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from ...configuration_utils import PretrainedConfig
class A ( UpperCAmelCase__ ):
'''simple docstring'''
A__ = '''bert-generation'''
def __init__(self : Dict , _UpperCAmelCase : Optional[int]=5_0358 , _UpperCAmelCase : List[Any]=1024 , _UpperCAmelCase : Optional[int]=24 , _UpperCAmelCase : Optional[int]=16 , _UpperCAmelCase : Union[str, Any]=4096 , _UpperCAmelCase : Dict="gelu" , _UpperCAmelCase : str=0.1 , _UpperCAmelCase : int=0.1 , _UpperCAmelCase : Optional[int]=512 , _UpperCAmelCase : Tuple=0.02 , _UpperCAmelCase : int=1E-1_2 , _UpperCAmelCase : str=0 , _UpperCAmelCase : Optional[Any]=2 , _UpperCAmelCase : List[str]=1 , _UpperCAmelCase : Any="absolute" , _UpperCAmelCase : Optional[Any]=True , **_UpperCAmelCase : Union[str, Any] , ) -> Tuple:
"""simple docstring"""
super().__init__(pad_token_id=_UpperCAmelCase , bos_token_id=_UpperCAmelCase , eos_token_id=_UpperCAmelCase , **_UpperCAmelCase )
lowercase__ = vocab_size
lowercase__ = hidden_size
lowercase__ = num_hidden_layers
lowercase__ = num_attention_heads
lowercase__ = hidden_act
lowercase__ = intermediate_size
lowercase__ = hidden_dropout_prob
lowercase__ = attention_probs_dropout_prob
lowercase__ = max_position_embeddings
lowercase__ = initializer_range
lowercase__ = layer_norm_eps
lowercase__ = position_embedding_type
lowercase__ = use_cache
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def UpperCamelCase ( __magic_name__ : str ) -> list:
"""simple docstring"""
if n_term == "":
return []
lowercase__ = []
for temp in range(int(__magic_name__ ) ):
series.append(f'''1/{temp + 1}''' if series else """1""" )
return series
if __name__ == "__main__":
A : Tuple = input('Enter the last number (nth term) of the Harmonic Series')
print('Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n')
print(harmonic_series(nth_term))
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def UpperCamelCase ( __magic_name__ : int = 10 , __magic_name__ : int = 1000 , __magic_name__ : bool = True ) -> int:
"""simple docstring"""
assert (
isinstance(__magic_name__ , __magic_name__ )
and isinstance(__magic_name__ , __magic_name__ )
and isinstance(__magic_name__ , __magic_name__ )
), "Invalid type of value(s) specified to function!"
if min_val > max_val:
raise ValueError("""Invalid value for min_val or max_val (min_value < max_value)""" )
return min_val if option else max_val
def UpperCamelCase ( __magic_name__ : int , __magic_name__ : int ) -> int:
"""simple docstring"""
return int((number_a + number_a) / 2 )
def UpperCamelCase ( __magic_name__ : int , __magic_name__ : int , __magic_name__ : int ) -> None:
"""simple docstring"""
assert (
isinstance(__magic_name__ , __magic_name__ ) and isinstance(__magic_name__ , __magic_name__ ) and isinstance(__magic_name__ , __magic_name__ )
), 'argument values must be type of "int"'
if lower > higher:
raise ValueError("""argument value for lower and higher must be(lower > higher)""" )
if not lower < to_guess < higher:
raise ValueError(
"""guess value must be within the range of lower and higher value""" )
def answer(__magic_name__ : int ) -> str:
if number > to_guess:
return "high"
elif number < to_guess:
return "low"
else:
return "same"
print("""started...""" )
lowercase__ = lower
lowercase__ = higher
lowercase__ = []
while True:
lowercase__ = get_avg(__magic_name__ , __magic_name__ )
last_numbers.append(__magic_name__ )
if answer(__magic_name__ ) == "low":
lowercase__ = number
elif answer(__magic_name__ ) == "high":
lowercase__ = number
else:
break
print(f'''guess the number : {last_numbers[-1]}''' )
print(f'''details : {last_numbers!s}''' )
def UpperCamelCase ( ) -> None:
"""simple docstring"""
lowercase__ = int(input("""Enter lower value : """ ).strip() )
lowercase__ = int(input("""Enter high value : """ ).strip() )
lowercase__ = int(input("""Enter value to guess : """ ).strip() )
guess_the_number(__magic_name__ , __magic_name__ , __magic_name__ )
if __name__ == "__main__":
main()
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|
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class A ( UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
A__ = ShapEImgaImgPipeline
A__ = ['''image''']
A__ = ['''image''']
A__ = [
'''num_images_per_prompt''',
'''num_inference_steps''',
'''generator''',
'''latents''',
'''guidance_scale''',
'''frame_size''',
'''output_type''',
'''return_dict''',
]
A__ = False
@property
def lowerCamelCase__ (self : Optional[Any] ) -> List[str]:
"""simple docstring"""
return 32
@property
def lowerCamelCase__ (self : str ) -> Any:
"""simple docstring"""
return 32
@property
def lowerCamelCase__ (self : str ) -> List[str]:
"""simple docstring"""
return self.time_input_dim * 4
@property
def lowerCamelCase__ (self : List[Any] ) -> Any:
"""simple docstring"""
return 8
@property
def lowerCamelCase__ (self : int ) -> List[str]:
"""simple docstring"""
torch.manual_seed(0 )
lowercase__ = CLIPVisionConfig(
hidden_size=self.text_embedder_hidden_size , image_size=64 , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_channels=3 , num_hidden_layers=5 , patch_size=1 , )
lowercase__ = CLIPVisionModel(_UpperCAmelCase )
return model
@property
def lowerCamelCase__ (self : Any ) -> List[Any]:
"""simple docstring"""
lowercase__ = CLIPImageProcessor(
crop_size=224 , do_center_crop=_UpperCAmelCase , do_normalize=_UpperCAmelCase , do_resize=_UpperCAmelCase , image_mean=[0.48_145_466, 0.4_578_275, 0.40_821_073] , image_std=[0.26_862_954, 0.26_130_258, 0.27_577_711] , resample=3 , size=224 , )
return image_processor
@property
def lowerCamelCase__ (self : int ) -> Optional[Any]:
"""simple docstring"""
torch.manual_seed(0 )
lowercase__ = {
"""num_attention_heads""": 2,
"""attention_head_dim""": 16,
"""embedding_dim""": self.time_input_dim,
"""num_embeddings""": 32,
"""embedding_proj_dim""": self.text_embedder_hidden_size,
"""time_embed_dim""": self.time_embed_dim,
"""num_layers""": 1,
"""clip_embed_dim""": self.time_input_dim * 2,
"""additional_embeddings""": 0,
"""time_embed_act_fn""": """gelu""",
"""norm_in_type""": """layer""",
"""embedding_proj_norm_type""": """layer""",
"""encoder_hid_proj_type""": None,
"""added_emb_type""": None,
}
lowercase__ = PriorTransformer(**_UpperCAmelCase )
return model
@property
def lowerCamelCase__ (self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
torch.manual_seed(0 )
lowercase__ = {
"""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,
),
}
lowercase__ = ShapERenderer(**_UpperCAmelCase )
return model
def lowerCamelCase__ (self : int ) -> Optional[int]:
"""simple docstring"""
lowercase__ = self.dummy_prior
lowercase__ = self.dummy_image_encoder
lowercase__ = self.dummy_image_processor
lowercase__ = self.dummy_renderer
lowercase__ = HeunDiscreteScheduler(
beta_schedule="""exp""" , num_train_timesteps=1024 , prediction_type="""sample""" , use_karras_sigmas=_UpperCAmelCase , clip_sample=_UpperCAmelCase , clip_sample_range=1.0 , )
lowercase__ = {
"""prior""": prior,
"""image_encoder""": image_encoder,
"""image_processor""": image_processor,
"""renderer""": renderer,
"""scheduler""": scheduler,
}
return components
def lowerCamelCase__ (self : Dict , _UpperCAmelCase : List[Any] , _UpperCAmelCase : str=0 ) -> str:
"""simple docstring"""
lowercase__ = floats_tensor((1, 3, 64, 64) , rng=random.Random(_UpperCAmelCase ) ).to(_UpperCAmelCase )
if str(_UpperCAmelCase ).startswith("""mps""" ):
lowercase__ = torch.manual_seed(_UpperCAmelCase )
else:
lowercase__ = torch.Generator(device=_UpperCAmelCase ).manual_seed(_UpperCAmelCase )
lowercase__ = {
"""image""": input_image,
"""generator""": generator,
"""num_inference_steps""": 1,
"""frame_size""": 32,
"""output_type""": """np""",
}
return inputs
def lowerCamelCase__ (self : str ) -> List[str]:
"""simple docstring"""
lowercase__ = """cpu"""
lowercase__ = self.get_dummy_components()
lowercase__ = self.pipeline_class(**_UpperCAmelCase )
lowercase__ = pipe.to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
lowercase__ = pipe(**self.get_dummy_inputs(_UpperCAmelCase ) )
lowercase__ = output.images[0]
lowercase__ = image[0, -3:, -3:, -1]
assert image.shape == (20, 32, 32, 3)
lowercase__ = np.array(
[
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def lowerCamelCase__ (self : str ) -> Any:
"""simple docstring"""
self._test_inference_batch_consistent(batch_sizes=[1, 2] )
def lowerCamelCase__ (self : Optional[int] ) -> str:
"""simple docstring"""
lowercase__ = torch_device == """cpu"""
lowercase__ = True
self._test_inference_batch_single_identical(
batch_size=2 , test_max_difference=_UpperCAmelCase , relax_max_difference=_UpperCAmelCase , )
def lowerCamelCase__ (self : Union[str, Any] ) -> int:
"""simple docstring"""
lowercase__ = self.get_dummy_components()
lowercase__ = self.pipeline_class(**_UpperCAmelCase )
lowercase__ = pipe.to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
lowercase__ = 1
lowercase__ = 2
lowercase__ = self.get_dummy_inputs(_UpperCAmelCase )
for key in inputs.keys():
if key in self.batch_params:
lowercase__ = batch_size * [inputs[key]]
lowercase__ = pipe(**_UpperCAmelCase , num_images_per_prompt=_UpperCAmelCase )[0]
assert images.shape[0] == batch_size * num_images_per_prompt
@slow
@require_torch_gpu
class A ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase__ (self : Dict ) -> List[Any]:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase__ (self : Any ) -> str:
"""simple docstring"""
lowercase__ = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/shap_e/corgi.png""" )
lowercase__ = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/shap_e/test_shap_e_img2img_out.npy""" )
lowercase__ = ShapEImgaImgPipeline.from_pretrained("""openai/shap-e-img2img""" )
lowercase__ = pipe.to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
lowercase__ = torch.Generator(device=_UpperCAmelCase ).manual_seed(0 )
lowercase__ = pipe(
_UpperCAmelCase , generator=_UpperCAmelCase , guidance_scale=3.0 , num_inference_steps=64 , frame_size=64 , output_type="""np""" , ).images[0]
assert images.shape == (20, 64, 64, 3)
assert_mean_pixel_difference(_UpperCAmelCase , _UpperCAmelCase )
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|
import itertools
import math
def UpperCamelCase ( __magic_name__ : int ) -> bool:
"""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(__magic_name__ ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def UpperCamelCase ( ) -> str:
"""simple docstring"""
lowercase__ = 2
while True:
if is_prime(__magic_name__ ):
yield num
num += 1
def UpperCamelCase ( __magic_name__ : int = 1_0001 ) -> int:
"""simple docstring"""
return next(itertools.islice(prime_generator() , nth - 1 , __magic_name__ ) )
if __name__ == "__main__":
print(F'{solution() = }')
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|
import requests
from bsa import BeautifulSoup
def UpperCamelCase ( __magic_name__ : str = "AAPL" ) -> str:
"""simple docstring"""
lowercase__ = f'''https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'''
lowercase__ = BeautifulSoup(requests.get(__magic_name__ ).text , """html.parser""" )
lowercase__ = """My(6px) Pos(r) smartphone_Mt(6px)"""
return soup.find("""div""" , class_=class_ ).find("""span""" ).text
if __name__ == "__main__":
for symbol in "AAPL AMZN IBM GOOG MSFT ORCL".split():
print(F'Current {symbol:<4} stock price is {stock_price(symbol):>8}')
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|
import os
import tempfile
import unittest
from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, 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 (
MODEL_FOR_PRETRAINING_MAPPING,
NezhaForMaskedLM,
NezhaForMultipleChoice,
NezhaForNextSentencePrediction,
NezhaForPreTraining,
NezhaForQuestionAnswering,
NezhaForSequenceClassification,
NezhaForTokenClassification,
NezhaModel,
)
from transformers.models.nezha.modeling_nezha import NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST
class A :
'''simple docstring'''
def __init__(self : str , _UpperCAmelCase : Tuple , _UpperCAmelCase : List[str]=13 , _UpperCAmelCase : Tuple=7 , _UpperCAmelCase : int=True , _UpperCAmelCase : List[Any]=True , _UpperCAmelCase : List[str]=True , _UpperCAmelCase : Tuple=True , _UpperCAmelCase : Union[str, Any]=99 , _UpperCAmelCase : str=32 , _UpperCAmelCase : str=5 , _UpperCAmelCase : Union[str, Any]=4 , _UpperCAmelCase : List[Any]=37 , _UpperCAmelCase : Dict="gelu" , _UpperCAmelCase : int=0.1 , _UpperCAmelCase : List[Any]=0.1 , _UpperCAmelCase : Any=128 , _UpperCAmelCase : Optional[int]=32 , _UpperCAmelCase : List[str]=16 , _UpperCAmelCase : Dict=2 , _UpperCAmelCase : int=0.02 , _UpperCAmelCase : List[Any]=3 , _UpperCAmelCase : Dict=4 , _UpperCAmelCase : Tuple=None , ) -> Any:
"""simple docstring"""
lowercase__ = parent
lowercase__ = batch_size
lowercase__ = seq_length
lowercase__ = is_training
lowercase__ = use_input_mask
lowercase__ = use_token_type_ids
lowercase__ = use_labels
lowercase__ = vocab_size
lowercase__ = hidden_size
lowercase__ = num_hidden_layers
lowercase__ = num_attention_heads
lowercase__ = intermediate_size
lowercase__ = hidden_act
lowercase__ = hidden_dropout_prob
lowercase__ = attention_probs_dropout_prob
lowercase__ = max_position_embeddings
lowercase__ = type_vocab_size
lowercase__ = type_sequence_label_size
lowercase__ = initializer_range
lowercase__ = num_labels
lowercase__ = num_choices
lowercase__ = scope
def lowerCamelCase__ (self : int ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase__ = None
if self.use_input_mask:
lowercase__ = random_attention_mask([self.batch_size, self.seq_length] )
lowercase__ = None
if self.use_token_type_ids:
lowercase__ = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowercase__ = None
lowercase__ = None
lowercase__ = None
if self.use_labels:
lowercase__ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase__ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowercase__ = ids_tensor([self.batch_size] , self.num_choices )
lowercase__ = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def lowerCamelCase__ (self : str ) -> str:
"""simple docstring"""
return NezhaConfig(
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=_UpperCAmelCase , initializer_range=self.initializer_range , )
def lowerCamelCase__ (self : List[Any] ) -> Tuple:
"""simple docstring"""
(
(
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) ,
) = self.prepare_config_and_inputs()
lowercase__ = True
lowercase__ = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
lowercase__ = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def lowerCamelCase__ (self : Optional[int] , _UpperCAmelCase : str , _UpperCAmelCase : str , _UpperCAmelCase : List[Any] , _UpperCAmelCase : str , _UpperCAmelCase : str , _UpperCAmelCase : Any , _UpperCAmelCase : List[Any] ) -> Optional[int]:
"""simple docstring"""
lowercase__ = NezhaModel(config=_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
lowercase__ = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase , token_type_ids=_UpperCAmelCase )
lowercase__ = model(_UpperCAmelCase , token_type_ids=_UpperCAmelCase )
lowercase__ = model(_UpperCAmelCase )
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 lowerCamelCase__ (self : Any , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Any , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Any , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Tuple , _UpperCAmelCase : str , _UpperCAmelCase : List[str] , ) -> List[str]:
"""simple docstring"""
lowercase__ = True
lowercase__ = NezhaModel(_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
lowercase__ = model(
_UpperCAmelCase , attention_mask=_UpperCAmelCase , token_type_ids=_UpperCAmelCase , encoder_hidden_states=_UpperCAmelCase , encoder_attention_mask=_UpperCAmelCase , )
lowercase__ = model(
_UpperCAmelCase , attention_mask=_UpperCAmelCase , token_type_ids=_UpperCAmelCase , encoder_hidden_states=_UpperCAmelCase , )
lowercase__ = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase , token_type_ids=_UpperCAmelCase )
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 lowerCamelCase__ (self : Tuple , _UpperCAmelCase : Any , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : str , _UpperCAmelCase : List[Any] , _UpperCAmelCase : str , _UpperCAmelCase : List[str] , _UpperCAmelCase : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = NezhaForMaskedLM(config=_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
lowercase__ = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase , token_type_ids=_UpperCAmelCase , labels=_UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowerCamelCase__ (self : Tuple , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Tuple , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : int , _UpperCAmelCase : List[str] , _UpperCAmelCase : Any ) -> Any:
"""simple docstring"""
lowercase__ = NezhaForNextSentencePrediction(config=_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
lowercase__ = model(
_UpperCAmelCase , attention_mask=_UpperCAmelCase , token_type_ids=_UpperCAmelCase , labels=_UpperCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 2) )
def lowerCamelCase__ (self : Dict , _UpperCAmelCase : Any , _UpperCAmelCase : str , _UpperCAmelCase : str , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Dict ) -> List[str]:
"""simple docstring"""
lowercase__ = NezhaForPreTraining(config=_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
lowercase__ = model(
_UpperCAmelCase , attention_mask=_UpperCAmelCase , token_type_ids=_UpperCAmelCase , labels=_UpperCAmelCase , next_sentence_label=_UpperCAmelCase , )
self.parent.assertEqual(result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.seq_relationship_logits.shape , (self.batch_size, 2) )
def lowerCamelCase__ (self : Optional[Any] , _UpperCAmelCase : Tuple , _UpperCAmelCase : Dict , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Dict , _UpperCAmelCase : List[str] , _UpperCAmelCase : Dict , _UpperCAmelCase : Optional[Any] ) -> int:
"""simple docstring"""
lowercase__ = NezhaForQuestionAnswering(config=_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
lowercase__ = model(
_UpperCAmelCase , attention_mask=_UpperCAmelCase , token_type_ids=_UpperCAmelCase , start_positions=_UpperCAmelCase , end_positions=_UpperCAmelCase , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def lowerCamelCase__ (self : Any , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Any , _UpperCAmelCase : Tuple , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : str , _UpperCAmelCase : str ) -> Tuple:
"""simple docstring"""
lowercase__ = self.num_labels
lowercase__ = NezhaForSequenceClassification(_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
lowercase__ = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase , token_type_ids=_UpperCAmelCase , labels=_UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCamelCase__ (self : Any , _UpperCAmelCase : Any , _UpperCAmelCase : Any , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : str ) -> str:
"""simple docstring"""
lowercase__ = self.num_labels
lowercase__ = NezhaForTokenClassification(config=_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
lowercase__ = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase , token_type_ids=_UpperCAmelCase , labels=_UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def lowerCamelCase__ (self : int , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Tuple , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Dict , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Any ) -> List[str]:
"""simple docstring"""
lowercase__ = self.num_choices
lowercase__ = NezhaForMultipleChoice(config=_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
lowercase__ = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowercase__ = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowercase__ = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowercase__ = model(
_UpperCAmelCase , attention_mask=_UpperCAmelCase , token_type_ids=_UpperCAmelCase , labels=_UpperCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def lowerCamelCase__ (self : Optional[int] ) -> int:
"""simple docstring"""
lowercase__ = self.prepare_config_and_inputs()
(
(
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) ,
) = config_and_inputs
lowercase__ = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class A ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
A__ = (
(
NezhaModel,
NezhaForMaskedLM,
NezhaForMultipleChoice,
NezhaForNextSentencePrediction,
NezhaForPreTraining,
NezhaForQuestionAnswering,
NezhaForSequenceClassification,
NezhaForTokenClassification,
)
if is_torch_available()
else ()
)
A__ = (
{
'''feature-extraction''': NezhaModel,
'''fill-mask''': NezhaForMaskedLM,
'''question-answering''': NezhaForQuestionAnswering,
'''text-classification''': NezhaForSequenceClassification,
'''token-classification''': NezhaForTokenClassification,
'''zero-shot''': NezhaForSequenceClassification,
}
if is_torch_available()
else {}
)
A__ = True
def lowerCamelCase__ (self : Dict , _UpperCAmelCase : List[str] , _UpperCAmelCase : int , _UpperCAmelCase : Optional[int]=False ) -> Dict:
"""simple docstring"""
lowercase__ = super()._prepare_for_class(_UpperCAmelCase , _UpperCAmelCase , return_labels=_UpperCAmelCase )
if return_labels:
if model_class in get_values(_UpperCAmelCase ):
lowercase__ = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=_UpperCAmelCase )
lowercase__ = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=_UpperCAmelCase )
return inputs_dict
def lowerCamelCase__ (self : Optional[Any] ) -> Dict:
"""simple docstring"""
lowercase__ = NezhaModelTester(self )
lowercase__ = ConfigTester(self , config_class=_UpperCAmelCase , hidden_size=37 )
def lowerCamelCase__ (self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
self.config_tester.run_common_tests()
def lowerCamelCase__ (self : str ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_UpperCAmelCase )
def lowerCamelCase__ (self : Tuple ) -> Tuple:
"""simple docstring"""
lowercase__ = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(*_UpperCAmelCase )
def lowerCamelCase__ (self : Any ) -> Optional[Any]:
"""simple docstring"""
(
(
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) ,
) = self.model_tester.prepare_config_and_inputs_for_decoder()
lowercase__ = None
self.model_tester.create_and_check_model_as_decoder(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , )
def lowerCamelCase__ (self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*_UpperCAmelCase )
def lowerCamelCase__ (self : List[Any] ) -> List[str]:
"""simple docstring"""
lowercase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*_UpperCAmelCase )
def lowerCamelCase__ (self : List[str] ) -> Any:
"""simple docstring"""
lowercase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_next_sequence_prediction(*_UpperCAmelCase )
def lowerCamelCase__ (self : int ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*_UpperCAmelCase )
def lowerCamelCase__ (self : Any ) -> int:
"""simple docstring"""
lowercase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*_UpperCAmelCase )
def lowerCamelCase__ (self : int ) -> List[str]:
"""simple docstring"""
lowercase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*_UpperCAmelCase )
def lowerCamelCase__ (self : List[str] ) -> int:
"""simple docstring"""
lowercase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*_UpperCAmelCase )
@slow
def lowerCamelCase__ (self : List[Any] ) -> Dict:
"""simple docstring"""
for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ = NezhaModel.from_pretrained(_UpperCAmelCase )
self.assertIsNotNone(_UpperCAmelCase )
@slow
@require_torch_gpu
def lowerCamelCase__ (self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ , lowercase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# NezhaForMultipleChoice behaves incorrectly in JIT environments.
if model_class == NezhaForMultipleChoice:
return
lowercase__ = True
lowercase__ = model_class(config=_UpperCAmelCase )
lowercase__ = self._prepare_for_class(_UpperCAmelCase , _UpperCAmelCase )
lowercase__ = torch.jit.trace(
_UpperCAmelCase , (inputs_dict["""input_ids"""].to("""cpu""" ), inputs_dict["""attention_mask"""].to("""cpu""" )) )
with tempfile.TemporaryDirectory() as tmp:
torch.jit.save(_UpperCAmelCase , os.path.join(_UpperCAmelCase , """bert.pt""" ) )
lowercase__ = torch.jit.load(os.path.join(_UpperCAmelCase , """bert.pt""" ) , map_location=_UpperCAmelCase )
loaded(inputs_dict["""input_ids"""].to(_UpperCAmelCase ) , inputs_dict["""attention_mask"""].to(_UpperCAmelCase ) )
@require_torch
class A ( unittest.TestCase ):
'''simple docstring'''
@slow
def lowerCamelCase__ (self : List[Any] ) -> Optional[int]:
"""simple docstring"""
lowercase__ = NezhaModel.from_pretrained("""sijunhe/nezha-cn-base""" )
lowercase__ = torch.tensor([[0, 1, 2, 3, 4, 5]] )
lowercase__ = torch.tensor([[0, 1, 1, 1, 1, 1]] )
with torch.no_grad():
lowercase__ = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase )[0]
lowercase__ = torch.Size((1, 6, 768) )
self.assertEqual(output.shape , _UpperCAmelCase )
lowercase__ = torch.tensor([[[0.0_685, 0.2_441, 0.1_102], [0.0_600, 0.1_906, 0.1_349], [0.0_221, 0.0_819, 0.0_586]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , _UpperCAmelCase , atol=1E-4 ) )
@slow
def lowerCamelCase__ (self : Optional[int] ) -> int:
"""simple docstring"""
lowercase__ = NezhaForMaskedLM.from_pretrained("""sijunhe/nezha-cn-base""" )
lowercase__ = torch.tensor([[0, 1, 2, 3, 4, 5]] )
lowercase__ = torch.tensor([[1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
lowercase__ = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase )[0]
lowercase__ = torch.Size((1, 6, 2_1128) )
self.assertEqual(output.shape , _UpperCAmelCase )
lowercase__ = torch.tensor(
[[-2.7_939, -1.7_902, -2.2_189], [-2.8_585, -1.8_908, -2.3_723], [-2.6_499, -1.7_750, -2.2_558]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , _UpperCAmelCase , atol=1E-4 ) )
| 305
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : List[str] = logging.get_logger(__name__)
A : Any = {
'tiiuae/falcon-40b': 'https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json',
'tiiuae/falcon-7b': 'https://huggingface.co/tiiuae/falcon-7b/resolve/main/config.json',
}
class A ( UpperCAmelCase__ ):
'''simple docstring'''
A__ = '''falcon'''
A__ = ['''past_key_values''']
def __init__(self : str , _UpperCAmelCase : Dict=6_5024 , _UpperCAmelCase : Optional[Any]=4544 , _UpperCAmelCase : Optional[int]=32 , _UpperCAmelCase : Optional[Any]=71 , _UpperCAmelCase : List[Any]=1E-5 , _UpperCAmelCase : int=0.02 , _UpperCAmelCase : str=True , _UpperCAmelCase : Tuple=0.0 , _UpperCAmelCase : Any=0.0 , _UpperCAmelCase : str=None , _UpperCAmelCase : Optional[int]=False , _UpperCAmelCase : int=False , _UpperCAmelCase : Union[str, Any]=True , _UpperCAmelCase : List[Any]=True , _UpperCAmelCase : List[Any]=False , _UpperCAmelCase : Optional[int]=11 , _UpperCAmelCase : Optional[Any]=11 , **_UpperCAmelCase : Union[str, Any] , ) -> List[str]:
"""simple docstring"""
lowercase__ = vocab_size
# Backward compatibility with n_embed kwarg
lowercase__ = kwargs.pop("""n_embed""" , _UpperCAmelCase )
lowercase__ = hidden_size if n_embed is None else n_embed
lowercase__ = num_hidden_layers
lowercase__ = num_attention_heads
lowercase__ = layer_norm_epsilon
lowercase__ = initializer_range
lowercase__ = use_cache
lowercase__ = hidden_dropout
lowercase__ = attention_dropout
lowercase__ = bos_token_id
lowercase__ = eos_token_id
lowercase__ = num_attention_heads if num_kv_heads is None else num_kv_heads
lowercase__ = alibi
lowercase__ = new_decoder_architecture
lowercase__ = multi_query # Ignored when new_decoder_architecture is True
lowercase__ = parallel_attn
lowercase__ = bias
super().__init__(bos_token_id=_UpperCAmelCase , eos_token_id=_UpperCAmelCase , **_UpperCAmelCase )
@property
def lowerCamelCase__ (self : Tuple ) -> int:
"""simple docstring"""
return self.hidden_size // self.num_attention_heads
@property
def lowerCamelCase__ (self : List[str] ) -> Tuple:
"""simple docstring"""
return not self.alibi
| 305
| 1
|
import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_MAPPING,
BeitForImageClassification,
BeitForMaskedImageModeling,
BeitForSemanticSegmentation,
BeitModel,
)
from transformers.models.beit.modeling_beit import BEIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
import PIL
from PIL import Image
from transformers import BeitImageProcessor
class A :
'''simple docstring'''
def __init__(self : Tuple , _UpperCAmelCase : List[Any] , _UpperCAmelCase : List[str]=100 , _UpperCAmelCase : Union[str, Any]=13 , _UpperCAmelCase : Any=30 , _UpperCAmelCase : Union[str, Any]=2 , _UpperCAmelCase : Union[str, Any]=3 , _UpperCAmelCase : Dict=True , _UpperCAmelCase : Dict=True , _UpperCAmelCase : Any=32 , _UpperCAmelCase : Tuple=4 , _UpperCAmelCase : Union[str, Any]=4 , _UpperCAmelCase : List[Any]=37 , _UpperCAmelCase : List[str]="gelu" , _UpperCAmelCase : Tuple=0.1 , _UpperCAmelCase : Any=0.1 , _UpperCAmelCase : Union[str, Any]=10 , _UpperCAmelCase : Union[str, Any]=0.02 , _UpperCAmelCase : str=3 , _UpperCAmelCase : Tuple=None , _UpperCAmelCase : Union[str, Any]=[0, 1, 2, 3] , ) -> Dict:
"""simple docstring"""
lowercase__ = parent
lowercase__ = 100
lowercase__ = batch_size
lowercase__ = image_size
lowercase__ = patch_size
lowercase__ = num_channels
lowercase__ = is_training
lowercase__ = use_labels
lowercase__ = hidden_size
lowercase__ = num_hidden_layers
lowercase__ = num_attention_heads
lowercase__ = intermediate_size
lowercase__ = hidden_act
lowercase__ = hidden_dropout_prob
lowercase__ = attention_probs_dropout_prob
lowercase__ = type_sequence_label_size
lowercase__ = initializer_range
lowercase__ = scope
lowercase__ = out_indices
lowercase__ = num_labels
# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
lowercase__ = (image_size // patch_size) ** 2
lowercase__ = num_patches + 1
def lowerCamelCase__ (self : Tuple ) -> List[Any]:
"""simple docstring"""
lowercase__ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowercase__ = None
lowercase__ = None
if self.use_labels:
lowercase__ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase__ = ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels )
lowercase__ = self.get_config()
return config, pixel_values, labels, pixel_labels
def lowerCamelCase__ (self : Dict ) -> List[str]:
"""simple docstring"""
return BeitConfig(
vocab_size=self.vocab_size , image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=_UpperCAmelCase , initializer_range=self.initializer_range , out_indices=self.out_indices , )
def lowerCamelCase__ (self : Dict , _UpperCAmelCase : str , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : int ) -> List[str]:
"""simple docstring"""
lowercase__ = BeitModel(config=_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
lowercase__ = model(_UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCamelCase__ (self : Tuple , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : str , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : List[str] ) -> Tuple:
"""simple docstring"""
lowercase__ = BeitForMaskedImageModeling(config=_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
lowercase__ = model(_UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length - 1, self.vocab_size) )
def lowerCamelCase__ (self : Optional[Any] , _UpperCAmelCase : Tuple , _UpperCAmelCase : Dict , _UpperCAmelCase : Any , _UpperCAmelCase : str ) -> Optional[int]:
"""simple docstring"""
lowercase__ = self.type_sequence_label_size
lowercase__ = BeitForImageClassification(_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
lowercase__ = model(_UpperCAmelCase , labels=_UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
lowercase__ = 1
lowercase__ = BeitForImageClassification(_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
lowercase__ = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
lowercase__ = model(_UpperCAmelCase , labels=_UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def lowerCamelCase__ (self : int , _UpperCAmelCase : Tuple , _UpperCAmelCase : List[Any] , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Dict ) -> Optional[int]:
"""simple docstring"""
lowercase__ = self.num_labels
lowercase__ = BeitForSemanticSegmentation(_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
lowercase__ = model(_UpperCAmelCase )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) )
lowercase__ = model(_UpperCAmelCase , labels=_UpperCAmelCase )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) )
def lowerCamelCase__ (self : str ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = self.prepare_config_and_inputs()
lowercase__ , lowercase__ , lowercase__ , lowercase__ = config_and_inputs
lowercase__ = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class A ( UpperCAmelCase__ , UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
A__ = (
(BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
if is_torch_available()
else ()
)
A__ = (
{
'''feature-extraction''': BeitModel,
'''image-classification''': BeitForImageClassification,
'''image-segmentation''': BeitForSemanticSegmentation,
}
if is_torch_available()
else {}
)
A__ = False
A__ = False
A__ = False
def lowerCamelCase__ (self : List[str] ) -> int:
"""simple docstring"""
lowercase__ = BeitModelTester(self )
lowercase__ = ConfigTester(self , config_class=_UpperCAmelCase , has_text_modality=_UpperCAmelCase , hidden_size=37 )
def lowerCamelCase__ (self : Optional[int] ) -> Any:
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason="""BEiT does not use inputs_embeds""" )
def lowerCamelCase__ (self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
pass
@require_torch_multi_gpu
@unittest.skip(reason="""BEiT has some layers using `add_module` which doesn't work well with `nn.DataParallel`""" )
def lowerCamelCase__ (self : Dict ) -> Optional[Any]:
"""simple docstring"""
pass
def lowerCamelCase__ (self : Dict ) -> int:
"""simple docstring"""
lowercase__ , lowercase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ = model_class(_UpperCAmelCase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
lowercase__ = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_UpperCAmelCase , nn.Linear ) )
def lowerCamelCase__ (self : Optional[int] ) -> int:
"""simple docstring"""
lowercase__ , lowercase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ = model_class(_UpperCAmelCase )
lowercase__ = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase__ = [*signature.parameters.keys()]
lowercase__ = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , _UpperCAmelCase )
def lowerCamelCase__ (self : int ) -> Any:
"""simple docstring"""
lowercase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_UpperCAmelCase )
def lowerCamelCase__ (self : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*_UpperCAmelCase )
def lowerCamelCase__ (self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
lowercase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase )
def lowerCamelCase__ (self : Optional[int] ) -> Tuple:
"""simple docstring"""
lowercase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*_UpperCAmelCase )
def lowerCamelCase__ (self : Union[str, Any] ) -> str:
"""simple docstring"""
if not self.model_tester.is_training:
return
lowercase__ , lowercase__ = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ = True
for model_class in self.all_model_classes:
# we don't test BeitForMaskedImageModeling
if model_class in [*get_values(_UpperCAmelCase ), BeitForMaskedImageModeling]:
continue
lowercase__ = model_class(_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.train()
lowercase__ = self._prepare_for_class(_UpperCAmelCase , _UpperCAmelCase , return_labels=_UpperCAmelCase )
lowercase__ = model(**_UpperCAmelCase ).loss
loss.backward()
def lowerCamelCase__ (self : str ) -> Any:
"""simple docstring"""
lowercase__ , lowercase__ = self.model_tester.prepare_config_and_inputs_for_common()
if not self.model_tester.is_training:
return
lowercase__ = False
lowercase__ = True
for model_class in self.all_model_classes:
# we don't test BeitForMaskedImageModeling
if (
model_class in [*get_values(_UpperCAmelCase ), BeitForMaskedImageModeling]
or not model_class.supports_gradient_checkpointing
):
continue
lowercase__ = model_class(_UpperCAmelCase )
model.gradient_checkpointing_enable()
model.to(_UpperCAmelCase )
model.train()
lowercase__ = self._prepare_for_class(_UpperCAmelCase , _UpperCAmelCase , return_labels=_UpperCAmelCase )
lowercase__ = model(**_UpperCAmelCase ).loss
loss.backward()
def lowerCamelCase__ (self : str ) -> List[Any]:
"""simple docstring"""
lowercase__ , lowercase__ = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ = _config_zero_init(_UpperCAmelCase )
for model_class in self.all_model_classes:
lowercase__ = model_class(config=_UpperCAmelCase )
for name, param in model.named_parameters():
# we skip lambda parameters as these require special initial values
# determined by config.layer_scale_init_value
if "lambda" in name:
continue
if param.requires_grad:
self.assertIn(
((param.data.mean() * 1E9).round() / 1E9).item() , [0.0, 1.0] , msg=f'''Parameter {name} of model {model_class} seems not properly initialized''' , )
@slow
def lowerCamelCase__ (self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ = BeitModel.from_pretrained(_UpperCAmelCase )
self.assertIsNotNone(_UpperCAmelCase )
def UpperCamelCase ( ) -> Optional[int]:
"""simple docstring"""
lowercase__ = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
@require_vision
class A ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def lowerCamelCase__ (self : Dict ) -> Optional[int]:
"""simple docstring"""
return BeitImageProcessor.from_pretrained("""microsoft/beit-base-patch16-224""" ) if is_vision_available() else None
@slow
def lowerCamelCase__ (self : str ) -> Tuple:
"""simple docstring"""
lowercase__ = BeitForMaskedImageModeling.from_pretrained("""microsoft/beit-base-patch16-224-pt22k""" ).to(_UpperCAmelCase )
lowercase__ = self.default_image_processor
lowercase__ = prepare_img()
lowercase__ = image_processor(images=_UpperCAmelCase , return_tensors="""pt""" ).pixel_values.to(_UpperCAmelCase )
# prepare bool_masked_pos
lowercase__ = torch.ones((1, 196) , dtype=torch.bool ).to(_UpperCAmelCase )
# forward pass
with torch.no_grad():
lowercase__ = model(pixel_values=_UpperCAmelCase , bool_masked_pos=_UpperCAmelCase )
lowercase__ = outputs.logits
# verify the logits
lowercase__ = torch.Size((1, 196, 8192) )
self.assertEqual(logits.shape , _UpperCAmelCase )
lowercase__ = torch.tensor(
[[-3.2_437, 0.5_072, -13.9_174], [-3.2_456, 0.4_948, -13.9_401], [-3.2_033, 0.5_121, -13.8_550]] ).to(_UpperCAmelCase )
self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3] , _UpperCAmelCase , atol=1E-2 ) )
@slow
def lowerCamelCase__ (self : Any ) -> int:
"""simple docstring"""
lowercase__ = BeitForImageClassification.from_pretrained("""microsoft/beit-base-patch16-224""" ).to(_UpperCAmelCase )
lowercase__ = self.default_image_processor
lowercase__ = prepare_img()
lowercase__ = image_processor(images=_UpperCAmelCase , return_tensors="""pt""" ).to(_UpperCAmelCase )
# forward pass
with torch.no_grad():
lowercase__ = model(**_UpperCAmelCase )
lowercase__ = outputs.logits
# verify the logits
lowercase__ = torch.Size((1, 1000) )
self.assertEqual(logits.shape , _UpperCAmelCase )
lowercase__ = torch.tensor([-1.2_385, -1.0_987, -1.0_108] ).to(_UpperCAmelCase )
self.assertTrue(torch.allclose(logits[0, :3] , _UpperCAmelCase , atol=1E-4 ) )
lowercase__ = 281
self.assertEqual(logits.argmax(-1 ).item() , _UpperCAmelCase )
@slow
def lowerCamelCase__ (self : Dict ) -> Any:
"""simple docstring"""
lowercase__ = BeitForImageClassification.from_pretrained("""microsoft/beit-large-patch16-224-pt22k-ft22k""" ).to(
_UpperCAmelCase )
lowercase__ = self.default_image_processor
lowercase__ = prepare_img()
lowercase__ = image_processor(images=_UpperCAmelCase , return_tensors="""pt""" ).to(_UpperCAmelCase )
# forward pass
with torch.no_grad():
lowercase__ = model(**_UpperCAmelCase )
lowercase__ = outputs.logits
# verify the logits
lowercase__ = torch.Size((1, 2_1841) )
self.assertEqual(logits.shape , _UpperCAmelCase )
lowercase__ = torch.tensor([1.6_881, -0.2_787, 0.5_901] ).to(_UpperCAmelCase )
self.assertTrue(torch.allclose(logits[0, :3] , _UpperCAmelCase , atol=1E-4 ) )
lowercase__ = 2396
self.assertEqual(logits.argmax(-1 ).item() , _UpperCAmelCase )
@slow
def lowerCamelCase__ (self : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = BeitForSemanticSegmentation.from_pretrained("""microsoft/beit-base-finetuned-ade-640-640""" )
lowercase__ = model.to(_UpperCAmelCase )
lowercase__ = BeitImageProcessor(do_resize=_UpperCAmelCase , size=640 , do_center_crop=_UpperCAmelCase )
lowercase__ = load_dataset("""hf-internal-testing/fixtures_ade20k""" , split="""test""" )
lowercase__ = Image.open(ds[0]["""file"""] )
lowercase__ = image_processor(images=_UpperCAmelCase , return_tensors="""pt""" ).to(_UpperCAmelCase )
# forward pass
with torch.no_grad():
lowercase__ = model(**_UpperCAmelCase )
lowercase__ = outputs.logits
# verify the logits
lowercase__ = torch.Size((1, 150, 160, 160) )
self.assertEqual(logits.shape , _UpperCAmelCase )
lowercase__ = version.parse(PIL.__version__ ) < version.parse("""9.0.0""" )
if is_pillow_less_than_a:
lowercase__ = torch.tensor(
[
[[-4.9_225, -2.3_954, -3.0_522], [-2.8_822, -1.0_046, -1.7_561], [-2.9_549, -1.3_228, -2.1_347]],
[[-5.8_168, -3.4_129, -4.0_778], [-3.8_651, -2.2_214, -3.0_277], [-3.8_356, -2.4_643, -3.3_535]],
[[-0.0_078, 3.9_952, 4.0_754], [2.9_856, 4.6_944, 5.0_035], [3.2_413, 4.7_813, 4.9_969]],
] , device=_UpperCAmelCase , )
else:
lowercase__ = torch.tensor(
[
[[-4.8_960, -2.3_688, -3.0_355], [-2.8_478, -0.9_836, -1.7_418], [-2.9_449, -1.3_332, -2.1_456]],
[[-5.8_081, -3.4_124, -4.1_006], [-3.8_561, -2.2_081, -3.0_323], [-3.8_365, -2.4_601, -3.3_669]],
[[-0.0_309, 3.9_868, 4.0_540], [2.9_640, 4.6_877, 4.9_976], [3.2_081, 4.7_690, 4.9_942]],
] , device=_UpperCAmelCase , )
self.assertTrue(torch.allclose(logits[0, :3, :3, :3] , _UpperCAmelCase , atol=1E-4 ) )
@slow
def lowerCamelCase__ (self : List[Any] ) -> Optional[int]:
"""simple docstring"""
lowercase__ = BeitForSemanticSegmentation.from_pretrained("""microsoft/beit-base-finetuned-ade-640-640""" )
lowercase__ = model.to(_UpperCAmelCase )
lowercase__ = BeitImageProcessor(do_resize=_UpperCAmelCase , size=640 , do_center_crop=_UpperCAmelCase )
lowercase__ = load_dataset("""hf-internal-testing/fixtures_ade20k""" , split="""test""" )
lowercase__ = Image.open(ds[0]["""file"""] )
lowercase__ = image_processor(images=_UpperCAmelCase , return_tensors="""pt""" ).to(_UpperCAmelCase )
# forward pass
with torch.no_grad():
lowercase__ = model(**_UpperCAmelCase )
lowercase__ = outputs.logits.detach().cpu()
lowercase__ = image_processor.post_process_semantic_segmentation(outputs=_UpperCAmelCase , target_sizes=[(500, 300)] )
lowercase__ = torch.Size((500, 300) )
self.assertEqual(segmentation[0].shape , _UpperCAmelCase )
lowercase__ = image_processor.post_process_semantic_segmentation(outputs=_UpperCAmelCase )
lowercase__ = torch.Size((160, 160) )
self.assertEqual(segmentation[0].shape , _UpperCAmelCase )
| 305
|
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AlignProcessor, EfficientNetImageProcessor
@require_vision
class A ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase__ (self : Union[str, Any] ) -> Any:
"""simple docstring"""
lowercase__ = tempfile.mkdtemp()
lowercase__ = [
"""[UNK]""",
"""[CLS]""",
"""[SEP]""",
"""[PAD]""",
"""[MASK]""",
"""want""",
"""##want""",
"""##ed""",
"""wa""",
"""un""",
"""runn""",
"""##ing""",
""",""",
"""low""",
"""lowest""",
]
lowercase__ = 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] ) )
lowercase__ = {
"""do_resize""": True,
"""size""": 20,
"""do_center_crop""": True,
"""crop_size""": 18,
"""do_normalize""": True,
"""image_mean""": [0.48_145_466, 0.4_578_275, 0.40_821_073],
"""image_std""": [0.26_862_954, 0.26_130_258, 0.27_577_711],
}
lowercase__ = os.path.join(self.tmpdirname , _UpperCAmelCase )
with open(self.image_processor_file , """w""" , encoding="""utf-8""" ) as fp:
json.dump(_UpperCAmelCase , _UpperCAmelCase )
def lowerCamelCase__ (self : Dict , **_UpperCAmelCase : Any ) -> Optional[Any]:
"""simple docstring"""
return BertTokenizer.from_pretrained(self.tmpdirname , **_UpperCAmelCase )
def lowerCamelCase__ (self : Union[str, Any] , **_UpperCAmelCase : Any ) -> Dict:
"""simple docstring"""
return BertTokenizerFast.from_pretrained(self.tmpdirname , **_UpperCAmelCase )
def lowerCamelCase__ (self : Optional[int] , **_UpperCAmelCase : str ) -> Dict:
"""simple docstring"""
return EfficientNetImageProcessor.from_pretrained(self.tmpdirname , **_UpperCAmelCase )
def lowerCamelCase__ (self : Optional[int] ) -> List[str]:
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def lowerCamelCase__ (self : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
lowercase__ = [Image.fromarray(np.moveaxis(_UpperCAmelCase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def lowerCamelCase__ (self : Optional[int] ) -> Tuple:
"""simple docstring"""
lowercase__ = self.get_tokenizer()
lowercase__ = self.get_rust_tokenizer()
lowercase__ = self.get_image_processor()
lowercase__ = AlignProcessor(tokenizer=_UpperCAmelCase , image_processor=_UpperCAmelCase )
processor_slow.save_pretrained(self.tmpdirname )
lowercase__ = AlignProcessor.from_pretrained(self.tmpdirname , use_fast=_UpperCAmelCase )
lowercase__ = AlignProcessor(tokenizer=_UpperCAmelCase , image_processor=_UpperCAmelCase )
processor_fast.save_pretrained(self.tmpdirname )
lowercase__ = AlignProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer , _UpperCAmelCase )
self.assertIsInstance(processor_fast.tokenizer , _UpperCAmelCase )
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor , _UpperCAmelCase )
self.assertIsInstance(processor_fast.image_processor , _UpperCAmelCase )
def lowerCamelCase__ (self : Any ) -> List[str]:
"""simple docstring"""
lowercase__ = AlignProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
lowercase__ = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
lowercase__ = self.get_image_processor(do_normalize=_UpperCAmelCase , padding_value=1.0 )
lowercase__ = AlignProcessor.from_pretrained(
self.tmpdirname , bos_token="""(BOS)""" , eos_token="""(EOS)""" , do_normalize=_UpperCAmelCase , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , _UpperCAmelCase )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , _UpperCAmelCase )
def lowerCamelCase__ (self : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = self.get_image_processor()
lowercase__ = self.get_tokenizer()
lowercase__ = AlignProcessor(tokenizer=_UpperCAmelCase , image_processor=_UpperCAmelCase )
lowercase__ = self.prepare_image_inputs()
lowercase__ = image_processor(_UpperCAmelCase , return_tensors="""np""" )
lowercase__ = processor(images=_UpperCAmelCase , return_tensors="""np""" )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1E-2 )
def lowerCamelCase__ (self : Dict ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = self.get_image_processor()
lowercase__ = self.get_tokenizer()
lowercase__ = AlignProcessor(tokenizer=_UpperCAmelCase , image_processor=_UpperCAmelCase )
lowercase__ = """lower newer"""
lowercase__ = processor(text=_UpperCAmelCase )
lowercase__ = tokenizer(_UpperCAmelCase , padding="""max_length""" , max_length=64 )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def lowerCamelCase__ (self : List[Any] ) -> Tuple:
"""simple docstring"""
lowercase__ = self.get_image_processor()
lowercase__ = self.get_tokenizer()
lowercase__ = AlignProcessor(tokenizer=_UpperCAmelCase , image_processor=_UpperCAmelCase )
lowercase__ = """lower newer"""
lowercase__ = self.prepare_image_inputs()
lowercase__ = processor(text=_UpperCAmelCase , images=_UpperCAmelCase )
self.assertListEqual(list(inputs.keys() ) , ["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""] )
# test if it raises when no input is passed
with pytest.raises(_UpperCAmelCase ):
processor()
def lowerCamelCase__ (self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = self.get_image_processor()
lowercase__ = self.get_tokenizer()
lowercase__ = AlignProcessor(tokenizer=_UpperCAmelCase , image_processor=_UpperCAmelCase )
lowercase__ = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
lowercase__ = processor.batch_decode(_UpperCAmelCase )
lowercase__ = tokenizer.batch_decode(_UpperCAmelCase )
self.assertListEqual(_UpperCAmelCase , _UpperCAmelCase )
def lowerCamelCase__ (self : List[str] ) -> Tuple:
"""simple docstring"""
lowercase__ = self.get_image_processor()
lowercase__ = self.get_tokenizer()
lowercase__ = AlignProcessor(tokenizer=_UpperCAmelCase , image_processor=_UpperCAmelCase )
lowercase__ = """lower newer"""
lowercase__ = self.prepare_image_inputs()
lowercase__ = processor(text=_UpperCAmelCase , images=_UpperCAmelCase )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 305
| 1
|
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class A ( UpperCAmelCase__ ):
'''simple docstring'''
def __init__(self : Optional[int] , _UpperCAmelCase : str , _UpperCAmelCase : int ) -> str:
"""simple docstring"""
lowercase__ = params
lowercase__ = np.array(_UpperCAmelCase )
lowercase__ = np.array([len(_UpperCAmelCase ) for t in data] )
self.check()
self.remove_long_sequences()
self.remove_empty_sequences()
self.remove_unknown_sequences()
self.check()
self.print_statistics()
def __getitem__(self : Union[str, Any] , _UpperCAmelCase : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
return (self.token_ids[index], self.lengths[index])
def __len__(self : str ) -> Union[str, Any]:
"""simple docstring"""
return len(self.lengths )
def lowerCamelCase__ (self : Dict ) -> Union[str, Any]:
"""simple docstring"""
assert len(self.token_ids ) == len(self.lengths )
assert all(self.lengths[i] == len(self.token_ids[i] ) for i in range(len(self.lengths ) ) )
def lowerCamelCase__ (self : Dict ) -> Tuple:
"""simple docstring"""
lowercase__ = self.params.max_model_input_size
lowercase__ = self.lengths > max_len
logger.info(f'''Splitting {sum(_UpperCAmelCase )} too long sequences.''' )
def divide_chunks(_UpperCAmelCase : Optional[Any] , _UpperCAmelCase : List[Any] ):
return [l[i : i + n] for i in range(0 , len(_UpperCAmelCase ) , _UpperCAmelCase )]
lowercase__ = []
lowercase__ = []
if self.params.mlm:
lowercase__ , lowercase__ = self.params.special_tok_ids["""cls_token"""], self.params.special_tok_ids["""sep_token"""]
else:
lowercase__ , lowercase__ = self.params.special_tok_ids["""bos_token"""], self.params.special_tok_ids["""eos_token"""]
for seq_, len_ in zip(self.token_ids , self.lengths ):
assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
if len_ <= max_len:
new_tok_ids.append(seq_ )
new_lengths.append(len_ )
else:
lowercase__ = []
for sub_s in divide_chunks(seq_ , max_len - 2 ):
if sub_s[0] != cls_id:
lowercase__ = np.insert(_UpperCAmelCase , 0 , _UpperCAmelCase )
if sub_s[-1] != sep_id:
lowercase__ = np.insert(_UpperCAmelCase , len(_UpperCAmelCase ) , _UpperCAmelCase )
assert len(_UpperCAmelCase ) <= max_len
assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
sub_seqs.append(_UpperCAmelCase )
new_tok_ids.extend(_UpperCAmelCase )
new_lengths.extend([len(_UpperCAmelCase ) for l in sub_seqs] )
lowercase__ = np.array(_UpperCAmelCase )
lowercase__ = np.array(_UpperCAmelCase )
def lowerCamelCase__ (self : int ) -> List[Any]:
"""simple docstring"""
lowercase__ = len(self )
lowercase__ = self.lengths > 11
lowercase__ = self.token_ids[indices]
lowercase__ = self.lengths[indices]
lowercase__ = len(self )
logger.info(f'''Remove {init_size - new_size} too short (<=11 tokens) sequences.''' )
def lowerCamelCase__ (self : Optional[int] ) -> str:
"""simple docstring"""
if "unk_token" not in self.params.special_tok_ids:
return
else:
lowercase__ = self.params.special_tok_ids["""unk_token"""]
lowercase__ = len(self )
lowercase__ = np.array([np.count_nonzero(a == unk_token_id ) for a in self.token_ids] )
lowercase__ = (unk_occs / self.lengths) < 0.5
lowercase__ = self.token_ids[indices]
lowercase__ = self.lengths[indices]
lowercase__ = len(self )
logger.info(f'''Remove {init_size - new_size} sequences with a high level of unknown tokens (50%).''' )
def lowerCamelCase__ (self : List[Any] ) -> str:
"""simple docstring"""
if not self.params.is_master:
return
logger.info(f'''{len(self )} sequences''' )
# data_len = sum(self.lengths)
# nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
# logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')
# unk_idx = self.params.special_tok_ids['unk_token']
# nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
# logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')
def lowerCamelCase__ (self : List[Any] , _UpperCAmelCase : int ) -> Optional[int]:
"""simple docstring"""
lowercase__ = [t[0] for t in batch]
lowercase__ = [t[1] for t in batch]
assert len(_UpperCAmelCase ) == len(_UpperCAmelCase )
# Max for paddings
lowercase__ = max(_UpperCAmelCase )
# Pad token ids
if self.params.mlm:
lowercase__ = self.params.special_tok_ids["""pad_token"""]
else:
lowercase__ = self.params.special_tok_ids["""unk_token"""]
lowercase__ = [list(t.astype(_UpperCAmelCase ) ) + [pad_idx] * (max_seq_len_ - len(_UpperCAmelCase )) for t in token_ids]
assert len(tk_ ) == len(_UpperCAmelCase )
assert all(len(_UpperCAmelCase ) == max_seq_len_ for t in tk_ )
lowercase__ = torch.tensor(tk_ ) # (bs, max_seq_len_)
lowercase__ = torch.tensor(_UpperCAmelCase ) # (bs)
return tk_t, lg_t
| 305
|
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def UpperCamelCase ( __magic_name__ : List[Any] ) -> Optional[int]:
"""simple docstring"""
return x + 2
class A ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase__ (self : Optional[Any] ) -> Any:
"""simple docstring"""
lowercase__ = """x = 3"""
lowercase__ = {}
lowercase__ = evaluate(_UpperCAmelCase , {} , state=_UpperCAmelCase )
assert result == 3
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3} )
lowercase__ = """x = y"""
lowercase__ = {"""y""": 5}
lowercase__ = evaluate(_UpperCAmelCase , {} , state=_UpperCAmelCase )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(_UpperCAmelCase , {"""x""": 5, """y""": 5} )
def lowerCamelCase__ (self : str ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = """y = add_two(x)"""
lowercase__ = {"""x""": 3}
lowercase__ = evaluate(_UpperCAmelCase , {"""add_two""": add_two} , state=_UpperCAmelCase )
assert result == 5
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3, """y""": 5} )
# Won't work without the tool
with CaptureStdout() as out:
lowercase__ = evaluate(_UpperCAmelCase , {} , state=_UpperCAmelCase )
assert result is None
assert "tried to execute add_two" in out.out
def lowerCamelCase__ (self : List[Any] ) -> Optional[int]:
"""simple docstring"""
lowercase__ = """x = 3"""
lowercase__ = {}
lowercase__ = evaluate(_UpperCAmelCase , {} , state=_UpperCAmelCase )
assert result == 3
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3} )
def lowerCamelCase__ (self : Optional[int] ) -> List[Any]:
"""simple docstring"""
lowercase__ = """test_dict = {'x': x, 'y': add_two(x)}"""
lowercase__ = {"""x""": 3}
lowercase__ = evaluate(_UpperCAmelCase , {"""add_two""": add_two} , state=_UpperCAmelCase )
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3, """y""": 5} )
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3, """test_dict""": {"""x""": 3, """y""": 5}} )
def lowerCamelCase__ (self : List[str] ) -> List[Any]:
"""simple docstring"""
lowercase__ = """x = 3\ny = 5"""
lowercase__ = {}
lowercase__ = evaluate(_UpperCAmelCase , {} , state=_UpperCAmelCase )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3, """y""": 5} )
def lowerCamelCase__ (self : List[Any] ) -> Dict:
"""simple docstring"""
lowercase__ = """text = f'This is x: {x}.'"""
lowercase__ = {"""x""": 3}
lowercase__ = evaluate(_UpperCAmelCase , {} , state=_UpperCAmelCase )
# evaluate returns the value of the last assignment.
assert result == "This is x: 3."
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3, """text""": """This is x: 3."""} )
def lowerCamelCase__ (self : List[str] ) -> int:
"""simple docstring"""
lowercase__ = """if x <= 3:\n y = 2\nelse:\n y = 5"""
lowercase__ = {"""x""": 3}
lowercase__ = evaluate(_UpperCAmelCase , {} , state=_UpperCAmelCase )
# evaluate returns the value of the last assignment.
assert result == 2
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3, """y""": 2} )
lowercase__ = {"""x""": 8}
lowercase__ = evaluate(_UpperCAmelCase , {} , state=_UpperCAmelCase )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(_UpperCAmelCase , {"""x""": 8, """y""": 5} )
def lowerCamelCase__ (self : Dict ) -> int:
"""simple docstring"""
lowercase__ = """test_list = [x, add_two(x)]"""
lowercase__ = {"""x""": 3}
lowercase__ = evaluate(_UpperCAmelCase , {"""add_two""": add_two} , state=_UpperCAmelCase )
self.assertListEqual(_UpperCAmelCase , [3, 5] )
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3, """test_list""": [3, 5]} )
def lowerCamelCase__ (self : Any ) -> int:
"""simple docstring"""
lowercase__ = """y = x"""
lowercase__ = {"""x""": 3}
lowercase__ = evaluate(_UpperCAmelCase , {} , state=_UpperCAmelCase )
assert result == 3
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3, """y""": 3} )
def lowerCamelCase__ (self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
lowercase__ = """test_list = [x, add_two(x)]\ntest_list[1]"""
lowercase__ = {"""x""": 3}
lowercase__ = evaluate(_UpperCAmelCase , {"""add_two""": add_two} , state=_UpperCAmelCase )
assert result == 5
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3, """test_list""": [3, 5]} )
lowercase__ = """test_dict = {'x': x, 'y': add_two(x)}\ntest_dict['y']"""
lowercase__ = {"""x""": 3}
lowercase__ = evaluate(_UpperCAmelCase , {"""add_two""": add_two} , state=_UpperCAmelCase )
assert result == 5
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3, """test_dict""": {"""x""": 3, """y""": 5}} )
def lowerCamelCase__ (self : Union[str, Any] ) -> Any:
"""simple docstring"""
lowercase__ = """x = 0\nfor i in range(3):\n x = i"""
lowercase__ = {}
lowercase__ = evaluate(_UpperCAmelCase , {"""range""": range} , state=_UpperCAmelCase )
assert result == 2
self.assertDictEqual(_UpperCAmelCase , {"""x""": 2, """i""": 2} )
| 305
| 1
|
import inspect
import os
import unittest
from pathlib import Path
import torch
import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command
class A ( unittest.TestCase ):
'''simple docstring'''
A__ = inspect.getfile(accelerate.test_utils )
A__ = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ['''scripts''', '''test_cli.py'''] )
A__ = ['''accelerate''', '''launch''']
A__ = Path.home() / '''.cache/huggingface/accelerate'''
A__ = '''default_config.yaml'''
A__ = config_folder / config_file
A__ = config_folder / '''_default_config.yaml'''
A__ = Path('''tests/test_configs''' )
@classmethod
def lowerCamelCase__ (cls : List[str] ) -> int:
"""simple docstring"""
if cls.config_path.is_file():
cls.config_path.rename(cls.changed_path )
@classmethod
def lowerCamelCase__ (cls : str ) -> List[str]:
"""simple docstring"""
if cls.changed_path.is_file():
cls.changed_path.rename(cls.config_path )
def lowerCamelCase__ (self : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = self.base_cmd
if torch.cuda.is_available() and (torch.cuda.device_count() > 1):
cmd += ["--multi_gpu"]
execute_subprocess_async(cmd + [self.test_file_path] , env=os.environ.copy() )
def lowerCamelCase__ (self : Any ) -> Any:
"""simple docstring"""
for config in sorted(self.test_config_path.glob("""**/*.yaml""" ) ):
with self.subTest(config_file=_UpperCAmelCase ):
execute_subprocess_async(
self.base_cmd + ["""--config_file""", str(_UpperCAmelCase ), self.test_file_path] , env=os.environ.copy() )
def lowerCamelCase__ (self : str ) -> Any:
"""simple docstring"""
execute_subprocess_async(["""accelerate""", """test"""] , env=os.environ.copy() )
class A ( unittest.TestCase ):
'''simple docstring'''
A__ = '''test-tpu'''
A__ = '''us-central1-a'''
A__ = '''ls'''
A__ = ['''accelerate''', '''tpu-config''']
A__ = '''cd /usr/share'''
A__ = '''tests/test_samples/test_command_file.sh'''
A__ = '''Running gcloud compute tpus tpu-vm ssh'''
def lowerCamelCase__ (self : List[Any] ) -> int:
"""simple docstring"""
lowercase__ = run_command(
self.cmd
+ ["""--command""", self.command, """--tpu_zone""", self.tpu_zone, """--tpu_name""", self.tpu_name, """--debug"""] , return_stdout=_UpperCAmelCase , )
self.assertIn(
f'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all''' , _UpperCAmelCase , )
def lowerCamelCase__ (self : Tuple ) -> List[str]:
"""simple docstring"""
lowercase__ = run_command(
self.cmd
+ [
"""--config_file""",
"""tests/test_configs/0_12_0.yaml""",
"""--command""",
self.command,
"""--tpu_zone""",
self.tpu_zone,
"""--tpu_name""",
self.tpu_name,
"""--debug""",
] , return_stdout=_UpperCAmelCase , )
self.assertIn(
f'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all''' , _UpperCAmelCase , )
def lowerCamelCase__ (self : Tuple ) -> int:
"""simple docstring"""
lowercase__ = run_command(
self.cmd + ["""--config_file""", """tests/test_configs/latest.yaml""", """--debug"""] , return_stdout=_UpperCAmelCase )
self.assertIn(
f'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo "hello world"; echo "this is a second command" --worker all''' , _UpperCAmelCase , )
def lowerCamelCase__ (self : int ) -> Dict:
"""simple docstring"""
lowercase__ = run_command(
self.cmd + ["""--config_file""", """tests/test_configs/latest.yaml""", """--command""", self.command, """--debug"""] , return_stdout=_UpperCAmelCase , )
self.assertIn(
f'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all''' , _UpperCAmelCase , )
def lowerCamelCase__ (self : str ) -> Tuple:
"""simple docstring"""
lowercase__ = run_command(
self.cmd
+ [
"""--config_file""",
"""tests/test_configs/latest.yaml""",
"""--command""",
self.command,
"""--command""",
"""echo \"Hello World\"""",
"""--debug""",
] , return_stdout=_UpperCAmelCase , )
self.assertIn(
f'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls; echo "Hello World" --worker all''' , _UpperCAmelCase , )
def lowerCamelCase__ (self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
lowercase__ = run_command(
self.cmd
+ ["""--config_file""", """tests/test_configs/latest.yaml""", """--command_file""", self.command_file, """--debug"""] , return_stdout=_UpperCAmelCase , )
self.assertIn(
f'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo "hello world"; echo "this is a second command" --worker all''' , _UpperCAmelCase , )
def lowerCamelCase__ (self : str ) -> Tuple:
"""simple docstring"""
lowercase__ = run_command(
self.cmd
+ [
"""--config_file""",
"""tests/test_configs/0_12_0.yaml""",
"""--command_file""",
self.command_file,
"""--tpu_zone""",
self.tpu_zone,
"""--tpu_name""",
self.tpu_name,
"""--debug""",
] , return_stdout=_UpperCAmelCase , )
self.assertIn(
f'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo "hello world"; echo "this is a second command" --worker all''' , _UpperCAmelCase , )
def lowerCamelCase__ (self : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = run_command(
self.cmd + ["""--config_file""", """tests/test_configs/latest.yaml""", """--install_accelerate""", """--debug"""] , return_stdout=_UpperCAmelCase , )
self.assertIn(
f'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate -U; echo "hello world"; echo "this is a second command" --worker all''' , _UpperCAmelCase , )
def lowerCamelCase__ (self : Union[str, Any] ) -> str:
"""simple docstring"""
lowercase__ = run_command(
self.cmd
+ [
"""--config_file""",
"""tests/test_configs/latest.yaml""",
"""--install_accelerate""",
"""--accelerate_version""",
"""12.0.0""",
"""--debug""",
] , return_stdout=_UpperCAmelCase , )
self.assertIn(
f'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate==12.0.0; echo "hello world"; echo "this is a second command" --worker all''' , _UpperCAmelCase , )
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|
class A :
'''simple docstring'''
def __init__(self : List[str] ) -> Tuple:
"""simple docstring"""
lowercase__ = 0
lowercase__ = 0
lowercase__ = {}
def lowerCamelCase__ (self : Dict , _UpperCAmelCase : Tuple ) -> Optional[int]:
"""simple docstring"""
if vertex not in self.adjacency:
lowercase__ = {}
self.num_vertices += 1
def lowerCamelCase__ (self : List[Any] , _UpperCAmelCase : List[Any] , _UpperCAmelCase : int , _UpperCAmelCase : List[str] ) -> Tuple:
"""simple docstring"""
self.add_vertex(_UpperCAmelCase )
self.add_vertex(_UpperCAmelCase )
if head == tail:
return
lowercase__ = weight
lowercase__ = weight
def lowerCamelCase__ (self : List[str] ) -> Optional[int]:
"""simple docstring"""
lowercase__ = self.get_edges()
for edge in edges:
lowercase__ , lowercase__ , lowercase__ = edge
edges.remove((tail, head, weight) )
for i in range(len(_UpperCAmelCase ) ):
lowercase__ = list(edges[i] )
edges.sort(key=lambda _UpperCAmelCase : e[2] )
for i in range(len(_UpperCAmelCase ) - 1 ):
if edges[i][2] >= edges[i + 1][2]:
lowercase__ = edges[i][2] + 1
for edge in edges:
lowercase__ , lowercase__ , lowercase__ = edge
lowercase__ = weight
lowercase__ = weight
def __str__(self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = """"""
for tail in self.adjacency:
for head in self.adjacency[tail]:
lowercase__ = self.adjacency[head][tail]
string += f'''{head} -> {tail} == {weight}\n'''
return string.rstrip("""\n""" )
def lowerCamelCase__ (self : Any ) -> str:
"""simple docstring"""
lowercase__ = []
for tail in self.adjacency:
for head in self.adjacency[tail]:
output.append((tail, head, self.adjacency[head][tail]) )
return output
def lowerCamelCase__ (self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
return self.adjacency.keys()
@staticmethod
def lowerCamelCase__ (_UpperCAmelCase : List[str]=None , _UpperCAmelCase : Any=None ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = Graph()
if vertices is None:
lowercase__ = []
if edges is None:
lowercase__ = []
for vertex in vertices:
g.add_vertex(_UpperCAmelCase )
for edge in edges:
g.add_edge(*_UpperCAmelCase )
return g
class A :
'''simple docstring'''
def __init__(self : Optional[Any] ) -> str:
"""simple docstring"""
lowercase__ = {}
lowercase__ = {}
def __len__(self : Optional[Any] ) -> Dict:
"""simple docstring"""
return len(self.parent )
def lowerCamelCase__ (self : str , _UpperCAmelCase : Dict ) -> Any:
"""simple docstring"""
if item in self.parent:
return self.find(_UpperCAmelCase )
lowercase__ = item
lowercase__ = 0
return item
def lowerCamelCase__ (self : List[str] , _UpperCAmelCase : Dict ) -> Any:
"""simple docstring"""
if item not in self.parent:
return self.make_set(_UpperCAmelCase )
if item != self.parent[item]:
lowercase__ = self.find(self.parent[item] )
return self.parent[item]
def lowerCamelCase__ (self : List[Any] , _UpperCAmelCase : Any , _UpperCAmelCase : List[Any] ) -> Optional[int]:
"""simple docstring"""
lowercase__ = self.find(_UpperCAmelCase )
lowercase__ = self.find(_UpperCAmelCase )
if roota == roota:
return roota
if self.rank[roota] > self.rank[roota]:
lowercase__ = roota
return roota
if self.rank[roota] < self.rank[roota]:
lowercase__ = roota
return roota
if self.rank[roota] == self.rank[roota]:
self.rank[roota] += 1
lowercase__ = roota
return roota
return None
@staticmethod
def lowerCamelCase__ (_UpperCAmelCase : str ) -> Optional[int]:
"""simple docstring"""
lowercase__ = graph.num_vertices
lowercase__ = Graph.UnionFind()
lowercase__ = []
while num_components > 1:
lowercase__ = {}
for vertex in graph.get_vertices():
lowercase__ = -1
lowercase__ = graph.get_edges()
for edge in edges:
lowercase__ , lowercase__ , lowercase__ = edge
edges.remove((tail, head, weight) )
for edge in edges:
lowercase__ , lowercase__ , lowercase__ = edge
lowercase__ = union_find.find(_UpperCAmelCase )
lowercase__ = union_find.find(_UpperCAmelCase )
if seta != seta:
if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
lowercase__ = [head, tail, weight]
if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
lowercase__ = [head, tail, weight]
for vertex in cheap_edge:
if cheap_edge[vertex] != -1:
lowercase__ , lowercase__ , lowercase__ = cheap_edge[vertex]
if union_find.find(_UpperCAmelCase ) != union_find.find(_UpperCAmelCase ):
union_find.union(_UpperCAmelCase , _UpperCAmelCase )
mst_edges.append(cheap_edge[vertex] )
lowercase__ = num_components - 1
lowercase__ = Graph.build(edges=_UpperCAmelCase )
return mst
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| 1
|
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging
if is_torch_available():
import torch
A : List[Any] = logging.get_logger(__name__)
class A ( UpperCAmelCase__ ):
'''simple docstring'''
A__ = ['''pixel_values''']
def __init__(self : Dict , _UpperCAmelCase : bool = True , _UpperCAmelCase : Optional[Dict[str, int]] = None , _UpperCAmelCase : PILImageResampling = PILImageResampling.BILINEAR , _UpperCAmelCase : bool = True , _UpperCAmelCase : Dict[str, int] = None , _UpperCAmelCase : bool = True , _UpperCAmelCase : Union[int, float] = 1 / 255 , _UpperCAmelCase : bool = True , _UpperCAmelCase : Optional[Union[float, List[float]]] = None , _UpperCAmelCase : Optional[Union[float, List[float]]] = None , **_UpperCAmelCase : Union[str, Any] , ) -> None:
"""simple docstring"""
super().__init__(**_UpperCAmelCase )
lowercase__ = size if size is not None else {"""shortest_edge""": 256}
lowercase__ = get_size_dict(_UpperCAmelCase , default_to_square=_UpperCAmelCase )
lowercase__ = crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
lowercase__ = get_size_dict(_UpperCAmelCase , param_name="""crop_size""" )
lowercase__ = do_resize
lowercase__ = size
lowercase__ = resample
lowercase__ = do_center_crop
lowercase__ = crop_size
lowercase__ = do_rescale
lowercase__ = rescale_factor
lowercase__ = do_normalize
lowercase__ = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
lowercase__ = image_std if image_std is not None else IMAGENET_STANDARD_STD
def lowerCamelCase__ (self : Union[str, Any] , _UpperCAmelCase : np.ndarray , _UpperCAmelCase : Dict[str, int] , _UpperCAmelCase : PILImageResampling = PILImageResampling.BICUBIC , _UpperCAmelCase : Optional[Union[str, ChannelDimension]] = None , **_UpperCAmelCase : str , ) -> np.ndarray:
"""simple docstring"""
lowercase__ = get_size_dict(_UpperCAmelCase , default_to_square=_UpperCAmelCase )
if "shortest_edge" not in size:
raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}''' )
lowercase__ = get_resize_output_image_size(_UpperCAmelCase , size=size["""shortest_edge"""] , default_to_square=_UpperCAmelCase )
return resize(_UpperCAmelCase , size=_UpperCAmelCase , resample=_UpperCAmelCase , data_format=_UpperCAmelCase , **_UpperCAmelCase )
def lowerCamelCase__ (self : Tuple , _UpperCAmelCase : np.ndarray , _UpperCAmelCase : Dict[str, int] , _UpperCAmelCase : Optional[Union[str, ChannelDimension]] = None , **_UpperCAmelCase : Union[str, Any] , ) -> np.ndarray:
"""simple docstring"""
lowercase__ = get_size_dict(_UpperCAmelCase )
if "height" not in size or "width" not in size:
raise ValueError(f'''The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}''' )
return center_crop(_UpperCAmelCase , size=(size["""height"""], size["""width"""]) , data_format=_UpperCAmelCase , **_UpperCAmelCase )
def lowerCamelCase__ (self : Optional[int] , _UpperCAmelCase : np.ndarray , _UpperCAmelCase : float , _UpperCAmelCase : Optional[Union[str, ChannelDimension]] = None , **_UpperCAmelCase : int ) -> np.ndarray:
"""simple docstring"""
return rescale(_UpperCAmelCase , scale=_UpperCAmelCase , data_format=_UpperCAmelCase , **_UpperCAmelCase )
def lowerCamelCase__ (self : List[Any] , _UpperCAmelCase : np.ndarray , _UpperCAmelCase : Union[float, List[float]] , _UpperCAmelCase : Union[float, List[float]] , _UpperCAmelCase : Optional[Union[str, ChannelDimension]] = None , **_UpperCAmelCase : List[str] , ) -> np.ndarray:
"""simple docstring"""
return normalize(_UpperCAmelCase , mean=_UpperCAmelCase , std=_UpperCAmelCase , data_format=_UpperCAmelCase , **_UpperCAmelCase )
def lowerCamelCase__ (self : int , _UpperCAmelCase : ImageInput , _UpperCAmelCase : Optional[bool] = None , _UpperCAmelCase : Dict[str, int] = None , _UpperCAmelCase : PILImageResampling = None , _UpperCAmelCase : bool = None , _UpperCAmelCase : Dict[str, int] = None , _UpperCAmelCase : Optional[bool] = None , _UpperCAmelCase : Optional[float] = None , _UpperCAmelCase : Optional[bool] = None , _UpperCAmelCase : Optional[Union[float, List[float]]] = None , _UpperCAmelCase : Optional[Union[float, List[float]]] = None , _UpperCAmelCase : Optional[Union[str, TensorType]] = None , _UpperCAmelCase : Union[str, ChannelDimension] = ChannelDimension.FIRST , **_UpperCAmelCase : Tuple , ) -> int:
"""simple docstring"""
lowercase__ = do_resize if do_resize is not None else self.do_resize
lowercase__ = size if size is not None else self.size
lowercase__ = get_size_dict(_UpperCAmelCase , default_to_square=_UpperCAmelCase )
lowercase__ = resample if resample is not None else self.resample
lowercase__ = do_center_crop if do_center_crop is not None else self.do_center_crop
lowercase__ = crop_size if crop_size is not None else self.crop_size
lowercase__ = get_size_dict(_UpperCAmelCase , param_name="""crop_size""" )
lowercase__ = do_rescale if do_rescale is not None else self.do_rescale
lowercase__ = rescale_factor if rescale_factor is not None else self.rescale_factor
lowercase__ = do_normalize if do_normalize is not None else self.do_normalize
lowercase__ = image_mean if image_mean is not None else self.image_mean
lowercase__ = image_std if image_std is not None else self.image_std
lowercase__ = make_list_of_images(_UpperCAmelCase )
if not valid_images(_UpperCAmelCase ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
if do_resize and size is None:
raise ValueError("""Size must be specified if do_resize is True.""" )
if do_center_crop and crop_size is None:
raise ValueError("""Crop size must be specified if do_center_crop is True.""" )
if do_rescale and rescale_factor is None:
raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("""Image mean and std must be specified if do_normalize is True.""" )
# All transformations expect numpy arrays.
lowercase__ = [to_numpy_array(_UpperCAmelCase ) for image in images]
if do_resize:
lowercase__ = [self.resize(image=_UpperCAmelCase , size=_UpperCAmelCase , resample=_UpperCAmelCase ) for image in images]
if do_center_crop:
lowercase__ = [self.center_crop(image=_UpperCAmelCase , size=_UpperCAmelCase ) for image in images]
if do_rescale:
lowercase__ = [self.rescale(image=_UpperCAmelCase , scale=_UpperCAmelCase ) for image in images]
if do_normalize:
lowercase__ = [self.normalize(image=_UpperCAmelCase , mean=_UpperCAmelCase , std=_UpperCAmelCase ) for image in images]
lowercase__ = [to_channel_dimension_format(_UpperCAmelCase , _UpperCAmelCase ) for image in images]
lowercase__ = {"""pixel_values""": images}
return BatchFeature(data=_UpperCAmelCase , tensor_type=_UpperCAmelCase )
def lowerCamelCase__ (self : Tuple , _UpperCAmelCase : Tuple , _UpperCAmelCase : List[Tuple] = None ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(_UpperCAmelCase ) != len(_UpperCAmelCase ):
raise ValueError(
"""Make sure that you pass in as many target sizes as the batch dimension of the logits""" )
if is_torch_tensor(_UpperCAmelCase ):
lowercase__ = target_sizes.numpy()
lowercase__ = []
for idx in range(len(_UpperCAmelCase ) ):
lowercase__ = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode="""bilinear""" , align_corners=_UpperCAmelCase )
lowercase__ = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(_UpperCAmelCase )
else:
lowercase__ = logits.argmax(dim=1 )
lowercase__ = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 305
|
def UpperCamelCase ( __magic_name__ : int , __magic_name__ : int ) -> int:
"""simple docstring"""
return int((input_a, input_a).count(1 ) != 0 )
def UpperCamelCase ( ) -> None:
"""simple docstring"""
assert or_gate(0 , 0 ) == 0
assert or_gate(0 , 1 ) == 1
assert or_gate(1 , 0 ) == 1
assert or_gate(1 , 1 ) == 1
if __name__ == "__main__":
print(or_gate(0, 1))
print(or_gate(1, 0))
print(or_gate(0, 0))
print(or_gate(1, 1))
| 305
| 1
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : Dict = logging.get_logger(__name__)
A : Optional[Any] = {
'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 A ( UpperCAmelCase__ ):
'''simple docstring'''
A__ = '''trocr'''
A__ = ['''past_key_values''']
A__ = {
'''num_attention_heads''': '''decoder_attention_heads''',
'''hidden_size''': '''d_model''',
'''num_hidden_layers''': '''decoder_layers''',
}
def __init__(self : Tuple , _UpperCAmelCase : Dict=5_0265 , _UpperCAmelCase : int=1024 , _UpperCAmelCase : Optional[int]=12 , _UpperCAmelCase : List[str]=16 , _UpperCAmelCase : List[Any]=4096 , _UpperCAmelCase : Optional[Any]="gelu" , _UpperCAmelCase : str=512 , _UpperCAmelCase : List[Any]=0.1 , _UpperCAmelCase : Tuple=0.0 , _UpperCAmelCase : str=0.0 , _UpperCAmelCase : Optional[Any]=2 , _UpperCAmelCase : Dict=0.02 , _UpperCAmelCase : List[str]=0.0 , _UpperCAmelCase : Optional[Any]=True , _UpperCAmelCase : Optional[Any]=False , _UpperCAmelCase : Dict=True , _UpperCAmelCase : Dict=True , _UpperCAmelCase : int=1 , _UpperCAmelCase : str=0 , _UpperCAmelCase : List[Any]=2 , **_UpperCAmelCase : str , ) -> Any:
"""simple docstring"""
lowercase__ = vocab_size
lowercase__ = d_model
lowercase__ = decoder_layers
lowercase__ = decoder_attention_heads
lowercase__ = decoder_ffn_dim
lowercase__ = activation_function
lowercase__ = max_position_embeddings
lowercase__ = dropout
lowercase__ = attention_dropout
lowercase__ = activation_dropout
lowercase__ = init_std
lowercase__ = decoder_layerdrop
lowercase__ = use_cache
lowercase__ = scale_embedding
lowercase__ = use_learned_position_embeddings
lowercase__ = layernorm_embedding
super().__init__(
pad_token_id=_UpperCAmelCase , bos_token_id=_UpperCAmelCase , eos_token_id=_UpperCAmelCase , decoder_start_token_id=_UpperCAmelCase , **_UpperCAmelCase , )
| 305
|
import argparse
from torch import nn
# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)
from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging
A : Any = logging.get_logger(__name__)
logging.set_verbosity_info()
def UpperCamelCase ( __magic_name__ : str , __magic_name__ : str ) -> List[str]:
"""simple docstring"""
if "xprophetnet" in prophetnet_checkpoint_path:
lowercase__ = XLMProphetNetForConditionalGenerationOld.from_pretrained(__magic_name__ )
lowercase__ , lowercase__ = XLMProphetNetForConditionalGeneration.from_pretrained(
__magic_name__ , output_loading_info=__magic_name__ )
else:
lowercase__ = ProphetNetForConditionalGenerationOld.from_pretrained(__magic_name__ )
lowercase__ , lowercase__ = ProphetNetForConditionalGeneration.from_pretrained(
__magic_name__ , output_loading_info=__magic_name__ )
lowercase__ = ["""key_proj""", """value_proj""", """query_proj"""]
lowercase__ = {
"""self_attn""": """ngram_self_attn""",
"""cross_attn""": """encoder_attn""",
"""cross_attn_layer_norm""": """encoder_attn_layer_norm""",
"""feed_forward_layer_norm""": """final_layer_norm""",
"""feed_forward""": """""",
"""intermediate""": """fc1""",
"""output""": """fc2""",
"""key_proj""": """k_proj""",
"""query_proj""": """q_proj""",
"""value_proj""": """v_proj""",
"""word_embeddings""": """embed_tokens""",
"""embeddings_layer_norm""": """emb_layer_norm""",
"""relative_pos_embeddings""": """relative_linear""",
"""ngram_embeddings""": """ngram_input_embed""",
"""position_embeddings""": """embed_positions""",
}
for key in loading_info["missing_keys"]:
lowercase__ = key.split(""".""" )
if attributes[0] == "lm_head":
lowercase__ = prophet
lowercase__ = prophet_old
else:
lowercase__ = prophet.prophetnet
lowercase__ = prophet_old.model
lowercase__ = False
for attribute in attributes:
if attribute in mapping:
lowercase__ = mapping[attribute]
if not hasattr(__magic_name__ , __magic_name__ ) and len(__magic_name__ ) > 0:
lowercase__ = attribute
elif hasattr(__magic_name__ , __magic_name__ ):
lowercase__ = attribute
if attribute == "weight":
assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
lowercase__ = old_model.weight
logger.info(f'''{attribute} is initialized.''' )
lowercase__ = True
break
elif attribute == "bias":
assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
lowercase__ = old_model.bias
logger.info(f'''{attribute} is initialized''' )
lowercase__ = True
break
elif attribute in special_keys and hasattr(__magic_name__ , """in_proj_weight""" ):
lowercase__ = old_model.in_proj_weight.shape[0] // 3
lowercase__ = getattr(__magic_name__ , __magic_name__ )
param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
if attribute == "query_proj":
lowercase__ = nn.Parameter(old_model.in_proj_weight[:embed_dim, :] )
lowercase__ = nn.Parameter(old_model.in_proj_bias[:embed_dim] )
elif attribute == "key_proj":
lowercase__ = nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :] )
lowercase__ = nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim] )
elif attribute == "value_proj":
lowercase__ = nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :] )
lowercase__ = nn.Parameter(old_model.in_proj_bias[2 * embed_dim :] )
lowercase__ = True
break
elif attribute == "position_embeddings":
assert (
model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
), "Hidden size has to match"
assert model.position_embeddings.weight.shape[0] == 512, "We want 512 position_embeddings."
lowercase__ = nn.Parameter(old_model.embed_positions.weight[:512, :] )
lowercase__ = True
break
if attribute.isdigit():
lowercase__ = model[int(__magic_name__ )]
lowercase__ = old_model[int(__magic_name__ )]
else:
lowercase__ = getattr(__magic_name__ , __magic_name__ )
if old_attribute == "":
lowercase__ = old_model
else:
if not hasattr(__magic_name__ , __magic_name__ ):
raise ValueError(f'''{old_model} does not have {old_attribute}''' )
lowercase__ = getattr(__magic_name__ , __magic_name__ )
if not is_key_init:
raise ValueError(f'''{key} was not correctly initialized!''' )
print(f'''Saving model to {pytorch_dump_folder_path}''' )
prophet.save_pretrained(__magic_name__ )
if __name__ == "__main__":
A : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--prophetnet_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
A : str = parser.parse_args()
convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)
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from sklearn.metrics import matthews_corrcoef
import datasets
A : Dict = '\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classifications. It takes\ninto account true and false positives and negatives and is generally\nregarded as a balanced measure which can be used even if the classes are of\nvery different sizes. The MCC is in essence a correlation coefficient value\nbetween -1 and +1. A coefficient of +1 represents a perfect prediction, 0\nan average random prediction and -1 an inverse prediction. The statistic\nis also known as the phi coefficient. [source: Wikipedia]\n'
A : Optional[int] = '\nArgs:\n predictions (list of int): Predicted labels, as returned by a model.\n references (list of int): Ground truth labels.\n sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.\nReturns:\n matthews_correlation (dict containing float): Matthews correlation.\nExamples:\n Example 1, a basic example with only predictions and references as inputs:\n >>> matthews_metric = datasets.load_metric("matthews_correlation")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3])\n >>> print(round(results[\'matthews_correlation\'], 2))\n 0.54\n\n Example 2, the same example as above, but also including sample weights:\n >>> matthews_metric = datasets.load_metric("matthews_correlation")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3],\n ... sample_weight=[0.5, 3, 1, 1, 1, 2])\n >>> print(round(results[\'matthews_correlation\'], 2))\n 0.1\n\n Example 3, the same example as above, but with sample weights that cause a negative correlation:\n >>> matthews_metric = datasets.load_metric("matthews_correlation")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3],\n ... sample_weight=[0.5, 1, 0, 0, 0, 1])\n >>> print(round(results[\'matthews_correlation\'], 2))\n -0.25\n'
A : str = '\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class A ( datasets.Metric ):
'''simple docstring'''
def lowerCamelCase__ (self : Any ) -> Union[str, Any]:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Value("""int32""" ),
"""references""": datasets.Value("""int32""" ),
} ) , reference_urls=[
"""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html"""
] , )
def lowerCamelCase__ (self : Any , _UpperCAmelCase : int , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Dict=None ) -> Dict:
"""simple docstring"""
return {
"matthews_correlation": float(matthews_corrcoef(_UpperCAmelCase , _UpperCAmelCase , sample_weight=_UpperCAmelCase ) ),
}
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import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class A ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__(self : Any , _UpperCAmelCase : Dict[str, int] , _UpperCAmelCase : List[str] , _UpperCAmelCase : int = None , _UpperCAmelCase : int = None ) -> Dict:
"""simple docstring"""
super().__init__()
lowercase__ = pad_token_id
lowercase__ = max_length
lowercase__ = vocab
lowercase__ = merges
lowercase__ = BytePairTokenizer(_UpperCAmelCase , _UpperCAmelCase , sequence_length=_UpperCAmelCase )
@classmethod
def lowerCamelCase__ (cls : Optional[int] , _UpperCAmelCase : GPTaTokenizer , *_UpperCAmelCase : List[Any] , **_UpperCAmelCase : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = [""" """.join(_UpperCAmelCase ) for m in tokenizer.bpe_ranks.keys()]
lowercase__ = tokenizer.get_vocab()
return cls(_UpperCAmelCase , _UpperCAmelCase , *_UpperCAmelCase , **_UpperCAmelCase )
@classmethod
def lowerCamelCase__ (cls : Union[str, Any] , _UpperCAmelCase : Union[str, os.PathLike] , *_UpperCAmelCase : str , **_UpperCAmelCase : List[Any] ) -> Any:
"""simple docstring"""
lowercase__ = GPTaTokenizer.from_pretrained(_UpperCAmelCase , *_UpperCAmelCase , **_UpperCAmelCase )
return cls.from_tokenizer(_UpperCAmelCase , *_UpperCAmelCase , **_UpperCAmelCase )
@classmethod
def lowerCamelCase__ (cls : Any , _UpperCAmelCase : Tuple ) -> Union[str, Any]:
"""simple docstring"""
return cls(**_UpperCAmelCase )
def lowerCamelCase__ (self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
return {
"vocab": self.vocab,
"merges": self.merges,
"max_length": self.max_length,
"pad_token_id": self.pad_token_id,
}
def lowerCamelCase__ (self : str , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : int = None ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = self.tf_tokenizer(_UpperCAmelCase )
lowercase__ = tf.ones_like(_UpperCAmelCase )
if self.pad_token_id is not None:
# pad the tokens up to max length
lowercase__ = max_length if max_length is not None else self.max_length
if max_length is not None:
lowercase__ , lowercase__ = pad_model_inputs(
_UpperCAmelCase , max_seq_length=_UpperCAmelCase , pad_value=self.pad_token_id )
return {"attention_mask": attention_mask, "input_ids": input_ids}
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|
import os
import zipfile
import pytest
from datasets.utils.extract import (
BzipaExtractor,
Extractor,
GzipExtractor,
LzaExtractor,
SevenZipExtractor,
TarExtractor,
XzExtractor,
ZipExtractor,
ZstdExtractor,
)
from .utils import require_lza, require_pyazr, require_zstandard
@pytest.mark.parametrize(
"""compression_format, is_archive""" , [
("""7z""", True),
("""bz2""", False),
("""gzip""", False),
("""lz4""", False),
("""tar""", True),
("""xz""", False),
("""zip""", True),
("""zstd""", False),
] , )
def UpperCamelCase ( __magic_name__ : Union[str, Any] , __magic_name__ : Any , __magic_name__ : str , __magic_name__ : List[str] , __magic_name__ : List[Any] , __magic_name__ : int , __magic_name__ : List[Any] , __magic_name__ : Any , __magic_name__ : int , __magic_name__ : List[Any] , __magic_name__ : int , __magic_name__ : Any , ) -> Tuple:
"""simple docstring"""
lowercase__ = {
"""7z""": (seven_zip_file, SevenZipExtractor),
"""bz2""": (bza_file, BzipaExtractor),
"""gzip""": (gz_file, GzipExtractor),
"""lz4""": (lza_file, LzaExtractor),
"""tar""": (tar_file, TarExtractor),
"""xz""": (xz_file, XzExtractor),
"""zip""": (zip_file, ZipExtractor),
"""zstd""": (zstd_file, ZstdExtractor),
}
lowercase__ , lowercase__ = input_paths_and_base_extractors[compression_format]
if input_path is None:
lowercase__ = f'''for \'{compression_format}\' compression_format, '''
if compression_format == "7z":
reason += require_pyazr.kwargs["reason"]
elif compression_format == "lz4":
reason += require_lza.kwargs["reason"]
elif compression_format == "zstd":
reason += require_zstandard.kwargs["reason"]
pytest.skip(__magic_name__ )
assert base_extractor.is_extractable(__magic_name__ )
lowercase__ = tmp_path / ("""extracted""" if is_archive else """extracted.txt""")
base_extractor.extract(__magic_name__ , __magic_name__ )
if is_archive:
assert output_path.is_dir()
for file_path in output_path.iterdir():
assert file_path.name == text_file.name
lowercase__ = file_path.read_text(encoding="""utf-8""" )
else:
lowercase__ = output_path.read_text(encoding="""utf-8""" )
lowercase__ = text_file.read_text(encoding="""utf-8""" )
assert extracted_file_content == expected_file_content
@pytest.mark.parametrize(
"""compression_format, is_archive""" , [
("""7z""", True),
("""bz2""", False),
("""gzip""", False),
("""lz4""", False),
("""tar""", True),
("""xz""", False),
("""zip""", True),
("""zstd""", False),
] , )
def UpperCamelCase ( __magic_name__ : Union[str, Any] , __magic_name__ : List[str] , __magic_name__ : List[str] , __magic_name__ : List[Any] , __magic_name__ : int , __magic_name__ : List[str] , __magic_name__ : Optional[Any] , __magic_name__ : str , __magic_name__ : int , __magic_name__ : Optional[int] , __magic_name__ : int , __magic_name__ : int , ) -> Dict:
"""simple docstring"""
lowercase__ = {
"""7z""": seven_zip_file,
"""bz2""": bza_file,
"""gzip""": gz_file,
"""lz4""": lza_file,
"""tar""": tar_file,
"""xz""": xz_file,
"""zip""": zip_file,
"""zstd""": zstd_file,
}
lowercase__ = input_paths[compression_format]
if input_path is None:
lowercase__ = f'''for \'{compression_format}\' compression_format, '''
if compression_format == "7z":
reason += require_pyazr.kwargs["reason"]
elif compression_format == "lz4":
reason += require_lza.kwargs["reason"]
elif compression_format == "zstd":
reason += require_zstandard.kwargs["reason"]
pytest.skip(__magic_name__ )
lowercase__ = Extractor.infer_extractor_format(__magic_name__ )
assert extractor_format is not None
lowercase__ = tmp_path / ("""extracted""" if is_archive else """extracted.txt""")
Extractor.extract(__magic_name__ , __magic_name__ , __magic_name__ )
if is_archive:
assert output_path.is_dir()
for file_path in output_path.iterdir():
assert file_path.name == text_file.name
lowercase__ = file_path.read_text(encoding="""utf-8""" )
else:
lowercase__ = output_path.read_text(encoding="""utf-8""" )
lowercase__ = text_file.read_text(encoding="""utf-8""" )
assert extracted_file_content == expected_file_content
@pytest.fixture
def UpperCamelCase ( __magic_name__ : List[Any] , __magic_name__ : Optional[Any] ) -> Dict:
"""simple docstring"""
import tarfile
lowercase__ = tmp_path / """data_dot_dot"""
directory.mkdir()
lowercase__ = directory / """tar_file_with_dot_dot.tar"""
with tarfile.TarFile(__magic_name__ , """w""" ) as f:
f.add(__magic_name__ , arcname=os.path.join("""..""" , text_file.name ) )
return path
@pytest.fixture
def UpperCamelCase ( __magic_name__ : List[Any] ) -> Optional[int]:
"""simple docstring"""
import tarfile
lowercase__ = tmp_path / """data_sym_link"""
directory.mkdir()
lowercase__ = directory / """tar_file_with_sym_link.tar"""
os.symlink("""..""" , directory / """subdir""" , target_is_directory=__magic_name__ )
with tarfile.TarFile(__magic_name__ , """w""" ) as f:
f.add(str(directory / """subdir""" ) , arcname="""subdir""" ) # str required by os.readlink on Windows and Python < 3.8
return path
@pytest.mark.parametrize(
"""insecure_tar_file, error_log""" , [("""tar_file_with_dot_dot""", """illegal path"""), ("""tar_file_with_sym_link""", """Symlink""")] , )
def UpperCamelCase ( __magic_name__ : List[str] , __magic_name__ : Optional[Any] , __magic_name__ : Tuple , __magic_name__ : Optional[int] , __magic_name__ : Tuple , __magic_name__ : int ) -> List[Any]:
"""simple docstring"""
lowercase__ = {
"""tar_file_with_dot_dot""": tar_file_with_dot_dot,
"""tar_file_with_sym_link""": tar_file_with_sym_link,
}
lowercase__ = insecure_tar_files[insecure_tar_file]
lowercase__ = tmp_path / """extracted"""
TarExtractor.extract(__magic_name__ , __magic_name__ )
assert caplog.text
for record in caplog.records:
assert record.levelname == "ERROR"
assert error_log in record.msg
def UpperCamelCase ( __magic_name__ : Optional[int] ) -> Any:
"""simple docstring"""
lowercase__ = tmpdir / """not_a_zip_file"""
# From: https://github.com/python/cpython/pull/5053
lowercase__ = (
B"""\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00\x01\x00\x00"""
B"""\x00\x02\x08\x06\x00\x00\x00\x99\x81\xb6'\x00\x00\x00\x15I"""
B"""DATx\x01\x01\n\x00\xf5\xff\x00PK\x05\x06\x00PK\x06\x06\x07"""
B"""\xac\x01N\xc6|a\r\x00\x00\x00\x00IEND\xaeB`\x82"""
)
with not_a_zip_file.open("""wb""" ) as f:
f.write(__magic_name__ )
assert zipfile.is_zipfile(str(__magic_name__ ) ) # is a false positive for `zipfile`
assert not ZipExtractor.is_extractable(__magic_name__ ) # but we're right
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from __future__ import annotations
from functools import lru_cache
from math import ceil
A : Optional[int] = 1_0_0
A : int = set(range(3, NUM_PRIMES, 2))
primes.add(2)
A : int
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prime not in primes:
continue
primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime)))
@lru_cache(maxsize=100 )
def UpperCamelCase ( __magic_name__ : int ) -> set[int]:
"""simple docstring"""
if number_to_partition < 0:
return set()
elif number_to_partition == 0:
return {1}
lowercase__ = set()
lowercase__ = 42
lowercase__ = 42
for prime in primes:
if prime > number_to_partition:
continue
for sub in partition(number_to_partition - prime ):
ret.add(sub * prime )
return ret
def UpperCamelCase ( __magic_name__ : int = 5000 ) -> int | None:
"""simple docstring"""
for number_to_partition in range(1 , __magic_name__ ):
if len(partition(__magic_name__ ) ) > number_unique_partitions:
return number_to_partition
return None
if __name__ == "__main__":
print(F'{solution() = }')
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from collections.abc import Callable
import numpy as np
def UpperCamelCase ( __magic_name__ : Callable , __magic_name__ : float , __magic_name__ : float , __magic_name__ : float , __magic_name__ : float ) -> np.array:
"""simple docstring"""
lowercase__ = int(np.ceil((x_end - xa) / step_size ) )
lowercase__ = np.zeros((n + 1,) )
lowercase__ = ya
lowercase__ = xa
for k in range(__magic_name__ ):
lowercase__ = y[k] + step_size * ode_func(__magic_name__ , y[k] )
lowercase__ = y[k] + (
(step_size / 2) * (ode_func(__magic_name__ , y[k] ) + ode_func(x + step_size , __magic_name__ ))
)
x += step_size
return y
if __name__ == "__main__":
import doctest
doctest.testmod()
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def UpperCamelCase ( __magic_name__ : List[Any] ) -> Optional[int]:
"""simple docstring"""
lowercase__ = [0] * len(__magic_name__ )
lowercase__ = []
lowercase__ = [1] * len(__magic_name__ )
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(__magic_name__ ) ):
if indegree[i] == 0:
queue.append(__magic_name__ )
while queue:
lowercase__ = queue.pop(0 )
for x in graph[vertex]:
indegree[x] -= 1
if long_dist[vertex] + 1 > long_dist[x]:
lowercase__ = long_dist[vertex] + 1
if indegree[x] == 0:
queue.append(__magic_name__ )
print(max(__magic_name__ ) )
# Adjacency list of Graph
A : Union[str, Any] = {0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}
longest_distance(graph)
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import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures
# Importing the dataset
A : List[Any] = pd.read_csv(
'https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/'
'position_salaries.csv'
)
A : Union[str, Any] = dataset.iloc[:, 1:2].values
A : Tuple = dataset.iloc[:, 2].values
A , A , A , A : Any = train_test_split(X, y, test_size=0.2, random_state=0)
A : int = PolynomialFeatures(degree=4)
A : List[Any] = poly_reg.fit_transform(X)
A : Optional[int] = LinearRegression()
pol_reg.fit(X_poly, y)
def UpperCamelCase ( ) -> List[Any]:
"""simple docstring"""
plt.scatter(__magic_name__ , __magic_name__ , color="""red""" )
plt.plot(__magic_name__ , pol_reg.predict(poly_reg.fit_transform(__magic_name__ ) ) , color="""blue""" )
plt.title("""Truth or Bluff (Linear Regression)""" )
plt.xlabel("""Position level""" )
plt.ylabel("""Salary""" )
plt.show()
if __name__ == "__main__":
viz_polymonial()
# Predicting a new result with Polymonial Regression
pol_reg.predict(poly_reg.fit_transform([[5.5]]))
# output should be 132148.43750003
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# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
from accelerate import PartialState
from accelerate.utils.operations import broadcast, gather, gather_object, pad_across_processes, reduce
def UpperCamelCase ( __magic_name__ : Any ) -> Optional[int]:
"""simple docstring"""
return (torch.arange(state.num_processes ) + 1.0 + (state.num_processes * state.process_index)).to(state.device )
def UpperCamelCase ( __magic_name__ : int ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = create_tensor(__magic_name__ )
lowercase__ = gather(__magic_name__ )
assert gathered_tensor.tolist() == list(range(1 , state.num_processes**2 + 1 ) )
def UpperCamelCase ( __magic_name__ : Optional[int] ) -> Tuple:
"""simple docstring"""
lowercase__ = [state.process_index]
lowercase__ = gather_object(__magic_name__ )
assert len(__magic_name__ ) == state.num_processes, f'''{gathered_obj}, {len(__magic_name__ )} != {state.num_processes}'''
assert gathered_obj == list(range(state.num_processes ) ), f'''{gathered_obj} != {list(range(state.num_processes ) )}'''
def UpperCamelCase ( __magic_name__ : str ) -> Dict:
"""simple docstring"""
lowercase__ = create_tensor(__magic_name__ )
lowercase__ = broadcast(__magic_name__ )
assert broadcasted_tensor.shape == torch.Size([state.num_processes] )
assert broadcasted_tensor.tolist() == list(range(1 , state.num_processes + 1 ) )
def UpperCamelCase ( __magic_name__ : str ) -> Dict:
"""simple docstring"""
if state.is_main_process:
lowercase__ = torch.arange(state.num_processes + 1 ).to(state.device )
else:
lowercase__ = torch.arange(state.num_processes ).to(state.device )
lowercase__ = pad_across_processes(__magic_name__ )
assert padded_tensor.shape == torch.Size([state.num_processes + 1] )
if not state.is_main_process:
assert padded_tensor.tolist() == list(range(0 , state.num_processes ) ) + [0]
def UpperCamelCase ( __magic_name__ : List[Any] ) -> Optional[int]:
"""simple docstring"""
if state.num_processes != 2:
return
lowercase__ = create_tensor(__magic_name__ )
lowercase__ = reduce(__magic_name__ , """sum""" )
lowercase__ = torch.tensor([4.0, 6] ).to(state.device )
assert torch.allclose(__magic_name__ , __magic_name__ ), f'''{reduced_tensor} != {truth_tensor}'''
def UpperCamelCase ( __magic_name__ : Dict ) -> int:
"""simple docstring"""
if state.num_processes != 2:
return
lowercase__ = create_tensor(__magic_name__ )
lowercase__ = reduce(__magic_name__ , """mean""" )
lowercase__ = torch.tensor([2.0, 3] ).to(state.device )
assert torch.allclose(__magic_name__ , __magic_name__ ), f'''{reduced_tensor} != {truth_tensor}'''
def UpperCamelCase ( __magic_name__ : str ) -> int:
"""simple docstring"""
main()
def UpperCamelCase ( ) -> Optional[int]:
"""simple docstring"""
lowercase__ = PartialState()
state.print(f'''State: {state}''' )
state.print("""testing gather""" )
test_gather(__magic_name__ )
state.print("""testing gather_object""" )
test_gather_object(__magic_name__ )
state.print("""testing broadcast""" )
test_broadcast(__magic_name__ )
state.print("""testing pad_across_processes""" )
test_pad_across_processes(__magic_name__ )
state.print("""testing reduce_sum""" )
test_reduce_sum(__magic_name__ )
state.print("""testing reduce_mean""" )
test_reduce_mean(__magic_name__ )
if __name__ == "__main__":
main()
| 305
| 1
|
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class A ( UpperCAmelCase__ ):
'''simple docstring'''
A__ = ['''image_processor''', '''tokenizer''']
A__ = '''LayoutLMv2ImageProcessor'''
A__ = ('''LayoutXLMTokenizer''', '''LayoutXLMTokenizerFast''')
def __init__(self : List[str] , _UpperCAmelCase : Optional[Any]=None , _UpperCAmelCase : Tuple=None , **_UpperCAmelCase : Optional[Any] ) -> Tuple:
"""simple docstring"""
if "feature_extractor" in kwargs:
warnings.warn(
"""The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
""" instead.""" , _UpperCAmelCase , )
lowercase__ = kwargs.pop("""feature_extractor""" )
lowercase__ = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("""You need to specify an `image_processor`.""" )
if tokenizer is None:
raise ValueError("""You need to specify a `tokenizer`.""" )
super().__init__(_UpperCAmelCase , _UpperCAmelCase )
def __call__(self : Optional[Any] , _UpperCAmelCase : int , _UpperCAmelCase : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None , _UpperCAmelCase : Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None , _UpperCAmelCase : Union[List[List[int]], List[List[List[int]]]] = None , _UpperCAmelCase : Optional[Union[List[int], List[List[int]]]] = None , _UpperCAmelCase : bool = True , _UpperCAmelCase : Union[bool, str, PaddingStrategy] = False , _UpperCAmelCase : Union[bool, str, TruncationStrategy] = None , _UpperCAmelCase : Optional[int] = None , _UpperCAmelCase : int = 0 , _UpperCAmelCase : Optional[int] = None , _UpperCAmelCase : Optional[bool] = None , _UpperCAmelCase : Optional[bool] = None , _UpperCAmelCase : bool = False , _UpperCAmelCase : bool = False , _UpperCAmelCase : bool = False , _UpperCAmelCase : bool = False , _UpperCAmelCase : bool = True , _UpperCAmelCase : Optional[Union[str, TensorType]] = None , **_UpperCAmelCase : Optional[int] , ) -> BatchEncoding:
"""simple docstring"""
if self.image_processor.apply_ocr and (boxes is not None):
raise ValueError(
"""You cannot provide bounding boxes """
"""if you initialized the image processor with apply_ocr set to True.""" )
if self.image_processor.apply_ocr and (word_labels is not None):
raise ValueError(
"""You cannot provide word labels if you initialized the image processor with apply_ocr set to True.""" )
if return_overflowing_tokens is True and return_offsets_mapping is False:
raise ValueError("""You cannot return overflowing tokens without returning the offsets mapping.""" )
# first, apply the image processor
lowercase__ = self.image_processor(images=_UpperCAmelCase , return_tensors=_UpperCAmelCase )
# second, apply the tokenizer
if text is not None and self.image_processor.apply_ocr and text_pair is None:
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
lowercase__ = [text] # add batch dimension (as the image processor always adds a batch dimension)
lowercase__ = features["""words"""]
lowercase__ = self.tokenizer(
text=text if text is not None else features["""words"""] , text_pair=text_pair if text_pair is not None else None , boxes=boxes if boxes is not None else features["""boxes"""] , word_labels=_UpperCAmelCase , add_special_tokens=_UpperCAmelCase , padding=_UpperCAmelCase , truncation=_UpperCAmelCase , max_length=_UpperCAmelCase , stride=_UpperCAmelCase , pad_to_multiple_of=_UpperCAmelCase , return_token_type_ids=_UpperCAmelCase , return_attention_mask=_UpperCAmelCase , return_overflowing_tokens=_UpperCAmelCase , return_special_tokens_mask=_UpperCAmelCase , return_offsets_mapping=_UpperCAmelCase , return_length=_UpperCAmelCase , verbose=_UpperCAmelCase , return_tensors=_UpperCAmelCase , **_UpperCAmelCase , )
# add pixel values
lowercase__ = features.pop("""pixel_values""" )
if return_overflowing_tokens is True:
lowercase__ = self.get_overflowing_images(_UpperCAmelCase , encoded_inputs["""overflow_to_sample_mapping"""] )
lowercase__ = images
return encoded_inputs
def lowerCamelCase__ (self : Optional[Any] , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Union[str, Any] ) -> Dict:
"""simple docstring"""
lowercase__ = []
for sample_idx in overflow_to_sample_mapping:
images_with_overflow.append(images[sample_idx] )
if len(_UpperCAmelCase ) != len(_UpperCAmelCase ):
raise ValueError(
"""Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"""
f''' {len(_UpperCAmelCase )} and {len(_UpperCAmelCase )}''' )
return images_with_overflow
def lowerCamelCase__ (self : Tuple , *_UpperCAmelCase : int , **_UpperCAmelCase : Any ) -> Optional[int]:
"""simple docstring"""
return self.tokenizer.batch_decode(*_UpperCAmelCase , **_UpperCAmelCase )
def lowerCamelCase__ (self : List[str] , *_UpperCAmelCase : str , **_UpperCAmelCase : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
return self.tokenizer.decode(*_UpperCAmelCase , **_UpperCAmelCase )
@property
def lowerCamelCase__ (self : Tuple ) -> int:
"""simple docstring"""
return ["input_ids", "bbox", "attention_mask", "image"]
@property
def lowerCamelCase__ (self : Optional[Any] ) -> str:
"""simple docstring"""
warnings.warn(
"""`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.""" , _UpperCAmelCase , )
return self.image_processor_class
@property
def lowerCamelCase__ (self : Union[str, Any] ) -> int:
"""simple docstring"""
warnings.warn(
"""`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.""" , _UpperCAmelCase , )
return self.image_processor
| 305
|
def UpperCamelCase ( __magic_name__ : str ) -> int:
"""simple docstring"""
assert column_title.isupper()
lowercase__ = 0
lowercase__ = len(__magic_name__ ) - 1
lowercase__ = 0
while index >= 0:
lowercase__ = (ord(column_title[index] ) - 64) * pow(26 , __magic_name__ )
answer += value
power += 1
index -= 1
return answer
if __name__ == "__main__":
from doctest import testmod
testmod()
| 305
| 1
|
def UpperCamelCase ( __magic_name__ : Optional[int] , __magic_name__ : int , __magic_name__ : Any , __magic_name__ : Optional[int] ) -> str:
"""simple docstring"""
lowercase__ = [False] * len(__magic_name__ )
lowercase__ = []
queue.append(__magic_name__ )
lowercase__ = True
while queue:
lowercase__ = queue.pop(0 )
for ind in range(len(graph[u] ) ):
if visited[ind] is False and graph[u][ind] > 0:
queue.append(__magic_name__ )
lowercase__ = True
lowercase__ = u
return visited[t]
def UpperCamelCase ( __magic_name__ : Optional[int] , __magic_name__ : Optional[Any] , __magic_name__ : Dict ) -> str:
"""simple docstring"""
lowercase__ = [-1] * (len(__magic_name__ ))
lowercase__ = 0
while bfs(__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ ):
lowercase__ = float("""Inf""" )
lowercase__ = sink
while s != source:
# Find the minimum value in select path
lowercase__ = min(__magic_name__ , graph[parent[s]][s] )
lowercase__ = parent[s]
max_flow += path_flow
lowercase__ = sink
while v != source:
lowercase__ = parent[v]
graph[u][v] -= path_flow
graph[v][u] += path_flow
lowercase__ = parent[v]
return max_flow
A : Optional[Any] = [
[0, 1_6, 1_3, 0, 0, 0],
[0, 0, 1_0, 1_2, 0, 0],
[0, 4, 0, 0, 1_4, 0],
[0, 0, 9, 0, 0, 2_0],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
A , A : str = 0, 5
print(ford_fulkerson(graph, source, sink))
| 305
|
import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def UpperCamelCase ( __magic_name__ : list , __magic_name__ : list , __magic_name__ : list , __magic_name__ : list , __magic_name__ : list ) -> float:
"""simple docstring"""
lowercase__ = np.array([[1, item, train_mtch[i]] for i, item in enumerate(__magic_name__ )] )
lowercase__ = np.array(__magic_name__ )
lowercase__ = np.dot(np.dot(np.linalg.inv(np.dot(x.transpose() , __magic_name__ ) ) , x.transpose() ) , __magic_name__ )
return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2] )
def UpperCamelCase ( __magic_name__ : list , __magic_name__ : list , __magic_name__ : list ) -> float:
"""simple docstring"""
lowercase__ = (1, 2, 1)
lowercase__ = (1, 1, 0, 7)
lowercase__ = SARIMAX(
__magic_name__ , exog=__magic_name__ , order=__magic_name__ , seasonal_order=__magic_name__ )
lowercase__ = model.fit(disp=__magic_name__ , maxiter=600 , method="""nm""" )
lowercase__ = model_fit.predict(1 , len(__magic_name__ ) , exog=[test_match] )
return result[0]
def UpperCamelCase ( __magic_name__ : list , __magic_name__ : list , __magic_name__ : list ) -> float:
"""simple docstring"""
lowercase__ = SVR(kernel="""rbf""" , C=1 , gamma=0.1 , epsilon=0.1 )
regressor.fit(__magic_name__ , __magic_name__ )
lowercase__ = regressor.predict(__magic_name__ )
return y_pred[0]
def UpperCamelCase ( __magic_name__ : list ) -> float:
"""simple docstring"""
train_user.sort()
lowercase__ = np.percentile(__magic_name__ , 25 )
lowercase__ = np.percentile(__magic_name__ , 75 )
lowercase__ = qa - qa
lowercase__ = qa - (iqr * 0.1)
return low_lim
def UpperCamelCase ( __magic_name__ : list , __magic_name__ : float ) -> bool:
"""simple docstring"""
lowercase__ = 0
lowercase__ = 0
for i in list_vote:
if i > actual_result:
lowercase__ = not_safe + 1
else:
if abs(abs(__magic_name__ ) - abs(__magic_name__ ) ) <= 0.1:
safe += 1
else:
not_safe += 1
return safe > not_safe
if __name__ == "__main__":
# data_input_df = pd.read_csv("ex_data.csv", header=None)
A : Dict = [[1_8_2_3_1, 0.0, 1], [2_2_6_2_1, 1.0, 2], [1_5_6_7_5, 0.0, 3], [2_3_5_8_3, 1.0, 4]]
A : str = pd.DataFrame(
data_input, columns=['total_user', 'total_even', 'days']
)
A : Any = Normalizer().fit_transform(data_input_df.values)
# split data
A : Optional[int] = normalize_df[:, 2].tolist()
A : Any = normalize_df[:, 0].tolist()
A : str = normalize_df[:, 1].tolist()
# for svr (input variable = total date and total match)
A : int = normalize_df[:, [1, 2]].tolist()
A : Any = x[: len(x) - 1]
A : Tuple = x[len(x) - 1 :]
# for linear regression & sarimax
A : Optional[int] = total_date[: len(total_date) - 1]
A : Optional[int] = total_user[: len(total_user) - 1]
A : str = total_match[: len(total_match) - 1]
A : Union[str, Any] = total_date[len(total_date) - 1 :]
A : List[str] = total_user[len(total_user) - 1 :]
A : str = total_match[len(total_match) - 1 :]
# voting system with forecasting
A : int = [
linear_regression_prediction(
trn_date, trn_user, trn_match, tst_date, tst_match
),
sarimax_predictor(trn_user, trn_match, tst_match),
support_vector_regressor(x_train, x_test, trn_user),
]
# check the safety of today's data
A : int = '' if data_safety_checker(res_vote, tst_user) else 'not '
print('Today\'s data is {not_str}safe.')
| 305
| 1
|
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class A ( UpperCAmelCase__ ):
'''simple docstring'''
A__ = ['''image_processor''', '''tokenizer''']
A__ = '''ViltImageProcessor'''
A__ = ('''BertTokenizer''', '''BertTokenizerFast''')
def __init__(self : List[Any] , _UpperCAmelCase : Optional[Any]=None , _UpperCAmelCase : int=None , **_UpperCAmelCase : Tuple ) -> str:
"""simple docstring"""
lowercase__ = None
if "feature_extractor" in kwargs:
warnings.warn(
"""The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
""" instead.""" , _UpperCAmelCase , )
lowercase__ = kwargs.pop("""feature_extractor""" )
lowercase__ = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("""You need to specify an `image_processor`.""" )
if tokenizer is None:
raise ValueError("""You need to specify a `tokenizer`.""" )
super().__init__(_UpperCAmelCase , _UpperCAmelCase )
lowercase__ = self.image_processor
def __call__(self : int , _UpperCAmelCase : Dict , _UpperCAmelCase : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None , _UpperCAmelCase : bool = True , _UpperCAmelCase : Union[bool, str, PaddingStrategy] = False , _UpperCAmelCase : Union[bool, str, TruncationStrategy] = None , _UpperCAmelCase : Optional[int] = None , _UpperCAmelCase : int = 0 , _UpperCAmelCase : Optional[int] = None , _UpperCAmelCase : Optional[bool] = None , _UpperCAmelCase : Optional[bool] = None , _UpperCAmelCase : bool = False , _UpperCAmelCase : bool = False , _UpperCAmelCase : bool = False , _UpperCAmelCase : bool = False , _UpperCAmelCase : bool = True , _UpperCAmelCase : Optional[Union[str, TensorType]] = None , **_UpperCAmelCase : Optional[int] , ) -> BatchEncoding:
"""simple docstring"""
lowercase__ = self.tokenizer(
text=_UpperCAmelCase , add_special_tokens=_UpperCAmelCase , padding=_UpperCAmelCase , truncation=_UpperCAmelCase , max_length=_UpperCAmelCase , stride=_UpperCAmelCase , pad_to_multiple_of=_UpperCAmelCase , return_token_type_ids=_UpperCAmelCase , return_attention_mask=_UpperCAmelCase , return_overflowing_tokens=_UpperCAmelCase , return_special_tokens_mask=_UpperCAmelCase , return_offsets_mapping=_UpperCAmelCase , return_length=_UpperCAmelCase , verbose=_UpperCAmelCase , return_tensors=_UpperCAmelCase , **_UpperCAmelCase , )
# add pixel_values + pixel_mask
lowercase__ = self.image_processor(_UpperCAmelCase , return_tensors=_UpperCAmelCase )
encoding.update(_UpperCAmelCase )
return encoding
def lowerCamelCase__ (self : Any , *_UpperCAmelCase : str , **_UpperCAmelCase : str ) -> Any:
"""simple docstring"""
return self.tokenizer.batch_decode(*_UpperCAmelCase , **_UpperCAmelCase )
def lowerCamelCase__ (self : int , *_UpperCAmelCase : List[str] , **_UpperCAmelCase : List[str] ) -> Dict:
"""simple docstring"""
return self.tokenizer.decode(*_UpperCAmelCase , **_UpperCAmelCase )
@property
def lowerCamelCase__ (self : List[str] ) -> Optional[int]:
"""simple docstring"""
lowercase__ = self.tokenizer.model_input_names
lowercase__ = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def lowerCamelCase__ (self : int ) -> int:
"""simple docstring"""
warnings.warn(
"""`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.""" , _UpperCAmelCase , )
return self.image_processor_class
@property
def lowerCamelCase__ (self : Any ) -> List[Any]:
"""simple docstring"""
warnings.warn(
"""`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.""" , _UpperCAmelCase , )
return self.image_processor
| 305
|
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def UpperCamelCase ( __magic_name__ : Optional[Any] ) -> List[Any]:
"""simple docstring"""
lowercase__ = tmp_path / """file.csv"""
lowercase__ = textwrap.dedent(
"""\
header1,header2
1,2
10,20
""" )
with open(__magic_name__ , """w""" ) as f:
f.write(__magic_name__ )
return str(__magic_name__ )
@pytest.fixture
def UpperCamelCase ( __magic_name__ : str ) -> Tuple:
"""simple docstring"""
lowercase__ = tmp_path / """malformed_file.csv"""
lowercase__ = textwrap.dedent(
"""\
header1,header2
1,2
10,20,
""" )
with open(__magic_name__ , """w""" ) as f:
f.write(__magic_name__ )
return str(__magic_name__ )
@pytest.fixture
def UpperCamelCase ( __magic_name__ : List[Any] , __magic_name__ : List[str] ) -> str:
"""simple docstring"""
lowercase__ = tmp_path / """csv_with_image.csv"""
lowercase__ = textwrap.dedent(
f'''\
image
{image_file}
''' )
with open(__magic_name__ , """w""" ) as f:
f.write(__magic_name__ )
return str(__magic_name__ )
@pytest.fixture
def UpperCamelCase ( __magic_name__ : Tuple ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = tmp_path / """csv_with_label.csv"""
lowercase__ = textwrap.dedent(
"""\
label
good
bad
good
""" )
with open(__magic_name__ , """w""" ) as f:
f.write(__magic_name__ )
return str(__magic_name__ )
@pytest.fixture
def UpperCamelCase ( __magic_name__ : Dict ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = tmp_path / """csv_with_int_list.csv"""
lowercase__ = textwrap.dedent(
"""\
int_list
1 2 3
4 5 6
7 8 9
""" )
with open(__magic_name__ , """w""" ) as f:
f.write(__magic_name__ )
return str(__magic_name__ )
def UpperCamelCase ( __magic_name__ : Tuple , __magic_name__ : Tuple , __magic_name__ : Tuple ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = Csv()
lowercase__ = csv._generate_tables([[csv_file, malformed_csv_file]] )
with pytest.raises(__magic_name__ , match="""Error tokenizing data""" ):
for _ in generator:
pass
assert any(
record.levelname == """ERROR"""
and """Failed to read file""" in record.message
and os.path.basename(__magic_name__ ) in record.message
for record in caplog.records )
@require_pil
def UpperCamelCase ( __magic_name__ : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
with open(__magic_name__ , encoding="""utf-8""" ) as f:
lowercase__ = f.read().splitlines()[1]
lowercase__ = Csv(encoding="""utf-8""" , features=Features({"""image""": Image()} ) )
lowercase__ = csv._generate_tables([[csv_file_with_image]] )
lowercase__ = pa.concat_tables([table for _, table in generator] )
assert pa_table.schema.field("""image""" ).type == Image()()
lowercase__ = pa_table.to_pydict()["""image"""]
assert generated_content == [{"path": image_file, "bytes": None}]
def UpperCamelCase ( __magic_name__ : Optional[Any] ) -> str:
"""simple docstring"""
with open(__magic_name__ , encoding="""utf-8""" ) as f:
lowercase__ = f.read().splitlines()[1:]
lowercase__ = Csv(encoding="""utf-8""" , features=Features({"""label""": ClassLabel(names=["""good""", """bad"""] )} ) )
lowercase__ = csv._generate_tables([[csv_file_with_label]] )
lowercase__ = pa.concat_tables([table for _, table in generator] )
assert pa_table.schema.field("""label""" ).type == ClassLabel(names=["""good""", """bad"""] )()
lowercase__ = pa_table.to_pydict()["""label"""]
assert generated_content == [ClassLabel(names=["""good""", """bad"""] ).straint(__magic_name__ ) for label in labels]
def UpperCamelCase ( __magic_name__ : Any ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = Csv(encoding="""utf-8""" , sep=""",""" , converters={"""int_list""": lambda __magic_name__ : [int(__magic_name__ ) for i in x.split()]} )
lowercase__ = csv._generate_tables([[csv_file_with_int_list]] )
lowercase__ = pa.concat_tables([table for _, table in generator] )
assert pa.types.is_list(pa_table.schema.field("""int_list""" ).type )
lowercase__ = pa_table.to_pydict()["""int_list"""]
assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
| 305
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|
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
class A ( TensorFormatter[Mapping, '''torch.Tensor''', Mapping] ):
'''simple docstring'''
def __init__(self : List[Any] , _UpperCAmelCase : Union[str, Any]=None , **_UpperCAmelCase : Tuple ) -> int:
"""simple docstring"""
super().__init__(features=_UpperCAmelCase )
lowercase__ = torch_tensor_kwargs
import torch # noqa import torch at initialization
def lowerCamelCase__ (self : List[Any] , _UpperCAmelCase : Optional[int] ) -> Tuple:
"""simple docstring"""
import torch
if isinstance(_UpperCAmelCase , _UpperCAmelCase ) and column:
if all(
isinstance(_UpperCAmelCase , torch.Tensor ) and x.shape == column[0].shape and x.dtype == column[0].dtype
for x in column ):
return torch.stack(_UpperCAmelCase )
return column
def lowerCamelCase__ (self : Optional[int] , _UpperCAmelCase : Dict ) -> List[str]:
"""simple docstring"""
import torch
if isinstance(_UpperCAmelCase , (str, bytes, type(_UpperCAmelCase )) ):
return value
elif isinstance(_UpperCAmelCase , (np.character, np.ndarray) ) and np.issubdtype(value.dtype , np.character ):
return value.tolist()
lowercase__ = {}
if isinstance(_UpperCAmelCase , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.integer ):
lowercase__ = {"""dtype""": torch.intaa}
elif isinstance(_UpperCAmelCase , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.floating ):
lowercase__ = {"""dtype""": torch.floataa}
elif config.PIL_AVAILABLE and "PIL" in sys.modules:
import PIL.Image
if isinstance(_UpperCAmelCase , PIL.Image.Image ):
lowercase__ = np.asarray(_UpperCAmelCase )
return torch.tensor(_UpperCAmelCase , **{**default_dtype, **self.torch_tensor_kwargs} )
def lowerCamelCase__ (self : int , _UpperCAmelCase : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
import torch
# support for torch, tf, jax etc.
if hasattr(_UpperCAmelCase , """__array__""" ) and not isinstance(_UpperCAmelCase , torch.Tensor ):
lowercase__ = data_struct.__array__()
# support for nested types like struct of list of struct
if isinstance(_UpperCAmelCase , np.ndarray ):
if data_struct.dtype == object: # torch tensors cannot be instantied from an array of objects
return self._consolidate([self.recursive_tensorize(_UpperCAmelCase ) for substruct in data_struct] )
elif isinstance(_UpperCAmelCase , (list, tuple) ):
return self._consolidate([self.recursive_tensorize(_UpperCAmelCase ) for substruct in data_struct] )
return self._tensorize(_UpperCAmelCase )
def lowerCamelCase__ (self : Any , _UpperCAmelCase : dict ) -> Union[str, Any]:
"""simple docstring"""
return map_nested(self._recursive_tensorize , _UpperCAmelCase , map_list=_UpperCAmelCase )
def lowerCamelCase__ (self : List[Any] , _UpperCAmelCase : pa.Table ) -> Mapping:
"""simple docstring"""
lowercase__ = self.numpy_arrow_extractor().extract_row(_UpperCAmelCase )
lowercase__ = self.python_features_decoder.decode_row(_UpperCAmelCase )
return self.recursive_tensorize(_UpperCAmelCase )
def lowerCamelCase__ (self : List[str] , _UpperCAmelCase : pa.Table ) -> "torch.Tensor":
"""simple docstring"""
lowercase__ = self.numpy_arrow_extractor().extract_column(_UpperCAmelCase )
lowercase__ = self.python_features_decoder.decode_column(_UpperCAmelCase , pa_table.column_names[0] )
lowercase__ = self.recursive_tensorize(_UpperCAmelCase )
lowercase__ = self._consolidate(_UpperCAmelCase )
return column
def lowerCamelCase__ (self : Optional[int] , _UpperCAmelCase : pa.Table ) -> Mapping:
"""simple docstring"""
lowercase__ = self.numpy_arrow_extractor().extract_batch(_UpperCAmelCase )
lowercase__ = self.python_features_decoder.decode_batch(_UpperCAmelCase )
lowercase__ = self.recursive_tensorize(_UpperCAmelCase )
for column_name in batch:
lowercase__ = self._consolidate(batch[column_name] )
return batch
| 305
|
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
A : int = {'configuration_dpt': ['DPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DPTConfig']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Union[str, Any] = ['DPTFeatureExtractor']
A : int = ['DPTImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Tuple = [
'DPT_PRETRAINED_MODEL_ARCHIVE_LIST',
'DPTForDepthEstimation',
'DPTForSemanticSegmentation',
'DPTModel',
'DPTPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_dpt import DPTFeatureExtractor
from .image_processing_dpt import DPTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_dpt import (
DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
DPTForDepthEstimation,
DPTForSemanticSegmentation,
DPTModel,
DPTPreTrainedModel,
)
else:
import sys
A : str = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 305
| 1
|
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def UpperCamelCase ( __magic_name__ : str , __magic_name__ : complex , __magic_name__ : str = "x" , __magic_name__ : float = 10**-10 , __magic_name__ : int = 1 , ) -> complex:
"""simple docstring"""
lowercase__ = symbols(__magic_name__ )
lowercase__ = lambdify(__magic_name__ , __magic_name__ )
lowercase__ = lambdify(__magic_name__ , diff(__magic_name__ , __magic_name__ ) )
lowercase__ = starting_point
while True:
if diff_function(__magic_name__ ) != 0:
lowercase__ = prev_guess - multiplicity * func(__magic_name__ ) / diff_function(
__magic_name__ )
else:
raise ZeroDivisionError("""Could not find root""" ) from None
# Precision is checked by comparing the difference of consecutive guesses
if abs(next_guess - prev_guess ) < precision:
return next_guess
lowercase__ = next_guess
# Let's Execute
if __name__ == "__main__":
# Find root of trigonometric function
# Find value of pi
print(F'The root of sin(x) = 0 is {newton_raphson("sin(x)", 2)}')
# Find root of polynomial
# Find fourth Root of 5
print(F'The root of x**4 - 5 = 0 is {newton_raphson("x**4 -5", 0.4 +5j)}')
# Find value of e
print(
'The root of log(y) - 1 = 0 is ',
F'{newton_raphson("log(y) - 1", 2, variable="y")}',
)
# Exponential Roots
print(
'The root of exp(x) - 1 = 0 is',
F'{newton_raphson("exp(x) - 1", 1_0, precision=0.005)}',
)
# Find root of cos(x)
print(F'The root of cos(x) = 0 is {newton_raphson("cos(x)", 0)}')
| 305
|
from __future__ import annotations
def UpperCamelCase ( __magic_name__ : list[float] , __magic_name__ : list[float] ) -> float:
"""simple docstring"""
lowercase__ = sorted(numsa + numsa )
lowercase__ , lowercase__ = divmod(len(__magic_name__ ) , 2 )
if mod == 1:
return all_numbers[div]
else:
return (all_numbers[div] + all_numbers[div - 1]) / 2
if __name__ == "__main__":
import doctest
doctest.testmod()
A : Any = [float(x) for x in input('Enter the elements of first array: ').split()]
A : Union[str, Any] = [float(x) for x in input('Enter the elements of second array: ').split()]
print(F'The median of two arrays is: {median_of_two_arrays(array_a, array_a)}')
| 305
| 1
|
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCamelCase ( __magic_name__ : Optional[Any] , __magic_name__ : List[Any] , __magic_name__ : Union[str, Any] ) -> Tuple:
"""simple docstring"""
lowercase__ = TaConfig.from_json_file(__magic_name__ )
print(f'''Building PyTorch model from configuration: {config}''' )
lowercase__ = TaForConditionalGeneration(__magic_name__ )
# Load weights from tf checkpoint
load_tf_weights_in_ta(__magic_name__ , __magic_name__ , __magic_name__ )
# Save pytorch-model
print(f'''Save PyTorch model to {pytorch_dump_path}''' )
model.save_pretrained(__magic_name__ )
if __name__ == "__main__":
A : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
)
parser.add_argument(
'--config_file',
default=None,
type=str,
required=True,
help=(
'The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'
),
)
parser.add_argument(
'--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
A : str = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
| 305
|
A : Union[str, Any] = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
A : List[Any] = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def UpperCamelCase ( __magic_name__ : dict[int, list[int]] , __magic_name__ : int , __magic_name__ : list[bool] ) -> list[int]:
"""simple docstring"""
lowercase__ = True
lowercase__ = []
for neighbour in graph[vert]:
if not visited[neighbour]:
order += topology_sort(__magic_name__ , __magic_name__ , __magic_name__ )
order.append(__magic_name__ )
return order
def UpperCamelCase ( __magic_name__ : dict[int, list[int]] , __magic_name__ : int , __magic_name__ : list[bool] ) -> list[int]:
"""simple docstring"""
lowercase__ = True
lowercase__ = [vert]
for neighbour in reversed_graph[vert]:
if not visited[neighbour]:
component += find_components(__magic_name__ , __magic_name__ , __magic_name__ )
return component
def UpperCamelCase ( __magic_name__ : dict[int, list[int]] ) -> list[list[int]]:
"""simple docstring"""
lowercase__ = len(__magic_name__ ) * [False]
lowercase__ = {vert: [] for vert in range(len(__magic_name__ ) )}
for vert, neighbours in graph.items():
for neighbour in neighbours:
reversed_graph[neighbour].append(__magic_name__ )
lowercase__ = []
for i, was_visited in enumerate(__magic_name__ ):
if not was_visited:
order += topology_sort(__magic_name__ , __magic_name__ , __magic_name__ )
lowercase__ = []
lowercase__ = len(__magic_name__ ) * [False]
for i in range(len(__magic_name__ ) ):
lowercase__ = order[len(__magic_name__ ) - i - 1]
if not visited[vert]:
lowercase__ = find_components(__magic_name__ , __magic_name__ , __magic_name__ )
components_list.append(__magic_name__ )
return components_list
| 305
| 1
|
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
A : Union[str, Any] = logging.get_logger(__name__)
class A ( UpperCAmelCase__ ):
'''simple docstring'''
def __init__(self : int , *_UpperCAmelCase : Any , **_UpperCAmelCase : Tuple ) -> None:
"""simple docstring"""
warnings.warn(
"""The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
""" use BeitImageProcessor instead.""" , _UpperCAmelCase , )
super().__init__(*_UpperCAmelCase , **_UpperCAmelCase )
| 305
|
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepInverseScheduler,
DPMSolverMultistepScheduler,
StableDiffusionDiffEditPipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device
from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class A ( UpperCAmelCase__ , UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
A__ = StableDiffusionDiffEditPipeline
A__ = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''height''', '''width''', '''image'''} | {'''image_latents'''}
A__ = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {'''image'''} | {'''image_latents'''}
A__ = frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
A__ = frozenset([] )
def lowerCamelCase__ (self : List[str] ) -> Optional[int]:
"""simple docstring"""
torch.manual_seed(0 )
lowercase__ = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , cross_attention_dim=32 , attention_head_dim=(2, 4) , use_linear_projection=_UpperCAmelCase , )
lowercase__ = DDIMScheduler(
beta_start=0.00_085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , clip_sample=_UpperCAmelCase , set_alpha_to_one=_UpperCAmelCase , )
lowercase__ = DDIMInverseScheduler(
beta_start=0.00_085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , clip_sample=_UpperCAmelCase , set_alpha_to_zero=_UpperCAmelCase , )
torch.manual_seed(0 )
lowercase__ = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , sample_size=128 , )
torch.manual_seed(0 )
lowercase__ = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , hidden_act="""gelu""" , projection_dim=512 , )
lowercase__ = CLIPTextModel(_UpperCAmelCase )
lowercase__ = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
lowercase__ = {
"""unet""": unet,
"""scheduler""": scheduler,
"""inverse_scheduler""": inverse_scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""safety_checker""": None,
"""feature_extractor""": None,
}
return components
def lowerCamelCase__ (self : Optional[Any] , _UpperCAmelCase : Tuple , _UpperCAmelCase : Tuple=0 ) -> Dict:
"""simple docstring"""
lowercase__ = floats_tensor((1, 16, 16) , rng=random.Random(_UpperCAmelCase ) ).to(_UpperCAmelCase )
lowercase__ = floats_tensor((1, 2, 4, 16, 16) , rng=random.Random(_UpperCAmelCase ) ).to(_UpperCAmelCase )
if str(_UpperCAmelCase ).startswith("""mps""" ):
lowercase__ = torch.manual_seed(_UpperCAmelCase )
else:
lowercase__ = torch.Generator(device=_UpperCAmelCase ).manual_seed(_UpperCAmelCase )
lowercase__ = {
"""prompt""": """a dog and a newt""",
"""mask_image""": mask,
"""image_latents""": latents,
"""generator""": generator,
"""num_inference_steps""": 2,
"""inpaint_strength""": 1.0,
"""guidance_scale""": 6.0,
"""output_type""": """numpy""",
}
return inputs
def lowerCamelCase__ (self : List[Any] , _UpperCAmelCase : Dict , _UpperCAmelCase : Tuple=0 ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = floats_tensor((1, 3, 32, 32) , rng=random.Random(_UpperCAmelCase ) ).to(_UpperCAmelCase )
lowercase__ = image.cpu().permute(0 , 2 , 3 , 1 )[0]
lowercase__ = Image.fromarray(np.uinta(_UpperCAmelCase ) ).convert("""RGB""" )
if str(_UpperCAmelCase ).startswith("""mps""" ):
lowercase__ = torch.manual_seed(_UpperCAmelCase )
else:
lowercase__ = torch.Generator(device=_UpperCAmelCase ).manual_seed(_UpperCAmelCase )
lowercase__ = {
"""image""": image,
"""source_prompt""": """a cat and a frog""",
"""target_prompt""": """a dog and a newt""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""num_maps_per_mask""": 2,
"""mask_encode_strength""": 1.0,
"""guidance_scale""": 6.0,
"""output_type""": """numpy""",
}
return inputs
def lowerCamelCase__ (self : Optional[Any] , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Dict=0 ) -> str:
"""simple docstring"""
lowercase__ = floats_tensor((1, 3, 32, 32) , rng=random.Random(_UpperCAmelCase ) ).to(_UpperCAmelCase )
lowercase__ = image.cpu().permute(0 , 2 , 3 , 1 )[0]
lowercase__ = Image.fromarray(np.uinta(_UpperCAmelCase ) ).convert("""RGB""" )
if str(_UpperCAmelCase ).startswith("""mps""" ):
lowercase__ = torch.manual_seed(_UpperCAmelCase )
else:
lowercase__ = torch.Generator(device=_UpperCAmelCase ).manual_seed(_UpperCAmelCase )
lowercase__ = {
"""image""": image,
"""prompt""": """a cat and a frog""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""inpaint_strength""": 1.0,
"""guidance_scale""": 6.0,
"""decode_latents""": True,
"""output_type""": """numpy""",
}
return inputs
def lowerCamelCase__ (self : int ) -> Dict:
"""simple docstring"""
if not hasattr(self.pipeline_class , """_optional_components""" ):
return
lowercase__ = self.get_dummy_components()
lowercase__ = self.pipeline_class(**_UpperCAmelCase )
pipe.to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
# set all optional components to None and update pipeline config accordingly
for optional_component in pipe._optional_components:
setattr(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components} )
lowercase__ = self.get_dummy_inputs(_UpperCAmelCase )
lowercase__ = pipe(**_UpperCAmelCase )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(_UpperCAmelCase )
lowercase__ = self.pipeline_class.from_pretrained(_UpperCAmelCase )
pipe_loaded.to(_UpperCAmelCase )
pipe_loaded.set_progress_bar_config(disable=_UpperCAmelCase )
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(_UpperCAmelCase , _UpperCAmelCase ) is None , f'''`{optional_component}` did not stay set to None after loading.''' , )
lowercase__ = self.get_dummy_inputs(_UpperCAmelCase )
lowercase__ = pipe_loaded(**_UpperCAmelCase )[0]
lowercase__ = np.abs(output - output_loaded ).max()
self.assertLess(_UpperCAmelCase , 1E-4 )
def lowerCamelCase__ (self : List[str] ) -> int:
"""simple docstring"""
lowercase__ = """cpu"""
lowercase__ = self.get_dummy_components()
lowercase__ = self.pipeline_class(**_UpperCAmelCase )
pipe.to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
lowercase__ = self.get_dummy_mask_inputs(_UpperCAmelCase )
lowercase__ = pipe.generate_mask(**_UpperCAmelCase )
lowercase__ = mask[0, -3:, -3:]
self.assertEqual(mask.shape , (1, 16, 16) )
lowercase__ = np.array([0] * 9 )
lowercase__ = np.abs(mask_slice.flatten() - expected_slice ).max()
self.assertLessEqual(_UpperCAmelCase , 1E-3 )
self.assertEqual(mask[0, -3, -4] , 0 )
def lowerCamelCase__ (self : List[Any] ) -> str:
"""simple docstring"""
lowercase__ = """cpu"""
lowercase__ = self.get_dummy_components()
lowercase__ = self.pipeline_class(**_UpperCAmelCase )
pipe.to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
lowercase__ = self.get_dummy_inversion_inputs(_UpperCAmelCase )
lowercase__ = pipe.invert(**_UpperCAmelCase ).images
lowercase__ = image[0, -1, -3:, -3:]
self.assertEqual(image.shape , (2, 32, 32, 3) )
lowercase__ = np.array(
[0.5_150, 0.5_134, 0.5_043, 0.5_376, 0.4_694, 0.51_050, 0.5_015, 0.4_407, 0.4_799] , )
lowercase__ = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(_UpperCAmelCase , 1E-3 )
def lowerCamelCase__ (self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
super().test_inference_batch_single_identical(expected_max_diff=5E-3 )
def lowerCamelCase__ (self : str ) -> List[str]:
"""simple docstring"""
lowercase__ = """cpu"""
lowercase__ = self.get_dummy_components()
lowercase__ = {"""beta_start""": 0.00_085, """beta_end""": 0.012, """beta_schedule""": """scaled_linear"""}
lowercase__ = DPMSolverMultistepScheduler(**_UpperCAmelCase )
lowercase__ = DPMSolverMultistepInverseScheduler(**_UpperCAmelCase )
lowercase__ = self.pipeline_class(**_UpperCAmelCase )
pipe.to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
lowercase__ = self.get_dummy_inversion_inputs(_UpperCAmelCase )
lowercase__ = pipe.invert(**_UpperCAmelCase ).images
lowercase__ = image[0, -1, -3:, -3:]
self.assertEqual(image.shape , (2, 32, 32, 3) )
lowercase__ = np.array(
[0.5_150, 0.5_134, 0.5_043, 0.5_376, 0.4_694, 0.51_050, 0.5_015, 0.4_407, 0.4_799] , )
lowercase__ = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(_UpperCAmelCase , 1E-3 )
@require_torch_gpu
@slow
class A ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase__ (self : Any ) -> Any:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@classmethod
def lowerCamelCase__ (cls : str ) -> Optional[int]:
"""simple docstring"""
lowercase__ = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png""" )
lowercase__ = raw_image.convert("""RGB""" ).resize((768, 768) )
lowercase__ = raw_image
def lowerCamelCase__ (self : Optional[int] ) -> Any:
"""simple docstring"""
lowercase__ = torch.manual_seed(0 )
lowercase__ = StableDiffusionDiffEditPipeline.from_pretrained(
"""stabilityai/stable-diffusion-2-1""" , safety_checker=_UpperCAmelCase , torch_dtype=torch.floataa )
lowercase__ = DDIMScheduler.from_config(pipe.scheduler.config )
lowercase__ = DDIMInverseScheduler.from_config(pipe.scheduler.config )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
lowercase__ = """a bowl of fruit"""
lowercase__ = """a bowl of pears"""
lowercase__ = pipe.generate_mask(
image=self.raw_image , source_prompt=_UpperCAmelCase , target_prompt=_UpperCAmelCase , generator=_UpperCAmelCase , )
lowercase__ = pipe.invert(
prompt=_UpperCAmelCase , image=self.raw_image , inpaint_strength=0.7 , generator=_UpperCAmelCase ).latents
lowercase__ = pipe(
prompt=_UpperCAmelCase , mask_image=_UpperCAmelCase , image_latents=_UpperCAmelCase , generator=_UpperCAmelCase , negative_prompt=_UpperCAmelCase , inpaint_strength=0.7 , output_type="""numpy""" , ).images[0]
lowercase__ = (
np.array(
load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/diffedit/pears.png""" ).resize((768, 768) ) )
/ 255
)
assert np.abs((expected_image - image).max() ) < 5E-1
def lowerCamelCase__ (self : int ) -> Any:
"""simple docstring"""
lowercase__ = torch.manual_seed(0 )
lowercase__ = StableDiffusionDiffEditPipeline.from_pretrained(
"""stabilityai/stable-diffusion-2-1""" , safety_checker=_UpperCAmelCase , torch_dtype=torch.floataa )
lowercase__ = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
lowercase__ = DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
lowercase__ = """a bowl of fruit"""
lowercase__ = """a bowl of pears"""
lowercase__ = pipe.generate_mask(
image=self.raw_image , source_prompt=_UpperCAmelCase , target_prompt=_UpperCAmelCase , generator=_UpperCAmelCase , )
lowercase__ = pipe.invert(
prompt=_UpperCAmelCase , image=self.raw_image , inpaint_strength=0.7 , generator=_UpperCAmelCase , num_inference_steps=25 , ).latents
lowercase__ = pipe(
prompt=_UpperCAmelCase , mask_image=_UpperCAmelCase , image_latents=_UpperCAmelCase , generator=_UpperCAmelCase , negative_prompt=_UpperCAmelCase , inpaint_strength=0.7 , num_inference_steps=25 , output_type="""numpy""" , ).images[0]
lowercase__ = (
np.array(
load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/diffedit/pears.png""" ).resize((768, 768) ) )
/ 255
)
assert np.abs((expected_image - image).max() ) < 5E-1
| 305
| 1
|
import inspect
import unittest
from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import YolosForObjectDetection, YolosModel
from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class A :
'''simple docstring'''
def __init__(self : Dict , _UpperCAmelCase : int , _UpperCAmelCase : Dict=13 , _UpperCAmelCase : Tuple=[30, 30] , _UpperCAmelCase : Any=2 , _UpperCAmelCase : Optional[Any]=3 , _UpperCAmelCase : Tuple=True , _UpperCAmelCase : Any=True , _UpperCAmelCase : Dict=32 , _UpperCAmelCase : List[Any]=5 , _UpperCAmelCase : str=4 , _UpperCAmelCase : List[str]=37 , _UpperCAmelCase : Dict="gelu" , _UpperCAmelCase : Dict=0.1 , _UpperCAmelCase : Tuple=0.1 , _UpperCAmelCase : Tuple=10 , _UpperCAmelCase : Any=0.02 , _UpperCAmelCase : List[str]=3 , _UpperCAmelCase : Dict=None , _UpperCAmelCase : Union[str, Any]=8 , _UpperCAmelCase : List[Any]=10 , ) -> List[str]:
"""simple docstring"""
lowercase__ = parent
lowercase__ = batch_size
lowercase__ = image_size
lowercase__ = patch_size
lowercase__ = num_channels
lowercase__ = is_training
lowercase__ = use_labels
lowercase__ = hidden_size
lowercase__ = num_hidden_layers
lowercase__ = num_attention_heads
lowercase__ = intermediate_size
lowercase__ = hidden_act
lowercase__ = hidden_dropout_prob
lowercase__ = attention_probs_dropout_prob
lowercase__ = type_sequence_label_size
lowercase__ = initializer_range
lowercase__ = num_labels
lowercase__ = scope
lowercase__ = n_targets
lowercase__ = num_detection_tokens
# we set the expected sequence length (which is used in several tests)
# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
lowercase__ = (image_size[1] // patch_size) * (image_size[0] // patch_size)
lowercase__ = num_patches + 1 + self.num_detection_tokens
def lowerCamelCase__ (self : int ) -> str:
"""simple docstring"""
lowercase__ = floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]] )
lowercase__ = None
if self.use_labels:
# labels is a list of Dict (each Dict being the labels for a given example in the batch)
lowercase__ = []
for i in range(self.batch_size ):
lowercase__ = {}
lowercase__ = torch.randint(
high=self.num_labels , size=(self.n_targets,) , device=_UpperCAmelCase )
lowercase__ = torch.rand(self.n_targets , 4 , device=_UpperCAmelCase )
labels.append(_UpperCAmelCase )
lowercase__ = self.get_config()
return config, pixel_values, labels
def lowerCamelCase__ (self : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
return YolosConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=_UpperCAmelCase , initializer_range=self.initializer_range , num_detection_tokens=self.num_detection_tokens , num_labels=self.num_labels , )
def lowerCamelCase__ (self : List[Any] , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Tuple , _UpperCAmelCase : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = YolosModel(config=_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
lowercase__ = model(_UpperCAmelCase )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.expected_seq_len, self.hidden_size) )
def lowerCamelCase__ (self : List[str] , _UpperCAmelCase : Tuple , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = YolosForObjectDetection(_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
lowercase__ = model(pixel_values=_UpperCAmelCase )
lowercase__ = model(_UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_detection_tokens, self.num_labels + 1) )
self.parent.assertEqual(result.pred_boxes.shape , (self.batch_size, self.num_detection_tokens, 4) )
lowercase__ = model(pixel_values=_UpperCAmelCase , labels=_UpperCAmelCase )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_detection_tokens, self.num_labels + 1) )
self.parent.assertEqual(result.pred_boxes.shape , (self.batch_size, self.num_detection_tokens, 4) )
def lowerCamelCase__ (self : List[Any] ) -> List[str]:
"""simple docstring"""
lowercase__ = self.prepare_config_and_inputs()
lowercase__ , lowercase__ , lowercase__ = config_and_inputs
lowercase__ = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class A ( UpperCAmelCase__ , UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
A__ = (YolosModel, YolosForObjectDetection) if is_torch_available() else ()
A__ = (
{'''feature-extraction''': YolosModel, '''object-detection''': YolosForObjectDetection} if is_torch_available() else {}
)
A__ = False
A__ = False
A__ = False
A__ = False
def lowerCamelCase__ (self : List[str] , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : List[str] , _UpperCAmelCase : Optional[int]=False ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = super()._prepare_for_class(_UpperCAmelCase , _UpperCAmelCase , return_labels=_UpperCAmelCase )
if return_labels:
if model_class.__name__ == "YolosForObjectDetection":
lowercase__ = []
for i in range(self.model_tester.batch_size ):
lowercase__ = {}
lowercase__ = torch.ones(
size=(self.model_tester.n_targets,) , device=_UpperCAmelCase , dtype=torch.long )
lowercase__ = torch.ones(
self.model_tester.n_targets , 4 , device=_UpperCAmelCase , dtype=torch.float )
labels.append(_UpperCAmelCase )
lowercase__ = labels
return inputs_dict
def lowerCamelCase__ (self : List[Any] ) -> Tuple:
"""simple docstring"""
lowercase__ = YolosModelTester(self )
lowercase__ = ConfigTester(self , config_class=_UpperCAmelCase , has_text_modality=_UpperCAmelCase , hidden_size=37 )
def lowerCamelCase__ (self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
self.config_tester.run_common_tests()
def lowerCamelCase__ (self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
pass
def lowerCamelCase__ (self : str ) -> int:
"""simple docstring"""
lowercase__ , lowercase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ = model_class(_UpperCAmelCase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
lowercase__ = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_UpperCAmelCase , nn.Linear ) )
def lowerCamelCase__ (self : Optional[Any] ) -> Tuple:
"""simple docstring"""
lowercase__ , lowercase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ = model_class(_UpperCAmelCase )
lowercase__ = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase__ = [*signature.parameters.keys()]
lowercase__ = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , _UpperCAmelCase )
def lowerCamelCase__ (self : Union[str, Any] ) -> Dict:
"""simple docstring"""
lowercase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_UpperCAmelCase )
def lowerCamelCase__ (self : List[str] ) -> int:
"""simple docstring"""
lowercase__ , lowercase__ = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ = True
# in YOLOS, the seq_len is different
lowercase__ = self.model_tester.expected_seq_len
for model_class in self.all_model_classes:
lowercase__ = True
lowercase__ = False
lowercase__ = True
lowercase__ = model_class(_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
with torch.no_grad():
lowercase__ = model(**self._prepare_for_class(_UpperCAmelCase , _UpperCAmelCase ) )
lowercase__ = outputs.attentions
self.assertEqual(len(_UpperCAmelCase ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
lowercase__ = True
lowercase__ = model_class(_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
with torch.no_grad():
lowercase__ = model(**self._prepare_for_class(_UpperCAmelCase , _UpperCAmelCase ) )
lowercase__ = outputs.attentions
self.assertEqual(len(_UpperCAmelCase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len, seq_len] , )
lowercase__ = len(_UpperCAmelCase )
# Check attention is always last and order is fine
lowercase__ = True
lowercase__ = True
lowercase__ = model_class(_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
with torch.no_grad():
lowercase__ = model(**self._prepare_for_class(_UpperCAmelCase , _UpperCAmelCase ) )
lowercase__ = 1
self.assertEqual(out_len + added_hidden_states , len(_UpperCAmelCase ) )
lowercase__ = outputs.attentions
self.assertEqual(len(_UpperCAmelCase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len, seq_len] , )
def lowerCamelCase__ (self : int ) -> Any:
"""simple docstring"""
def check_hidden_states_output(_UpperCAmelCase : Optional[int] , _UpperCAmelCase : Any , _UpperCAmelCase : Optional[int] ):
lowercase__ = model_class(_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
with torch.no_grad():
lowercase__ = model(**self._prepare_for_class(_UpperCAmelCase , _UpperCAmelCase ) )
lowercase__ = outputs.hidden_states
lowercase__ = getattr(
self.model_tester , """expected_num_hidden_layers""" , self.model_tester.num_hidden_layers + 1 )
self.assertEqual(len(_UpperCAmelCase ) , _UpperCAmelCase )
# YOLOS has a different seq_length
lowercase__ = self.model_tester.expected_seq_len
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [seq_length, self.model_tester.hidden_size] , )
lowercase__ , lowercase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ = True
check_hidden_states_output(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowercase__ = True
check_hidden_states_output(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
def lowerCamelCase__ (self : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_object_detection(*_UpperCAmelCase )
@slow
def lowerCamelCase__ (self : List[str] ) -> Dict:
"""simple docstring"""
for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ = YolosModel.from_pretrained(_UpperCAmelCase )
self.assertIsNotNone(_UpperCAmelCase )
def UpperCamelCase ( ) -> Any:
"""simple docstring"""
lowercase__ = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
@require_vision
class A ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def lowerCamelCase__ (self : str ) -> Optional[int]:
"""simple docstring"""
return AutoImageProcessor.from_pretrained("""hustvl/yolos-small""" ) if is_vision_available() else None
@slow
def lowerCamelCase__ (self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = YolosForObjectDetection.from_pretrained("""hustvl/yolos-small""" ).to(_UpperCAmelCase )
lowercase__ = self.default_image_processor
lowercase__ = prepare_img()
lowercase__ = image_processor(images=_UpperCAmelCase , return_tensors="""pt""" ).to(_UpperCAmelCase )
# forward pass
with torch.no_grad():
lowercase__ = model(inputs.pixel_values )
# verify outputs
lowercase__ = torch.Size((1, 100, 92) )
self.assertEqual(outputs.logits.shape , _UpperCAmelCase )
lowercase__ = torch.tensor(
[[-24.0_248, -10.3_024, -14.8_290], [-42.0_392, -16.8_200, -27.4_334], [-27.2_743, -11.8_154, -18.7_148]] , device=_UpperCAmelCase , )
lowercase__ = torch.tensor(
[[0.2_559, 0.5_455, 0.4_706], [0.2_989, 0.7_279, 0.1_875], [0.7_732, 0.4_017, 0.4_462]] , device=_UpperCAmelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] , _UpperCAmelCase , atol=1E-4 ) )
self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3] , _UpperCAmelCase , atol=1E-4 ) )
# verify postprocessing
lowercase__ = image_processor.post_process_object_detection(
_UpperCAmelCase , threshold=0.3 , target_sizes=[image.size[::-1]] )[0]
lowercase__ = torch.tensor([0.9_994, 0.9_790, 0.9_964, 0.9_972, 0.9_861] ).to(_UpperCAmelCase )
lowercase__ = [75, 75, 17, 63, 17]
lowercase__ = torch.tensor([335.0_609, 79.3_848, 375.4_216, 187.2_495] ).to(_UpperCAmelCase )
self.assertEqual(len(results["""scores"""] ) , 5 )
self.assertTrue(torch.allclose(results["""scores"""] , _UpperCAmelCase , atol=1E-4 ) )
self.assertSequenceEqual(results["""labels"""].tolist() , _UpperCAmelCase )
self.assertTrue(torch.allclose(results["""boxes"""][0, :] , _UpperCAmelCase ) )
| 305
|
from __future__ import annotations
import math
def UpperCamelCase ( __magic_name__ : list , __magic_name__ : list ) -> list:
"""simple docstring"""
if len(__magic_name__ ) != 2 or len(a[0] ) != 2 or len(__magic_name__ ) != 2 or len(b[0] ) != 2:
raise Exception("""Matrices are not 2x2""" )
lowercase__ = [
[a[0][0] * b[0][0] + a[0][1] * b[1][0], a[0][0] * b[0][1] + a[0][1] * b[1][1]],
[a[1][0] * b[0][0] + a[1][1] * b[1][0], a[1][0] * b[0][1] + a[1][1] * b[1][1]],
]
return new_matrix
def UpperCamelCase ( __magic_name__ : list , __magic_name__ : list ) -> Union[str, Any]:
"""simple docstring"""
return [
[matrix_a[row][col] + matrix_b[row][col] for col in range(len(matrix_a[row] ) )]
for row in range(len(__magic_name__ ) )
]
def UpperCamelCase ( __magic_name__ : list , __magic_name__ : list ) -> int:
"""simple docstring"""
return [
[matrix_a[row][col] - matrix_b[row][col] for col in range(len(matrix_a[row] ) )]
for row in range(len(__magic_name__ ) )
]
def UpperCamelCase ( __magic_name__ : list ) -> tuple[list, list, list, list]:
"""simple docstring"""
if len(__magic_name__ ) % 2 != 0 or len(a[0] ) % 2 != 0:
raise Exception("""Odd matrices are not supported!""" )
lowercase__ = len(__magic_name__ )
lowercase__ = matrix_length // 2
lowercase__ = [[a[i][j] for j in range(__magic_name__ , __magic_name__ )] for i in range(__magic_name__ )]
lowercase__ = [
[a[i][j] for j in range(__magic_name__ , __magic_name__ )] for i in range(__magic_name__ , __magic_name__ )
]
lowercase__ = [[a[i][j] for j in range(__magic_name__ )] for i in range(__magic_name__ )]
lowercase__ = [[a[i][j] for j in range(__magic_name__ )] for i in range(__magic_name__ , __magic_name__ )]
return top_left, top_right, bot_left, bot_right
def UpperCamelCase ( __magic_name__ : list ) -> tuple[int, int]:
"""simple docstring"""
return len(__magic_name__ ), len(matrix[0] )
def UpperCamelCase ( __magic_name__ : list ) -> None:
"""simple docstring"""
print("""\n""".join(str(__magic_name__ ) for line in matrix ) )
def UpperCamelCase ( __magic_name__ : list , __magic_name__ : list ) -> list:
"""simple docstring"""
if matrix_dimensions(__magic_name__ ) == (2, 2):
return default_matrix_multiplication(__magic_name__ , __magic_name__ )
lowercase__ , lowercase__ , lowercase__ , lowercase__ = split_matrix(__magic_name__ )
lowercase__ , lowercase__ , lowercase__ , lowercase__ = split_matrix(__magic_name__ )
lowercase__ = actual_strassen(__magic_name__ , matrix_subtraction(__magic_name__ , __magic_name__ ) )
lowercase__ = actual_strassen(matrix_addition(__magic_name__ , __magic_name__ ) , __magic_name__ )
lowercase__ = actual_strassen(matrix_addition(__magic_name__ , __magic_name__ ) , __magic_name__ )
lowercase__ = actual_strassen(__magic_name__ , matrix_subtraction(__magic_name__ , __magic_name__ ) )
lowercase__ = actual_strassen(matrix_addition(__magic_name__ , __magic_name__ ) , matrix_addition(__magic_name__ , __magic_name__ ) )
lowercase__ = actual_strassen(matrix_subtraction(__magic_name__ , __magic_name__ ) , matrix_addition(__magic_name__ , __magic_name__ ) )
lowercase__ = actual_strassen(matrix_subtraction(__magic_name__ , __magic_name__ ) , matrix_addition(__magic_name__ , __magic_name__ ) )
lowercase__ = matrix_addition(matrix_subtraction(matrix_addition(__magic_name__ , __magic_name__ ) , __magic_name__ ) , __magic_name__ )
lowercase__ = matrix_addition(__magic_name__ , __magic_name__ )
lowercase__ = matrix_addition(__magic_name__ , __magic_name__ )
lowercase__ = matrix_subtraction(matrix_subtraction(matrix_addition(__magic_name__ , __magic_name__ ) , __magic_name__ ) , __magic_name__ )
# construct the new matrix from our 4 quadrants
lowercase__ = []
for i in range(len(__magic_name__ ) ):
new_matrix.append(top_left[i] + top_right[i] )
for i in range(len(__magic_name__ ) ):
new_matrix.append(bot_left[i] + bot_right[i] )
return new_matrix
def UpperCamelCase ( __magic_name__ : list , __magic_name__ : list ) -> list:
"""simple docstring"""
if matrix_dimensions(__magic_name__ )[1] != matrix_dimensions(__magic_name__ )[0]:
lowercase__ = (
"""Unable to multiply these matrices, please check the dimensions.\n"""
f'''Matrix A: {matrixa}\n'''
f'''Matrix B: {matrixa}'''
)
raise Exception(__magic_name__ )
lowercase__ = matrix_dimensions(__magic_name__ )
lowercase__ = matrix_dimensions(__magic_name__ )
if dimensiona[0] == dimensiona[1] and dimensiona[0] == dimensiona[1]:
return [matrixa, matrixa]
lowercase__ = max(*__magic_name__ , *__magic_name__ )
lowercase__ = int(math.pow(2 , math.ceil(math.loga(__magic_name__ ) ) ) )
lowercase__ = matrixa
lowercase__ = matrixa
# Adding zeros to the matrices so that the arrays dimensions are the same and also
# power of 2
for i in range(0 , __magic_name__ ):
if i < dimensiona[0]:
for _ in range(dimensiona[1] , __magic_name__ ):
new_matrixa[i].append(0 )
else:
new_matrixa.append([0] * maxim )
if i < dimensiona[0]:
for _ in range(dimensiona[1] , __magic_name__ ):
new_matrixa[i].append(0 )
else:
new_matrixa.append([0] * maxim )
lowercase__ = actual_strassen(__magic_name__ , __magic_name__ )
# Removing the additional zeros
for i in range(0 , __magic_name__ ):
if i < dimensiona[0]:
for _ in range(dimensiona[1] , __magic_name__ ):
final_matrix[i].pop()
else:
final_matrix.pop()
return final_matrix
if __name__ == "__main__":
A : Optional[Any] = [
[2, 3, 4, 5],
[6, 4, 3, 1],
[2, 3, 6, 7],
[3, 1, 2, 4],
[2, 3, 4, 5],
[6, 4, 3, 1],
[2, 3, 6, 7],
[3, 1, 2, 4],
[2, 3, 4, 5],
[6, 2, 3, 1],
]
A : List[Any] = [[0, 2, 1, 1], [1_6, 2, 3, 3], [2, 2, 7, 7], [1_3, 1_1, 2_2, 4]]
print(strassen(matrixa, matrixa))
| 305
| 1
|
import argparse
import os
import re
import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints
from transformers import (
AutoTokenizer,
PixaStructConfig,
PixaStructForConditionalGeneration,
PixaStructImageProcessor,
PixaStructProcessor,
PixaStructTextConfig,
PixaStructVisionConfig,
)
def UpperCamelCase ( __magic_name__ : Optional[int] ) -> Optional[int]:
"""simple docstring"""
lowercase__ = checkpoints.load_tax_checkpoint(__magic_name__ )
lowercase__ = flatten_dict(__magic_name__ )
return flax_params
def UpperCamelCase ( __magic_name__ : List[Any] ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = {}
lowercase__ = {
"""token_embedder""": """embeddings""",
"""encoder_norm""": """layernorm""",
"""kernel""": """weight""",
""".out""": """.output""",
"""scale""": """weight""",
"""embedders_0.pos_embedding""": """row_embedder.weight""",
"""embedders_1.pos_embedding""": """column_embedder.weight""",
}
lowercase__ = {
"""query""": """attention.query""",
"""key""": """attention.key""",
"""value""": """attention.value""",
"""output.dense""": """output""",
"""encoder_decoder_attention.o""": """encoder_decoder_attention.attention.o""",
"""pre_self_attention_layer_norm""": """self_attention.layer_norm""",
"""pre_cross_attention_layer_norm""": """encoder_decoder_attention.layer_norm""",
"""mlp.""": """mlp.DenseReluDense.""",
"""pre_mlp_layer_norm""": """mlp.layer_norm""",
"""self_attention.o""": """self_attention.attention.o""",
"""decoder.embeddings.embedding""": """decoder.embed_tokens.weight""",
"""decoder.relpos_bias.rel_embedding""": """decoder.layer.0.self_attention.attention.relative_attention_bias.weight""",
"""decoder.decoder_norm.weight""": """decoder.final_layer_norm.weight""",
"""decoder.logits_dense.weight""": """decoder.lm_head.weight""",
}
for key in flax_dict.keys():
if "target" in key:
# remove the first prefix from the key
lowercase__ = """.""".join(key[1:] )
# rename the key
for old, new in CONVERSION_MAPPING.items():
lowercase__ = new_key.replace(__magic_name__ , __magic_name__ )
if "decoder" in new_key:
for old, new in DECODER_CONVERSION_MAPPING.items():
lowercase__ = new_key.replace(__magic_name__ , __magic_name__ )
if "layers" in new_key and "decoder" not in new_key:
# use regex to replace the layer number
lowercase__ = re.sub(R"""layers_(\d+)""" , R"""layer.\1""" , __magic_name__ )
lowercase__ = new_key.replace("""encoder""" , """encoder.encoder""" )
elif "layers" in new_key and "decoder" in new_key:
# use regex to replace the layer number
lowercase__ = re.sub(R"""layers_(\d+)""" , R"""layer.\1""" , __magic_name__ )
lowercase__ = flax_dict[key]
lowercase__ = {}
# convert converted_dict into torch format
for key in converted_dict.keys():
if ("embed_tokens" not in key) and ("embedder" not in key):
lowercase__ = torch.from_numpy(converted_dict[key].T )
else:
lowercase__ = torch.from_numpy(converted_dict[key] )
return converted_torch_dict
def UpperCamelCase ( __magic_name__ : Optional[int] , __magic_name__ : Optional[Any] , __magic_name__ : Optional[Any]=False , __magic_name__ : List[Any]=False ) -> int:
"""simple docstring"""
lowercase__ = get_flax_param(__magic_name__ )
if not use_large:
lowercase__ = PixaStructVisionConfig()
lowercase__ = PixaStructTextConfig()
else:
lowercase__ = PixaStructVisionConfig(
hidden_size=1536 , d_ff=3968 , num_attention_heads=24 , num_hidden_layers=18 )
lowercase__ = PixaStructTextConfig(hidden_size=1536 , d_ff=3968 , num_heads=24 , num_layers=18 )
lowercase__ = PixaStructConfig(
vision_config=encoder_config.to_dict() , text_config=decoder_config.to_dict() , is_vqa=__magic_name__ )
lowercase__ = PixaStructForConditionalGeneration(__magic_name__ )
lowercase__ = rename_and_convert_flax_params(__magic_name__ )
model.load_state_dict(__magic_name__ )
lowercase__ = AutoTokenizer.from_pretrained("""ybelkada/test-pix2struct-tokenizer""" )
lowercase__ = PixaStructImageProcessor()
lowercase__ = PixaStructProcessor(image_processor=__magic_name__ , tokenizer=__magic_name__ )
if use_large:
lowercase__ = 4096
lowercase__ = True
# mkdir if needed
os.makedirs(__magic_name__ , exist_ok=__magic_name__ )
model.save_pretrained(__magic_name__ )
processor.save_pretrained(__magic_name__ )
print("""Model saved in {}""".format(__magic_name__ ) )
if __name__ == "__main__":
A : Optional[Any] = argparse.ArgumentParser()
parser.add_argument('--t5x_checkpoint_path', default=None, type=str, help='Path to the original T5x checkpoint.')
parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
parser.add_argument('--use_large', action='store_true', help='Use large model.')
parser.add_argument('--is_vqa', action='store_true', help='Use large model.')
A : Union[str, Any] = parser.parse_args()
convert_pixastruct_original_pytorch_checkpoint_to_hf(
args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
)
| 305
|
import unittest
import numpy as np
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transformers.models.bert.modeling_flax_bert import (
FlaxBertForMaskedLM,
FlaxBertForMultipleChoice,
FlaxBertForNextSentencePrediction,
FlaxBertForPreTraining,
FlaxBertForQuestionAnswering,
FlaxBertForSequenceClassification,
FlaxBertForTokenClassification,
FlaxBertModel,
)
class A ( unittest.TestCase ):
'''simple docstring'''
def __init__(self : Optional[Any] , _UpperCAmelCase : Tuple , _UpperCAmelCase : str=13 , _UpperCAmelCase : List[str]=7 , _UpperCAmelCase : Union[str, Any]=True , _UpperCAmelCase : Dict=True , _UpperCAmelCase : str=True , _UpperCAmelCase : str=True , _UpperCAmelCase : Dict=99 , _UpperCAmelCase : Any=32 , _UpperCAmelCase : List[str]=5 , _UpperCAmelCase : Union[str, Any]=4 , _UpperCAmelCase : str=37 , _UpperCAmelCase : Union[str, Any]="gelu" , _UpperCAmelCase : Any=0.1 , _UpperCAmelCase : int=0.1 , _UpperCAmelCase : Dict=512 , _UpperCAmelCase : Optional[int]=16 , _UpperCAmelCase : str=2 , _UpperCAmelCase : List[Any]=0.02 , _UpperCAmelCase : List[str]=4 , ) -> List[Any]:
"""simple docstring"""
lowercase__ = parent
lowercase__ = batch_size
lowercase__ = seq_length
lowercase__ = is_training
lowercase__ = use_attention_mask
lowercase__ = use_token_type_ids
lowercase__ = use_labels
lowercase__ = vocab_size
lowercase__ = hidden_size
lowercase__ = num_hidden_layers
lowercase__ = num_attention_heads
lowercase__ = intermediate_size
lowercase__ = hidden_act
lowercase__ = hidden_dropout_prob
lowercase__ = attention_probs_dropout_prob
lowercase__ = max_position_embeddings
lowercase__ = type_vocab_size
lowercase__ = type_sequence_label_size
lowercase__ = initializer_range
lowercase__ = num_choices
def lowerCamelCase__ (self : List[str] ) -> Dict:
"""simple docstring"""
lowercase__ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase__ = None
if self.use_attention_mask:
lowercase__ = random_attention_mask([self.batch_size, self.seq_length] )
lowercase__ = None
if self.use_token_type_ids:
lowercase__ = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowercase__ = 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=_UpperCAmelCase , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def lowerCamelCase__ (self : int ) -> Any:
"""simple docstring"""
lowercase__ = self.prepare_config_and_inputs()
lowercase__ , lowercase__ , lowercase__ , lowercase__ = config_and_inputs
lowercase__ = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
return config, inputs_dict
def lowerCamelCase__ (self : Tuple ) -> str:
"""simple docstring"""
lowercase__ = self.prepare_config_and_inputs()
lowercase__ , lowercase__ , lowercase__ , lowercase__ = config_and_inputs
lowercase__ = True
lowercase__ = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
lowercase__ = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
attention_mask,
encoder_hidden_states,
encoder_attention_mask,
)
@require_flax
class A ( UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
A__ = True
A__ = (
(
FlaxBertModel,
FlaxBertForPreTraining,
FlaxBertForMaskedLM,
FlaxBertForMultipleChoice,
FlaxBertForQuestionAnswering,
FlaxBertForNextSentencePrediction,
FlaxBertForSequenceClassification,
FlaxBertForTokenClassification,
FlaxBertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def lowerCamelCase__ (self : Optional[int] ) -> List[str]:
"""simple docstring"""
lowercase__ = FlaxBertModelTester(self )
@slow
def lowerCamelCase__ (self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = FlaxBertModel.from_pretrained("""bert-base-cased""" )
lowercase__ = model(np.ones((1, 1) ) )
self.assertIsNotNone(_UpperCAmelCase )
| 305
| 1
|
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def UpperCamelCase ( __magic_name__ : List[Any] ) -> Optional[int]:
"""simple docstring"""
return x + 2
class A ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase__ (self : Optional[Any] ) -> Any:
"""simple docstring"""
lowercase__ = """x = 3"""
lowercase__ = {}
lowercase__ = evaluate(_UpperCAmelCase , {} , state=_UpperCAmelCase )
assert result == 3
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3} )
lowercase__ = """x = y"""
lowercase__ = {"""y""": 5}
lowercase__ = evaluate(_UpperCAmelCase , {} , state=_UpperCAmelCase )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(_UpperCAmelCase , {"""x""": 5, """y""": 5} )
def lowerCamelCase__ (self : str ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = """y = add_two(x)"""
lowercase__ = {"""x""": 3}
lowercase__ = evaluate(_UpperCAmelCase , {"""add_two""": add_two} , state=_UpperCAmelCase )
assert result == 5
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3, """y""": 5} )
# Won't work without the tool
with CaptureStdout() as out:
lowercase__ = evaluate(_UpperCAmelCase , {} , state=_UpperCAmelCase )
assert result is None
assert "tried to execute add_two" in out.out
def lowerCamelCase__ (self : List[Any] ) -> Optional[int]:
"""simple docstring"""
lowercase__ = """x = 3"""
lowercase__ = {}
lowercase__ = evaluate(_UpperCAmelCase , {} , state=_UpperCAmelCase )
assert result == 3
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3} )
def lowerCamelCase__ (self : Optional[int] ) -> List[Any]:
"""simple docstring"""
lowercase__ = """test_dict = {'x': x, 'y': add_two(x)}"""
lowercase__ = {"""x""": 3}
lowercase__ = evaluate(_UpperCAmelCase , {"""add_two""": add_two} , state=_UpperCAmelCase )
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3, """y""": 5} )
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3, """test_dict""": {"""x""": 3, """y""": 5}} )
def lowerCamelCase__ (self : List[str] ) -> List[Any]:
"""simple docstring"""
lowercase__ = """x = 3\ny = 5"""
lowercase__ = {}
lowercase__ = evaluate(_UpperCAmelCase , {} , state=_UpperCAmelCase )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3, """y""": 5} )
def lowerCamelCase__ (self : List[Any] ) -> Dict:
"""simple docstring"""
lowercase__ = """text = f'This is x: {x}.'"""
lowercase__ = {"""x""": 3}
lowercase__ = evaluate(_UpperCAmelCase , {} , state=_UpperCAmelCase )
# evaluate returns the value of the last assignment.
assert result == "This is x: 3."
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3, """text""": """This is x: 3."""} )
def lowerCamelCase__ (self : List[str] ) -> int:
"""simple docstring"""
lowercase__ = """if x <= 3:\n y = 2\nelse:\n y = 5"""
lowercase__ = {"""x""": 3}
lowercase__ = evaluate(_UpperCAmelCase , {} , state=_UpperCAmelCase )
# evaluate returns the value of the last assignment.
assert result == 2
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3, """y""": 2} )
lowercase__ = {"""x""": 8}
lowercase__ = evaluate(_UpperCAmelCase , {} , state=_UpperCAmelCase )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(_UpperCAmelCase , {"""x""": 8, """y""": 5} )
def lowerCamelCase__ (self : Dict ) -> int:
"""simple docstring"""
lowercase__ = """test_list = [x, add_two(x)]"""
lowercase__ = {"""x""": 3}
lowercase__ = evaluate(_UpperCAmelCase , {"""add_two""": add_two} , state=_UpperCAmelCase )
self.assertListEqual(_UpperCAmelCase , [3, 5] )
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3, """test_list""": [3, 5]} )
def lowerCamelCase__ (self : Any ) -> int:
"""simple docstring"""
lowercase__ = """y = x"""
lowercase__ = {"""x""": 3}
lowercase__ = evaluate(_UpperCAmelCase , {} , state=_UpperCAmelCase )
assert result == 3
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3, """y""": 3} )
def lowerCamelCase__ (self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
lowercase__ = """test_list = [x, add_two(x)]\ntest_list[1]"""
lowercase__ = {"""x""": 3}
lowercase__ = evaluate(_UpperCAmelCase , {"""add_two""": add_two} , state=_UpperCAmelCase )
assert result == 5
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3, """test_list""": [3, 5]} )
lowercase__ = """test_dict = {'x': x, 'y': add_two(x)}\ntest_dict['y']"""
lowercase__ = {"""x""": 3}
lowercase__ = evaluate(_UpperCAmelCase , {"""add_two""": add_two} , state=_UpperCAmelCase )
assert result == 5
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3, """test_dict""": {"""x""": 3, """y""": 5}} )
def lowerCamelCase__ (self : Union[str, Any] ) -> Any:
"""simple docstring"""
lowercase__ = """x = 0\nfor i in range(3):\n x = i"""
lowercase__ = {}
lowercase__ = evaluate(_UpperCAmelCase , {"""range""": range} , state=_UpperCAmelCase )
assert result == 2
self.assertDictEqual(_UpperCAmelCase , {"""x""": 2, """i""": 2} )
| 305
|
def UpperCamelCase ( __magic_name__ : str ) -> list:
"""simple docstring"""
if n_term == "":
return []
lowercase__ = []
for temp in range(int(__magic_name__ ) ):
series.append(f'''1/{temp + 1}''' if series else """1""" )
return series
if __name__ == "__main__":
A : Tuple = input('Enter the last number (nth term) of the Harmonic Series')
print('Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n')
print(harmonic_series(nth_term))
| 305
| 1
|
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def UpperCamelCase ( __magic_name__ : Union[dict, list, tuple, torch.Tensor] ) -> List[Tuple[int, ...]]:
"""simple docstring"""
lowercase__ = []
if isinstance(__magic_name__ , __magic_name__ ):
for v in tree.values():
shapes.extend(_fetch_dims(__magic_name__ ) )
elif isinstance(__magic_name__ , (list, tuple) ):
for t in tree:
shapes.extend(_fetch_dims(__magic_name__ ) )
elif isinstance(__magic_name__ , torch.Tensor ):
shapes.append(tree.shape )
else:
raise ValueError("""Not supported""" )
return shapes
@torch.jit.ignore
def UpperCamelCase ( __magic_name__ : int , __magic_name__ : Tuple[int, ...] ) -> Tuple[int, ...]:
"""simple docstring"""
lowercase__ = []
for d in reversed(__magic_name__ ):
idx.append(flat_idx % d )
lowercase__ = flat_idx // d
return tuple(reversed(__magic_name__ ) )
@torch.jit.ignore
def UpperCamelCase ( __magic_name__ : Sequence[int] , __magic_name__ : Sequence[int] , __magic_name__ : Sequence[int] , __magic_name__ : Optional[Sequence[bool]] = None , __magic_name__ : Optional[Sequence[bool]] = None , ) -> List[Tuple[slice, ...]]:
"""simple docstring"""
def reduce_edge_list(__magic_name__ : List[bool] ) -> None:
lowercase__ = True
for i in range(len(__magic_name__ ) ):
lowercase__ = -1 * (i + 1)
l[reversed_idx] &= tally
lowercase__ = l[reversed_idx]
if start_edges is None:
lowercase__ = [s == 0 for s in start]
reduce_edge_list(__magic_name__ )
if end_edges is None:
lowercase__ = [e == (d - 1) for e, d in zip(__magic_name__ , __magic_name__ )]
reduce_edge_list(__magic_name__ )
# Base cases. Either start/end are empty and we're done, or the final,
# one-dimensional tensor can be simply sliced
if len(__magic_name__ ) == 0:
return [()]
elif len(__magic_name__ ) == 1:
return [(slice(start[0] , end[0] + 1 ),)]
lowercase__ = []
lowercase__ = []
# Dimensions common to start and end can be selected directly
for s, e in zip(__magic_name__ , __magic_name__ ):
if s == e:
path_list.append(slice(__magic_name__ , s + 1 ) )
else:
break
lowercase__ = tuple(__magic_name__ )
lowercase__ = len(__magic_name__ )
# start == end, and we're done
if divergence_idx == len(__magic_name__ ):
return [path]
def upper() -> Tuple[Tuple[slice, ...], ...]:
assert start_edges is not None
assert end_edges is not None
lowercase__ = start[divergence_idx]
return tuple(
path + (slice(__magic_name__ , sdi + 1 ),) + s
for s in _get_minimal_slice_set(
start[divergence_idx + 1 :] , [d - 1 for d in dims[divergence_idx + 1 :]] , dims[divergence_idx + 1 :] , start_edges=start_edges[divergence_idx + 1 :] , end_edges=[True for _ in end_edges[divergence_idx + 1 :]] , ) )
def lower() -> Tuple[Tuple[slice, ...], ...]:
assert start_edges is not None
assert end_edges is not None
lowercase__ = end[divergence_idx]
return tuple(
path + (slice(__magic_name__ , edi + 1 ),) + s
for s in _get_minimal_slice_set(
[0 for _ in start[divergence_idx + 1 :]] , end[divergence_idx + 1 :] , dims[divergence_idx + 1 :] , start_edges=[True for _ in start_edges[divergence_idx + 1 :]] , end_edges=end_edges[divergence_idx + 1 :] , ) )
# If both start and end are at the edges of the subtree rooted at
# divergence_idx, we can just select the whole subtree at once
if start_edges[divergence_idx] and end_edges[divergence_idx]:
slices.append(path + (slice(start[divergence_idx] , end[divergence_idx] + 1 ),) )
# If just start is at the edge, we can grab almost all of the subtree,
# treating only the ragged bottom edge as an edge case
elif start_edges[divergence_idx]:
slices.append(path + (slice(start[divergence_idx] , end[divergence_idx] ),) )
slices.extend(lower() )
# Analogous to the previous case, but the top is ragged this time
elif end_edges[divergence_idx]:
slices.extend(upper() )
slices.append(path + (slice(start[divergence_idx] + 1 , end[divergence_idx] + 1 ),) )
# If both sides of the range are ragged, we need to handle both sides
# separately. If there's contiguous meat in between them, we can index it
# in one big chunk
else:
slices.extend(upper() )
lowercase__ = end[divergence_idx] - start[divergence_idx]
if middle_ground > 1:
slices.append(path + (slice(start[divergence_idx] + 1 , end[divergence_idx] ),) )
slices.extend(lower() )
return slices
@torch.jit.ignore
def UpperCamelCase ( __magic_name__ : torch.Tensor , __magic_name__ : int , __magic_name__ : int , __magic_name__ : int ) -> torch.Tensor:
"""simple docstring"""
lowercase__ = t.shape[:no_batch_dims]
lowercase__ = list(_flat_idx_to_idx(__magic_name__ , __magic_name__ ) )
# _get_minimal_slice_set is inclusive
lowercase__ = list(_flat_idx_to_idx(flat_end - 1 , __magic_name__ ) )
# Get an ordered list of slices to perform
lowercase__ = _get_minimal_slice_set(
__magic_name__ , __magic_name__ , __magic_name__ , )
lowercase__ = [t[s] for s in slices]
return torch.cat([s.view((-1,) + t.shape[no_batch_dims:] ) for s in sliced_tensors] )
def UpperCamelCase ( __magic_name__ : Callable , __magic_name__ : Dict[str, Any] , __magic_name__ : int , __magic_name__ : int , __magic_name__ : bool = False , __magic_name__ : Any = None , __magic_name__ : bool = False , ) -> Any:
"""simple docstring"""
if not (len(__magic_name__ ) > 0):
raise ValueError("""Must provide at least one input""" )
lowercase__ = [shape[:no_batch_dims] for shape in _fetch_dims(__magic_name__ )]
lowercase__ = tuple([max(__magic_name__ ) for s in zip(*__magic_name__ )] )
def _prep_inputs(__magic_name__ : torch.Tensor ) -> torch.Tensor:
if not low_mem:
if not sum(t.shape[:no_batch_dims] ) == no_batch_dims:
lowercase__ = t.expand(orig_batch_dims + t.shape[no_batch_dims:] )
lowercase__ = t.reshape(-1 , *t.shape[no_batch_dims:] )
else:
lowercase__ = t.expand(orig_batch_dims + t.shape[no_batch_dims:] )
return t
lowercase__ = tensor_tree_map(_prep_inputs , __magic_name__ )
lowercase__ = None
if _out is not None:
lowercase__ = tensor_tree_map(lambda __magic_name__ : t.view([-1] + list(t.shape[no_batch_dims:] ) ) , _out )
lowercase__ = 1
for d in orig_batch_dims:
flat_batch_dim *= d
lowercase__ = flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)
def _select_chunk(__magic_name__ : torch.Tensor ) -> torch.Tensor:
return t[i : i + chunk_size] if t.shape[0] != 1 else t
lowercase__ = 0
lowercase__ = prepped_outputs
for _ in range(__magic_name__ ):
# Chunk the input
if not low_mem:
lowercase__ = _select_chunk
else:
lowercase__ = partial(
_chunk_slice , flat_start=__magic_name__ , flat_end=min(__magic_name__ , i + chunk_size ) , no_batch_dims=len(__magic_name__ ) , )
lowercase__ = tensor_tree_map(__magic_name__ , __magic_name__ )
# Run the layer on the chunk
lowercase__ = layer(**__magic_name__ )
# Allocate space for the output
if out is None:
lowercase__ = tensor_tree_map(lambda __magic_name__ : t.new_zeros((flat_batch_dim,) + t.shape[1:] ) , __magic_name__ )
# Put the chunk in its pre-allocated space
if isinstance(__magic_name__ , __magic_name__ ):
def assign(__magic_name__ : dict , __magic_name__ : dict ) -> None:
for k, v in da.items():
if isinstance(__magic_name__ , __magic_name__ ):
assign(__magic_name__ , da[k] )
else:
if _add_into_out:
v[i : i + chunk_size] += da[k]
else:
lowercase__ = da[k]
assign(__magic_name__ , __magic_name__ )
elif isinstance(__magic_name__ , __magic_name__ ):
for xa, xa in zip(__magic_name__ , __magic_name__ ):
if _add_into_out:
xa[i : i + chunk_size] += xa
else:
lowercase__ = xa
elif isinstance(__magic_name__ , torch.Tensor ):
if _add_into_out:
out[i : i + chunk_size] += output_chunk
else:
lowercase__ = output_chunk
else:
raise ValueError("""Not supported""" )
i += chunk_size
lowercase__ = tensor_tree_map(lambda __magic_name__ : t.view(orig_batch_dims + t.shape[1:] ) , __magic_name__ )
return out
class A :
'''simple docstring'''
def __init__(self : Tuple , _UpperCAmelCase : int = 512 , ) -> Any:
"""simple docstring"""
lowercase__ = max_chunk_size
lowercase__ = None
lowercase__ = None
def lowerCamelCase__ (self : Optional[Any] , _UpperCAmelCase : Callable , _UpperCAmelCase : tuple , _UpperCAmelCase : int ) -> int:
"""simple docstring"""
logging.info("""Tuning chunk size...""" )
if min_chunk_size >= self.max_chunk_size:
return min_chunk_size
lowercase__ = [2**l for l in range(int(math.log(self.max_chunk_size , 2 ) ) + 1 )]
lowercase__ = [c for c in candidates if c > min_chunk_size]
lowercase__ = [min_chunk_size] + candidates
candidates[-1] += 4
def test_chunk_size(_UpperCAmelCase : int ) -> bool:
try:
with torch.no_grad():
fn(*_UpperCAmelCase , chunk_size=_UpperCAmelCase )
return True
except RuntimeError:
return False
lowercase__ = 0
lowercase__ = len(_UpperCAmelCase ) - 1
while i > min_viable_chunk_size_index:
lowercase__ = test_chunk_size(candidates[i] )
if not viable:
lowercase__ = (min_viable_chunk_size_index + i) // 2
else:
lowercase__ = i
lowercase__ = (i + len(_UpperCAmelCase ) - 1) // 2
return candidates[min_viable_chunk_size_index]
def lowerCamelCase__ (self : Union[str, Any] , _UpperCAmelCase : Iterable , _UpperCAmelCase : Iterable ) -> bool:
"""simple docstring"""
lowercase__ = True
for aa, aa in zip(_UpperCAmelCase , _UpperCAmelCase ):
assert type(_UpperCAmelCase ) == type(_UpperCAmelCase )
if isinstance(_UpperCAmelCase , (list, tuple) ):
consistent &= self._compare_arg_caches(_UpperCAmelCase , _UpperCAmelCase )
elif isinstance(_UpperCAmelCase , _UpperCAmelCase ):
lowercase__ = [v for _, v in sorted(aa.items() , key=lambda _UpperCAmelCase : x[0] )]
lowercase__ = [v for _, v in sorted(aa.items() , key=lambda _UpperCAmelCase : x[0] )]
consistent &= self._compare_arg_caches(_UpperCAmelCase , _UpperCAmelCase )
else:
consistent &= aa == aa
return consistent
def lowerCamelCase__ (self : Dict , _UpperCAmelCase : Callable , _UpperCAmelCase : tuple , _UpperCAmelCase : int , ) -> int:
"""simple docstring"""
lowercase__ = True
lowercase__ = tree_map(lambda _UpperCAmelCase : a.shape if isinstance(_UpperCAmelCase , torch.Tensor ) else a , _UpperCAmelCase , _UpperCAmelCase )
if self.cached_arg_data is not None:
# If args have changed shape/value, we need to re-tune
assert len(self.cached_arg_data ) == len(_UpperCAmelCase )
lowercase__ = self._compare_arg_caches(self.cached_arg_data , _UpperCAmelCase )
else:
# Otherwise, we can reuse the precomputed value
lowercase__ = False
if not consistent:
lowercase__ = self._determine_favorable_chunk_size(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , )
lowercase__ = arg_data
assert self.cached_chunk_size is not None
return self.cached_chunk_size
| 305
|
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class A ( UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
A__ = ShapEImgaImgPipeline
A__ = ['''image''']
A__ = ['''image''']
A__ = [
'''num_images_per_prompt''',
'''num_inference_steps''',
'''generator''',
'''latents''',
'''guidance_scale''',
'''frame_size''',
'''output_type''',
'''return_dict''',
]
A__ = False
@property
def lowerCamelCase__ (self : Optional[Any] ) -> List[str]:
"""simple docstring"""
return 32
@property
def lowerCamelCase__ (self : str ) -> Any:
"""simple docstring"""
return 32
@property
def lowerCamelCase__ (self : str ) -> List[str]:
"""simple docstring"""
return self.time_input_dim * 4
@property
def lowerCamelCase__ (self : List[Any] ) -> Any:
"""simple docstring"""
return 8
@property
def lowerCamelCase__ (self : int ) -> List[str]:
"""simple docstring"""
torch.manual_seed(0 )
lowercase__ = CLIPVisionConfig(
hidden_size=self.text_embedder_hidden_size , image_size=64 , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_channels=3 , num_hidden_layers=5 , patch_size=1 , )
lowercase__ = CLIPVisionModel(_UpperCAmelCase )
return model
@property
def lowerCamelCase__ (self : Any ) -> List[Any]:
"""simple docstring"""
lowercase__ = CLIPImageProcessor(
crop_size=224 , do_center_crop=_UpperCAmelCase , do_normalize=_UpperCAmelCase , do_resize=_UpperCAmelCase , image_mean=[0.48_145_466, 0.4_578_275, 0.40_821_073] , image_std=[0.26_862_954, 0.26_130_258, 0.27_577_711] , resample=3 , size=224 , )
return image_processor
@property
def lowerCamelCase__ (self : int ) -> Optional[Any]:
"""simple docstring"""
torch.manual_seed(0 )
lowercase__ = {
"""num_attention_heads""": 2,
"""attention_head_dim""": 16,
"""embedding_dim""": self.time_input_dim,
"""num_embeddings""": 32,
"""embedding_proj_dim""": self.text_embedder_hidden_size,
"""time_embed_dim""": self.time_embed_dim,
"""num_layers""": 1,
"""clip_embed_dim""": self.time_input_dim * 2,
"""additional_embeddings""": 0,
"""time_embed_act_fn""": """gelu""",
"""norm_in_type""": """layer""",
"""embedding_proj_norm_type""": """layer""",
"""encoder_hid_proj_type""": None,
"""added_emb_type""": None,
}
lowercase__ = PriorTransformer(**_UpperCAmelCase )
return model
@property
def lowerCamelCase__ (self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
torch.manual_seed(0 )
lowercase__ = {
"""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,
),
}
lowercase__ = ShapERenderer(**_UpperCAmelCase )
return model
def lowerCamelCase__ (self : int ) -> Optional[int]:
"""simple docstring"""
lowercase__ = self.dummy_prior
lowercase__ = self.dummy_image_encoder
lowercase__ = self.dummy_image_processor
lowercase__ = self.dummy_renderer
lowercase__ = HeunDiscreteScheduler(
beta_schedule="""exp""" , num_train_timesteps=1024 , prediction_type="""sample""" , use_karras_sigmas=_UpperCAmelCase , clip_sample=_UpperCAmelCase , clip_sample_range=1.0 , )
lowercase__ = {
"""prior""": prior,
"""image_encoder""": image_encoder,
"""image_processor""": image_processor,
"""renderer""": renderer,
"""scheduler""": scheduler,
}
return components
def lowerCamelCase__ (self : Dict , _UpperCAmelCase : List[Any] , _UpperCAmelCase : str=0 ) -> str:
"""simple docstring"""
lowercase__ = floats_tensor((1, 3, 64, 64) , rng=random.Random(_UpperCAmelCase ) ).to(_UpperCAmelCase )
if str(_UpperCAmelCase ).startswith("""mps""" ):
lowercase__ = torch.manual_seed(_UpperCAmelCase )
else:
lowercase__ = torch.Generator(device=_UpperCAmelCase ).manual_seed(_UpperCAmelCase )
lowercase__ = {
"""image""": input_image,
"""generator""": generator,
"""num_inference_steps""": 1,
"""frame_size""": 32,
"""output_type""": """np""",
}
return inputs
def lowerCamelCase__ (self : str ) -> List[str]:
"""simple docstring"""
lowercase__ = """cpu"""
lowercase__ = self.get_dummy_components()
lowercase__ = self.pipeline_class(**_UpperCAmelCase )
lowercase__ = pipe.to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
lowercase__ = pipe(**self.get_dummy_inputs(_UpperCAmelCase ) )
lowercase__ = output.images[0]
lowercase__ = image[0, -3:, -3:, -1]
assert image.shape == (20, 32, 32, 3)
lowercase__ = np.array(
[
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def lowerCamelCase__ (self : str ) -> Any:
"""simple docstring"""
self._test_inference_batch_consistent(batch_sizes=[1, 2] )
def lowerCamelCase__ (self : Optional[int] ) -> str:
"""simple docstring"""
lowercase__ = torch_device == """cpu"""
lowercase__ = True
self._test_inference_batch_single_identical(
batch_size=2 , test_max_difference=_UpperCAmelCase , relax_max_difference=_UpperCAmelCase , )
def lowerCamelCase__ (self : Union[str, Any] ) -> int:
"""simple docstring"""
lowercase__ = self.get_dummy_components()
lowercase__ = self.pipeline_class(**_UpperCAmelCase )
lowercase__ = pipe.to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
lowercase__ = 1
lowercase__ = 2
lowercase__ = self.get_dummy_inputs(_UpperCAmelCase )
for key in inputs.keys():
if key in self.batch_params:
lowercase__ = batch_size * [inputs[key]]
lowercase__ = pipe(**_UpperCAmelCase , num_images_per_prompt=_UpperCAmelCase )[0]
assert images.shape[0] == batch_size * num_images_per_prompt
@slow
@require_torch_gpu
class A ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase__ (self : Dict ) -> List[Any]:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase__ (self : Any ) -> str:
"""simple docstring"""
lowercase__ = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/shap_e/corgi.png""" )
lowercase__ = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/shap_e/test_shap_e_img2img_out.npy""" )
lowercase__ = ShapEImgaImgPipeline.from_pretrained("""openai/shap-e-img2img""" )
lowercase__ = pipe.to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
lowercase__ = torch.Generator(device=_UpperCAmelCase ).manual_seed(0 )
lowercase__ = pipe(
_UpperCAmelCase , generator=_UpperCAmelCase , guidance_scale=3.0 , num_inference_steps=64 , frame_size=64 , output_type="""np""" , ).images[0]
assert images.shape == (20, 64, 64, 3)
assert_mean_pixel_difference(_UpperCAmelCase , _UpperCAmelCase )
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def UpperCamelCase ( __magic_name__ : str ) -> int:
"""simple docstring"""
assert column_title.isupper()
lowercase__ = 0
lowercase__ = len(__magic_name__ ) - 1
lowercase__ = 0
while index >= 0:
lowercase__ = (ord(column_title[index] ) - 64) * pow(26 , __magic_name__ )
answer += value
power += 1
index -= 1
return answer
if __name__ == "__main__":
from doctest import testmod
testmod()
| 305
|
import requests
from bsa import BeautifulSoup
def UpperCamelCase ( __magic_name__ : str = "AAPL" ) -> str:
"""simple docstring"""
lowercase__ = f'''https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'''
lowercase__ = BeautifulSoup(requests.get(__magic_name__ ).text , """html.parser""" )
lowercase__ = """My(6px) Pos(r) smartphone_Mt(6px)"""
return soup.find("""div""" , class_=class_ ).find("""span""" ).text
if __name__ == "__main__":
for symbol in "AAPL AMZN IBM GOOG MSFT ORCL".split():
print(F'Current {symbol:<4} stock price is {stock_price(symbol):>8}')
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|
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
A : str = datasets.utils.logging.get_logger(__name__)
@dataclass
class A ( datasets.BuilderConfig ):
'''simple docstring'''
A__ = None
A__ = "utf-8"
A__ = None
A__ = None
A__ = True # deprecated
A__ = None # deprecated
A__ = 10 << 20 # 10MB
A__ = None
class A ( datasets.ArrowBasedBuilder ):
'''simple docstring'''
A__ = JsonConfig
def lowerCamelCase__ (self : List[Any] ) -> List[Any]:
"""simple docstring"""
if self.config.block_size is not None:
logger.warning("""The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead""" )
lowercase__ = self.config.block_size
if self.config.use_threads is not True:
logger.warning(
"""The JSON loader parameter `use_threads` is deprecated and doesn't have any effect anymore.""" )
if self.config.newlines_in_values is not None:
raise ValueError("""The JSON loader parameter `newlines_in_values` is no longer supported""" )
return datasets.DatasetInfo(features=self.config.features )
def lowerCamelCase__ (self : Optional[Any] , _UpperCAmelCase : Optional[Any] ) -> Any:
"""simple docstring"""
if not self.config.data_files:
raise ValueError(f'''At least one data file must be specified, but got data_files={self.config.data_files}''' )
lowercase__ = dl_manager.download_and_extract(self.config.data_files )
if isinstance(_UpperCAmelCase , (str, list, tuple) ):
lowercase__ = data_files
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
lowercase__ = [files]
lowercase__ = [dl_manager.iter_files(_UpperCAmelCase ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={"""files""": files} )]
lowercase__ = []
for split_name, files in data_files.items():
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
lowercase__ = [files]
lowercase__ = [dl_manager.iter_files(_UpperCAmelCase ) for file in files]
splits.append(datasets.SplitGenerator(name=_UpperCAmelCase , gen_kwargs={"""files""": files} ) )
return splits
def lowerCamelCase__ (self : str , _UpperCAmelCase : pa.Table ) -> pa.Table:
"""simple docstring"""
if self.config.features is not None:
# adding missing columns
for column_name in set(self.config.features ) - set(pa_table.column_names ):
lowercase__ = self.config.features.arrow_schema.field(_UpperCAmelCase ).type
lowercase__ = pa_table.append_column(_UpperCAmelCase , pa.array([None] * len(_UpperCAmelCase ) , type=_UpperCAmelCase ) )
# more expensive cast to support nested structures with keys in a different order
# allows str <-> int/float or str to Audio for example
lowercase__ = table_cast(_UpperCAmelCase , self.config.features.arrow_schema )
return pa_table
def lowerCamelCase__ (self : Optional[Any] , _UpperCAmelCase : Tuple ) -> Tuple:
"""simple docstring"""
for file_idx, file in enumerate(itertools.chain.from_iterable(_UpperCAmelCase ) ):
# If the file is one json object and if we need to look at the list of items in one specific field
if self.config.field is not None:
with open(_UpperCAmelCase , encoding=self.config.encoding , errors=self.config.encoding_errors ) as f:
lowercase__ = json.load(_UpperCAmelCase )
# We keep only the field we are interested in
lowercase__ = dataset[self.config.field]
# We accept two format: a list of dicts or a dict of lists
if isinstance(_UpperCAmelCase , (list, tuple) ):
lowercase__ = set().union(*[row.keys() for row in dataset] )
lowercase__ = {col: [row.get(_UpperCAmelCase ) for row in dataset] for col in keys}
else:
lowercase__ = dataset
lowercase__ = pa.Table.from_pydict(_UpperCAmelCase )
yield file_idx, self._cast_table(_UpperCAmelCase )
# If the file has one json object per line
else:
with open(_UpperCAmelCase , """rb""" ) as f:
lowercase__ = 0
# Use block_size equal to the chunk size divided by 32 to leverage multithreading
# Set a default minimum value of 16kB if the chunk size is really small
lowercase__ = max(self.config.chunksize // 32 , 16 << 10 )
lowercase__ = (
self.config.encoding_errors if self.config.encoding_errors is not None else """strict"""
)
while True:
lowercase__ = f.read(self.config.chunksize )
if not batch:
break
# Finish current line
try:
batch += f.readline()
except (AttributeError, io.UnsupportedOperation):
batch += readline(_UpperCAmelCase )
# PyArrow only accepts utf-8 encoded bytes
if self.config.encoding != "utf-8":
lowercase__ = batch.decode(self.config.encoding , errors=_UpperCAmelCase ).encode("""utf-8""" )
try:
while True:
try:
lowercase__ = paj.read_json(
io.BytesIO(_UpperCAmelCase ) , read_options=paj.ReadOptions(block_size=_UpperCAmelCase ) )
break
except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
if (
isinstance(_UpperCAmelCase , pa.ArrowInvalid )
and "straddling" not in str(_UpperCAmelCase )
or block_size > len(_UpperCAmelCase )
):
raise
else:
# Increase the block size in case it was too small.
# The block size will be reset for the next file.
logger.debug(
f'''Batch of {len(_UpperCAmelCase )} bytes couldn\'t be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}.''' )
block_size *= 2
except pa.ArrowInvalid as e:
try:
with open(
_UpperCAmelCase , encoding=self.config.encoding , errors=self.config.encoding_errors ) as f:
lowercase__ = json.load(_UpperCAmelCase )
except json.JSONDecodeError:
logger.error(f'''Failed to read file \'{file}\' with error {type(_UpperCAmelCase )}: {e}''' )
raise e
# If possible, parse the file as a list of json objects and exit the loop
if isinstance(_UpperCAmelCase , _UpperCAmelCase ): # list is the only sequence type supported in JSON
try:
lowercase__ = set().union(*[row.keys() for row in dataset] )
lowercase__ = {col: [row.get(_UpperCAmelCase ) for row in dataset] for col in keys}
lowercase__ = pa.Table.from_pydict(_UpperCAmelCase )
except (pa.ArrowInvalid, AttributeError) as e:
logger.error(f'''Failed to read file \'{file}\' with error {type(_UpperCAmelCase )}: {e}''' )
raise ValueError(f'''Not able to read records in the JSON file at {file}.''' ) from None
yield file_idx, self._cast_table(_UpperCAmelCase )
break
else:
logger.error(f'''Failed to read file \'{file}\' with error {type(_UpperCAmelCase )}: {e}''' )
raise ValueError(
f'''Not able to read records in the JSON file at {file}. '''
f'''You should probably indicate the field of the JSON file containing your records. '''
f'''This JSON file contain the following fields: {str(list(dataset.keys() ) )}. '''
f'''Select the correct one and provide it as `field=\'XXX\'` to the dataset loading method. ''' ) from None
# Uncomment for debugging (will print the Arrow table size and elements)
# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
yield (file_idx, batch_idx), self._cast_table(_UpperCAmelCase )
batch_idx += 1
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|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : List[str] = logging.get_logger(__name__)
A : Any = {
'tiiuae/falcon-40b': 'https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json',
'tiiuae/falcon-7b': 'https://huggingface.co/tiiuae/falcon-7b/resolve/main/config.json',
}
class A ( UpperCAmelCase__ ):
'''simple docstring'''
A__ = '''falcon'''
A__ = ['''past_key_values''']
def __init__(self : str , _UpperCAmelCase : Dict=6_5024 , _UpperCAmelCase : Optional[Any]=4544 , _UpperCAmelCase : Optional[int]=32 , _UpperCAmelCase : Optional[Any]=71 , _UpperCAmelCase : List[Any]=1E-5 , _UpperCAmelCase : int=0.02 , _UpperCAmelCase : str=True , _UpperCAmelCase : Tuple=0.0 , _UpperCAmelCase : Any=0.0 , _UpperCAmelCase : str=None , _UpperCAmelCase : Optional[int]=False , _UpperCAmelCase : int=False , _UpperCAmelCase : Union[str, Any]=True , _UpperCAmelCase : List[Any]=True , _UpperCAmelCase : List[Any]=False , _UpperCAmelCase : Optional[int]=11 , _UpperCAmelCase : Optional[Any]=11 , **_UpperCAmelCase : Union[str, Any] , ) -> List[str]:
"""simple docstring"""
lowercase__ = vocab_size
# Backward compatibility with n_embed kwarg
lowercase__ = kwargs.pop("""n_embed""" , _UpperCAmelCase )
lowercase__ = hidden_size if n_embed is None else n_embed
lowercase__ = num_hidden_layers
lowercase__ = num_attention_heads
lowercase__ = layer_norm_epsilon
lowercase__ = initializer_range
lowercase__ = use_cache
lowercase__ = hidden_dropout
lowercase__ = attention_dropout
lowercase__ = bos_token_id
lowercase__ = eos_token_id
lowercase__ = num_attention_heads if num_kv_heads is None else num_kv_heads
lowercase__ = alibi
lowercase__ = new_decoder_architecture
lowercase__ = multi_query # Ignored when new_decoder_architecture is True
lowercase__ = parallel_attn
lowercase__ = bias
super().__init__(bos_token_id=_UpperCAmelCase , eos_token_id=_UpperCAmelCase , **_UpperCAmelCase )
@property
def lowerCamelCase__ (self : Tuple ) -> int:
"""simple docstring"""
return self.hidden_size // self.num_attention_heads
@property
def lowerCamelCase__ (self : List[str] ) -> Tuple:
"""simple docstring"""
return not self.alibi
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|
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBertForMaskedLM,
DistilBertTokenizer,
GPTaConfig,
GPTaLMHeadModel,
GPTaTokenizer,
RobertaConfig,
RobertaForMaskedLM,
RobertaTokenizer,
)
from utils import git_log, init_gpu_params, logger, set_seed
A : Tuple = {
'distilbert': (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
'roberta': (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
'bert': (BertConfig, BertForMaskedLM, BertTokenizer),
'gpt2': (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}
def UpperCamelCase ( __magic_name__ : Tuple ) -> int:
"""simple docstring"""
assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
if args.mlm:
assert os.path.isfile(args.token_counts )
assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
else:
assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])
assert args.teacher_type == args.student_type or (
args.student_type == "distilbert" and args.teacher_type == "bert"
)
assert os.path.isfile(args.student_config )
if args.student_pretrained_weights is not None:
assert os.path.isfile(args.student_pretrained_weights )
if args.freeze_token_type_embds:
assert args.student_type in ["roberta"]
assert args.alpha_ce >= 0.0
assert args.alpha_mlm >= 0.0
assert args.alpha_clm >= 0.0
assert args.alpha_mse >= 0.0
assert args.alpha_cos >= 0.0
assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0
def UpperCamelCase ( __magic_name__ : List[str] , __magic_name__ : Tuple ) -> List[str]:
"""simple docstring"""
if args.student_type == "roberta":
lowercase__ = False
elif args.student_type == "gpt2":
lowercase__ = False
def UpperCamelCase ( __magic_name__ : int , __magic_name__ : List[str] ) -> Optional[int]:
"""simple docstring"""
if args.student_type == "roberta":
lowercase__ = False
def UpperCamelCase ( ) -> str:
"""simple docstring"""
lowercase__ = argparse.ArgumentParser(description="""Training""" )
parser.add_argument("""--force""" , action="""store_true""" , help="""Overwrite dump_path if it already exists.""" )
parser.add_argument(
"""--dump_path""" , type=__magic_name__ , required=__magic_name__ , help="""The output directory (log, checkpoints, parameters, etc.)""" )
parser.add_argument(
"""--data_file""" , type=__magic_name__ , required=__magic_name__ , help="""The binarized file (tokenized + tokens_to_ids) and grouped by sequence.""" , )
parser.add_argument(
"""--student_type""" , type=__magic_name__ , choices=["""distilbert""", """roberta""", """gpt2"""] , required=__magic_name__ , help="""The student type (DistilBERT, RoBERTa).""" , )
parser.add_argument("""--student_config""" , type=__magic_name__ , required=__magic_name__ , help="""Path to the student configuration.""" )
parser.add_argument(
"""--student_pretrained_weights""" , default=__magic_name__ , type=__magic_name__ , help="""Load student initialization checkpoint.""" )
parser.add_argument(
"""--teacher_type""" , choices=["""bert""", """roberta""", """gpt2"""] , required=__magic_name__ , help="""Teacher type (BERT, RoBERTa).""" )
parser.add_argument("""--teacher_name""" , type=__magic_name__ , required=__magic_name__ , help="""The teacher model.""" )
parser.add_argument("""--temperature""" , default=2.0 , type=__magic_name__ , help="""Temperature for the softmax temperature.""" )
parser.add_argument(
"""--alpha_ce""" , default=0.5 , type=__magic_name__ , help="""Linear weight for the distillation loss. Must be >=0.""" )
parser.add_argument(
"""--alpha_mlm""" , default=0.0 , type=__magic_name__ , help="""Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag.""" , )
parser.add_argument("""--alpha_clm""" , default=0.5 , type=__magic_name__ , help="""Linear weight for the CLM loss. Must be >=0.""" )
parser.add_argument("""--alpha_mse""" , default=0.0 , type=__magic_name__ , help="""Linear weight of the MSE loss. Must be >=0.""" )
parser.add_argument(
"""--alpha_cos""" , default=0.0 , type=__magic_name__ , help="""Linear weight of the cosine embedding loss. Must be >=0.""" )
parser.add_argument(
"""--mlm""" , action="""store_true""" , help="""The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM.""" )
parser.add_argument(
"""--mlm_mask_prop""" , default=0.1_5 , type=__magic_name__ , help="""Proportion of tokens for which we need to make a prediction.""" , )
parser.add_argument("""--word_mask""" , default=0.8 , type=__magic_name__ , help="""Proportion of tokens to mask out.""" )
parser.add_argument("""--word_keep""" , default=0.1 , type=__magic_name__ , help="""Proportion of tokens to keep.""" )
parser.add_argument("""--word_rand""" , default=0.1 , type=__magic_name__ , help="""Proportion of tokens to randomly replace.""" )
parser.add_argument(
"""--mlm_smoothing""" , default=0.7 , type=__magic_name__ , help="""Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec).""" , )
parser.add_argument("""--token_counts""" , type=__magic_name__ , help="""The token counts in the data_file for MLM.""" )
parser.add_argument(
"""--restrict_ce_to_mask""" , action="""store_true""" , help="""If true, compute the distillation loss only the [MLM] prediction distribution.""" , )
parser.add_argument(
"""--freeze_pos_embs""" , action="""store_true""" , help="""Freeze positional embeddings during distillation. For student_type in ['roberta', 'gpt2'] only.""" , )
parser.add_argument(
"""--freeze_token_type_embds""" , action="""store_true""" , help="""Freeze token type embeddings during distillation if existent. For student_type in ['roberta'] only.""" , )
parser.add_argument("""--n_epoch""" , type=__magic_name__ , default=3 , help="""Number of pass on the whole dataset.""" )
parser.add_argument("""--batch_size""" , type=__magic_name__ , default=5 , help="""Batch size (for each process).""" )
parser.add_argument(
"""--group_by_size""" , action="""store_false""" , help="""If true, group sequences that have similar length into the same batch. Default is true.""" , )
parser.add_argument(
"""--gradient_accumulation_steps""" , type=__magic_name__ , default=50 , help="""Gradient accumulation for larger training batches.""" , )
parser.add_argument("""--warmup_prop""" , default=0.0_5 , type=__magic_name__ , help="""Linear warmup proportion.""" )
parser.add_argument("""--weight_decay""" , default=0.0 , type=__magic_name__ , help="""Weight decay if we apply some.""" )
parser.add_argument("""--learning_rate""" , default=5E-4 , type=__magic_name__ , help="""The initial learning rate for Adam.""" )
parser.add_argument("""--adam_epsilon""" , default=1E-6 , type=__magic_name__ , help="""Epsilon for Adam optimizer.""" )
parser.add_argument("""--max_grad_norm""" , default=5.0 , type=__magic_name__ , help="""Max gradient norm.""" )
parser.add_argument("""--initializer_range""" , default=0.0_2 , type=__magic_name__ , help="""Random initialization range.""" )
parser.add_argument(
"""--fp16""" , action="""store_true""" , help="""Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit""" , )
parser.add_argument(
"""--fp16_opt_level""" , type=__magic_name__ , default="""O1""" , help=(
"""For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."""
"""See details at https://nvidia.github.io/apex/amp.html"""
) , )
parser.add_argument("""--n_gpu""" , type=__magic_name__ , default=1 , help="""Number of GPUs in the node.""" )
parser.add_argument("""--local_rank""" , type=__magic_name__ , default=-1 , help="""Distributed training - Local rank""" )
parser.add_argument("""--seed""" , type=__magic_name__ , default=56 , help="""Random seed""" )
parser.add_argument("""--log_interval""" , type=__magic_name__ , default=500 , help="""Tensorboard logging interval.""" )
parser.add_argument("""--checkpoint_interval""" , type=__magic_name__ , default=4000 , help="""Checkpoint interval.""" )
lowercase__ = parser.parse_args()
sanity_checks(__magic_name__ )
# ARGS #
init_gpu_params(__magic_name__ )
set_seed(__magic_name__ )
if args.is_master:
if os.path.exists(args.dump_path ):
if not args.force:
raise ValueError(
f'''Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite'''
""" itUse `--force` if you want to overwrite it""" )
else:
shutil.rmtree(args.dump_path )
if not os.path.exists(args.dump_path ):
os.makedirs(args.dump_path )
logger.info(f'''Experiment will be dumped and logged in {args.dump_path}''' )
# SAVE PARAMS #
logger.info(f'''Param: {args}''' )
with open(os.path.join(args.dump_path , """parameters.json""" ) , """w""" ) as f:
json.dump(vars(__magic_name__ ) , __magic_name__ , indent=4 )
git_log(args.dump_path )
lowercase__ , lowercase__ , lowercase__ = MODEL_CLASSES[args.student_type]
lowercase__ , lowercase__ , lowercase__ = MODEL_CLASSES[args.teacher_type]
# TOKENIZER #
lowercase__ = teacher_tokenizer_class.from_pretrained(args.teacher_name )
lowercase__ = {}
for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
lowercase__ = tokenizer.all_special_tokens.index(__magic_name__ )
lowercase__ = tokenizer.all_special_ids[idx]
logger.info(f'''Special tokens {special_tok_ids}''' )
lowercase__ = special_tok_ids
lowercase__ = tokenizer.max_model_input_sizes[args.teacher_name]
# DATA LOADER #
logger.info(f'''Loading data from {args.data_file}''' )
with open(args.data_file , """rb""" ) as fp:
lowercase__ = pickle.load(__magic_name__ )
if args.mlm:
logger.info(f'''Loading token counts from {args.token_counts} (already pre-computed)''' )
with open(args.token_counts , """rb""" ) as fp:
lowercase__ = pickle.load(__magic_name__ )
lowercase__ = np.maximum(__magic_name__ , 1 ) ** -args.mlm_smoothing
for idx in special_tok_ids.values():
lowercase__ = 0.0 # do not predict special tokens
lowercase__ = torch.from_numpy(__magic_name__ )
else:
lowercase__ = None
lowercase__ = LmSeqsDataset(params=__magic_name__ , data=__magic_name__ )
logger.info("""Data loader created.""" )
# STUDENT #
logger.info(f'''Loading student config from {args.student_config}''' )
lowercase__ = student_config_class.from_pretrained(args.student_config )
lowercase__ = True
if args.student_pretrained_weights is not None:
logger.info(f'''Loading pretrained weights from {args.student_pretrained_weights}''' )
lowercase__ = student_model_class.from_pretrained(args.student_pretrained_weights , config=__magic_name__ )
else:
lowercase__ = student_model_class(__magic_name__ )
if args.n_gpu > 0:
student.to(f'''cuda:{args.local_rank}''' )
logger.info("""Student loaded.""" )
# TEACHER #
lowercase__ = teacher_model_class.from_pretrained(args.teacher_name , output_hidden_states=__magic_name__ )
if args.n_gpu > 0:
teacher.to(f'''cuda:{args.local_rank}''' )
logger.info(f'''Teacher loaded from {args.teacher_name}.''' )
# FREEZING #
if args.freeze_pos_embs:
freeze_pos_embeddings(__magic_name__ , __magic_name__ )
if args.freeze_token_type_embds:
freeze_token_type_embeddings(__magic_name__ , __magic_name__ )
# SANITY CHECKS #
assert student.config.vocab_size == teacher.config.vocab_size
assert student.config.hidden_size == teacher.config.hidden_size
assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
if args.mlm:
assert token_probs.size(0 ) == stu_architecture_config.vocab_size
# DISTILLER #
torch.cuda.empty_cache()
lowercase__ = Distiller(
params=__magic_name__ , dataset=__magic_name__ , token_probs=__magic_name__ , student=__magic_name__ , teacher=__magic_name__ )
distiller.train()
logger.info("""Let's go get some drinks.""" )
if __name__ == "__main__":
main()
| 305
|
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AlignProcessor, EfficientNetImageProcessor
@require_vision
class A ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase__ (self : Union[str, Any] ) -> Any:
"""simple docstring"""
lowercase__ = tempfile.mkdtemp()
lowercase__ = [
"""[UNK]""",
"""[CLS]""",
"""[SEP]""",
"""[PAD]""",
"""[MASK]""",
"""want""",
"""##want""",
"""##ed""",
"""wa""",
"""un""",
"""runn""",
"""##ing""",
""",""",
"""low""",
"""lowest""",
]
lowercase__ = 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] ) )
lowercase__ = {
"""do_resize""": True,
"""size""": 20,
"""do_center_crop""": True,
"""crop_size""": 18,
"""do_normalize""": True,
"""image_mean""": [0.48_145_466, 0.4_578_275, 0.40_821_073],
"""image_std""": [0.26_862_954, 0.26_130_258, 0.27_577_711],
}
lowercase__ = os.path.join(self.tmpdirname , _UpperCAmelCase )
with open(self.image_processor_file , """w""" , encoding="""utf-8""" ) as fp:
json.dump(_UpperCAmelCase , _UpperCAmelCase )
def lowerCamelCase__ (self : Dict , **_UpperCAmelCase : Any ) -> Optional[Any]:
"""simple docstring"""
return BertTokenizer.from_pretrained(self.tmpdirname , **_UpperCAmelCase )
def lowerCamelCase__ (self : Union[str, Any] , **_UpperCAmelCase : Any ) -> Dict:
"""simple docstring"""
return BertTokenizerFast.from_pretrained(self.tmpdirname , **_UpperCAmelCase )
def lowerCamelCase__ (self : Optional[int] , **_UpperCAmelCase : str ) -> Dict:
"""simple docstring"""
return EfficientNetImageProcessor.from_pretrained(self.tmpdirname , **_UpperCAmelCase )
def lowerCamelCase__ (self : Optional[int] ) -> List[str]:
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def lowerCamelCase__ (self : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
lowercase__ = [Image.fromarray(np.moveaxis(_UpperCAmelCase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def lowerCamelCase__ (self : Optional[int] ) -> Tuple:
"""simple docstring"""
lowercase__ = self.get_tokenizer()
lowercase__ = self.get_rust_tokenizer()
lowercase__ = self.get_image_processor()
lowercase__ = AlignProcessor(tokenizer=_UpperCAmelCase , image_processor=_UpperCAmelCase )
processor_slow.save_pretrained(self.tmpdirname )
lowercase__ = AlignProcessor.from_pretrained(self.tmpdirname , use_fast=_UpperCAmelCase )
lowercase__ = AlignProcessor(tokenizer=_UpperCAmelCase , image_processor=_UpperCAmelCase )
processor_fast.save_pretrained(self.tmpdirname )
lowercase__ = AlignProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer , _UpperCAmelCase )
self.assertIsInstance(processor_fast.tokenizer , _UpperCAmelCase )
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor , _UpperCAmelCase )
self.assertIsInstance(processor_fast.image_processor , _UpperCAmelCase )
def lowerCamelCase__ (self : Any ) -> List[str]:
"""simple docstring"""
lowercase__ = AlignProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
lowercase__ = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
lowercase__ = self.get_image_processor(do_normalize=_UpperCAmelCase , padding_value=1.0 )
lowercase__ = AlignProcessor.from_pretrained(
self.tmpdirname , bos_token="""(BOS)""" , eos_token="""(EOS)""" , do_normalize=_UpperCAmelCase , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , _UpperCAmelCase )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , _UpperCAmelCase )
def lowerCamelCase__ (self : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = self.get_image_processor()
lowercase__ = self.get_tokenizer()
lowercase__ = AlignProcessor(tokenizer=_UpperCAmelCase , image_processor=_UpperCAmelCase )
lowercase__ = self.prepare_image_inputs()
lowercase__ = image_processor(_UpperCAmelCase , return_tensors="""np""" )
lowercase__ = processor(images=_UpperCAmelCase , return_tensors="""np""" )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1E-2 )
def lowerCamelCase__ (self : Dict ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = self.get_image_processor()
lowercase__ = self.get_tokenizer()
lowercase__ = AlignProcessor(tokenizer=_UpperCAmelCase , image_processor=_UpperCAmelCase )
lowercase__ = """lower newer"""
lowercase__ = processor(text=_UpperCAmelCase )
lowercase__ = tokenizer(_UpperCAmelCase , padding="""max_length""" , max_length=64 )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def lowerCamelCase__ (self : List[Any] ) -> Tuple:
"""simple docstring"""
lowercase__ = self.get_image_processor()
lowercase__ = self.get_tokenizer()
lowercase__ = AlignProcessor(tokenizer=_UpperCAmelCase , image_processor=_UpperCAmelCase )
lowercase__ = """lower newer"""
lowercase__ = self.prepare_image_inputs()
lowercase__ = processor(text=_UpperCAmelCase , images=_UpperCAmelCase )
self.assertListEqual(list(inputs.keys() ) , ["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""] )
# test if it raises when no input is passed
with pytest.raises(_UpperCAmelCase ):
processor()
def lowerCamelCase__ (self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = self.get_image_processor()
lowercase__ = self.get_tokenizer()
lowercase__ = AlignProcessor(tokenizer=_UpperCAmelCase , image_processor=_UpperCAmelCase )
lowercase__ = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
lowercase__ = processor.batch_decode(_UpperCAmelCase )
lowercase__ = tokenizer.batch_decode(_UpperCAmelCase )
self.assertListEqual(_UpperCAmelCase , _UpperCAmelCase )
def lowerCamelCase__ (self : List[str] ) -> Tuple:
"""simple docstring"""
lowercase__ = self.get_image_processor()
lowercase__ = self.get_tokenizer()
lowercase__ = AlignProcessor(tokenizer=_UpperCAmelCase , image_processor=_UpperCAmelCase )
lowercase__ = """lower newer"""
lowercase__ = self.prepare_image_inputs()
lowercase__ = processor(text=_UpperCAmelCase , images=_UpperCAmelCase )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 305
| 1
|
from __future__ import annotations
import numpy as np
def UpperCamelCase ( __magic_name__ : np.ndarray ) -> tuple[np.ndarray, np.ndarray]:
"""simple docstring"""
lowercase__ , lowercase__ = np.shape(__magic_name__ )
if rows != columns:
lowercase__ = (
"""'table' has to be of square shaped array but got a """
f'''{rows}x{columns} array:\n{table}'''
)
raise ValueError(__magic_name__ )
lowercase__ = np.zeros((rows, columns) )
lowercase__ = np.zeros((rows, columns) )
for i in range(__magic_name__ ):
for j in range(__magic_name__ ):
lowercase__ = sum(lower[i][k] * upper[k][j] for k in range(__magic_name__ ) )
if upper[j][j] == 0:
raise ArithmeticError("""No LU decomposition exists""" )
lowercase__ = (table[i][j] - total) / upper[j][j]
lowercase__ = 1
for j in range(__magic_name__ , __magic_name__ ):
lowercase__ = sum(lower[i][k] * upper[k][j] for k in range(__magic_name__ ) )
lowercase__ = table[i][j] - total
return lower, upper
if __name__ == "__main__":
import doctest
doctest.testmod()
| 305
|
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def UpperCamelCase ( __magic_name__ : List[Any] ) -> Optional[int]:
"""simple docstring"""
return x + 2
class A ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase__ (self : Optional[Any] ) -> Any:
"""simple docstring"""
lowercase__ = """x = 3"""
lowercase__ = {}
lowercase__ = evaluate(_UpperCAmelCase , {} , state=_UpperCAmelCase )
assert result == 3
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3} )
lowercase__ = """x = y"""
lowercase__ = {"""y""": 5}
lowercase__ = evaluate(_UpperCAmelCase , {} , state=_UpperCAmelCase )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(_UpperCAmelCase , {"""x""": 5, """y""": 5} )
def lowerCamelCase__ (self : str ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = """y = add_two(x)"""
lowercase__ = {"""x""": 3}
lowercase__ = evaluate(_UpperCAmelCase , {"""add_two""": add_two} , state=_UpperCAmelCase )
assert result == 5
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3, """y""": 5} )
# Won't work without the tool
with CaptureStdout() as out:
lowercase__ = evaluate(_UpperCAmelCase , {} , state=_UpperCAmelCase )
assert result is None
assert "tried to execute add_two" in out.out
def lowerCamelCase__ (self : List[Any] ) -> Optional[int]:
"""simple docstring"""
lowercase__ = """x = 3"""
lowercase__ = {}
lowercase__ = evaluate(_UpperCAmelCase , {} , state=_UpperCAmelCase )
assert result == 3
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3} )
def lowerCamelCase__ (self : Optional[int] ) -> List[Any]:
"""simple docstring"""
lowercase__ = """test_dict = {'x': x, 'y': add_two(x)}"""
lowercase__ = {"""x""": 3}
lowercase__ = evaluate(_UpperCAmelCase , {"""add_two""": add_two} , state=_UpperCAmelCase )
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3, """y""": 5} )
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3, """test_dict""": {"""x""": 3, """y""": 5}} )
def lowerCamelCase__ (self : List[str] ) -> List[Any]:
"""simple docstring"""
lowercase__ = """x = 3\ny = 5"""
lowercase__ = {}
lowercase__ = evaluate(_UpperCAmelCase , {} , state=_UpperCAmelCase )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3, """y""": 5} )
def lowerCamelCase__ (self : List[Any] ) -> Dict:
"""simple docstring"""
lowercase__ = """text = f'This is x: {x}.'"""
lowercase__ = {"""x""": 3}
lowercase__ = evaluate(_UpperCAmelCase , {} , state=_UpperCAmelCase )
# evaluate returns the value of the last assignment.
assert result == "This is x: 3."
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3, """text""": """This is x: 3."""} )
def lowerCamelCase__ (self : List[str] ) -> int:
"""simple docstring"""
lowercase__ = """if x <= 3:\n y = 2\nelse:\n y = 5"""
lowercase__ = {"""x""": 3}
lowercase__ = evaluate(_UpperCAmelCase , {} , state=_UpperCAmelCase )
# evaluate returns the value of the last assignment.
assert result == 2
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3, """y""": 2} )
lowercase__ = {"""x""": 8}
lowercase__ = evaluate(_UpperCAmelCase , {} , state=_UpperCAmelCase )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(_UpperCAmelCase , {"""x""": 8, """y""": 5} )
def lowerCamelCase__ (self : Dict ) -> int:
"""simple docstring"""
lowercase__ = """test_list = [x, add_two(x)]"""
lowercase__ = {"""x""": 3}
lowercase__ = evaluate(_UpperCAmelCase , {"""add_two""": add_two} , state=_UpperCAmelCase )
self.assertListEqual(_UpperCAmelCase , [3, 5] )
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3, """test_list""": [3, 5]} )
def lowerCamelCase__ (self : Any ) -> int:
"""simple docstring"""
lowercase__ = """y = x"""
lowercase__ = {"""x""": 3}
lowercase__ = evaluate(_UpperCAmelCase , {} , state=_UpperCAmelCase )
assert result == 3
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3, """y""": 3} )
def lowerCamelCase__ (self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
lowercase__ = """test_list = [x, add_two(x)]\ntest_list[1]"""
lowercase__ = {"""x""": 3}
lowercase__ = evaluate(_UpperCAmelCase , {"""add_two""": add_two} , state=_UpperCAmelCase )
assert result == 5
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3, """test_list""": [3, 5]} )
lowercase__ = """test_dict = {'x': x, 'y': add_two(x)}\ntest_dict['y']"""
lowercase__ = {"""x""": 3}
lowercase__ = evaluate(_UpperCAmelCase , {"""add_two""": add_two} , state=_UpperCAmelCase )
assert result == 5
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3, """test_dict""": {"""x""": 3, """y""": 5}} )
def lowerCamelCase__ (self : Union[str, Any] ) -> Any:
"""simple docstring"""
lowercase__ = """x = 0\nfor i in range(3):\n x = i"""
lowercase__ = {}
lowercase__ = evaluate(_UpperCAmelCase , {"""range""": range} , state=_UpperCAmelCase )
assert result == 2
self.assertDictEqual(_UpperCAmelCase , {"""x""": 2, """i""": 2} )
| 305
| 1
|
from collections.abc import Generator
from math import sin
def UpperCamelCase ( __magic_name__ : bytes ) -> bytes:
"""simple docstring"""
if len(__magic_name__ ) != 32:
raise ValueError("""Input must be of length 32""" )
lowercase__ = B""""""
for i in [3, 2, 1, 0]:
little_endian += string_aa[8 * i : 8 * i + 8]
return little_endian
def UpperCamelCase ( __magic_name__ : int ) -> bytes:
"""simple docstring"""
if i < 0:
raise ValueError("""Input must be non-negative""" )
lowercase__ = format(__magic_name__ , """08x""" )[-8:]
lowercase__ = B""""""
for i in [3, 2, 1, 0]:
little_endian_hex += hex_rep[2 * i : 2 * i + 2].encode("""utf-8""" )
return little_endian_hex
def UpperCamelCase ( __magic_name__ : bytes ) -> bytes:
"""simple docstring"""
lowercase__ = B""""""
for char in message:
bit_string += format(__magic_name__ , """08b""" ).encode("""utf-8""" )
lowercase__ = format(len(__magic_name__ ) , """064b""" ).encode("""utf-8""" )
# Pad bit_string to a multiple of 512 chars
bit_string += b"1"
while len(__magic_name__ ) % 512 != 448:
bit_string += b"0"
bit_string += to_little_endian(start_len[32:] ) + to_little_endian(start_len[:32] )
return bit_string
def UpperCamelCase ( __magic_name__ : bytes ) -> Generator[list[int], None, None]:
"""simple docstring"""
if len(__magic_name__ ) % 512 != 0:
raise ValueError("""Input must have length that's a multiple of 512""" )
for pos in range(0 , len(__magic_name__ ) , 512 ):
lowercase__ = bit_string[pos : pos + 512]
lowercase__ = []
for i in range(0 , 512 , 32 ):
block_words.append(int(to_little_endian(block[i : i + 32] ) , 2 ) )
yield block_words
def UpperCamelCase ( __magic_name__ : int ) -> int:
"""simple docstring"""
if i < 0:
raise ValueError("""Input must be non-negative""" )
lowercase__ = format(__magic_name__ , """032b""" )
lowercase__ = """"""
for c in i_str:
new_str += "1" if c == "0" else "0"
return int(__magic_name__ , 2 )
def UpperCamelCase ( __magic_name__ : int , __magic_name__ : int ) -> int:
"""simple docstring"""
return (a + b) % 2**32
def UpperCamelCase ( __magic_name__ : int , __magic_name__ : int ) -> int:
"""simple docstring"""
if i < 0:
raise ValueError("""Input must be non-negative""" )
if shift < 0:
raise ValueError("""Shift must be non-negative""" )
return ((i << shift) ^ (i >> (32 - shift))) % 2**32
def UpperCamelCase ( __magic_name__ : bytes ) -> bytes:
"""simple docstring"""
lowercase__ = preprocess(__magic_name__ )
lowercase__ = [int(2**32 * abs(sin(i + 1 ) ) ) for i in range(64 )]
# Starting states
lowercase__ = 0x6745_2301
lowercase__ = 0xEFCD_AB89
lowercase__ = 0x98BA_DCFE
lowercase__ = 0x1032_5476
lowercase__ = [
7,
12,
17,
22,
7,
12,
17,
22,
7,
12,
17,
22,
7,
12,
17,
22,
5,
9,
14,
20,
5,
9,
14,
20,
5,
9,
14,
20,
5,
9,
14,
20,
4,
11,
16,
23,
4,
11,
16,
23,
4,
11,
16,
23,
4,
11,
16,
23,
6,
10,
15,
21,
6,
10,
15,
21,
6,
10,
15,
21,
6,
10,
15,
21,
]
# Process bit string in chunks, each with 16 32-char words
for block_words in get_block_words(__magic_name__ ):
lowercase__ = aa
lowercase__ = ba
lowercase__ = ca
lowercase__ = da
# Hash current chunk
for i in range(64 ):
if i <= 15:
# f = (b & c) | (not_32(b) & d) # Alternate definition for f
lowercase__ = d ^ (b & (c ^ d))
lowercase__ = i
elif i <= 31:
# f = (d & b) | (not_32(d) & c) # Alternate definition for f
lowercase__ = c ^ (d & (b ^ c))
lowercase__ = (5 * i + 1) % 16
elif i <= 47:
lowercase__ = b ^ c ^ d
lowercase__ = (3 * i + 5) % 16
else:
lowercase__ = c ^ (b | not_aa(__magic_name__ ))
lowercase__ = (7 * i) % 16
lowercase__ = (f + a + added_consts[i] + block_words[g]) % 2**32
lowercase__ = d
lowercase__ = c
lowercase__ = b
lowercase__ = sum_aa(__magic_name__ , left_rotate_aa(__magic_name__ , shift_amounts[i] ) )
# Add hashed chunk to running total
lowercase__ = sum_aa(__magic_name__ , __magic_name__ )
lowercase__ = sum_aa(__magic_name__ , __magic_name__ )
lowercase__ = sum_aa(__magic_name__ , __magic_name__ )
lowercase__ = sum_aa(__magic_name__ , __magic_name__ )
lowercase__ = reformat_hex(__magic_name__ ) + reformat_hex(__magic_name__ ) + reformat_hex(__magic_name__ ) + reformat_hex(__magic_name__ )
return digest
if __name__ == "__main__":
import doctest
doctest.testmod()
| 305
|
class A :
'''simple docstring'''
def __init__(self : List[str] ) -> Tuple:
"""simple docstring"""
lowercase__ = 0
lowercase__ = 0
lowercase__ = {}
def lowerCamelCase__ (self : Dict , _UpperCAmelCase : Tuple ) -> Optional[int]:
"""simple docstring"""
if vertex not in self.adjacency:
lowercase__ = {}
self.num_vertices += 1
def lowerCamelCase__ (self : List[Any] , _UpperCAmelCase : List[Any] , _UpperCAmelCase : int , _UpperCAmelCase : List[str] ) -> Tuple:
"""simple docstring"""
self.add_vertex(_UpperCAmelCase )
self.add_vertex(_UpperCAmelCase )
if head == tail:
return
lowercase__ = weight
lowercase__ = weight
def lowerCamelCase__ (self : List[str] ) -> Optional[int]:
"""simple docstring"""
lowercase__ = self.get_edges()
for edge in edges:
lowercase__ , lowercase__ , lowercase__ = edge
edges.remove((tail, head, weight) )
for i in range(len(_UpperCAmelCase ) ):
lowercase__ = list(edges[i] )
edges.sort(key=lambda _UpperCAmelCase : e[2] )
for i in range(len(_UpperCAmelCase ) - 1 ):
if edges[i][2] >= edges[i + 1][2]:
lowercase__ = edges[i][2] + 1
for edge in edges:
lowercase__ , lowercase__ , lowercase__ = edge
lowercase__ = weight
lowercase__ = weight
def __str__(self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = """"""
for tail in self.adjacency:
for head in self.adjacency[tail]:
lowercase__ = self.adjacency[head][tail]
string += f'''{head} -> {tail} == {weight}\n'''
return string.rstrip("""\n""" )
def lowerCamelCase__ (self : Any ) -> str:
"""simple docstring"""
lowercase__ = []
for tail in self.adjacency:
for head in self.adjacency[tail]:
output.append((tail, head, self.adjacency[head][tail]) )
return output
def lowerCamelCase__ (self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
return self.adjacency.keys()
@staticmethod
def lowerCamelCase__ (_UpperCAmelCase : List[str]=None , _UpperCAmelCase : Any=None ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = Graph()
if vertices is None:
lowercase__ = []
if edges is None:
lowercase__ = []
for vertex in vertices:
g.add_vertex(_UpperCAmelCase )
for edge in edges:
g.add_edge(*_UpperCAmelCase )
return g
class A :
'''simple docstring'''
def __init__(self : Optional[Any] ) -> str:
"""simple docstring"""
lowercase__ = {}
lowercase__ = {}
def __len__(self : Optional[Any] ) -> Dict:
"""simple docstring"""
return len(self.parent )
def lowerCamelCase__ (self : str , _UpperCAmelCase : Dict ) -> Any:
"""simple docstring"""
if item in self.parent:
return self.find(_UpperCAmelCase )
lowercase__ = item
lowercase__ = 0
return item
def lowerCamelCase__ (self : List[str] , _UpperCAmelCase : Dict ) -> Any:
"""simple docstring"""
if item not in self.parent:
return self.make_set(_UpperCAmelCase )
if item != self.parent[item]:
lowercase__ = self.find(self.parent[item] )
return self.parent[item]
def lowerCamelCase__ (self : List[Any] , _UpperCAmelCase : Any , _UpperCAmelCase : List[Any] ) -> Optional[int]:
"""simple docstring"""
lowercase__ = self.find(_UpperCAmelCase )
lowercase__ = self.find(_UpperCAmelCase )
if roota == roota:
return roota
if self.rank[roota] > self.rank[roota]:
lowercase__ = roota
return roota
if self.rank[roota] < self.rank[roota]:
lowercase__ = roota
return roota
if self.rank[roota] == self.rank[roota]:
self.rank[roota] += 1
lowercase__ = roota
return roota
return None
@staticmethod
def lowerCamelCase__ (_UpperCAmelCase : str ) -> Optional[int]:
"""simple docstring"""
lowercase__ = graph.num_vertices
lowercase__ = Graph.UnionFind()
lowercase__ = []
while num_components > 1:
lowercase__ = {}
for vertex in graph.get_vertices():
lowercase__ = -1
lowercase__ = graph.get_edges()
for edge in edges:
lowercase__ , lowercase__ , lowercase__ = edge
edges.remove((tail, head, weight) )
for edge in edges:
lowercase__ , lowercase__ , lowercase__ = edge
lowercase__ = union_find.find(_UpperCAmelCase )
lowercase__ = union_find.find(_UpperCAmelCase )
if seta != seta:
if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
lowercase__ = [head, tail, weight]
if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
lowercase__ = [head, tail, weight]
for vertex in cheap_edge:
if cheap_edge[vertex] != -1:
lowercase__ , lowercase__ , lowercase__ = cheap_edge[vertex]
if union_find.find(_UpperCAmelCase ) != union_find.find(_UpperCAmelCase ):
union_find.union(_UpperCAmelCase , _UpperCAmelCase )
mst_edges.append(cheap_edge[vertex] )
lowercase__ = num_components - 1
lowercase__ = Graph.build(edges=_UpperCAmelCase )
return mst
| 305
| 1
|
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A : Optional[Any] = logging.get_logger(__name__)
A : int = {
'google/mobilenet_v1_1.0_224': 'https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json',
'google/mobilenet_v1_0.75_192': 'https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json',
# See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}
class A ( UpperCAmelCase__ ):
'''simple docstring'''
A__ = '''mobilenet_v1'''
def __init__(self : List[str] , _UpperCAmelCase : int=3 , _UpperCAmelCase : Optional[Any]=224 , _UpperCAmelCase : List[Any]=1.0 , _UpperCAmelCase : int=8 , _UpperCAmelCase : int="relu6" , _UpperCAmelCase : Any=True , _UpperCAmelCase : Union[str, Any]=0.999 , _UpperCAmelCase : str=0.02 , _UpperCAmelCase : Optional[Any]=0.001 , **_UpperCAmelCase : Optional[Any] , ) -> Dict:
"""simple docstring"""
super().__init__(**_UpperCAmelCase )
if depth_multiplier <= 0:
raise ValueError("""depth_multiplier must be greater than zero.""" )
lowercase__ = num_channels
lowercase__ = image_size
lowercase__ = depth_multiplier
lowercase__ = min_depth
lowercase__ = hidden_act
lowercase__ = tf_padding
lowercase__ = classifier_dropout_prob
lowercase__ = initializer_range
lowercase__ = layer_norm_eps
class A ( UpperCAmelCase__ ):
'''simple docstring'''
A__ = version.parse('''1.11''' )
@property
def lowerCamelCase__ (self : int ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
return OrderedDict([("""pixel_values""", {0: """batch"""})] )
@property
def lowerCamelCase__ (self : List[Any] ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
if self.task == "image-classification":
return OrderedDict([("""logits""", {0: """batch"""})] )
else:
return OrderedDict([("""last_hidden_state""", {0: """batch"""}), ("""pooler_output""", {0: """batch"""})] )
@property
def lowerCamelCase__ (self : Dict ) -> float:
"""simple docstring"""
return 1E-4
| 305
|
def UpperCamelCase ( __magic_name__ : int , __magic_name__ : int ) -> int:
"""simple docstring"""
return int((input_a, input_a).count(1 ) != 0 )
def UpperCamelCase ( ) -> None:
"""simple docstring"""
assert or_gate(0 , 0 ) == 0
assert or_gate(0 , 1 ) == 1
assert or_gate(1 , 0 ) == 1
assert or_gate(1 , 1 ) == 1
if __name__ == "__main__":
print(or_gate(0, 1))
print(or_gate(1, 0))
print(or_gate(0, 0))
print(or_gate(1, 1))
| 305
| 1
|
A : Union[str, Any] = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
A : List[Any] = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def UpperCamelCase ( __magic_name__ : dict[int, list[int]] , __magic_name__ : int , __magic_name__ : list[bool] ) -> list[int]:
"""simple docstring"""
lowercase__ = True
lowercase__ = []
for neighbour in graph[vert]:
if not visited[neighbour]:
order += topology_sort(__magic_name__ , __magic_name__ , __magic_name__ )
order.append(__magic_name__ )
return order
def UpperCamelCase ( __magic_name__ : dict[int, list[int]] , __magic_name__ : int , __magic_name__ : list[bool] ) -> list[int]:
"""simple docstring"""
lowercase__ = True
lowercase__ = [vert]
for neighbour in reversed_graph[vert]:
if not visited[neighbour]:
component += find_components(__magic_name__ , __magic_name__ , __magic_name__ )
return component
def UpperCamelCase ( __magic_name__ : dict[int, list[int]] ) -> list[list[int]]:
"""simple docstring"""
lowercase__ = len(__magic_name__ ) * [False]
lowercase__ = {vert: [] for vert in range(len(__magic_name__ ) )}
for vert, neighbours in graph.items():
for neighbour in neighbours:
reversed_graph[neighbour].append(__magic_name__ )
lowercase__ = []
for i, was_visited in enumerate(__magic_name__ ):
if not was_visited:
order += topology_sort(__magic_name__ , __magic_name__ , __magic_name__ )
lowercase__ = []
lowercase__ = len(__magic_name__ ) * [False]
for i in range(len(__magic_name__ ) ):
lowercase__ = order[len(__magic_name__ ) - i - 1]
if not visited[vert]:
lowercase__ = find_components(__magic_name__ , __magic_name__ , __magic_name__ )
components_list.append(__magic_name__ )
return components_list
| 305
|
import argparse
from torch import nn
# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)
from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging
A : Any = logging.get_logger(__name__)
logging.set_verbosity_info()
def UpperCamelCase ( __magic_name__ : str , __magic_name__ : str ) -> List[str]:
"""simple docstring"""
if "xprophetnet" in prophetnet_checkpoint_path:
lowercase__ = XLMProphetNetForConditionalGenerationOld.from_pretrained(__magic_name__ )
lowercase__ , lowercase__ = XLMProphetNetForConditionalGeneration.from_pretrained(
__magic_name__ , output_loading_info=__magic_name__ )
else:
lowercase__ = ProphetNetForConditionalGenerationOld.from_pretrained(__magic_name__ )
lowercase__ , lowercase__ = ProphetNetForConditionalGeneration.from_pretrained(
__magic_name__ , output_loading_info=__magic_name__ )
lowercase__ = ["""key_proj""", """value_proj""", """query_proj"""]
lowercase__ = {
"""self_attn""": """ngram_self_attn""",
"""cross_attn""": """encoder_attn""",
"""cross_attn_layer_norm""": """encoder_attn_layer_norm""",
"""feed_forward_layer_norm""": """final_layer_norm""",
"""feed_forward""": """""",
"""intermediate""": """fc1""",
"""output""": """fc2""",
"""key_proj""": """k_proj""",
"""query_proj""": """q_proj""",
"""value_proj""": """v_proj""",
"""word_embeddings""": """embed_tokens""",
"""embeddings_layer_norm""": """emb_layer_norm""",
"""relative_pos_embeddings""": """relative_linear""",
"""ngram_embeddings""": """ngram_input_embed""",
"""position_embeddings""": """embed_positions""",
}
for key in loading_info["missing_keys"]:
lowercase__ = key.split(""".""" )
if attributes[0] == "lm_head":
lowercase__ = prophet
lowercase__ = prophet_old
else:
lowercase__ = prophet.prophetnet
lowercase__ = prophet_old.model
lowercase__ = False
for attribute in attributes:
if attribute in mapping:
lowercase__ = mapping[attribute]
if not hasattr(__magic_name__ , __magic_name__ ) and len(__magic_name__ ) > 0:
lowercase__ = attribute
elif hasattr(__magic_name__ , __magic_name__ ):
lowercase__ = attribute
if attribute == "weight":
assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
lowercase__ = old_model.weight
logger.info(f'''{attribute} is initialized.''' )
lowercase__ = True
break
elif attribute == "bias":
assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
lowercase__ = old_model.bias
logger.info(f'''{attribute} is initialized''' )
lowercase__ = True
break
elif attribute in special_keys and hasattr(__magic_name__ , """in_proj_weight""" ):
lowercase__ = old_model.in_proj_weight.shape[0] // 3
lowercase__ = getattr(__magic_name__ , __magic_name__ )
param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
if attribute == "query_proj":
lowercase__ = nn.Parameter(old_model.in_proj_weight[:embed_dim, :] )
lowercase__ = nn.Parameter(old_model.in_proj_bias[:embed_dim] )
elif attribute == "key_proj":
lowercase__ = nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :] )
lowercase__ = nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim] )
elif attribute == "value_proj":
lowercase__ = nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :] )
lowercase__ = nn.Parameter(old_model.in_proj_bias[2 * embed_dim :] )
lowercase__ = True
break
elif attribute == "position_embeddings":
assert (
model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
), "Hidden size has to match"
assert model.position_embeddings.weight.shape[0] == 512, "We want 512 position_embeddings."
lowercase__ = nn.Parameter(old_model.embed_positions.weight[:512, :] )
lowercase__ = True
break
if attribute.isdigit():
lowercase__ = model[int(__magic_name__ )]
lowercase__ = old_model[int(__magic_name__ )]
else:
lowercase__ = getattr(__magic_name__ , __magic_name__ )
if old_attribute == "":
lowercase__ = old_model
else:
if not hasattr(__magic_name__ , __magic_name__ ):
raise ValueError(f'''{old_model} does not have {old_attribute}''' )
lowercase__ = getattr(__magic_name__ , __magic_name__ )
if not is_key_init:
raise ValueError(f'''{key} was not correctly initialized!''' )
print(f'''Saving model to {pytorch_dump_folder_path}''' )
prophet.save_pretrained(__magic_name__ )
if __name__ == "__main__":
A : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--prophetnet_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
A : str = parser.parse_args()
convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)
| 305
| 1
|
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AlignProcessor, EfficientNetImageProcessor
@require_vision
class A ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase__ (self : Union[str, Any] ) -> Any:
"""simple docstring"""
lowercase__ = tempfile.mkdtemp()
lowercase__ = [
"""[UNK]""",
"""[CLS]""",
"""[SEP]""",
"""[PAD]""",
"""[MASK]""",
"""want""",
"""##want""",
"""##ed""",
"""wa""",
"""un""",
"""runn""",
"""##ing""",
""",""",
"""low""",
"""lowest""",
]
lowercase__ = 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] ) )
lowercase__ = {
"""do_resize""": True,
"""size""": 20,
"""do_center_crop""": True,
"""crop_size""": 18,
"""do_normalize""": True,
"""image_mean""": [0.48_145_466, 0.4_578_275, 0.40_821_073],
"""image_std""": [0.26_862_954, 0.26_130_258, 0.27_577_711],
}
lowercase__ = os.path.join(self.tmpdirname , _UpperCAmelCase )
with open(self.image_processor_file , """w""" , encoding="""utf-8""" ) as fp:
json.dump(_UpperCAmelCase , _UpperCAmelCase )
def lowerCamelCase__ (self : Dict , **_UpperCAmelCase : Any ) -> Optional[Any]:
"""simple docstring"""
return BertTokenizer.from_pretrained(self.tmpdirname , **_UpperCAmelCase )
def lowerCamelCase__ (self : Union[str, Any] , **_UpperCAmelCase : Any ) -> Dict:
"""simple docstring"""
return BertTokenizerFast.from_pretrained(self.tmpdirname , **_UpperCAmelCase )
def lowerCamelCase__ (self : Optional[int] , **_UpperCAmelCase : str ) -> Dict:
"""simple docstring"""
return EfficientNetImageProcessor.from_pretrained(self.tmpdirname , **_UpperCAmelCase )
def lowerCamelCase__ (self : Optional[int] ) -> List[str]:
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def lowerCamelCase__ (self : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
lowercase__ = [Image.fromarray(np.moveaxis(_UpperCAmelCase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def lowerCamelCase__ (self : Optional[int] ) -> Tuple:
"""simple docstring"""
lowercase__ = self.get_tokenizer()
lowercase__ = self.get_rust_tokenizer()
lowercase__ = self.get_image_processor()
lowercase__ = AlignProcessor(tokenizer=_UpperCAmelCase , image_processor=_UpperCAmelCase )
processor_slow.save_pretrained(self.tmpdirname )
lowercase__ = AlignProcessor.from_pretrained(self.tmpdirname , use_fast=_UpperCAmelCase )
lowercase__ = AlignProcessor(tokenizer=_UpperCAmelCase , image_processor=_UpperCAmelCase )
processor_fast.save_pretrained(self.tmpdirname )
lowercase__ = AlignProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer , _UpperCAmelCase )
self.assertIsInstance(processor_fast.tokenizer , _UpperCAmelCase )
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor , _UpperCAmelCase )
self.assertIsInstance(processor_fast.image_processor , _UpperCAmelCase )
def lowerCamelCase__ (self : Any ) -> List[str]:
"""simple docstring"""
lowercase__ = AlignProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
lowercase__ = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
lowercase__ = self.get_image_processor(do_normalize=_UpperCAmelCase , padding_value=1.0 )
lowercase__ = AlignProcessor.from_pretrained(
self.tmpdirname , bos_token="""(BOS)""" , eos_token="""(EOS)""" , do_normalize=_UpperCAmelCase , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , _UpperCAmelCase )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , _UpperCAmelCase )
def lowerCamelCase__ (self : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = self.get_image_processor()
lowercase__ = self.get_tokenizer()
lowercase__ = AlignProcessor(tokenizer=_UpperCAmelCase , image_processor=_UpperCAmelCase )
lowercase__ = self.prepare_image_inputs()
lowercase__ = image_processor(_UpperCAmelCase , return_tensors="""np""" )
lowercase__ = processor(images=_UpperCAmelCase , return_tensors="""np""" )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1E-2 )
def lowerCamelCase__ (self : Dict ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = self.get_image_processor()
lowercase__ = self.get_tokenizer()
lowercase__ = AlignProcessor(tokenizer=_UpperCAmelCase , image_processor=_UpperCAmelCase )
lowercase__ = """lower newer"""
lowercase__ = processor(text=_UpperCAmelCase )
lowercase__ = tokenizer(_UpperCAmelCase , padding="""max_length""" , max_length=64 )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def lowerCamelCase__ (self : List[Any] ) -> Tuple:
"""simple docstring"""
lowercase__ = self.get_image_processor()
lowercase__ = self.get_tokenizer()
lowercase__ = AlignProcessor(tokenizer=_UpperCAmelCase , image_processor=_UpperCAmelCase )
lowercase__ = """lower newer"""
lowercase__ = self.prepare_image_inputs()
lowercase__ = processor(text=_UpperCAmelCase , images=_UpperCAmelCase )
self.assertListEqual(list(inputs.keys() ) , ["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""] )
# test if it raises when no input is passed
with pytest.raises(_UpperCAmelCase ):
processor()
def lowerCamelCase__ (self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = self.get_image_processor()
lowercase__ = self.get_tokenizer()
lowercase__ = AlignProcessor(tokenizer=_UpperCAmelCase , image_processor=_UpperCAmelCase )
lowercase__ = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
lowercase__ = processor.batch_decode(_UpperCAmelCase )
lowercase__ = tokenizer.batch_decode(_UpperCAmelCase )
self.assertListEqual(_UpperCAmelCase , _UpperCAmelCase )
def lowerCamelCase__ (self : List[str] ) -> Tuple:
"""simple docstring"""
lowercase__ = self.get_image_processor()
lowercase__ = self.get_tokenizer()
lowercase__ = AlignProcessor(tokenizer=_UpperCAmelCase , image_processor=_UpperCAmelCase )
lowercase__ = """lower newer"""
lowercase__ = self.prepare_image_inputs()
lowercase__ = processor(text=_UpperCAmelCase , images=_UpperCAmelCase )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 305
|
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class A ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__(self : Any , _UpperCAmelCase : Dict[str, int] , _UpperCAmelCase : List[str] , _UpperCAmelCase : int = None , _UpperCAmelCase : int = None ) -> Dict:
"""simple docstring"""
super().__init__()
lowercase__ = pad_token_id
lowercase__ = max_length
lowercase__ = vocab
lowercase__ = merges
lowercase__ = BytePairTokenizer(_UpperCAmelCase , _UpperCAmelCase , sequence_length=_UpperCAmelCase )
@classmethod
def lowerCamelCase__ (cls : Optional[int] , _UpperCAmelCase : GPTaTokenizer , *_UpperCAmelCase : List[Any] , **_UpperCAmelCase : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = [""" """.join(_UpperCAmelCase ) for m in tokenizer.bpe_ranks.keys()]
lowercase__ = tokenizer.get_vocab()
return cls(_UpperCAmelCase , _UpperCAmelCase , *_UpperCAmelCase , **_UpperCAmelCase )
@classmethod
def lowerCamelCase__ (cls : Union[str, Any] , _UpperCAmelCase : Union[str, os.PathLike] , *_UpperCAmelCase : str , **_UpperCAmelCase : List[Any] ) -> Any:
"""simple docstring"""
lowercase__ = GPTaTokenizer.from_pretrained(_UpperCAmelCase , *_UpperCAmelCase , **_UpperCAmelCase )
return cls.from_tokenizer(_UpperCAmelCase , *_UpperCAmelCase , **_UpperCAmelCase )
@classmethod
def lowerCamelCase__ (cls : Any , _UpperCAmelCase : Tuple ) -> Union[str, Any]:
"""simple docstring"""
return cls(**_UpperCAmelCase )
def lowerCamelCase__ (self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
return {
"vocab": self.vocab,
"merges": self.merges,
"max_length": self.max_length,
"pad_token_id": self.pad_token_id,
}
def lowerCamelCase__ (self : str , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : int = None ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = self.tf_tokenizer(_UpperCAmelCase )
lowercase__ = tf.ones_like(_UpperCAmelCase )
if self.pad_token_id is not None:
# pad the tokens up to max length
lowercase__ = max_length if max_length is not None else self.max_length
if max_length is not None:
lowercase__ , lowercase__ = pad_model_inputs(
_UpperCAmelCase , max_seq_length=_UpperCAmelCase , pad_value=self.pad_token_id )
return {"attention_mask": attention_mask, "input_ids": input_ids}
| 305
| 1
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_torch_available,
is_vision_available,
)
A : Optional[Any] = {'configuration_beit': ['BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BeitConfig', 'BeitOnnxConfig']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Optional[int] = ['BeitFeatureExtractor']
A : Dict = ['BeitImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : List[Any] = [
'BEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
'BeitForImageClassification',
'BeitForMaskedImageModeling',
'BeitForSemanticSegmentation',
'BeitModel',
'BeitPreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Tuple = [
'FlaxBeitForImageClassification',
'FlaxBeitForMaskedImageModeling',
'FlaxBeitModel',
'FlaxBeitPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig, BeitOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_beit import BeitFeatureExtractor
from .image_processing_beit import BeitImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_beit import (
BEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
BeitForImageClassification,
BeitForMaskedImageModeling,
BeitForSemanticSegmentation,
BeitModel,
BeitPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_beit import (
FlaxBeitForImageClassification,
FlaxBeitForMaskedImageModeling,
FlaxBeitModel,
FlaxBeitPreTrainedModel,
)
else:
import sys
A : Any = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 305
|
from __future__ import annotations
from functools import lru_cache
from math import ceil
A : Optional[int] = 1_0_0
A : int = set(range(3, NUM_PRIMES, 2))
primes.add(2)
A : int
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prime not in primes:
continue
primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime)))
@lru_cache(maxsize=100 )
def UpperCamelCase ( __magic_name__ : int ) -> set[int]:
"""simple docstring"""
if number_to_partition < 0:
return set()
elif number_to_partition == 0:
return {1}
lowercase__ = set()
lowercase__ = 42
lowercase__ = 42
for prime in primes:
if prime > number_to_partition:
continue
for sub in partition(number_to_partition - prime ):
ret.add(sub * prime )
return ret
def UpperCamelCase ( __magic_name__ : int = 5000 ) -> int | None:
"""simple docstring"""
for number_to_partition in range(1 , __magic_name__ ):
if len(partition(__magic_name__ ) ) > number_unique_partitions:
return number_to_partition
return None
if __name__ == "__main__":
print(F'{solution() = }')
| 305
| 1
|
import math
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : Any = logging.get_logger(__name__)
A : List[Any] = {
'facebook/data2vec-base-960h': 'https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json',
# See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}
class A ( UpperCAmelCase__ ):
'''simple docstring'''
A__ = '''data2vec-audio'''
def __init__(self : Dict , _UpperCAmelCase : Optional[int]=32 , _UpperCAmelCase : str=768 , _UpperCAmelCase : Optional[int]=12 , _UpperCAmelCase : Optional[Any]=12 , _UpperCAmelCase : Any=3072 , _UpperCAmelCase : List[Any]="gelu" , _UpperCAmelCase : Union[str, Any]=0.1 , _UpperCAmelCase : Dict=0.1 , _UpperCAmelCase : Optional[int]=0.1 , _UpperCAmelCase : Optional[int]=0.0 , _UpperCAmelCase : Tuple=0.1 , _UpperCAmelCase : List[Any]=0.1 , _UpperCAmelCase : Union[str, Any]=0.02 , _UpperCAmelCase : Tuple=1E-5 , _UpperCAmelCase : Optional[int]="gelu" , _UpperCAmelCase : Any=(512, 512, 512, 512, 512, 512, 512) , _UpperCAmelCase : List[str]=(5, 2, 2, 2, 2, 2, 2) , _UpperCAmelCase : Tuple=(10, 3, 3, 3, 3, 2, 2) , _UpperCAmelCase : Optional[int]=False , _UpperCAmelCase : int=16 , _UpperCAmelCase : Tuple=19 , _UpperCAmelCase : str=5 , _UpperCAmelCase : Union[str, Any]=0.05 , _UpperCAmelCase : Union[str, Any]=10 , _UpperCAmelCase : int=2 , _UpperCAmelCase : int=0.0 , _UpperCAmelCase : Any=10 , _UpperCAmelCase : Any=0 , _UpperCAmelCase : Any="sum" , _UpperCAmelCase : Tuple=False , _UpperCAmelCase : List[str]=False , _UpperCAmelCase : List[str]=256 , _UpperCAmelCase : Tuple=(512, 512, 512, 512, 1500) , _UpperCAmelCase : Optional[int]=(5, 3, 3, 1, 1) , _UpperCAmelCase : List[Any]=(1, 2, 3, 1, 1) , _UpperCAmelCase : Any=512 , _UpperCAmelCase : Any=0 , _UpperCAmelCase : Any=1 , _UpperCAmelCase : Union[str, Any]=2 , _UpperCAmelCase : Any=False , _UpperCAmelCase : Optional[int]=3 , _UpperCAmelCase : Union[str, Any]=2 , _UpperCAmelCase : Any=3 , _UpperCAmelCase : Union[str, Any]=None , **_UpperCAmelCase : str , ) -> Optional[Any]:
"""simple docstring"""
super().__init__(**_UpperCAmelCase , pad_token_id=_UpperCAmelCase , bos_token_id=_UpperCAmelCase , eos_token_id=_UpperCAmelCase )
lowercase__ = hidden_size
lowercase__ = feat_extract_activation
lowercase__ = list(_UpperCAmelCase )
lowercase__ = list(_UpperCAmelCase )
lowercase__ = list(_UpperCAmelCase )
lowercase__ = conv_bias
lowercase__ = num_conv_pos_embeddings
lowercase__ = num_conv_pos_embedding_groups
lowercase__ = conv_pos_kernel_size
lowercase__ = len(self.conv_dim )
lowercase__ = num_hidden_layers
lowercase__ = intermediate_size
lowercase__ = hidden_act
lowercase__ = num_attention_heads
lowercase__ = hidden_dropout
lowercase__ = attention_dropout
lowercase__ = activation_dropout
lowercase__ = feat_proj_dropout
lowercase__ = final_dropout
lowercase__ = layerdrop
lowercase__ = layer_norm_eps
lowercase__ = initializer_range
lowercase__ = vocab_size
lowercase__ = use_weighted_layer_sum
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)`, but is `len(config.conv_dim) ="""
f''' {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,'''
f''' `len(config.conv_kernel) = {len(self.conv_kernel )}`.''' )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
lowercase__ = mask_time_prob
lowercase__ = mask_time_length
lowercase__ = mask_time_min_masks
lowercase__ = mask_feature_prob
lowercase__ = mask_feature_length
lowercase__ = mask_feature_min_masks
# ctc loss
lowercase__ = ctc_loss_reduction
lowercase__ = ctc_zero_infinity
# adapter
lowercase__ = add_adapter
lowercase__ = adapter_kernel_size
lowercase__ = adapter_stride
lowercase__ = num_adapter_layers
lowercase__ = output_hidden_size or hidden_size
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
lowercase__ = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
lowercase__ = list(_UpperCAmelCase )
lowercase__ = list(_UpperCAmelCase )
lowercase__ = list(_UpperCAmelCase )
lowercase__ = xvector_output_dim
@property
def lowerCamelCase__ (self : Optional[Any] ) -> Dict:
"""simple docstring"""
return math.prod(self.conv_stride )
| 305
|
def UpperCamelCase ( __magic_name__ : List[Any] ) -> Optional[int]:
"""simple docstring"""
lowercase__ = [0] * len(__magic_name__ )
lowercase__ = []
lowercase__ = [1] * len(__magic_name__ )
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(__magic_name__ ) ):
if indegree[i] == 0:
queue.append(__magic_name__ )
while queue:
lowercase__ = queue.pop(0 )
for x in graph[vertex]:
indegree[x] -= 1
if long_dist[vertex] + 1 > long_dist[x]:
lowercase__ = long_dist[vertex] + 1
if indegree[x] == 0:
queue.append(__magic_name__ )
print(max(__magic_name__ ) )
# Adjacency list of Graph
A : Union[str, Any] = {0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}
longest_distance(graph)
| 305
| 1
|
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 305
|
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
from accelerate import PartialState
from accelerate.utils.operations import broadcast, gather, gather_object, pad_across_processes, reduce
def UpperCamelCase ( __magic_name__ : Any ) -> Optional[int]:
"""simple docstring"""
return (torch.arange(state.num_processes ) + 1.0 + (state.num_processes * state.process_index)).to(state.device )
def UpperCamelCase ( __magic_name__ : int ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = create_tensor(__magic_name__ )
lowercase__ = gather(__magic_name__ )
assert gathered_tensor.tolist() == list(range(1 , state.num_processes**2 + 1 ) )
def UpperCamelCase ( __magic_name__ : Optional[int] ) -> Tuple:
"""simple docstring"""
lowercase__ = [state.process_index]
lowercase__ = gather_object(__magic_name__ )
assert len(__magic_name__ ) == state.num_processes, f'''{gathered_obj}, {len(__magic_name__ )} != {state.num_processes}'''
assert gathered_obj == list(range(state.num_processes ) ), f'''{gathered_obj} != {list(range(state.num_processes ) )}'''
def UpperCamelCase ( __magic_name__ : str ) -> Dict:
"""simple docstring"""
lowercase__ = create_tensor(__magic_name__ )
lowercase__ = broadcast(__magic_name__ )
assert broadcasted_tensor.shape == torch.Size([state.num_processes] )
assert broadcasted_tensor.tolist() == list(range(1 , state.num_processes + 1 ) )
def UpperCamelCase ( __magic_name__ : str ) -> Dict:
"""simple docstring"""
if state.is_main_process:
lowercase__ = torch.arange(state.num_processes + 1 ).to(state.device )
else:
lowercase__ = torch.arange(state.num_processes ).to(state.device )
lowercase__ = pad_across_processes(__magic_name__ )
assert padded_tensor.shape == torch.Size([state.num_processes + 1] )
if not state.is_main_process:
assert padded_tensor.tolist() == list(range(0 , state.num_processes ) ) + [0]
def UpperCamelCase ( __magic_name__ : List[Any] ) -> Optional[int]:
"""simple docstring"""
if state.num_processes != 2:
return
lowercase__ = create_tensor(__magic_name__ )
lowercase__ = reduce(__magic_name__ , """sum""" )
lowercase__ = torch.tensor([4.0, 6] ).to(state.device )
assert torch.allclose(__magic_name__ , __magic_name__ ), f'''{reduced_tensor} != {truth_tensor}'''
def UpperCamelCase ( __magic_name__ : Dict ) -> int:
"""simple docstring"""
if state.num_processes != 2:
return
lowercase__ = create_tensor(__magic_name__ )
lowercase__ = reduce(__magic_name__ , """mean""" )
lowercase__ = torch.tensor([2.0, 3] ).to(state.device )
assert torch.allclose(__magic_name__ , __magic_name__ ), f'''{reduced_tensor} != {truth_tensor}'''
def UpperCamelCase ( __magic_name__ : str ) -> int:
"""simple docstring"""
main()
def UpperCamelCase ( ) -> Optional[int]:
"""simple docstring"""
lowercase__ = PartialState()
state.print(f'''State: {state}''' )
state.print("""testing gather""" )
test_gather(__magic_name__ )
state.print("""testing gather_object""" )
test_gather_object(__magic_name__ )
state.print("""testing broadcast""" )
test_broadcast(__magic_name__ )
state.print("""testing pad_across_processes""" )
test_pad_across_processes(__magic_name__ )
state.print("""testing reduce_sum""" )
test_reduce_sum(__magic_name__ )
state.print("""testing reduce_mean""" )
test_reduce_mean(__magic_name__ )
if __name__ == "__main__":
main()
| 305
| 1
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : List[str] = logging.get_logger(__name__)
A : Any = {
'tiiuae/falcon-40b': 'https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json',
'tiiuae/falcon-7b': 'https://huggingface.co/tiiuae/falcon-7b/resolve/main/config.json',
}
class A ( UpperCAmelCase__ ):
'''simple docstring'''
A__ = '''falcon'''
A__ = ['''past_key_values''']
def __init__(self : str , _UpperCAmelCase : Dict=6_5024 , _UpperCAmelCase : Optional[Any]=4544 , _UpperCAmelCase : Optional[int]=32 , _UpperCAmelCase : Optional[Any]=71 , _UpperCAmelCase : List[Any]=1E-5 , _UpperCAmelCase : int=0.02 , _UpperCAmelCase : str=True , _UpperCAmelCase : Tuple=0.0 , _UpperCAmelCase : Any=0.0 , _UpperCAmelCase : str=None , _UpperCAmelCase : Optional[int]=False , _UpperCAmelCase : int=False , _UpperCAmelCase : Union[str, Any]=True , _UpperCAmelCase : List[Any]=True , _UpperCAmelCase : List[Any]=False , _UpperCAmelCase : Optional[int]=11 , _UpperCAmelCase : Optional[Any]=11 , **_UpperCAmelCase : Union[str, Any] , ) -> List[str]:
"""simple docstring"""
lowercase__ = vocab_size
# Backward compatibility with n_embed kwarg
lowercase__ = kwargs.pop("""n_embed""" , _UpperCAmelCase )
lowercase__ = hidden_size if n_embed is None else n_embed
lowercase__ = num_hidden_layers
lowercase__ = num_attention_heads
lowercase__ = layer_norm_epsilon
lowercase__ = initializer_range
lowercase__ = use_cache
lowercase__ = hidden_dropout
lowercase__ = attention_dropout
lowercase__ = bos_token_id
lowercase__ = eos_token_id
lowercase__ = num_attention_heads if num_kv_heads is None else num_kv_heads
lowercase__ = alibi
lowercase__ = new_decoder_architecture
lowercase__ = multi_query # Ignored when new_decoder_architecture is True
lowercase__ = parallel_attn
lowercase__ = bias
super().__init__(bos_token_id=_UpperCAmelCase , eos_token_id=_UpperCAmelCase , **_UpperCAmelCase )
@property
def lowerCamelCase__ (self : Tuple ) -> int:
"""simple docstring"""
return self.hidden_size // self.num_attention_heads
@property
def lowerCamelCase__ (self : List[str] ) -> Tuple:
"""simple docstring"""
return not self.alibi
| 305
|
def UpperCamelCase ( __magic_name__ : str ) -> int:
"""simple docstring"""
assert column_title.isupper()
lowercase__ = 0
lowercase__ = len(__magic_name__ ) - 1
lowercase__ = 0
while index >= 0:
lowercase__ = (ord(column_title[index] ) - 64) * pow(26 , __magic_name__ )
answer += value
power += 1
index -= 1
return answer
if __name__ == "__main__":
from doctest import testmod
testmod()
| 305
| 1
|
from math import factorial
A : dict[str, int] = {str(digit): factorial(digit) for digit in range(1_0)}
def UpperCamelCase ( __magic_name__ : int ) -> int:
"""simple docstring"""
if not isinstance(__magic_name__ , __magic_name__ ):
raise TypeError("""Parameter number must be int""" )
if number < 0:
raise ValueError("""Parameter number must be greater than or equal to 0""" )
# Converts number in string to iterate on its digits and adds its factorial.
return sum(DIGIT_FACTORIAL[digit] for digit in str(__magic_name__ ) )
def UpperCamelCase ( __magic_name__ : int = 60 , __magic_name__ : int = 100_0000 ) -> int:
"""simple docstring"""
if not isinstance(__magic_name__ , __magic_name__ ) or not isinstance(__magic_name__ , __magic_name__ ):
raise TypeError("""Parameters chain_length and number_limit must be int""" )
if chain_length <= 0 or number_limit <= 0:
raise ValueError(
"""Parameters chain_length and number_limit must be greater than 0""" )
# the counter for the chains with the exact desired length
lowercase__ = 0
# the cached sizes of the previous chains
lowercase__ = {}
for start_chain_element in range(1 , __magic_name__ ):
# The temporary set will contain the elements of the chain
lowercase__ = set()
lowercase__ = 0
# Stop computing the chain when you find a cached size, a repeating item or the
# length is greater then the desired one.
lowercase__ = start_chain_element
while (
chain_element not in chain_sets_lengths
and chain_element not in chain_set
and chain_set_length <= chain_length
):
chain_set.add(__magic_name__ )
chain_set_length += 1
lowercase__ = digit_factorial_sum(__magic_name__ )
if chain_element in chain_sets_lengths:
chain_set_length += chain_sets_lengths[chain_element]
lowercase__ = chain_set_length
# If chain contains the exact amount of elements increase the counter
if chain_set_length == chain_length:
chains_counter += 1
return chains_counter
if __name__ == "__main__":
import doctest
doctest.testmod()
print(F'{solution()}')
| 305
|
import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def UpperCamelCase ( __magic_name__ : list , __magic_name__ : list , __magic_name__ : list , __magic_name__ : list , __magic_name__ : list ) -> float:
"""simple docstring"""
lowercase__ = np.array([[1, item, train_mtch[i]] for i, item in enumerate(__magic_name__ )] )
lowercase__ = np.array(__magic_name__ )
lowercase__ = np.dot(np.dot(np.linalg.inv(np.dot(x.transpose() , __magic_name__ ) ) , x.transpose() ) , __magic_name__ )
return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2] )
def UpperCamelCase ( __magic_name__ : list , __magic_name__ : list , __magic_name__ : list ) -> float:
"""simple docstring"""
lowercase__ = (1, 2, 1)
lowercase__ = (1, 1, 0, 7)
lowercase__ = SARIMAX(
__magic_name__ , exog=__magic_name__ , order=__magic_name__ , seasonal_order=__magic_name__ )
lowercase__ = model.fit(disp=__magic_name__ , maxiter=600 , method="""nm""" )
lowercase__ = model_fit.predict(1 , len(__magic_name__ ) , exog=[test_match] )
return result[0]
def UpperCamelCase ( __magic_name__ : list , __magic_name__ : list , __magic_name__ : list ) -> float:
"""simple docstring"""
lowercase__ = SVR(kernel="""rbf""" , C=1 , gamma=0.1 , epsilon=0.1 )
regressor.fit(__magic_name__ , __magic_name__ )
lowercase__ = regressor.predict(__magic_name__ )
return y_pred[0]
def UpperCamelCase ( __magic_name__ : list ) -> float:
"""simple docstring"""
train_user.sort()
lowercase__ = np.percentile(__magic_name__ , 25 )
lowercase__ = np.percentile(__magic_name__ , 75 )
lowercase__ = qa - qa
lowercase__ = qa - (iqr * 0.1)
return low_lim
def UpperCamelCase ( __magic_name__ : list , __magic_name__ : float ) -> bool:
"""simple docstring"""
lowercase__ = 0
lowercase__ = 0
for i in list_vote:
if i > actual_result:
lowercase__ = not_safe + 1
else:
if abs(abs(__magic_name__ ) - abs(__magic_name__ ) ) <= 0.1:
safe += 1
else:
not_safe += 1
return safe > not_safe
if __name__ == "__main__":
# data_input_df = pd.read_csv("ex_data.csv", header=None)
A : Dict = [[1_8_2_3_1, 0.0, 1], [2_2_6_2_1, 1.0, 2], [1_5_6_7_5, 0.0, 3], [2_3_5_8_3, 1.0, 4]]
A : str = pd.DataFrame(
data_input, columns=['total_user', 'total_even', 'days']
)
A : Any = Normalizer().fit_transform(data_input_df.values)
# split data
A : Optional[int] = normalize_df[:, 2].tolist()
A : Any = normalize_df[:, 0].tolist()
A : str = normalize_df[:, 1].tolist()
# for svr (input variable = total date and total match)
A : int = normalize_df[:, [1, 2]].tolist()
A : Any = x[: len(x) - 1]
A : Tuple = x[len(x) - 1 :]
# for linear regression & sarimax
A : Optional[int] = total_date[: len(total_date) - 1]
A : Optional[int] = total_user[: len(total_user) - 1]
A : str = total_match[: len(total_match) - 1]
A : Union[str, Any] = total_date[len(total_date) - 1 :]
A : List[str] = total_user[len(total_user) - 1 :]
A : str = total_match[len(total_match) - 1 :]
# voting system with forecasting
A : int = [
linear_regression_prediction(
trn_date, trn_user, trn_match, tst_date, tst_match
),
sarimax_predictor(trn_user, trn_match, tst_match),
support_vector_regressor(x_train, x_test, trn_user),
]
# check the safety of today's data
A : int = '' if data_safety_checker(res_vote, tst_user) else 'not '
print('Today\'s data is {not_str}safe.')
| 305
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|
# flake8: noqa
# Lint as: python3
from typing import Dict, List, Optional, Type
from .. import config
from ..utils import logging
from .formatting import (
ArrowFormatter,
CustomFormatter,
Formatter,
PandasFormatter,
PythonFormatter,
TensorFormatter,
format_table,
query_table,
)
from .np_formatter import NumpyFormatter
A : Union[str, Any] = logging.get_logger(__name__)
A : Dict[Optional[str], Type[Formatter]] = {}
A : Dict[Optional[str], str] = {}
A : Dict[Optional[str], Exception] = {}
def UpperCamelCase ( __magic_name__ : type , __magic_name__ : Optional[str] , __magic_name__ : Optional[List[str]] = None , ) -> List[str]:
"""simple docstring"""
lowercase__ = aliases if aliases is not None else []
if format_type in _FORMAT_TYPES:
logger.warning(
f'''Overwriting format type \'{format_type}\' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})''' )
lowercase__ = formatter_cls
for alias in set(aliases + [format_type] ):
if alias in _FORMAT_TYPES_ALIASES:
logger.warning(
f'''Overwriting format type alias \'{alias}\' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})''' )
lowercase__ = format_type
def UpperCamelCase ( __magic_name__ : Exception , __magic_name__ : Optional[str] , __magic_name__ : Optional[List[str]] = None ) -> int:
"""simple docstring"""
lowercase__ = aliases if aliases is not None else []
for alias in set(aliases + [format_type] ):
lowercase__ = unavailable_error
# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=['python'])
_register_formatter(ArrowFormatter, 'arrow', aliases=['pa', 'pyarrow'])
_register_formatter(NumpyFormatter, 'numpy', aliases=['np'])
_register_formatter(PandasFormatter, 'pandas', aliases=['pd'])
_register_formatter(CustomFormatter, 'custom')
if config.TORCH_AVAILABLE:
from .torch_formatter import TorchFormatter
_register_formatter(TorchFormatter, 'torch', aliases=['pt', 'pytorch'])
else:
A : List[str] = ValueError('PyTorch needs to be installed to be able to return PyTorch tensors.')
_register_unavailable_formatter(_torch_error, 'torch', aliases=['pt', 'pytorch'])
if config.TF_AVAILABLE:
from .tf_formatter import TFFormatter
_register_formatter(TFFormatter, 'tensorflow', aliases=['tf'])
else:
A : Union[str, Any] = ValueError('Tensorflow needs to be installed to be able to return Tensorflow tensors.')
_register_unavailable_formatter(_tf_error, 'tensorflow', aliases=['tf'])
if config.JAX_AVAILABLE:
from .jax_formatter import JaxFormatter
_register_formatter(JaxFormatter, 'jax', aliases=[])
else:
A : Optional[int] = ValueError('JAX needs to be installed to be able to return JAX arrays.')
_register_unavailable_formatter(_jax_error, 'jax', aliases=[])
def UpperCamelCase ( __magic_name__ : Optional[str] ) -> Optional[str]:
"""simple docstring"""
if format_type in _FORMAT_TYPES_ALIASES:
return _FORMAT_TYPES_ALIASES[format_type]
else:
return format_type
def UpperCamelCase ( __magic_name__ : Optional[str] , **__magic_name__ : List[Any] ) -> Formatter:
"""simple docstring"""
lowercase__ = get_format_type_from_alias(__magic_name__ )
if format_type in _FORMAT_TYPES:
return _FORMAT_TYPES[format_type](**__magic_name__ )
if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
else:
raise ValueError(
f'''Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None )}, but got \'{format_type}\'''' )
| 305
|
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def UpperCamelCase ( __magic_name__ : Optional[Any] ) -> List[Any]:
"""simple docstring"""
lowercase__ = tmp_path / """file.csv"""
lowercase__ = textwrap.dedent(
"""\
header1,header2
1,2
10,20
""" )
with open(__magic_name__ , """w""" ) as f:
f.write(__magic_name__ )
return str(__magic_name__ )
@pytest.fixture
def UpperCamelCase ( __magic_name__ : str ) -> Tuple:
"""simple docstring"""
lowercase__ = tmp_path / """malformed_file.csv"""
lowercase__ = textwrap.dedent(
"""\
header1,header2
1,2
10,20,
""" )
with open(__magic_name__ , """w""" ) as f:
f.write(__magic_name__ )
return str(__magic_name__ )
@pytest.fixture
def UpperCamelCase ( __magic_name__ : List[Any] , __magic_name__ : List[str] ) -> str:
"""simple docstring"""
lowercase__ = tmp_path / """csv_with_image.csv"""
lowercase__ = textwrap.dedent(
f'''\
image
{image_file}
''' )
with open(__magic_name__ , """w""" ) as f:
f.write(__magic_name__ )
return str(__magic_name__ )
@pytest.fixture
def UpperCamelCase ( __magic_name__ : Tuple ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = tmp_path / """csv_with_label.csv"""
lowercase__ = textwrap.dedent(
"""\
label
good
bad
good
""" )
with open(__magic_name__ , """w""" ) as f:
f.write(__magic_name__ )
return str(__magic_name__ )
@pytest.fixture
def UpperCamelCase ( __magic_name__ : Dict ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = tmp_path / """csv_with_int_list.csv"""
lowercase__ = textwrap.dedent(
"""\
int_list
1 2 3
4 5 6
7 8 9
""" )
with open(__magic_name__ , """w""" ) as f:
f.write(__magic_name__ )
return str(__magic_name__ )
def UpperCamelCase ( __magic_name__ : Tuple , __magic_name__ : Tuple , __magic_name__ : Tuple ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = Csv()
lowercase__ = csv._generate_tables([[csv_file, malformed_csv_file]] )
with pytest.raises(__magic_name__ , match="""Error tokenizing data""" ):
for _ in generator:
pass
assert any(
record.levelname == """ERROR"""
and """Failed to read file""" in record.message
and os.path.basename(__magic_name__ ) in record.message
for record in caplog.records )
@require_pil
def UpperCamelCase ( __magic_name__ : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
with open(__magic_name__ , encoding="""utf-8""" ) as f:
lowercase__ = f.read().splitlines()[1]
lowercase__ = Csv(encoding="""utf-8""" , features=Features({"""image""": Image()} ) )
lowercase__ = csv._generate_tables([[csv_file_with_image]] )
lowercase__ = pa.concat_tables([table for _, table in generator] )
assert pa_table.schema.field("""image""" ).type == Image()()
lowercase__ = pa_table.to_pydict()["""image"""]
assert generated_content == [{"path": image_file, "bytes": None}]
def UpperCamelCase ( __magic_name__ : Optional[Any] ) -> str:
"""simple docstring"""
with open(__magic_name__ , encoding="""utf-8""" ) as f:
lowercase__ = f.read().splitlines()[1:]
lowercase__ = Csv(encoding="""utf-8""" , features=Features({"""label""": ClassLabel(names=["""good""", """bad"""] )} ) )
lowercase__ = csv._generate_tables([[csv_file_with_label]] )
lowercase__ = pa.concat_tables([table for _, table in generator] )
assert pa_table.schema.field("""label""" ).type == ClassLabel(names=["""good""", """bad"""] )()
lowercase__ = pa_table.to_pydict()["""label"""]
assert generated_content == [ClassLabel(names=["""good""", """bad"""] ).straint(__magic_name__ ) for label in labels]
def UpperCamelCase ( __magic_name__ : Any ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = Csv(encoding="""utf-8""" , sep=""",""" , converters={"""int_list""": lambda __magic_name__ : [int(__magic_name__ ) for i in x.split()]} )
lowercase__ = csv._generate_tables([[csv_file_with_int_list]] )
lowercase__ = pa.concat_tables([table for _, table in generator] )
assert pa.types.is_list(pa_table.schema.field("""int_list""" ).type )
lowercase__ = pa_table.to_pydict()["""int_list"""]
assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
| 305
| 1
|
def UpperCamelCase ( __magic_name__ : Any ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = []
lowercase__ = set({"""(""", """[""", """{"""} )
lowercase__ = set({""")""", """]""", """}"""} )
lowercase__ = {"""{""": """}""", """[""": """]""", """(""": """)"""}
for i in range(len(__magic_name__ ) ):
if s[i] in open_brackets:
stack.append(s[i] )
elif s[i] in closed_brackets and (
len(__magic_name__ ) == 0 or (len(__magic_name__ ) > 0 and open_to_closed[stack.pop()] != s[i])
):
return False
return len(__magic_name__ ) == 0
def UpperCamelCase ( ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = input("""Enter sequence of brackets: """ )
if is_balanced(__magic_name__ ):
print(__magic_name__ , """is balanced""" )
else:
print(__magic_name__ , """is not balanced""" )
if __name__ == "__main__":
main()
| 305
|
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
A : int = {'configuration_dpt': ['DPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DPTConfig']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Union[str, Any] = ['DPTFeatureExtractor']
A : int = ['DPTImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Tuple = [
'DPT_PRETRAINED_MODEL_ARCHIVE_LIST',
'DPTForDepthEstimation',
'DPTForSemanticSegmentation',
'DPTModel',
'DPTPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_dpt import DPTFeatureExtractor
from .image_processing_dpt import DPTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_dpt import (
DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
DPTForDepthEstimation,
DPTForSemanticSegmentation,
DPTModel,
DPTPreTrainedModel,
)
else:
import sys
A : str = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 305
| 1
|
from typing import Dict, Optional
import numpy as np
import datasets
A : Optional[Any] = '\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (two classes) or multi-class segmentation,\nthe mean IoU of the image is calculated by taking the IoU of each class and averaging them.\n'
A : int = '\nArgs:\n predictions (`List[ndarray]`):\n List of predicted segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n references (`List[ndarray]`):\n List of ground truth segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n num_labels (`int`):\n Number of classes (categories).\n ignore_index (`int`):\n Index that will be ignored during evaluation.\n nan_to_num (`int`, *optional*):\n If specified, NaN values will be replaced by the number defined by the user.\n label_map (`dict`, *optional*):\n If specified, dictionary mapping old label indices to new label indices.\n reduce_labels (`bool`, *optional*, defaults to `False`):\n Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background,\n and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255.\n\nReturns:\n `Dict[str, float | ndarray]` comprising various elements:\n - *mean_iou* (`float`):\n Mean Intersection-over-Union (IoU averaged over all categories).\n - *mean_accuracy* (`float`):\n Mean accuracy (averaged over all categories).\n - *overall_accuracy* (`float`):\n Overall accuracy on all images.\n - *per_category_accuracy* (`ndarray` of shape `(num_labels,)`):\n Per category accuracy.\n - *per_category_iou* (`ndarray` of shape `(num_labels,)`):\n Per category IoU.\n\nExamples:\n\n >>> import numpy as np\n\n >>> mean_iou = datasets.load_metric("mean_iou")\n\n >>> # suppose one has 3 different segmentation maps predicted\n >>> predicted_1 = np.array([[1, 2], [3, 4], [5, 255]])\n >>> actual_1 = np.array([[0, 3], [5, 4], [6, 255]])\n\n >>> predicted_2 = np.array([[2, 7], [9, 2], [3, 6]])\n >>> actual_2 = np.array([[1, 7], [9, 2], [3, 6]])\n\n >>> predicted_3 = np.array([[2, 2, 3], [8, 2, 4], [3, 255, 2]])\n >>> actual_3 = np.array([[1, 2, 2], [8, 2, 1], [3, 255, 1]])\n\n >>> predicted = [predicted_1, predicted_2, predicted_3]\n >>> ground_truth = [actual_1, actual_2, actual_3]\n\n >>> results = mean_iou.compute(predictions=predicted, references=ground_truth, num_labels=10, ignore_index=255, reduce_labels=False)\n >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n {\'mean_iou\': 0.47750000000000004, \'mean_accuracy\': 0.5916666666666666, \'overall_accuracy\': 0.5263157894736842, \'per_category_iou\': array([0. , 0. , 0.375, 0.4 , 0.5 , 0. , 0.5 , 1. , 1. , 1. ]), \'per_category_accuracy\': array([0. , 0. , 0.75 , 0.66666667, 1. , 0. , 0.5 , 1. , 1. , 1. ])}\n'
A : int = '\\n@software{MMSegmentation_Contributors_OpenMMLab_Semantic_Segmentation_2020,\nauthor = {{MMSegmentation Contributors}},\nlicense = {Apache-2.0},\nmonth = {7},\ntitle = {{OpenMMLab Semantic Segmentation Toolbox and Benchmark}},\nurl = {https://github.com/open-mmlab/mmsegmentation},\nyear = {2020}\n}'
def UpperCamelCase ( __magic_name__ : Union[str, Any] , __magic_name__ : str , __magic_name__ : Optional[int] , __magic_name__ : bool , __magic_name__ : Optional[Dict[int, int]] = None , __magic_name__ : bool = False , ) -> Dict:
"""simple docstring"""
if label_map is not None:
for old_id, new_id in label_map.items():
lowercase__ = new_id
# turn into Numpy arrays
lowercase__ = np.array(__magic_name__ )
lowercase__ = np.array(__magic_name__ )
if reduce_labels:
lowercase__ = 255
lowercase__ = label - 1
lowercase__ = 255
lowercase__ = label != ignore_index
lowercase__ = np.not_equal(__magic_name__ , __magic_name__ )
lowercase__ = pred_label[mask]
lowercase__ = np.array(__magic_name__ )[mask]
lowercase__ = pred_label[pred_label == label]
lowercase__ = np.histogram(__magic_name__ , bins=__magic_name__ , range=(0, num_labels - 1) )[0]
lowercase__ = np.histogram(__magic_name__ , bins=__magic_name__ , range=(0, num_labels - 1) )[0]
lowercase__ = np.histogram(__magic_name__ , bins=__magic_name__ , range=(0, num_labels - 1) )[0]
lowercase__ = area_pred_label + area_label - area_intersect
return area_intersect, area_union, area_pred_label, area_label
def UpperCamelCase ( __magic_name__ : Optional[Any] , __magic_name__ : List[str] , __magic_name__ : Dict , __magic_name__ : bool , __magic_name__ : Optional[Dict[int, int]] = None , __magic_name__ : bool = False , ) -> int:
"""simple docstring"""
lowercase__ = np.zeros((num_labels,) , dtype=np.floataa )
lowercase__ = np.zeros((num_labels,) , dtype=np.floataa )
lowercase__ = np.zeros((num_labels,) , dtype=np.floataa )
lowercase__ = np.zeros((num_labels,) , dtype=np.floataa )
for result, gt_seg_map in zip(__magic_name__ , __magic_name__ ):
lowercase__ , lowercase__ , lowercase__ , lowercase__ = intersect_and_union(
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ )
total_area_intersect += area_intersect
total_area_union += area_union
total_area_pred_label += area_pred_label
total_area_label += area_label
return total_area_intersect, total_area_union, total_area_pred_label, total_area_label
def UpperCamelCase ( __magic_name__ : Tuple , __magic_name__ : Tuple , __magic_name__ : Union[str, Any] , __magic_name__ : bool , __magic_name__ : Optional[int] = None , __magic_name__ : Optional[Dict[int, int]] = None , __magic_name__ : bool = False , ) -> Optional[Any]:
"""simple docstring"""
lowercase__ , lowercase__ , lowercase__ , lowercase__ = total_intersect_and_union(
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ )
# compute metrics
lowercase__ = {}
lowercase__ = total_area_intersect.sum() / total_area_label.sum()
lowercase__ = total_area_intersect / total_area_union
lowercase__ = total_area_intersect / total_area_label
lowercase__ = np.nanmean(__magic_name__ )
lowercase__ = np.nanmean(__magic_name__ )
lowercase__ = all_acc
lowercase__ = iou
lowercase__ = acc
if nan_to_num is not None:
lowercase__ = {metric: np.nan_to_num(__magic_name__ , nan=__magic_name__ ) for metric, metric_value in metrics.items()}
return metrics
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class A ( datasets.Metric ):
'''simple docstring'''
def lowerCamelCase__ (self : List[str] ) -> List[str]:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
# 1st Seq - height dim, 2nd - width dim
{
"""predictions""": datasets.Sequence(datasets.Sequence(datasets.Value("""uint16""" ) ) ),
"""references""": datasets.Sequence(datasets.Sequence(datasets.Value("""uint16""" ) ) ),
} ) , reference_urls=[
"""https://github.com/open-mmlab/mmsegmentation/blob/71c201b1813267d78764f306a297ca717827c4bf/mmseg/core/evaluation/metrics.py"""
] , )
def lowerCamelCase__ (self : Tuple , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Tuple , _UpperCAmelCase : int , _UpperCAmelCase : bool , _UpperCAmelCase : Optional[int] = None , _UpperCAmelCase : Optional[Dict[int, int]] = None , _UpperCAmelCase : bool = False , ) -> Optional[int]:
"""simple docstring"""
lowercase__ = mean_iou(
results=_UpperCAmelCase , gt_seg_maps=_UpperCAmelCase , num_labels=_UpperCAmelCase , ignore_index=_UpperCAmelCase , nan_to_num=_UpperCAmelCase , label_map=_UpperCAmelCase , reduce_labels=_UpperCAmelCase , )
return iou_result
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from __future__ import annotations
def UpperCamelCase ( __magic_name__ : list[float] , __magic_name__ : list[float] ) -> float:
"""simple docstring"""
lowercase__ = sorted(numsa + numsa )
lowercase__ , lowercase__ = divmod(len(__magic_name__ ) , 2 )
if mod == 1:
return all_numbers[div]
else:
return (all_numbers[div] + all_numbers[div - 1]) / 2
if __name__ == "__main__":
import doctest
doctest.testmod()
A : Any = [float(x) for x in input('Enter the elements of first array: ').split()]
A : Union[str, Any] = [float(x) for x in input('Enter the elements of second array: ').split()]
print(F'The median of two arrays is: {median_of_two_arrays(array_a, array_a)}')
| 305
| 1
|
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
A : Any = logging.get_logger(__name__)
A : Any = {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}
A : Union[str, Any] = {
'vocab_file': {
'allegro/herbert-base-cased': 'https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'
},
'merges_file': {
'allegro/herbert-base-cased': 'https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'
},
}
A : List[Any] = {'allegro/herbert-base-cased': 5_1_4}
A : Optional[Any] = {}
class A ( UpperCAmelCase__ ):
'''simple docstring'''
A__ = VOCAB_FILES_NAMES
A__ = PRETRAINED_VOCAB_FILES_MAP
A__ = PRETRAINED_INIT_CONFIGURATION
A__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A__ = HerbertTokenizer
def __init__(self : Tuple , _UpperCAmelCase : str=None , _UpperCAmelCase : int=None , _UpperCAmelCase : Any=None , _UpperCAmelCase : Dict="<s>" , _UpperCAmelCase : int="<unk>" , _UpperCAmelCase : Any="<pad>" , _UpperCAmelCase : int="<mask>" , _UpperCAmelCase : Optional[Any]="</s>" , **_UpperCAmelCase : str , ) -> Any:
"""simple docstring"""
super().__init__(
_UpperCAmelCase , _UpperCAmelCase , tokenizer_file=_UpperCAmelCase , cls_token=_UpperCAmelCase , unk_token=_UpperCAmelCase , pad_token=_UpperCAmelCase , mask_token=_UpperCAmelCase , sep_token=_UpperCAmelCase , **_UpperCAmelCase , )
def lowerCamelCase__ (self : Dict , _UpperCAmelCase : List[int] , _UpperCAmelCase : Optional[List[int]] = None ) -> List[int]:
"""simple docstring"""
lowercase__ = [self.cls_token_id]
lowercase__ = [self.sep_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def lowerCamelCase__ (self : Any , _UpperCAmelCase : List[int] , _UpperCAmelCase : Optional[List[int]] = None , _UpperCAmelCase : bool = False ) -> List[int]:
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_UpperCAmelCase , token_ids_a=_UpperCAmelCase , already_has_special_tokens=_UpperCAmelCase )
if token_ids_a is None:
return [1] + ([0] * len(_UpperCAmelCase )) + [1]
return [1] + ([0] * len(_UpperCAmelCase )) + [1] + ([0] * len(_UpperCAmelCase )) + [1]
def lowerCamelCase__ (self : Union[str, Any] , _UpperCAmelCase : List[int] , _UpperCAmelCase : Optional[List[int]] = None ) -> List[int]:
"""simple docstring"""
lowercase__ = [self.sep_token_id]
lowercase__ = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def lowerCamelCase__ (self : List[Any] , _UpperCAmelCase : str , _UpperCAmelCase : Optional[str] = None ) -> Tuple[str]:
"""simple docstring"""
lowercase__ = self._tokenizer.model.save(_UpperCAmelCase , name=_UpperCAmelCase )
return tuple(_UpperCAmelCase )
| 305
|
A : Union[str, Any] = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
A : List[Any] = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def UpperCamelCase ( __magic_name__ : dict[int, list[int]] , __magic_name__ : int , __magic_name__ : list[bool] ) -> list[int]:
"""simple docstring"""
lowercase__ = True
lowercase__ = []
for neighbour in graph[vert]:
if not visited[neighbour]:
order += topology_sort(__magic_name__ , __magic_name__ , __magic_name__ )
order.append(__magic_name__ )
return order
def UpperCamelCase ( __magic_name__ : dict[int, list[int]] , __magic_name__ : int , __magic_name__ : list[bool] ) -> list[int]:
"""simple docstring"""
lowercase__ = True
lowercase__ = [vert]
for neighbour in reversed_graph[vert]:
if not visited[neighbour]:
component += find_components(__magic_name__ , __magic_name__ , __magic_name__ )
return component
def UpperCamelCase ( __magic_name__ : dict[int, list[int]] ) -> list[list[int]]:
"""simple docstring"""
lowercase__ = len(__magic_name__ ) * [False]
lowercase__ = {vert: [] for vert in range(len(__magic_name__ ) )}
for vert, neighbours in graph.items():
for neighbour in neighbours:
reversed_graph[neighbour].append(__magic_name__ )
lowercase__ = []
for i, was_visited in enumerate(__magic_name__ ):
if not was_visited:
order += topology_sort(__magic_name__ , __magic_name__ , __magic_name__ )
lowercase__ = []
lowercase__ = len(__magic_name__ ) * [False]
for i in range(len(__magic_name__ ) ):
lowercase__ = order[len(__magic_name__ ) - i - 1]
if not visited[vert]:
lowercase__ = find_components(__magic_name__ , __magic_name__ , __magic_name__ )
components_list.append(__magic_name__ )
return components_list
| 305
| 1
|
import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict
if TYPE_CHECKING:
import PIL.Image
from .features import FeatureType
A : Optional[List[str]] = None
A : Optional[Any] = '<' if sys.byteorder == 'little' else '>'
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
A : Optional[int] = [
np.dtype('|b1'),
np.dtype('|u1'),
np.dtype('<u2'),
np.dtype('>u2'),
np.dtype('<i2'),
np.dtype('>i2'),
np.dtype('<u4'),
np.dtype('>u4'),
np.dtype('<i4'),
np.dtype('>i4'),
np.dtype('<f4'),
np.dtype('>f4'),
np.dtype('<f8'),
np.dtype('>f8'),
]
@dataclass
class A :
'''simple docstring'''
A__ = True
A__ = None
# Automatically constructed
A__ = "PIL.Image.Image"
A__ = pa.struct({'''bytes''': pa.binary(), '''path''': pa.string()} )
A__ = field(default='''Image''' , init=UpperCAmelCase__ , repr=UpperCAmelCase__ )
def __call__(self : Union[str, Any] ) -> int:
"""simple docstring"""
return self.pa_type
def lowerCamelCase__ (self : Any , _UpperCAmelCase : Union[str, bytes, dict, np.ndarray, "PIL.Image.Image"] ) -> dict:
"""simple docstring"""
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support encoding images, please install 'Pillow'.""" )
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
lowercase__ = np.array(_UpperCAmelCase )
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
return {"path": value, "bytes": None}
elif isinstance(_UpperCAmelCase , _UpperCAmelCase ):
return {"path": None, "bytes": value}
elif isinstance(_UpperCAmelCase , np.ndarray ):
# convert the image array to PNG/TIFF bytes
return encode_np_array(_UpperCAmelCase )
elif isinstance(_UpperCAmelCase , PIL.Image.Image ):
# convert the PIL image to bytes (default format is PNG/TIFF)
return encode_pil_image(_UpperCAmelCase )
elif value.get("""path""" ) is not None and os.path.isfile(value["""path"""] ):
# we set "bytes": None to not duplicate the data if they're already available locally
return {"bytes": None, "path": value.get("""path""" )}
elif value.get("""bytes""" ) is not None or value.get("""path""" ) is not None:
# store the image bytes, and path is used to infer the image format using the file extension
return {"bytes": value.get("""bytes""" ), "path": value.get("""path""" )}
else:
raise ValueError(
f'''An image sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.''' )
def lowerCamelCase__ (self : str , _UpperCAmelCase : dict , _UpperCAmelCase : Tuple=None ) -> "PIL.Image.Image":
"""simple docstring"""
if not self.decode:
raise RuntimeError("""Decoding is disabled for this feature. Please use Image(decode=True) instead.""" )
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support decoding images, please install 'Pillow'.""" )
if token_per_repo_id is None:
lowercase__ = {}
lowercase__ , lowercase__ = value["""path"""], value["""bytes"""]
if bytes_ is None:
if path is None:
raise ValueError(f'''An image should have one of \'path\' or \'bytes\' but both are None in {value}.''' )
else:
if is_local_path(_UpperCAmelCase ):
lowercase__ = PIL.Image.open(_UpperCAmelCase )
else:
lowercase__ = path.split("""::""" )[-1]
try:
lowercase__ = string_to_dict(_UpperCAmelCase , config.HUB_DATASETS_URL )["""repo_id"""]
lowercase__ = token_per_repo_id.get(_UpperCAmelCase )
except ValueError:
lowercase__ = None
with xopen(_UpperCAmelCase , """rb""" , use_auth_token=_UpperCAmelCase ) as f:
lowercase__ = BytesIO(f.read() )
lowercase__ = PIL.Image.open(bytes_ )
else:
lowercase__ = PIL.Image.open(BytesIO(bytes_ ) )
image.load() # to avoid "Too many open files" errors
return image
def lowerCamelCase__ (self : int ) -> Union["FeatureType", Dict[str, "FeatureType"]]:
"""simple docstring"""
from .features import Value
return (
self
if self.decode
else {
"bytes": Value("""binary""" ),
"path": Value("""string""" ),
}
)
def lowerCamelCase__ (self : List[str] , _UpperCAmelCase : Union[pa.StringArray, pa.StructArray, pa.ListArray] ) -> pa.StructArray:
"""simple docstring"""
if pa.types.is_string(storage.type ):
lowercase__ = pa.array([None] * len(_UpperCAmelCase ) , type=pa.binary() )
lowercase__ = pa.StructArray.from_arrays([bytes_array, storage] , ["""bytes""", """path"""] , mask=storage.is_null() )
elif pa.types.is_binary(storage.type ):
lowercase__ = pa.array([None] * len(_UpperCAmelCase ) , type=pa.string() )
lowercase__ = pa.StructArray.from_arrays([storage, path_array] , ["""bytes""", """path"""] , mask=storage.is_null() )
elif pa.types.is_struct(storage.type ):
if storage.type.get_field_index("""bytes""" ) >= 0:
lowercase__ = storage.field("""bytes""" )
else:
lowercase__ = pa.array([None] * len(_UpperCAmelCase ) , type=pa.binary() )
if storage.type.get_field_index("""path""" ) >= 0:
lowercase__ = storage.field("""path""" )
else:
lowercase__ = pa.array([None] * len(_UpperCAmelCase ) , type=pa.string() )
lowercase__ = pa.StructArray.from_arrays([bytes_array, path_array] , ["""bytes""", """path"""] , mask=storage.is_null() )
elif pa.types.is_list(storage.type ):
lowercase__ = pa.array(
[encode_np_array(np.array(_UpperCAmelCase ) )["""bytes"""] if arr is not None else None for arr in storage.to_pylist()] , type=pa.binary() , )
lowercase__ = pa.array([None] * len(_UpperCAmelCase ) , type=pa.string() )
lowercase__ = pa.StructArray.from_arrays(
[bytes_array, path_array] , ["""bytes""", """path"""] , mask=bytes_array.is_null() )
return array_cast(_UpperCAmelCase , self.pa_type )
def lowerCamelCase__ (self : Optional[int] , _UpperCAmelCase : pa.StructArray ) -> pa.StructArray:
"""simple docstring"""
@no_op_if_value_is_null
def path_to_bytes(_UpperCAmelCase : Any ):
with xopen(_UpperCAmelCase , """rb""" ) as f:
lowercase__ = f.read()
return bytes_
lowercase__ = pa.array(
[
(path_to_bytes(x["""path"""] ) if x["""bytes"""] is None else x["""bytes"""]) if x is not None else None
for x in storage.to_pylist()
] , type=pa.binary() , )
lowercase__ = pa.array(
[os.path.basename(_UpperCAmelCase ) if path is not None else None for path in storage.field("""path""" ).to_pylist()] , type=pa.string() , )
lowercase__ = pa.StructArray.from_arrays([bytes_array, path_array] , ["""bytes""", """path"""] , mask=bytes_array.is_null() )
return array_cast(_UpperCAmelCase , self.pa_type )
def UpperCamelCase ( ) -> List[str]:
"""simple docstring"""
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support encoding images, please install 'Pillow'.""" )
global _IMAGE_COMPRESSION_FORMATS
if _IMAGE_COMPRESSION_FORMATS is None:
PIL.Image.init()
lowercase__ = list(set(PIL.Image.OPEN.keys() ) & set(PIL.Image.SAVE.keys() ) )
return _IMAGE_COMPRESSION_FORMATS
def UpperCamelCase ( __magic_name__ : "PIL.Image.Image" ) -> bytes:
"""simple docstring"""
lowercase__ = BytesIO()
if image.format in list_image_compression_formats():
lowercase__ = image.format
else:
lowercase__ = """PNG""" if image.mode in ["""1""", """L""", """LA""", """RGB""", """RGBA"""] else """TIFF"""
image.save(__magic_name__ , format=__magic_name__ )
return buffer.getvalue()
def UpperCamelCase ( __magic_name__ : "PIL.Image.Image" ) -> dict:
"""simple docstring"""
if hasattr(__magic_name__ , """filename""" ) and image.filename != "":
return {"path": image.filename, "bytes": None}
else:
return {"path": None, "bytes": image_to_bytes(__magic_name__ )}
def UpperCamelCase ( __magic_name__ : np.ndarray ) -> dict:
"""simple docstring"""
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support encoding images, please install 'Pillow'.""" )
lowercase__ = array.dtype
lowercase__ = dtype.byteorder if dtype.byteorder != """=""" else _NATIVE_BYTEORDER
lowercase__ = dtype.kind
lowercase__ = dtype.itemsize
lowercase__ = None
# Multi-channel array case (only np.dtype("|u1") is allowed)
if array.shape[2:]:
lowercase__ = np.dtype("""|u1""" )
if dtype_kind not in ["u", "i"]:
raise TypeError(
f'''Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays.''' )
if dtype is not dest_dtype:
warnings.warn(f'''Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\'''' )
# Exact match
elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
lowercase__ = dtype
else: # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
while dtype_itemsize >= 1:
lowercase__ = dtype_byteorder + dtype_kind + str(__magic_name__ )
lowercase__ = np.dtype(__magic_name__ )
if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
warnings.warn(f'''Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\'''' )
break
else:
dtype_itemsize //= 2
if dest_dtype is None:
raise TypeError(
f'''Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}''' )
lowercase__ = PIL.Image.fromarray(array.astype(__magic_name__ ) )
return {"path": None, "bytes": image_to_bytes(__magic_name__ )}
def UpperCamelCase ( __magic_name__ : Union[List[str], List[dict], List[np.ndarray], List["PIL.Image.Image"]] ) -> List[dict]:
"""simple docstring"""
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support encoding images, please install 'Pillow'.""" )
if objs:
lowercase__ , lowercase__ = first_non_null_value(__magic_name__ )
if isinstance(__magic_name__ , __magic_name__ ):
return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
if isinstance(__magic_name__ , np.ndarray ):
lowercase__ = no_op_if_value_is_null(__magic_name__ )
return [obj_to_image_dict_func(__magic_name__ ) for obj in objs]
elif isinstance(__magic_name__ , PIL.Image.Image ):
lowercase__ = no_op_if_value_is_null(__magic_name__ )
return [obj_to_image_dict_func(__magic_name__ ) for obj in objs]
else:
return objs
else:
return objs
| 305
|
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepInverseScheduler,
DPMSolverMultistepScheduler,
StableDiffusionDiffEditPipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device
from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class A ( UpperCAmelCase__ , UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
A__ = StableDiffusionDiffEditPipeline
A__ = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''height''', '''width''', '''image'''} | {'''image_latents'''}
A__ = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {'''image'''} | {'''image_latents'''}
A__ = frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
A__ = frozenset([] )
def lowerCamelCase__ (self : List[str] ) -> Optional[int]:
"""simple docstring"""
torch.manual_seed(0 )
lowercase__ = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , cross_attention_dim=32 , attention_head_dim=(2, 4) , use_linear_projection=_UpperCAmelCase , )
lowercase__ = DDIMScheduler(
beta_start=0.00_085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , clip_sample=_UpperCAmelCase , set_alpha_to_one=_UpperCAmelCase , )
lowercase__ = DDIMInverseScheduler(
beta_start=0.00_085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , clip_sample=_UpperCAmelCase , set_alpha_to_zero=_UpperCAmelCase , )
torch.manual_seed(0 )
lowercase__ = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , sample_size=128 , )
torch.manual_seed(0 )
lowercase__ = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , hidden_act="""gelu""" , projection_dim=512 , )
lowercase__ = CLIPTextModel(_UpperCAmelCase )
lowercase__ = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
lowercase__ = {
"""unet""": unet,
"""scheduler""": scheduler,
"""inverse_scheduler""": inverse_scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""safety_checker""": None,
"""feature_extractor""": None,
}
return components
def lowerCamelCase__ (self : Optional[Any] , _UpperCAmelCase : Tuple , _UpperCAmelCase : Tuple=0 ) -> Dict:
"""simple docstring"""
lowercase__ = floats_tensor((1, 16, 16) , rng=random.Random(_UpperCAmelCase ) ).to(_UpperCAmelCase )
lowercase__ = floats_tensor((1, 2, 4, 16, 16) , rng=random.Random(_UpperCAmelCase ) ).to(_UpperCAmelCase )
if str(_UpperCAmelCase ).startswith("""mps""" ):
lowercase__ = torch.manual_seed(_UpperCAmelCase )
else:
lowercase__ = torch.Generator(device=_UpperCAmelCase ).manual_seed(_UpperCAmelCase )
lowercase__ = {
"""prompt""": """a dog and a newt""",
"""mask_image""": mask,
"""image_latents""": latents,
"""generator""": generator,
"""num_inference_steps""": 2,
"""inpaint_strength""": 1.0,
"""guidance_scale""": 6.0,
"""output_type""": """numpy""",
}
return inputs
def lowerCamelCase__ (self : List[Any] , _UpperCAmelCase : Dict , _UpperCAmelCase : Tuple=0 ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = floats_tensor((1, 3, 32, 32) , rng=random.Random(_UpperCAmelCase ) ).to(_UpperCAmelCase )
lowercase__ = image.cpu().permute(0 , 2 , 3 , 1 )[0]
lowercase__ = Image.fromarray(np.uinta(_UpperCAmelCase ) ).convert("""RGB""" )
if str(_UpperCAmelCase ).startswith("""mps""" ):
lowercase__ = torch.manual_seed(_UpperCAmelCase )
else:
lowercase__ = torch.Generator(device=_UpperCAmelCase ).manual_seed(_UpperCAmelCase )
lowercase__ = {
"""image""": image,
"""source_prompt""": """a cat and a frog""",
"""target_prompt""": """a dog and a newt""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""num_maps_per_mask""": 2,
"""mask_encode_strength""": 1.0,
"""guidance_scale""": 6.0,
"""output_type""": """numpy""",
}
return inputs
def lowerCamelCase__ (self : Optional[Any] , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Dict=0 ) -> str:
"""simple docstring"""
lowercase__ = floats_tensor((1, 3, 32, 32) , rng=random.Random(_UpperCAmelCase ) ).to(_UpperCAmelCase )
lowercase__ = image.cpu().permute(0 , 2 , 3 , 1 )[0]
lowercase__ = Image.fromarray(np.uinta(_UpperCAmelCase ) ).convert("""RGB""" )
if str(_UpperCAmelCase ).startswith("""mps""" ):
lowercase__ = torch.manual_seed(_UpperCAmelCase )
else:
lowercase__ = torch.Generator(device=_UpperCAmelCase ).manual_seed(_UpperCAmelCase )
lowercase__ = {
"""image""": image,
"""prompt""": """a cat and a frog""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""inpaint_strength""": 1.0,
"""guidance_scale""": 6.0,
"""decode_latents""": True,
"""output_type""": """numpy""",
}
return inputs
def lowerCamelCase__ (self : int ) -> Dict:
"""simple docstring"""
if not hasattr(self.pipeline_class , """_optional_components""" ):
return
lowercase__ = self.get_dummy_components()
lowercase__ = self.pipeline_class(**_UpperCAmelCase )
pipe.to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
# set all optional components to None and update pipeline config accordingly
for optional_component in pipe._optional_components:
setattr(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components} )
lowercase__ = self.get_dummy_inputs(_UpperCAmelCase )
lowercase__ = pipe(**_UpperCAmelCase )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(_UpperCAmelCase )
lowercase__ = self.pipeline_class.from_pretrained(_UpperCAmelCase )
pipe_loaded.to(_UpperCAmelCase )
pipe_loaded.set_progress_bar_config(disable=_UpperCAmelCase )
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(_UpperCAmelCase , _UpperCAmelCase ) is None , f'''`{optional_component}` did not stay set to None after loading.''' , )
lowercase__ = self.get_dummy_inputs(_UpperCAmelCase )
lowercase__ = pipe_loaded(**_UpperCAmelCase )[0]
lowercase__ = np.abs(output - output_loaded ).max()
self.assertLess(_UpperCAmelCase , 1E-4 )
def lowerCamelCase__ (self : List[str] ) -> int:
"""simple docstring"""
lowercase__ = """cpu"""
lowercase__ = self.get_dummy_components()
lowercase__ = self.pipeline_class(**_UpperCAmelCase )
pipe.to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
lowercase__ = self.get_dummy_mask_inputs(_UpperCAmelCase )
lowercase__ = pipe.generate_mask(**_UpperCAmelCase )
lowercase__ = mask[0, -3:, -3:]
self.assertEqual(mask.shape , (1, 16, 16) )
lowercase__ = np.array([0] * 9 )
lowercase__ = np.abs(mask_slice.flatten() - expected_slice ).max()
self.assertLessEqual(_UpperCAmelCase , 1E-3 )
self.assertEqual(mask[0, -3, -4] , 0 )
def lowerCamelCase__ (self : List[Any] ) -> str:
"""simple docstring"""
lowercase__ = """cpu"""
lowercase__ = self.get_dummy_components()
lowercase__ = self.pipeline_class(**_UpperCAmelCase )
pipe.to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
lowercase__ = self.get_dummy_inversion_inputs(_UpperCAmelCase )
lowercase__ = pipe.invert(**_UpperCAmelCase ).images
lowercase__ = image[0, -1, -3:, -3:]
self.assertEqual(image.shape , (2, 32, 32, 3) )
lowercase__ = np.array(
[0.5_150, 0.5_134, 0.5_043, 0.5_376, 0.4_694, 0.51_050, 0.5_015, 0.4_407, 0.4_799] , )
lowercase__ = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(_UpperCAmelCase , 1E-3 )
def lowerCamelCase__ (self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
super().test_inference_batch_single_identical(expected_max_diff=5E-3 )
def lowerCamelCase__ (self : str ) -> List[str]:
"""simple docstring"""
lowercase__ = """cpu"""
lowercase__ = self.get_dummy_components()
lowercase__ = {"""beta_start""": 0.00_085, """beta_end""": 0.012, """beta_schedule""": """scaled_linear"""}
lowercase__ = DPMSolverMultistepScheduler(**_UpperCAmelCase )
lowercase__ = DPMSolverMultistepInverseScheduler(**_UpperCAmelCase )
lowercase__ = self.pipeline_class(**_UpperCAmelCase )
pipe.to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
lowercase__ = self.get_dummy_inversion_inputs(_UpperCAmelCase )
lowercase__ = pipe.invert(**_UpperCAmelCase ).images
lowercase__ = image[0, -1, -3:, -3:]
self.assertEqual(image.shape , (2, 32, 32, 3) )
lowercase__ = np.array(
[0.5_150, 0.5_134, 0.5_043, 0.5_376, 0.4_694, 0.51_050, 0.5_015, 0.4_407, 0.4_799] , )
lowercase__ = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(_UpperCAmelCase , 1E-3 )
@require_torch_gpu
@slow
class A ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase__ (self : Any ) -> Any:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@classmethod
def lowerCamelCase__ (cls : str ) -> Optional[int]:
"""simple docstring"""
lowercase__ = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png""" )
lowercase__ = raw_image.convert("""RGB""" ).resize((768, 768) )
lowercase__ = raw_image
def lowerCamelCase__ (self : Optional[int] ) -> Any:
"""simple docstring"""
lowercase__ = torch.manual_seed(0 )
lowercase__ = StableDiffusionDiffEditPipeline.from_pretrained(
"""stabilityai/stable-diffusion-2-1""" , safety_checker=_UpperCAmelCase , torch_dtype=torch.floataa )
lowercase__ = DDIMScheduler.from_config(pipe.scheduler.config )
lowercase__ = DDIMInverseScheduler.from_config(pipe.scheduler.config )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
lowercase__ = """a bowl of fruit"""
lowercase__ = """a bowl of pears"""
lowercase__ = pipe.generate_mask(
image=self.raw_image , source_prompt=_UpperCAmelCase , target_prompt=_UpperCAmelCase , generator=_UpperCAmelCase , )
lowercase__ = pipe.invert(
prompt=_UpperCAmelCase , image=self.raw_image , inpaint_strength=0.7 , generator=_UpperCAmelCase ).latents
lowercase__ = pipe(
prompt=_UpperCAmelCase , mask_image=_UpperCAmelCase , image_latents=_UpperCAmelCase , generator=_UpperCAmelCase , negative_prompt=_UpperCAmelCase , inpaint_strength=0.7 , output_type="""numpy""" , ).images[0]
lowercase__ = (
np.array(
load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/diffedit/pears.png""" ).resize((768, 768) ) )
/ 255
)
assert np.abs((expected_image - image).max() ) < 5E-1
def lowerCamelCase__ (self : int ) -> Any:
"""simple docstring"""
lowercase__ = torch.manual_seed(0 )
lowercase__ = StableDiffusionDiffEditPipeline.from_pretrained(
"""stabilityai/stable-diffusion-2-1""" , safety_checker=_UpperCAmelCase , torch_dtype=torch.floataa )
lowercase__ = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
lowercase__ = DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
lowercase__ = """a bowl of fruit"""
lowercase__ = """a bowl of pears"""
lowercase__ = pipe.generate_mask(
image=self.raw_image , source_prompt=_UpperCAmelCase , target_prompt=_UpperCAmelCase , generator=_UpperCAmelCase , )
lowercase__ = pipe.invert(
prompt=_UpperCAmelCase , image=self.raw_image , inpaint_strength=0.7 , generator=_UpperCAmelCase , num_inference_steps=25 , ).latents
lowercase__ = pipe(
prompt=_UpperCAmelCase , mask_image=_UpperCAmelCase , image_latents=_UpperCAmelCase , generator=_UpperCAmelCase , negative_prompt=_UpperCAmelCase , inpaint_strength=0.7 , num_inference_steps=25 , output_type="""numpy""" , ).images[0]
lowercase__ = (
np.array(
load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/diffedit/pears.png""" ).resize((768, 768) ) )
/ 255
)
assert np.abs((expected_image - image).max() ) < 5E-1
| 305
| 1
|
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepInverseScheduler,
DPMSolverMultistepScheduler,
StableDiffusionDiffEditPipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device
from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class A ( UpperCAmelCase__ , UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
A__ = StableDiffusionDiffEditPipeline
A__ = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''height''', '''width''', '''image'''} | {'''image_latents'''}
A__ = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {'''image'''} | {'''image_latents'''}
A__ = frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
A__ = frozenset([] )
def lowerCamelCase__ (self : List[str] ) -> Optional[int]:
"""simple docstring"""
torch.manual_seed(0 )
lowercase__ = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , cross_attention_dim=32 , attention_head_dim=(2, 4) , use_linear_projection=_UpperCAmelCase , )
lowercase__ = DDIMScheduler(
beta_start=0.00_085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , clip_sample=_UpperCAmelCase , set_alpha_to_one=_UpperCAmelCase , )
lowercase__ = DDIMInverseScheduler(
beta_start=0.00_085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , clip_sample=_UpperCAmelCase , set_alpha_to_zero=_UpperCAmelCase , )
torch.manual_seed(0 )
lowercase__ = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , sample_size=128 , )
torch.manual_seed(0 )
lowercase__ = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , hidden_act="""gelu""" , projection_dim=512 , )
lowercase__ = CLIPTextModel(_UpperCAmelCase )
lowercase__ = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
lowercase__ = {
"""unet""": unet,
"""scheduler""": scheduler,
"""inverse_scheduler""": inverse_scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""safety_checker""": None,
"""feature_extractor""": None,
}
return components
def lowerCamelCase__ (self : Optional[Any] , _UpperCAmelCase : Tuple , _UpperCAmelCase : Tuple=0 ) -> Dict:
"""simple docstring"""
lowercase__ = floats_tensor((1, 16, 16) , rng=random.Random(_UpperCAmelCase ) ).to(_UpperCAmelCase )
lowercase__ = floats_tensor((1, 2, 4, 16, 16) , rng=random.Random(_UpperCAmelCase ) ).to(_UpperCAmelCase )
if str(_UpperCAmelCase ).startswith("""mps""" ):
lowercase__ = torch.manual_seed(_UpperCAmelCase )
else:
lowercase__ = torch.Generator(device=_UpperCAmelCase ).manual_seed(_UpperCAmelCase )
lowercase__ = {
"""prompt""": """a dog and a newt""",
"""mask_image""": mask,
"""image_latents""": latents,
"""generator""": generator,
"""num_inference_steps""": 2,
"""inpaint_strength""": 1.0,
"""guidance_scale""": 6.0,
"""output_type""": """numpy""",
}
return inputs
def lowerCamelCase__ (self : List[Any] , _UpperCAmelCase : Dict , _UpperCAmelCase : Tuple=0 ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = floats_tensor((1, 3, 32, 32) , rng=random.Random(_UpperCAmelCase ) ).to(_UpperCAmelCase )
lowercase__ = image.cpu().permute(0 , 2 , 3 , 1 )[0]
lowercase__ = Image.fromarray(np.uinta(_UpperCAmelCase ) ).convert("""RGB""" )
if str(_UpperCAmelCase ).startswith("""mps""" ):
lowercase__ = torch.manual_seed(_UpperCAmelCase )
else:
lowercase__ = torch.Generator(device=_UpperCAmelCase ).manual_seed(_UpperCAmelCase )
lowercase__ = {
"""image""": image,
"""source_prompt""": """a cat and a frog""",
"""target_prompt""": """a dog and a newt""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""num_maps_per_mask""": 2,
"""mask_encode_strength""": 1.0,
"""guidance_scale""": 6.0,
"""output_type""": """numpy""",
}
return inputs
def lowerCamelCase__ (self : Optional[Any] , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Dict=0 ) -> str:
"""simple docstring"""
lowercase__ = floats_tensor((1, 3, 32, 32) , rng=random.Random(_UpperCAmelCase ) ).to(_UpperCAmelCase )
lowercase__ = image.cpu().permute(0 , 2 , 3 , 1 )[0]
lowercase__ = Image.fromarray(np.uinta(_UpperCAmelCase ) ).convert("""RGB""" )
if str(_UpperCAmelCase ).startswith("""mps""" ):
lowercase__ = torch.manual_seed(_UpperCAmelCase )
else:
lowercase__ = torch.Generator(device=_UpperCAmelCase ).manual_seed(_UpperCAmelCase )
lowercase__ = {
"""image""": image,
"""prompt""": """a cat and a frog""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""inpaint_strength""": 1.0,
"""guidance_scale""": 6.0,
"""decode_latents""": True,
"""output_type""": """numpy""",
}
return inputs
def lowerCamelCase__ (self : int ) -> Dict:
"""simple docstring"""
if not hasattr(self.pipeline_class , """_optional_components""" ):
return
lowercase__ = self.get_dummy_components()
lowercase__ = self.pipeline_class(**_UpperCAmelCase )
pipe.to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
# set all optional components to None and update pipeline config accordingly
for optional_component in pipe._optional_components:
setattr(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components} )
lowercase__ = self.get_dummy_inputs(_UpperCAmelCase )
lowercase__ = pipe(**_UpperCAmelCase )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(_UpperCAmelCase )
lowercase__ = self.pipeline_class.from_pretrained(_UpperCAmelCase )
pipe_loaded.to(_UpperCAmelCase )
pipe_loaded.set_progress_bar_config(disable=_UpperCAmelCase )
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(_UpperCAmelCase , _UpperCAmelCase ) is None , f'''`{optional_component}` did not stay set to None after loading.''' , )
lowercase__ = self.get_dummy_inputs(_UpperCAmelCase )
lowercase__ = pipe_loaded(**_UpperCAmelCase )[0]
lowercase__ = np.abs(output - output_loaded ).max()
self.assertLess(_UpperCAmelCase , 1E-4 )
def lowerCamelCase__ (self : List[str] ) -> int:
"""simple docstring"""
lowercase__ = """cpu"""
lowercase__ = self.get_dummy_components()
lowercase__ = self.pipeline_class(**_UpperCAmelCase )
pipe.to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
lowercase__ = self.get_dummy_mask_inputs(_UpperCAmelCase )
lowercase__ = pipe.generate_mask(**_UpperCAmelCase )
lowercase__ = mask[0, -3:, -3:]
self.assertEqual(mask.shape , (1, 16, 16) )
lowercase__ = np.array([0] * 9 )
lowercase__ = np.abs(mask_slice.flatten() - expected_slice ).max()
self.assertLessEqual(_UpperCAmelCase , 1E-3 )
self.assertEqual(mask[0, -3, -4] , 0 )
def lowerCamelCase__ (self : List[Any] ) -> str:
"""simple docstring"""
lowercase__ = """cpu"""
lowercase__ = self.get_dummy_components()
lowercase__ = self.pipeline_class(**_UpperCAmelCase )
pipe.to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
lowercase__ = self.get_dummy_inversion_inputs(_UpperCAmelCase )
lowercase__ = pipe.invert(**_UpperCAmelCase ).images
lowercase__ = image[0, -1, -3:, -3:]
self.assertEqual(image.shape , (2, 32, 32, 3) )
lowercase__ = np.array(
[0.5_150, 0.5_134, 0.5_043, 0.5_376, 0.4_694, 0.51_050, 0.5_015, 0.4_407, 0.4_799] , )
lowercase__ = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(_UpperCAmelCase , 1E-3 )
def lowerCamelCase__ (self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
super().test_inference_batch_single_identical(expected_max_diff=5E-3 )
def lowerCamelCase__ (self : str ) -> List[str]:
"""simple docstring"""
lowercase__ = """cpu"""
lowercase__ = self.get_dummy_components()
lowercase__ = {"""beta_start""": 0.00_085, """beta_end""": 0.012, """beta_schedule""": """scaled_linear"""}
lowercase__ = DPMSolverMultistepScheduler(**_UpperCAmelCase )
lowercase__ = DPMSolverMultistepInverseScheduler(**_UpperCAmelCase )
lowercase__ = self.pipeline_class(**_UpperCAmelCase )
pipe.to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
lowercase__ = self.get_dummy_inversion_inputs(_UpperCAmelCase )
lowercase__ = pipe.invert(**_UpperCAmelCase ).images
lowercase__ = image[0, -1, -3:, -3:]
self.assertEqual(image.shape , (2, 32, 32, 3) )
lowercase__ = np.array(
[0.5_150, 0.5_134, 0.5_043, 0.5_376, 0.4_694, 0.51_050, 0.5_015, 0.4_407, 0.4_799] , )
lowercase__ = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(_UpperCAmelCase , 1E-3 )
@require_torch_gpu
@slow
class A ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase__ (self : Any ) -> Any:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@classmethod
def lowerCamelCase__ (cls : str ) -> Optional[int]:
"""simple docstring"""
lowercase__ = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png""" )
lowercase__ = raw_image.convert("""RGB""" ).resize((768, 768) )
lowercase__ = raw_image
def lowerCamelCase__ (self : Optional[int] ) -> Any:
"""simple docstring"""
lowercase__ = torch.manual_seed(0 )
lowercase__ = StableDiffusionDiffEditPipeline.from_pretrained(
"""stabilityai/stable-diffusion-2-1""" , safety_checker=_UpperCAmelCase , torch_dtype=torch.floataa )
lowercase__ = DDIMScheduler.from_config(pipe.scheduler.config )
lowercase__ = DDIMInverseScheduler.from_config(pipe.scheduler.config )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
lowercase__ = """a bowl of fruit"""
lowercase__ = """a bowl of pears"""
lowercase__ = pipe.generate_mask(
image=self.raw_image , source_prompt=_UpperCAmelCase , target_prompt=_UpperCAmelCase , generator=_UpperCAmelCase , )
lowercase__ = pipe.invert(
prompt=_UpperCAmelCase , image=self.raw_image , inpaint_strength=0.7 , generator=_UpperCAmelCase ).latents
lowercase__ = pipe(
prompt=_UpperCAmelCase , mask_image=_UpperCAmelCase , image_latents=_UpperCAmelCase , generator=_UpperCAmelCase , negative_prompt=_UpperCAmelCase , inpaint_strength=0.7 , output_type="""numpy""" , ).images[0]
lowercase__ = (
np.array(
load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/diffedit/pears.png""" ).resize((768, 768) ) )
/ 255
)
assert np.abs((expected_image - image).max() ) < 5E-1
def lowerCamelCase__ (self : int ) -> Any:
"""simple docstring"""
lowercase__ = torch.manual_seed(0 )
lowercase__ = StableDiffusionDiffEditPipeline.from_pretrained(
"""stabilityai/stable-diffusion-2-1""" , safety_checker=_UpperCAmelCase , torch_dtype=torch.floataa )
lowercase__ = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
lowercase__ = DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
lowercase__ = """a bowl of fruit"""
lowercase__ = """a bowl of pears"""
lowercase__ = pipe.generate_mask(
image=self.raw_image , source_prompt=_UpperCAmelCase , target_prompt=_UpperCAmelCase , generator=_UpperCAmelCase , )
lowercase__ = pipe.invert(
prompt=_UpperCAmelCase , image=self.raw_image , inpaint_strength=0.7 , generator=_UpperCAmelCase , num_inference_steps=25 , ).latents
lowercase__ = pipe(
prompt=_UpperCAmelCase , mask_image=_UpperCAmelCase , image_latents=_UpperCAmelCase , generator=_UpperCAmelCase , negative_prompt=_UpperCAmelCase , inpaint_strength=0.7 , num_inference_steps=25 , output_type="""numpy""" , ).images[0]
lowercase__ = (
np.array(
load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/diffedit/pears.png""" ).resize((768, 768) ) )
/ 255
)
assert np.abs((expected_image - image).max() ) < 5E-1
| 305
|
from __future__ import annotations
import math
def UpperCamelCase ( __magic_name__ : list , __magic_name__ : list ) -> list:
"""simple docstring"""
if len(__magic_name__ ) != 2 or len(a[0] ) != 2 or len(__magic_name__ ) != 2 or len(b[0] ) != 2:
raise Exception("""Matrices are not 2x2""" )
lowercase__ = [
[a[0][0] * b[0][0] + a[0][1] * b[1][0], a[0][0] * b[0][1] + a[0][1] * b[1][1]],
[a[1][0] * b[0][0] + a[1][1] * b[1][0], a[1][0] * b[0][1] + a[1][1] * b[1][1]],
]
return new_matrix
def UpperCamelCase ( __magic_name__ : list , __magic_name__ : list ) -> Union[str, Any]:
"""simple docstring"""
return [
[matrix_a[row][col] + matrix_b[row][col] for col in range(len(matrix_a[row] ) )]
for row in range(len(__magic_name__ ) )
]
def UpperCamelCase ( __magic_name__ : list , __magic_name__ : list ) -> int:
"""simple docstring"""
return [
[matrix_a[row][col] - matrix_b[row][col] for col in range(len(matrix_a[row] ) )]
for row in range(len(__magic_name__ ) )
]
def UpperCamelCase ( __magic_name__ : list ) -> tuple[list, list, list, list]:
"""simple docstring"""
if len(__magic_name__ ) % 2 != 0 or len(a[0] ) % 2 != 0:
raise Exception("""Odd matrices are not supported!""" )
lowercase__ = len(__magic_name__ )
lowercase__ = matrix_length // 2
lowercase__ = [[a[i][j] for j in range(__magic_name__ , __magic_name__ )] for i in range(__magic_name__ )]
lowercase__ = [
[a[i][j] for j in range(__magic_name__ , __magic_name__ )] for i in range(__magic_name__ , __magic_name__ )
]
lowercase__ = [[a[i][j] for j in range(__magic_name__ )] for i in range(__magic_name__ )]
lowercase__ = [[a[i][j] for j in range(__magic_name__ )] for i in range(__magic_name__ , __magic_name__ )]
return top_left, top_right, bot_left, bot_right
def UpperCamelCase ( __magic_name__ : list ) -> tuple[int, int]:
"""simple docstring"""
return len(__magic_name__ ), len(matrix[0] )
def UpperCamelCase ( __magic_name__ : list ) -> None:
"""simple docstring"""
print("""\n""".join(str(__magic_name__ ) for line in matrix ) )
def UpperCamelCase ( __magic_name__ : list , __magic_name__ : list ) -> list:
"""simple docstring"""
if matrix_dimensions(__magic_name__ ) == (2, 2):
return default_matrix_multiplication(__magic_name__ , __magic_name__ )
lowercase__ , lowercase__ , lowercase__ , lowercase__ = split_matrix(__magic_name__ )
lowercase__ , lowercase__ , lowercase__ , lowercase__ = split_matrix(__magic_name__ )
lowercase__ = actual_strassen(__magic_name__ , matrix_subtraction(__magic_name__ , __magic_name__ ) )
lowercase__ = actual_strassen(matrix_addition(__magic_name__ , __magic_name__ ) , __magic_name__ )
lowercase__ = actual_strassen(matrix_addition(__magic_name__ , __magic_name__ ) , __magic_name__ )
lowercase__ = actual_strassen(__magic_name__ , matrix_subtraction(__magic_name__ , __magic_name__ ) )
lowercase__ = actual_strassen(matrix_addition(__magic_name__ , __magic_name__ ) , matrix_addition(__magic_name__ , __magic_name__ ) )
lowercase__ = actual_strassen(matrix_subtraction(__magic_name__ , __magic_name__ ) , matrix_addition(__magic_name__ , __magic_name__ ) )
lowercase__ = actual_strassen(matrix_subtraction(__magic_name__ , __magic_name__ ) , matrix_addition(__magic_name__ , __magic_name__ ) )
lowercase__ = matrix_addition(matrix_subtraction(matrix_addition(__magic_name__ , __magic_name__ ) , __magic_name__ ) , __magic_name__ )
lowercase__ = matrix_addition(__magic_name__ , __magic_name__ )
lowercase__ = matrix_addition(__magic_name__ , __magic_name__ )
lowercase__ = matrix_subtraction(matrix_subtraction(matrix_addition(__magic_name__ , __magic_name__ ) , __magic_name__ ) , __magic_name__ )
# construct the new matrix from our 4 quadrants
lowercase__ = []
for i in range(len(__magic_name__ ) ):
new_matrix.append(top_left[i] + top_right[i] )
for i in range(len(__magic_name__ ) ):
new_matrix.append(bot_left[i] + bot_right[i] )
return new_matrix
def UpperCamelCase ( __magic_name__ : list , __magic_name__ : list ) -> list:
"""simple docstring"""
if matrix_dimensions(__magic_name__ )[1] != matrix_dimensions(__magic_name__ )[0]:
lowercase__ = (
"""Unable to multiply these matrices, please check the dimensions.\n"""
f'''Matrix A: {matrixa}\n'''
f'''Matrix B: {matrixa}'''
)
raise Exception(__magic_name__ )
lowercase__ = matrix_dimensions(__magic_name__ )
lowercase__ = matrix_dimensions(__magic_name__ )
if dimensiona[0] == dimensiona[1] and dimensiona[0] == dimensiona[1]:
return [matrixa, matrixa]
lowercase__ = max(*__magic_name__ , *__magic_name__ )
lowercase__ = int(math.pow(2 , math.ceil(math.loga(__magic_name__ ) ) ) )
lowercase__ = matrixa
lowercase__ = matrixa
# Adding zeros to the matrices so that the arrays dimensions are the same and also
# power of 2
for i in range(0 , __magic_name__ ):
if i < dimensiona[0]:
for _ in range(dimensiona[1] , __magic_name__ ):
new_matrixa[i].append(0 )
else:
new_matrixa.append([0] * maxim )
if i < dimensiona[0]:
for _ in range(dimensiona[1] , __magic_name__ ):
new_matrixa[i].append(0 )
else:
new_matrixa.append([0] * maxim )
lowercase__ = actual_strassen(__magic_name__ , __magic_name__ )
# Removing the additional zeros
for i in range(0 , __magic_name__ ):
if i < dimensiona[0]:
for _ in range(dimensiona[1] , __magic_name__ ):
final_matrix[i].pop()
else:
final_matrix.pop()
return final_matrix
if __name__ == "__main__":
A : Optional[Any] = [
[2, 3, 4, 5],
[6, 4, 3, 1],
[2, 3, 6, 7],
[3, 1, 2, 4],
[2, 3, 4, 5],
[6, 4, 3, 1],
[2, 3, 6, 7],
[3, 1, 2, 4],
[2, 3, 4, 5],
[6, 2, 3, 1],
]
A : List[Any] = [[0, 2, 1, 1], [1_6, 2, 3, 3], [2, 2, 7, 7], [1_3, 1_1, 2_2, 4]]
print(strassen(matrixa, matrixa))
| 305
| 1
|
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A : str = logging.get_logger(__name__)
A : Union[str, Any] = '▁'
A : Optional[int] = {
'vocab_file': 'vocab.json',
'spm_file': 'sentencepiece.bpe.model',
}
A : str = {
'vocab_file': {
'facebook/s2t-small-librispeech-asr': (
'https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json'
),
},
'spm_file': {
'facebook/s2t-small-librispeech-asr': (
'https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model'
)
},
}
A : Any = {
'facebook/s2t-small-librispeech-asr': 1_0_2_4,
}
A : Dict = ['pt', 'fr', 'ru', 'nl', 'ro', 'it', 'es', 'de']
A : Union[str, Any] = {'mustc': MUSTC_LANGS}
class A ( UpperCAmelCase__ ):
'''simple docstring'''
A__ = VOCAB_FILES_NAMES
A__ = PRETRAINED_VOCAB_FILES_MAP
A__ = MAX_MODEL_INPUT_SIZES
A__ = ['''input_ids''', '''attention_mask''']
A__ = []
def __init__(self : Dict , _UpperCAmelCase : int , _UpperCAmelCase : List[str] , _UpperCAmelCase : Optional[int]="<s>" , _UpperCAmelCase : Optional[int]="</s>" , _UpperCAmelCase : Optional[Any]="<pad>" , _UpperCAmelCase : int="<unk>" , _UpperCAmelCase : Any=False , _UpperCAmelCase : Dict=False , _UpperCAmelCase : List[str]=None , _UpperCAmelCase : List[str]=None , _UpperCAmelCase : Optional[Dict[str, Any]] = None , **_UpperCAmelCase : List[Any] , ) -> None:
"""simple docstring"""
lowercase__ = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=_UpperCAmelCase , eos_token=_UpperCAmelCase , unk_token=_UpperCAmelCase , pad_token=_UpperCAmelCase , do_upper_case=_UpperCAmelCase , do_lower_case=_UpperCAmelCase , tgt_lang=_UpperCAmelCase , lang_codes=_UpperCAmelCase , sp_model_kwargs=self.sp_model_kwargs , **_UpperCAmelCase , )
lowercase__ = do_upper_case
lowercase__ = do_lower_case
lowercase__ = load_json(_UpperCAmelCase )
lowercase__ = {v: k for k, v in self.encoder.items()}
lowercase__ = spm_file
lowercase__ = load_spm(_UpperCAmelCase , self.sp_model_kwargs )
if lang_codes is not None:
lowercase__ = lang_codes
lowercase__ = LANGUAGES[lang_codes]
lowercase__ = [f'''<lang:{lang}>''' for lang in self.langs]
lowercase__ = {lang: self.sp_model.PieceToId(f'''<lang:{lang}>''' ) for lang in self.langs}
lowercase__ = self.lang_tokens
lowercase__ = tgt_lang if tgt_lang is not None else self.langs[0]
self.set_tgt_lang_special_tokens(self._tgt_lang )
else:
lowercase__ = {}
@property
def lowerCamelCase__ (self : str ) -> int:
"""simple docstring"""
return len(self.encoder )
@property
def lowerCamelCase__ (self : List[Any] ) -> str:
"""simple docstring"""
return self._tgt_lang
@tgt_lang.setter
def lowerCamelCase__ (self : Any , _UpperCAmelCase : int ) -> None:
"""simple docstring"""
lowercase__ = new_tgt_lang
self.set_tgt_lang_special_tokens(_UpperCAmelCase )
def lowerCamelCase__ (self : str , _UpperCAmelCase : str ) -> None:
"""simple docstring"""
lowercase__ = self.lang_code_to_id[tgt_lang]
lowercase__ = [lang_code_id]
def lowerCamelCase__ (self : Tuple , _UpperCAmelCase : str ) -> List[str]:
"""simple docstring"""
return self.sp_model.encode(_UpperCAmelCase , out_type=_UpperCAmelCase )
def lowerCamelCase__ (self : Tuple , _UpperCAmelCase : int ) -> Dict:
"""simple docstring"""
return self.encoder.get(_UpperCAmelCase , self.encoder[self.unk_token] )
def lowerCamelCase__ (self : Optional[int] , _UpperCAmelCase : int ) -> str:
"""simple docstring"""
return self.decoder.get(_UpperCAmelCase , self.unk_token )
def lowerCamelCase__ (self : Optional[Any] , _UpperCAmelCase : List[str] ) -> str:
"""simple docstring"""
lowercase__ = []
lowercase__ = """"""
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
lowercase__ = self.sp_model.decode(_UpperCAmelCase )
out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
lowercase__ = []
else:
current_sub_tokens.append(_UpperCAmelCase )
lowercase__ = self.sp_model.decode(_UpperCAmelCase )
out_string += decoded.upper() if self.do_upper_case else decoded
return out_string.strip()
def lowerCamelCase__ (self : Dict , _UpperCAmelCase : List[Any] , _UpperCAmelCase : str=None ) -> List[int]:
"""simple docstring"""
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + [self.eos_token_id]
def lowerCamelCase__ (self : List[str] , _UpperCAmelCase : List[int] , _UpperCAmelCase : Optional[List[int]] = None , _UpperCAmelCase : bool = False ) -> List[int]:
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_UpperCAmelCase , token_ids_a=_UpperCAmelCase , already_has_special_tokens=_UpperCAmelCase )
lowercase__ = [1] * len(self.prefix_tokens )
lowercase__ = [1]
if token_ids_a is None:
return prefix_ones + ([0] * len(_UpperCAmelCase )) + suffix_ones
return prefix_ones + ([0] * len(_UpperCAmelCase )) + ([0] * len(_UpperCAmelCase )) + suffix_ones
def lowerCamelCase__ (self : List[str] ) -> Dict:
"""simple docstring"""
lowercase__ = self.encoder.copy()
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__(self : List[Any] ) -> Dict:
"""simple docstring"""
lowercase__ = self.__dict__.copy()
lowercase__ = None
return state
def __setstate__(self : Optional[Any] , _UpperCAmelCase : Dict ) -> None:
"""simple docstring"""
lowercase__ = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
lowercase__ = {}
lowercase__ = load_spm(self.spm_file , self.sp_model_kwargs )
def lowerCamelCase__ (self : Any , _UpperCAmelCase : str , _UpperCAmelCase : Optional[str] = None ) -> Tuple[str]:
"""simple docstring"""
lowercase__ = Path(_UpperCAmelCase )
assert save_dir.is_dir(), f'''{save_directory} should be a directory'''
lowercase__ = save_dir / (
(filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""vocab_file"""]
)
lowercase__ = save_dir / (
(filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""spm_file"""]
)
save_json(self.encoder , _UpperCAmelCase )
if os.path.abspath(self.spm_file ) != os.path.abspath(_UpperCAmelCase ) and os.path.isfile(self.spm_file ):
copyfile(self.spm_file , _UpperCAmelCase )
elif not os.path.isfile(self.spm_file ):
with open(_UpperCAmelCase , """wb""" ) as fi:
lowercase__ = self.sp_model.serialized_model_proto()
fi.write(_UpperCAmelCase )
return (str(_UpperCAmelCase ), str(_UpperCAmelCase ))
def UpperCamelCase ( __magic_name__ : str , __magic_name__ : Dict[str, Any] ) -> sentencepiece.SentencePieceProcessor:
"""simple docstring"""
lowercase__ = sentencepiece.SentencePieceProcessor(**__magic_name__ )
spm.Load(str(__magic_name__ ) )
return spm
def UpperCamelCase ( __magic_name__ : str ) -> Union[Dict, List]:
"""simple docstring"""
with open(__magic_name__ , """r""" ) as f:
return json.load(__magic_name__ )
def UpperCamelCase ( __magic_name__ : List[str] , __magic_name__ : str ) -> None:
"""simple docstring"""
with open(__magic_name__ , """w""" ) as f:
json.dump(__magic_name__ , __magic_name__ , indent=2 )
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|
import unittest
import numpy as np
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transformers.models.bert.modeling_flax_bert import (
FlaxBertForMaskedLM,
FlaxBertForMultipleChoice,
FlaxBertForNextSentencePrediction,
FlaxBertForPreTraining,
FlaxBertForQuestionAnswering,
FlaxBertForSequenceClassification,
FlaxBertForTokenClassification,
FlaxBertModel,
)
class A ( unittest.TestCase ):
'''simple docstring'''
def __init__(self : Optional[Any] , _UpperCAmelCase : Tuple , _UpperCAmelCase : str=13 , _UpperCAmelCase : List[str]=7 , _UpperCAmelCase : Union[str, Any]=True , _UpperCAmelCase : Dict=True , _UpperCAmelCase : str=True , _UpperCAmelCase : str=True , _UpperCAmelCase : Dict=99 , _UpperCAmelCase : Any=32 , _UpperCAmelCase : List[str]=5 , _UpperCAmelCase : Union[str, Any]=4 , _UpperCAmelCase : str=37 , _UpperCAmelCase : Union[str, Any]="gelu" , _UpperCAmelCase : Any=0.1 , _UpperCAmelCase : int=0.1 , _UpperCAmelCase : Dict=512 , _UpperCAmelCase : Optional[int]=16 , _UpperCAmelCase : str=2 , _UpperCAmelCase : List[Any]=0.02 , _UpperCAmelCase : List[str]=4 , ) -> List[Any]:
"""simple docstring"""
lowercase__ = parent
lowercase__ = batch_size
lowercase__ = seq_length
lowercase__ = is_training
lowercase__ = use_attention_mask
lowercase__ = use_token_type_ids
lowercase__ = use_labels
lowercase__ = vocab_size
lowercase__ = hidden_size
lowercase__ = num_hidden_layers
lowercase__ = num_attention_heads
lowercase__ = intermediate_size
lowercase__ = hidden_act
lowercase__ = hidden_dropout_prob
lowercase__ = attention_probs_dropout_prob
lowercase__ = max_position_embeddings
lowercase__ = type_vocab_size
lowercase__ = type_sequence_label_size
lowercase__ = initializer_range
lowercase__ = num_choices
def lowerCamelCase__ (self : List[str] ) -> Dict:
"""simple docstring"""
lowercase__ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase__ = None
if self.use_attention_mask:
lowercase__ = random_attention_mask([self.batch_size, self.seq_length] )
lowercase__ = None
if self.use_token_type_ids:
lowercase__ = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowercase__ = 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=_UpperCAmelCase , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def lowerCamelCase__ (self : int ) -> Any:
"""simple docstring"""
lowercase__ = self.prepare_config_and_inputs()
lowercase__ , lowercase__ , lowercase__ , lowercase__ = config_and_inputs
lowercase__ = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
return config, inputs_dict
def lowerCamelCase__ (self : Tuple ) -> str:
"""simple docstring"""
lowercase__ = self.prepare_config_and_inputs()
lowercase__ , lowercase__ , lowercase__ , lowercase__ = config_and_inputs
lowercase__ = True
lowercase__ = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
lowercase__ = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
attention_mask,
encoder_hidden_states,
encoder_attention_mask,
)
@require_flax
class A ( UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
A__ = True
A__ = (
(
FlaxBertModel,
FlaxBertForPreTraining,
FlaxBertForMaskedLM,
FlaxBertForMultipleChoice,
FlaxBertForQuestionAnswering,
FlaxBertForNextSentencePrediction,
FlaxBertForSequenceClassification,
FlaxBertForTokenClassification,
FlaxBertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def lowerCamelCase__ (self : Optional[int] ) -> List[str]:
"""simple docstring"""
lowercase__ = FlaxBertModelTester(self )
@slow
def lowerCamelCase__ (self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = FlaxBertModel.from_pretrained("""bert-base-cased""" )
lowercase__ = model(np.ones((1, 1) ) )
self.assertIsNotNone(_UpperCAmelCase )
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|
def UpperCamelCase ( __magic_name__ : Dict , __magic_name__ : Dict ) -> Any:
"""simple docstring"""
lowercase__ = [0 for i in range(r + 1 )]
# nc0 = 1
lowercase__ = 1
for i in range(1 , n + 1 ):
# to compute current row from previous row.
lowercase__ = min(__magic_name__ , __magic_name__ )
while j > 0:
c[j] += c[j - 1]
j -= 1
return c[r]
print(binomial_coefficient(n=1_0, r=5))
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def UpperCamelCase ( __magic_name__ : str ) -> list:
"""simple docstring"""
if n_term == "":
return []
lowercase__ = []
for temp in range(int(__magic_name__ ) ):
series.append(f'''1/{temp + 1}''' if series else """1""" )
return series
if __name__ == "__main__":
A : Tuple = input('Enter the last number (nth term) of the Harmonic Series')
print('Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n')
print(harmonic_series(nth_term))
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| 1
|
def UpperCamelCase ( __magic_name__ : int ) -> int:
"""simple docstring"""
if not isinstance(__magic_name__ , __magic_name__ ):
raise TypeError("""Input value must be an 'int' type""" )
lowercase__ = 0
while number:
position += 1
number >>= 1
return position
if __name__ == "__main__":
import doctest
doctest.testmod()
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|
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class A ( UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
A__ = ShapEImgaImgPipeline
A__ = ['''image''']
A__ = ['''image''']
A__ = [
'''num_images_per_prompt''',
'''num_inference_steps''',
'''generator''',
'''latents''',
'''guidance_scale''',
'''frame_size''',
'''output_type''',
'''return_dict''',
]
A__ = False
@property
def lowerCamelCase__ (self : Optional[Any] ) -> List[str]:
"""simple docstring"""
return 32
@property
def lowerCamelCase__ (self : str ) -> Any:
"""simple docstring"""
return 32
@property
def lowerCamelCase__ (self : str ) -> List[str]:
"""simple docstring"""
return self.time_input_dim * 4
@property
def lowerCamelCase__ (self : List[Any] ) -> Any:
"""simple docstring"""
return 8
@property
def lowerCamelCase__ (self : int ) -> List[str]:
"""simple docstring"""
torch.manual_seed(0 )
lowercase__ = CLIPVisionConfig(
hidden_size=self.text_embedder_hidden_size , image_size=64 , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_channels=3 , num_hidden_layers=5 , patch_size=1 , )
lowercase__ = CLIPVisionModel(_UpperCAmelCase )
return model
@property
def lowerCamelCase__ (self : Any ) -> List[Any]:
"""simple docstring"""
lowercase__ = CLIPImageProcessor(
crop_size=224 , do_center_crop=_UpperCAmelCase , do_normalize=_UpperCAmelCase , do_resize=_UpperCAmelCase , image_mean=[0.48_145_466, 0.4_578_275, 0.40_821_073] , image_std=[0.26_862_954, 0.26_130_258, 0.27_577_711] , resample=3 , size=224 , )
return image_processor
@property
def lowerCamelCase__ (self : int ) -> Optional[Any]:
"""simple docstring"""
torch.manual_seed(0 )
lowercase__ = {
"""num_attention_heads""": 2,
"""attention_head_dim""": 16,
"""embedding_dim""": self.time_input_dim,
"""num_embeddings""": 32,
"""embedding_proj_dim""": self.text_embedder_hidden_size,
"""time_embed_dim""": self.time_embed_dim,
"""num_layers""": 1,
"""clip_embed_dim""": self.time_input_dim * 2,
"""additional_embeddings""": 0,
"""time_embed_act_fn""": """gelu""",
"""norm_in_type""": """layer""",
"""embedding_proj_norm_type""": """layer""",
"""encoder_hid_proj_type""": None,
"""added_emb_type""": None,
}
lowercase__ = PriorTransformer(**_UpperCAmelCase )
return model
@property
def lowerCamelCase__ (self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
torch.manual_seed(0 )
lowercase__ = {
"""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,
),
}
lowercase__ = ShapERenderer(**_UpperCAmelCase )
return model
def lowerCamelCase__ (self : int ) -> Optional[int]:
"""simple docstring"""
lowercase__ = self.dummy_prior
lowercase__ = self.dummy_image_encoder
lowercase__ = self.dummy_image_processor
lowercase__ = self.dummy_renderer
lowercase__ = HeunDiscreteScheduler(
beta_schedule="""exp""" , num_train_timesteps=1024 , prediction_type="""sample""" , use_karras_sigmas=_UpperCAmelCase , clip_sample=_UpperCAmelCase , clip_sample_range=1.0 , )
lowercase__ = {
"""prior""": prior,
"""image_encoder""": image_encoder,
"""image_processor""": image_processor,
"""renderer""": renderer,
"""scheduler""": scheduler,
}
return components
def lowerCamelCase__ (self : Dict , _UpperCAmelCase : List[Any] , _UpperCAmelCase : str=0 ) -> str:
"""simple docstring"""
lowercase__ = floats_tensor((1, 3, 64, 64) , rng=random.Random(_UpperCAmelCase ) ).to(_UpperCAmelCase )
if str(_UpperCAmelCase ).startswith("""mps""" ):
lowercase__ = torch.manual_seed(_UpperCAmelCase )
else:
lowercase__ = torch.Generator(device=_UpperCAmelCase ).manual_seed(_UpperCAmelCase )
lowercase__ = {
"""image""": input_image,
"""generator""": generator,
"""num_inference_steps""": 1,
"""frame_size""": 32,
"""output_type""": """np""",
}
return inputs
def lowerCamelCase__ (self : str ) -> List[str]:
"""simple docstring"""
lowercase__ = """cpu"""
lowercase__ = self.get_dummy_components()
lowercase__ = self.pipeline_class(**_UpperCAmelCase )
lowercase__ = pipe.to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
lowercase__ = pipe(**self.get_dummy_inputs(_UpperCAmelCase ) )
lowercase__ = output.images[0]
lowercase__ = image[0, -3:, -3:, -1]
assert image.shape == (20, 32, 32, 3)
lowercase__ = np.array(
[
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def lowerCamelCase__ (self : str ) -> Any:
"""simple docstring"""
self._test_inference_batch_consistent(batch_sizes=[1, 2] )
def lowerCamelCase__ (self : Optional[int] ) -> str:
"""simple docstring"""
lowercase__ = torch_device == """cpu"""
lowercase__ = True
self._test_inference_batch_single_identical(
batch_size=2 , test_max_difference=_UpperCAmelCase , relax_max_difference=_UpperCAmelCase , )
def lowerCamelCase__ (self : Union[str, Any] ) -> int:
"""simple docstring"""
lowercase__ = self.get_dummy_components()
lowercase__ = self.pipeline_class(**_UpperCAmelCase )
lowercase__ = pipe.to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
lowercase__ = 1
lowercase__ = 2
lowercase__ = self.get_dummy_inputs(_UpperCAmelCase )
for key in inputs.keys():
if key in self.batch_params:
lowercase__ = batch_size * [inputs[key]]
lowercase__ = pipe(**_UpperCAmelCase , num_images_per_prompt=_UpperCAmelCase )[0]
assert images.shape[0] == batch_size * num_images_per_prompt
@slow
@require_torch_gpu
class A ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase__ (self : Dict ) -> List[Any]:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase__ (self : Any ) -> str:
"""simple docstring"""
lowercase__ = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/shap_e/corgi.png""" )
lowercase__ = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/shap_e/test_shap_e_img2img_out.npy""" )
lowercase__ = ShapEImgaImgPipeline.from_pretrained("""openai/shap-e-img2img""" )
lowercase__ = pipe.to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
lowercase__ = torch.Generator(device=_UpperCAmelCase ).manual_seed(0 )
lowercase__ = pipe(
_UpperCAmelCase , generator=_UpperCAmelCase , guidance_scale=3.0 , num_inference_steps=64 , frame_size=64 , output_type="""np""" , ).images[0]
assert images.shape == (20, 64, 64, 3)
assert_mean_pixel_difference(_UpperCAmelCase , _UpperCAmelCase )
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|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : Dict = logging.get_logger(__name__)
A : str = {
'weiweishi/roc-bert-base-zh': 'https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json',
}
class A ( UpperCAmelCase__ ):
'''simple docstring'''
A__ = '''roc_bert'''
def __init__(self : int , _UpperCAmelCase : List[str]=3_0522 , _UpperCAmelCase : Optional[int]=768 , _UpperCAmelCase : Optional[Any]=12 , _UpperCAmelCase : int=12 , _UpperCAmelCase : Union[str, Any]=3072 , _UpperCAmelCase : Dict="gelu" , _UpperCAmelCase : int=0.1 , _UpperCAmelCase : Any=0.1 , _UpperCAmelCase : Optional[Any]=512 , _UpperCAmelCase : int=2 , _UpperCAmelCase : Optional[int]=0.02 , _UpperCAmelCase : List[Any]=1E-1_2 , _UpperCAmelCase : Tuple=True , _UpperCAmelCase : int=0 , _UpperCAmelCase : Optional[int]="absolute" , _UpperCAmelCase : Optional[Any]=None , _UpperCAmelCase : List[str]=True , _UpperCAmelCase : List[Any]=True , _UpperCAmelCase : str=768 , _UpperCAmelCase : Dict=910 , _UpperCAmelCase : Optional[int]=512 , _UpperCAmelCase : Optional[int]=2_4858 , _UpperCAmelCase : List[Any]=True , **_UpperCAmelCase : List[str] , ) -> str:
"""simple docstring"""
lowercase__ = vocab_size
lowercase__ = max_position_embeddings
lowercase__ = hidden_size
lowercase__ = num_hidden_layers
lowercase__ = num_attention_heads
lowercase__ = intermediate_size
lowercase__ = hidden_act
lowercase__ = hidden_dropout_prob
lowercase__ = attention_probs_dropout_prob
lowercase__ = initializer_range
lowercase__ = type_vocab_size
lowercase__ = layer_norm_eps
lowercase__ = use_cache
lowercase__ = enable_pronunciation
lowercase__ = enable_shape
lowercase__ = pronunciation_embed_dim
lowercase__ = pronunciation_vocab_size
lowercase__ = shape_embed_dim
lowercase__ = shape_vocab_size
lowercase__ = concat_input
lowercase__ = position_embedding_type
lowercase__ = classifier_dropout
super().__init__(pad_token_id=_UpperCAmelCase , **_UpperCAmelCase )
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import requests
from bsa import BeautifulSoup
def UpperCamelCase ( __magic_name__ : str = "AAPL" ) -> str:
"""simple docstring"""
lowercase__ = f'''https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'''
lowercase__ = BeautifulSoup(requests.get(__magic_name__ ).text , """html.parser""" )
lowercase__ = """My(6px) Pos(r) smartphone_Mt(6px)"""
return soup.find("""div""" , class_=class_ ).find("""span""" ).text
if __name__ == "__main__":
for symbol in "AAPL AMZN IBM GOOG MSFT ORCL".split():
print(F'Current {symbol:<4} stock price is {stock_price(symbol):>8}')
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|
from math import pi, sqrt, tan
def UpperCamelCase ( __magic_name__ : float ) -> float:
"""simple docstring"""
if side_length < 0:
raise ValueError("""surface_area_cube() only accepts non-negative values""" )
return 6 * side_length**2
def UpperCamelCase ( __magic_name__ : float , __magic_name__ : float , __magic_name__ : float ) -> float:
"""simple docstring"""
if length < 0 or breadth < 0 or height < 0:
raise ValueError("""surface_area_cuboid() only accepts non-negative values""" )
return 2 * ((length * breadth) + (breadth * height) + (length * height))
def UpperCamelCase ( __magic_name__ : float ) -> float:
"""simple docstring"""
if radius < 0:
raise ValueError("""surface_area_sphere() only accepts non-negative values""" )
return 4 * pi * radius**2
def UpperCamelCase ( __magic_name__ : float ) -> float:
"""simple docstring"""
if radius < 0:
raise ValueError("""surface_area_hemisphere() only accepts non-negative values""" )
return 3 * pi * radius**2
def UpperCamelCase ( __magic_name__ : float , __magic_name__ : float ) -> float:
"""simple docstring"""
if radius < 0 or height < 0:
raise ValueError("""surface_area_cone() only accepts non-negative values""" )
return pi * radius * (radius + (height**2 + radius**2) ** 0.5)
def UpperCamelCase ( __magic_name__ : float , __magic_name__ : float , __magic_name__ : float ) -> float:
"""simple docstring"""
if radius_a < 0 or radius_a < 0 or height < 0:
raise ValueError(
"""surface_area_conical_frustum() only accepts non-negative values""" )
lowercase__ = (height**2 + (radius_a - radius_a) ** 2) ** 0.5
return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)
def UpperCamelCase ( __magic_name__ : float , __magic_name__ : float ) -> float:
"""simple docstring"""
if radius < 0 or height < 0:
raise ValueError("""surface_area_cylinder() only accepts non-negative values""" )
return 2 * pi * radius * (height + radius)
def UpperCamelCase ( __magic_name__ : float , __magic_name__ : float ) -> float:
"""simple docstring"""
if torus_radius < 0 or tube_radius < 0:
raise ValueError("""surface_area_torus() only accepts non-negative values""" )
if torus_radius < tube_radius:
raise ValueError(
"""surface_area_torus() does not support spindle or self intersecting tori""" )
return 4 * pow(__magic_name__ , 2 ) * torus_radius * tube_radius
def UpperCamelCase ( __magic_name__ : float , __magic_name__ : float ) -> float:
"""simple docstring"""
if length < 0 or width < 0:
raise ValueError("""area_rectangle() only accepts non-negative values""" )
return length * width
def UpperCamelCase ( __magic_name__ : float ) -> float:
"""simple docstring"""
if side_length < 0:
raise ValueError("""area_square() only accepts non-negative values""" )
return side_length**2
def UpperCamelCase ( __magic_name__ : float , __magic_name__ : float ) -> float:
"""simple docstring"""
if base < 0 or height < 0:
raise ValueError("""area_triangle() only accepts non-negative values""" )
return (base * height) / 2
def UpperCamelCase ( __magic_name__ : float , __magic_name__ : float , __magic_name__ : float ) -> float:
"""simple docstring"""
if sidea < 0 or sidea < 0 or sidea < 0:
raise ValueError("""area_triangle_three_sides() only accepts non-negative values""" )
elif sidea + sidea < sidea or sidea + sidea < sidea or sidea + sidea < sidea:
raise ValueError("""Given three sides do not form a triangle""" )
lowercase__ = (sidea + sidea + sidea) / 2
lowercase__ = sqrt(
semi_perimeter
* (semi_perimeter - sidea)
* (semi_perimeter - sidea)
* (semi_perimeter - sidea) )
return area
def UpperCamelCase ( __magic_name__ : float , __magic_name__ : float ) -> float:
"""simple docstring"""
if base < 0 or height < 0:
raise ValueError("""area_parallelogram() only accepts non-negative values""" )
return base * height
def UpperCamelCase ( __magic_name__ : float , __magic_name__ : float , __magic_name__ : float ) -> float:
"""simple docstring"""
if basea < 0 or basea < 0 or height < 0:
raise ValueError("""area_trapezium() only accepts non-negative values""" )
return 1 / 2 * (basea + basea) * height
def UpperCamelCase ( __magic_name__ : float ) -> float:
"""simple docstring"""
if radius < 0:
raise ValueError("""area_circle() only accepts non-negative values""" )
return pi * radius**2
def UpperCamelCase ( __magic_name__ : float , __magic_name__ : float ) -> float:
"""simple docstring"""
if radius_x < 0 or radius_y < 0:
raise ValueError("""area_ellipse() only accepts non-negative values""" )
return pi * radius_x * radius_y
def UpperCamelCase ( __magic_name__ : float , __magic_name__ : float ) -> float:
"""simple docstring"""
if diagonal_a < 0 or diagonal_a < 0:
raise ValueError("""area_rhombus() only accepts non-negative values""" )
return 1 / 2 * diagonal_a * diagonal_a
def UpperCamelCase ( __magic_name__ : int , __magic_name__ : float ) -> float:
"""simple docstring"""
if not isinstance(__magic_name__ , __magic_name__ ) or sides < 3:
raise ValueError(
"""area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides""" )
elif length < 0:
raise ValueError(
"""area_reg_polygon() only accepts non-negative values as \
length of a side""" )
return (sides * length**2) / (4 * tan(pi / sides ))
return (sides * length**2) / (4 * tan(pi / sides ))
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True) # verbose so we can see methods missing tests
print('[DEMO] Areas of various geometric shapes: \n')
print(F'Rectangle: {area_rectangle(1_0, 2_0) = }')
print(F'Square: {area_square(1_0) = }')
print(F'Triangle: {area_triangle(1_0, 1_0) = }')
print(F'Triangle: {area_triangle_three_sides(5, 1_2, 1_3) = }')
print(F'Parallelogram: {area_parallelogram(1_0, 2_0) = }')
print(F'Rhombus: {area_rhombus(1_0, 2_0) = }')
print(F'Trapezium: {area_trapezium(1_0, 2_0, 3_0) = }')
print(F'Circle: {area_circle(2_0) = }')
print(F'Ellipse: {area_ellipse(1_0, 2_0) = }')
print('\nSurface Areas of various geometric shapes: \n')
print(F'Cube: {surface_area_cube(2_0) = }')
print(F'Cuboid: {surface_area_cuboid(1_0, 2_0, 3_0) = }')
print(F'Sphere: {surface_area_sphere(2_0) = }')
print(F'Hemisphere: {surface_area_hemisphere(2_0) = }')
print(F'Cone: {surface_area_cone(1_0, 2_0) = }')
print(F'Conical Frustum: {surface_area_conical_frustum(1_0, 2_0, 3_0) = }')
print(F'Cylinder: {surface_area_cylinder(1_0, 2_0) = }')
print(F'Torus: {surface_area_torus(2_0, 1_0) = }')
print(F'Equilateral Triangle: {area_reg_polygon(3, 1_0) = }')
print(F'Square: {area_reg_polygon(4, 1_0) = }')
print(F'Reqular Pentagon: {area_reg_polygon(5, 1_0) = }')
| 305
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : List[str] = logging.get_logger(__name__)
A : Any = {
'tiiuae/falcon-40b': 'https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json',
'tiiuae/falcon-7b': 'https://huggingface.co/tiiuae/falcon-7b/resolve/main/config.json',
}
class A ( UpperCAmelCase__ ):
'''simple docstring'''
A__ = '''falcon'''
A__ = ['''past_key_values''']
def __init__(self : str , _UpperCAmelCase : Dict=6_5024 , _UpperCAmelCase : Optional[Any]=4544 , _UpperCAmelCase : Optional[int]=32 , _UpperCAmelCase : Optional[Any]=71 , _UpperCAmelCase : List[Any]=1E-5 , _UpperCAmelCase : int=0.02 , _UpperCAmelCase : str=True , _UpperCAmelCase : Tuple=0.0 , _UpperCAmelCase : Any=0.0 , _UpperCAmelCase : str=None , _UpperCAmelCase : Optional[int]=False , _UpperCAmelCase : int=False , _UpperCAmelCase : Union[str, Any]=True , _UpperCAmelCase : List[Any]=True , _UpperCAmelCase : List[Any]=False , _UpperCAmelCase : Optional[int]=11 , _UpperCAmelCase : Optional[Any]=11 , **_UpperCAmelCase : Union[str, Any] , ) -> List[str]:
"""simple docstring"""
lowercase__ = vocab_size
# Backward compatibility with n_embed kwarg
lowercase__ = kwargs.pop("""n_embed""" , _UpperCAmelCase )
lowercase__ = hidden_size if n_embed is None else n_embed
lowercase__ = num_hidden_layers
lowercase__ = num_attention_heads
lowercase__ = layer_norm_epsilon
lowercase__ = initializer_range
lowercase__ = use_cache
lowercase__ = hidden_dropout
lowercase__ = attention_dropout
lowercase__ = bos_token_id
lowercase__ = eos_token_id
lowercase__ = num_attention_heads if num_kv_heads is None else num_kv_heads
lowercase__ = alibi
lowercase__ = new_decoder_architecture
lowercase__ = multi_query # Ignored when new_decoder_architecture is True
lowercase__ = parallel_attn
lowercase__ = bias
super().__init__(bos_token_id=_UpperCAmelCase , eos_token_id=_UpperCAmelCase , **_UpperCAmelCase )
@property
def lowerCamelCase__ (self : Tuple ) -> int:
"""simple docstring"""
return self.hidden_size // self.num_attention_heads
@property
def lowerCamelCase__ (self : List[str] ) -> Tuple:
"""simple docstring"""
return not self.alibi
| 305
| 1
|
import argparse
import torch
from transformers import (
UniSpeechSatConfig,
UniSpeechSatForAudioFrameClassification,
UniSpeechSatForSequenceClassification,
UniSpeechSatForXVector,
WavaVecaFeatureExtractor,
logging,
)
logging.set_verbosity_info()
A : List[str] = logging.get_logger(__name__)
def UpperCamelCase ( __magic_name__ : Tuple , __magic_name__ : str , __magic_name__ : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = UniSpeechSatForSequenceClassification.from_pretrained(__magic_name__ , config=__magic_name__ )
lowercase__ = downstream_dict["""projector.weight"""]
lowercase__ = downstream_dict["""projector.bias"""]
lowercase__ = downstream_dict["""model.post_net.linear.weight"""]
lowercase__ = downstream_dict["""model.post_net.linear.bias"""]
return model
def UpperCamelCase ( __magic_name__ : Optional[int] , __magic_name__ : Dict , __magic_name__ : Tuple ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = UniSpeechSatForAudioFrameClassification.from_pretrained(__magic_name__ , config=__magic_name__ )
lowercase__ = downstream_dict["""model.linear.weight"""]
lowercase__ = downstream_dict["""model.linear.bias"""]
return model
def UpperCamelCase ( __magic_name__ : int , __magic_name__ : List[Any] , __magic_name__ : List[Any] ) -> List[str]:
"""simple docstring"""
lowercase__ = UniSpeechSatForXVector.from_pretrained(__magic_name__ , config=__magic_name__ )
lowercase__ = downstream_dict["""connector.weight"""]
lowercase__ = downstream_dict["""connector.bias"""]
for i, kernel_size in enumerate(hf_config.tdnn_kernel ):
lowercase__ = downstream_dict[
f'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
]
lowercase__ = downstream_dict[f'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']
lowercase__ = downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
lowercase__ = downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
lowercase__ = downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
lowercase__ = downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
lowercase__ = downstream_dict["""objective.W"""]
return model
@torch.no_grad()
def UpperCamelCase ( __magic_name__ : Optional[int] , __magic_name__ : Any , __magic_name__ : Optional[int] , __magic_name__ : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = torch.load(__magic_name__ , map_location="""cpu""" )
lowercase__ = checkpoint["""Downstream"""]
lowercase__ = UniSpeechSatConfig.from_pretrained(__magic_name__ )
lowercase__ = WavaVecaFeatureExtractor.from_pretrained(
__magic_name__ , return_attention_mask=__magic_name__ , do_normalize=__magic_name__ )
lowercase__ = hf_config.architectures[0]
if arch.endswith("""ForSequenceClassification""" ):
lowercase__ = convert_classification(__magic_name__ , __magic_name__ , __magic_name__ )
elif arch.endswith("""ForAudioFrameClassification""" ):
lowercase__ = convert_diarization(__magic_name__ , __magic_name__ , __magic_name__ )
elif arch.endswith("""ForXVector""" ):
lowercase__ = convert_xvector(__magic_name__ , __magic_name__ , __magic_name__ )
else:
raise NotImplementedError(f'''S3PRL weights conversion is not supported for {arch}''' )
if hf_config.use_weighted_layer_sum:
lowercase__ = checkpoint["""Featurizer"""]["""weights"""]
hf_feature_extractor.save_pretrained(__magic_name__ )
hf_model.save_pretrained(__magic_name__ )
if __name__ == "__main__":
A : Union[str, Any] = argparse.ArgumentParser()
parser.add_argument(
'--base_model_name', default=None, type=str, help='Name of the huggingface pretrained base model.'
)
parser.add_argument('--config_path', default=None, type=str, help='Path to the huggingface classifier config.')
parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to the s3prl checkpoint.')
parser.add_argument('--model_dump_path', default=None, type=str, help='Path to the final converted model.')
A : int = parser.parse_args()
convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
| 305
|
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AlignProcessor, EfficientNetImageProcessor
@require_vision
class A ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase__ (self : Union[str, Any] ) -> Any:
"""simple docstring"""
lowercase__ = tempfile.mkdtemp()
lowercase__ = [
"""[UNK]""",
"""[CLS]""",
"""[SEP]""",
"""[PAD]""",
"""[MASK]""",
"""want""",
"""##want""",
"""##ed""",
"""wa""",
"""un""",
"""runn""",
"""##ing""",
""",""",
"""low""",
"""lowest""",
]
lowercase__ = 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] ) )
lowercase__ = {
"""do_resize""": True,
"""size""": 20,
"""do_center_crop""": True,
"""crop_size""": 18,
"""do_normalize""": True,
"""image_mean""": [0.48_145_466, 0.4_578_275, 0.40_821_073],
"""image_std""": [0.26_862_954, 0.26_130_258, 0.27_577_711],
}
lowercase__ = os.path.join(self.tmpdirname , _UpperCAmelCase )
with open(self.image_processor_file , """w""" , encoding="""utf-8""" ) as fp:
json.dump(_UpperCAmelCase , _UpperCAmelCase )
def lowerCamelCase__ (self : Dict , **_UpperCAmelCase : Any ) -> Optional[Any]:
"""simple docstring"""
return BertTokenizer.from_pretrained(self.tmpdirname , **_UpperCAmelCase )
def lowerCamelCase__ (self : Union[str, Any] , **_UpperCAmelCase : Any ) -> Dict:
"""simple docstring"""
return BertTokenizerFast.from_pretrained(self.tmpdirname , **_UpperCAmelCase )
def lowerCamelCase__ (self : Optional[int] , **_UpperCAmelCase : str ) -> Dict:
"""simple docstring"""
return EfficientNetImageProcessor.from_pretrained(self.tmpdirname , **_UpperCAmelCase )
def lowerCamelCase__ (self : Optional[int] ) -> List[str]:
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def lowerCamelCase__ (self : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
lowercase__ = [Image.fromarray(np.moveaxis(_UpperCAmelCase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def lowerCamelCase__ (self : Optional[int] ) -> Tuple:
"""simple docstring"""
lowercase__ = self.get_tokenizer()
lowercase__ = self.get_rust_tokenizer()
lowercase__ = self.get_image_processor()
lowercase__ = AlignProcessor(tokenizer=_UpperCAmelCase , image_processor=_UpperCAmelCase )
processor_slow.save_pretrained(self.tmpdirname )
lowercase__ = AlignProcessor.from_pretrained(self.tmpdirname , use_fast=_UpperCAmelCase )
lowercase__ = AlignProcessor(tokenizer=_UpperCAmelCase , image_processor=_UpperCAmelCase )
processor_fast.save_pretrained(self.tmpdirname )
lowercase__ = AlignProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer , _UpperCAmelCase )
self.assertIsInstance(processor_fast.tokenizer , _UpperCAmelCase )
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor , _UpperCAmelCase )
self.assertIsInstance(processor_fast.image_processor , _UpperCAmelCase )
def lowerCamelCase__ (self : Any ) -> List[str]:
"""simple docstring"""
lowercase__ = AlignProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
lowercase__ = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
lowercase__ = self.get_image_processor(do_normalize=_UpperCAmelCase , padding_value=1.0 )
lowercase__ = AlignProcessor.from_pretrained(
self.tmpdirname , bos_token="""(BOS)""" , eos_token="""(EOS)""" , do_normalize=_UpperCAmelCase , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , _UpperCAmelCase )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , _UpperCAmelCase )
def lowerCamelCase__ (self : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = self.get_image_processor()
lowercase__ = self.get_tokenizer()
lowercase__ = AlignProcessor(tokenizer=_UpperCAmelCase , image_processor=_UpperCAmelCase )
lowercase__ = self.prepare_image_inputs()
lowercase__ = image_processor(_UpperCAmelCase , return_tensors="""np""" )
lowercase__ = processor(images=_UpperCAmelCase , return_tensors="""np""" )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1E-2 )
def lowerCamelCase__ (self : Dict ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = self.get_image_processor()
lowercase__ = self.get_tokenizer()
lowercase__ = AlignProcessor(tokenizer=_UpperCAmelCase , image_processor=_UpperCAmelCase )
lowercase__ = """lower newer"""
lowercase__ = processor(text=_UpperCAmelCase )
lowercase__ = tokenizer(_UpperCAmelCase , padding="""max_length""" , max_length=64 )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def lowerCamelCase__ (self : List[Any] ) -> Tuple:
"""simple docstring"""
lowercase__ = self.get_image_processor()
lowercase__ = self.get_tokenizer()
lowercase__ = AlignProcessor(tokenizer=_UpperCAmelCase , image_processor=_UpperCAmelCase )
lowercase__ = """lower newer"""
lowercase__ = self.prepare_image_inputs()
lowercase__ = processor(text=_UpperCAmelCase , images=_UpperCAmelCase )
self.assertListEqual(list(inputs.keys() ) , ["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""] )
# test if it raises when no input is passed
with pytest.raises(_UpperCAmelCase ):
processor()
def lowerCamelCase__ (self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = self.get_image_processor()
lowercase__ = self.get_tokenizer()
lowercase__ = AlignProcessor(tokenizer=_UpperCAmelCase , image_processor=_UpperCAmelCase )
lowercase__ = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
lowercase__ = processor.batch_decode(_UpperCAmelCase )
lowercase__ = tokenizer.batch_decode(_UpperCAmelCase )
self.assertListEqual(_UpperCAmelCase , _UpperCAmelCase )
def lowerCamelCase__ (self : List[str] ) -> Tuple:
"""simple docstring"""
lowercase__ = self.get_image_processor()
lowercase__ = self.get_tokenizer()
lowercase__ = AlignProcessor(tokenizer=_UpperCAmelCase , image_processor=_UpperCAmelCase )
lowercase__ = """lower newer"""
lowercase__ = self.prepare_image_inputs()
lowercase__ = processor(text=_UpperCAmelCase , images=_UpperCAmelCase )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 305
| 1
|
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
A : List[str] = logging.getLogger(__name__)
@dataclass
@add_start_docstrings(TrainingArguments.__doc__ )
class A ( UpperCAmelCase__ ):
'''simple docstring'''
A__ = field(default=UpperCAmelCase__ , metadata={'''help''': '''Whether to use SortishSampler or not.'''} )
A__ = field(
default=UpperCAmelCase__ , metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''} )
A__ = field(
default=UpperCAmelCase__ , metadata={
'''help''': (
'''The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default '''
'''to the `max_length` value of the model configuration.'''
)
} , )
A__ = field(
default=UpperCAmelCase__ , metadata={
'''help''': (
'''The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default '''
'''to the `num_beams` value of the model configuration.'''
)
} , )
A__ = field(
default=UpperCAmelCase__ , metadata={
'''help''': '''Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'''
} , )
def lowerCamelCase__ (self : List[Any] ) -> int:
"""simple docstring"""
lowercase__ = super().to_dict()
for k, v in d.items():
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
lowercase__ = v.to_dict()
return d
| 305
|
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def UpperCamelCase ( __magic_name__ : List[Any] ) -> Optional[int]:
"""simple docstring"""
return x + 2
class A ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase__ (self : Optional[Any] ) -> Any:
"""simple docstring"""
lowercase__ = """x = 3"""
lowercase__ = {}
lowercase__ = evaluate(_UpperCAmelCase , {} , state=_UpperCAmelCase )
assert result == 3
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3} )
lowercase__ = """x = y"""
lowercase__ = {"""y""": 5}
lowercase__ = evaluate(_UpperCAmelCase , {} , state=_UpperCAmelCase )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(_UpperCAmelCase , {"""x""": 5, """y""": 5} )
def lowerCamelCase__ (self : str ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = """y = add_two(x)"""
lowercase__ = {"""x""": 3}
lowercase__ = evaluate(_UpperCAmelCase , {"""add_two""": add_two} , state=_UpperCAmelCase )
assert result == 5
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3, """y""": 5} )
# Won't work without the tool
with CaptureStdout() as out:
lowercase__ = evaluate(_UpperCAmelCase , {} , state=_UpperCAmelCase )
assert result is None
assert "tried to execute add_two" in out.out
def lowerCamelCase__ (self : List[Any] ) -> Optional[int]:
"""simple docstring"""
lowercase__ = """x = 3"""
lowercase__ = {}
lowercase__ = evaluate(_UpperCAmelCase , {} , state=_UpperCAmelCase )
assert result == 3
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3} )
def lowerCamelCase__ (self : Optional[int] ) -> List[Any]:
"""simple docstring"""
lowercase__ = """test_dict = {'x': x, 'y': add_two(x)}"""
lowercase__ = {"""x""": 3}
lowercase__ = evaluate(_UpperCAmelCase , {"""add_two""": add_two} , state=_UpperCAmelCase )
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3, """y""": 5} )
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3, """test_dict""": {"""x""": 3, """y""": 5}} )
def lowerCamelCase__ (self : List[str] ) -> List[Any]:
"""simple docstring"""
lowercase__ = """x = 3\ny = 5"""
lowercase__ = {}
lowercase__ = evaluate(_UpperCAmelCase , {} , state=_UpperCAmelCase )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3, """y""": 5} )
def lowerCamelCase__ (self : List[Any] ) -> Dict:
"""simple docstring"""
lowercase__ = """text = f'This is x: {x}.'"""
lowercase__ = {"""x""": 3}
lowercase__ = evaluate(_UpperCAmelCase , {} , state=_UpperCAmelCase )
# evaluate returns the value of the last assignment.
assert result == "This is x: 3."
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3, """text""": """This is x: 3."""} )
def lowerCamelCase__ (self : List[str] ) -> int:
"""simple docstring"""
lowercase__ = """if x <= 3:\n y = 2\nelse:\n y = 5"""
lowercase__ = {"""x""": 3}
lowercase__ = evaluate(_UpperCAmelCase , {} , state=_UpperCAmelCase )
# evaluate returns the value of the last assignment.
assert result == 2
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3, """y""": 2} )
lowercase__ = {"""x""": 8}
lowercase__ = evaluate(_UpperCAmelCase , {} , state=_UpperCAmelCase )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(_UpperCAmelCase , {"""x""": 8, """y""": 5} )
def lowerCamelCase__ (self : Dict ) -> int:
"""simple docstring"""
lowercase__ = """test_list = [x, add_two(x)]"""
lowercase__ = {"""x""": 3}
lowercase__ = evaluate(_UpperCAmelCase , {"""add_two""": add_two} , state=_UpperCAmelCase )
self.assertListEqual(_UpperCAmelCase , [3, 5] )
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3, """test_list""": [3, 5]} )
def lowerCamelCase__ (self : Any ) -> int:
"""simple docstring"""
lowercase__ = """y = x"""
lowercase__ = {"""x""": 3}
lowercase__ = evaluate(_UpperCAmelCase , {} , state=_UpperCAmelCase )
assert result == 3
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3, """y""": 3} )
def lowerCamelCase__ (self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
lowercase__ = """test_list = [x, add_two(x)]\ntest_list[1]"""
lowercase__ = {"""x""": 3}
lowercase__ = evaluate(_UpperCAmelCase , {"""add_two""": add_two} , state=_UpperCAmelCase )
assert result == 5
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3, """test_list""": [3, 5]} )
lowercase__ = """test_dict = {'x': x, 'y': add_two(x)}\ntest_dict['y']"""
lowercase__ = {"""x""": 3}
lowercase__ = evaluate(_UpperCAmelCase , {"""add_two""": add_two} , state=_UpperCAmelCase )
assert result == 5
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3, """test_dict""": {"""x""": 3, """y""": 5}} )
def lowerCamelCase__ (self : Union[str, Any] ) -> Any:
"""simple docstring"""
lowercase__ = """x = 0\nfor i in range(3):\n x = i"""
lowercase__ = {}
lowercase__ = evaluate(_UpperCAmelCase , {"""range""": range} , state=_UpperCAmelCase )
assert result == 2
self.assertDictEqual(_UpperCAmelCase , {"""x""": 2, """i""": 2} )
| 305
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import faiss # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import requests # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn # noqa: F401 # Here to have a nice missing dependency error message early on
import tqdm # noqa: F401 # Here to have a nice missing dependency error message early on
from mauve import compute_mauve # From: mauve-text
import datasets
A : str = '\\n@inproceedings{pillutla-etal:mauve:neurips2021,\n title={MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers},\n author={Pillutla, Krishna and Swayamdipta, Swabha and Zellers, Rowan and Thickstun, John and Welleck, Sean and Choi, Yejin and Harchaoui, Zaid},\n booktitle = {NeurIPS},\n year = {2021}\n}\n\n'
A : Tuple = '\\nMAUVE is a library built on PyTorch and HuggingFace Transformers to measure the gap between neural text and human text with the eponymous MAUVE measure.\n\nMAUVE summarizes both Type I and Type II errors measured softly using Kullback–Leibler (KL) divergences.\n\nFor details, see the MAUVE paper: https://arxiv.org/abs/2102.01454 (Neurips, 2021).\n\nThis metrics is a wrapper around the official implementation of MAUVE:\nhttps://github.com/krishnap25/mauve\n'
A : List[Any] = '\nCalculates MAUVE scores between two lists of generated text and reference text.\nArgs:\n predictions: list of generated text to score. Each predictions\n should be a string with tokens separated by spaces.\n references: list of reference for each prediction. Each\n reference should be a string with tokens separated by spaces.\nOptional Args:\n num_buckets: the size of the histogram to quantize P and Q. Options: \'auto\' (default) or an integer\n pca_max_data: the number data points to use for PCA dimensionality reduction prior to clustering. If -1, use all the data. Default -1\n kmeans_explained_var: amount of variance of the data to keep in dimensionality reduction by PCA. Default 0.9\n kmeans_num_redo: number of times to redo k-means clustering (the best objective is kept). Default 5\n kmeans_max_iter: maximum number of k-means iterations. Default 500\n featurize_model_name: name of the model from which features are obtained. Default \'gpt2-large\' Use one of [\'gpt2\', \'gpt2-medium\', \'gpt2-large\', \'gpt2-xl\'].\n device_id: Device for featurization. Supply a GPU id (e.g. 0 or 3) to use GPU. If no GPU with this id is found, use CPU\n max_text_length: maximum number of tokens to consider. Default 1024\n divergence_curve_discretization_size: Number of points to consider on the divergence curve. Default 25\n mauve_scaling_factor: "c" from the paper. Default 5.\n verbose: If True (default), print running time updates\n seed: random seed to initialize k-means cluster assignments.\nReturns:\n mauve: MAUVE score, a number between 0 and 1. Larger values indicate that P and Q are closer,\n frontier_integral: Frontier Integral, a number between 0 and 1. Smaller values indicate that P and Q are closer,\n divergence_curve: a numpy.ndarray of shape (m, 2); plot it with matplotlib to view the divergence curve,\n p_hist: a discrete distribution, which is a quantized version of the text distribution p_text,\n q_hist: same as above, but with q_text.\nExamples:\n\n >>> # faiss segfaults in doctest for some reason, so the .compute call is not tested with doctest\n >>> import datasets\n >>> mauve = datasets.load_metric(\'mauve\')\n >>> predictions = ["hello there", "general kenobi"]\n >>> references = ["hello there", "general kenobi"]\n >>> out = mauve.compute(predictions=predictions, references=references) # doctest: +SKIP\n >>> print(out.mauve) # doctest: +SKIP\n 1.0\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class A ( datasets.Metric ):
'''simple docstring'''
def lowerCamelCase__ (self : Union[str, Any] ) -> Any:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , homepage="""https://github.com/krishnap25/mauve""" , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Value("""string""" , id="""sequence""" ),
"""references""": datasets.Value("""string""" , id="""sequence""" ),
} ) , codebase_urls=["""https://github.com/krishnap25/mauve"""] , reference_urls=[
"""https://arxiv.org/abs/2102.01454""",
"""https://github.com/krishnap25/mauve""",
] , )
def lowerCamelCase__ (self : Tuple , _UpperCAmelCase : List[str] , _UpperCAmelCase : Dict , _UpperCAmelCase : Any=None , _UpperCAmelCase : Optional[Any]=None , _UpperCAmelCase : Optional[int]=None , _UpperCAmelCase : List[str]=None , _UpperCAmelCase : Any="auto" , _UpperCAmelCase : Optional[int]=-1 , _UpperCAmelCase : Tuple=0.9 , _UpperCAmelCase : List[str]=5 , _UpperCAmelCase : Any=500 , _UpperCAmelCase : int="gpt2-large" , _UpperCAmelCase : Optional[Any]=-1 , _UpperCAmelCase : Any=1024 , _UpperCAmelCase : int=25 , _UpperCAmelCase : Dict=5 , _UpperCAmelCase : Optional[Any]=True , _UpperCAmelCase : Dict=25 , ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = compute_mauve(
p_text=_UpperCAmelCase , q_text=_UpperCAmelCase , p_features=_UpperCAmelCase , q_features=_UpperCAmelCase , p_tokens=_UpperCAmelCase , q_tokens=_UpperCAmelCase , num_buckets=_UpperCAmelCase , pca_max_data=_UpperCAmelCase , kmeans_explained_var=_UpperCAmelCase , kmeans_num_redo=_UpperCAmelCase , kmeans_max_iter=_UpperCAmelCase , featurize_model_name=_UpperCAmelCase , device_id=_UpperCAmelCase , max_text_length=_UpperCAmelCase , divergence_curve_discretization_size=_UpperCAmelCase , mauve_scaling_factor=_UpperCAmelCase , verbose=_UpperCAmelCase , seed=_UpperCAmelCase , )
return out
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|
class A :
'''simple docstring'''
def __init__(self : List[str] ) -> Tuple:
"""simple docstring"""
lowercase__ = 0
lowercase__ = 0
lowercase__ = {}
def lowerCamelCase__ (self : Dict , _UpperCAmelCase : Tuple ) -> Optional[int]:
"""simple docstring"""
if vertex not in self.adjacency:
lowercase__ = {}
self.num_vertices += 1
def lowerCamelCase__ (self : List[Any] , _UpperCAmelCase : List[Any] , _UpperCAmelCase : int , _UpperCAmelCase : List[str] ) -> Tuple:
"""simple docstring"""
self.add_vertex(_UpperCAmelCase )
self.add_vertex(_UpperCAmelCase )
if head == tail:
return
lowercase__ = weight
lowercase__ = weight
def lowerCamelCase__ (self : List[str] ) -> Optional[int]:
"""simple docstring"""
lowercase__ = self.get_edges()
for edge in edges:
lowercase__ , lowercase__ , lowercase__ = edge
edges.remove((tail, head, weight) )
for i in range(len(_UpperCAmelCase ) ):
lowercase__ = list(edges[i] )
edges.sort(key=lambda _UpperCAmelCase : e[2] )
for i in range(len(_UpperCAmelCase ) - 1 ):
if edges[i][2] >= edges[i + 1][2]:
lowercase__ = edges[i][2] + 1
for edge in edges:
lowercase__ , lowercase__ , lowercase__ = edge
lowercase__ = weight
lowercase__ = weight
def __str__(self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = """"""
for tail in self.adjacency:
for head in self.adjacency[tail]:
lowercase__ = self.adjacency[head][tail]
string += f'''{head} -> {tail} == {weight}\n'''
return string.rstrip("""\n""" )
def lowerCamelCase__ (self : Any ) -> str:
"""simple docstring"""
lowercase__ = []
for tail in self.adjacency:
for head in self.adjacency[tail]:
output.append((tail, head, self.adjacency[head][tail]) )
return output
def lowerCamelCase__ (self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
return self.adjacency.keys()
@staticmethod
def lowerCamelCase__ (_UpperCAmelCase : List[str]=None , _UpperCAmelCase : Any=None ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = Graph()
if vertices is None:
lowercase__ = []
if edges is None:
lowercase__ = []
for vertex in vertices:
g.add_vertex(_UpperCAmelCase )
for edge in edges:
g.add_edge(*_UpperCAmelCase )
return g
class A :
'''simple docstring'''
def __init__(self : Optional[Any] ) -> str:
"""simple docstring"""
lowercase__ = {}
lowercase__ = {}
def __len__(self : Optional[Any] ) -> Dict:
"""simple docstring"""
return len(self.parent )
def lowerCamelCase__ (self : str , _UpperCAmelCase : Dict ) -> Any:
"""simple docstring"""
if item in self.parent:
return self.find(_UpperCAmelCase )
lowercase__ = item
lowercase__ = 0
return item
def lowerCamelCase__ (self : List[str] , _UpperCAmelCase : Dict ) -> Any:
"""simple docstring"""
if item not in self.parent:
return self.make_set(_UpperCAmelCase )
if item != self.parent[item]:
lowercase__ = self.find(self.parent[item] )
return self.parent[item]
def lowerCamelCase__ (self : List[Any] , _UpperCAmelCase : Any , _UpperCAmelCase : List[Any] ) -> Optional[int]:
"""simple docstring"""
lowercase__ = self.find(_UpperCAmelCase )
lowercase__ = self.find(_UpperCAmelCase )
if roota == roota:
return roota
if self.rank[roota] > self.rank[roota]:
lowercase__ = roota
return roota
if self.rank[roota] < self.rank[roota]:
lowercase__ = roota
return roota
if self.rank[roota] == self.rank[roota]:
self.rank[roota] += 1
lowercase__ = roota
return roota
return None
@staticmethod
def lowerCamelCase__ (_UpperCAmelCase : str ) -> Optional[int]:
"""simple docstring"""
lowercase__ = graph.num_vertices
lowercase__ = Graph.UnionFind()
lowercase__ = []
while num_components > 1:
lowercase__ = {}
for vertex in graph.get_vertices():
lowercase__ = -1
lowercase__ = graph.get_edges()
for edge in edges:
lowercase__ , lowercase__ , lowercase__ = edge
edges.remove((tail, head, weight) )
for edge in edges:
lowercase__ , lowercase__ , lowercase__ = edge
lowercase__ = union_find.find(_UpperCAmelCase )
lowercase__ = union_find.find(_UpperCAmelCase )
if seta != seta:
if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
lowercase__ = [head, tail, weight]
if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
lowercase__ = [head, tail, weight]
for vertex in cheap_edge:
if cheap_edge[vertex] != -1:
lowercase__ , lowercase__ , lowercase__ = cheap_edge[vertex]
if union_find.find(_UpperCAmelCase ) != union_find.find(_UpperCAmelCase ):
union_find.union(_UpperCAmelCase , _UpperCAmelCase )
mst_edges.append(cheap_edge[vertex] )
lowercase__ = num_components - 1
lowercase__ = Graph.build(edges=_UpperCAmelCase )
return mst
| 305
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|
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 A ( UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
A__ = CpmAntTokenizer
A__ = False
def lowerCamelCase__ (self : Dict ) -> int:
"""simple docstring"""
super().setUp()
lowercase__ = [
"""<d>""",
"""</d>""",
"""<s>""",
"""</s>""",
"""</_>""",
"""<unk>""",
"""<pad>""",
"""</n>""",
"""我""",
"""是""",
"""C""",
"""P""",
"""M""",
"""A""",
"""n""",
"""t""",
]
lowercase__ = 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 lowerCamelCase__ (self : List[str] ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = CpmAntTokenizer.from_pretrained("""openbmb/cpm-ant-10b""" )
lowercase__ = """今天天气真好！"""
lowercase__ = ["""今天""", """天气""", """真""", """好""", """！"""]
lowercase__ = tokenizer.tokenize(_UpperCAmelCase )
self.assertListEqual(_UpperCAmelCase , _UpperCAmelCase )
lowercase__ = """今天天气真好！"""
lowercase__ = [tokenizer.bos_token] + tokens
lowercase__ = [6, 9802, 1_4962, 2082, 831, 244]
self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase ) , _UpperCAmelCase )
lowercase__ = tokenizer.decode(_UpperCAmelCase )
self.assertEqual(_UpperCAmelCase , _UpperCAmelCase )
| 305
|
def UpperCamelCase ( __magic_name__ : int , __magic_name__ : int ) -> int:
"""simple docstring"""
return int((input_a, input_a).count(1 ) != 0 )
def UpperCamelCase ( ) -> None:
"""simple docstring"""
assert or_gate(0 , 0 ) == 0
assert or_gate(0 , 1 ) == 1
assert or_gate(1 , 0 ) == 1
assert or_gate(1 , 1 ) == 1
if __name__ == "__main__":
print(or_gate(0, 1))
print(or_gate(1, 0))
print(or_gate(0, 0))
print(or_gate(1, 1))
| 305
| 1
|
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
A : Any = logging.get_logger(__name__)
A : List[Any] = {
'microsoft/table-transformer-detection': (
'https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json'
),
}
class A ( UpperCAmelCase__ ):
'''simple docstring'''
A__ = '''table-transformer'''
A__ = ['''past_key_values''']
A__ = {
'''hidden_size''': '''d_model''',
'''num_attention_heads''': '''encoder_attention_heads''',
}
def __init__(self : Optional[int] , _UpperCAmelCase : List[Any]=True , _UpperCAmelCase : Any=None , _UpperCAmelCase : int=3 , _UpperCAmelCase : str=100 , _UpperCAmelCase : List[Any]=6 , _UpperCAmelCase : Any=2048 , _UpperCAmelCase : Optional[Any]=8 , _UpperCAmelCase : Optional[Any]=6 , _UpperCAmelCase : Tuple=2048 , _UpperCAmelCase : Optional[int]=8 , _UpperCAmelCase : int=0.0 , _UpperCAmelCase : Dict=0.0 , _UpperCAmelCase : Any=True , _UpperCAmelCase : List[str]="relu" , _UpperCAmelCase : Tuple=256 , _UpperCAmelCase : List[str]=0.1 , _UpperCAmelCase : List[Any]=0.0 , _UpperCAmelCase : Any=0.0 , _UpperCAmelCase : int=0.02 , _UpperCAmelCase : Dict=1.0 , _UpperCAmelCase : Any=False , _UpperCAmelCase : str="sine" , _UpperCAmelCase : Optional[int]="resnet50" , _UpperCAmelCase : Optional[Any]=True , _UpperCAmelCase : Dict=False , _UpperCAmelCase : Optional[Any]=1 , _UpperCAmelCase : Tuple=5 , _UpperCAmelCase : Any=2 , _UpperCAmelCase : Optional[Any]=1 , _UpperCAmelCase : Union[str, Any]=1 , _UpperCAmelCase : Any=5 , _UpperCAmelCase : List[str]=2 , _UpperCAmelCase : Optional[Any]=0.1 , **_UpperCAmelCase : int , ) -> str:
"""simple docstring"""
if backbone_config is not None and use_timm_backbone:
raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`.""" )
if not use_timm_backbone:
if backbone_config is None:
logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.""" )
lowercase__ = CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""] )
elif isinstance(_UpperCAmelCase , _UpperCAmelCase ):
lowercase__ = backbone_config.get("""model_type""" )
lowercase__ = CONFIG_MAPPING[backbone_model_type]
lowercase__ = config_class.from_dict(_UpperCAmelCase )
# set timm attributes to None
lowercase__ , lowercase__ , lowercase__ = None, None, None
lowercase__ = use_timm_backbone
lowercase__ = backbone_config
lowercase__ = num_channels
lowercase__ = num_queries
lowercase__ = d_model
lowercase__ = encoder_ffn_dim
lowercase__ = encoder_layers
lowercase__ = encoder_attention_heads
lowercase__ = decoder_ffn_dim
lowercase__ = decoder_layers
lowercase__ = decoder_attention_heads
lowercase__ = dropout
lowercase__ = attention_dropout
lowercase__ = activation_dropout
lowercase__ = activation_function
lowercase__ = init_std
lowercase__ = init_xavier_std
lowercase__ = encoder_layerdrop
lowercase__ = decoder_layerdrop
lowercase__ = encoder_layers
lowercase__ = auxiliary_loss
lowercase__ = position_embedding_type
lowercase__ = backbone
lowercase__ = use_pretrained_backbone
lowercase__ = dilation
# Hungarian matcher
lowercase__ = class_cost
lowercase__ = bbox_cost
lowercase__ = giou_cost
# Loss coefficients
lowercase__ = mask_loss_coefficient
lowercase__ = dice_loss_coefficient
lowercase__ = bbox_loss_coefficient
lowercase__ = giou_loss_coefficient
lowercase__ = eos_coefficient
super().__init__(is_encoder_decoder=_UpperCAmelCase , **_UpperCAmelCase )
@property
def lowerCamelCase__ (self : Union[str, Any] ) -> int:
"""simple docstring"""
return self.encoder_attention_heads
@property
def lowerCamelCase__ (self : List[str] ) -> int:
"""simple docstring"""
return self.d_model
class A ( UpperCAmelCase__ ):
'''simple docstring'''
A__ = version.parse('''1.11''' )
@property
def lowerCamelCase__ (self : List[Any] ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
("""pixel_mask""", {0: """batch"""}),
] )
@property
def lowerCamelCase__ (self : int ) -> float:
"""simple docstring"""
return 1E-5
@property
def lowerCamelCase__ (self : Any ) -> int:
"""simple docstring"""
return 12
| 305
|
import argparse
from torch import nn
# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)
from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging
A : Any = logging.get_logger(__name__)
logging.set_verbosity_info()
def UpperCamelCase ( __magic_name__ : str , __magic_name__ : str ) -> List[str]:
"""simple docstring"""
if "xprophetnet" in prophetnet_checkpoint_path:
lowercase__ = XLMProphetNetForConditionalGenerationOld.from_pretrained(__magic_name__ )
lowercase__ , lowercase__ = XLMProphetNetForConditionalGeneration.from_pretrained(
__magic_name__ , output_loading_info=__magic_name__ )
else:
lowercase__ = ProphetNetForConditionalGenerationOld.from_pretrained(__magic_name__ )
lowercase__ , lowercase__ = ProphetNetForConditionalGeneration.from_pretrained(
__magic_name__ , output_loading_info=__magic_name__ )
lowercase__ = ["""key_proj""", """value_proj""", """query_proj"""]
lowercase__ = {
"""self_attn""": """ngram_self_attn""",
"""cross_attn""": """encoder_attn""",
"""cross_attn_layer_norm""": """encoder_attn_layer_norm""",
"""feed_forward_layer_norm""": """final_layer_norm""",
"""feed_forward""": """""",
"""intermediate""": """fc1""",
"""output""": """fc2""",
"""key_proj""": """k_proj""",
"""query_proj""": """q_proj""",
"""value_proj""": """v_proj""",
"""word_embeddings""": """embed_tokens""",
"""embeddings_layer_norm""": """emb_layer_norm""",
"""relative_pos_embeddings""": """relative_linear""",
"""ngram_embeddings""": """ngram_input_embed""",
"""position_embeddings""": """embed_positions""",
}
for key in loading_info["missing_keys"]:
lowercase__ = key.split(""".""" )
if attributes[0] == "lm_head":
lowercase__ = prophet
lowercase__ = prophet_old
else:
lowercase__ = prophet.prophetnet
lowercase__ = prophet_old.model
lowercase__ = False
for attribute in attributes:
if attribute in mapping:
lowercase__ = mapping[attribute]
if not hasattr(__magic_name__ , __magic_name__ ) and len(__magic_name__ ) > 0:
lowercase__ = attribute
elif hasattr(__magic_name__ , __magic_name__ ):
lowercase__ = attribute
if attribute == "weight":
assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
lowercase__ = old_model.weight
logger.info(f'''{attribute} is initialized.''' )
lowercase__ = True
break
elif attribute == "bias":
assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
lowercase__ = old_model.bias
logger.info(f'''{attribute} is initialized''' )
lowercase__ = True
break
elif attribute in special_keys and hasattr(__magic_name__ , """in_proj_weight""" ):
lowercase__ = old_model.in_proj_weight.shape[0] // 3
lowercase__ = getattr(__magic_name__ , __magic_name__ )
param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
if attribute == "query_proj":
lowercase__ = nn.Parameter(old_model.in_proj_weight[:embed_dim, :] )
lowercase__ = nn.Parameter(old_model.in_proj_bias[:embed_dim] )
elif attribute == "key_proj":
lowercase__ = nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :] )
lowercase__ = nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim] )
elif attribute == "value_proj":
lowercase__ = nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :] )
lowercase__ = nn.Parameter(old_model.in_proj_bias[2 * embed_dim :] )
lowercase__ = True
break
elif attribute == "position_embeddings":
assert (
model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
), "Hidden size has to match"
assert model.position_embeddings.weight.shape[0] == 512, "We want 512 position_embeddings."
lowercase__ = nn.Parameter(old_model.embed_positions.weight[:512, :] )
lowercase__ = True
break
if attribute.isdigit():
lowercase__ = model[int(__magic_name__ )]
lowercase__ = old_model[int(__magic_name__ )]
else:
lowercase__ = getattr(__magic_name__ , __magic_name__ )
if old_attribute == "":
lowercase__ = old_model
else:
if not hasattr(__magic_name__ , __magic_name__ ):
raise ValueError(f'''{old_model} does not have {old_attribute}''' )
lowercase__ = getattr(__magic_name__ , __magic_name__ )
if not is_key_init:
raise ValueError(f'''{key} was not correctly initialized!''' )
print(f'''Saving model to {pytorch_dump_folder_path}''' )
prophet.save_pretrained(__magic_name__ )
if __name__ == "__main__":
A : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--prophetnet_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
A : str = parser.parse_args()
convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)
| 305
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|
from __future__ import annotations
import unittest
from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import numpy
import tensorflow as tf
from transformers.models.esm.modeling_tf_esm import (
TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFEsmForMaskedLM,
TFEsmForSequenceClassification,
TFEsmForTokenClassification,
TFEsmModel,
)
class A :
'''simple docstring'''
def __init__(self : Tuple , _UpperCAmelCase : str , ) -> List[str]:
"""simple docstring"""
lowercase__ = parent
lowercase__ = 13
lowercase__ = 7
lowercase__ = True
lowercase__ = True
lowercase__ = True
lowercase__ = 99
lowercase__ = 32
lowercase__ = 2
lowercase__ = 4
lowercase__ = 37
lowercase__ = """gelu"""
lowercase__ = 0.1
lowercase__ = 0.1
lowercase__ = 512
lowercase__ = 16
lowercase__ = 2
lowercase__ = 0.02
lowercase__ = 3
lowercase__ = 4
lowercase__ = None
def lowerCamelCase__ (self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase__ = None
if self.use_input_mask:
lowercase__ = random_attention_mask([self.batch_size, self.seq_length] )
lowercase__ = None
lowercase__ = None
lowercase__ = None
if self.use_labels:
lowercase__ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase__ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowercase__ = ids_tensor([self.batch_size] , self.num_choices )
lowercase__ = EsmConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , pad_token_id=1 , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , )
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def lowerCamelCase__ (self : List[str] ) -> Tuple:
"""simple docstring"""
(
(
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) ,
) = self.prepare_config_and_inputs()
lowercase__ = True
lowercase__ = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
lowercase__ = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def lowerCamelCase__ (self : List[Any] , _UpperCAmelCase : str , _UpperCAmelCase : List[str] , _UpperCAmelCase : List[str] , _UpperCAmelCase : str , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Union[str, Any] ) -> Tuple:
"""simple docstring"""
lowercase__ = TFEsmModel(config=_UpperCAmelCase )
lowercase__ = {"""input_ids""": input_ids, """attention_mask""": input_mask}
lowercase__ = model(_UpperCAmelCase )
lowercase__ = [input_ids, input_mask]
lowercase__ = model(_UpperCAmelCase )
lowercase__ = model(_UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCamelCase__ (self : str , _UpperCAmelCase : Tuple , _UpperCAmelCase : List[Any] , _UpperCAmelCase : List[str] , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : str , _UpperCAmelCase : str , _UpperCAmelCase : Union[str, Any] , ) -> int:
"""simple docstring"""
lowercase__ = True
lowercase__ = TFEsmModel(config=_UpperCAmelCase )
lowercase__ = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""encoder_hidden_states""": encoder_hidden_states,
"""encoder_attention_mask""": encoder_attention_mask,
}
lowercase__ = model(_UpperCAmelCase )
lowercase__ = [input_ids, input_mask]
lowercase__ = model(_UpperCAmelCase , encoder_hidden_states=_UpperCAmelCase )
# Also check the case where encoder outputs are not passed
lowercase__ = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCamelCase__ (self : Optional[Any] , _UpperCAmelCase : int , _UpperCAmelCase : Tuple , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : int , _UpperCAmelCase : Tuple , _UpperCAmelCase : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = TFEsmForMaskedLM(config=_UpperCAmelCase )
lowercase__ = model([input_ids, input_mask] )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowerCamelCase__ (self : List[str] , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : List[str] , _UpperCAmelCase : Dict , _UpperCAmelCase : Any , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Dict ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = self.num_labels
lowercase__ = TFEsmForTokenClassification(config=_UpperCAmelCase )
lowercase__ = {"""input_ids""": input_ids, """attention_mask""": input_mask}
lowercase__ = model(_UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def lowerCamelCase__ (self : Optional[int] ) -> str:
"""simple docstring"""
lowercase__ = self.prepare_config_and_inputs()
(
(
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) ,
) = config_and_inputs
lowercase__ = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_tf
class A ( UpperCAmelCase__ , UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
A__ = (
(
TFEsmModel,
TFEsmForMaskedLM,
TFEsmForSequenceClassification,
TFEsmForTokenClassification,
)
if is_tf_available()
else ()
)
A__ = (
{
'''feature-extraction''': TFEsmModel,
'''fill-mask''': TFEsmForMaskedLM,
'''text-classification''': TFEsmForSequenceClassification,
'''token-classification''': TFEsmForTokenClassification,
'''zero-shot''': TFEsmForSequenceClassification,
}
if is_tf_available()
else {}
)
A__ = False
A__ = False
def lowerCamelCase__ (self : List[Any] ) -> Tuple:
"""simple docstring"""
lowercase__ = TFEsmModelTester(self )
lowercase__ = ConfigTester(self , config_class=_UpperCAmelCase , hidden_size=37 )
def lowerCamelCase__ (self : Union[str, Any] ) -> str:
"""simple docstring"""
self.config_tester.run_common_tests()
def lowerCamelCase__ (self : Tuple ) -> Optional[int]:
"""simple docstring"""
lowercase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_UpperCAmelCase )
def lowerCamelCase__ (self : Dict ) -> Tuple:
"""simple docstring"""
lowercase__ = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(*_UpperCAmelCase )
def lowerCamelCase__ (self : List[Any] ) -> Optional[int]:
"""simple docstring"""
lowercase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*_UpperCAmelCase )
def lowerCamelCase__ (self : Union[str, Any] ) -> Dict:
"""simple docstring"""
lowercase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*_UpperCAmelCase )
@slow
def lowerCamelCase__ (self : str ) -> List[str]:
"""simple docstring"""
for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ = TFEsmModel.from_pretrained(_UpperCAmelCase )
self.assertIsNotNone(_UpperCAmelCase )
@unittest.skip("""Protein models do not support embedding resizing.""" )
def lowerCamelCase__ (self : List[str] ) -> Optional[Any]:
"""simple docstring"""
pass
@unittest.skip("""Protein models do not support embedding resizing.""" )
def lowerCamelCase__ (self : Tuple ) -> Tuple:
"""simple docstring"""
pass
def lowerCamelCase__ (self : List[str] ) -> Optional[int]:
"""simple docstring"""
lowercase__ , lowercase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ = model_class(_UpperCAmelCase )
assert isinstance(model.get_input_embeddings() , tf.keras.layers.Layer )
if model_class is TFEsmForMaskedLM:
# Output embedding test differs from the main test because they're a matrix, not a layer
lowercase__ = model.get_bias()
assert isinstance(_UpperCAmelCase , _UpperCAmelCase )
for k, v in name.items():
assert isinstance(_UpperCAmelCase , tf.Variable )
else:
lowercase__ = model.get_output_embeddings()
assert x is None
lowercase__ = model.get_bias()
assert name is None
@require_tf
class A ( unittest.TestCase ):
'''simple docstring'''
@slow
def lowerCamelCase__ (self : int ) -> str:
"""simple docstring"""
lowercase__ = TFEsmForMaskedLM.from_pretrained("""facebook/esm2_t6_8M_UR50D""" )
lowercase__ = tf.constant([[0, 1, 2, 3, 4, 5]] )
lowercase__ = model(_UpperCAmelCase )[0]
lowercase__ = [1, 6, 33]
self.assertEqual(list(output.numpy().shape ) , _UpperCAmelCase )
# compare the actual values for a slice.
lowercase__ = tf.constant(
[
[
[8.921_518, -10.589_814, -6.4_671_307],
[-6.3_967_156, -13.911_377, -1.1_211_915],
[-7.781_247, -13.951_557, -3.740_592],
]
] )
self.assertTrue(numpy.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1E-2 ) )
@slow
def lowerCamelCase__ (self : List[Any] ) -> Optional[int]:
"""simple docstring"""
lowercase__ = TFEsmModel.from_pretrained("""facebook/esm2_t6_8M_UR50D""" )
lowercase__ = tf.constant([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]] )
lowercase__ = model(_UpperCAmelCase )[0]
# compare the actual values for a slice.
lowercase__ = tf.constant(
[
[
[0.14_443_092, 0.54_125_327, 0.3_247_739],
[0.30_340_484, 0.00_526_676, 0.31_077_722],
[0.32_278_043, -0.24_987_096, 0.3_414_628],
]
] )
self.assertTrue(numpy.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1E-4 ) )
| 305
|
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class A ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__(self : Any , _UpperCAmelCase : Dict[str, int] , _UpperCAmelCase : List[str] , _UpperCAmelCase : int = None , _UpperCAmelCase : int = None ) -> Dict:
"""simple docstring"""
super().__init__()
lowercase__ = pad_token_id
lowercase__ = max_length
lowercase__ = vocab
lowercase__ = merges
lowercase__ = BytePairTokenizer(_UpperCAmelCase , _UpperCAmelCase , sequence_length=_UpperCAmelCase )
@classmethod
def lowerCamelCase__ (cls : Optional[int] , _UpperCAmelCase : GPTaTokenizer , *_UpperCAmelCase : List[Any] , **_UpperCAmelCase : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = [""" """.join(_UpperCAmelCase ) for m in tokenizer.bpe_ranks.keys()]
lowercase__ = tokenizer.get_vocab()
return cls(_UpperCAmelCase , _UpperCAmelCase , *_UpperCAmelCase , **_UpperCAmelCase )
@classmethod
def lowerCamelCase__ (cls : Union[str, Any] , _UpperCAmelCase : Union[str, os.PathLike] , *_UpperCAmelCase : str , **_UpperCAmelCase : List[Any] ) -> Any:
"""simple docstring"""
lowercase__ = GPTaTokenizer.from_pretrained(_UpperCAmelCase , *_UpperCAmelCase , **_UpperCAmelCase )
return cls.from_tokenizer(_UpperCAmelCase , *_UpperCAmelCase , **_UpperCAmelCase )
@classmethod
def lowerCamelCase__ (cls : Any , _UpperCAmelCase : Tuple ) -> Union[str, Any]:
"""simple docstring"""
return cls(**_UpperCAmelCase )
def lowerCamelCase__ (self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
return {
"vocab": self.vocab,
"merges": self.merges,
"max_length": self.max_length,
"pad_token_id": self.pad_token_id,
}
def lowerCamelCase__ (self : str , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : int = None ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = self.tf_tokenizer(_UpperCAmelCase )
lowercase__ = tf.ones_like(_UpperCAmelCase )
if self.pad_token_id is not None:
# pad the tokens up to max length
lowercase__ = max_length if max_length is not None else self.max_length
if max_length is not None:
lowercase__ , lowercase__ = pad_model_inputs(
_UpperCAmelCase , max_seq_length=_UpperCAmelCase , pad_value=self.pad_token_id )
return {"attention_mask": attention_mask, "input_ids": input_ids}
| 305
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|
def UpperCamelCase ( __magic_name__ : int , __magic_name__ : int ) -> int:
"""simple docstring"""
return int((input_a, input_a).count(1 ) != 0 )
def UpperCamelCase ( ) -> None:
"""simple docstring"""
assert or_gate(0 , 0 ) == 0
assert or_gate(0 , 1 ) == 1
assert or_gate(1 , 0 ) == 1
assert or_gate(1 , 1 ) == 1
if __name__ == "__main__":
print(or_gate(0, 1))
print(or_gate(1, 0))
print(or_gate(0, 0))
print(or_gate(1, 1))
| 305
|
from __future__ import annotations
from functools import lru_cache
from math import ceil
A : Optional[int] = 1_0_0
A : int = set(range(3, NUM_PRIMES, 2))
primes.add(2)
A : int
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prime not in primes:
continue
primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime)))
@lru_cache(maxsize=100 )
def UpperCamelCase ( __magic_name__ : int ) -> set[int]:
"""simple docstring"""
if number_to_partition < 0:
return set()
elif number_to_partition == 0:
return {1}
lowercase__ = set()
lowercase__ = 42
lowercase__ = 42
for prime in primes:
if prime > number_to_partition:
continue
for sub in partition(number_to_partition - prime ):
ret.add(sub * prime )
return ret
def UpperCamelCase ( __magic_name__ : int = 5000 ) -> int | None:
"""simple docstring"""
for number_to_partition in range(1 , __magic_name__ ):
if len(partition(__magic_name__ ) ) > number_unique_partitions:
return number_to_partition
return None
if __name__ == "__main__":
print(F'{solution() = }')
| 305
| 1
|
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def UpperCamelCase ( __magic_name__ : str ) -> str:
"""simple docstring"""
return "".join(sorted(__magic_name__ ) )
def UpperCamelCase ( __magic_name__ : str ) -> list[str]:
"""simple docstring"""
return word_by_signature[signature(__magic_name__ )]
A : str = Path(__file__).parent.joinpath('words.txt').read_text(encoding='utf-8')
A : Tuple = sorted({word.strip().lower() for word in data.splitlines()})
A : str = collections.defaultdict(list)
for word in word_list:
word_by_signature[signature(word)].append(word)
if __name__ == "__main__":
A : Optional[int] = {word: anagram(word) for word in word_list if len(anagram(word)) > 1}
with open('anagrams.txt', 'w') as file:
file.write('all_anagrams = \n ')
file.write(pprint.pformat(all_anagrams))
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|
def UpperCamelCase ( __magic_name__ : List[Any] ) -> Optional[int]:
"""simple docstring"""
lowercase__ = [0] * len(__magic_name__ )
lowercase__ = []
lowercase__ = [1] * len(__magic_name__ )
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(__magic_name__ ) ):
if indegree[i] == 0:
queue.append(__magic_name__ )
while queue:
lowercase__ = queue.pop(0 )
for x in graph[vertex]:
indegree[x] -= 1
if long_dist[vertex] + 1 > long_dist[x]:
lowercase__ = long_dist[vertex] + 1
if indegree[x] == 0:
queue.append(__magic_name__ )
print(max(__magic_name__ ) )
# Adjacency list of Graph
A : Union[str, Any] = {0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}
longest_distance(graph)
| 305
| 1
|
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
from accelerate import PartialState
from accelerate.utils.operations import broadcast, gather, gather_object, pad_across_processes, reduce
def UpperCamelCase ( __magic_name__ : Any ) -> Optional[int]:
"""simple docstring"""
return (torch.arange(state.num_processes ) + 1.0 + (state.num_processes * state.process_index)).to(state.device )
def UpperCamelCase ( __magic_name__ : int ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = create_tensor(__magic_name__ )
lowercase__ = gather(__magic_name__ )
assert gathered_tensor.tolist() == list(range(1 , state.num_processes**2 + 1 ) )
def UpperCamelCase ( __magic_name__ : Optional[int] ) -> Tuple:
"""simple docstring"""
lowercase__ = [state.process_index]
lowercase__ = gather_object(__magic_name__ )
assert len(__magic_name__ ) == state.num_processes, f'''{gathered_obj}, {len(__magic_name__ )} != {state.num_processes}'''
assert gathered_obj == list(range(state.num_processes ) ), f'''{gathered_obj} != {list(range(state.num_processes ) )}'''
def UpperCamelCase ( __magic_name__ : str ) -> Dict:
"""simple docstring"""
lowercase__ = create_tensor(__magic_name__ )
lowercase__ = broadcast(__magic_name__ )
assert broadcasted_tensor.shape == torch.Size([state.num_processes] )
assert broadcasted_tensor.tolist() == list(range(1 , state.num_processes + 1 ) )
def UpperCamelCase ( __magic_name__ : str ) -> Dict:
"""simple docstring"""
if state.is_main_process:
lowercase__ = torch.arange(state.num_processes + 1 ).to(state.device )
else:
lowercase__ = torch.arange(state.num_processes ).to(state.device )
lowercase__ = pad_across_processes(__magic_name__ )
assert padded_tensor.shape == torch.Size([state.num_processes + 1] )
if not state.is_main_process:
assert padded_tensor.tolist() == list(range(0 , state.num_processes ) ) + [0]
def UpperCamelCase ( __magic_name__ : List[Any] ) -> Optional[int]:
"""simple docstring"""
if state.num_processes != 2:
return
lowercase__ = create_tensor(__magic_name__ )
lowercase__ = reduce(__magic_name__ , """sum""" )
lowercase__ = torch.tensor([4.0, 6] ).to(state.device )
assert torch.allclose(__magic_name__ , __magic_name__ ), f'''{reduced_tensor} != {truth_tensor}'''
def UpperCamelCase ( __magic_name__ : Dict ) -> int:
"""simple docstring"""
if state.num_processes != 2:
return
lowercase__ = create_tensor(__magic_name__ )
lowercase__ = reduce(__magic_name__ , """mean""" )
lowercase__ = torch.tensor([2.0, 3] ).to(state.device )
assert torch.allclose(__magic_name__ , __magic_name__ ), f'''{reduced_tensor} != {truth_tensor}'''
def UpperCamelCase ( __magic_name__ : str ) -> int:
"""simple docstring"""
main()
def UpperCamelCase ( ) -> Optional[int]:
"""simple docstring"""
lowercase__ = PartialState()
state.print(f'''State: {state}''' )
state.print("""testing gather""" )
test_gather(__magic_name__ )
state.print("""testing gather_object""" )
test_gather_object(__magic_name__ )
state.print("""testing broadcast""" )
test_broadcast(__magic_name__ )
state.print("""testing pad_across_processes""" )
test_pad_across_processes(__magic_name__ )
state.print("""testing reduce_sum""" )
test_reduce_sum(__magic_name__ )
state.print("""testing reduce_mean""" )
test_reduce_mean(__magic_name__ )
if __name__ == "__main__":
main()
| 305
|
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
from accelerate import PartialState
from accelerate.utils.operations import broadcast, gather, gather_object, pad_across_processes, reduce
def UpperCamelCase ( __magic_name__ : Any ) -> Optional[int]:
"""simple docstring"""
return (torch.arange(state.num_processes ) + 1.0 + (state.num_processes * state.process_index)).to(state.device )
def UpperCamelCase ( __magic_name__ : int ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = create_tensor(__magic_name__ )
lowercase__ = gather(__magic_name__ )
assert gathered_tensor.tolist() == list(range(1 , state.num_processes**2 + 1 ) )
def UpperCamelCase ( __magic_name__ : Optional[int] ) -> Tuple:
"""simple docstring"""
lowercase__ = [state.process_index]
lowercase__ = gather_object(__magic_name__ )
assert len(__magic_name__ ) == state.num_processes, f'''{gathered_obj}, {len(__magic_name__ )} != {state.num_processes}'''
assert gathered_obj == list(range(state.num_processes ) ), f'''{gathered_obj} != {list(range(state.num_processes ) )}'''
def UpperCamelCase ( __magic_name__ : str ) -> Dict:
"""simple docstring"""
lowercase__ = create_tensor(__magic_name__ )
lowercase__ = broadcast(__magic_name__ )
assert broadcasted_tensor.shape == torch.Size([state.num_processes] )
assert broadcasted_tensor.tolist() == list(range(1 , state.num_processes + 1 ) )
def UpperCamelCase ( __magic_name__ : str ) -> Dict:
"""simple docstring"""
if state.is_main_process:
lowercase__ = torch.arange(state.num_processes + 1 ).to(state.device )
else:
lowercase__ = torch.arange(state.num_processes ).to(state.device )
lowercase__ = pad_across_processes(__magic_name__ )
assert padded_tensor.shape == torch.Size([state.num_processes + 1] )
if not state.is_main_process:
assert padded_tensor.tolist() == list(range(0 , state.num_processes ) ) + [0]
def UpperCamelCase ( __magic_name__ : List[Any] ) -> Optional[int]:
"""simple docstring"""
if state.num_processes != 2:
return
lowercase__ = create_tensor(__magic_name__ )
lowercase__ = reduce(__magic_name__ , """sum""" )
lowercase__ = torch.tensor([4.0, 6] ).to(state.device )
assert torch.allclose(__magic_name__ , __magic_name__ ), f'''{reduced_tensor} != {truth_tensor}'''
def UpperCamelCase ( __magic_name__ : Dict ) -> int:
"""simple docstring"""
if state.num_processes != 2:
return
lowercase__ = create_tensor(__magic_name__ )
lowercase__ = reduce(__magic_name__ , """mean""" )
lowercase__ = torch.tensor([2.0, 3] ).to(state.device )
assert torch.allclose(__magic_name__ , __magic_name__ ), f'''{reduced_tensor} != {truth_tensor}'''
def UpperCamelCase ( __magic_name__ : str ) -> int:
"""simple docstring"""
main()
def UpperCamelCase ( ) -> Optional[int]:
"""simple docstring"""
lowercase__ = PartialState()
state.print(f'''State: {state}''' )
state.print("""testing gather""" )
test_gather(__magic_name__ )
state.print("""testing gather_object""" )
test_gather_object(__magic_name__ )
state.print("""testing broadcast""" )
test_broadcast(__magic_name__ )
state.print("""testing pad_across_processes""" )
test_pad_across_processes(__magic_name__ )
state.print("""testing reduce_sum""" )
test_reduce_sum(__magic_name__ )
state.print("""testing reduce_mean""" )
test_reduce_mean(__magic_name__ )
if __name__ == "__main__":
main()
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import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed
class A ( UpperCAmelCase__ ):
'''simple docstring'''
def __init__(self : Union[str, Any] , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Tuple , _UpperCAmelCase : Any=1024 , _UpperCAmelCase : Tuple=1024 , _UpperCAmelCase : str=3.6 ) -> str:
"""simple docstring"""
lowercase__ = tokenizer
lowercase__ = tokenizer.bos_token_id
lowercase__ = dataset
lowercase__ = seq_length
lowercase__ = seq_length * chars_per_token * num_of_sequences
def __iter__(self : Any ) -> Tuple:
"""simple docstring"""
lowercase__ = iter(self.dataset )
lowercase__ = True
while more_examples:
lowercase__ , lowercase__ = [], 0
while True:
if buffer_len >= self.input_characters:
break
try:
buffer.append(next(_UpperCAmelCase )["""content"""] )
buffer_len += len(buffer[-1] )
except StopIteration:
lowercase__ = False
break
lowercase__ = tokenizer(_UpperCAmelCase , truncation=_UpperCAmelCase )["""input_ids"""]
lowercase__ = []
for tokenized_input in tokenized_inputs:
all_token_ids.extend(tokenized_input + [self.concat_token_id] )
for i in range(0 , len(_UpperCAmelCase ) , self.seq_length ):
lowercase__ = all_token_ids[i : i + self.seq_length]
if len(_UpperCAmelCase ) == self.seq_length:
yield torch.tensor(_UpperCAmelCase )
def UpperCamelCase ( __magic_name__ : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = {"""streaming""": True}
lowercase__ = load_dataset(args.dataset_name , split="""train""" , **__magic_name__ )
lowercase__ = ConstantLengthDataset(__magic_name__ , __magic_name__ , seq_length=args.seq_length )
lowercase__ = DataLoader(__magic_name__ , batch_size=args.batch_size )
return eval_dataloader
def UpperCamelCase ( __magic_name__ : Dict ) -> int:
"""simple docstring"""
model.eval()
lowercase__ = []
for step, batch in enumerate(__magic_name__ ):
with torch.no_grad():
lowercase__ = model(__magic_name__ , labels=__magic_name__ )
lowercase__ = outputs.loss.repeat(args.batch_size )
losses.append(accelerator.gather(__magic_name__ ) )
if args.max_eval_steps > 0 and step >= args.max_eval_steps:
break
lowercase__ = torch.mean(torch.cat(__magic_name__ ) )
try:
lowercase__ = torch.exp(__magic_name__ )
except OverflowError:
lowercase__ = float("""inf""" )
return loss.item(), perplexity.item()
# Setup Accelerator
A : int = Accelerator()
# Parse configuration
A : List[Any] = HfArgumentParser(EvaluationArguments)
A : List[Any] = parser.parse_args()
set_seed(args.seed)
# Logging
A : Union[str, Any] = logging.getLogger(__name__)
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
# Load model and tokenizer
A : str = AutoModelForCausalLM.from_pretrained(args.model_ckpt)
A : int = AutoTokenizer.from_pretrained(args.model_ckpt)
# Load dataset and dataloader
A : Dict = create_dataloader(args)
# Prepare everything with our `accelerator`.
A , A : List[str] = accelerator.prepare(model, eval_dataloader)
# Evaluate and save the last checkpoint
logger.info('Evaluating and saving model after training')
A , A : List[str] = evaluate(args)
logger.info(F'loss/eval: {eval_loss}, perplexity: {perplexity}')
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def UpperCamelCase ( __magic_name__ : str ) -> int:
"""simple docstring"""
assert column_title.isupper()
lowercase__ = 0
lowercase__ = len(__magic_name__ ) - 1
lowercase__ = 0
while index >= 0:
lowercase__ = (ord(column_title[index] ) - 64) * pow(26 , __magic_name__ )
answer += value
power += 1
index -= 1
return answer
if __name__ == "__main__":
from doctest import testmod
testmod()
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from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging
A : Dict = logging.get_logger(__name__)
A : List[str] = {
'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json',
# See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}
class A ( UpperCAmelCase__ ):
'''simple docstring'''
A__ = '''blenderbot-small'''
A__ = ['''past_key_values''']
A__ = {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
def __init__(self : Union[str, Any] , _UpperCAmelCase : str=5_0265 , _UpperCAmelCase : Tuple=512 , _UpperCAmelCase : List[Any]=8 , _UpperCAmelCase : Any=2048 , _UpperCAmelCase : str=16 , _UpperCAmelCase : List[str]=8 , _UpperCAmelCase : str=2048 , _UpperCAmelCase : int=16 , _UpperCAmelCase : Dict=0.0 , _UpperCAmelCase : List[Any]=0.0 , _UpperCAmelCase : str=True , _UpperCAmelCase : Any=True , _UpperCAmelCase : int="gelu" , _UpperCAmelCase : Union[str, Any]=512 , _UpperCAmelCase : Any=0.1 , _UpperCAmelCase : str=0.0 , _UpperCAmelCase : List[str]=0.0 , _UpperCAmelCase : Dict=0.02 , _UpperCAmelCase : Tuple=1 , _UpperCAmelCase : Optional[int]=False , _UpperCAmelCase : Optional[Any]=0 , _UpperCAmelCase : Tuple=1 , _UpperCAmelCase : Union[str, Any]=2 , _UpperCAmelCase : Dict=2 , **_UpperCAmelCase : Any , ) -> Optional[int]:
"""simple docstring"""
lowercase__ = vocab_size
lowercase__ = max_position_embeddings
lowercase__ = d_model
lowercase__ = encoder_ffn_dim
lowercase__ = encoder_layers
lowercase__ = encoder_attention_heads
lowercase__ = decoder_ffn_dim
lowercase__ = decoder_layers
lowercase__ = decoder_attention_heads
lowercase__ = dropout
lowercase__ = attention_dropout
lowercase__ = activation_dropout
lowercase__ = activation_function
lowercase__ = init_std
lowercase__ = encoder_layerdrop
lowercase__ = decoder_layerdrop
lowercase__ = use_cache
lowercase__ = encoder_layers
lowercase__ = scale_embedding # scale factor will be sqrt(d_model) if True
super().__init__(
pad_token_id=_UpperCAmelCase , bos_token_id=_UpperCAmelCase , eos_token_id=_UpperCAmelCase , is_encoder_decoder=_UpperCAmelCase , decoder_start_token_id=_UpperCAmelCase , forced_eos_token_id=_UpperCAmelCase , **_UpperCAmelCase , )
class A ( UpperCAmelCase__ ):
'''simple docstring'''
@property
def lowerCamelCase__ (self : str ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
if self.task in ["default", "seq2seq-lm"]:
lowercase__ = OrderedDict(
[
("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
] )
if self.use_past:
lowercase__ = {0: """batch"""}
lowercase__ = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
else:
lowercase__ = {0: """batch""", 1: """decoder_sequence"""}
lowercase__ = {0: """batch""", 1: """decoder_sequence"""}
if self.use_past:
self.fill_with_past_key_values_(_UpperCAmelCase , direction="""inputs""" )
elif self.task == "causal-lm":
# TODO: figure this case out.
lowercase__ = OrderedDict(
[
("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
] )
if self.use_past:
lowercase__ , lowercase__ = self.num_layers
for i in range(_UpperCAmelCase ):
lowercase__ = {0: """batch""", 2: """past_sequence + sequence"""}
lowercase__ = {0: """batch""", 2: """past_sequence + sequence"""}
else:
lowercase__ = OrderedDict(
[
("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
("""decoder_input_ids""", {0: """batch""", 1: """decoder_sequence"""}),
("""decoder_attention_mask""", {0: """batch""", 1: """decoder_sequence"""}),
] )
return common_inputs
@property
def lowerCamelCase__ (self : Union[str, Any] ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
if self.task in ["default", "seq2seq-lm"]:
lowercase__ = super().outputs
else:
lowercase__ = super(_UpperCAmelCase , self ).outputs
if self.use_past:
lowercase__ , lowercase__ = self.num_layers
for i in range(_UpperCAmelCase ):
lowercase__ = {0: """batch""", 2: """past_sequence + sequence"""}
lowercase__ = {0: """batch""", 2: """past_sequence + sequence"""}
return common_outputs
def lowerCamelCase__ (self : int , _UpperCAmelCase : PreTrainedTokenizer , _UpperCAmelCase : int = -1 , _UpperCAmelCase : int = -1 , _UpperCAmelCase : bool = False , _UpperCAmelCase : Optional[TensorType] = None , ) -> Mapping[str, Any]:
"""simple docstring"""
lowercase__ = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
# Generate decoder inputs
lowercase__ = seq_length if not self.use_past else 1
lowercase__ = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
lowercase__ = {f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
lowercase__ = dict(**_UpperCAmelCase , **_UpperCAmelCase )
if self.use_past:
if not is_torch_available():
raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed.""" )
else:
import torch
lowercase__ , lowercase__ = common_inputs["""input_ids"""].shape
lowercase__ = common_inputs["""decoder_input_ids"""].shape[1]
lowercase__ , lowercase__ = self.num_attention_heads
lowercase__ = (
batch,
num_encoder_attention_heads,
encoder_seq_length,
self._config.hidden_size // num_encoder_attention_heads,
)
lowercase__ = decoder_seq_length + 3
lowercase__ = (
batch,
num_decoder_attention_heads,
decoder_past_length,
self._config.hidden_size // num_decoder_attention_heads,
)
lowercase__ = torch.cat(
[common_inputs["""decoder_attention_mask"""], torch.ones(_UpperCAmelCase , _UpperCAmelCase )] , dim=1 )
lowercase__ = []
# If the number of encoder and decoder layers are present in the model configuration, both are considered
lowercase__ , lowercase__ = self.num_layers
lowercase__ = min(_UpperCAmelCase , _UpperCAmelCase )
lowercase__ = max(_UpperCAmelCase , _UpperCAmelCase ) - min_num_layers
lowercase__ = """encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""
for _ in range(_UpperCAmelCase ):
common_inputs["past_key_values"].append(
(
torch.zeros(_UpperCAmelCase ),
torch.zeros(_UpperCAmelCase ),
torch.zeros(_UpperCAmelCase ),
torch.zeros(_UpperCAmelCase ),
) )
# TODO: test this.
lowercase__ = encoder_shape if remaining_side_name == """encoder""" else decoder_shape
for _ in range(_UpperCAmelCase , _UpperCAmelCase ):
common_inputs["past_key_values"].append((torch.zeros(_UpperCAmelCase ), torch.zeros(_UpperCAmelCase )) )
return common_inputs
def lowerCamelCase__ (self : Any , _UpperCAmelCase : PreTrainedTokenizer , _UpperCAmelCase : int = -1 , _UpperCAmelCase : int = -1 , _UpperCAmelCase : bool = False , _UpperCAmelCase : Optional[TensorType] = None , ) -> Mapping[str, Any]:
"""simple docstring"""
lowercase__ = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
if self.use_past:
if not is_torch_available():
raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed.""" )
else:
import torch
lowercase__ , lowercase__ = common_inputs["""input_ids"""].shape
# Not using the same length for past_key_values
lowercase__ = seqlen + 2
lowercase__ , lowercase__ = self.num_layers
lowercase__ , lowercase__ = self.num_attention_heads
lowercase__ = (
batch,
num_encoder_attention_heads,
past_key_values_length,
self._config.hidden_size // num_encoder_attention_heads,
)
lowercase__ = common_inputs["""attention_mask"""].dtype
lowercase__ = torch.cat(
[common_inputs["""attention_mask"""], torch.ones(_UpperCAmelCase , _UpperCAmelCase , dtype=_UpperCAmelCase )] , dim=1 )
lowercase__ = [
(torch.zeros(_UpperCAmelCase ), torch.zeros(_UpperCAmelCase )) for _ in range(_UpperCAmelCase )
]
return common_inputs
def lowerCamelCase__ (self : Optional[int] , _UpperCAmelCase : PreTrainedTokenizer , _UpperCAmelCase : int = -1 , _UpperCAmelCase : int = -1 , _UpperCAmelCase : bool = False , _UpperCAmelCase : Optional[TensorType] = None , ) -> Mapping[str, Any]:
"""simple docstring"""
lowercase__ = compute_effective_axis_dimension(
_UpperCAmelCase , fixed_dimension=OnnxConfig.default_fixed_batch , num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
lowercase__ = tokenizer.num_special_tokens_to_add(_UpperCAmelCase )
lowercase__ = compute_effective_axis_dimension(
_UpperCAmelCase , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=_UpperCAmelCase )
# Generate dummy inputs according to compute batch and sequence
lowercase__ = [""" """.join([tokenizer.unk_token] ) * seq_length] * batch_size
lowercase__ = dict(tokenizer(_UpperCAmelCase , return_tensors=_UpperCAmelCase ) )
return common_inputs
def lowerCamelCase__ (self : List[Any] , _UpperCAmelCase : PreTrainedTokenizer , _UpperCAmelCase : int = -1 , _UpperCAmelCase : int = -1 , _UpperCAmelCase : bool = False , _UpperCAmelCase : Optional[TensorType] = None , ) -> Mapping[str, Any]:
"""simple docstring"""
if self.task in ["default", "seq2seq-lm"]:
lowercase__ = self._generate_dummy_inputs_for_default_and_seqaseq_lm(
_UpperCAmelCase , batch_size=_UpperCAmelCase , seq_length=_UpperCAmelCase , is_pair=_UpperCAmelCase , framework=_UpperCAmelCase )
elif self.task == "causal-lm":
lowercase__ = self._generate_dummy_inputs_for_causal_lm(
_UpperCAmelCase , batch_size=_UpperCAmelCase , seq_length=_UpperCAmelCase , is_pair=_UpperCAmelCase , framework=_UpperCAmelCase )
else:
lowercase__ = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
_UpperCAmelCase , batch_size=_UpperCAmelCase , seq_length=_UpperCAmelCase , is_pair=_UpperCAmelCase , framework=_UpperCAmelCase )
return common_inputs
def lowerCamelCase__ (self : List[Any] , _UpperCAmelCase : int , _UpperCAmelCase : List[str] , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Tuple ) -> str:
"""simple docstring"""
if self.task in ["default", "seq2seq-lm"]:
lowercase__ = super()._flatten_past_key_values_(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
else:
lowercase__ = super(_UpperCAmelCase , self )._flatten_past_key_values_(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
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import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def UpperCamelCase ( __magic_name__ : list , __magic_name__ : list , __magic_name__ : list , __magic_name__ : list , __magic_name__ : list ) -> float:
"""simple docstring"""
lowercase__ = np.array([[1, item, train_mtch[i]] for i, item in enumerate(__magic_name__ )] )
lowercase__ = np.array(__magic_name__ )
lowercase__ = np.dot(np.dot(np.linalg.inv(np.dot(x.transpose() , __magic_name__ ) ) , x.transpose() ) , __magic_name__ )
return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2] )
def UpperCamelCase ( __magic_name__ : list , __magic_name__ : list , __magic_name__ : list ) -> float:
"""simple docstring"""
lowercase__ = (1, 2, 1)
lowercase__ = (1, 1, 0, 7)
lowercase__ = SARIMAX(
__magic_name__ , exog=__magic_name__ , order=__magic_name__ , seasonal_order=__magic_name__ )
lowercase__ = model.fit(disp=__magic_name__ , maxiter=600 , method="""nm""" )
lowercase__ = model_fit.predict(1 , len(__magic_name__ ) , exog=[test_match] )
return result[0]
def UpperCamelCase ( __magic_name__ : list , __magic_name__ : list , __magic_name__ : list ) -> float:
"""simple docstring"""
lowercase__ = SVR(kernel="""rbf""" , C=1 , gamma=0.1 , epsilon=0.1 )
regressor.fit(__magic_name__ , __magic_name__ )
lowercase__ = regressor.predict(__magic_name__ )
return y_pred[0]
def UpperCamelCase ( __magic_name__ : list ) -> float:
"""simple docstring"""
train_user.sort()
lowercase__ = np.percentile(__magic_name__ , 25 )
lowercase__ = np.percentile(__magic_name__ , 75 )
lowercase__ = qa - qa
lowercase__ = qa - (iqr * 0.1)
return low_lim
def UpperCamelCase ( __magic_name__ : list , __magic_name__ : float ) -> bool:
"""simple docstring"""
lowercase__ = 0
lowercase__ = 0
for i in list_vote:
if i > actual_result:
lowercase__ = not_safe + 1
else:
if abs(abs(__magic_name__ ) - abs(__magic_name__ ) ) <= 0.1:
safe += 1
else:
not_safe += 1
return safe > not_safe
if __name__ == "__main__":
# data_input_df = pd.read_csv("ex_data.csv", header=None)
A : Dict = [[1_8_2_3_1, 0.0, 1], [2_2_6_2_1, 1.0, 2], [1_5_6_7_5, 0.0, 3], [2_3_5_8_3, 1.0, 4]]
A : str = pd.DataFrame(
data_input, columns=['total_user', 'total_even', 'days']
)
A : Any = Normalizer().fit_transform(data_input_df.values)
# split data
A : Optional[int] = normalize_df[:, 2].tolist()
A : Any = normalize_df[:, 0].tolist()
A : str = normalize_df[:, 1].tolist()
# for svr (input variable = total date and total match)
A : int = normalize_df[:, [1, 2]].tolist()
A : Any = x[: len(x) - 1]
A : Tuple = x[len(x) - 1 :]
# for linear regression & sarimax
A : Optional[int] = total_date[: len(total_date) - 1]
A : Optional[int] = total_user[: len(total_user) - 1]
A : str = total_match[: len(total_match) - 1]
A : Union[str, Any] = total_date[len(total_date) - 1 :]
A : List[str] = total_user[len(total_user) - 1 :]
A : str = total_match[len(total_match) - 1 :]
# voting system with forecasting
A : int = [
linear_regression_prediction(
trn_date, trn_user, trn_match, tst_date, tst_match
),
sarimax_predictor(trn_user, trn_match, tst_match),
support_vector_regressor(x_train, x_test, trn_user),
]
# check the safety of today's data
A : int = '' if data_safety_checker(res_vote, tst_user) else 'not '
print('Today\'s data is {not_str}safe.')
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import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipeline,
)
from transformers.testing_utils import (
is_torch_available,
require_accelerate,
require_bitsandbytes,
require_torch,
require_torch_gpu,
require_torch_multi_gpu,
slow,
)
def UpperCamelCase ( __magic_name__ : Tuple ) -> int:
"""simple docstring"""
if model.config.model_type == "gpt2":
return model.transformer.h[0].mlp.c_fc
return model.transformer.h[0].mlp.dense_ah_to_h
if is_torch_available():
import torch
import torch.nn as nn
class A ( nn.Module ):
'''simple docstring'''
def __init__(self : List[Any] , _UpperCAmelCase : nn.Module , _UpperCAmelCase : int ) -> Optional[Any]:
"""simple docstring"""
super().__init__()
lowercase__ = module
lowercase__ = nn.Sequential(
nn.Linear(module.in_features , _UpperCAmelCase , bias=_UpperCAmelCase ) , nn.Linear(_UpperCAmelCase , module.out_features , bias=_UpperCAmelCase ) , )
lowercase__ = (2.0 / (5 * min(module.in_features , module.out_features ))) ** 0.5
nn.init.normal_(self.adapter[0].weight , std=_UpperCAmelCase )
nn.init.zeros_(self.adapter[1].weight )
self.adapter.to(module.weight.device )
def lowerCamelCase__ (self : Dict , _UpperCAmelCase : Dict , *_UpperCAmelCase : int , **_UpperCAmelCase : Any ) -> Dict:
"""simple docstring"""
return self.module(_UpperCAmelCase , *_UpperCAmelCase , **_UpperCAmelCase ) + self.adapter(_UpperCAmelCase )
@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class A ( unittest.TestCase ):
'''simple docstring'''
A__ = '''bigscience/bloom-1b7'''
# Constant values
A__ = 2.1_09_65_95_52_69_25_74
A__ = '''Hello my name is'''
A__ = set()
EXPECTED_OUTPUTS.add('''Hello my name is John and I am a professional photographer. I''' )
EXPECTED_OUTPUTS.add('''Hello my name is John.\nI am a friend of your father.\n''' )
EXPECTED_OUTPUTS.add('''Hello my name is John Doe, I am a student at the University''' )
A__ = 10
def lowerCamelCase__ (self : Any ) -> List[Any]:
"""simple docstring"""
lowercase__ = AutoTokenizer.from_pretrained(self.model_name )
class A ( UpperCAmelCase__ ):
'''simple docstring'''
def lowerCamelCase__ (self : Optional[Any] ) -> Tuple:
"""simple docstring"""
super().setUp()
# Models and tokenizer
lowercase__ = AutoModelForCausalLM.from_pretrained(
self.model_name , torch_dtype=torch.floataa , device_map="""auto""" )
lowercase__ = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=_UpperCAmelCase , device_map="""auto""" )
def lowerCamelCase__ (self : int ) -> List[Any]:
"""simple docstring"""
del self.model_fpaa
del self.model_abit
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase__ (self : Any ) -> Tuple:
"""simple docstring"""
lowercase__ = self.model_abit.config
self.assertTrue(hasattr(_UpperCAmelCase , """quantization_config""" ) )
lowercase__ = config.to_dict()
lowercase__ = config.to_diff_dict()
lowercase__ = config.to_json_string()
def lowerCamelCase__ (self : Union[str, Any] ) -> int:
"""simple docstring"""
from bitsandbytes.nn import Paramsabit
lowercase__ = self.model_fpaa.get_memory_footprint()
lowercase__ = self.model_abit.get_memory_footprint()
self.assertAlmostEqual(mem_fpaa / mem_abit , self.EXPECTED_RELATIVE_DIFFERENCE )
lowercase__ = get_some_linear_layer(self.model_abit )
self.assertTrue(linear.weight.__class__ == Paramsabit )
def lowerCamelCase__ (self : List[Any] ) -> int:
"""simple docstring"""
from transformers import TaPreTrainedModel
self.model_fpaa.get_memory_footprint()
self.model_abit.get_memory_footprint()
for name, module in self.model_abit.named_modules():
if isinstance(_UpperCAmelCase , torch.nn.Linear ):
if name not in ["lm_head"] + TaPreTrainedModel._keep_in_fpaa_modules:
# 4-bit parameters are packed in uint8 variables
self.assertTrue(module.weight.dtype == torch.uinta )
def lowerCamelCase__ (self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
lowercase__ = self.tokenizer(self.input_text , return_tensors="""pt""" )
lowercase__ = self.model_abit.generate(input_ids=encoded_input["""input_ids"""].to(0 ) , max_new_tokens=10 )
self.assertIn(self.tokenizer.decode(output_sequences[0] , skip_special_tokens=_UpperCAmelCase ) , self.EXPECTED_OUTPUTS )
def lowerCamelCase__ (self : Union[str, Any] ) -> Dict:
"""simple docstring"""
lowercase__ = BitsAndBytesConfig()
lowercase__ = True
lowercase__ = AutoModelForCausalLM.from_pretrained(
self.model_name , quantization_config=_UpperCAmelCase , device_map="""auto""" )
lowercase__ = self.tokenizer(self.input_text , return_tensors="""pt""" )
lowercase__ = model_abit_from_config.generate(
input_ids=encoded_input["""input_ids"""].to(0 ) , max_new_tokens=10 )
self.assertIn(self.tokenizer.decode(output_sequences[0] , skip_special_tokens=_UpperCAmelCase ) , self.EXPECTED_OUTPUTS )
def lowerCamelCase__ (self : Dict ) -> str:
"""simple docstring"""
with self.assertRaises(_UpperCAmelCase ), tempfile.TemporaryDirectory() as tmpdirname:
self.model_abit.save_pretrained(_UpperCAmelCase )
def lowerCamelCase__ (self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = BitsAndBytesConfig()
with self.assertRaises(_UpperCAmelCase ):
lowercase__ = AutoModelForCausalLM.from_pretrained(
self.model_name , quantization_config=_UpperCAmelCase , load_in_abit=_UpperCAmelCase , device_map="""auto""" , bnb_abit_quant_type="""nf4""" , )
def lowerCamelCase__ (self : Tuple ) -> Any:
"""simple docstring"""
with self.assertRaises(_UpperCAmelCase ):
# Tries with `str`
self.model_abit.to("""cpu""" )
with self.assertRaises(_UpperCAmelCase ):
# Tries with a `dtype``
self.model_abit.to(torch.floataa )
with self.assertRaises(_UpperCAmelCase ):
# Tries with a `device`
self.model_abit.to(torch.device("""cuda:0""" ) )
with self.assertRaises(_UpperCAmelCase ):
# Tries with a `device`
self.model_abit.float()
with self.assertRaises(_UpperCAmelCase ):
# Tries with a `device`
self.model_abit.half()
# Test if we did not break anything
lowercase__ = self.tokenizer(self.input_text , return_tensors="""pt""" )
lowercase__ = self.model_fpaa.to(torch.floataa )
lowercase__ = self.model_fpaa.generate(input_ids=encoded_input["""input_ids"""].to(0 ) , max_new_tokens=10 )
# Check this does not throw an error
lowercase__ = self.model_fpaa.to("""cpu""" )
# Check this does not throw an error
lowercase__ = self.model_fpaa.half()
# Check this does not throw an error
lowercase__ = self.model_fpaa.float()
def lowerCamelCase__ (self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
lowercase__ = AutoModelForSeqaSeqLM.from_pretrained("""t5-small""" , load_in_abit=_UpperCAmelCase , device_map="""auto""" )
self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.floataa )
@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class A ( unittest.TestCase ):
'''simple docstring'''
@classmethod
def lowerCamelCase__ (cls : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = """t5-small"""
lowercase__ = """google/flan-t5-small""" # flan-t5 uses dense-act instead of dense-relu-dense
lowercase__ = AutoTokenizer.from_pretrained(cls.model_name )
lowercase__ = """Translate in German: Hello, my dog is cute"""
def lowerCamelCase__ (self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase__ (self : str ) -> Tuple:
"""simple docstring"""
from transformers import TaForConditionalGeneration
lowercase__ = TaForConditionalGeneration._keep_in_fpaa_modules
lowercase__ = None
# test with `t5-small`
lowercase__ = TaForConditionalGeneration.from_pretrained(self.model_name , load_in_abit=_UpperCAmelCase , device_map="""auto""" )
lowercase__ = self.tokenizer(self.input_text , return_tensors="""pt""" ).to(0 )
lowercase__ = model.generate(**_UpperCAmelCase )
# test with `flan-t5-small`
lowercase__ = TaForConditionalGeneration.from_pretrained(
self.dense_act_model_name , load_in_abit=_UpperCAmelCase , device_map="""auto""" )
lowercase__ = self.tokenizer(self.input_text , return_tensors="""pt""" ).to(0 )
lowercase__ = model.generate(**_UpperCAmelCase )
lowercase__ = modules
def lowerCamelCase__ (self : Dict ) -> Dict:
"""simple docstring"""
import bitsandbytes as bnb
from transformers import TaForConditionalGeneration
# test with `t5-small`
lowercase__ = TaForConditionalGeneration.from_pretrained(self.model_name , load_in_abit=_UpperCAmelCase , device_map="""auto""" )
# there was a bug with decoders - this test checks that it is fixed
self.assertTrue(isinstance(model.decoder.block[0].layer[0].SelfAttention.q , bnb.nn.Linearabit ) )
lowercase__ = self.tokenizer(self.input_text , return_tensors="""pt""" ).to(0 )
lowercase__ = model.generate(**_UpperCAmelCase )
# test with `flan-t5-small`
lowercase__ = TaForConditionalGeneration.from_pretrained(
self.dense_act_model_name , load_in_abit=_UpperCAmelCase , device_map="""auto""" )
lowercase__ = self.tokenizer(self.input_text , return_tensors="""pt""" ).to(0 )
lowercase__ = model.generate(**_UpperCAmelCase )
class A ( UpperCAmelCase__ ):
'''simple docstring'''
def lowerCamelCase__ (self : List[Any] ) -> Any:
"""simple docstring"""
super().setUp()
# model_name
lowercase__ = """bigscience/bloom-560m"""
lowercase__ = """t5-small"""
# Different types of model
lowercase__ = AutoModel.from_pretrained(self.model_name , load_in_abit=_UpperCAmelCase , device_map="""auto""" )
# Sequence classification model
lowercase__ = AutoModelForSequenceClassification.from_pretrained(
self.model_name , load_in_abit=_UpperCAmelCase , device_map="""auto""" )
# CausalLM model
lowercase__ = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=_UpperCAmelCase , device_map="""auto""" )
# Seq2seq model
lowercase__ = AutoModelForSeqaSeqLM.from_pretrained(
self.seq_to_seq_name , load_in_abit=_UpperCAmelCase , device_map="""auto""" )
def lowerCamelCase__ (self : str ) -> List[Any]:
"""simple docstring"""
del self.base_model
del self.sequence_model
del self.model_abit
del self.seq_to_seq_model
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase__ (self : Optional[int] ) -> int:
"""simple docstring"""
from bitsandbytes.nn import Paramsabit
self.assertTrue(self.base_model.h[-1].mlp.dense_ah_to_h.weight.__class__ == Paramsabit )
# Other heads should be nn.Parameter
self.assertTrue(self.model_abit.lm_head.weight.__class__ == torch.nn.Parameter )
self.assertTrue(self.sequence_model.score.weight.__class__ == torch.nn.Parameter )
self.assertTrue(self.seq_to_seq_model.lm_head.weight.__class__ == torch.nn.Parameter )
class A ( UpperCAmelCase__ ):
'''simple docstring'''
def lowerCamelCase__ (self : Optional[Any] ) -> int:
"""simple docstring"""
super().setUp()
def lowerCamelCase__ (self : str ) -> Optional[int]:
"""simple docstring"""
del self.pipe
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase__ (self : Optional[Any] ) -> List[Any]:
"""simple docstring"""
lowercase__ = pipeline(
"""text-generation""" , model=self.model_name , model_kwargs={"""device_map""": """auto""", """load_in_4bit""": True, """torch_dtype""": torch.floataa} , max_new_tokens=self.MAX_NEW_TOKENS , )
# Real second forward pass
lowercase__ = self.pipe(self.input_text )
self.assertIn(pipeline_output[0]["""generated_text"""] , self.EXPECTED_OUTPUTS )
@require_torch_multi_gpu
class A ( UpperCAmelCase__ ):
'''simple docstring'''
def lowerCamelCase__ (self : Any ) -> Dict:
"""simple docstring"""
super().setUp()
def lowerCamelCase__ (self : Any ) -> List[Any]:
"""simple docstring"""
lowercase__ = AutoModelForCausalLM.from_pretrained(
self.model_name , load_in_abit=_UpperCAmelCase , device_map="""balanced""" )
# Check correct device map
self.assertEqual(set(model_parallel.hf_device_map.values() ) , {0, 1} )
# Check that inference pass works on the model
lowercase__ = self.tokenizer(self.input_text , return_tensors="""pt""" )
# Second real batch
lowercase__ = model_parallel.generate(input_ids=encoded_input["""input_ids"""].to(0 ) , max_new_tokens=10 )
self.assertIn(self.tokenizer.decode(output_parallel[0] , skip_special_tokens=_UpperCAmelCase ) , self.EXPECTED_OUTPUTS )
class A ( UpperCAmelCase__ ):
'''simple docstring'''
def lowerCamelCase__ (self : List[str] ) -> List[Any]:
"""simple docstring"""
lowercase__ = """facebook/opt-350m"""
super().setUp()
def lowerCamelCase__ (self : Any ) -> Dict:
"""simple docstring"""
if version.parse(importlib.metadata.version("""bitsandbytes""" ) ) < version.parse("""0.37.0""" ):
return
# Step 1: freeze all parameters
lowercase__ = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=_UpperCAmelCase )
self.assertEqual(set(model.hf_device_map.values() ) , {torch.cuda.current_device()} )
for param in model.parameters():
lowercase__ = False # freeze the model - train adapters later
if param.ndim == 1:
# cast the small parameters (e.g. layernorm) to fp32 for stability
lowercase__ = param.data.to(torch.floataa )
# Step 2: add adapters
for _, module in model.named_modules():
if "OPTAttention" in repr(type(_UpperCAmelCase ) ):
lowercase__ = LoRALayer(module.q_proj , rank=16 )
lowercase__ = LoRALayer(module.k_proj , rank=16 )
lowercase__ = LoRALayer(module.v_proj , rank=16 )
# Step 3: dummy batch
lowercase__ = self.tokenizer("""Test batch """ , return_tensors="""pt""" ).to(0 )
# Step 4: Check if the gradient is not None
with torch.cuda.amp.autocast():
lowercase__ = model.forward(**_UpperCAmelCase )
out.logits.norm().backward()
for module in model.modules():
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
self.assertTrue(module.adapter[1].weight.grad is not None )
self.assertTrue(module.adapter[1].weight.grad.norm().item() > 0 )
elif isinstance(_UpperCAmelCase , nn.Embedding ):
self.assertTrue(module.weight.grad is None )
class A ( UpperCAmelCase__ ):
'''simple docstring'''
A__ = '''gpt2-xl'''
A__ = 3.31_91_85_48_54_15_21_87
| 305
|
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def UpperCamelCase ( __magic_name__ : Optional[Any] ) -> List[Any]:
"""simple docstring"""
lowercase__ = tmp_path / """file.csv"""
lowercase__ = textwrap.dedent(
"""\
header1,header2
1,2
10,20
""" )
with open(__magic_name__ , """w""" ) as f:
f.write(__magic_name__ )
return str(__magic_name__ )
@pytest.fixture
def UpperCamelCase ( __magic_name__ : str ) -> Tuple:
"""simple docstring"""
lowercase__ = tmp_path / """malformed_file.csv"""
lowercase__ = textwrap.dedent(
"""\
header1,header2
1,2
10,20,
""" )
with open(__magic_name__ , """w""" ) as f:
f.write(__magic_name__ )
return str(__magic_name__ )
@pytest.fixture
def UpperCamelCase ( __magic_name__ : List[Any] , __magic_name__ : List[str] ) -> str:
"""simple docstring"""
lowercase__ = tmp_path / """csv_with_image.csv"""
lowercase__ = textwrap.dedent(
f'''\
image
{image_file}
''' )
with open(__magic_name__ , """w""" ) as f:
f.write(__magic_name__ )
return str(__magic_name__ )
@pytest.fixture
def UpperCamelCase ( __magic_name__ : Tuple ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = tmp_path / """csv_with_label.csv"""
lowercase__ = textwrap.dedent(
"""\
label
good
bad
good
""" )
with open(__magic_name__ , """w""" ) as f:
f.write(__magic_name__ )
return str(__magic_name__ )
@pytest.fixture
def UpperCamelCase ( __magic_name__ : Dict ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = tmp_path / """csv_with_int_list.csv"""
lowercase__ = textwrap.dedent(
"""\
int_list
1 2 3
4 5 6
7 8 9
""" )
with open(__magic_name__ , """w""" ) as f:
f.write(__magic_name__ )
return str(__magic_name__ )
def UpperCamelCase ( __magic_name__ : Tuple , __magic_name__ : Tuple , __magic_name__ : Tuple ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = Csv()
lowercase__ = csv._generate_tables([[csv_file, malformed_csv_file]] )
with pytest.raises(__magic_name__ , match="""Error tokenizing data""" ):
for _ in generator:
pass
assert any(
record.levelname == """ERROR"""
and """Failed to read file""" in record.message
and os.path.basename(__magic_name__ ) in record.message
for record in caplog.records )
@require_pil
def UpperCamelCase ( __magic_name__ : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
with open(__magic_name__ , encoding="""utf-8""" ) as f:
lowercase__ = f.read().splitlines()[1]
lowercase__ = Csv(encoding="""utf-8""" , features=Features({"""image""": Image()} ) )
lowercase__ = csv._generate_tables([[csv_file_with_image]] )
lowercase__ = pa.concat_tables([table for _, table in generator] )
assert pa_table.schema.field("""image""" ).type == Image()()
lowercase__ = pa_table.to_pydict()["""image"""]
assert generated_content == [{"path": image_file, "bytes": None}]
def UpperCamelCase ( __magic_name__ : Optional[Any] ) -> str:
"""simple docstring"""
with open(__magic_name__ , encoding="""utf-8""" ) as f:
lowercase__ = f.read().splitlines()[1:]
lowercase__ = Csv(encoding="""utf-8""" , features=Features({"""label""": ClassLabel(names=["""good""", """bad"""] )} ) )
lowercase__ = csv._generate_tables([[csv_file_with_label]] )
lowercase__ = pa.concat_tables([table for _, table in generator] )
assert pa_table.schema.field("""label""" ).type == ClassLabel(names=["""good""", """bad"""] )()
lowercase__ = pa_table.to_pydict()["""label"""]
assert generated_content == [ClassLabel(names=["""good""", """bad"""] ).straint(__magic_name__ ) for label in labels]
def UpperCamelCase ( __magic_name__ : Any ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = Csv(encoding="""utf-8""" , sep=""",""" , converters={"""int_list""": lambda __magic_name__ : [int(__magic_name__ ) for i in x.split()]} )
lowercase__ = csv._generate_tables([[csv_file_with_int_list]] )
lowercase__ = pa.concat_tables([table for _, table in generator] )
assert pa.types.is_list(pa_table.schema.field("""int_list""" ).type )
lowercase__ = pa_table.to_pydict()["""int_list"""]
assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
| 305
| 1
|
import argparse
import os
import torch
from transformers import FlavaImageCodebook, FlavaImageCodebookConfig
def UpperCamelCase ( __magic_name__ : List[str] , __magic_name__ : Tuple , __magic_name__ : Union[str, Any] , __magic_name__ : Union[str, Any] ) -> Dict:
"""simple docstring"""
lowercase__ = s.rsplit(__magic_name__ , __magic_name__ )
return new.join(__magic_name__ )
def UpperCamelCase ( __magic_name__ : int ) -> int:
"""simple docstring"""
return sum(param.float().sum() if """encoder.embeddings""" not in key else 0 for key, param in state_dict.items() )
def UpperCamelCase ( __magic_name__ : Tuple ) -> Any:
"""simple docstring"""
lowercase__ = {}
lowercase__ = ["""group_1""", """group_2""", """group_3""", """group_4"""]
for key, value in state_dict.items():
for group_key in group_keys:
if group_key in key:
lowercase__ = key.replace(f'''{group_key}.''' , f'''{group_key}.group.''' )
if "res_path" in key:
lowercase__ = key.replace("""res_path.""" , """res_path.path.""" )
if key.endswith(""".w""" ):
lowercase__ = rreplace(__magic_name__ , """.w""" , """.weight""" , 1 )
if key.endswith(""".b""" ):
lowercase__ = rreplace(__magic_name__ , """.b""" , """.bias""" , 1 )
lowercase__ = value.float()
return upgrade
@torch.no_grad()
def UpperCamelCase ( __magic_name__ : int , __magic_name__ : List[str] , __magic_name__ : str=None , __magic_name__ : Tuple=True ) -> Any:
"""simple docstring"""
from dall_e import Encoder
lowercase__ = Encoder()
if os.path.exists(__magic_name__ ):
lowercase__ = torch.load(__magic_name__ )
else:
lowercase__ = torch.hub.load_state_dict_from_url(__magic_name__ )
if isinstance(__magic_name__ , __magic_name__ ):
lowercase__ = ckpt.state_dict()
encoder.load_state_dict(__magic_name__ )
if config_path is not None:
lowercase__ = FlavaImageCodebookConfig.from_pretrained(__magic_name__ )
else:
lowercase__ = FlavaImageCodebookConfig()
lowercase__ = FlavaImageCodebook(__magic_name__ ).eval()
lowercase__ = encoder.state_dict()
lowercase__ = upgrade_state_dict(__magic_name__ )
hf_model.load_state_dict(__magic_name__ )
lowercase__ = hf_model.state_dict()
lowercase__ = count_parameters(__magic_name__ )
lowercase__ = count_parameters(__magic_name__ )
assert torch.allclose(__magic_name__ , __magic_name__ , atol=1E-3 )
if save_checkpoint:
hf_model.save_pretrained(__magic_name__ )
else:
return hf_state_dict
if __name__ == "__main__":
A : str = argparse.ArgumentParser()
parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to flava checkpoint')
parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
A : Dict = parser.parse_args()
convert_dalle_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
| 305
|
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
A : int = {'configuration_dpt': ['DPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DPTConfig']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Union[str, Any] = ['DPTFeatureExtractor']
A : int = ['DPTImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Tuple = [
'DPT_PRETRAINED_MODEL_ARCHIVE_LIST',
'DPTForDepthEstimation',
'DPTForSemanticSegmentation',
'DPTModel',
'DPTPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_dpt import DPTFeatureExtractor
from .image_processing_dpt import DPTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_dpt import (
DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
DPTForDepthEstimation,
DPTForSemanticSegmentation,
DPTModel,
DPTPreTrainedModel,
)
else:
import sys
A : str = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 305
| 1
|
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class A ( UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
A__ = ShapEImgaImgPipeline
A__ = ['''image''']
A__ = ['''image''']
A__ = [
'''num_images_per_prompt''',
'''num_inference_steps''',
'''generator''',
'''latents''',
'''guidance_scale''',
'''frame_size''',
'''output_type''',
'''return_dict''',
]
A__ = False
@property
def lowerCamelCase__ (self : Optional[Any] ) -> List[str]:
"""simple docstring"""
return 32
@property
def lowerCamelCase__ (self : str ) -> Any:
"""simple docstring"""
return 32
@property
def lowerCamelCase__ (self : str ) -> List[str]:
"""simple docstring"""
return self.time_input_dim * 4
@property
def lowerCamelCase__ (self : List[Any] ) -> Any:
"""simple docstring"""
return 8
@property
def lowerCamelCase__ (self : int ) -> List[str]:
"""simple docstring"""
torch.manual_seed(0 )
lowercase__ = CLIPVisionConfig(
hidden_size=self.text_embedder_hidden_size , image_size=64 , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_channels=3 , num_hidden_layers=5 , patch_size=1 , )
lowercase__ = CLIPVisionModel(_UpperCAmelCase )
return model
@property
def lowerCamelCase__ (self : Any ) -> List[Any]:
"""simple docstring"""
lowercase__ = CLIPImageProcessor(
crop_size=224 , do_center_crop=_UpperCAmelCase , do_normalize=_UpperCAmelCase , do_resize=_UpperCAmelCase , image_mean=[0.48_145_466, 0.4_578_275, 0.40_821_073] , image_std=[0.26_862_954, 0.26_130_258, 0.27_577_711] , resample=3 , size=224 , )
return image_processor
@property
def lowerCamelCase__ (self : int ) -> Optional[Any]:
"""simple docstring"""
torch.manual_seed(0 )
lowercase__ = {
"""num_attention_heads""": 2,
"""attention_head_dim""": 16,
"""embedding_dim""": self.time_input_dim,
"""num_embeddings""": 32,
"""embedding_proj_dim""": self.text_embedder_hidden_size,
"""time_embed_dim""": self.time_embed_dim,
"""num_layers""": 1,
"""clip_embed_dim""": self.time_input_dim * 2,
"""additional_embeddings""": 0,
"""time_embed_act_fn""": """gelu""",
"""norm_in_type""": """layer""",
"""embedding_proj_norm_type""": """layer""",
"""encoder_hid_proj_type""": None,
"""added_emb_type""": None,
}
lowercase__ = PriorTransformer(**_UpperCAmelCase )
return model
@property
def lowerCamelCase__ (self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
torch.manual_seed(0 )
lowercase__ = {
"""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,
),
}
lowercase__ = ShapERenderer(**_UpperCAmelCase )
return model
def lowerCamelCase__ (self : int ) -> Optional[int]:
"""simple docstring"""
lowercase__ = self.dummy_prior
lowercase__ = self.dummy_image_encoder
lowercase__ = self.dummy_image_processor
lowercase__ = self.dummy_renderer
lowercase__ = HeunDiscreteScheduler(
beta_schedule="""exp""" , num_train_timesteps=1024 , prediction_type="""sample""" , use_karras_sigmas=_UpperCAmelCase , clip_sample=_UpperCAmelCase , clip_sample_range=1.0 , )
lowercase__ = {
"""prior""": prior,
"""image_encoder""": image_encoder,
"""image_processor""": image_processor,
"""renderer""": renderer,
"""scheduler""": scheduler,
}
return components
def lowerCamelCase__ (self : Dict , _UpperCAmelCase : List[Any] , _UpperCAmelCase : str=0 ) -> str:
"""simple docstring"""
lowercase__ = floats_tensor((1, 3, 64, 64) , rng=random.Random(_UpperCAmelCase ) ).to(_UpperCAmelCase )
if str(_UpperCAmelCase ).startswith("""mps""" ):
lowercase__ = torch.manual_seed(_UpperCAmelCase )
else:
lowercase__ = torch.Generator(device=_UpperCAmelCase ).manual_seed(_UpperCAmelCase )
lowercase__ = {
"""image""": input_image,
"""generator""": generator,
"""num_inference_steps""": 1,
"""frame_size""": 32,
"""output_type""": """np""",
}
return inputs
def lowerCamelCase__ (self : str ) -> List[str]:
"""simple docstring"""
lowercase__ = """cpu"""
lowercase__ = self.get_dummy_components()
lowercase__ = self.pipeline_class(**_UpperCAmelCase )
lowercase__ = pipe.to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
lowercase__ = pipe(**self.get_dummy_inputs(_UpperCAmelCase ) )
lowercase__ = output.images[0]
lowercase__ = image[0, -3:, -3:, -1]
assert image.shape == (20, 32, 32, 3)
lowercase__ = np.array(
[
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def lowerCamelCase__ (self : str ) -> Any:
"""simple docstring"""
self._test_inference_batch_consistent(batch_sizes=[1, 2] )
def lowerCamelCase__ (self : Optional[int] ) -> str:
"""simple docstring"""
lowercase__ = torch_device == """cpu"""
lowercase__ = True
self._test_inference_batch_single_identical(
batch_size=2 , test_max_difference=_UpperCAmelCase , relax_max_difference=_UpperCAmelCase , )
def lowerCamelCase__ (self : Union[str, Any] ) -> int:
"""simple docstring"""
lowercase__ = self.get_dummy_components()
lowercase__ = self.pipeline_class(**_UpperCAmelCase )
lowercase__ = pipe.to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
lowercase__ = 1
lowercase__ = 2
lowercase__ = self.get_dummy_inputs(_UpperCAmelCase )
for key in inputs.keys():
if key in self.batch_params:
lowercase__ = batch_size * [inputs[key]]
lowercase__ = pipe(**_UpperCAmelCase , num_images_per_prompt=_UpperCAmelCase )[0]
assert images.shape[0] == batch_size * num_images_per_prompt
@slow
@require_torch_gpu
class A ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase__ (self : Dict ) -> List[Any]:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase__ (self : Any ) -> str:
"""simple docstring"""
lowercase__ = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/shap_e/corgi.png""" )
lowercase__ = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/shap_e/test_shap_e_img2img_out.npy""" )
lowercase__ = ShapEImgaImgPipeline.from_pretrained("""openai/shap-e-img2img""" )
lowercase__ = pipe.to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
lowercase__ = torch.Generator(device=_UpperCAmelCase ).manual_seed(0 )
lowercase__ = pipe(
_UpperCAmelCase , generator=_UpperCAmelCase , guidance_scale=3.0 , num_inference_steps=64 , frame_size=64 , output_type="""np""" , ).images[0]
assert images.shape == (20, 64, 64, 3)
assert_mean_pixel_difference(_UpperCAmelCase , _UpperCAmelCase )
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|
from __future__ import annotations
def UpperCamelCase ( __magic_name__ : list[float] , __magic_name__ : list[float] ) -> float:
"""simple docstring"""
lowercase__ = sorted(numsa + numsa )
lowercase__ , lowercase__ = divmod(len(__magic_name__ ) , 2 )
if mod == 1:
return all_numbers[div]
else:
return (all_numbers[div] + all_numbers[div - 1]) / 2
if __name__ == "__main__":
import doctest
doctest.testmod()
A : Any = [float(x) for x in input('Enter the elements of first array: ').split()]
A : Union[str, Any] = [float(x) for x in input('Enter the elements of second array: ').split()]
print(F'The median of two arrays is: {median_of_two_arrays(array_a, array_a)}')
| 305
| 1
|
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow
if is_tf_available():
import tensorflow as tf
if is_tensorflow_text_available():
from transformers.models.bert import TFBertTokenizer
A : Optional[Any] = ['bert-base-uncased', 'bert-base-cased']
A : Any = 'hf-internal-testing/tiny-bert-tf-only'
if is_tf_available():
class A ( tf.keras.Model ):
'''simple docstring'''
def __init__(self : Optional[Any] , _UpperCAmelCase : Dict ) -> Any:
"""simple docstring"""
super().__init__()
lowercase__ = tokenizer
lowercase__ = AutoConfig.from_pretrained(_UpperCAmelCase )
lowercase__ = TFAutoModel.from_config(_UpperCAmelCase )
def lowerCamelCase__ (self : Any , _UpperCAmelCase : str ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = self.tokenizer(_UpperCAmelCase )
lowercase__ = self.bert(**_UpperCAmelCase )
return out["pooler_output"]
@require_tf
@require_tensorflow_text
class A ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase__ (self : str ) -> Any:
"""simple docstring"""
super().setUp()
lowercase__ = [
BertTokenizer.from_pretrained(_UpperCAmelCase ) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
] # repeat for when fast_bert_tokenizer=false
lowercase__ = [TFBertTokenizer.from_pretrained(_UpperCAmelCase ) for checkpoint in TOKENIZER_CHECKPOINTS] + [
TFBertTokenizer.from_pretrained(_UpperCAmelCase , use_fast_bert_tokenizer=_UpperCAmelCase )
for checkpoint in TOKENIZER_CHECKPOINTS
]
assert len(self.tokenizers ) == len(self.tf_tokenizers )
lowercase__ = [
"""This is a straightforward English test sentence.""",
"""This one has some weird characters\rto\nsee\r\nif those\u00E9break things.""",
"""Now we're going to add some Chinese: 一 二 三 一二三""",
"""And some much more rare Chinese: 齉 堃 齉堃""",
"""Je vais aussi écrire en français pour tester les accents""",
"""Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ""",
]
lowercase__ = list(zip(self.test_sentences , self.test_sentences[::-1] ) )
def lowerCamelCase__ (self : Tuple ) -> List[str]:
"""simple docstring"""
for tokenizer, tf_tokenizer in zip(self.tokenizers , self.tf_tokenizers ):
for test_inputs in (self.test_sentences, self.paired_sentences):
lowercase__ = tokenizer(_UpperCAmelCase , return_tensors="""tf""" , padding="""longest""" )
lowercase__ = tf_tokenizer(_UpperCAmelCase )
for key in python_outputs.keys():
self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape ) )
self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key] , tf.intaa ) == tf_outputs[key] ) )
@slow
def lowerCamelCase__ (self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
for tf_tokenizer in self.tf_tokenizers:
lowercase__ = tf_tokenizer(self.paired_sentences )
lowercase__ = tf_tokenizer(
text=[sentence[0] for sentence in self.paired_sentences] , text_pair=[sentence[1] for sentence in self.paired_sentences] , )
for key in merged_outputs.keys():
self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key] , tf.intaa ) == separated_outputs[key] ) )
@slow
def lowerCamelCase__ (self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
for tf_tokenizer in self.tf_tokenizers:
lowercase__ = tf.function(_UpperCAmelCase )
for test_inputs in (self.test_sentences, self.paired_sentences):
lowercase__ = tf.constant(_UpperCAmelCase )
lowercase__ = compiled_tokenizer(_UpperCAmelCase )
lowercase__ = tf_tokenizer(_UpperCAmelCase )
for key in eager_outputs.keys():
self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key] ) )
@slow
def lowerCamelCase__ (self : str ) -> Tuple:
"""simple docstring"""
for tf_tokenizer in self.tf_tokenizers:
lowercase__ = ModelToSave(tokenizer=_UpperCAmelCase )
lowercase__ = tf.convert_to_tensor(self.test_sentences )
lowercase__ = model(_UpperCAmelCase ) # Build model with some sample inputs
with TemporaryDirectory() as tempdir:
lowercase__ = Path(_UpperCAmelCase ) / """saved.model"""
model.save(_UpperCAmelCase )
lowercase__ = tf.keras.models.load_model(_UpperCAmelCase )
lowercase__ = loaded_model(_UpperCAmelCase )
# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output ) ) , 1E-5 )
| 305
|
A : Union[str, Any] = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
A : List[Any] = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def UpperCamelCase ( __magic_name__ : dict[int, list[int]] , __magic_name__ : int , __magic_name__ : list[bool] ) -> list[int]:
"""simple docstring"""
lowercase__ = True
lowercase__ = []
for neighbour in graph[vert]:
if not visited[neighbour]:
order += topology_sort(__magic_name__ , __magic_name__ , __magic_name__ )
order.append(__magic_name__ )
return order
def UpperCamelCase ( __magic_name__ : dict[int, list[int]] , __magic_name__ : int , __magic_name__ : list[bool] ) -> list[int]:
"""simple docstring"""
lowercase__ = True
lowercase__ = [vert]
for neighbour in reversed_graph[vert]:
if not visited[neighbour]:
component += find_components(__magic_name__ , __magic_name__ , __magic_name__ )
return component
def UpperCamelCase ( __magic_name__ : dict[int, list[int]] ) -> list[list[int]]:
"""simple docstring"""
lowercase__ = len(__magic_name__ ) * [False]
lowercase__ = {vert: [] for vert in range(len(__magic_name__ ) )}
for vert, neighbours in graph.items():
for neighbour in neighbours:
reversed_graph[neighbour].append(__magic_name__ )
lowercase__ = []
for i, was_visited in enumerate(__magic_name__ ):
if not was_visited:
order += topology_sort(__magic_name__ , __magic_name__ , __magic_name__ )
lowercase__ = []
lowercase__ = len(__magic_name__ ) * [False]
for i in range(len(__magic_name__ ) ):
lowercase__ = order[len(__magic_name__ ) - i - 1]
if not visited[vert]:
lowercase__ = find_components(__magic_name__ , __magic_name__ , __magic_name__ )
components_list.append(__magic_name__ )
return components_list
| 305
| 1
|
from __future__ import annotations
A : Any = '#'
class A :
'''simple docstring'''
def __init__(self : int ) -> None:
"""simple docstring"""
lowercase__ = {}
def lowerCamelCase__ (self : Any , _UpperCAmelCase : str ) -> None:
"""simple docstring"""
lowercase__ = self._trie
for char in text:
if char not in trie:
lowercase__ = {}
lowercase__ = trie[char]
lowercase__ = True
def lowerCamelCase__ (self : Tuple , _UpperCAmelCase : str ) -> tuple | list:
"""simple docstring"""
lowercase__ = self._trie
for char in prefix:
if char in trie:
lowercase__ = trie[char]
else:
return []
return self._elements(_UpperCAmelCase )
def lowerCamelCase__ (self : Any , _UpperCAmelCase : dict ) -> tuple:
"""simple docstring"""
lowercase__ = []
for c, v in d.items():
lowercase__ = [""" """] if c == END else [(c + s) for s in self._elements(_UpperCAmelCase )]
result.extend(_UpperCAmelCase )
return tuple(_UpperCAmelCase )
A : str = Trie()
A : Tuple = ('depart', 'detergent', 'daring', 'dog', 'deer', 'deal')
for word in words:
trie.insert_word(word)
def UpperCamelCase ( __magic_name__ : str ) -> tuple:
"""simple docstring"""
lowercase__ = trie.find_word(__magic_name__ )
return tuple(string + word for word in suffixes )
def UpperCamelCase ( ) -> None:
"""simple docstring"""
print(autocomplete_using_trie("""de""" ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 305
|
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepInverseScheduler,
DPMSolverMultistepScheduler,
StableDiffusionDiffEditPipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device
from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class A ( UpperCAmelCase__ , UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
A__ = StableDiffusionDiffEditPipeline
A__ = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''height''', '''width''', '''image'''} | {'''image_latents'''}
A__ = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {'''image'''} | {'''image_latents'''}
A__ = frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
A__ = frozenset([] )
def lowerCamelCase__ (self : List[str] ) -> Optional[int]:
"""simple docstring"""
torch.manual_seed(0 )
lowercase__ = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , cross_attention_dim=32 , attention_head_dim=(2, 4) , use_linear_projection=_UpperCAmelCase , )
lowercase__ = DDIMScheduler(
beta_start=0.00_085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , clip_sample=_UpperCAmelCase , set_alpha_to_one=_UpperCAmelCase , )
lowercase__ = DDIMInverseScheduler(
beta_start=0.00_085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , clip_sample=_UpperCAmelCase , set_alpha_to_zero=_UpperCAmelCase , )
torch.manual_seed(0 )
lowercase__ = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , sample_size=128 , )
torch.manual_seed(0 )
lowercase__ = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , hidden_act="""gelu""" , projection_dim=512 , )
lowercase__ = CLIPTextModel(_UpperCAmelCase )
lowercase__ = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
lowercase__ = {
"""unet""": unet,
"""scheduler""": scheduler,
"""inverse_scheduler""": inverse_scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""safety_checker""": None,
"""feature_extractor""": None,
}
return components
def lowerCamelCase__ (self : Optional[Any] , _UpperCAmelCase : Tuple , _UpperCAmelCase : Tuple=0 ) -> Dict:
"""simple docstring"""
lowercase__ = floats_tensor((1, 16, 16) , rng=random.Random(_UpperCAmelCase ) ).to(_UpperCAmelCase )
lowercase__ = floats_tensor((1, 2, 4, 16, 16) , rng=random.Random(_UpperCAmelCase ) ).to(_UpperCAmelCase )
if str(_UpperCAmelCase ).startswith("""mps""" ):
lowercase__ = torch.manual_seed(_UpperCAmelCase )
else:
lowercase__ = torch.Generator(device=_UpperCAmelCase ).manual_seed(_UpperCAmelCase )
lowercase__ = {
"""prompt""": """a dog and a newt""",
"""mask_image""": mask,
"""image_latents""": latents,
"""generator""": generator,
"""num_inference_steps""": 2,
"""inpaint_strength""": 1.0,
"""guidance_scale""": 6.0,
"""output_type""": """numpy""",
}
return inputs
def lowerCamelCase__ (self : List[Any] , _UpperCAmelCase : Dict , _UpperCAmelCase : Tuple=0 ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = floats_tensor((1, 3, 32, 32) , rng=random.Random(_UpperCAmelCase ) ).to(_UpperCAmelCase )
lowercase__ = image.cpu().permute(0 , 2 , 3 , 1 )[0]
lowercase__ = Image.fromarray(np.uinta(_UpperCAmelCase ) ).convert("""RGB""" )
if str(_UpperCAmelCase ).startswith("""mps""" ):
lowercase__ = torch.manual_seed(_UpperCAmelCase )
else:
lowercase__ = torch.Generator(device=_UpperCAmelCase ).manual_seed(_UpperCAmelCase )
lowercase__ = {
"""image""": image,
"""source_prompt""": """a cat and a frog""",
"""target_prompt""": """a dog and a newt""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""num_maps_per_mask""": 2,
"""mask_encode_strength""": 1.0,
"""guidance_scale""": 6.0,
"""output_type""": """numpy""",
}
return inputs
def lowerCamelCase__ (self : Optional[Any] , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Dict=0 ) -> str:
"""simple docstring"""
lowercase__ = floats_tensor((1, 3, 32, 32) , rng=random.Random(_UpperCAmelCase ) ).to(_UpperCAmelCase )
lowercase__ = image.cpu().permute(0 , 2 , 3 , 1 )[0]
lowercase__ = Image.fromarray(np.uinta(_UpperCAmelCase ) ).convert("""RGB""" )
if str(_UpperCAmelCase ).startswith("""mps""" ):
lowercase__ = torch.manual_seed(_UpperCAmelCase )
else:
lowercase__ = torch.Generator(device=_UpperCAmelCase ).manual_seed(_UpperCAmelCase )
lowercase__ = {
"""image""": image,
"""prompt""": """a cat and a frog""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""inpaint_strength""": 1.0,
"""guidance_scale""": 6.0,
"""decode_latents""": True,
"""output_type""": """numpy""",
}
return inputs
def lowerCamelCase__ (self : int ) -> Dict:
"""simple docstring"""
if not hasattr(self.pipeline_class , """_optional_components""" ):
return
lowercase__ = self.get_dummy_components()
lowercase__ = self.pipeline_class(**_UpperCAmelCase )
pipe.to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
# set all optional components to None and update pipeline config accordingly
for optional_component in pipe._optional_components:
setattr(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components} )
lowercase__ = self.get_dummy_inputs(_UpperCAmelCase )
lowercase__ = pipe(**_UpperCAmelCase )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(_UpperCAmelCase )
lowercase__ = self.pipeline_class.from_pretrained(_UpperCAmelCase )
pipe_loaded.to(_UpperCAmelCase )
pipe_loaded.set_progress_bar_config(disable=_UpperCAmelCase )
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(_UpperCAmelCase , _UpperCAmelCase ) is None , f'''`{optional_component}` did not stay set to None after loading.''' , )
lowercase__ = self.get_dummy_inputs(_UpperCAmelCase )
lowercase__ = pipe_loaded(**_UpperCAmelCase )[0]
lowercase__ = np.abs(output - output_loaded ).max()
self.assertLess(_UpperCAmelCase , 1E-4 )
def lowerCamelCase__ (self : List[str] ) -> int:
"""simple docstring"""
lowercase__ = """cpu"""
lowercase__ = self.get_dummy_components()
lowercase__ = self.pipeline_class(**_UpperCAmelCase )
pipe.to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
lowercase__ = self.get_dummy_mask_inputs(_UpperCAmelCase )
lowercase__ = pipe.generate_mask(**_UpperCAmelCase )
lowercase__ = mask[0, -3:, -3:]
self.assertEqual(mask.shape , (1, 16, 16) )
lowercase__ = np.array([0] * 9 )
lowercase__ = np.abs(mask_slice.flatten() - expected_slice ).max()
self.assertLessEqual(_UpperCAmelCase , 1E-3 )
self.assertEqual(mask[0, -3, -4] , 0 )
def lowerCamelCase__ (self : List[Any] ) -> str:
"""simple docstring"""
lowercase__ = """cpu"""
lowercase__ = self.get_dummy_components()
lowercase__ = self.pipeline_class(**_UpperCAmelCase )
pipe.to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
lowercase__ = self.get_dummy_inversion_inputs(_UpperCAmelCase )
lowercase__ = pipe.invert(**_UpperCAmelCase ).images
lowercase__ = image[0, -1, -3:, -3:]
self.assertEqual(image.shape , (2, 32, 32, 3) )
lowercase__ = np.array(
[0.5_150, 0.5_134, 0.5_043, 0.5_376, 0.4_694, 0.51_050, 0.5_015, 0.4_407, 0.4_799] , )
lowercase__ = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(_UpperCAmelCase , 1E-3 )
def lowerCamelCase__ (self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
super().test_inference_batch_single_identical(expected_max_diff=5E-3 )
def lowerCamelCase__ (self : str ) -> List[str]:
"""simple docstring"""
lowercase__ = """cpu"""
lowercase__ = self.get_dummy_components()
lowercase__ = {"""beta_start""": 0.00_085, """beta_end""": 0.012, """beta_schedule""": """scaled_linear"""}
lowercase__ = DPMSolverMultistepScheduler(**_UpperCAmelCase )
lowercase__ = DPMSolverMultistepInverseScheduler(**_UpperCAmelCase )
lowercase__ = self.pipeline_class(**_UpperCAmelCase )
pipe.to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
lowercase__ = self.get_dummy_inversion_inputs(_UpperCAmelCase )
lowercase__ = pipe.invert(**_UpperCAmelCase ).images
lowercase__ = image[0, -1, -3:, -3:]
self.assertEqual(image.shape , (2, 32, 32, 3) )
lowercase__ = np.array(
[0.5_150, 0.5_134, 0.5_043, 0.5_376, 0.4_694, 0.51_050, 0.5_015, 0.4_407, 0.4_799] , )
lowercase__ = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(_UpperCAmelCase , 1E-3 )
@require_torch_gpu
@slow
class A ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase__ (self : Any ) -> Any:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@classmethod
def lowerCamelCase__ (cls : str ) -> Optional[int]:
"""simple docstring"""
lowercase__ = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png""" )
lowercase__ = raw_image.convert("""RGB""" ).resize((768, 768) )
lowercase__ = raw_image
def lowerCamelCase__ (self : Optional[int] ) -> Any:
"""simple docstring"""
lowercase__ = torch.manual_seed(0 )
lowercase__ = StableDiffusionDiffEditPipeline.from_pretrained(
"""stabilityai/stable-diffusion-2-1""" , safety_checker=_UpperCAmelCase , torch_dtype=torch.floataa )
lowercase__ = DDIMScheduler.from_config(pipe.scheduler.config )
lowercase__ = DDIMInverseScheduler.from_config(pipe.scheduler.config )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
lowercase__ = """a bowl of fruit"""
lowercase__ = """a bowl of pears"""
lowercase__ = pipe.generate_mask(
image=self.raw_image , source_prompt=_UpperCAmelCase , target_prompt=_UpperCAmelCase , generator=_UpperCAmelCase , )
lowercase__ = pipe.invert(
prompt=_UpperCAmelCase , image=self.raw_image , inpaint_strength=0.7 , generator=_UpperCAmelCase ).latents
lowercase__ = pipe(
prompt=_UpperCAmelCase , mask_image=_UpperCAmelCase , image_latents=_UpperCAmelCase , generator=_UpperCAmelCase , negative_prompt=_UpperCAmelCase , inpaint_strength=0.7 , output_type="""numpy""" , ).images[0]
lowercase__ = (
np.array(
load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/diffedit/pears.png""" ).resize((768, 768) ) )
/ 255
)
assert np.abs((expected_image - image).max() ) < 5E-1
def lowerCamelCase__ (self : int ) -> Any:
"""simple docstring"""
lowercase__ = torch.manual_seed(0 )
lowercase__ = StableDiffusionDiffEditPipeline.from_pretrained(
"""stabilityai/stable-diffusion-2-1""" , safety_checker=_UpperCAmelCase , torch_dtype=torch.floataa )
lowercase__ = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
lowercase__ = DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
lowercase__ = """a bowl of fruit"""
lowercase__ = """a bowl of pears"""
lowercase__ = pipe.generate_mask(
image=self.raw_image , source_prompt=_UpperCAmelCase , target_prompt=_UpperCAmelCase , generator=_UpperCAmelCase , )
lowercase__ = pipe.invert(
prompt=_UpperCAmelCase , image=self.raw_image , inpaint_strength=0.7 , generator=_UpperCAmelCase , num_inference_steps=25 , ).latents
lowercase__ = pipe(
prompt=_UpperCAmelCase , mask_image=_UpperCAmelCase , image_latents=_UpperCAmelCase , generator=_UpperCAmelCase , negative_prompt=_UpperCAmelCase , inpaint_strength=0.7 , num_inference_steps=25 , output_type="""numpy""" , ).images[0]
lowercase__ = (
np.array(
load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/diffedit/pears.png""" ).resize((768, 768) ) )
/ 255
)
assert np.abs((expected_image - image).max() ) < 5E-1
| 305
| 1
|
from math import isqrt
def UpperCamelCase ( __magic_name__ : int ) -> list[int]:
"""simple docstring"""
lowercase__ = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , __magic_name__ , __magic_name__ ):
lowercase__ = False
return [i for i in range(2 , __magic_name__ ) if is_prime[i]]
def UpperCamelCase ( __magic_name__ : int = 10**8 ) -> int:
"""simple docstring"""
lowercase__ = calculate_prime_numbers(max_number // 2 )
lowercase__ = 0
lowercase__ = 0
lowercase__ = len(__magic_name__ ) - 1
while left <= right:
while prime_numbers[left] * prime_numbers[right] >= max_number:
right -= 1
semiprimes_count += right - left + 1
left += 1
return semiprimes_count
if __name__ == "__main__":
print(F'{solution() = }')
| 305
|
from __future__ import annotations
import math
def UpperCamelCase ( __magic_name__ : list , __magic_name__ : list ) -> list:
"""simple docstring"""
if len(__magic_name__ ) != 2 or len(a[0] ) != 2 or len(__magic_name__ ) != 2 or len(b[0] ) != 2:
raise Exception("""Matrices are not 2x2""" )
lowercase__ = [
[a[0][0] * b[0][0] + a[0][1] * b[1][0], a[0][0] * b[0][1] + a[0][1] * b[1][1]],
[a[1][0] * b[0][0] + a[1][1] * b[1][0], a[1][0] * b[0][1] + a[1][1] * b[1][1]],
]
return new_matrix
def UpperCamelCase ( __magic_name__ : list , __magic_name__ : list ) -> Union[str, Any]:
"""simple docstring"""
return [
[matrix_a[row][col] + matrix_b[row][col] for col in range(len(matrix_a[row] ) )]
for row in range(len(__magic_name__ ) )
]
def UpperCamelCase ( __magic_name__ : list , __magic_name__ : list ) -> int:
"""simple docstring"""
return [
[matrix_a[row][col] - matrix_b[row][col] for col in range(len(matrix_a[row] ) )]
for row in range(len(__magic_name__ ) )
]
def UpperCamelCase ( __magic_name__ : list ) -> tuple[list, list, list, list]:
"""simple docstring"""
if len(__magic_name__ ) % 2 != 0 or len(a[0] ) % 2 != 0:
raise Exception("""Odd matrices are not supported!""" )
lowercase__ = len(__magic_name__ )
lowercase__ = matrix_length // 2
lowercase__ = [[a[i][j] for j in range(__magic_name__ , __magic_name__ )] for i in range(__magic_name__ )]
lowercase__ = [
[a[i][j] for j in range(__magic_name__ , __magic_name__ )] for i in range(__magic_name__ , __magic_name__ )
]
lowercase__ = [[a[i][j] for j in range(__magic_name__ )] for i in range(__magic_name__ )]
lowercase__ = [[a[i][j] for j in range(__magic_name__ )] for i in range(__magic_name__ , __magic_name__ )]
return top_left, top_right, bot_left, bot_right
def UpperCamelCase ( __magic_name__ : list ) -> tuple[int, int]:
"""simple docstring"""
return len(__magic_name__ ), len(matrix[0] )
def UpperCamelCase ( __magic_name__ : list ) -> None:
"""simple docstring"""
print("""\n""".join(str(__magic_name__ ) for line in matrix ) )
def UpperCamelCase ( __magic_name__ : list , __magic_name__ : list ) -> list:
"""simple docstring"""
if matrix_dimensions(__magic_name__ ) == (2, 2):
return default_matrix_multiplication(__magic_name__ , __magic_name__ )
lowercase__ , lowercase__ , lowercase__ , lowercase__ = split_matrix(__magic_name__ )
lowercase__ , lowercase__ , lowercase__ , lowercase__ = split_matrix(__magic_name__ )
lowercase__ = actual_strassen(__magic_name__ , matrix_subtraction(__magic_name__ , __magic_name__ ) )
lowercase__ = actual_strassen(matrix_addition(__magic_name__ , __magic_name__ ) , __magic_name__ )
lowercase__ = actual_strassen(matrix_addition(__magic_name__ , __magic_name__ ) , __magic_name__ )
lowercase__ = actual_strassen(__magic_name__ , matrix_subtraction(__magic_name__ , __magic_name__ ) )
lowercase__ = actual_strassen(matrix_addition(__magic_name__ , __magic_name__ ) , matrix_addition(__magic_name__ , __magic_name__ ) )
lowercase__ = actual_strassen(matrix_subtraction(__magic_name__ , __magic_name__ ) , matrix_addition(__magic_name__ , __magic_name__ ) )
lowercase__ = actual_strassen(matrix_subtraction(__magic_name__ , __magic_name__ ) , matrix_addition(__magic_name__ , __magic_name__ ) )
lowercase__ = matrix_addition(matrix_subtraction(matrix_addition(__magic_name__ , __magic_name__ ) , __magic_name__ ) , __magic_name__ )
lowercase__ = matrix_addition(__magic_name__ , __magic_name__ )
lowercase__ = matrix_addition(__magic_name__ , __magic_name__ )
lowercase__ = matrix_subtraction(matrix_subtraction(matrix_addition(__magic_name__ , __magic_name__ ) , __magic_name__ ) , __magic_name__ )
# construct the new matrix from our 4 quadrants
lowercase__ = []
for i in range(len(__magic_name__ ) ):
new_matrix.append(top_left[i] + top_right[i] )
for i in range(len(__magic_name__ ) ):
new_matrix.append(bot_left[i] + bot_right[i] )
return new_matrix
def UpperCamelCase ( __magic_name__ : list , __magic_name__ : list ) -> list:
"""simple docstring"""
if matrix_dimensions(__magic_name__ )[1] != matrix_dimensions(__magic_name__ )[0]:
lowercase__ = (
"""Unable to multiply these matrices, please check the dimensions.\n"""
f'''Matrix A: {matrixa}\n'''
f'''Matrix B: {matrixa}'''
)
raise Exception(__magic_name__ )
lowercase__ = matrix_dimensions(__magic_name__ )
lowercase__ = matrix_dimensions(__magic_name__ )
if dimensiona[0] == dimensiona[1] and dimensiona[0] == dimensiona[1]:
return [matrixa, matrixa]
lowercase__ = max(*__magic_name__ , *__magic_name__ )
lowercase__ = int(math.pow(2 , math.ceil(math.loga(__magic_name__ ) ) ) )
lowercase__ = matrixa
lowercase__ = matrixa
# Adding zeros to the matrices so that the arrays dimensions are the same and also
# power of 2
for i in range(0 , __magic_name__ ):
if i < dimensiona[0]:
for _ in range(dimensiona[1] , __magic_name__ ):
new_matrixa[i].append(0 )
else:
new_matrixa.append([0] * maxim )
if i < dimensiona[0]:
for _ in range(dimensiona[1] , __magic_name__ ):
new_matrixa[i].append(0 )
else:
new_matrixa.append([0] * maxim )
lowercase__ = actual_strassen(__magic_name__ , __magic_name__ )
# Removing the additional zeros
for i in range(0 , __magic_name__ ):
if i < dimensiona[0]:
for _ in range(dimensiona[1] , __magic_name__ ):
final_matrix[i].pop()
else:
final_matrix.pop()
return final_matrix
if __name__ == "__main__":
A : Optional[Any] = [
[2, 3, 4, 5],
[6, 4, 3, 1],
[2, 3, 6, 7],
[3, 1, 2, 4],
[2, 3, 4, 5],
[6, 4, 3, 1],
[2, 3, 6, 7],
[3, 1, 2, 4],
[2, 3, 4, 5],
[6, 2, 3, 1],
]
A : List[Any] = [[0, 2, 1, 1], [1_6, 2, 3, 3], [2, 2, 7, 7], [1_3, 1_1, 2_2, 4]]
print(strassen(matrixa, matrixa))
| 305
| 1
|
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaInpaintPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class A ( UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
A__ = KandinskyVaaInpaintPipeline
A__ = ['''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
A__ = [
'''image_embeds''',
'''negative_image_embeds''',
'''image''',
'''mask_image''',
]
A__ = [
'''generator''',
'''height''',
'''width''',
'''latents''',
'''guidance_scale''',
'''num_inference_steps''',
'''return_dict''',
'''guidance_scale''',
'''num_images_per_prompt''',
'''output_type''',
'''return_dict''',
]
A__ = False
@property
def lowerCamelCase__ (self : Tuple ) -> List[str]:
"""simple docstring"""
return 32
@property
def lowerCamelCase__ (self : List[str] ) -> List[Any]:
"""simple docstring"""
return 32
@property
def lowerCamelCase__ (self : Dict ) -> Tuple:
"""simple docstring"""
return self.time_input_dim
@property
def lowerCamelCase__ (self : List[str] ) -> str:
"""simple docstring"""
return self.time_input_dim * 4
@property
def lowerCamelCase__ (self : Optional[int] ) -> int:
"""simple docstring"""
return 100
@property
def lowerCamelCase__ (self : Dict ) -> List[Any]:
"""simple docstring"""
torch.manual_seed(0 )
lowercase__ = {
"""in_channels""": 9,
# Out channels is double in channels because predicts mean and variance
"""out_channels""": 8,
"""addition_embed_type""": """image""",
"""down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
"""up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
"""mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
"""block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
"""layers_per_block""": 1,
"""encoder_hid_dim""": self.text_embedder_hidden_size,
"""encoder_hid_dim_type""": """image_proj""",
"""cross_attention_dim""": self.cross_attention_dim,
"""attention_head_dim""": 4,
"""resnet_time_scale_shift""": """scale_shift""",
"""class_embed_type""": None,
}
lowercase__ = UNetaDConditionModel(**_UpperCAmelCase )
return model
@property
def lowerCamelCase__ (self : int ) -> Tuple:
"""simple docstring"""
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def lowerCamelCase__ (self : Tuple ) -> List[str]:
"""simple docstring"""
torch.manual_seed(0 )
lowercase__ = VQModel(**self.dummy_movq_kwargs )
return model
def lowerCamelCase__ (self : int ) -> Any:
"""simple docstring"""
lowercase__ = self.dummy_unet
lowercase__ = self.dummy_movq
lowercase__ = DDIMScheduler(
num_train_timesteps=1000 , beta_schedule="""linear""" , beta_start=0.00_085 , beta_end=0.012 , clip_sample=_UpperCAmelCase , set_alpha_to_one=_UpperCAmelCase , steps_offset=1 , prediction_type="""epsilon""" , thresholding=_UpperCAmelCase , )
lowercase__ = {
"""unet""": unet,
"""scheduler""": scheduler,
"""movq""": movq,
}
return components
def lowerCamelCase__ (self : Optional[int] , _UpperCAmelCase : int , _UpperCAmelCase : List[Any]=0 ) -> Tuple:
"""simple docstring"""
lowercase__ = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(_UpperCAmelCase ) ).to(_UpperCAmelCase )
lowercase__ = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
_UpperCAmelCase )
# create init_image
lowercase__ = floats_tensor((1, 3, 64, 64) , rng=random.Random(_UpperCAmelCase ) ).to(_UpperCAmelCase )
lowercase__ = image.cpu().permute(0 , 2 , 3 , 1 )[0]
lowercase__ = Image.fromarray(np.uinta(_UpperCAmelCase ) ).convert("""RGB""" ).resize((256, 256) )
# create mask
lowercase__ = np.ones((64, 64) , dtype=np.floataa )
lowercase__ = 0
if str(_UpperCAmelCase ).startswith("""mps""" ):
lowercase__ = torch.manual_seed(_UpperCAmelCase )
else:
lowercase__ = torch.Generator(device=_UpperCAmelCase ).manual_seed(_UpperCAmelCase )
lowercase__ = {
"""image""": init_image,
"""mask_image""": mask,
"""image_embeds""": image_embeds,
"""negative_image_embeds""": negative_image_embeds,
"""generator""": generator,
"""height""": 64,
"""width""": 64,
"""num_inference_steps""": 2,
"""guidance_scale""": 4.0,
"""output_type""": """np""",
}
return inputs
def lowerCamelCase__ (self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
lowercase__ = """cpu"""
lowercase__ = self.get_dummy_components()
lowercase__ = self.pipeline_class(**_UpperCAmelCase )
lowercase__ = pipe.to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
lowercase__ = pipe(**self.get_dummy_inputs(_UpperCAmelCase ) )
lowercase__ = output.images
lowercase__ = pipe(
**self.get_dummy_inputs(_UpperCAmelCase ) , return_dict=_UpperCAmelCase , )[0]
lowercase__ = image[0, -3:, -3:, -1]
lowercase__ = image_from_tuple[0, -3:, -3:, -1]
print(f'''image.shape {image.shape}''' )
assert image.shape == (1, 64, 64, 3)
lowercase__ = np.array(
[0.50_775_903, 0.49_527_195, 0.48_824_543, 0.50_192_237, 0.48_644_906, 0.49_373_814, 0.4_780_598, 0.47_234_827, 0.48_327_848] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
), f''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
), f''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''
def lowerCamelCase__ (self : int ) -> Optional[int]:
"""simple docstring"""
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
@slow
@require_torch_gpu
class A ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase__ (self : int ) -> Union[str, Any]:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase__ (self : Any ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy""" )
lowercase__ = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png""" )
lowercase__ = np.ones((768, 768) , dtype=np.floataa )
lowercase__ = 0
lowercase__ = """a hat"""
lowercase__ = KandinskyVaaPriorPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-2-prior""" , torch_dtype=torch.floataa )
pipe_prior.to(_UpperCAmelCase )
lowercase__ = KandinskyVaaInpaintPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-2-decoder-inpaint""" , torch_dtype=torch.floataa )
lowercase__ = pipeline.to(_UpperCAmelCase )
pipeline.set_progress_bar_config(disable=_UpperCAmelCase )
lowercase__ = torch.Generator(device="""cpu""" ).manual_seed(0 )
lowercase__ , lowercase__ = pipe_prior(
_UpperCAmelCase , generator=_UpperCAmelCase , num_inference_steps=5 , negative_prompt="""""" , ).to_tuple()
lowercase__ = pipeline(
image=_UpperCAmelCase , mask_image=_UpperCAmelCase , image_embeds=_UpperCAmelCase , negative_image_embeds=_UpperCAmelCase , generator=_UpperCAmelCase , num_inference_steps=100 , height=768 , width=768 , output_type="""np""" , )
lowercase__ = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(_UpperCAmelCase , _UpperCAmelCase )
| 305
|
import unittest
import numpy as np
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transformers.models.bert.modeling_flax_bert import (
FlaxBertForMaskedLM,
FlaxBertForMultipleChoice,
FlaxBertForNextSentencePrediction,
FlaxBertForPreTraining,
FlaxBertForQuestionAnswering,
FlaxBertForSequenceClassification,
FlaxBertForTokenClassification,
FlaxBertModel,
)
class A ( unittest.TestCase ):
'''simple docstring'''
def __init__(self : Optional[Any] , _UpperCAmelCase : Tuple , _UpperCAmelCase : str=13 , _UpperCAmelCase : List[str]=7 , _UpperCAmelCase : Union[str, Any]=True , _UpperCAmelCase : Dict=True , _UpperCAmelCase : str=True , _UpperCAmelCase : str=True , _UpperCAmelCase : Dict=99 , _UpperCAmelCase : Any=32 , _UpperCAmelCase : List[str]=5 , _UpperCAmelCase : Union[str, Any]=4 , _UpperCAmelCase : str=37 , _UpperCAmelCase : Union[str, Any]="gelu" , _UpperCAmelCase : Any=0.1 , _UpperCAmelCase : int=0.1 , _UpperCAmelCase : Dict=512 , _UpperCAmelCase : Optional[int]=16 , _UpperCAmelCase : str=2 , _UpperCAmelCase : List[Any]=0.02 , _UpperCAmelCase : List[str]=4 , ) -> List[Any]:
"""simple docstring"""
lowercase__ = parent
lowercase__ = batch_size
lowercase__ = seq_length
lowercase__ = is_training
lowercase__ = use_attention_mask
lowercase__ = use_token_type_ids
lowercase__ = use_labels
lowercase__ = vocab_size
lowercase__ = hidden_size
lowercase__ = num_hidden_layers
lowercase__ = num_attention_heads
lowercase__ = intermediate_size
lowercase__ = hidden_act
lowercase__ = hidden_dropout_prob
lowercase__ = attention_probs_dropout_prob
lowercase__ = max_position_embeddings
lowercase__ = type_vocab_size
lowercase__ = type_sequence_label_size
lowercase__ = initializer_range
lowercase__ = num_choices
def lowerCamelCase__ (self : List[str] ) -> Dict:
"""simple docstring"""
lowercase__ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase__ = None
if self.use_attention_mask:
lowercase__ = random_attention_mask([self.batch_size, self.seq_length] )
lowercase__ = None
if self.use_token_type_ids:
lowercase__ = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowercase__ = 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=_UpperCAmelCase , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def lowerCamelCase__ (self : int ) -> Any:
"""simple docstring"""
lowercase__ = self.prepare_config_and_inputs()
lowercase__ , lowercase__ , lowercase__ , lowercase__ = config_and_inputs
lowercase__ = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
return config, inputs_dict
def lowerCamelCase__ (self : Tuple ) -> str:
"""simple docstring"""
lowercase__ = self.prepare_config_and_inputs()
lowercase__ , lowercase__ , lowercase__ , lowercase__ = config_and_inputs
lowercase__ = True
lowercase__ = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
lowercase__ = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
attention_mask,
encoder_hidden_states,
encoder_attention_mask,
)
@require_flax
class A ( UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
A__ = True
A__ = (
(
FlaxBertModel,
FlaxBertForPreTraining,
FlaxBertForMaskedLM,
FlaxBertForMultipleChoice,
FlaxBertForQuestionAnswering,
FlaxBertForNextSentencePrediction,
FlaxBertForSequenceClassification,
FlaxBertForTokenClassification,
FlaxBertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def lowerCamelCase__ (self : Optional[int] ) -> List[str]:
"""simple docstring"""
lowercase__ = FlaxBertModelTester(self )
@slow
def lowerCamelCase__ (self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = FlaxBertModel.from_pretrained("""bert-base-cased""" )
lowercase__ = model(np.ones((1, 1) ) )
self.assertIsNotNone(_UpperCAmelCase )
| 305
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|
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
A : Dict = logging.get_logger(__name__)
A : int = list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
A : Optional[Any] = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class A :
'''simple docstring'''
A__ = field(
default=UpperCAmelCase__ , metadata={'''help''': '''Model type selected in the list: ''' + ''', '''.join(UpperCAmelCase__ )} )
A__ = field(
default=UpperCAmelCase__ , metadata={'''help''': '''The input data dir. Should contain the .json files for the SQuAD task.'''} )
A__ = field(
default=1_28 , metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
A__ = field(
default=1_28 , metadata={'''help''': '''When splitting up a long document into chunks, how much stride to take between chunks.'''} , )
A__ = field(
default=64 , metadata={
'''help''': (
'''The maximum number of tokens for the question. Questions longer than this will '''
'''be truncated to this length.'''
)
} , )
A__ = field(
default=30 , metadata={
'''help''': (
'''The maximum length of an answer that can be generated. This is needed because the start '''
'''and end predictions are not conditioned on one another.'''
)
} , )
A__ = field(
default=UpperCAmelCase__ , metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )
A__ = field(
default=UpperCAmelCase__ , metadata={'''help''': '''If true, the SQuAD examples contain some that do not have an answer.'''} )
A__ = field(
default=0.0 , metadata={'''help''': '''If null_score - best_non_null is greater than the threshold predict null.'''} )
A__ = field(
default=20 , metadata={'''help''': '''If null_score - best_non_null is greater than the threshold predict null.'''} )
A__ = field(
default=0 , metadata={
'''help''': (
'''language id of input for language-specific xlm models (see'''
''' tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)'''
)
} , )
A__ = field(default=1 , metadata={'''help''': '''multiple threads for converting example to features'''} )
class A ( UpperCAmelCase__ ):
'''simple docstring'''
A__ = '''train'''
A__ = '''dev'''
class A ( UpperCAmelCase__ ):
'''simple docstring'''
A__ = 42
A__ = 42
A__ = 42
A__ = 42
def __init__(self : Optional[int] , _UpperCAmelCase : SquadDataTrainingArguments , _UpperCAmelCase : PreTrainedTokenizer , _UpperCAmelCase : Optional[int] = None , _UpperCAmelCase : Union[str, Split] = Split.train , _UpperCAmelCase : Optional[bool] = False , _UpperCAmelCase : Optional[str] = None , _UpperCAmelCase : Optional[str] = "pt" , ) -> Tuple:
"""simple docstring"""
lowercase__ = args
lowercase__ = is_language_sensitive
lowercase__ = SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
try:
lowercase__ = Split[mode]
except KeyError:
raise KeyError("""mode is not a valid split name""" )
lowercase__ = mode
# Load data features from cache or dataset file
lowercase__ = """v2""" if args.version_2_with_negative else """v1"""
lowercase__ = os.path.join(
cache_dir if cache_dir is not None else args.data_dir , f'''cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}''' , )
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
lowercase__ = cached_features_file + """.lock"""
with FileLock(_UpperCAmelCase ):
if os.path.exists(_UpperCAmelCase ) and not args.overwrite_cache:
lowercase__ = time.time()
lowercase__ = torch.load(_UpperCAmelCase )
# Legacy cache files have only features, while new cache files
# will have dataset and examples also.
lowercase__ = self.old_features["""features"""]
lowercase__ = self.old_features.get("""dataset""" , _UpperCAmelCase )
lowercase__ = self.old_features.get("""examples""" , _UpperCAmelCase )
logger.info(
f'''Loading features from cached file {cached_features_file} [took %.3f s]''' , time.time() - start )
if self.dataset is None or self.examples is None:
logger.warning(
f'''Deleting cached file {cached_features_file} will allow dataset and examples to be cached in'''
""" future run""" )
else:
if mode == Split.dev:
lowercase__ = self.processor.get_dev_examples(args.data_dir )
else:
lowercase__ = self.processor.get_train_examples(args.data_dir )
lowercase__ , lowercase__ = squad_convert_examples_to_features(
examples=self.examples , tokenizer=_UpperCAmelCase , max_seq_length=args.max_seq_length , doc_stride=args.doc_stride , max_query_length=args.max_query_length , is_training=mode == Split.train , threads=args.threads , return_dataset=_UpperCAmelCase , )
lowercase__ = time.time()
torch.save(
{"""features""": self.features, """dataset""": self.dataset, """examples""": self.examples} , _UpperCAmelCase , )
# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
logger.info(
f'''Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]''' )
def __len__(self : List[str] ) -> Dict:
"""simple docstring"""
return len(self.features )
def __getitem__(self : Union[str, Any] , _UpperCAmelCase : Dict ) -> Dict[str, torch.Tensor]:
"""simple docstring"""
lowercase__ = self.features[i]
lowercase__ = torch.tensor(feature.input_ids , dtype=torch.long )
lowercase__ = torch.tensor(feature.attention_mask , dtype=torch.long )
lowercase__ = torch.tensor(feature.token_type_ids , dtype=torch.long )
lowercase__ = torch.tensor(feature.cls_index , dtype=torch.long )
lowercase__ = torch.tensor(feature.p_mask , dtype=torch.float )
lowercase__ = torch.tensor(feature.is_impossible , dtype=torch.float )
lowercase__ = {
"""input_ids""": input_ids,
"""attention_mask""": attention_mask,
"""token_type_ids""": token_type_ids,
}
if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
del inputs["token_type_ids"]
if self.args.model_type in ["xlnet", "xlm"]:
inputs.update({"""cls_index""": cls_index, """p_mask""": p_mask} )
if self.args.version_2_with_negative:
inputs.update({"""is_impossible""": is_impossible} )
if self.is_language_sensitive:
inputs.update({"""langs""": (torch.ones(input_ids.shape , dtype=torch.intaa ) * self.args.lang_id)} )
if self.mode == Split.train:
lowercase__ = torch.tensor(feature.start_position , dtype=torch.long )
lowercase__ = torch.tensor(feature.end_position , dtype=torch.long )
inputs.update({"""start_positions""": start_positions, """end_positions""": end_positions} )
return inputs
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|
def UpperCamelCase ( __magic_name__ : str ) -> list:
"""simple docstring"""
if n_term == "":
return []
lowercase__ = []
for temp in range(int(__magic_name__ ) ):
series.append(f'''1/{temp + 1}''' if series else """1""" )
return series
if __name__ == "__main__":
A : Tuple = input('Enter the last number (nth term) of the Harmonic Series')
print('Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n')
print(harmonic_series(nth_term))
| 305
| 1
|
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
A : int = logging.get_logger(__name__)
class A ( UpperCAmelCase__ ):
'''simple docstring'''
A__ = ['''pixel_values''']
def __init__(self : Tuple , _UpperCAmelCase : bool = True , _UpperCAmelCase : Optional[Dict[str, int]] = None , _UpperCAmelCase : PILImageResampling = PILImageResampling.BILINEAR , _UpperCAmelCase : bool = True , _UpperCAmelCase : Dict[str, int] = None , _UpperCAmelCase : bool = True , _UpperCAmelCase : Union[int, float] = 1 / 255 , _UpperCAmelCase : bool = True , _UpperCAmelCase : Optional[Union[float, List[float]]] = None , _UpperCAmelCase : Optional[Union[float, List[float]]] = None , **_UpperCAmelCase : Optional[Any] , ) -> None:
"""simple docstring"""
super().__init__(**_UpperCAmelCase )
lowercase__ = size if size is not None else {"""shortest_edge""": 256}
lowercase__ = get_size_dict(_UpperCAmelCase , default_to_square=_UpperCAmelCase )
lowercase__ = crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
lowercase__ = get_size_dict(_UpperCAmelCase )
lowercase__ = do_resize
lowercase__ = size
lowercase__ = resample
lowercase__ = do_center_crop
lowercase__ = crop_size
lowercase__ = do_rescale
lowercase__ = rescale_factor
lowercase__ = do_normalize
lowercase__ = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
lowercase__ = image_std if image_std is not None else IMAGENET_STANDARD_STD
def lowerCamelCase__ (self : Any , _UpperCAmelCase : np.ndarray , _UpperCAmelCase : Dict[str, int] , _UpperCAmelCase : PILImageResampling = PILImageResampling.BICUBIC , _UpperCAmelCase : Optional[Union[str, ChannelDimension]] = None , **_UpperCAmelCase : Union[str, Any] , ) -> np.ndarray:
"""simple docstring"""
lowercase__ = get_size_dict(_UpperCAmelCase , default_to_square=_UpperCAmelCase )
if "shortest_edge" not in size:
raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}''' )
lowercase__ = get_resize_output_image_size(_UpperCAmelCase , size=size["""shortest_edge"""] , default_to_square=_UpperCAmelCase )
return resize(_UpperCAmelCase , size=_UpperCAmelCase , resample=_UpperCAmelCase , data_format=_UpperCAmelCase , **_UpperCAmelCase )
def lowerCamelCase__ (self : Optional[int] , _UpperCAmelCase : np.ndarray , _UpperCAmelCase : Dict[str, int] , _UpperCAmelCase : Optional[Union[str, ChannelDimension]] = None , **_UpperCAmelCase : int , ) -> np.ndarray:
"""simple docstring"""
lowercase__ = get_size_dict(_UpperCAmelCase )
return center_crop(_UpperCAmelCase , size=(size["""height"""], size["""width"""]) , data_format=_UpperCAmelCase , **_UpperCAmelCase )
def lowerCamelCase__ (self : List[str] , _UpperCAmelCase : np.ndarray , _UpperCAmelCase : float , _UpperCAmelCase : Optional[Union[str, ChannelDimension]] = None , **_UpperCAmelCase : Optional[Any] ) -> np.ndarray:
"""simple docstring"""
return rescale(_UpperCAmelCase , scale=_UpperCAmelCase , data_format=_UpperCAmelCase , **_UpperCAmelCase )
def lowerCamelCase__ (self : List[Any] , _UpperCAmelCase : np.ndarray , _UpperCAmelCase : Union[float, List[float]] , _UpperCAmelCase : Union[float, List[float]] , _UpperCAmelCase : Optional[Union[str, ChannelDimension]] = None , **_UpperCAmelCase : int , ) -> np.ndarray:
"""simple docstring"""
return normalize(_UpperCAmelCase , mean=_UpperCAmelCase , std=_UpperCAmelCase , data_format=_UpperCAmelCase , **_UpperCAmelCase )
def lowerCamelCase__ (self : Optional[Any] , _UpperCAmelCase : ImageInput , _UpperCAmelCase : Optional[bool] = None , _UpperCAmelCase : Dict[str, int] = None , _UpperCAmelCase : PILImageResampling = None , _UpperCAmelCase : bool = None , _UpperCAmelCase : Dict[str, int] = None , _UpperCAmelCase : Optional[bool] = None , _UpperCAmelCase : Optional[float] = None , _UpperCAmelCase : Optional[bool] = None , _UpperCAmelCase : Optional[Union[float, List[float]]] = None , _UpperCAmelCase : Optional[Union[float, List[float]]] = None , _UpperCAmelCase : Optional[Union[str, TensorType]] = None , _UpperCAmelCase : Union[str, ChannelDimension] = ChannelDimension.FIRST , **_UpperCAmelCase : int , ) -> str:
"""simple docstring"""
lowercase__ = do_resize if do_resize is not None else self.do_resize
lowercase__ = size if size is not None else self.size
lowercase__ = get_size_dict(_UpperCAmelCase , default_to_square=_UpperCAmelCase )
lowercase__ = resample if resample is not None else self.resample
lowercase__ = do_center_crop if do_center_crop is not None else self.do_center_crop
lowercase__ = crop_size if crop_size is not None else self.crop_size
lowercase__ = get_size_dict(_UpperCAmelCase )
lowercase__ = do_rescale if do_rescale is not None else self.do_rescale
lowercase__ = rescale_factor if rescale_factor is not None else self.rescale_factor
lowercase__ = do_normalize if do_normalize is not None else self.do_normalize
lowercase__ = image_mean if image_mean is not None else self.image_mean
lowercase__ = image_std if image_std is not None else self.image_std
lowercase__ = make_list_of_images(_UpperCAmelCase )
if not valid_images(_UpperCAmelCase ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
if do_resize and size is None:
raise ValueError("""Size must be specified if do_resize is True.""" )
if do_center_crop and crop_size is None:
raise ValueError("""Crop size must be specified if do_center_crop is True.""" )
if do_rescale and rescale_factor is None:
raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("""Image mean and std must be specified if do_normalize is True.""" )
# All transformations expect numpy arrays.
lowercase__ = [to_numpy_array(_UpperCAmelCase ) for image in images]
if do_resize:
lowercase__ = [self.resize(image=_UpperCAmelCase , size=_UpperCAmelCase , resample=_UpperCAmelCase ) for image in images]
if do_center_crop:
lowercase__ = [self.center_crop(image=_UpperCAmelCase , size=_UpperCAmelCase ) for image in images]
if do_rescale:
lowercase__ = [self.rescale(image=_UpperCAmelCase , scale=_UpperCAmelCase ) for image in images]
if do_normalize:
lowercase__ = [self.normalize(image=_UpperCAmelCase , mean=_UpperCAmelCase , std=_UpperCAmelCase ) for image in images]
lowercase__ = [to_channel_dimension_format(_UpperCAmelCase , _UpperCAmelCase ) for image in images]
lowercase__ = {"""pixel_values""": images}
return BatchFeature(data=_UpperCAmelCase , tensor_type=_UpperCAmelCase )
| 305
|
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class A ( UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
A__ = ShapEImgaImgPipeline
A__ = ['''image''']
A__ = ['''image''']
A__ = [
'''num_images_per_prompt''',
'''num_inference_steps''',
'''generator''',
'''latents''',
'''guidance_scale''',
'''frame_size''',
'''output_type''',
'''return_dict''',
]
A__ = False
@property
def lowerCamelCase__ (self : Optional[Any] ) -> List[str]:
"""simple docstring"""
return 32
@property
def lowerCamelCase__ (self : str ) -> Any:
"""simple docstring"""
return 32
@property
def lowerCamelCase__ (self : str ) -> List[str]:
"""simple docstring"""
return self.time_input_dim * 4
@property
def lowerCamelCase__ (self : List[Any] ) -> Any:
"""simple docstring"""
return 8
@property
def lowerCamelCase__ (self : int ) -> List[str]:
"""simple docstring"""
torch.manual_seed(0 )
lowercase__ = CLIPVisionConfig(
hidden_size=self.text_embedder_hidden_size , image_size=64 , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_channels=3 , num_hidden_layers=5 , patch_size=1 , )
lowercase__ = CLIPVisionModel(_UpperCAmelCase )
return model
@property
def lowerCamelCase__ (self : Any ) -> List[Any]:
"""simple docstring"""
lowercase__ = CLIPImageProcessor(
crop_size=224 , do_center_crop=_UpperCAmelCase , do_normalize=_UpperCAmelCase , do_resize=_UpperCAmelCase , image_mean=[0.48_145_466, 0.4_578_275, 0.40_821_073] , image_std=[0.26_862_954, 0.26_130_258, 0.27_577_711] , resample=3 , size=224 , )
return image_processor
@property
def lowerCamelCase__ (self : int ) -> Optional[Any]:
"""simple docstring"""
torch.manual_seed(0 )
lowercase__ = {
"""num_attention_heads""": 2,
"""attention_head_dim""": 16,
"""embedding_dim""": self.time_input_dim,
"""num_embeddings""": 32,
"""embedding_proj_dim""": self.text_embedder_hidden_size,
"""time_embed_dim""": self.time_embed_dim,
"""num_layers""": 1,
"""clip_embed_dim""": self.time_input_dim * 2,
"""additional_embeddings""": 0,
"""time_embed_act_fn""": """gelu""",
"""norm_in_type""": """layer""",
"""embedding_proj_norm_type""": """layer""",
"""encoder_hid_proj_type""": None,
"""added_emb_type""": None,
}
lowercase__ = PriorTransformer(**_UpperCAmelCase )
return model
@property
def lowerCamelCase__ (self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
torch.manual_seed(0 )
lowercase__ = {
"""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,
),
}
lowercase__ = ShapERenderer(**_UpperCAmelCase )
return model
def lowerCamelCase__ (self : int ) -> Optional[int]:
"""simple docstring"""
lowercase__ = self.dummy_prior
lowercase__ = self.dummy_image_encoder
lowercase__ = self.dummy_image_processor
lowercase__ = self.dummy_renderer
lowercase__ = HeunDiscreteScheduler(
beta_schedule="""exp""" , num_train_timesteps=1024 , prediction_type="""sample""" , use_karras_sigmas=_UpperCAmelCase , clip_sample=_UpperCAmelCase , clip_sample_range=1.0 , )
lowercase__ = {
"""prior""": prior,
"""image_encoder""": image_encoder,
"""image_processor""": image_processor,
"""renderer""": renderer,
"""scheduler""": scheduler,
}
return components
def lowerCamelCase__ (self : Dict , _UpperCAmelCase : List[Any] , _UpperCAmelCase : str=0 ) -> str:
"""simple docstring"""
lowercase__ = floats_tensor((1, 3, 64, 64) , rng=random.Random(_UpperCAmelCase ) ).to(_UpperCAmelCase )
if str(_UpperCAmelCase ).startswith("""mps""" ):
lowercase__ = torch.manual_seed(_UpperCAmelCase )
else:
lowercase__ = torch.Generator(device=_UpperCAmelCase ).manual_seed(_UpperCAmelCase )
lowercase__ = {
"""image""": input_image,
"""generator""": generator,
"""num_inference_steps""": 1,
"""frame_size""": 32,
"""output_type""": """np""",
}
return inputs
def lowerCamelCase__ (self : str ) -> List[str]:
"""simple docstring"""
lowercase__ = """cpu"""
lowercase__ = self.get_dummy_components()
lowercase__ = self.pipeline_class(**_UpperCAmelCase )
lowercase__ = pipe.to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
lowercase__ = pipe(**self.get_dummy_inputs(_UpperCAmelCase ) )
lowercase__ = output.images[0]
lowercase__ = image[0, -3:, -3:, -1]
assert image.shape == (20, 32, 32, 3)
lowercase__ = np.array(
[
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def lowerCamelCase__ (self : str ) -> Any:
"""simple docstring"""
self._test_inference_batch_consistent(batch_sizes=[1, 2] )
def lowerCamelCase__ (self : Optional[int] ) -> str:
"""simple docstring"""
lowercase__ = torch_device == """cpu"""
lowercase__ = True
self._test_inference_batch_single_identical(
batch_size=2 , test_max_difference=_UpperCAmelCase , relax_max_difference=_UpperCAmelCase , )
def lowerCamelCase__ (self : Union[str, Any] ) -> int:
"""simple docstring"""
lowercase__ = self.get_dummy_components()
lowercase__ = self.pipeline_class(**_UpperCAmelCase )
lowercase__ = pipe.to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
lowercase__ = 1
lowercase__ = 2
lowercase__ = self.get_dummy_inputs(_UpperCAmelCase )
for key in inputs.keys():
if key in self.batch_params:
lowercase__ = batch_size * [inputs[key]]
lowercase__ = pipe(**_UpperCAmelCase , num_images_per_prompt=_UpperCAmelCase )[0]
assert images.shape[0] == batch_size * num_images_per_prompt
@slow
@require_torch_gpu
class A ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase__ (self : Dict ) -> List[Any]:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase__ (self : Any ) -> str:
"""simple docstring"""
lowercase__ = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/shap_e/corgi.png""" )
lowercase__ = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/shap_e/test_shap_e_img2img_out.npy""" )
lowercase__ = ShapEImgaImgPipeline.from_pretrained("""openai/shap-e-img2img""" )
lowercase__ = pipe.to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
lowercase__ = torch.Generator(device=_UpperCAmelCase ).manual_seed(0 )
lowercase__ = pipe(
_UpperCAmelCase , generator=_UpperCAmelCase , guidance_scale=3.0 , num_inference_steps=64 , frame_size=64 , output_type="""np""" , ).images[0]
assert images.shape == (20, 64, 64, 3)
assert_mean_pixel_difference(_UpperCAmelCase , _UpperCAmelCase )
| 305
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|
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 UpperCamelCase ( __magic_name__ : Union[str, Any] , __magic_name__ : Dict ) -> Any:
"""simple docstring"""
if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
# expert layer
lowercase__ = flax_key_tuple[:-1] + ("""weight""",)
lowercase__ = torch.permute(__magic_name__ , (0, 2, 1) )
elif flax_key_tuple[-1] == "kernel" and ".".join(__magic_name__ ):
# linear layer
lowercase__ = flax_key_tuple[:-1] + ("""weight""",)
lowercase__ = flax_tensor.T
elif flax_key_tuple[-1] in ["scale", "embedding"]:
lowercase__ = flax_key_tuple[:-1] + ("""weight""",)
return flax_key_tuple, flax_tensor
def UpperCamelCase ( __magic_name__ : Tuple , __magic_name__ : Optional[int] , __magic_name__ : Dict ) -> str:
"""simple docstring"""
if "metadata" in layer:
lowercase__ = layer.split("""metadata""" )
lowercase__ = """""".join(split_layer[0] )[:-1]
lowercase__ = [tuple(("""metadata""" + split_layer[1]).split("""/""" ) )]
elif "kvstore" in layer:
lowercase__ = layer.split("""kvstore""" )
lowercase__ = """""".join(split_layer[0] )[:-1]
lowercase__ = [tuple(("""kvstore""" + split_layer[1]).split("""/""" ) )]
else:
lowercase__ = layer.split("""/""" )
lowercase__ = """/""".join(split_layer[:-1] )
lowercase__ = (split_layer[-1],)
if "kvstore/path" in layer:
lowercase__ = f'''{switch_checkpoint_path}/{checkpoint_info[layer]}'''
elif "kvstore/driver" in layer:
lowercase__ = """file"""
else:
lowercase__ = checkpoint_info[layer]
return curr_real_layer_name, split_layer, content
def UpperCamelCase ( __magic_name__ : Any , __magic_name__ : List[str] ) -> List[Any]:
"""simple docstring"""
lowercase__ = rename_keys(__magic_name__ )
lowercase__ = {}
for k, v in current_block.items():
lowercase__ = v
lowercase__ = new_current_block
torch.save(__magic_name__ , __magic_name__ )
def UpperCamelCase ( __magic_name__ : List[str] , __magic_name__ : Optional[int] , __magic_name__ : Any , __magic_name__ : Dict , __magic_name__ : str = WEIGHTS_NAME ) -> int:
"""simple docstring"""
lowercase__ = convert_file_size_to_int(__magic_name__ )
lowercase__ = []
lowercase__ = {}
lowercase__ = 0
lowercase__ = 0
os.makedirs(__magic_name__ , exist_ok=__magic_name__ )
with gfile.GFile(switch_checkpoint_path + """/checkpoint""" , """rb""" ) as fp:
lowercase__ = serialization.msgpack_restore(fp.read() )["""optimizer"""]["""target"""]
lowercase__ = flatten_dict(__magic_name__ , sep="""/""" )
lowercase__ = {}
for layer in checkpoint_info.keys():
lowercase__ , lowercase__ , lowercase__ = get_key_and_tensorstore_dict(
__magic_name__ , __magic_name__ , __magic_name__ )
if curr_real_layer_name in all_layers:
lowercase__ = content
else:
lowercase__ = {split_layer[-1]: content}
for key in all_layers.keys():
# open tensorstore file
lowercase__ = ts.open(unflatten_dict(all_layers[key] ) ).result().read().result()
lowercase__ = torch.tensor(__magic_name__ )
lowercase__ = raw_weights.numel() * dtype_byte_size(raw_weights.dtype )
# use the renaming pattern from the small conversion scripts
lowercase__ , lowercase__ = rename_base_flax_keys(tuple(key.split("""/""" ) ) , __magic_name__ )
lowercase__ = """/""".join(__magic_name__ )
# If this weight is going to tip up over the maximal size, we split.
if current_block_size + weight_size > max_shard_size:
lowercase__ = os.path.join(
__magic_name__ , weights_name.replace(""".bin""" , f'''-{len(__magic_name__ )+1:05d}-of-???.bin''' ) )
rename_and_save_block(__magic_name__ , __magic_name__ )
sharded_state_dicts.append(current_block.keys() )
del current_block
lowercase__ = {}
lowercase__ = 0
lowercase__ = raw_weights.to(getattr(__magic_name__ , __magic_name__ ) )
current_block_size += weight_size
total_size += weight_size
# Add the last block
lowercase__ = os.path.join(__magic_name__ , weights_name.replace(""".bin""" , f'''-{len(__magic_name__ )+1:05d}-of-???.bin''' ) )
rename_and_save_block(__magic_name__ , __magic_name__ )
sharded_state_dicts.append(current_block.keys() )
# If we only have one shard, we return it
if len(__magic_name__ ) == 1:
return {weights_name: sharded_state_dicts[0]}, None
# Otherwise, let's build the index
lowercase__ = {}
lowercase__ = {}
for idx, shard in enumerate(__magic_name__ ):
lowercase__ = weights_name.replace(
""".bin""" , f'''-{idx+1:05d}-of-{len(__magic_name__ ):05d}.bin''' ) # len(sharded_state_dicts):05d}
lowercase__ = os.path.join(__magic_name__ , weights_name.replace(""".bin""" , f'''-{idx+1:05d}-of-???.bin''' ) )
os.rename(__magic_name__ , os.path.join(__magic_name__ , __magic_name__ ) )
lowercase__ = shard
for key in shard:
lowercase__ = shard_file
# Add the metadata
lowercase__ = {"""total_size""": total_size}
lowercase__ = {"""metadata""": metadata, """weight_map""": weight_map}
with open(os.path.join(__magic_name__ , __magic_name__ ) , """w""" , encoding="""utf-8""" ) as f:
lowercase__ = json.dumps(__magic_name__ , indent=2 , sort_keys=__magic_name__ ) + """\n"""
f.write(__magic_name__ )
return metadata, index
if __name__ == "__main__":
A : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--switch_t5x_checkpoint_path',
default='/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128/checkpoint_634600',
type=str,
required=False,
help='Path to a directory containing a folder per layer. Follows the original Google format.',
)
parser.add_argument('--max_shard_size', default='10GB', required=False, help='Max shard size')
parser.add_argument('--dtype', default='bfloat16', type=str, required=False, help='dtype of the saved model')
parser.add_argument(
'--pytorch_dump_folder_path',
default='/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128-converted',
type=str,
required=False,
help='Path to the output pytorch model.',
)
A : Optional[int] = parser.parse_args()
shard_on_the_fly(
args.switch_tax_checkpoint_path,
args.pytorch_dump_folder_path,
args.max_shard_size,
args.dtype,
)
def UpperCamelCase ( ) -> Dict:
"""simple docstring"""
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer
lowercase__ = SwitchTransformersConfig.from_pretrained("""google/switch-base-8""" )
config.save_pretrained("""/home/arthur_huggingface_co/transformers/switch_converted""" )
lowercase__ = SwitchTransformersForConditionalGeneration.from_pretrained(
"""/home/arthur_huggingface_co/transformers/switch_converted""" , device_map="""auto""" )
lowercase__ = TaTokenizer.from_pretrained("""t5-small""" )
lowercase__ = """A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."""
lowercase__ = tokenizer(__magic_name__ , return_tensors="""pt""" ).input_ids
lowercase__ = model.generate(__magic_name__ , decoder_start_token_id=0 )
print(tokenizer.decode(out[0] ) )
| 305
|
import requests
from bsa import BeautifulSoup
def UpperCamelCase ( __magic_name__ : str = "AAPL" ) -> str:
"""simple docstring"""
lowercase__ = f'''https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'''
lowercase__ = BeautifulSoup(requests.get(__magic_name__ ).text , """html.parser""" )
lowercase__ = """My(6px) Pos(r) smartphone_Mt(6px)"""
return soup.find("""div""" , class_=class_ ).find("""span""" ).text
if __name__ == "__main__":
for symbol in "AAPL AMZN IBM GOOG MSFT ORCL".split():
print(F'Current {symbol:<4} stock price is {stock_price(symbol):>8}')
| 305
| 1
|
from collections.abc import Callable
class A :
'''simple docstring'''
def __init__(self : List[str] , _UpperCAmelCase : Callable | None = None ) -> None:
"""simple docstring"""
lowercase__ = []
# Stores indexes of each item for supporting updates and deletion.
lowercase__ = {}
# Stores current size of heap.
lowercase__ = 0
# Stores function used to evaluate the score of an item on which basis ordering
# will be done.
lowercase__ = key or (lambda _UpperCAmelCase : x)
def lowerCamelCase__ (self : Tuple , _UpperCAmelCase : int ) -> int | None:
"""simple docstring"""
return int((i - 1) / 2 ) if i > 0 else None
def lowerCamelCase__ (self : int , _UpperCAmelCase : int ) -> int | None:
"""simple docstring"""
lowercase__ = int(2 * i + 1 )
return left if 0 < left < self.size else None
def lowerCamelCase__ (self : int , _UpperCAmelCase : int ) -> int | None:
"""simple docstring"""
lowercase__ = int(2 * i + 2 )
return right if 0 < right < self.size else None
def lowerCamelCase__ (self : List[Any] , _UpperCAmelCase : int , _UpperCAmelCase : int ) -> None:
"""simple docstring"""
lowercase__ , lowercase__ = (
self.pos_map[self.arr[j][0]],
self.pos_map[self.arr[i][0]],
)
# Then swap the items in the list.
lowercase__ , lowercase__ = self.arr[j], self.arr[i]
def lowerCamelCase__ (self : List[Any] , _UpperCAmelCase : int , _UpperCAmelCase : int ) -> bool:
"""simple docstring"""
return self.arr[i][1] < self.arr[j][1]
def lowerCamelCase__ (self : Optional[Any] , _UpperCAmelCase : int ) -> int:
"""simple docstring"""
lowercase__ = self._left(_UpperCAmelCase )
lowercase__ = self._right(_UpperCAmelCase )
lowercase__ = i
if left is not None and not self._cmp(_UpperCAmelCase , _UpperCAmelCase ):
lowercase__ = left
if right is not None and not self._cmp(_UpperCAmelCase , _UpperCAmelCase ):
lowercase__ = right
return valid_parent
def lowerCamelCase__ (self : Optional[int] , _UpperCAmelCase : int ) -> None:
"""simple docstring"""
lowercase__ = self._parent(_UpperCAmelCase )
while parent is not None and not self._cmp(_UpperCAmelCase , _UpperCAmelCase ):
self._swap(_UpperCAmelCase , _UpperCAmelCase )
lowercase__ , lowercase__ = parent, self._parent(_UpperCAmelCase )
def lowerCamelCase__ (self : Tuple , _UpperCAmelCase : int ) -> None:
"""simple docstring"""
lowercase__ = self._get_valid_parent(_UpperCAmelCase )
while valid_parent != index:
self._swap(_UpperCAmelCase , _UpperCAmelCase )
lowercase__ , lowercase__ = valid_parent, self._get_valid_parent(_UpperCAmelCase )
def lowerCamelCase__ (self : List[str] , _UpperCAmelCase : int , _UpperCAmelCase : int ) -> None:
"""simple docstring"""
if item not in self.pos_map:
return
lowercase__ = self.pos_map[item]
lowercase__ = [item, self.key(_UpperCAmelCase )]
# Make sure heap is right in both up and down direction.
# Ideally only one of them will make any change.
self._heapify_up(_UpperCAmelCase )
self._heapify_down(_UpperCAmelCase )
def lowerCamelCase__ (self : List[Any] , _UpperCAmelCase : int ) -> None:
"""simple docstring"""
if item not in self.pos_map:
return
lowercase__ = self.pos_map[item]
del self.pos_map[item]
lowercase__ = self.arr[self.size - 1]
lowercase__ = index
self.size -= 1
# Make sure heap is right in both up and down direction. Ideally only one
# of them will make any change- so no performance loss in calling both.
if self.size > index:
self._heapify_up(_UpperCAmelCase )
self._heapify_down(_UpperCAmelCase )
def lowerCamelCase__ (self : int , _UpperCAmelCase : int , _UpperCAmelCase : int ) -> None:
"""simple docstring"""
lowercase__ = len(self.arr )
if arr_len == self.size:
self.arr.append([item, self.key(_UpperCAmelCase )] )
else:
lowercase__ = [item, self.key(_UpperCAmelCase )]
lowercase__ = self.size
self.size += 1
self._heapify_up(self.size - 1 )
def lowerCamelCase__ (self : List[str] ) -> tuple | None:
"""simple docstring"""
return self.arr[0] if self.size else None
def lowerCamelCase__ (self : Dict ) -> tuple | None:
"""simple docstring"""
lowercase__ = self.get_top()
if top_item_tuple:
self.delete_item(top_item_tuple[0] )
return top_item_tuple
def UpperCamelCase ( ) -> None:
"""simple docstring"""
if __name__ == "__main__":
import doctest
doctest.testmod()
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|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : List[str] = logging.get_logger(__name__)
A : Any = {
'tiiuae/falcon-40b': 'https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json',
'tiiuae/falcon-7b': 'https://huggingface.co/tiiuae/falcon-7b/resolve/main/config.json',
}
class A ( UpperCAmelCase__ ):
'''simple docstring'''
A__ = '''falcon'''
A__ = ['''past_key_values''']
def __init__(self : str , _UpperCAmelCase : Dict=6_5024 , _UpperCAmelCase : Optional[Any]=4544 , _UpperCAmelCase : Optional[int]=32 , _UpperCAmelCase : Optional[Any]=71 , _UpperCAmelCase : List[Any]=1E-5 , _UpperCAmelCase : int=0.02 , _UpperCAmelCase : str=True , _UpperCAmelCase : Tuple=0.0 , _UpperCAmelCase : Any=0.0 , _UpperCAmelCase : str=None , _UpperCAmelCase : Optional[int]=False , _UpperCAmelCase : int=False , _UpperCAmelCase : Union[str, Any]=True , _UpperCAmelCase : List[Any]=True , _UpperCAmelCase : List[Any]=False , _UpperCAmelCase : Optional[int]=11 , _UpperCAmelCase : Optional[Any]=11 , **_UpperCAmelCase : Union[str, Any] , ) -> List[str]:
"""simple docstring"""
lowercase__ = vocab_size
# Backward compatibility with n_embed kwarg
lowercase__ = kwargs.pop("""n_embed""" , _UpperCAmelCase )
lowercase__ = hidden_size if n_embed is None else n_embed
lowercase__ = num_hidden_layers
lowercase__ = num_attention_heads
lowercase__ = layer_norm_epsilon
lowercase__ = initializer_range
lowercase__ = use_cache
lowercase__ = hidden_dropout
lowercase__ = attention_dropout
lowercase__ = bos_token_id
lowercase__ = eos_token_id
lowercase__ = num_attention_heads if num_kv_heads is None else num_kv_heads
lowercase__ = alibi
lowercase__ = new_decoder_architecture
lowercase__ = multi_query # Ignored when new_decoder_architecture is True
lowercase__ = parallel_attn
lowercase__ = bias
super().__init__(bos_token_id=_UpperCAmelCase , eos_token_id=_UpperCAmelCase , **_UpperCAmelCase )
@property
def lowerCamelCase__ (self : Tuple ) -> int:
"""simple docstring"""
return self.hidden_size // self.num_attention_heads
@property
def lowerCamelCase__ (self : List[str] ) -> Tuple:
"""simple docstring"""
return not self.alibi
| 305
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|
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.tokenization_utils import AddedToken
logging.set_verbosity_info()
A : Optional[Any] = logging.get_logger('transformers.models.speecht5')
A : List[Any] = {
'speech_encoder_prenet.layer_norm': 'speecht5.encoder.prenet.feature_projection.layer_norm',
'speech_encoder_prenet.post_extract_proj': 'speecht5.encoder.prenet.feature_projection.projection',
'speech_encoder_prenet.pos_conv.0': 'speecht5.encoder.prenet.pos_conv_embed.conv',
'speech_encoder_prenet.mask_emb': 'speecht5.encoder.prenet.masked_spec_embed',
}
A : Tuple = {
'text_encoder_prenet.encoder_prenet.0': 'speecht5.encoder.prenet.embed_tokens',
'text_encoder_prenet.encoder_prenet.1.alpha': 'speecht5.encoder.prenet.encode_positions.alpha',
}
A : Optional[Any] = {
'speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0': 'speecht5.decoder.prenet.layers.0',
'speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0': 'speecht5.decoder.prenet.layers.1',
'speech_decoder_prenet.decoder_prenet.0.1': 'speecht5.decoder.prenet.final_layer',
'speech_decoder_prenet.decoder_prenet.1.alpha': 'speecht5.decoder.prenet.encode_positions.alpha',
'speech_decoder_prenet.spkembs_layer.0': 'speecht5.decoder.prenet.speaker_embeds_layer',
}
A : Dict = {
'speech_decoder_postnet.feat_out': 'speech_decoder_postnet.feat_out',
'speech_decoder_postnet.prob_out': 'speech_decoder_postnet.prob_out',
'speech_decoder_postnet.postnet.postnet.0.0': 'speech_decoder_postnet.layers.0.conv',
'speech_decoder_postnet.postnet.postnet.0.1': 'speech_decoder_postnet.layers.0.batch_norm',
'speech_decoder_postnet.postnet.postnet.1.0': 'speech_decoder_postnet.layers.1.conv',
'speech_decoder_postnet.postnet.postnet.1.1': 'speech_decoder_postnet.layers.1.batch_norm',
'speech_decoder_postnet.postnet.postnet.2.0': 'speech_decoder_postnet.layers.2.conv',
'speech_decoder_postnet.postnet.postnet.2.1': 'speech_decoder_postnet.layers.2.batch_norm',
'speech_decoder_postnet.postnet.postnet.3.0': 'speech_decoder_postnet.layers.3.conv',
'speech_decoder_postnet.postnet.postnet.3.1': 'speech_decoder_postnet.layers.3.batch_norm',
'speech_decoder_postnet.postnet.postnet.4.0': 'speech_decoder_postnet.layers.4.conv',
'speech_decoder_postnet.postnet.postnet.4.1': 'speech_decoder_postnet.layers.4.batch_norm',
}
A : int = {
'text_decoder_prenet.embed_tokens': 'speecht5.decoder.prenet.embed_tokens',
}
A : List[str] = {
'text_decoder_postnet.output_projection': 'text_decoder_postnet.lm_head',
}
A : Dict = {
'encoder.layers.*.self_attn.k_proj': 'speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj',
'encoder.layers.*.self_attn.v_proj': 'speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj',
'encoder.layers.*.self_attn.q_proj': 'speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj',
'encoder.layers.*.self_attn.out_proj': 'speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj',
'encoder.layers.*.self_attn_layer_norm': 'speecht5.encoder.wrapped_encoder.layers.*.layer_norm',
'encoder.layers.*.fc1': 'speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense',
'encoder.layers.*.fc2': 'speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense',
'encoder.layers.*.final_layer_norm': 'speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm',
'encoder.layer_norm': 'speecht5.encoder.wrapped_encoder.layer_norm',
'encoder.pos_emb.pe_k': 'speecht5.encoder.wrapped_encoder.embed_positions.pe_k',
}
A : str = {
'decoder.layers.*.self_attn.k_proj': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj',
'decoder.layers.*.self_attn.v_proj': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj',
'decoder.layers.*.self_attn.q_proj': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj',
'decoder.layers.*.self_attn.out_proj': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj',
'decoder.layers.*.self_attn_layer_norm': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm',
'decoder.layers.*.encoder_attn.k_proj': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj',
'decoder.layers.*.encoder_attn.v_proj': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj',
'decoder.layers.*.encoder_attn.q_proj': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj',
'decoder.layers.*.encoder_attn.out_proj': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj',
'decoder.layers.*.encoder_attn_layer_norm': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm',
'decoder.layers.*.fc1': 'speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense',
'decoder.layers.*.fc2': 'speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense',
'decoder.layers.*.final_layer_norm': 'speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm',
}
A : List[str] = {
**MAPPING_SPEECH_ENCODER_PRENET,
**MAPPING_ENCODER,
**MAPPING_DECODER,
**MAPPING_TEXT_DECODER_PRENET,
**MAPPING_TEXT_DECODER_POSTNET,
}
A : str = {
**MAPPING_TEXT_ENCODER_PRENET,
**MAPPING_ENCODER,
**MAPPING_DECODER,
**MAPPING_SPEECH_DECODER_PRENET,
**MAPPING_SPEECH_DECODER_POSTNET,
}
A : Dict = {
**MAPPING_SPEECH_ENCODER_PRENET,
**MAPPING_ENCODER,
**MAPPING_DECODER,
**MAPPING_SPEECH_DECODER_PRENET,
**MAPPING_SPEECH_DECODER_POSTNET,
}
A : str = []
A : int = [
'encoder.version',
'encoder.layers.*.norm_k.weight',
'encoder.layers.*.norm_k.bias',
'decoder.version',
'decoder.layers.*.norm_k.weight',
'decoder.layers.*.norm_k.bias',
'decoder.pos_emb.pe_k',
'speech_encoder_prenet.embed_positions._float_tensor',
'text_decoder_prenet.embed_positions._float_tensor',
]
A : Tuple = IGNORE_KEYS + [
'encoder.proj',
'text_encoder_prenet.*',
'speech_decoder_prenet.*',
'speech_decoder_postnet.*',
]
A : Optional[int] = IGNORE_KEYS + [
'encoder.proj',
'speech_encoder_prenet.*',
'text_decoder_prenet.*',
'text_decoder_postnet.*',
]
A : Optional[Any] = IGNORE_KEYS + [
'encoder.proj',
'text_encoder_prenet.*',
'text_decoder_prenet.*',
'text_decoder_postnet.*',
]
def UpperCamelCase ( __magic_name__ : Optional[Any] , __magic_name__ : Optional[Any] , __magic_name__ : Optional[Any] , __magic_name__ : Tuple , __magic_name__ : str ) -> Optional[int]:
"""simple docstring"""
for attribute in key.split(""".""" ):
lowercase__ = getattr(__magic_name__ , __magic_name__ )
if weight_type is not None:
lowercase__ = getattr(__magic_name__ , __magic_name__ ).shape
else:
lowercase__ = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
f'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
f''' {value.shape} for {full_name}''' )
if weight_type == "weight":
lowercase__ = value
elif weight_type == "weight_g":
lowercase__ = value
elif weight_type == "weight_v":
lowercase__ = value
elif weight_type == "bias":
lowercase__ = value
elif weight_type == "running_mean":
lowercase__ = value
elif weight_type == "running_var":
lowercase__ = value
elif weight_type == "num_batches_tracked":
lowercase__ = value
else:
lowercase__ = value
logger.info(f'''{key + ("." + weight_type if weight_type is not None else "")} was initialized from {full_name}.''' )
def UpperCamelCase ( __magic_name__ : Union[str, Any] , __magic_name__ : Tuple ) -> Any:
"""simple docstring"""
for key in ignore_keys:
if key.endswith(""".*""" ):
if name.startswith(key[:-1] ):
return True
elif ".*." in key:
lowercase__ , lowercase__ = key.split(""".*.""" )
if prefix in name and suffix in name:
return True
elif key in name:
return True
return False
def UpperCamelCase ( __magic_name__ : Optional[int] , __magic_name__ : Optional[Any] , __magic_name__ : Dict ) -> int:
"""simple docstring"""
lowercase__ = []
if task == "s2t":
lowercase__ = hf_model.speechta.encoder.prenet.feature_encoder
lowercase__ = MAPPING_S2T
lowercase__ = IGNORE_KEYS_S2T
elif task == "t2s":
lowercase__ = None
lowercase__ = MAPPING_T2S
lowercase__ = IGNORE_KEYS_T2S
elif task == "s2s":
lowercase__ = hf_model.speechta.encoder.prenet.feature_encoder
lowercase__ = MAPPING_S2S
lowercase__ = IGNORE_KEYS_S2S
else:
raise ValueError(f'''Unsupported task: {task}''' )
for name, value in fairseq_dict.items():
if should_ignore(__magic_name__ , __magic_name__ ):
logger.info(f'''{name} was ignored''' )
continue
lowercase__ = False
if "conv_layers" in name:
load_conv_layer(
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , hf_model.config.feat_extract_norm == """group""" , )
lowercase__ = True
else:
for key, mapped_key in MAPPING.items():
# mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if "*" in key:
lowercase__ , lowercase__ = key.split(""".*.""" )
if prefix in name and suffix in name:
lowercase__ = suffix
# if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
if key in name:
lowercase__ = True
if "*" in mapped_key:
lowercase__ = name.split(__magic_name__ )[0].split(""".""" )[-2]
lowercase__ = mapped_key.replace("""*""" , __magic_name__ )
if "weight_g" in name:
lowercase__ = """weight_g"""
elif "weight_v" in name:
lowercase__ = """weight_v"""
elif "bias" in name:
lowercase__ = """bias"""
elif "weight" in name:
lowercase__ = """weight"""
elif "running_mean" in name:
lowercase__ = """running_mean"""
elif "running_var" in name:
lowercase__ = """running_var"""
elif "num_batches_tracked" in name:
lowercase__ = """num_batches_tracked"""
else:
lowercase__ = None
set_recursively(__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ )
continue
if not is_used:
unused_weights.append(__magic_name__ )
logger.warning(f'''Unused weights: {unused_weights}''' )
def UpperCamelCase ( __magic_name__ : Any , __magic_name__ : Optional[Any] , __magic_name__ : List[str] , __magic_name__ : Union[str, Any] , __magic_name__ : Optional[int] ) -> List[Any]:
"""simple docstring"""
lowercase__ = full_name.split("""conv_layers.""" )[-1]
lowercase__ = name.split(""".""" )
lowercase__ = int(items[0] )
lowercase__ = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
f'''{full_name} has size {value.shape}, but'''
f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.''' )
lowercase__ = value
logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
f'''{full_name} has size {value.shape}, but'''
f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.''' )
lowercase__ = value
logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
f'''{full_name} has size {value.shape}, but'''
f''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.''' )
lowercase__ = value
logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
f'''{full_name} has size {value.shape}, but'''
f''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.''' )
lowercase__ = value
logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
else:
unused_weights.append(__magic_name__ )
@torch.no_grad()
def UpperCamelCase ( __magic_name__ : int , __magic_name__ : Dict , __magic_name__ : Tuple , __magic_name__ : List[str]=None , __magic_name__ : Dict=None , __magic_name__ : Dict=None , ) -> Tuple:
"""simple docstring"""
if config_path is not None:
lowercase__ = SpeechTaConfig.from_pretrained(__magic_name__ )
else:
lowercase__ = SpeechTaConfig()
if task == "s2t":
lowercase__ = config.max_text_positions
lowercase__ = SpeechTaForSpeechToText(__magic_name__ )
elif task == "t2s":
lowercase__ = 1876
lowercase__ = 600
lowercase__ = config.max_speech_positions
lowercase__ = SpeechTaForTextToSpeech(__magic_name__ )
elif task == "s2s":
lowercase__ = 1876
lowercase__ = config.max_speech_positions
lowercase__ = SpeechTaForSpeechToSpeech(__magic_name__ )
else:
raise ValueError(f'''Unknown task name: {task}''' )
if vocab_path:
lowercase__ = SpeechTaTokenizer(__magic_name__ , model_max_length=config.max_text_positions )
# Mask token behaves like a normal word, i.e. include the space before it
lowercase__ = AddedToken("""<mask>""" , lstrip=__magic_name__ , rstrip=__magic_name__ )
lowercase__ = mask_token
tokenizer.add_special_tokens({"""mask_token""": mask_token} )
tokenizer.add_tokens(["""<ctc_blank>"""] )
lowercase__ = SpeechTaFeatureExtractor()
lowercase__ = SpeechTaProcessor(tokenizer=__magic_name__ , feature_extractor=__magic_name__ )
processor.save_pretrained(__magic_name__ )
lowercase__ = torch.load(__magic_name__ )
recursively_load_weights(fairseq_checkpoint["""model"""] , __magic_name__ , __magic_name__ )
model.save_pretrained(__magic_name__ )
if repo_id:
print("""Pushing to the hub...""" )
processor.push_to_hub(__magic_name__ )
model.push_to_hub(__magic_name__ )
if __name__ == "__main__":
A : Union[str, Any] = argparse.ArgumentParser()
parser.add_argument(
'--task',
default='s2t',
type=str,
help='Type of the SpeechT5 model you\'d like to convert. Should be one of \'s2t\', \'t2s\', \'s2s\'.',
)
parser.add_argument('--checkpoint_path', required=True, default=None, type=str, help='Path to fairseq checkpoint')
parser.add_argument('--vocab_path', default=None, type=str, help='Path to SentencePiece model')
parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
parser.add_argument(
'--pytorch_dump_folder_path', required=True, default=None, type=str, help='Path to the output PyTorch model.'
)
parser.add_argument(
'--push_to_hub', default=None, type=str, help='Where to upload the converted model on the 🤗 hub.'
)
A : Optional[int] = parser.parse_args()
convert_speechta_checkpoint(
args.task,
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.config_path,
args.vocab_path,
args.push_to_hub,
)
| 305
|
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AlignProcessor, EfficientNetImageProcessor
@require_vision
class A ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase__ (self : Union[str, Any] ) -> Any:
"""simple docstring"""
lowercase__ = tempfile.mkdtemp()
lowercase__ = [
"""[UNK]""",
"""[CLS]""",
"""[SEP]""",
"""[PAD]""",
"""[MASK]""",
"""want""",
"""##want""",
"""##ed""",
"""wa""",
"""un""",
"""runn""",
"""##ing""",
""",""",
"""low""",
"""lowest""",
]
lowercase__ = 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] ) )
lowercase__ = {
"""do_resize""": True,
"""size""": 20,
"""do_center_crop""": True,
"""crop_size""": 18,
"""do_normalize""": True,
"""image_mean""": [0.48_145_466, 0.4_578_275, 0.40_821_073],
"""image_std""": [0.26_862_954, 0.26_130_258, 0.27_577_711],
}
lowercase__ = os.path.join(self.tmpdirname , _UpperCAmelCase )
with open(self.image_processor_file , """w""" , encoding="""utf-8""" ) as fp:
json.dump(_UpperCAmelCase , _UpperCAmelCase )
def lowerCamelCase__ (self : Dict , **_UpperCAmelCase : Any ) -> Optional[Any]:
"""simple docstring"""
return BertTokenizer.from_pretrained(self.tmpdirname , **_UpperCAmelCase )
def lowerCamelCase__ (self : Union[str, Any] , **_UpperCAmelCase : Any ) -> Dict:
"""simple docstring"""
return BertTokenizerFast.from_pretrained(self.tmpdirname , **_UpperCAmelCase )
def lowerCamelCase__ (self : Optional[int] , **_UpperCAmelCase : str ) -> Dict:
"""simple docstring"""
return EfficientNetImageProcessor.from_pretrained(self.tmpdirname , **_UpperCAmelCase )
def lowerCamelCase__ (self : Optional[int] ) -> List[str]:
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def lowerCamelCase__ (self : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
lowercase__ = [Image.fromarray(np.moveaxis(_UpperCAmelCase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def lowerCamelCase__ (self : Optional[int] ) -> Tuple:
"""simple docstring"""
lowercase__ = self.get_tokenizer()
lowercase__ = self.get_rust_tokenizer()
lowercase__ = self.get_image_processor()
lowercase__ = AlignProcessor(tokenizer=_UpperCAmelCase , image_processor=_UpperCAmelCase )
processor_slow.save_pretrained(self.tmpdirname )
lowercase__ = AlignProcessor.from_pretrained(self.tmpdirname , use_fast=_UpperCAmelCase )
lowercase__ = AlignProcessor(tokenizer=_UpperCAmelCase , image_processor=_UpperCAmelCase )
processor_fast.save_pretrained(self.tmpdirname )
lowercase__ = AlignProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer , _UpperCAmelCase )
self.assertIsInstance(processor_fast.tokenizer , _UpperCAmelCase )
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor , _UpperCAmelCase )
self.assertIsInstance(processor_fast.image_processor , _UpperCAmelCase )
def lowerCamelCase__ (self : Any ) -> List[str]:
"""simple docstring"""
lowercase__ = AlignProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
lowercase__ = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
lowercase__ = self.get_image_processor(do_normalize=_UpperCAmelCase , padding_value=1.0 )
lowercase__ = AlignProcessor.from_pretrained(
self.tmpdirname , bos_token="""(BOS)""" , eos_token="""(EOS)""" , do_normalize=_UpperCAmelCase , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , _UpperCAmelCase )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , _UpperCAmelCase )
def lowerCamelCase__ (self : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = self.get_image_processor()
lowercase__ = self.get_tokenizer()
lowercase__ = AlignProcessor(tokenizer=_UpperCAmelCase , image_processor=_UpperCAmelCase )
lowercase__ = self.prepare_image_inputs()
lowercase__ = image_processor(_UpperCAmelCase , return_tensors="""np""" )
lowercase__ = processor(images=_UpperCAmelCase , return_tensors="""np""" )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1E-2 )
def lowerCamelCase__ (self : Dict ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = self.get_image_processor()
lowercase__ = self.get_tokenizer()
lowercase__ = AlignProcessor(tokenizer=_UpperCAmelCase , image_processor=_UpperCAmelCase )
lowercase__ = """lower newer"""
lowercase__ = processor(text=_UpperCAmelCase )
lowercase__ = tokenizer(_UpperCAmelCase , padding="""max_length""" , max_length=64 )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def lowerCamelCase__ (self : List[Any] ) -> Tuple:
"""simple docstring"""
lowercase__ = self.get_image_processor()
lowercase__ = self.get_tokenizer()
lowercase__ = AlignProcessor(tokenizer=_UpperCAmelCase , image_processor=_UpperCAmelCase )
lowercase__ = """lower newer"""
lowercase__ = self.prepare_image_inputs()
lowercase__ = processor(text=_UpperCAmelCase , images=_UpperCAmelCase )
self.assertListEqual(list(inputs.keys() ) , ["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""] )
# test if it raises when no input is passed
with pytest.raises(_UpperCAmelCase ):
processor()
def lowerCamelCase__ (self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = self.get_image_processor()
lowercase__ = self.get_tokenizer()
lowercase__ = AlignProcessor(tokenizer=_UpperCAmelCase , image_processor=_UpperCAmelCase )
lowercase__ = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
lowercase__ = processor.batch_decode(_UpperCAmelCase )
lowercase__ = tokenizer.batch_decode(_UpperCAmelCase )
self.assertListEqual(_UpperCAmelCase , _UpperCAmelCase )
def lowerCamelCase__ (self : List[str] ) -> Tuple:
"""simple docstring"""
lowercase__ = self.get_image_processor()
lowercase__ = self.get_tokenizer()
lowercase__ = AlignProcessor(tokenizer=_UpperCAmelCase , image_processor=_UpperCAmelCase )
lowercase__ = """lower newer"""
lowercase__ = self.prepare_image_inputs()
lowercase__ = processor(text=_UpperCAmelCase , images=_UpperCAmelCase )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 305
| 1
|
from __future__ import annotations
A : Optional[Any] = 1.6_0_2_1e-1_9 # units = C
def UpperCamelCase ( __magic_name__ : float , __magic_name__ : float , __magic_name__ : float , ) -> tuple[str, float]:
"""simple docstring"""
if (conductivity, electron_conc, mobility).count(0 ) != 1:
raise ValueError("""You cannot supply more or less than 2 values""" )
elif conductivity < 0:
raise ValueError("""Conductivity cannot be negative""" )
elif electron_conc < 0:
raise ValueError("""Electron concentration cannot be negative""" )
elif mobility < 0:
raise ValueError("""mobility cannot be negative""" )
elif conductivity == 0:
return (
"conductivity",
mobility * electron_conc * ELECTRON_CHARGE,
)
elif electron_conc == 0:
return (
"electron_conc",
conductivity / (mobility * ELECTRON_CHARGE),
)
else:
return (
"mobility",
conductivity / (electron_conc * ELECTRON_CHARGE),
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 305
|
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def UpperCamelCase ( __magic_name__ : List[Any] ) -> Optional[int]:
"""simple docstring"""
return x + 2
class A ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase__ (self : Optional[Any] ) -> Any:
"""simple docstring"""
lowercase__ = """x = 3"""
lowercase__ = {}
lowercase__ = evaluate(_UpperCAmelCase , {} , state=_UpperCAmelCase )
assert result == 3
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3} )
lowercase__ = """x = y"""
lowercase__ = {"""y""": 5}
lowercase__ = evaluate(_UpperCAmelCase , {} , state=_UpperCAmelCase )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(_UpperCAmelCase , {"""x""": 5, """y""": 5} )
def lowerCamelCase__ (self : str ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = """y = add_two(x)"""
lowercase__ = {"""x""": 3}
lowercase__ = evaluate(_UpperCAmelCase , {"""add_two""": add_two} , state=_UpperCAmelCase )
assert result == 5
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3, """y""": 5} )
# Won't work without the tool
with CaptureStdout() as out:
lowercase__ = evaluate(_UpperCAmelCase , {} , state=_UpperCAmelCase )
assert result is None
assert "tried to execute add_two" in out.out
def lowerCamelCase__ (self : List[Any] ) -> Optional[int]:
"""simple docstring"""
lowercase__ = """x = 3"""
lowercase__ = {}
lowercase__ = evaluate(_UpperCAmelCase , {} , state=_UpperCAmelCase )
assert result == 3
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3} )
def lowerCamelCase__ (self : Optional[int] ) -> List[Any]:
"""simple docstring"""
lowercase__ = """test_dict = {'x': x, 'y': add_two(x)}"""
lowercase__ = {"""x""": 3}
lowercase__ = evaluate(_UpperCAmelCase , {"""add_two""": add_two} , state=_UpperCAmelCase )
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3, """y""": 5} )
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3, """test_dict""": {"""x""": 3, """y""": 5}} )
def lowerCamelCase__ (self : List[str] ) -> List[Any]:
"""simple docstring"""
lowercase__ = """x = 3\ny = 5"""
lowercase__ = {}
lowercase__ = evaluate(_UpperCAmelCase , {} , state=_UpperCAmelCase )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3, """y""": 5} )
def lowerCamelCase__ (self : List[Any] ) -> Dict:
"""simple docstring"""
lowercase__ = """text = f'This is x: {x}.'"""
lowercase__ = {"""x""": 3}
lowercase__ = evaluate(_UpperCAmelCase , {} , state=_UpperCAmelCase )
# evaluate returns the value of the last assignment.
assert result == "This is x: 3."
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3, """text""": """This is x: 3."""} )
def lowerCamelCase__ (self : List[str] ) -> int:
"""simple docstring"""
lowercase__ = """if x <= 3:\n y = 2\nelse:\n y = 5"""
lowercase__ = {"""x""": 3}
lowercase__ = evaluate(_UpperCAmelCase , {} , state=_UpperCAmelCase )
# evaluate returns the value of the last assignment.
assert result == 2
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3, """y""": 2} )
lowercase__ = {"""x""": 8}
lowercase__ = evaluate(_UpperCAmelCase , {} , state=_UpperCAmelCase )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(_UpperCAmelCase , {"""x""": 8, """y""": 5} )
def lowerCamelCase__ (self : Dict ) -> int:
"""simple docstring"""
lowercase__ = """test_list = [x, add_two(x)]"""
lowercase__ = {"""x""": 3}
lowercase__ = evaluate(_UpperCAmelCase , {"""add_two""": add_two} , state=_UpperCAmelCase )
self.assertListEqual(_UpperCAmelCase , [3, 5] )
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3, """test_list""": [3, 5]} )
def lowerCamelCase__ (self : Any ) -> int:
"""simple docstring"""
lowercase__ = """y = x"""
lowercase__ = {"""x""": 3}
lowercase__ = evaluate(_UpperCAmelCase , {} , state=_UpperCAmelCase )
assert result == 3
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3, """y""": 3} )
def lowerCamelCase__ (self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
lowercase__ = """test_list = [x, add_two(x)]\ntest_list[1]"""
lowercase__ = {"""x""": 3}
lowercase__ = evaluate(_UpperCAmelCase , {"""add_two""": add_two} , state=_UpperCAmelCase )
assert result == 5
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3, """test_list""": [3, 5]} )
lowercase__ = """test_dict = {'x': x, 'y': add_two(x)}\ntest_dict['y']"""
lowercase__ = {"""x""": 3}
lowercase__ = evaluate(_UpperCAmelCase , {"""add_two""": add_two} , state=_UpperCAmelCase )
assert result == 5
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3, """test_dict""": {"""x""": 3, """y""": 5}} )
def lowerCamelCase__ (self : Union[str, Any] ) -> Any:
"""simple docstring"""
lowercase__ = """x = 0\nfor i in range(3):\n x = i"""
lowercase__ = {}
lowercase__ = evaluate(_UpperCAmelCase , {"""range""": range} , state=_UpperCAmelCase )
assert result == 2
self.assertDictEqual(_UpperCAmelCase , {"""x""": 2, """i""": 2} )
| 305
| 1
|
import argparse
from collections import defaultdict
import yaml
A : int = 'docs/source/en/_toctree.yml'
def UpperCamelCase ( __magic_name__ : Union[str, Any] ) -> List[str]:
"""simple docstring"""
lowercase__ = defaultdict(__magic_name__ )
for doc in model_doc:
counts[doc["local"]] += 1
lowercase__ = [key for key, value in counts.items() if value > 1]
lowercase__ = []
for duplicate_key in duplicates:
lowercase__ = list({doc["""title"""] for doc in model_doc if doc["""local"""] == duplicate_key} )
if len(__magic_name__ ) > 1:
raise ValueError(
f'''{duplicate_key} is present several times in the documentation table of content at '''
"""`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the """
"""others.""" )
# Only add this once
new_doc.append({"""local""": duplicate_key, """title""": titles[0]} )
# Add none duplicate-keys
new_doc.extend([doc for doc in model_doc if counts[doc["""local"""]] == 1] )
# Sort
return sorted(__magic_name__ , key=lambda __magic_name__ : s["title"].lower() )
def UpperCamelCase ( __magic_name__ : str=False ) -> Tuple:
"""simple docstring"""
with open(__magic_name__ , encoding="""utf-8""" ) as f:
lowercase__ = yaml.safe_load(f.read() )
# Get to the API doc
lowercase__ = 0
while content[api_idx]["title"] != "API":
api_idx += 1
lowercase__ = content[api_idx]["""sections"""]
# Then to the model doc
lowercase__ = 0
while api_doc[model_idx]["title"] != "Models":
model_idx += 1
lowercase__ = api_doc[model_idx]["""sections"""]
lowercase__ = [(idx, section) for idx, section in enumerate(__magic_name__ ) if """sections""" in section]
lowercase__ = False
for idx, modality_doc in modalities_docs:
lowercase__ = modality_doc["""sections"""]
lowercase__ = clean_model_doc_toc(__magic_name__ )
if old_modality_doc != new_modality_doc:
lowercase__ = True
if overwrite:
lowercase__ = new_modality_doc
if diff:
if overwrite:
lowercase__ = model_doc
lowercase__ = api_doc
with open(__magic_name__ , """w""" , encoding="""utf-8""" ) as f:
f.write(yaml.dump(__magic_name__ , allow_unicode=__magic_name__ ) )
else:
raise ValueError(
"""The model doc part of the table of content is not properly sorted, run `make style` to fix this.""" )
if __name__ == "__main__":
A : Any = argparse.ArgumentParser()
parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
A : Any = parser.parse_args()
check_model_doc(args.fix_and_overwrite)
| 305
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class A :
'''simple docstring'''
def __init__(self : List[str] ) -> Tuple:
"""simple docstring"""
lowercase__ = 0
lowercase__ = 0
lowercase__ = {}
def lowerCamelCase__ (self : Dict , _UpperCAmelCase : Tuple ) -> Optional[int]:
"""simple docstring"""
if vertex not in self.adjacency:
lowercase__ = {}
self.num_vertices += 1
def lowerCamelCase__ (self : List[Any] , _UpperCAmelCase : List[Any] , _UpperCAmelCase : int , _UpperCAmelCase : List[str] ) -> Tuple:
"""simple docstring"""
self.add_vertex(_UpperCAmelCase )
self.add_vertex(_UpperCAmelCase )
if head == tail:
return
lowercase__ = weight
lowercase__ = weight
def lowerCamelCase__ (self : List[str] ) -> Optional[int]:
"""simple docstring"""
lowercase__ = self.get_edges()
for edge in edges:
lowercase__ , lowercase__ , lowercase__ = edge
edges.remove((tail, head, weight) )
for i in range(len(_UpperCAmelCase ) ):
lowercase__ = list(edges[i] )
edges.sort(key=lambda _UpperCAmelCase : e[2] )
for i in range(len(_UpperCAmelCase ) - 1 ):
if edges[i][2] >= edges[i + 1][2]:
lowercase__ = edges[i][2] + 1
for edge in edges:
lowercase__ , lowercase__ , lowercase__ = edge
lowercase__ = weight
lowercase__ = weight
def __str__(self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = """"""
for tail in self.adjacency:
for head in self.adjacency[tail]:
lowercase__ = self.adjacency[head][tail]
string += f'''{head} -> {tail} == {weight}\n'''
return string.rstrip("""\n""" )
def lowerCamelCase__ (self : Any ) -> str:
"""simple docstring"""
lowercase__ = []
for tail in self.adjacency:
for head in self.adjacency[tail]:
output.append((tail, head, self.adjacency[head][tail]) )
return output
def lowerCamelCase__ (self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
return self.adjacency.keys()
@staticmethod
def lowerCamelCase__ (_UpperCAmelCase : List[str]=None , _UpperCAmelCase : Any=None ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = Graph()
if vertices is None:
lowercase__ = []
if edges is None:
lowercase__ = []
for vertex in vertices:
g.add_vertex(_UpperCAmelCase )
for edge in edges:
g.add_edge(*_UpperCAmelCase )
return g
class A :
'''simple docstring'''
def __init__(self : Optional[Any] ) -> str:
"""simple docstring"""
lowercase__ = {}
lowercase__ = {}
def __len__(self : Optional[Any] ) -> Dict:
"""simple docstring"""
return len(self.parent )
def lowerCamelCase__ (self : str , _UpperCAmelCase : Dict ) -> Any:
"""simple docstring"""
if item in self.parent:
return self.find(_UpperCAmelCase )
lowercase__ = item
lowercase__ = 0
return item
def lowerCamelCase__ (self : List[str] , _UpperCAmelCase : Dict ) -> Any:
"""simple docstring"""
if item not in self.parent:
return self.make_set(_UpperCAmelCase )
if item != self.parent[item]:
lowercase__ = self.find(self.parent[item] )
return self.parent[item]
def lowerCamelCase__ (self : List[Any] , _UpperCAmelCase : Any , _UpperCAmelCase : List[Any] ) -> Optional[int]:
"""simple docstring"""
lowercase__ = self.find(_UpperCAmelCase )
lowercase__ = self.find(_UpperCAmelCase )
if roota == roota:
return roota
if self.rank[roota] > self.rank[roota]:
lowercase__ = roota
return roota
if self.rank[roota] < self.rank[roota]:
lowercase__ = roota
return roota
if self.rank[roota] == self.rank[roota]:
self.rank[roota] += 1
lowercase__ = roota
return roota
return None
@staticmethod
def lowerCamelCase__ (_UpperCAmelCase : str ) -> Optional[int]:
"""simple docstring"""
lowercase__ = graph.num_vertices
lowercase__ = Graph.UnionFind()
lowercase__ = []
while num_components > 1:
lowercase__ = {}
for vertex in graph.get_vertices():
lowercase__ = -1
lowercase__ = graph.get_edges()
for edge in edges:
lowercase__ , lowercase__ , lowercase__ = edge
edges.remove((tail, head, weight) )
for edge in edges:
lowercase__ , lowercase__ , lowercase__ = edge
lowercase__ = union_find.find(_UpperCAmelCase )
lowercase__ = union_find.find(_UpperCAmelCase )
if seta != seta:
if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
lowercase__ = [head, tail, weight]
if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
lowercase__ = [head, tail, weight]
for vertex in cheap_edge:
if cheap_edge[vertex] != -1:
lowercase__ , lowercase__ , lowercase__ = cheap_edge[vertex]
if union_find.find(_UpperCAmelCase ) != union_find.find(_UpperCAmelCase ):
union_find.union(_UpperCAmelCase , _UpperCAmelCase )
mst_edges.append(cheap_edge[vertex] )
lowercase__ = num_components - 1
lowercase__ = Graph.build(edges=_UpperCAmelCase )
return mst
| 305
| 1
|
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class A ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__(self : Any , _UpperCAmelCase : Dict[str, int] , _UpperCAmelCase : List[str] , _UpperCAmelCase : int = None , _UpperCAmelCase : int = None ) -> Dict:
"""simple docstring"""
super().__init__()
lowercase__ = pad_token_id
lowercase__ = max_length
lowercase__ = vocab
lowercase__ = merges
lowercase__ = BytePairTokenizer(_UpperCAmelCase , _UpperCAmelCase , sequence_length=_UpperCAmelCase )
@classmethod
def lowerCamelCase__ (cls : Optional[int] , _UpperCAmelCase : GPTaTokenizer , *_UpperCAmelCase : List[Any] , **_UpperCAmelCase : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = [""" """.join(_UpperCAmelCase ) for m in tokenizer.bpe_ranks.keys()]
lowercase__ = tokenizer.get_vocab()
return cls(_UpperCAmelCase , _UpperCAmelCase , *_UpperCAmelCase , **_UpperCAmelCase )
@classmethod
def lowerCamelCase__ (cls : Union[str, Any] , _UpperCAmelCase : Union[str, os.PathLike] , *_UpperCAmelCase : str , **_UpperCAmelCase : List[Any] ) -> Any:
"""simple docstring"""
lowercase__ = GPTaTokenizer.from_pretrained(_UpperCAmelCase , *_UpperCAmelCase , **_UpperCAmelCase )
return cls.from_tokenizer(_UpperCAmelCase , *_UpperCAmelCase , **_UpperCAmelCase )
@classmethod
def lowerCamelCase__ (cls : Any , _UpperCAmelCase : Tuple ) -> Union[str, Any]:
"""simple docstring"""
return cls(**_UpperCAmelCase )
def lowerCamelCase__ (self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
return {
"vocab": self.vocab,
"merges": self.merges,
"max_length": self.max_length,
"pad_token_id": self.pad_token_id,
}
def lowerCamelCase__ (self : str , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : int = None ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = self.tf_tokenizer(_UpperCAmelCase )
lowercase__ = tf.ones_like(_UpperCAmelCase )
if self.pad_token_id is not None:
# pad the tokens up to max length
lowercase__ = max_length if max_length is not None else self.max_length
if max_length is not None:
lowercase__ , lowercase__ = pad_model_inputs(
_UpperCAmelCase , max_seq_length=_UpperCAmelCase , pad_value=self.pad_token_id )
return {"attention_mask": attention_mask, "input_ids": input_ids}
| 305
|
def UpperCamelCase ( __magic_name__ : int , __magic_name__ : int ) -> int:
"""simple docstring"""
return int((input_a, input_a).count(1 ) != 0 )
def UpperCamelCase ( ) -> None:
"""simple docstring"""
assert or_gate(0 , 0 ) == 0
assert or_gate(0 , 1 ) == 1
assert or_gate(1 , 0 ) == 1
assert or_gate(1 , 1 ) == 1
if __name__ == "__main__":
print(or_gate(0, 1))
print(or_gate(1, 0))
print(or_gate(0, 0))
print(or_gate(1, 1))
| 305
| 1
|
from __future__ import annotations
import pandas as pd
def UpperCamelCase ( __magic_name__ : list[int] , __magic_name__ : list[int] , __magic_name__ : int ) -> list[int]:
"""simple docstring"""
lowercase__ = [0] * no_of_processes
lowercase__ = [0] * no_of_processes
# Copy the burst time into remaining_time[]
for i in range(__magic_name__ ):
lowercase__ = burst_time[i]
lowercase__ = 0
lowercase__ = 0
lowercase__ = 9_9999_9999
lowercase__ = 0
lowercase__ = False
# Process until all processes are completed
while complete != no_of_processes:
for j in range(__magic_name__ ):
if arrival_time[j] <= increment_time and remaining_time[j] > 0:
if remaining_time[j] < minm:
lowercase__ = remaining_time[j]
lowercase__ = j
lowercase__ = True
if not check:
increment_time += 1
continue
remaining_time[short] -= 1
lowercase__ = remaining_time[short]
if minm == 0:
lowercase__ = 9_9999_9999
if remaining_time[short] == 0:
complete += 1
lowercase__ = False
# Find finish time of current process
lowercase__ = increment_time + 1
# Calculate waiting time
lowercase__ = finish_time - arrival_time[short]
lowercase__ = finar - burst_time[short]
if waiting_time[short] < 0:
lowercase__ = 0
# Increment time
increment_time += 1
return waiting_time
def UpperCamelCase ( __magic_name__ : list[int] , __magic_name__ : int , __magic_name__ : list[int] ) -> list[int]:
"""simple docstring"""
lowercase__ = [0] * no_of_processes
for i in range(__magic_name__ ):
lowercase__ = burst_time[i] + waiting_time[i]
return turn_around_time
def UpperCamelCase ( __magic_name__ : list[int] , __magic_name__ : list[int] , __magic_name__ : int ) -> None:
"""simple docstring"""
lowercase__ = 0
lowercase__ = 0
for i in range(__magic_name__ ):
lowercase__ = total_waiting_time + waiting_time[i]
lowercase__ = total_turn_around_time + turn_around_time[i]
print(f'''Average waiting time = {total_waiting_time / no_of_processes:.5f}''' )
print("""Average turn around time =""" , total_turn_around_time / no_of_processes )
if __name__ == "__main__":
print('Enter how many process you want to analyze')
A : str = int(input())
A : List[Any] = [0] * no_of_processes
A : List[Any] = [0] * no_of_processes
A : Any = list(range(1, no_of_processes + 1))
for i in range(no_of_processes):
print('Enter the arrival time and burst time for process:--' + str(i + 1))
A , A : Union[str, Any] = map(int, input().split())
A : Union[str, Any] = calculate_waitingtime(arrival_time, burst_time, no_of_processes)
A : Any = burst_time
A : Optional[int] = no_of_processes
A : Tuple = waiting_time
A : int = calculate_turnaroundtime(bt, n, wt)
calculate_average_times(waiting_time, turn_around_time, no_of_processes)
A : Any = pd.DataFrame(
list(zip(processes, burst_time, arrival_time, waiting_time, turn_around_time)),
columns=[
'Process',
'BurstTime',
'ArrivalTime',
'WaitingTime',
'TurnAroundTime',
],
)
# Printing the dataFrame
pd.set_option('display.max_rows', fcfs.shape[0] + 1)
print(fcfs)
| 305
|
import argparse
from torch import nn
# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)
from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging
A : Any = logging.get_logger(__name__)
logging.set_verbosity_info()
def UpperCamelCase ( __magic_name__ : str , __magic_name__ : str ) -> List[str]:
"""simple docstring"""
if "xprophetnet" in prophetnet_checkpoint_path:
lowercase__ = XLMProphetNetForConditionalGenerationOld.from_pretrained(__magic_name__ )
lowercase__ , lowercase__ = XLMProphetNetForConditionalGeneration.from_pretrained(
__magic_name__ , output_loading_info=__magic_name__ )
else:
lowercase__ = ProphetNetForConditionalGenerationOld.from_pretrained(__magic_name__ )
lowercase__ , lowercase__ = ProphetNetForConditionalGeneration.from_pretrained(
__magic_name__ , output_loading_info=__magic_name__ )
lowercase__ = ["""key_proj""", """value_proj""", """query_proj"""]
lowercase__ = {
"""self_attn""": """ngram_self_attn""",
"""cross_attn""": """encoder_attn""",
"""cross_attn_layer_norm""": """encoder_attn_layer_norm""",
"""feed_forward_layer_norm""": """final_layer_norm""",
"""feed_forward""": """""",
"""intermediate""": """fc1""",
"""output""": """fc2""",
"""key_proj""": """k_proj""",
"""query_proj""": """q_proj""",
"""value_proj""": """v_proj""",
"""word_embeddings""": """embed_tokens""",
"""embeddings_layer_norm""": """emb_layer_norm""",
"""relative_pos_embeddings""": """relative_linear""",
"""ngram_embeddings""": """ngram_input_embed""",
"""position_embeddings""": """embed_positions""",
}
for key in loading_info["missing_keys"]:
lowercase__ = key.split(""".""" )
if attributes[0] == "lm_head":
lowercase__ = prophet
lowercase__ = prophet_old
else:
lowercase__ = prophet.prophetnet
lowercase__ = prophet_old.model
lowercase__ = False
for attribute in attributes:
if attribute in mapping:
lowercase__ = mapping[attribute]
if not hasattr(__magic_name__ , __magic_name__ ) and len(__magic_name__ ) > 0:
lowercase__ = attribute
elif hasattr(__magic_name__ , __magic_name__ ):
lowercase__ = attribute
if attribute == "weight":
assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
lowercase__ = old_model.weight
logger.info(f'''{attribute} is initialized.''' )
lowercase__ = True
break
elif attribute == "bias":
assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
lowercase__ = old_model.bias
logger.info(f'''{attribute} is initialized''' )
lowercase__ = True
break
elif attribute in special_keys and hasattr(__magic_name__ , """in_proj_weight""" ):
lowercase__ = old_model.in_proj_weight.shape[0] // 3
lowercase__ = getattr(__magic_name__ , __magic_name__ )
param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
if attribute == "query_proj":
lowercase__ = nn.Parameter(old_model.in_proj_weight[:embed_dim, :] )
lowercase__ = nn.Parameter(old_model.in_proj_bias[:embed_dim] )
elif attribute == "key_proj":
lowercase__ = nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :] )
lowercase__ = nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim] )
elif attribute == "value_proj":
lowercase__ = nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :] )
lowercase__ = nn.Parameter(old_model.in_proj_bias[2 * embed_dim :] )
lowercase__ = True
break
elif attribute == "position_embeddings":
assert (
model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
), "Hidden size has to match"
assert model.position_embeddings.weight.shape[0] == 512, "We want 512 position_embeddings."
lowercase__ = nn.Parameter(old_model.embed_positions.weight[:512, :] )
lowercase__ = True
break
if attribute.isdigit():
lowercase__ = model[int(__magic_name__ )]
lowercase__ = old_model[int(__magic_name__ )]
else:
lowercase__ = getattr(__magic_name__ , __magic_name__ )
if old_attribute == "":
lowercase__ = old_model
else:
if not hasattr(__magic_name__ , __magic_name__ ):
raise ValueError(f'''{old_model} does not have {old_attribute}''' )
lowercase__ = getattr(__magic_name__ , __magic_name__ )
if not is_key_init:
raise ValueError(f'''{key} was not correctly initialized!''' )
print(f'''Saving model to {pytorch_dump_folder_path}''' )
prophet.save_pretrained(__magic_name__ )
if __name__ == "__main__":
A : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--prophetnet_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
A : str = parser.parse_args()
convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)
| 305
| 1
|
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def UpperCamelCase ( __magic_name__ : int ) -> str:
"""simple docstring"""
if "img_encoder.pos_embed" in name:
lowercase__ = name.replace("""img_encoder.pos_embed""" , """vision_model.embeddings.position_embeddings""" )
if "img_encoder.patch_embed.proj" in name:
lowercase__ = name.replace("""img_encoder.patch_embed.proj""" , """vision_model.embeddings.patch_embeddings.projection""" )
if "img_encoder.patch_embed.norm" in name:
lowercase__ = name.replace("""img_encoder.patch_embed.norm""" , """vision_model.embeddings.layernorm""" )
if "img_encoder.layers" in name:
lowercase__ = name.replace("""img_encoder.layers""" , """vision_model.encoder.stages""" )
if "blocks" in name and "res" not in name:
lowercase__ = name.replace("""blocks""" , """layers""" )
if "attn" in name and "pre_assign" not in name:
lowercase__ = name.replace("""attn""" , """self_attn""" )
if "proj" in name and "self_attn" in name and "text" not in name:
lowercase__ = name.replace("""proj""" , """out_proj""" )
if "pre_assign_attn.attn.proj" in name:
lowercase__ = name.replace("""pre_assign_attn.attn.proj""" , """pre_assign_attn.attn.out_proj""" )
if "norm1" in name:
lowercase__ = name.replace("""norm1""" , """layer_norm1""" )
if "norm2" in name and "pre_assign" not in name:
lowercase__ = name.replace("""norm2""" , """layer_norm2""" )
if "img_encoder.norm" in name:
lowercase__ = name.replace("""img_encoder.norm""" , """vision_model.layernorm""" )
# text encoder
if "text_encoder.token_embedding" in name:
lowercase__ = name.replace("""text_encoder.token_embedding""" , """text_model.embeddings.token_embedding""" )
if "text_encoder.positional_embedding" in name:
lowercase__ = name.replace("""text_encoder.positional_embedding""" , """text_model.embeddings.position_embedding.weight""" )
if "text_encoder.transformer.resblocks." in name:
lowercase__ = name.replace("""text_encoder.transformer.resblocks.""" , """text_model.encoder.layers.""" )
if "ln_1" in name:
lowercase__ = name.replace("""ln_1""" , """layer_norm1""" )
if "ln_2" in name:
lowercase__ = name.replace("""ln_2""" , """layer_norm2""" )
if "c_fc" in name:
lowercase__ = name.replace("""c_fc""" , """fc1""" )
if "c_proj" in name:
lowercase__ = name.replace("""c_proj""" , """fc2""" )
if "text_encoder" in name:
lowercase__ = name.replace("""text_encoder""" , """text_model""" )
if "ln_final" in name:
lowercase__ = name.replace("""ln_final""" , """final_layer_norm""" )
# projection layers
if "img_projector.linear_hidden." in name:
lowercase__ = name.replace("""img_projector.linear_hidden.""" , """visual_projection.""" )
if "img_projector.linear_out." in name:
lowercase__ = name.replace("""img_projector.linear_out.""" , """visual_projection.3.""" )
if "text_projector.linear_hidden" in name:
lowercase__ = name.replace("""text_projector.linear_hidden""" , """text_projection""" )
if "text_projector.linear_out" in name:
lowercase__ = name.replace("""text_projector.linear_out""" , """text_projection.3""" )
return name
def UpperCamelCase ( __magic_name__ : int , __magic_name__ : str ) -> Any:
"""simple docstring"""
for key in orig_state_dict.copy().keys():
lowercase__ = orig_state_dict.pop(__magic_name__ )
if "qkv" in key:
# weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
# we need to split them up into separate matrices/vectors
lowercase__ = key.split(""".""" )
lowercase__ , lowercase__ = int(key_split[2] ), int(key_split[4] )
lowercase__ = config.vision_config.hidden_size
if "weight" in key:
lowercase__ = val[:dim, :]
lowercase__ = val[dim : dim * 2, :]
lowercase__ = val[-dim:, :]
else:
lowercase__ = val[:dim]
lowercase__ = val[dim : dim * 2]
lowercase__ = val[-dim:]
elif "in_proj" in key:
# weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
# we need to split them up into separate matrices/vectors
lowercase__ = key.split(""".""" )
lowercase__ = int(key_split[3] )
lowercase__ = config.text_config.hidden_size
if "weight" in key:
lowercase__ = val[:dim, :]
lowercase__ = val[
dim : dim * 2, :
]
lowercase__ = val[-dim:, :]
else:
lowercase__ = val[:dim]
lowercase__ = val[dim : dim * 2]
lowercase__ = val[-dim:]
else:
lowercase__ = rename_key(__magic_name__ )
# squeeze if necessary
if (
"text_projection.0" in new_name
or "text_projection.3" in new_name
or "visual_projection.0" in new_name
or "visual_projection.3" in new_name
):
lowercase__ = val.squeeze_()
else:
lowercase__ = val
return orig_state_dict
def UpperCamelCase ( ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = """http://images.cocodataset.org/val2017/000000039769.jpg"""
lowercase__ = Image.open(requests.get(__magic_name__ , stream=__magic_name__ ).raw )
return im
@torch.no_grad()
def UpperCamelCase ( __magic_name__ : Union[str, Any] , __magic_name__ : Union[str, Any] , __magic_name__ : Union[str, Any]="groupvit-gcc-yfcc" , __magic_name__ : List[Any]=False ) -> str:
"""simple docstring"""
lowercase__ = GroupViTConfig()
lowercase__ = GroupViTModel(__magic_name__ ).eval()
lowercase__ = torch.load(__magic_name__ , map_location="""cpu""" )["""model"""]
lowercase__ = convert_state_dict(__magic_name__ , __magic_name__ )
lowercase__ , lowercase__ = model.load_state_dict(__magic_name__ , strict=__magic_name__ )
assert missing_keys == ["text_model.embeddings.position_ids"]
assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(__magic_name__ ) == 0)
# verify result
lowercase__ = CLIPProcessor.from_pretrained("""openai/clip-vit-base-patch32""" )
lowercase__ = prepare_img()
lowercase__ = processor(text=["""a photo of a cat""", """a photo of a dog"""] , images=__magic_name__ , padding=__magic_name__ , return_tensors="""pt""" )
with torch.no_grad():
lowercase__ = model(**__magic_name__ )
if model_name == "groupvit-gcc-yfcc":
lowercase__ = torch.tensor([[1_3.3_5_2_3, 6.3_6_2_9]] )
elif model_name == "groupvit-gcc-redcaps":
lowercase__ = torch.tensor([[1_6.1_8_7_3, 8.6_2_3_0]] )
else:
raise ValueError(f'''Model name {model_name} not supported.''' )
assert torch.allclose(outputs.logits_per_image , __magic_name__ , atol=1E-3 )
processor.save_pretrained(__magic_name__ )
model.save_pretrained(__magic_name__ )
print("""Successfully saved processor and model to""" , __magic_name__ )
if push_to_hub:
print("""Pushing to the hub...""" )
processor.push_to_hub(__magic_name__ , organization="""nielsr""" )
model.push_to_hub(__magic_name__ , organization="""nielsr""" )
if __name__ == "__main__":
A : List[Any] = argparse.ArgumentParser()
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to dump the processor and PyTorch model.'
)
parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to GroupViT checkpoint')
parser.add_argument(
'--model_name',
default='groupvit-gccy-fcc',
type=str,
help='Name of the model. Expecting either \'groupvit-gcc-yfcc\' or \'groupvit-gcc-redcaps\'',
)
parser.add_argument(
'--push_to_hub',
action='store_true',
help='Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.',
)
A : int = parser.parse_args()
convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 305
|
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class A ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__(self : Any , _UpperCAmelCase : Dict[str, int] , _UpperCAmelCase : List[str] , _UpperCAmelCase : int = None , _UpperCAmelCase : int = None ) -> Dict:
"""simple docstring"""
super().__init__()
lowercase__ = pad_token_id
lowercase__ = max_length
lowercase__ = vocab
lowercase__ = merges
lowercase__ = BytePairTokenizer(_UpperCAmelCase , _UpperCAmelCase , sequence_length=_UpperCAmelCase )
@classmethod
def lowerCamelCase__ (cls : Optional[int] , _UpperCAmelCase : GPTaTokenizer , *_UpperCAmelCase : List[Any] , **_UpperCAmelCase : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = [""" """.join(_UpperCAmelCase ) for m in tokenizer.bpe_ranks.keys()]
lowercase__ = tokenizer.get_vocab()
return cls(_UpperCAmelCase , _UpperCAmelCase , *_UpperCAmelCase , **_UpperCAmelCase )
@classmethod
def lowerCamelCase__ (cls : Union[str, Any] , _UpperCAmelCase : Union[str, os.PathLike] , *_UpperCAmelCase : str , **_UpperCAmelCase : List[Any] ) -> Any:
"""simple docstring"""
lowercase__ = GPTaTokenizer.from_pretrained(_UpperCAmelCase , *_UpperCAmelCase , **_UpperCAmelCase )
return cls.from_tokenizer(_UpperCAmelCase , *_UpperCAmelCase , **_UpperCAmelCase )
@classmethod
def lowerCamelCase__ (cls : Any , _UpperCAmelCase : Tuple ) -> Union[str, Any]:
"""simple docstring"""
return cls(**_UpperCAmelCase )
def lowerCamelCase__ (self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
return {
"vocab": self.vocab,
"merges": self.merges,
"max_length": self.max_length,
"pad_token_id": self.pad_token_id,
}
def lowerCamelCase__ (self : str , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : int = None ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = self.tf_tokenizer(_UpperCAmelCase )
lowercase__ = tf.ones_like(_UpperCAmelCase )
if self.pad_token_id is not None:
# pad the tokens up to max length
lowercase__ = max_length if max_length is not None else self.max_length
if max_length is not None:
lowercase__ , lowercase__ = pad_model_inputs(
_UpperCAmelCase , max_seq_length=_UpperCAmelCase , pad_value=self.pad_token_id )
return {"attention_mask": attention_mask, "input_ids": input_ids}
| 305
| 1
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
A : Any = {'tokenization_herbert': ['HerbertTokenizer']}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : List[str] = ['HerbertTokenizerFast']
if TYPE_CHECKING:
from .tokenization_herbert import HerbertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_herbert_fast import HerbertTokenizerFast
else:
import sys
A : Optional[Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 305
|
from __future__ import annotations
from functools import lru_cache
from math import ceil
A : Optional[int] = 1_0_0
A : int = set(range(3, NUM_PRIMES, 2))
primes.add(2)
A : int
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prime not in primes:
continue
primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime)))
@lru_cache(maxsize=100 )
def UpperCamelCase ( __magic_name__ : int ) -> set[int]:
"""simple docstring"""
if number_to_partition < 0:
return set()
elif number_to_partition == 0:
return {1}
lowercase__ = set()
lowercase__ = 42
lowercase__ = 42
for prime in primes:
if prime > number_to_partition:
continue
for sub in partition(number_to_partition - prime ):
ret.add(sub * prime )
return ret
def UpperCamelCase ( __magic_name__ : int = 5000 ) -> int | None:
"""simple docstring"""
for number_to_partition in range(1 , __magic_name__ ):
if len(partition(__magic_name__ ) ) > number_unique_partitions:
return number_to_partition
return None
if __name__ == "__main__":
print(F'{solution() = }')
| 305
| 1
|
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=UpperCAmelCase__ )
class A ( UpperCAmelCase__ ):
'''simple docstring'''
A__ = field(default='''summarization''' , metadata={'''include_in_asdict_even_if_is_default''': True} )
A__ = Features({'''text''': Value('''string''' )} )
A__ = Features({'''summary''': Value('''string''' )} )
A__ = "text"
A__ = "summary"
@property
def lowerCamelCase__ (self : Any ) -> Dict[str, str]:
"""simple docstring"""
return {self.text_column: "text", self.summary_column: "summary"}
| 305
|
def UpperCamelCase ( __magic_name__ : List[Any] ) -> Optional[int]:
"""simple docstring"""
lowercase__ = [0] * len(__magic_name__ )
lowercase__ = []
lowercase__ = [1] * len(__magic_name__ )
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(__magic_name__ ) ):
if indegree[i] == 0:
queue.append(__magic_name__ )
while queue:
lowercase__ = queue.pop(0 )
for x in graph[vertex]:
indegree[x] -= 1
if long_dist[vertex] + 1 > long_dist[x]:
lowercase__ = long_dist[vertex] + 1
if indegree[x] == 0:
queue.append(__magic_name__ )
print(max(__magic_name__ ) )
# Adjacency list of Graph
A : Union[str, Any] = {0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}
longest_distance(graph)
| 305
| 1
|
import os
import pickle
import unittest
from transformers import AutoTokenizer
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.models.bert_japanese.tokenization_bert_japanese import (
VOCAB_FILES_NAMES,
BertJapaneseTokenizer,
CharacterTokenizer,
JumanppTokenizer,
MecabTokenizer,
SudachiTokenizer,
WordpieceTokenizer,
)
from transformers.testing_utils import custom_tokenizers, require_jumanpp, require_sudachi
from ...test_tokenization_common import TokenizerTesterMixin
@custom_tokenizers
class A ( UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
A__ = BertJapaneseTokenizer
A__ = False
A__ = True
def lowerCamelCase__ (self : Union[str, Any] ) -> Any:
"""simple docstring"""
super().setUp()
lowercase__ = [
"""[UNK]""",
"""[CLS]""",
"""[SEP]""",
"""こんにちは""",
"""こん""",
"""にちは""",
"""ばんは""",
"""##こん""",
"""##にちは""",
"""##ばんは""",
"""世界""",
"""##世界""",
"""、""",
"""##、""",
"""。""",
"""##。""",
]
lowercase__ = 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] ) )
def lowerCamelCase__ (self : Tuple , _UpperCAmelCase : Tuple ) -> Dict:
"""simple docstring"""
lowercase__ = """こんにちは、世界。 \nこんばんは、世界。"""
lowercase__ = """こんにちは 、 世界 。 こんばんは 、 世界 。"""
return input_text, output_text
def lowerCamelCase__ (self : int , _UpperCAmelCase : int ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ , lowercase__ = self.get_input_output_texts(_UpperCAmelCase )
lowercase__ = tokenizer.encode(_UpperCAmelCase , add_special_tokens=_UpperCAmelCase )
lowercase__ = tokenizer.decode(_UpperCAmelCase , clean_up_tokenization_spaces=_UpperCAmelCase )
return text, ids
def lowerCamelCase__ (self : Any ) -> Dict:
"""simple docstring"""
pass # TODO add if relevant
def lowerCamelCase__ (self : Any ) -> Dict:
"""simple docstring"""
pass # TODO add if relevant
def lowerCamelCase__ (self : List[str] ) -> str:
"""simple docstring"""
pass # TODO add if relevant
def lowerCamelCase__ (self : Any ) -> List[str]:
"""simple docstring"""
lowercase__ = self.tokenizer_class(self.vocab_file )
lowercase__ = tokenizer.tokenize("""こんにちは、世界。\nこんばんは、世界。""" )
self.assertListEqual(_UpperCAmelCase , ["""こんにちは""", """、""", """世界""", """。""", """こん""", """##ばんは""", """、""", """世界""", """。"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase ) , [3, 12, 10, 14, 4, 9, 12, 10, 14] )
def lowerCamelCase__ (self : Any ) -> Dict:
"""simple docstring"""
lowercase__ = self.tokenizer_class(self.vocab_file , word_tokenizer_type="""mecab""" )
self.assertIsNotNone(_UpperCAmelCase )
lowercase__ = """こんにちは、世界。\nこんばんは、世界。"""
lowercase__ = tokenizer.tokenize(_UpperCAmelCase )
self.assertListEqual(_UpperCAmelCase , ["""こんにちは""", """、""", """世界""", """。""", """こん""", """##ばんは""", """、""", """世界""", """。"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase ) , [3, 12, 10, 14, 4, 9, 12, 10, 14] )
lowercase__ = os.path.join(self.tmpdirname , """tokenizer.bin""" )
with open(_UpperCAmelCase , """wb""" ) as handle:
pickle.dump(_UpperCAmelCase , _UpperCAmelCase )
with open(_UpperCAmelCase , """rb""" ) as handle:
lowercase__ = pickle.load(_UpperCAmelCase )
lowercase__ = tokenizer_new.tokenize(_UpperCAmelCase )
self.assertListEqual(_UpperCAmelCase , _UpperCAmelCase )
def lowerCamelCase__ (self : Tuple ) -> Optional[int]:
"""simple docstring"""
lowercase__ = MecabTokenizer(mecab_dic="""ipadic""" )
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ) , ["""アップルストア""", """で""", """iPhone""", """8""", """が""", """発売""", """さ""", """れ""", """た""", """。"""] , )
def lowerCamelCase__ (self : str ) -> Optional[int]:
"""simple docstring"""
try:
lowercase__ = MecabTokenizer(mecab_dic="""unidic_lite""" )
except ModuleNotFoundError:
return
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ) , ["""アップル""", """ストア""", """で""", """iPhone""", """8""", """が""", """発売""", """さ""", """れ""", """た""", """。"""] , )
def lowerCamelCase__ (self : Optional[int] ) -> str:
"""simple docstring"""
try:
lowercase__ = MecabTokenizer(mecab_dic="""unidic""" )
except ModuleNotFoundError:
return
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ) , ["""アップル""", """ストア""", """で""", """iPhone""", """8""", """が""", """発売""", """さ""", """れ""", """た""", """。"""] , )
def lowerCamelCase__ (self : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = MecabTokenizer(do_lower_case=_UpperCAmelCase , mecab_dic="""ipadic""" )
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ) , ["""アップルストア""", """で""", """iphone""", """8""", """が""", """発売""", """さ""", """れ""", """た""", """。"""] , )
def lowerCamelCase__ (self : str ) -> int:
"""simple docstring"""
try:
lowercase__ = MecabTokenizer(
do_lower_case=_UpperCAmelCase , normalize_text=_UpperCAmelCase , mecab_option="""-d /usr/local/lib/mecab/dic/jumandic""" )
except RuntimeError:
# if dict doesn't exist in the system, previous code raises this error.
return
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ) , ["""ｱｯﾌﾟﾙストア""", """で""", """iPhone""", """８""", """が""", """発売""", """さ""", """れた""", """\u3000""", """。"""] , )
def lowerCamelCase__ (self : Dict ) -> Any:
"""simple docstring"""
lowercase__ = MecabTokenizer(normalize_text=_UpperCAmelCase , mecab_dic="""ipadic""" )
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ) , ["""ｱｯﾌﾟﾙストア""", """で""", """iPhone""", """８""", """が""", """発売""", """さ""", """れ""", """た""", """ """, """。"""] , )
@require_sudachi
def lowerCamelCase__ (self : Tuple ) -> List[Any]:
"""simple docstring"""
lowercase__ = self.tokenizer_class(self.vocab_file , word_tokenizer_type="""sudachi""" )
self.assertIsNotNone(_UpperCAmelCase )
lowercase__ = """こんにちは、世界。\nこんばんは、世界。"""
lowercase__ = tokenizer.tokenize(_UpperCAmelCase )
self.assertListEqual(_UpperCAmelCase , ["""こんにちは""", """、""", """世界""", """。""", """こん""", """##ばんは""", """、""", """世界""", """。"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase ) , [3, 12, 10, 14, 4, 9, 12, 10, 14] )
lowercase__ = os.path.join(self.tmpdirname , """tokenizer.bin""" )
with open(_UpperCAmelCase , """wb""" ) as handle:
pickle.dump(_UpperCAmelCase , _UpperCAmelCase )
with open(_UpperCAmelCase , """rb""" ) as handle:
lowercase__ = pickle.load(_UpperCAmelCase )
lowercase__ = tokenizer_new.tokenize(_UpperCAmelCase )
self.assertListEqual(_UpperCAmelCase , _UpperCAmelCase )
@require_sudachi
def lowerCamelCase__ (self : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
lowercase__ = SudachiTokenizer(sudachi_dict_type="""core""" )
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ) , [""" """, """\t""", """アップル""", """ストア""", """で""", """iPhone""", """8""", """ """, """が""", """ """, """ """, """\n """, """発売""", """さ""", """れ""", """た""", """ """, """。""", """ """, """ """] , )
@require_sudachi
def lowerCamelCase__ (self : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = SudachiTokenizer(sudachi_dict_type="""core""" , sudachi_split_mode="""A""" )
self.assertListEqual(tokenizer.tokenize("""外国人参政権""" ) , ["""外国""", """人""", """参政""", """権"""] )
@require_sudachi
def lowerCamelCase__ (self : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = SudachiTokenizer(sudachi_dict_type="""core""" , sudachi_split_mode="""B""" )
self.assertListEqual(tokenizer.tokenize("""外国人参政権""" ) , ["""外国人""", """参政権"""] )
@require_sudachi
def lowerCamelCase__ (self : List[str] ) -> List[Any]:
"""simple docstring"""
lowercase__ = SudachiTokenizer(sudachi_dict_type="""core""" , sudachi_split_mode="""C""" )
self.assertListEqual(tokenizer.tokenize("""外国人参政権""" ) , ["""外国人参政権"""] )
@require_sudachi
def lowerCamelCase__ (self : Tuple ) -> List[str]:
"""simple docstring"""
lowercase__ = SudachiTokenizer(do_lower_case=_UpperCAmelCase , sudachi_dict_type="""core""" )
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ) , [""" """, """\t""", """アップル""", """ストア""", """で""", """iphone""", """8""", """ """, """が""", """ """, """ """, """\n """, """発売""", """さ""", """れ""", """た""", """ """, """。""", """ """, """ """] , )
@require_sudachi
def lowerCamelCase__ (self : Tuple ) -> List[Any]:
"""simple docstring"""
lowercase__ = SudachiTokenizer(normalize_text=_UpperCAmelCase , sudachi_dict_type="""core""" )
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ) , [""" """, """\t""", """ｱｯﾌﾟﾙ""", """ストア""", """で""", """iPhone""", """８""", """ """, """が""", """ """, """ """, """\n """, """発売""", """さ""", """れ""", """た""", """\u3000""", """。""", """ """, """ """] , )
@require_sudachi
def lowerCamelCase__ (self : Dict ) -> Any:
"""simple docstring"""
lowercase__ = SudachiTokenizer(trim_whitespace=_UpperCAmelCase , sudachi_dict_type="""core""" )
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ) , ["""アップル""", """ストア""", """で""", """iPhone""", """8""", """が""", """発売""", """さ""", """れ""", """た""", """。"""] , )
@require_jumanpp
def lowerCamelCase__ (self : Union[str, Any] ) -> Any:
"""simple docstring"""
lowercase__ = self.tokenizer_class(self.vocab_file , word_tokenizer_type="""jumanpp""" )
self.assertIsNotNone(_UpperCAmelCase )
lowercase__ = """こんにちは、世界。\nこんばんは、世界。"""
lowercase__ = tokenizer.tokenize(_UpperCAmelCase )
self.assertListEqual(_UpperCAmelCase , ["""こんにちは""", """、""", """世界""", """。""", """こん""", """##ばんは""", """、""", """世界""", """。"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase ) , [3, 12, 10, 14, 4, 9, 12, 10, 14] )
lowercase__ = os.path.join(self.tmpdirname , """tokenizer.bin""" )
with open(_UpperCAmelCase , """wb""" ) as handle:
pickle.dump(_UpperCAmelCase , _UpperCAmelCase )
with open(_UpperCAmelCase , """rb""" ) as handle:
lowercase__ = pickle.load(_UpperCAmelCase )
lowercase__ = tokenizer_new.tokenize(_UpperCAmelCase )
self.assertListEqual(_UpperCAmelCase , _UpperCAmelCase )
@require_jumanpp
def lowerCamelCase__ (self : Optional[Any] ) -> List[str]:
"""simple docstring"""
lowercase__ = JumanppTokenizer()
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ) , ["""アップル""", """ストア""", """で""", """iPhone""", """8""", """\u3000""", """が""", """\u3000""", """\u3000""", """\u3000""", """発売""", """さ""", """れた""", """\u3000""", """。"""] , )
@require_jumanpp
def lowerCamelCase__ (self : List[str] ) -> List[Any]:
"""simple docstring"""
lowercase__ = JumanppTokenizer(do_lower_case=_UpperCAmelCase )
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ) , ["""アップル""", """ストア""", """で""", """iphone""", """8""", """\u3000""", """が""", """\u3000""", """\u3000""", """\u3000""", """発売""", """さ""", """れた""", """\u3000""", """。"""] , )
@require_jumanpp
def lowerCamelCase__ (self : Dict ) -> Dict:
"""simple docstring"""
lowercase__ = JumanppTokenizer(normalize_text=_UpperCAmelCase )
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ) , ["""ｱ""", """ｯ""", """ﾌ""", """ﾟ""", """ﾙ""", """ストア""", """で""", """iPhone""", """８""", """\u3000""", """が""", """\u3000""", """\u3000""", """\u3000""", """発売""", """さ""", """れた""", """\u3000""", """。"""] , )
@require_jumanpp
def lowerCamelCase__ (self : List[Any] ) -> List[Any]:
"""simple docstring"""
lowercase__ = JumanppTokenizer(trim_whitespace=_UpperCAmelCase )
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ) , ["""アップル""", """ストア""", """で""", """iPhone""", """8""", """が""", """発売""", """さ""", """れた""", """。"""] , )
@require_jumanpp
def lowerCamelCase__ (self : List[str] ) -> Dict:
"""simple docstring"""
lowercase__ = JumanppTokenizer()
self.assertListEqual(
tokenizer.tokenize("""ありがとうございますm(_ _)ｍ見つけるのが大変です。""" ) , ["""ありがとう""", """ございます""", """m(_ _)m""", """見つける""", """の""", """が""", """大変です""", """。"""] , )
def lowerCamelCase__ (self : Tuple ) -> Any:
"""simple docstring"""
lowercase__ = ["""[UNK]""", """[CLS]""", """[SEP]""", """こんにちは""", """こん""", """にちは""", """ばんは""", """##こん""", """##にちは""", """##ばんは"""]
lowercase__ = {}
for i, token in enumerate(_UpperCAmelCase ):
lowercase__ = i
lowercase__ = WordpieceTokenizer(vocab=_UpperCAmelCase , unk_token="""[UNK]""" )
self.assertListEqual(tokenizer.tokenize("""""" ) , [] )
self.assertListEqual(tokenizer.tokenize("""こんにちは""" ) , ["""こんにちは"""] )
self.assertListEqual(tokenizer.tokenize("""こんばんは""" ) , ["""こん""", """##ばんは"""] )
self.assertListEqual(tokenizer.tokenize("""こんばんは こんばんにちは こんにちは""" ) , ["""こん""", """##ばんは""", """[UNK]""", """こんにちは"""] )
def lowerCamelCase__ (self : Tuple ) -> Any:
"""simple docstring"""
lowercase__ = BertJapaneseTokenizer.from_pretrained("""nlp-waseda/roberta-base-japanese-with-auto-jumanpp""" )
lowercase__ = tokenizer.subword_tokenizer
lowercase__ = subword_tokenizer.tokenize("""国境 の 長い トンネル を 抜ける と 雪国 であった 。""" )
self.assertListEqual(_UpperCAmelCase , ["""▁国境""", """▁の""", """▁長い""", """▁トンネル""", """▁を""", """▁抜ける""", """▁と""", """▁雪""", """国""", """▁であった""", """▁。"""] )
lowercase__ = subword_tokenizer.tokenize("""こんばんは こんばん にち は こんにちは""" )
self.assertListEqual(_UpperCAmelCase , ["""▁こん""", """ばん""", """は""", """▁こん""", """ばん""", """▁に""", """ち""", """▁は""", """▁こんにちは"""] )
def lowerCamelCase__ (self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
lowercase__ = self.tokenizer_class.from_pretrained("""cl-tohoku/bert-base-japanese""" )
lowercase__ = tokenizer.encode("""ありがとう。""" , add_special_tokens=_UpperCAmelCase )
lowercase__ = tokenizer.encode("""どういたしまして。""" , add_special_tokens=_UpperCAmelCase )
lowercase__ = tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase )
lowercase__ = tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase , _UpperCAmelCase )
# 2 is for "[CLS]", 3 is for "[SEP]"
assert encoded_sentence == [2] + text + [3]
assert encoded_pair == [2] + text + [3] + text_a + [3]
@custom_tokenizers
class A ( UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
A__ = BertJapaneseTokenizer
A__ = False
def lowerCamelCase__ (self : List[Any] ) -> List[str]:
"""simple docstring"""
super().setUp()
lowercase__ = ["""[UNK]""", """[CLS]""", """[SEP]""", """こ""", """ん""", """に""", """ち""", """は""", """ば""", """世""", """界""", """、""", """。"""]
lowercase__ = 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] ) )
def lowerCamelCase__ (self : Union[str, Any] , **_UpperCAmelCase : Any ) -> Union[str, Any]:
"""simple docstring"""
return BertJapaneseTokenizer.from_pretrained(self.tmpdirname , subword_tokenizer_type="""character""" , **_UpperCAmelCase )
def lowerCamelCase__ (self : Tuple , _UpperCAmelCase : int ) -> Dict:
"""simple docstring"""
lowercase__ = """こんにちは、世界。 \nこんばんは、世界。"""
lowercase__ = """こ ん に ち は 、 世 界 。 こ ん ば ん は 、 世 界 。"""
return input_text, output_text
def lowerCamelCase__ (self : List[Any] ) -> Optional[int]:
"""simple docstring"""
pass # TODO add if relevant
def lowerCamelCase__ (self : Any ) -> Any:
"""simple docstring"""
pass # TODO add if relevant
def lowerCamelCase__ (self : int ) -> Dict:
"""simple docstring"""
pass # TODO add if relevant
def lowerCamelCase__ (self : Union[str, Any] ) -> Any:
"""simple docstring"""
lowercase__ = self.tokenizer_class(self.vocab_file , subword_tokenizer_type="""character""" )
lowercase__ = tokenizer.tokenize("""こんにちは、世界。 \nこんばんは、世界。""" )
self.assertListEqual(
_UpperCAmelCase , ["""こ""", """ん""", """に""", """ち""", """は""", """、""", """世""", """界""", """。""", """こ""", """ん""", """ば""", """ん""", """は""", """、""", """世""", """界""", """。"""] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(_UpperCAmelCase ) , [3, 4, 5, 6, 7, 11, 9, 10, 12, 3, 4, 8, 4, 7, 11, 9, 10, 12] )
def lowerCamelCase__ (self : Dict ) -> str:
"""simple docstring"""
lowercase__ = ["""[UNK]""", """[CLS]""", """[SEP]""", """こ""", """ん""", """に""", """ち""", """は""", """ば""", """世""", """界""", """、""", """。"""]
lowercase__ = {}
for i, token in enumerate(_UpperCAmelCase ):
lowercase__ = i
lowercase__ = CharacterTokenizer(vocab=_UpperCAmelCase , unk_token="""[UNK]""" )
self.assertListEqual(tokenizer.tokenize("""""" ) , [] )
self.assertListEqual(tokenizer.tokenize("""こんにちは""" ) , ["""こ""", """ん""", """に""", """ち""", """は"""] )
self.assertListEqual(tokenizer.tokenize("""こんにちほ""" ) , ["""こ""", """ん""", """に""", """ち""", """[UNK]"""] )
def lowerCamelCase__ (self : List[str] ) -> List[str]:
"""simple docstring"""
lowercase__ = self.tokenizer_class.from_pretrained("""cl-tohoku/bert-base-japanese-char""" )
lowercase__ = tokenizer.encode("""ありがとう。""" , add_special_tokens=_UpperCAmelCase )
lowercase__ = tokenizer.encode("""どういたしまして。""" , add_special_tokens=_UpperCAmelCase )
lowercase__ = tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase )
lowercase__ = tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase , _UpperCAmelCase )
# 2 is for "[CLS]", 3 is for "[SEP]"
assert encoded_sentence == [2] + text + [3]
assert encoded_pair == [2] + text + [3] + text_a + [3]
@custom_tokenizers
class A ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase__ (self : str ) -> List[str]:
"""simple docstring"""
lowercase__ = """cl-tohoku/bert-base-japanese"""
lowercase__ = AutoTokenizer.from_pretrained(_UpperCAmelCase )
self.assertIsInstance(_UpperCAmelCase , _UpperCAmelCase )
class A ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase__ (self : Dict ) -> Any:
"""simple docstring"""
lowercase__ = """cl-tohoku/bert-base-japanese"""
with self.assertLogs("""transformers""" , level="""WARNING""" ) as cm:
BertTokenizer.from_pretrained(_UpperCAmelCase )
self.assertTrue(
cm.records[0].message.startswith(
"""The tokenizer class you load from this checkpoint is not the same type as the class this function"""
""" is called from.""" ) )
lowercase__ = """bert-base-cased"""
with self.assertLogs("""transformers""" , level="""WARNING""" ) as cm:
BertJapaneseTokenizer.from_pretrained(_UpperCAmelCase )
self.assertTrue(
cm.records[0].message.startswith(
"""The tokenizer class you load from this checkpoint is not the same type as the class this function"""
""" is called from.""" ) )
| 305
|
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
from accelerate import PartialState
from accelerate.utils.operations import broadcast, gather, gather_object, pad_across_processes, reduce
def UpperCamelCase ( __magic_name__ : Any ) -> Optional[int]:
"""simple docstring"""
return (torch.arange(state.num_processes ) + 1.0 + (state.num_processes * state.process_index)).to(state.device )
def UpperCamelCase ( __magic_name__ : int ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = create_tensor(__magic_name__ )
lowercase__ = gather(__magic_name__ )
assert gathered_tensor.tolist() == list(range(1 , state.num_processes**2 + 1 ) )
def UpperCamelCase ( __magic_name__ : Optional[int] ) -> Tuple:
"""simple docstring"""
lowercase__ = [state.process_index]
lowercase__ = gather_object(__magic_name__ )
assert len(__magic_name__ ) == state.num_processes, f'''{gathered_obj}, {len(__magic_name__ )} != {state.num_processes}'''
assert gathered_obj == list(range(state.num_processes ) ), f'''{gathered_obj} != {list(range(state.num_processes ) )}'''
def UpperCamelCase ( __magic_name__ : str ) -> Dict:
"""simple docstring"""
lowercase__ = create_tensor(__magic_name__ )
lowercase__ = broadcast(__magic_name__ )
assert broadcasted_tensor.shape == torch.Size([state.num_processes] )
assert broadcasted_tensor.tolist() == list(range(1 , state.num_processes + 1 ) )
def UpperCamelCase ( __magic_name__ : str ) -> Dict:
"""simple docstring"""
if state.is_main_process:
lowercase__ = torch.arange(state.num_processes + 1 ).to(state.device )
else:
lowercase__ = torch.arange(state.num_processes ).to(state.device )
lowercase__ = pad_across_processes(__magic_name__ )
assert padded_tensor.shape == torch.Size([state.num_processes + 1] )
if not state.is_main_process:
assert padded_tensor.tolist() == list(range(0 , state.num_processes ) ) + [0]
def UpperCamelCase ( __magic_name__ : List[Any] ) -> Optional[int]:
"""simple docstring"""
if state.num_processes != 2:
return
lowercase__ = create_tensor(__magic_name__ )
lowercase__ = reduce(__magic_name__ , """sum""" )
lowercase__ = torch.tensor([4.0, 6] ).to(state.device )
assert torch.allclose(__magic_name__ , __magic_name__ ), f'''{reduced_tensor} != {truth_tensor}'''
def UpperCamelCase ( __magic_name__ : Dict ) -> int:
"""simple docstring"""
if state.num_processes != 2:
return
lowercase__ = create_tensor(__magic_name__ )
lowercase__ = reduce(__magic_name__ , """mean""" )
lowercase__ = torch.tensor([2.0, 3] ).to(state.device )
assert torch.allclose(__magic_name__ , __magic_name__ ), f'''{reduced_tensor} != {truth_tensor}'''
def UpperCamelCase ( __magic_name__ : str ) -> int:
"""simple docstring"""
main()
def UpperCamelCase ( ) -> Optional[int]:
"""simple docstring"""
lowercase__ = PartialState()
state.print(f'''State: {state}''' )
state.print("""testing gather""" )
test_gather(__magic_name__ )
state.print("""testing gather_object""" )
test_gather_object(__magic_name__ )
state.print("""testing broadcast""" )
test_broadcast(__magic_name__ )
state.print("""testing pad_across_processes""" )
test_pad_across_processes(__magic_name__ )
state.print("""testing reduce_sum""" )
test_reduce_sum(__magic_name__ )
state.print("""testing reduce_mean""" )
test_reduce_mean(__magic_name__ )
if __name__ == "__main__":
main()
| 305
| 1
|
import math
def UpperCamelCase ( __magic_name__ : list , __magic_name__ : int ) -> int:
"""simple docstring"""
lowercase__ = len(__magic_name__ )
lowercase__ = int(math.floor(math.sqrt(__magic_name__ ) ) )
lowercase__ = 0
while arr[min(__magic_name__ , __magic_name__ ) - 1] < x:
lowercase__ = step
step += int(math.floor(math.sqrt(__magic_name__ ) ) )
if prev >= n:
return -1
while arr[prev] < x:
lowercase__ = prev + 1
if prev == min(__magic_name__ , __magic_name__ ):
return -1
if arr[prev] == x:
return prev
return -1
if __name__ == "__main__":
A : int = input('Enter numbers separated by a comma:\n').strip()
A : Tuple = [int(item) for item in user_input.split(',')]
A : Union[str, Any] = int(input('Enter the number to be searched:\n'))
A : Optional[Any] = jump_search(arr, x)
if res == -1:
print('Number not found!')
else:
print(F'Number {x} is at index {res}')
| 305
|
def UpperCamelCase ( __magic_name__ : str ) -> int:
"""simple docstring"""
assert column_title.isupper()
lowercase__ = 0
lowercase__ = len(__magic_name__ ) - 1
lowercase__ = 0
while index >= 0:
lowercase__ = (ord(column_title[index] ) - 64) * pow(26 , __magic_name__ )
answer += value
power += 1
index -= 1
return answer
if __name__ == "__main__":
from doctest import testmod
testmod()
| 305
| 1
|
import torch
def UpperCamelCase ( ) -> List[Any]:
"""simple docstring"""
if torch.cuda.is_available():
lowercase__ = torch.cuda.device_count()
else:
lowercase__ = 0
print(f'''Successfully ran on {num_gpus} GPUs''' )
if __name__ == "__main__":
main()
| 305
|
import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def UpperCamelCase ( __magic_name__ : list , __magic_name__ : list , __magic_name__ : list , __magic_name__ : list , __magic_name__ : list ) -> float:
"""simple docstring"""
lowercase__ = np.array([[1, item, train_mtch[i]] for i, item in enumerate(__magic_name__ )] )
lowercase__ = np.array(__magic_name__ )
lowercase__ = np.dot(np.dot(np.linalg.inv(np.dot(x.transpose() , __magic_name__ ) ) , x.transpose() ) , __magic_name__ )
return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2] )
def UpperCamelCase ( __magic_name__ : list , __magic_name__ : list , __magic_name__ : list ) -> float:
"""simple docstring"""
lowercase__ = (1, 2, 1)
lowercase__ = (1, 1, 0, 7)
lowercase__ = SARIMAX(
__magic_name__ , exog=__magic_name__ , order=__magic_name__ , seasonal_order=__magic_name__ )
lowercase__ = model.fit(disp=__magic_name__ , maxiter=600 , method="""nm""" )
lowercase__ = model_fit.predict(1 , len(__magic_name__ ) , exog=[test_match] )
return result[0]
def UpperCamelCase ( __magic_name__ : list , __magic_name__ : list , __magic_name__ : list ) -> float:
"""simple docstring"""
lowercase__ = SVR(kernel="""rbf""" , C=1 , gamma=0.1 , epsilon=0.1 )
regressor.fit(__magic_name__ , __magic_name__ )
lowercase__ = regressor.predict(__magic_name__ )
return y_pred[0]
def UpperCamelCase ( __magic_name__ : list ) -> float:
"""simple docstring"""
train_user.sort()
lowercase__ = np.percentile(__magic_name__ , 25 )
lowercase__ = np.percentile(__magic_name__ , 75 )
lowercase__ = qa - qa
lowercase__ = qa - (iqr * 0.1)
return low_lim
def UpperCamelCase ( __magic_name__ : list , __magic_name__ : float ) -> bool:
"""simple docstring"""
lowercase__ = 0
lowercase__ = 0
for i in list_vote:
if i > actual_result:
lowercase__ = not_safe + 1
else:
if abs(abs(__magic_name__ ) - abs(__magic_name__ ) ) <= 0.1:
safe += 1
else:
not_safe += 1
return safe > not_safe
if __name__ == "__main__":
# data_input_df = pd.read_csv("ex_data.csv", header=None)
A : Dict = [[1_8_2_3_1, 0.0, 1], [2_2_6_2_1, 1.0, 2], [1_5_6_7_5, 0.0, 3], [2_3_5_8_3, 1.0, 4]]
A : str = pd.DataFrame(
data_input, columns=['total_user', 'total_even', 'days']
)
A : Any = Normalizer().fit_transform(data_input_df.values)
# split data
A : Optional[int] = normalize_df[:, 2].tolist()
A : Any = normalize_df[:, 0].tolist()
A : str = normalize_df[:, 1].tolist()
# for svr (input variable = total date and total match)
A : int = normalize_df[:, [1, 2]].tolist()
A : Any = x[: len(x) - 1]
A : Tuple = x[len(x) - 1 :]
# for linear regression & sarimax
A : Optional[int] = total_date[: len(total_date) - 1]
A : Optional[int] = total_user[: len(total_user) - 1]
A : str = total_match[: len(total_match) - 1]
A : Union[str, Any] = total_date[len(total_date) - 1 :]
A : List[str] = total_user[len(total_user) - 1 :]
A : str = total_match[len(total_match) - 1 :]
# voting system with forecasting
A : int = [
linear_regression_prediction(
trn_date, trn_user, trn_match, tst_date, tst_match
),
sarimax_predictor(trn_user, trn_match, tst_match),
support_vector_regressor(x_train, x_test, trn_user),
]
# check the safety of today's data
A : int = '' if data_safety_checker(res_vote, tst_user) else 'not '
print('Today\'s data is {not_str}safe.')
| 305
| 1
|
import argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
BarkCoarseConfig,
BarkConfig,
BarkFineConfig,
BarkSemanticConfig,
)
from transformers.models.bark.generation_configuration_bark import (
BarkCoarseGenerationConfig,
BarkFineGenerationConfig,
BarkGenerationConfig,
BarkSemanticGenerationConfig,
)
from transformers.models.bark.modeling_bark import BarkCoarseModel, BarkFineModel, BarkModel, BarkSemanticModel
from transformers.utils import logging
logging.set_verbosity_info()
A : Dict = logging.get_logger(__name__)
set_seed(7_7_0)
A : int = {
'c_attn': 'att_proj',
'c_proj': 'out_proj',
'c_fc': 'in_proj',
'transformer.': '',
'h.': 'layers.',
'ln_1': 'layernorm_1',
'ln_2': 'layernorm_2',
'ln_f': 'layernorm_final',
'wpe': 'position_embeds_layer',
'wte': 'input_embeds_layer',
}
A : int = {
'text_small': {
'repo_id': 'suno/bark',
'file_name': 'text.pt',
},
'coarse_small': {
'repo_id': 'suno/bark',
'file_name': 'coarse.pt',
},
'fine_small': {
'repo_id': 'suno/bark',
'file_name': 'fine.pt',
},
'text': {
'repo_id': 'suno/bark',
'file_name': 'text_2.pt',
},
'coarse': {
'repo_id': 'suno/bark',
'file_name': 'coarse_2.pt',
},
'fine': {
'repo_id': 'suno/bark',
'file_name': 'fine_2.pt',
},
}
A : Dict = os.path.dirname(os.path.abspath(__file__))
A : Dict = os.path.join(os.path.expanduser('~'), '.cache')
A : str = os.path.join(os.getenv('XDG_CACHE_HOME', default_cache_dir), 'suno', 'bark_v0')
def UpperCamelCase ( __magic_name__ : Union[str, Any] , __magic_name__ : int=False ) -> int:
"""simple docstring"""
lowercase__ = model_type
if use_small:
key += "_small"
return os.path.join(__magic_name__ , REMOTE_MODEL_PATHS[key]["""file_name"""] )
def UpperCamelCase ( __magic_name__ : str , __magic_name__ : Any ) -> int:
"""simple docstring"""
os.makedirs(__magic_name__ , exist_ok=__magic_name__ )
hf_hub_download(repo_id=__magic_name__ , filename=__magic_name__ , local_dir=__magic_name__ )
def UpperCamelCase ( __magic_name__ : List[Any] , __magic_name__ : Optional[int] , __magic_name__ : str=False , __magic_name__ : str="text" ) -> int:
"""simple docstring"""
if model_type == "text":
lowercase__ = BarkSemanticModel
lowercase__ = BarkSemanticConfig
lowercase__ = BarkSemanticGenerationConfig
elif model_type == "coarse":
lowercase__ = BarkCoarseModel
lowercase__ = BarkCoarseConfig
lowercase__ = BarkCoarseGenerationConfig
elif model_type == "fine":
lowercase__ = BarkFineModel
lowercase__ = BarkFineConfig
lowercase__ = BarkFineGenerationConfig
else:
raise NotImplementedError()
lowercase__ = f'''{model_type}_small''' if use_small else model_type
lowercase__ = REMOTE_MODEL_PATHS[model_key]
if not os.path.exists(__magic_name__ ):
logger.info(f'''{model_type} model not found, downloading into `{CACHE_DIR}`.''' )
_download(model_info["""repo_id"""] , model_info["""file_name"""] )
lowercase__ = torch.load(__magic_name__ , map_location=__magic_name__ )
# this is a hack
lowercase__ = checkpoint["""model_args"""]
if "input_vocab_size" not in model_args:
lowercase__ = model_args["""vocab_size"""]
lowercase__ = model_args["""vocab_size"""]
del model_args["vocab_size"]
# convert Bark model arguments to HF Bark model arguments
lowercase__ = model_args.pop("""n_head""" )
lowercase__ = model_args.pop("""n_embd""" )
lowercase__ = model_args.pop("""n_layer""" )
lowercase__ = ConfigClass(**checkpoint["""model_args"""] )
lowercase__ = ModelClass(config=__magic_name__ )
lowercase__ = GenerationConfigClass()
lowercase__ = model_generation_config
lowercase__ = checkpoint["""model"""]
# fixup checkpoint
lowercase__ = """_orig_mod."""
for k, v in list(state_dict.items() ):
if k.startswith(__magic_name__ ):
# replace part of the key with corresponding layer name in HF implementation
lowercase__ = k[len(__magic_name__ ) :]
for old_layer_name in new_layer_name_dict:
lowercase__ = new_k.replace(__magic_name__ , new_layer_name_dict[old_layer_name] )
lowercase__ = state_dict.pop(__magic_name__ )
lowercase__ = set(state_dict.keys() ) - set(model.state_dict().keys() )
lowercase__ = {k for k in extra_keys if not k.endswith(""".attn.bias""" )}
lowercase__ = set(model.state_dict().keys() ) - set(state_dict.keys() )
lowercase__ = {k for k in missing_keys if not k.endswith(""".attn.bias""" )}
if len(__magic_name__ ) != 0:
raise ValueError(f'''extra keys found: {extra_keys}''' )
if len(__magic_name__ ) != 0:
raise ValueError(f'''missing keys: {missing_keys}''' )
model.load_state_dict(__magic_name__ , strict=__magic_name__ )
lowercase__ = model.num_parameters(exclude_embeddings=__magic_name__ )
lowercase__ = checkpoint["""best_val_loss"""].item()
logger.info(f'''model loaded: {round(n_params/1E6 , 1 )}M params, {round(__magic_name__ , 3 )} loss''' )
model.eval()
model.to(__magic_name__ )
del checkpoint, state_dict
return model
def UpperCamelCase ( __magic_name__ : Union[str, Any] , __magic_name__ : str=False , __magic_name__ : int="text" ) -> Dict:
"""simple docstring"""
if model_type not in ("text", "coarse", "fine"):
raise NotImplementedError()
lowercase__ = """cpu""" # do conversion on cpu
lowercase__ = _get_ckpt_path(__magic_name__ , use_small=__magic_name__ )
lowercase__ = _load_model(__magic_name__ , __magic_name__ , model_type=__magic_name__ , use_small=__magic_name__ )
# load bark initial model
lowercase__ = _bark_load_model(__magic_name__ , """cpu""" , model_type=__magic_name__ , use_small=__magic_name__ )
if model_type == "text":
lowercase__ = bark_model["""model"""]
if model.num_parameters(exclude_embeddings=__magic_name__ ) != bark_model.get_num_params():
raise ValueError("""initial and new models don't have the same number of parameters""" )
# check if same output as the bark model
lowercase__ = 5
lowercase__ = 10
if model_type in ["text", "coarse"]:
lowercase__ = torch.randint(256 , (batch_size, sequence_length) , dtype=torch.int )
lowercase__ = bark_model(__magic_name__ )[0]
lowercase__ = model(__magic_name__ )
# take last logits
lowercase__ = output_new_model_total.logits[:, [-1], :]
else:
lowercase__ = 3
lowercase__ = 8
lowercase__ = torch.randint(256 , (batch_size, sequence_length, n_codes_total) , dtype=torch.int )
lowercase__ = model(__magic_name__ , __magic_name__ )
lowercase__ = bark_model(__magic_name__ , __magic_name__ )
lowercase__ = output_new_model_total.logits
# output difference should come from the difference of self-attention implementation design
if output_new_model.shape != output_old_model.shape:
raise ValueError("""initial and new outputs don't have the same shape""" )
if (output_new_model - output_old_model).abs().max().item() > 1E-3:
raise ValueError("""initial and new outputs are not equal""" )
Path(__magic_name__ ).mkdir(exist_ok=__magic_name__ )
model.save_pretrained(__magic_name__ )
def UpperCamelCase ( __magic_name__ : str , __magic_name__ : Any , __magic_name__ : Optional[int] , __magic_name__ : Union[str, Any] , __magic_name__ : Dict , __magic_name__ : List[Any] , ) -> Any:
"""simple docstring"""
lowercase__ = os.path.join(__magic_name__ , __magic_name__ )
lowercase__ = BarkSemanticConfig.from_pretrained(os.path.join(__magic_name__ , """config.json""" ) )
lowercase__ = BarkCoarseConfig.from_pretrained(os.path.join(__magic_name__ , """config.json""" ) )
lowercase__ = BarkFineConfig.from_pretrained(os.path.join(__magic_name__ , """config.json""" ) )
lowercase__ = EncodecConfig.from_pretrained("""facebook/encodec_24khz""" )
lowercase__ = BarkSemanticModel.from_pretrained(__magic_name__ )
lowercase__ = BarkCoarseModel.from_pretrained(__magic_name__ )
lowercase__ = BarkFineModel.from_pretrained(__magic_name__ )
lowercase__ = EncodecModel.from_pretrained("""facebook/encodec_24khz""" )
lowercase__ = BarkConfig.from_sub_model_configs(
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ )
lowercase__ = BarkGenerationConfig.from_sub_model_configs(
semantic.generation_config , coarseAcoustic.generation_config , fineAcoustic.generation_config )
lowercase__ = BarkModel(__magic_name__ )
lowercase__ = semantic
lowercase__ = coarseAcoustic
lowercase__ = fineAcoustic
lowercase__ = codec
lowercase__ = bark_generation_config
Path(__magic_name__ ).mkdir(exist_ok=__magic_name__ )
bark.save_pretrained(__magic_name__ , repo_id=__magic_name__ , push_to_hub=__magic_name__ )
if __name__ == "__main__":
A : List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument('model_type', type=str, help='text, coarse or fine.')
parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
parser.add_argument('--is_small', action='store_true', help='convert the small version instead of the large.')
A : int = parser.parse_args()
load_model(args.pytorch_dump_folder_path, model_type=args.model_type, use_small=args.is_small)
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import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def UpperCamelCase ( __magic_name__ : Optional[Any] ) -> List[Any]:
"""simple docstring"""
lowercase__ = tmp_path / """file.csv"""
lowercase__ = textwrap.dedent(
"""\
header1,header2
1,2
10,20
""" )
with open(__magic_name__ , """w""" ) as f:
f.write(__magic_name__ )
return str(__magic_name__ )
@pytest.fixture
def UpperCamelCase ( __magic_name__ : str ) -> Tuple:
"""simple docstring"""
lowercase__ = tmp_path / """malformed_file.csv"""
lowercase__ = textwrap.dedent(
"""\
header1,header2
1,2
10,20,
""" )
with open(__magic_name__ , """w""" ) as f:
f.write(__magic_name__ )
return str(__magic_name__ )
@pytest.fixture
def UpperCamelCase ( __magic_name__ : List[Any] , __magic_name__ : List[str] ) -> str:
"""simple docstring"""
lowercase__ = tmp_path / """csv_with_image.csv"""
lowercase__ = textwrap.dedent(
f'''\
image
{image_file}
''' )
with open(__magic_name__ , """w""" ) as f:
f.write(__magic_name__ )
return str(__magic_name__ )
@pytest.fixture
def UpperCamelCase ( __magic_name__ : Tuple ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = tmp_path / """csv_with_label.csv"""
lowercase__ = textwrap.dedent(
"""\
label
good
bad
good
""" )
with open(__magic_name__ , """w""" ) as f:
f.write(__magic_name__ )
return str(__magic_name__ )
@pytest.fixture
def UpperCamelCase ( __magic_name__ : Dict ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = tmp_path / """csv_with_int_list.csv"""
lowercase__ = textwrap.dedent(
"""\
int_list
1 2 3
4 5 6
7 8 9
""" )
with open(__magic_name__ , """w""" ) as f:
f.write(__magic_name__ )
return str(__magic_name__ )
def UpperCamelCase ( __magic_name__ : Tuple , __magic_name__ : Tuple , __magic_name__ : Tuple ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = Csv()
lowercase__ = csv._generate_tables([[csv_file, malformed_csv_file]] )
with pytest.raises(__magic_name__ , match="""Error tokenizing data""" ):
for _ in generator:
pass
assert any(
record.levelname == """ERROR"""
and """Failed to read file""" in record.message
and os.path.basename(__magic_name__ ) in record.message
for record in caplog.records )
@require_pil
def UpperCamelCase ( __magic_name__ : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
with open(__magic_name__ , encoding="""utf-8""" ) as f:
lowercase__ = f.read().splitlines()[1]
lowercase__ = Csv(encoding="""utf-8""" , features=Features({"""image""": Image()} ) )
lowercase__ = csv._generate_tables([[csv_file_with_image]] )
lowercase__ = pa.concat_tables([table for _, table in generator] )
assert pa_table.schema.field("""image""" ).type == Image()()
lowercase__ = pa_table.to_pydict()["""image"""]
assert generated_content == [{"path": image_file, "bytes": None}]
def UpperCamelCase ( __magic_name__ : Optional[Any] ) -> str:
"""simple docstring"""
with open(__magic_name__ , encoding="""utf-8""" ) as f:
lowercase__ = f.read().splitlines()[1:]
lowercase__ = Csv(encoding="""utf-8""" , features=Features({"""label""": ClassLabel(names=["""good""", """bad"""] )} ) )
lowercase__ = csv._generate_tables([[csv_file_with_label]] )
lowercase__ = pa.concat_tables([table for _, table in generator] )
assert pa_table.schema.field("""label""" ).type == ClassLabel(names=["""good""", """bad"""] )()
lowercase__ = pa_table.to_pydict()["""label"""]
assert generated_content == [ClassLabel(names=["""good""", """bad"""] ).straint(__magic_name__ ) for label in labels]
def UpperCamelCase ( __magic_name__ : Any ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = Csv(encoding="""utf-8""" , sep=""",""" , converters={"""int_list""": lambda __magic_name__ : [int(__magic_name__ ) for i in x.split()]} )
lowercase__ = csv._generate_tables([[csv_file_with_int_list]] )
lowercase__ = pa.concat_tables([table for _, table in generator] )
assert pa.types.is_list(pa_table.schema.field("""int_list""" ).type )
lowercase__ = pa_table.to_pydict()["""int_list"""]
assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
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| 1
|
def UpperCamelCase ( __magic_name__ : Optional[int] , __magic_name__ : int ) -> List[str]:
"""simple docstring"""
lowercase__ = [1]
for i in range(2 , __magic_name__ ):
factorials.append(factorials[-1] * i )
assert 0 <= k < factorials[-1] * n, "k out of bounds"
lowercase__ = []
lowercase__ = list(range(__magic_name__ ) )
# Find permutation
while factorials:
lowercase__ = factorials.pop()
lowercase__ , lowercase__ = divmod(__magic_name__ , __magic_name__ )
permutation.append(elements[number] )
elements.remove(elements[number] )
permutation.append(elements[0] )
return permutation
if __name__ == "__main__":
import doctest
doctest.testmod()
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|
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
A : int = {'configuration_dpt': ['DPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DPTConfig']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Union[str, Any] = ['DPTFeatureExtractor']
A : int = ['DPTImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Tuple = [
'DPT_PRETRAINED_MODEL_ARCHIVE_LIST',
'DPTForDepthEstimation',
'DPTForSemanticSegmentation',
'DPTModel',
'DPTPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_dpt import DPTFeatureExtractor
from .image_processing_dpt import DPTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_dpt import (
DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
DPTForDepthEstimation,
DPTForSemanticSegmentation,
DPTModel,
DPTPreTrainedModel,
)
else:
import sys
A : str = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
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| 1
|
# using dfs for finding eulerian path traversal
def UpperCamelCase ( __magic_name__ : str , __magic_name__ : int , __magic_name__ : Union[str, Any] , __magic_name__ : Tuple=None ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = (path or []) + [u]
for v in graph[u]:
if visited_edge[u][v] is False:
lowercase__ , lowercase__ = True, True
lowercase__ = dfs(__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ )
return path
def UpperCamelCase ( __magic_name__ : List[str] , __magic_name__ : Any ) -> Tuple:
"""simple docstring"""
lowercase__ = 0
lowercase__ = -1
for i in range(__magic_name__ ):
if i not in graph.keys():
continue
if len(graph[i] ) % 2 == 1:
odd_degree_nodes += 1
lowercase__ = i
if odd_degree_nodes == 0:
return 1, odd_node
if odd_degree_nodes == 2:
return 2, odd_node
return 3, odd_node
def UpperCamelCase ( __magic_name__ : str , __magic_name__ : List[str] ) -> int:
"""simple docstring"""
lowercase__ = [[False for _ in range(max_node + 1 )] for _ in range(max_node + 1 )]
lowercase__ , lowercase__ = check_circuit_or_path(__magic_name__ , __magic_name__ )
if check == 3:
print("""graph is not Eulerian""" )
print("""no path""" )
return
lowercase__ = 1
if check == 2:
lowercase__ = odd_node
print("""graph has a Euler path""" )
if check == 1:
print("""graph has a Euler cycle""" )
lowercase__ = dfs(__magic_name__ , __magic_name__ , __magic_name__ )
print(__magic_name__ )
def UpperCamelCase ( ) -> int:
"""simple docstring"""
lowercase__ = {1: [2, 3, 4], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [4]}
lowercase__ = {1: [2, 3, 4, 5], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [1, 4]}
lowercase__ = {1: [2, 3, 4], 2: [1, 3, 4], 3: [1, 2], 4: [1, 2, 5], 5: [4]}
lowercase__ = {1: [2, 3], 2: [1, 3], 3: [1, 2]}
lowercase__ = {
1: [],
2: []
# all degree is zero
}
lowercase__ = 10
check_euler(__magic_name__ , __magic_name__ )
check_euler(__magic_name__ , __magic_name__ )
check_euler(__magic_name__ , __magic_name__ )
check_euler(__magic_name__ , __magic_name__ )
check_euler(__magic_name__ , __magic_name__ )
if __name__ == "__main__":
main()
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|
from __future__ import annotations
def UpperCamelCase ( __magic_name__ : list[float] , __magic_name__ : list[float] ) -> float:
"""simple docstring"""
lowercase__ = sorted(numsa + numsa )
lowercase__ , lowercase__ = divmod(len(__magic_name__ ) , 2 )
if mod == 1:
return all_numbers[div]
else:
return (all_numbers[div] + all_numbers[div - 1]) / 2
if __name__ == "__main__":
import doctest
doctest.testmod()
A : Any = [float(x) for x in input('Enter the elements of first array: ').split()]
A : Union[str, Any] = [float(x) for x in input('Enter the elements of second array: ').split()]
print(F'The median of two arrays is: {median_of_two_arrays(array_a, array_a)}')
| 305
| 1
|
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : Tuple = logging.get_logger(__name__)
A : Optional[Any] = {
'huggingface/informer-tourism-monthly': (
'https://huggingface.co/huggingface/informer-tourism-monthly/resolve/main/config.json'
),
# See all Informer models at https://huggingface.co/models?filter=informer
}
class A ( UpperCAmelCase__ ):
'''simple docstring'''
A__ = '''informer'''
A__ = {
'''hidden_size''': '''d_model''',
'''num_attention_heads''': '''encoder_attention_heads''',
'''num_hidden_layers''': '''encoder_layers''',
}
def __init__(self : Any , _UpperCAmelCase : Optional[int] = None , _UpperCAmelCase : Optional[int] = None , _UpperCAmelCase : str = "student_t" , _UpperCAmelCase : str = "nll" , _UpperCAmelCase : int = 1 , _UpperCAmelCase : List[int] = None , _UpperCAmelCase : Optional[Union[str, bool]] = "mean" , _UpperCAmelCase : int = 0 , _UpperCAmelCase : int = 0 , _UpperCAmelCase : int = 0 , _UpperCAmelCase : int = 0 , _UpperCAmelCase : Optional[List[int]] = None , _UpperCAmelCase : Optional[List[int]] = None , _UpperCAmelCase : int = 64 , _UpperCAmelCase : int = 32 , _UpperCAmelCase : int = 32 , _UpperCAmelCase : int = 2 , _UpperCAmelCase : int = 2 , _UpperCAmelCase : int = 2 , _UpperCAmelCase : int = 2 , _UpperCAmelCase : bool = True , _UpperCAmelCase : str = "gelu" , _UpperCAmelCase : float = 0.05 , _UpperCAmelCase : float = 0.1 , _UpperCAmelCase : float = 0.1 , _UpperCAmelCase : float = 0.1 , _UpperCAmelCase : float = 0.1 , _UpperCAmelCase : int = 100 , _UpperCAmelCase : float = 0.02 , _UpperCAmelCase : Dict=True , _UpperCAmelCase : str = "prob" , _UpperCAmelCase : int = 5 , _UpperCAmelCase : bool = True , **_UpperCAmelCase : Dict , ) -> int:
"""simple docstring"""
lowercase__ = prediction_length
lowercase__ = context_length or prediction_length
lowercase__ = distribution_output
lowercase__ = loss
lowercase__ = input_size
lowercase__ = num_time_features
lowercase__ = lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
lowercase__ = scaling
lowercase__ = num_dynamic_real_features
lowercase__ = num_static_real_features
lowercase__ = num_static_categorical_features
# set cardinality
if cardinality and num_static_categorical_features > 0:
if len(_UpperCAmelCase ) != num_static_categorical_features:
raise ValueError(
"""The cardinality should be a list of the same length as `num_static_categorical_features`""" )
lowercase__ = cardinality
else:
lowercase__ = [0]
# set embedding_dimension
if embedding_dimension and num_static_categorical_features > 0:
if len(_UpperCAmelCase ) != num_static_categorical_features:
raise ValueError(
"""The embedding dimension should be a list of the same length as `num_static_categorical_features`""" )
lowercase__ = embedding_dimension
else:
lowercase__ = [min(50 , (cat + 1) // 2 ) for cat in self.cardinality]
lowercase__ = num_parallel_samples
# Transformer architecture configuration
lowercase__ = input_size * len(self.lags_sequence ) + self._number_of_features
lowercase__ = d_model
lowercase__ = encoder_attention_heads
lowercase__ = decoder_attention_heads
lowercase__ = encoder_ffn_dim
lowercase__ = decoder_ffn_dim
lowercase__ = encoder_layers
lowercase__ = decoder_layers
lowercase__ = dropout
lowercase__ = attention_dropout
lowercase__ = activation_dropout
lowercase__ = encoder_layerdrop
lowercase__ = decoder_layerdrop
lowercase__ = activation_function
lowercase__ = init_std
lowercase__ = use_cache
# Informer
lowercase__ = attention_type
lowercase__ = sampling_factor
lowercase__ = distil
super().__init__(is_encoder_decoder=_UpperCAmelCase , **_UpperCAmelCase )
@property
def lowerCamelCase__ (self : List[str] ) -> int:
"""simple docstring"""
return (
sum(self.embedding_dimension )
+ self.num_dynamic_real_features
+ self.num_time_features
+ self.num_static_real_features
+ self.input_size * 2 # the log1p(abs(loc)) and log(scale) features
)
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|
A : Union[str, Any] = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
A : List[Any] = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def UpperCamelCase ( __magic_name__ : dict[int, list[int]] , __magic_name__ : int , __magic_name__ : list[bool] ) -> list[int]:
"""simple docstring"""
lowercase__ = True
lowercase__ = []
for neighbour in graph[vert]:
if not visited[neighbour]:
order += topology_sort(__magic_name__ , __magic_name__ , __magic_name__ )
order.append(__magic_name__ )
return order
def UpperCamelCase ( __magic_name__ : dict[int, list[int]] , __magic_name__ : int , __magic_name__ : list[bool] ) -> list[int]:
"""simple docstring"""
lowercase__ = True
lowercase__ = [vert]
for neighbour in reversed_graph[vert]:
if not visited[neighbour]:
component += find_components(__magic_name__ , __magic_name__ , __magic_name__ )
return component
def UpperCamelCase ( __magic_name__ : dict[int, list[int]] ) -> list[list[int]]:
"""simple docstring"""
lowercase__ = len(__magic_name__ ) * [False]
lowercase__ = {vert: [] for vert in range(len(__magic_name__ ) )}
for vert, neighbours in graph.items():
for neighbour in neighbours:
reversed_graph[neighbour].append(__magic_name__ )
lowercase__ = []
for i, was_visited in enumerate(__magic_name__ ):
if not was_visited:
order += topology_sort(__magic_name__ , __magic_name__ , __magic_name__ )
lowercase__ = []
lowercase__ = len(__magic_name__ ) * [False]
for i in range(len(__magic_name__ ) ):
lowercase__ = order[len(__magic_name__ ) - i - 1]
if not visited[vert]:
lowercase__ = find_components(__magic_name__ , __magic_name__ , __magic_name__ )
components_list.append(__magic_name__ )
return components_list
| 305
| 1
|
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
A : Any = datasets.utils.logging.get_logger(__name__)
@dataclass
class A ( datasets.BuilderConfig ):
'''simple docstring'''
A__ = 1_00_00
A__ = None
A__ = None
class A ( datasets.ArrowBasedBuilder ):
'''simple docstring'''
A__ = ParquetConfig
def lowerCamelCase__ (self : str ) -> Dict:
"""simple docstring"""
return datasets.DatasetInfo(features=self.config.features )
def lowerCamelCase__ (self : Dict , _UpperCAmelCase : Any ) -> List[str]:
"""simple docstring"""
if not self.config.data_files:
raise ValueError(f'''At least one data file must be specified, but got data_files={self.config.data_files}''' )
lowercase__ = dl_manager.download_and_extract(self.config.data_files )
if isinstance(_UpperCAmelCase , (str, list, tuple) ):
lowercase__ = data_files
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
lowercase__ = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
lowercase__ = [dl_manager.iter_files(_UpperCAmelCase ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={"""files""": files} )]
lowercase__ = []
for split_name, files in data_files.items():
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
lowercase__ = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
lowercase__ = [dl_manager.iter_files(_UpperCAmelCase ) for file in files]
# Infer features is they are stoed in the arrow schema
if self.info.features is None:
for file in itertools.chain.from_iterable(_UpperCAmelCase ):
with open(_UpperCAmelCase , """rb""" ) as f:
lowercase__ = datasets.Features.from_arrow_schema(pq.read_schema(_UpperCAmelCase ) )
break
splits.append(datasets.SplitGenerator(name=_UpperCAmelCase , gen_kwargs={"""files""": files} ) )
return splits
def lowerCamelCase__ (self : str , _UpperCAmelCase : pa.Table ) -> pa.Table:
"""simple docstring"""
if self.info.features is not None:
# more expensive cast to support nested features with keys in a different order
# allows str <-> int/float or str to Audio for example
lowercase__ = table_cast(_UpperCAmelCase , self.info.features.arrow_schema )
return pa_table
def lowerCamelCase__ (self : Union[str, Any] , _UpperCAmelCase : Tuple ) -> Optional[int]:
"""simple docstring"""
lowercase__ = self.info.features.arrow_schema if self.info.features is not None else None
if self.info.features is not None and self.config.columns is not None:
if sorted(field.name for field in schema ) != sorted(self.config.columns ):
raise ValueError(
f'''Tried to load parquet data with columns \'{self.config.columns}\' with mismatching features \'{self.info.features}\'''' )
for file_idx, file in enumerate(itertools.chain.from_iterable(_UpperCAmelCase ) ):
with open(_UpperCAmelCase , """rb""" ) as f:
lowercase__ = pq.ParquetFile(_UpperCAmelCase )
try:
for batch_idx, record_batch in enumerate(
parquet_file.iter_batches(batch_size=self.config.batch_size , columns=self.config.columns ) ):
lowercase__ = pa.Table.from_batches([record_batch] )
# Uncomment for debugging (will print the Arrow table size and elements)
# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
yield f'''{file_idx}_{batch_idx}''', self._cast_table(_UpperCAmelCase )
except ValueError as e:
logger.error(f'''Failed to read file \'{file}\' with error {type(_UpperCAmelCase )}: {e}''' )
raise
| 305
|
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepInverseScheduler,
DPMSolverMultistepScheduler,
StableDiffusionDiffEditPipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device
from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class A ( UpperCAmelCase__ , UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
A__ = StableDiffusionDiffEditPipeline
A__ = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''height''', '''width''', '''image'''} | {'''image_latents'''}
A__ = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {'''image'''} | {'''image_latents'''}
A__ = frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
A__ = frozenset([] )
def lowerCamelCase__ (self : List[str] ) -> Optional[int]:
"""simple docstring"""
torch.manual_seed(0 )
lowercase__ = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , cross_attention_dim=32 , attention_head_dim=(2, 4) , use_linear_projection=_UpperCAmelCase , )
lowercase__ = DDIMScheduler(
beta_start=0.00_085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , clip_sample=_UpperCAmelCase , set_alpha_to_one=_UpperCAmelCase , )
lowercase__ = DDIMInverseScheduler(
beta_start=0.00_085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , clip_sample=_UpperCAmelCase , set_alpha_to_zero=_UpperCAmelCase , )
torch.manual_seed(0 )
lowercase__ = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , sample_size=128 , )
torch.manual_seed(0 )
lowercase__ = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , hidden_act="""gelu""" , projection_dim=512 , )
lowercase__ = CLIPTextModel(_UpperCAmelCase )
lowercase__ = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
lowercase__ = {
"""unet""": unet,
"""scheduler""": scheduler,
"""inverse_scheduler""": inverse_scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""safety_checker""": None,
"""feature_extractor""": None,
}
return components
def lowerCamelCase__ (self : Optional[Any] , _UpperCAmelCase : Tuple , _UpperCAmelCase : Tuple=0 ) -> Dict:
"""simple docstring"""
lowercase__ = floats_tensor((1, 16, 16) , rng=random.Random(_UpperCAmelCase ) ).to(_UpperCAmelCase )
lowercase__ = floats_tensor((1, 2, 4, 16, 16) , rng=random.Random(_UpperCAmelCase ) ).to(_UpperCAmelCase )
if str(_UpperCAmelCase ).startswith("""mps""" ):
lowercase__ = torch.manual_seed(_UpperCAmelCase )
else:
lowercase__ = torch.Generator(device=_UpperCAmelCase ).manual_seed(_UpperCAmelCase )
lowercase__ = {
"""prompt""": """a dog and a newt""",
"""mask_image""": mask,
"""image_latents""": latents,
"""generator""": generator,
"""num_inference_steps""": 2,
"""inpaint_strength""": 1.0,
"""guidance_scale""": 6.0,
"""output_type""": """numpy""",
}
return inputs
def lowerCamelCase__ (self : List[Any] , _UpperCAmelCase : Dict , _UpperCAmelCase : Tuple=0 ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = floats_tensor((1, 3, 32, 32) , rng=random.Random(_UpperCAmelCase ) ).to(_UpperCAmelCase )
lowercase__ = image.cpu().permute(0 , 2 , 3 , 1 )[0]
lowercase__ = Image.fromarray(np.uinta(_UpperCAmelCase ) ).convert("""RGB""" )
if str(_UpperCAmelCase ).startswith("""mps""" ):
lowercase__ = torch.manual_seed(_UpperCAmelCase )
else:
lowercase__ = torch.Generator(device=_UpperCAmelCase ).manual_seed(_UpperCAmelCase )
lowercase__ = {
"""image""": image,
"""source_prompt""": """a cat and a frog""",
"""target_prompt""": """a dog and a newt""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""num_maps_per_mask""": 2,
"""mask_encode_strength""": 1.0,
"""guidance_scale""": 6.0,
"""output_type""": """numpy""",
}
return inputs
def lowerCamelCase__ (self : Optional[Any] , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Dict=0 ) -> str:
"""simple docstring"""
lowercase__ = floats_tensor((1, 3, 32, 32) , rng=random.Random(_UpperCAmelCase ) ).to(_UpperCAmelCase )
lowercase__ = image.cpu().permute(0 , 2 , 3 , 1 )[0]
lowercase__ = Image.fromarray(np.uinta(_UpperCAmelCase ) ).convert("""RGB""" )
if str(_UpperCAmelCase ).startswith("""mps""" ):
lowercase__ = torch.manual_seed(_UpperCAmelCase )
else:
lowercase__ = torch.Generator(device=_UpperCAmelCase ).manual_seed(_UpperCAmelCase )
lowercase__ = {
"""image""": image,
"""prompt""": """a cat and a frog""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""inpaint_strength""": 1.0,
"""guidance_scale""": 6.0,
"""decode_latents""": True,
"""output_type""": """numpy""",
}
return inputs
def lowerCamelCase__ (self : int ) -> Dict:
"""simple docstring"""
if not hasattr(self.pipeline_class , """_optional_components""" ):
return
lowercase__ = self.get_dummy_components()
lowercase__ = self.pipeline_class(**_UpperCAmelCase )
pipe.to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
# set all optional components to None and update pipeline config accordingly
for optional_component in pipe._optional_components:
setattr(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components} )
lowercase__ = self.get_dummy_inputs(_UpperCAmelCase )
lowercase__ = pipe(**_UpperCAmelCase )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(_UpperCAmelCase )
lowercase__ = self.pipeline_class.from_pretrained(_UpperCAmelCase )
pipe_loaded.to(_UpperCAmelCase )
pipe_loaded.set_progress_bar_config(disable=_UpperCAmelCase )
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(_UpperCAmelCase , _UpperCAmelCase ) is None , f'''`{optional_component}` did not stay set to None after loading.''' , )
lowercase__ = self.get_dummy_inputs(_UpperCAmelCase )
lowercase__ = pipe_loaded(**_UpperCAmelCase )[0]
lowercase__ = np.abs(output - output_loaded ).max()
self.assertLess(_UpperCAmelCase , 1E-4 )
def lowerCamelCase__ (self : List[str] ) -> int:
"""simple docstring"""
lowercase__ = """cpu"""
lowercase__ = self.get_dummy_components()
lowercase__ = self.pipeline_class(**_UpperCAmelCase )
pipe.to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
lowercase__ = self.get_dummy_mask_inputs(_UpperCAmelCase )
lowercase__ = pipe.generate_mask(**_UpperCAmelCase )
lowercase__ = mask[0, -3:, -3:]
self.assertEqual(mask.shape , (1, 16, 16) )
lowercase__ = np.array([0] * 9 )
lowercase__ = np.abs(mask_slice.flatten() - expected_slice ).max()
self.assertLessEqual(_UpperCAmelCase , 1E-3 )
self.assertEqual(mask[0, -3, -4] , 0 )
def lowerCamelCase__ (self : List[Any] ) -> str:
"""simple docstring"""
lowercase__ = """cpu"""
lowercase__ = self.get_dummy_components()
lowercase__ = self.pipeline_class(**_UpperCAmelCase )
pipe.to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
lowercase__ = self.get_dummy_inversion_inputs(_UpperCAmelCase )
lowercase__ = pipe.invert(**_UpperCAmelCase ).images
lowercase__ = image[0, -1, -3:, -3:]
self.assertEqual(image.shape , (2, 32, 32, 3) )
lowercase__ = np.array(
[0.5_150, 0.5_134, 0.5_043, 0.5_376, 0.4_694, 0.51_050, 0.5_015, 0.4_407, 0.4_799] , )
lowercase__ = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(_UpperCAmelCase , 1E-3 )
def lowerCamelCase__ (self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
super().test_inference_batch_single_identical(expected_max_diff=5E-3 )
def lowerCamelCase__ (self : str ) -> List[str]:
"""simple docstring"""
lowercase__ = """cpu"""
lowercase__ = self.get_dummy_components()
lowercase__ = {"""beta_start""": 0.00_085, """beta_end""": 0.012, """beta_schedule""": """scaled_linear"""}
lowercase__ = DPMSolverMultistepScheduler(**_UpperCAmelCase )
lowercase__ = DPMSolverMultistepInverseScheduler(**_UpperCAmelCase )
lowercase__ = self.pipeline_class(**_UpperCAmelCase )
pipe.to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
lowercase__ = self.get_dummy_inversion_inputs(_UpperCAmelCase )
lowercase__ = pipe.invert(**_UpperCAmelCase ).images
lowercase__ = image[0, -1, -3:, -3:]
self.assertEqual(image.shape , (2, 32, 32, 3) )
lowercase__ = np.array(
[0.5_150, 0.5_134, 0.5_043, 0.5_376, 0.4_694, 0.51_050, 0.5_015, 0.4_407, 0.4_799] , )
lowercase__ = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(_UpperCAmelCase , 1E-3 )
@require_torch_gpu
@slow
class A ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase__ (self : Any ) -> Any:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@classmethod
def lowerCamelCase__ (cls : str ) -> Optional[int]:
"""simple docstring"""
lowercase__ = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png""" )
lowercase__ = raw_image.convert("""RGB""" ).resize((768, 768) )
lowercase__ = raw_image
def lowerCamelCase__ (self : Optional[int] ) -> Any:
"""simple docstring"""
lowercase__ = torch.manual_seed(0 )
lowercase__ = StableDiffusionDiffEditPipeline.from_pretrained(
"""stabilityai/stable-diffusion-2-1""" , safety_checker=_UpperCAmelCase , torch_dtype=torch.floataa )
lowercase__ = DDIMScheduler.from_config(pipe.scheduler.config )
lowercase__ = DDIMInverseScheduler.from_config(pipe.scheduler.config )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
lowercase__ = """a bowl of fruit"""
lowercase__ = """a bowl of pears"""
lowercase__ = pipe.generate_mask(
image=self.raw_image , source_prompt=_UpperCAmelCase , target_prompt=_UpperCAmelCase , generator=_UpperCAmelCase , )
lowercase__ = pipe.invert(
prompt=_UpperCAmelCase , image=self.raw_image , inpaint_strength=0.7 , generator=_UpperCAmelCase ).latents
lowercase__ = pipe(
prompt=_UpperCAmelCase , mask_image=_UpperCAmelCase , image_latents=_UpperCAmelCase , generator=_UpperCAmelCase , negative_prompt=_UpperCAmelCase , inpaint_strength=0.7 , output_type="""numpy""" , ).images[0]
lowercase__ = (
np.array(
load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/diffedit/pears.png""" ).resize((768, 768) ) )
/ 255
)
assert np.abs((expected_image - image).max() ) < 5E-1
def lowerCamelCase__ (self : int ) -> Any:
"""simple docstring"""
lowercase__ = torch.manual_seed(0 )
lowercase__ = StableDiffusionDiffEditPipeline.from_pretrained(
"""stabilityai/stable-diffusion-2-1""" , safety_checker=_UpperCAmelCase , torch_dtype=torch.floataa )
lowercase__ = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
lowercase__ = DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
lowercase__ = """a bowl of fruit"""
lowercase__ = """a bowl of pears"""
lowercase__ = pipe.generate_mask(
image=self.raw_image , source_prompt=_UpperCAmelCase , target_prompt=_UpperCAmelCase , generator=_UpperCAmelCase , )
lowercase__ = pipe.invert(
prompt=_UpperCAmelCase , image=self.raw_image , inpaint_strength=0.7 , generator=_UpperCAmelCase , num_inference_steps=25 , ).latents
lowercase__ = pipe(
prompt=_UpperCAmelCase , mask_image=_UpperCAmelCase , image_latents=_UpperCAmelCase , generator=_UpperCAmelCase , negative_prompt=_UpperCAmelCase , inpaint_strength=0.7 , num_inference_steps=25 , output_type="""numpy""" , ).images[0]
lowercase__ = (
np.array(
load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/diffedit/pears.png""" ).resize((768, 768) ) )
/ 255
)
assert np.abs((expected_image - image).max() ) < 5E-1
| 305
| 1
|
import math
A : Optional[Any] = 1_0
A : List[str] = 7
A : str = BALLS_PER_COLOUR * NUM_COLOURS
def UpperCamelCase ( __magic_name__ : int = 20 ) -> str:
"""simple docstring"""
lowercase__ = math.comb(__magic_name__ , __magic_name__ )
lowercase__ = math.comb(NUM_BALLS - BALLS_PER_COLOUR , __magic_name__ )
lowercase__ = NUM_COLOURS * (1 - missing_colour / total)
return f'''{result:.9f}'''
if __name__ == "__main__":
print(solution(2_0))
| 305
|
from __future__ import annotations
import math
def UpperCamelCase ( __magic_name__ : list , __magic_name__ : list ) -> list:
"""simple docstring"""
if len(__magic_name__ ) != 2 or len(a[0] ) != 2 or len(__magic_name__ ) != 2 or len(b[0] ) != 2:
raise Exception("""Matrices are not 2x2""" )
lowercase__ = [
[a[0][0] * b[0][0] + a[0][1] * b[1][0], a[0][0] * b[0][1] + a[0][1] * b[1][1]],
[a[1][0] * b[0][0] + a[1][1] * b[1][0], a[1][0] * b[0][1] + a[1][1] * b[1][1]],
]
return new_matrix
def UpperCamelCase ( __magic_name__ : list , __magic_name__ : list ) -> Union[str, Any]:
"""simple docstring"""
return [
[matrix_a[row][col] + matrix_b[row][col] for col in range(len(matrix_a[row] ) )]
for row in range(len(__magic_name__ ) )
]
def UpperCamelCase ( __magic_name__ : list , __magic_name__ : list ) -> int:
"""simple docstring"""
return [
[matrix_a[row][col] - matrix_b[row][col] for col in range(len(matrix_a[row] ) )]
for row in range(len(__magic_name__ ) )
]
def UpperCamelCase ( __magic_name__ : list ) -> tuple[list, list, list, list]:
"""simple docstring"""
if len(__magic_name__ ) % 2 != 0 or len(a[0] ) % 2 != 0:
raise Exception("""Odd matrices are not supported!""" )
lowercase__ = len(__magic_name__ )
lowercase__ = matrix_length // 2
lowercase__ = [[a[i][j] for j in range(__magic_name__ , __magic_name__ )] for i in range(__magic_name__ )]
lowercase__ = [
[a[i][j] for j in range(__magic_name__ , __magic_name__ )] for i in range(__magic_name__ , __magic_name__ )
]
lowercase__ = [[a[i][j] for j in range(__magic_name__ )] for i in range(__magic_name__ )]
lowercase__ = [[a[i][j] for j in range(__magic_name__ )] for i in range(__magic_name__ , __magic_name__ )]
return top_left, top_right, bot_left, bot_right
def UpperCamelCase ( __magic_name__ : list ) -> tuple[int, int]:
"""simple docstring"""
return len(__magic_name__ ), len(matrix[0] )
def UpperCamelCase ( __magic_name__ : list ) -> None:
"""simple docstring"""
print("""\n""".join(str(__magic_name__ ) for line in matrix ) )
def UpperCamelCase ( __magic_name__ : list , __magic_name__ : list ) -> list:
"""simple docstring"""
if matrix_dimensions(__magic_name__ ) == (2, 2):
return default_matrix_multiplication(__magic_name__ , __magic_name__ )
lowercase__ , lowercase__ , lowercase__ , lowercase__ = split_matrix(__magic_name__ )
lowercase__ , lowercase__ , lowercase__ , lowercase__ = split_matrix(__magic_name__ )
lowercase__ = actual_strassen(__magic_name__ , matrix_subtraction(__magic_name__ , __magic_name__ ) )
lowercase__ = actual_strassen(matrix_addition(__magic_name__ , __magic_name__ ) , __magic_name__ )
lowercase__ = actual_strassen(matrix_addition(__magic_name__ , __magic_name__ ) , __magic_name__ )
lowercase__ = actual_strassen(__magic_name__ , matrix_subtraction(__magic_name__ , __magic_name__ ) )
lowercase__ = actual_strassen(matrix_addition(__magic_name__ , __magic_name__ ) , matrix_addition(__magic_name__ , __magic_name__ ) )
lowercase__ = actual_strassen(matrix_subtraction(__magic_name__ , __magic_name__ ) , matrix_addition(__magic_name__ , __magic_name__ ) )
lowercase__ = actual_strassen(matrix_subtraction(__magic_name__ , __magic_name__ ) , matrix_addition(__magic_name__ , __magic_name__ ) )
lowercase__ = matrix_addition(matrix_subtraction(matrix_addition(__magic_name__ , __magic_name__ ) , __magic_name__ ) , __magic_name__ )
lowercase__ = matrix_addition(__magic_name__ , __magic_name__ )
lowercase__ = matrix_addition(__magic_name__ , __magic_name__ )
lowercase__ = matrix_subtraction(matrix_subtraction(matrix_addition(__magic_name__ , __magic_name__ ) , __magic_name__ ) , __magic_name__ )
# construct the new matrix from our 4 quadrants
lowercase__ = []
for i in range(len(__magic_name__ ) ):
new_matrix.append(top_left[i] + top_right[i] )
for i in range(len(__magic_name__ ) ):
new_matrix.append(bot_left[i] + bot_right[i] )
return new_matrix
def UpperCamelCase ( __magic_name__ : list , __magic_name__ : list ) -> list:
"""simple docstring"""
if matrix_dimensions(__magic_name__ )[1] != matrix_dimensions(__magic_name__ )[0]:
lowercase__ = (
"""Unable to multiply these matrices, please check the dimensions.\n"""
f'''Matrix A: {matrixa}\n'''
f'''Matrix B: {matrixa}'''
)
raise Exception(__magic_name__ )
lowercase__ = matrix_dimensions(__magic_name__ )
lowercase__ = matrix_dimensions(__magic_name__ )
if dimensiona[0] == dimensiona[1] and dimensiona[0] == dimensiona[1]:
return [matrixa, matrixa]
lowercase__ = max(*__magic_name__ , *__magic_name__ )
lowercase__ = int(math.pow(2 , math.ceil(math.loga(__magic_name__ ) ) ) )
lowercase__ = matrixa
lowercase__ = matrixa
# Adding zeros to the matrices so that the arrays dimensions are the same and also
# power of 2
for i in range(0 , __magic_name__ ):
if i < dimensiona[0]:
for _ in range(dimensiona[1] , __magic_name__ ):
new_matrixa[i].append(0 )
else:
new_matrixa.append([0] * maxim )
if i < dimensiona[0]:
for _ in range(dimensiona[1] , __magic_name__ ):
new_matrixa[i].append(0 )
else:
new_matrixa.append([0] * maxim )
lowercase__ = actual_strassen(__magic_name__ , __magic_name__ )
# Removing the additional zeros
for i in range(0 , __magic_name__ ):
if i < dimensiona[0]:
for _ in range(dimensiona[1] , __magic_name__ ):
final_matrix[i].pop()
else:
final_matrix.pop()
return final_matrix
if __name__ == "__main__":
A : Optional[Any] = [
[2, 3, 4, 5],
[6, 4, 3, 1],
[2, 3, 6, 7],
[3, 1, 2, 4],
[2, 3, 4, 5],
[6, 4, 3, 1],
[2, 3, 6, 7],
[3, 1, 2, 4],
[2, 3, 4, 5],
[6, 2, 3, 1],
]
A : List[Any] = [[0, 2, 1, 1], [1_6, 2, 3, 3], [2, 2, 7, 7], [1_3, 1_1, 2_2, 4]]
print(strassen(matrixa, matrixa))
| 305
| 1
|
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def UpperCamelCase ( __magic_name__ : Dict , __magic_name__ : Union[str, Any] , __magic_name__ : Tuple ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = AlbertConfig.from_json_file(__magic_name__ )
print(f'''Building PyTorch model from configuration: {config}''' )
lowercase__ = AlbertForPreTraining(__magic_name__ )
# Load weights from tf checkpoint
load_tf_weights_in_albert(__magic_name__ , __magic_name__ , __magic_name__ )
# Save pytorch-model
print(f'''Save PyTorch model to {pytorch_dump_path}''' )
torch.save(model.state_dict() , __magic_name__ )
if __name__ == "__main__":
A : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
)
parser.add_argument(
'--albert_config_file',
default=None,
type=str,
required=True,
help=(
'The config json file corresponding to the pre-trained ALBERT model. \n'
'This specifies the model architecture.'
),
)
parser.add_argument(
'--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
A : Dict = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)
| 305
|
import unittest
import numpy as np
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transformers.models.bert.modeling_flax_bert import (
FlaxBertForMaskedLM,
FlaxBertForMultipleChoice,
FlaxBertForNextSentencePrediction,
FlaxBertForPreTraining,
FlaxBertForQuestionAnswering,
FlaxBertForSequenceClassification,
FlaxBertForTokenClassification,
FlaxBertModel,
)
class A ( unittest.TestCase ):
'''simple docstring'''
def __init__(self : Optional[Any] , _UpperCAmelCase : Tuple , _UpperCAmelCase : str=13 , _UpperCAmelCase : List[str]=7 , _UpperCAmelCase : Union[str, Any]=True , _UpperCAmelCase : Dict=True , _UpperCAmelCase : str=True , _UpperCAmelCase : str=True , _UpperCAmelCase : Dict=99 , _UpperCAmelCase : Any=32 , _UpperCAmelCase : List[str]=5 , _UpperCAmelCase : Union[str, Any]=4 , _UpperCAmelCase : str=37 , _UpperCAmelCase : Union[str, Any]="gelu" , _UpperCAmelCase : Any=0.1 , _UpperCAmelCase : int=0.1 , _UpperCAmelCase : Dict=512 , _UpperCAmelCase : Optional[int]=16 , _UpperCAmelCase : str=2 , _UpperCAmelCase : List[Any]=0.02 , _UpperCAmelCase : List[str]=4 , ) -> List[Any]:
"""simple docstring"""
lowercase__ = parent
lowercase__ = batch_size
lowercase__ = seq_length
lowercase__ = is_training
lowercase__ = use_attention_mask
lowercase__ = use_token_type_ids
lowercase__ = use_labels
lowercase__ = vocab_size
lowercase__ = hidden_size
lowercase__ = num_hidden_layers
lowercase__ = num_attention_heads
lowercase__ = intermediate_size
lowercase__ = hidden_act
lowercase__ = hidden_dropout_prob
lowercase__ = attention_probs_dropout_prob
lowercase__ = max_position_embeddings
lowercase__ = type_vocab_size
lowercase__ = type_sequence_label_size
lowercase__ = initializer_range
lowercase__ = num_choices
def lowerCamelCase__ (self : List[str] ) -> Dict:
"""simple docstring"""
lowercase__ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase__ = None
if self.use_attention_mask:
lowercase__ = random_attention_mask([self.batch_size, self.seq_length] )
lowercase__ = None
if self.use_token_type_ids:
lowercase__ = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowercase__ = 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=_UpperCAmelCase , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def lowerCamelCase__ (self : int ) -> Any:
"""simple docstring"""
lowercase__ = self.prepare_config_and_inputs()
lowercase__ , lowercase__ , lowercase__ , lowercase__ = config_and_inputs
lowercase__ = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
return config, inputs_dict
def lowerCamelCase__ (self : Tuple ) -> str:
"""simple docstring"""
lowercase__ = self.prepare_config_and_inputs()
lowercase__ , lowercase__ , lowercase__ , lowercase__ = config_and_inputs
lowercase__ = True
lowercase__ = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
lowercase__ = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
attention_mask,
encoder_hidden_states,
encoder_attention_mask,
)
@require_flax
class A ( UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
A__ = True
A__ = (
(
FlaxBertModel,
FlaxBertForPreTraining,
FlaxBertForMaskedLM,
FlaxBertForMultipleChoice,
FlaxBertForQuestionAnswering,
FlaxBertForNextSentencePrediction,
FlaxBertForSequenceClassification,
FlaxBertForTokenClassification,
FlaxBertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def lowerCamelCase__ (self : Optional[int] ) -> List[str]:
"""simple docstring"""
lowercase__ = FlaxBertModelTester(self )
@slow
def lowerCamelCase__ (self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = FlaxBertModel.from_pretrained("""bert-base-cased""" )
lowercase__ = model(np.ones((1, 1) ) )
self.assertIsNotNone(_UpperCAmelCase )
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def UpperCamelCase ( __magic_name__ : str ) -> list:
"""simple docstring"""
if n_term == "":
return []
lowercase__ = []
for temp in range(int(__magic_name__ ) ):
series.append(f'''1/{temp + 1}''' if series else """1""" )
return series
if __name__ == "__main__":
A : Tuple = input('Enter the last number (nth term) of the Harmonic Series')
print('Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n')
print(harmonic_series(nth_term))
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|
def UpperCamelCase ( __magic_name__ : str ) -> list:
"""simple docstring"""
if n_term == "":
return []
lowercase__ = []
for temp in range(int(__magic_name__ ) ):
series.append(f'''1/{temp + 1}''' if series else """1""" )
return series
if __name__ == "__main__":
A : Tuple = input('Enter the last number (nth term) of the Harmonic Series')
print('Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n')
print(harmonic_series(nth_term))
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from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class A :
'''simple docstring'''
A__ = 42
A__ = None
A__ = None
A : Union[str, Any] = namedtuple('CoinsDistribResult', 'moves excess')
def UpperCamelCase ( __magic_name__ : TreeNode | None ) -> int:
"""simple docstring"""
if root is None:
return 0
# Validation
def count_nodes(__magic_name__ : TreeNode | None ) -> int:
if node is None:
return 0
return count_nodes(node.left ) + count_nodes(node.right ) + 1
def count_coins(__magic_name__ : TreeNode | None ) -> int:
if node is None:
return 0
return count_coins(node.left ) + count_coins(node.right ) + node.data
if count_nodes(__magic_name__ ) != count_coins(__magic_name__ ):
raise ValueError("""The nodes number should be same as the number of coins""" )
# Main calculation
def get_distrib(__magic_name__ : TreeNode | None ) -> CoinsDistribResult:
if node is None:
return CoinsDistribResult(0 , 1 )
lowercase__ , lowercase__ = get_distrib(node.left )
lowercase__ , lowercase__ = get_distrib(node.right )
lowercase__ = 1 - left_distrib_excess
lowercase__ = 1 - right_distrib_excess
lowercase__ = (
left_distrib_moves
+ right_distrib_moves
+ abs(__magic_name__ )
+ abs(__magic_name__ )
)
lowercase__ = node.data - coins_to_left - coins_to_right
return CoinsDistribResult(__magic_name__ , __magic_name__ )
return get_distrib(__magic_name__ )[0]
if __name__ == "__main__":
import doctest
doctest.testmod()
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|
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class A ( UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
A__ = ShapEImgaImgPipeline
A__ = ['''image''']
A__ = ['''image''']
A__ = [
'''num_images_per_prompt''',
'''num_inference_steps''',
'''generator''',
'''latents''',
'''guidance_scale''',
'''frame_size''',
'''output_type''',
'''return_dict''',
]
A__ = False
@property
def lowerCamelCase__ (self : Optional[Any] ) -> List[str]:
"""simple docstring"""
return 32
@property
def lowerCamelCase__ (self : str ) -> Any:
"""simple docstring"""
return 32
@property
def lowerCamelCase__ (self : str ) -> List[str]:
"""simple docstring"""
return self.time_input_dim * 4
@property
def lowerCamelCase__ (self : List[Any] ) -> Any:
"""simple docstring"""
return 8
@property
def lowerCamelCase__ (self : int ) -> List[str]:
"""simple docstring"""
torch.manual_seed(0 )
lowercase__ = CLIPVisionConfig(
hidden_size=self.text_embedder_hidden_size , image_size=64 , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_channels=3 , num_hidden_layers=5 , patch_size=1 , )
lowercase__ = CLIPVisionModel(_UpperCAmelCase )
return model
@property
def lowerCamelCase__ (self : Any ) -> List[Any]:
"""simple docstring"""
lowercase__ = CLIPImageProcessor(
crop_size=224 , do_center_crop=_UpperCAmelCase , do_normalize=_UpperCAmelCase , do_resize=_UpperCAmelCase , image_mean=[0.48_145_466, 0.4_578_275, 0.40_821_073] , image_std=[0.26_862_954, 0.26_130_258, 0.27_577_711] , resample=3 , size=224 , )
return image_processor
@property
def lowerCamelCase__ (self : int ) -> Optional[Any]:
"""simple docstring"""
torch.manual_seed(0 )
lowercase__ = {
"""num_attention_heads""": 2,
"""attention_head_dim""": 16,
"""embedding_dim""": self.time_input_dim,
"""num_embeddings""": 32,
"""embedding_proj_dim""": self.text_embedder_hidden_size,
"""time_embed_dim""": self.time_embed_dim,
"""num_layers""": 1,
"""clip_embed_dim""": self.time_input_dim * 2,
"""additional_embeddings""": 0,
"""time_embed_act_fn""": """gelu""",
"""norm_in_type""": """layer""",
"""embedding_proj_norm_type""": """layer""",
"""encoder_hid_proj_type""": None,
"""added_emb_type""": None,
}
lowercase__ = PriorTransformer(**_UpperCAmelCase )
return model
@property
def lowerCamelCase__ (self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
torch.manual_seed(0 )
lowercase__ = {
"""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,
),
}
lowercase__ = ShapERenderer(**_UpperCAmelCase )
return model
def lowerCamelCase__ (self : int ) -> Optional[int]:
"""simple docstring"""
lowercase__ = self.dummy_prior
lowercase__ = self.dummy_image_encoder
lowercase__ = self.dummy_image_processor
lowercase__ = self.dummy_renderer
lowercase__ = HeunDiscreteScheduler(
beta_schedule="""exp""" , num_train_timesteps=1024 , prediction_type="""sample""" , use_karras_sigmas=_UpperCAmelCase , clip_sample=_UpperCAmelCase , clip_sample_range=1.0 , )
lowercase__ = {
"""prior""": prior,
"""image_encoder""": image_encoder,
"""image_processor""": image_processor,
"""renderer""": renderer,
"""scheduler""": scheduler,
}
return components
def lowerCamelCase__ (self : Dict , _UpperCAmelCase : List[Any] , _UpperCAmelCase : str=0 ) -> str:
"""simple docstring"""
lowercase__ = floats_tensor((1, 3, 64, 64) , rng=random.Random(_UpperCAmelCase ) ).to(_UpperCAmelCase )
if str(_UpperCAmelCase ).startswith("""mps""" ):
lowercase__ = torch.manual_seed(_UpperCAmelCase )
else:
lowercase__ = torch.Generator(device=_UpperCAmelCase ).manual_seed(_UpperCAmelCase )
lowercase__ = {
"""image""": input_image,
"""generator""": generator,
"""num_inference_steps""": 1,
"""frame_size""": 32,
"""output_type""": """np""",
}
return inputs
def lowerCamelCase__ (self : str ) -> List[str]:
"""simple docstring"""
lowercase__ = """cpu"""
lowercase__ = self.get_dummy_components()
lowercase__ = self.pipeline_class(**_UpperCAmelCase )
lowercase__ = pipe.to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
lowercase__ = pipe(**self.get_dummy_inputs(_UpperCAmelCase ) )
lowercase__ = output.images[0]
lowercase__ = image[0, -3:, -3:, -1]
assert image.shape == (20, 32, 32, 3)
lowercase__ = np.array(
[
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def lowerCamelCase__ (self : str ) -> Any:
"""simple docstring"""
self._test_inference_batch_consistent(batch_sizes=[1, 2] )
def lowerCamelCase__ (self : Optional[int] ) -> str:
"""simple docstring"""
lowercase__ = torch_device == """cpu"""
lowercase__ = True
self._test_inference_batch_single_identical(
batch_size=2 , test_max_difference=_UpperCAmelCase , relax_max_difference=_UpperCAmelCase , )
def lowerCamelCase__ (self : Union[str, Any] ) -> int:
"""simple docstring"""
lowercase__ = self.get_dummy_components()
lowercase__ = self.pipeline_class(**_UpperCAmelCase )
lowercase__ = pipe.to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
lowercase__ = 1
lowercase__ = 2
lowercase__ = self.get_dummy_inputs(_UpperCAmelCase )
for key in inputs.keys():
if key in self.batch_params:
lowercase__ = batch_size * [inputs[key]]
lowercase__ = pipe(**_UpperCAmelCase , num_images_per_prompt=_UpperCAmelCase )[0]
assert images.shape[0] == batch_size * num_images_per_prompt
@slow
@require_torch_gpu
class A ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase__ (self : Dict ) -> List[Any]:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase__ (self : Any ) -> str:
"""simple docstring"""
lowercase__ = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/shap_e/corgi.png""" )
lowercase__ = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/shap_e/test_shap_e_img2img_out.npy""" )
lowercase__ = ShapEImgaImgPipeline.from_pretrained("""openai/shap-e-img2img""" )
lowercase__ = pipe.to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
lowercase__ = torch.Generator(device=_UpperCAmelCase ).manual_seed(0 )
lowercase__ = pipe(
_UpperCAmelCase , generator=_UpperCAmelCase , guidance_scale=3.0 , num_inference_steps=64 , frame_size=64 , output_type="""np""" , ).images[0]
assert images.shape == (20, 64, 64, 3)
assert_mean_pixel_difference(_UpperCAmelCase , _UpperCAmelCase )
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|
import enum
import warnings
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
A : int = logging.get_logger(__name__)
class A ( enum.Enum ):
'''simple docstring'''
A__ = 0
A__ = 1
@add_end_docstrings(UpperCAmelCase__ )
class A ( UpperCAmelCase__ ):
'''simple docstring'''
A__ = '''generated'''
def __init__(self : Optional[int] , *_UpperCAmelCase : Optional[Any] , **_UpperCAmelCase : Optional[Any] ) -> List[Any]:
"""simple docstring"""
super().__init__(*_UpperCAmelCase , **_UpperCAmelCase )
self.check_model_type(
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
if self.framework == """tf"""
else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING )
def lowerCamelCase__ (self : str , _UpperCAmelCase : Optional[int]=None , _UpperCAmelCase : List[Any]=None , _UpperCAmelCase : List[str]=None , _UpperCAmelCase : List[str]=None , _UpperCAmelCase : Dict=None , _UpperCAmelCase : Tuple=None , **_UpperCAmelCase : Dict , ) -> List[str]:
"""simple docstring"""
lowercase__ = {}
if truncation is not None:
lowercase__ = truncation
lowercase__ = generate_kwargs
lowercase__ = {}
if return_tensors is not None and return_type is None:
lowercase__ = ReturnType.TENSORS if return_tensors else ReturnType.TEXT
if return_type is not None:
lowercase__ = return_type
if clean_up_tokenization_spaces is not None:
lowercase__ = clean_up_tokenization_spaces
if stop_sequence is not None:
lowercase__ = self.tokenizer.encode(_UpperCAmelCase , add_special_tokens=_UpperCAmelCase )
if len(_UpperCAmelCase ) > 1:
warnings.warn(
"""Stopping on a multiple token sequence is not yet supported on transformers. The first token of"""
""" the stop sequence will be used as the stop sequence string in the interim.""" )
lowercase__ = stop_sequence_ids[0]
return preprocess_params, forward_params, postprocess_params
def lowerCamelCase__ (self : Any , _UpperCAmelCase : int , _UpperCAmelCase : int , _UpperCAmelCase : int ) -> Union[str, Any]:
"""simple docstring"""
return True
def lowerCamelCase__ (self : Union[str, Any] , *_UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Tuple ) -> Optional[int]:
"""simple docstring"""
lowercase__ = self.model.config.prefix if self.model.config.prefix is not None else """"""
if isinstance(args[0] , _UpperCAmelCase ):
if self.tokenizer.pad_token_id is None:
raise ValueError("""Please make sure that the tokenizer has a pad_token_id when using a batch input""" )
lowercase__ = ([prefix + arg for arg in args[0]],)
lowercase__ = True
elif isinstance(args[0] , _UpperCAmelCase ):
lowercase__ = (prefix + args[0],)
lowercase__ = False
else:
raise ValueError(
f''' `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`''' )
lowercase__ = self.tokenizer(*_UpperCAmelCase , padding=_UpperCAmelCase , truncation=_UpperCAmelCase , return_tensors=self.framework )
# This is produced by tokenizers but is an invalid generate kwargs
if "token_type_ids" in inputs:
del inputs["token_type_ids"]
return inputs
def __call__(self : Optional[int] , *_UpperCAmelCase : Dict , **_UpperCAmelCase : Optional[int] ) -> str:
"""simple docstring"""
lowercase__ = super().__call__(*_UpperCAmelCase , **_UpperCAmelCase )
if (
isinstance(args[0] , _UpperCAmelCase )
and all(isinstance(_UpperCAmelCase , _UpperCAmelCase ) for el in args[0] )
and all(len(_UpperCAmelCase ) == 1 for res in result )
):
return [res[0] for res in result]
return result
def lowerCamelCase__ (self : Tuple , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Union[str, Any]=TruncationStrategy.DO_NOT_TRUNCATE , **_UpperCAmelCase : Any ) -> Optional[int]:
"""simple docstring"""
lowercase__ = self._parse_and_tokenize(_UpperCAmelCase , truncation=_UpperCAmelCase , **_UpperCAmelCase )
return inputs
def lowerCamelCase__ (self : Optional[int] , _UpperCAmelCase : str , **_UpperCAmelCase : Any ) -> List[str]:
"""simple docstring"""
if self.framework == "pt":
lowercase__ , lowercase__ = model_inputs["""input_ids"""].shape
elif self.framework == "tf":
lowercase__ , lowercase__ = tf.shape(model_inputs["""input_ids"""] ).numpy()
lowercase__ = generate_kwargs.get("""min_length""" , self.model.config.min_length )
lowercase__ = generate_kwargs.get("""max_length""" , self.model.config.max_length )
self.check_inputs(_UpperCAmelCase , generate_kwargs["""min_length"""] , generate_kwargs["""max_length"""] )
lowercase__ = self.model.generate(**_UpperCAmelCase , **_UpperCAmelCase )
lowercase__ = output_ids.shape[0]
if self.framework == "pt":
lowercase__ = output_ids.reshape(_UpperCAmelCase , out_b // in_b , *output_ids.shape[1:] )
elif self.framework == "tf":
lowercase__ = tf.reshape(_UpperCAmelCase , (in_b, out_b // in_b, *output_ids.shape[1:]) )
return {"output_ids": output_ids}
def lowerCamelCase__ (self : Optional[Any] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : int=ReturnType.TEXT , _UpperCAmelCase : Any=False ) -> Dict:
"""simple docstring"""
lowercase__ = []
for output_ids in model_outputs["output_ids"][0]:
if return_type == ReturnType.TENSORS:
lowercase__ = {f'''{self.return_name}_token_ids''': output_ids}
elif return_type == ReturnType.TEXT:
lowercase__ = {
f'''{self.return_name}_text''': self.tokenizer.decode(
_UpperCAmelCase , skip_special_tokens=_UpperCAmelCase , clean_up_tokenization_spaces=_UpperCAmelCase , )
}
records.append(_UpperCAmelCase )
return records
@add_end_docstrings(UpperCAmelCase__ )
class A ( UpperCAmelCase__ ):
'''simple docstring'''
A__ = '''summary'''
def __call__(self : List[str] , *_UpperCAmelCase : Union[str, Any] , **_UpperCAmelCase : str ) -> int:
"""simple docstring"""
return super().__call__(*_UpperCAmelCase , **_UpperCAmelCase )
def lowerCamelCase__ (self : List[str] , _UpperCAmelCase : int , _UpperCAmelCase : int , _UpperCAmelCase : int ) -> bool:
"""simple docstring"""
if max_length < min_length:
logger.warning(f'''Your min_length={min_length} must be inferior than your max_length={max_length}.''' )
if input_length < max_length:
logger.warning(
f'''Your max_length is set to {max_length}, but your input_length is only {input_length}. Since this is '''
"""a summarization task, where outputs shorter than the input are typically wanted, you might """
f'''consider decreasing max_length manually, e.g. summarizer(\'...\', max_length={input_length//2})''' )
@add_end_docstrings(UpperCAmelCase__ )
class A ( UpperCAmelCase__ ):
'''simple docstring'''
A__ = '''translation'''
def lowerCamelCase__ (self : Optional[Any] , _UpperCAmelCase : int , _UpperCAmelCase : int , _UpperCAmelCase : int ) -> List[str]:
"""simple docstring"""
if input_length > 0.9 * max_length:
logger.warning(
f'''Your input_length: {input_length} is bigger than 0.9 * max_length: {max_length}. You might consider '''
"""increasing your max_length manually, e.g. translator('...', max_length=400)""" )
return True
def lowerCamelCase__ (self : List[str] , *_UpperCAmelCase : Any , _UpperCAmelCase : Optional[int]=TruncationStrategy.DO_NOT_TRUNCATE , _UpperCAmelCase : Union[str, Any]=None , _UpperCAmelCase : List[str]=None ) -> Optional[int]:
"""simple docstring"""
if getattr(self.tokenizer , """_build_translation_inputs""" , _UpperCAmelCase ):
return self.tokenizer._build_translation_inputs(
*_UpperCAmelCase , return_tensors=self.framework , truncation=_UpperCAmelCase , src_lang=_UpperCAmelCase , tgt_lang=_UpperCAmelCase )
else:
return super()._parse_and_tokenize(*_UpperCAmelCase , truncation=_UpperCAmelCase )
def lowerCamelCase__ (self : str , _UpperCAmelCase : Any=None , _UpperCAmelCase : str=None , **_UpperCAmelCase : Any ) -> Optional[int]:
"""simple docstring"""
lowercase__ , lowercase__ , lowercase__ = super()._sanitize_parameters(**_UpperCAmelCase )
if src_lang is not None:
lowercase__ = src_lang
if tgt_lang is not None:
lowercase__ = tgt_lang
if src_lang is None and tgt_lang is None:
# Backward compatibility, direct arguments use is preferred.
lowercase__ = kwargs.get("""task""" , self.task )
lowercase__ = task.split("""_""" )
if task and len(_UpperCAmelCase ) == 4:
# translation, XX, to YY
lowercase__ = items[1]
lowercase__ = items[3]
return preprocess_params, forward_params, postprocess_params
def __call__(self : Union[str, Any] , *_UpperCAmelCase : Union[str, Any] , **_UpperCAmelCase : List[Any] ) -> Tuple:
"""simple docstring"""
return super().__call__(*_UpperCAmelCase , **_UpperCAmelCase )
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import requests
from bsa import BeautifulSoup
def UpperCamelCase ( __magic_name__ : str = "AAPL" ) -> str:
"""simple docstring"""
lowercase__ = f'''https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'''
lowercase__ = BeautifulSoup(requests.get(__magic_name__ ).text , """html.parser""" )
lowercase__ = """My(6px) Pos(r) smartphone_Mt(6px)"""
return soup.find("""div""" , class_=class_ ).find("""span""" ).text
if __name__ == "__main__":
for symbol in "AAPL AMZN IBM GOOG MSFT ORCL".split():
print(F'Current {symbol:<4} stock price is {stock_price(symbol):>8}')
| 305
| 1
|
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
ConditionalDetrConfig,
ConditionalDetrForObjectDetection,
ConditionalDetrForSegmentation,
ConditionalDetrImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
A : Optional[int] = logging.get_logger(__name__)
# here we list all keys to be renamed (original name on the left, our name on the right)
A : int = []
for i in range(6):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append(
(F'transformer.encoder.layers.{i}.self_attn.out_proj.weight', F'encoder.layers.{i}.self_attn.out_proj.weight')
)
rename_keys.append(
(F'transformer.encoder.layers.{i}.self_attn.out_proj.bias', F'encoder.layers.{i}.self_attn.out_proj.bias')
)
rename_keys.append((F'transformer.encoder.layers.{i}.linear1.weight', F'encoder.layers.{i}.fc1.weight'))
rename_keys.append((F'transformer.encoder.layers.{i}.linear1.bias', F'encoder.layers.{i}.fc1.bias'))
rename_keys.append((F'transformer.encoder.layers.{i}.linear2.weight', F'encoder.layers.{i}.fc2.weight'))
rename_keys.append((F'transformer.encoder.layers.{i}.linear2.bias', F'encoder.layers.{i}.fc2.bias'))
rename_keys.append(
(F'transformer.encoder.layers.{i}.norm1.weight', F'encoder.layers.{i}.self_attn_layer_norm.weight')
)
rename_keys.append((F'transformer.encoder.layers.{i}.norm1.bias', F'encoder.layers.{i}.self_attn_layer_norm.bias'))
rename_keys.append((F'transformer.encoder.layers.{i}.norm2.weight', F'encoder.layers.{i}.final_layer_norm.weight'))
rename_keys.append((F'transformer.encoder.layers.{i}.norm2.bias', F'encoder.layers.{i}.final_layer_norm.bias'))
# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
rename_keys.append(
(F'transformer.decoder.layers.{i}.self_attn.out_proj.weight', F'decoder.layers.{i}.self_attn.out_proj.weight')
)
rename_keys.append(
(F'transformer.decoder.layers.{i}.self_attn.out_proj.bias', F'decoder.layers.{i}.self_attn.out_proj.bias')
)
rename_keys.append(
(
F'transformer.decoder.layers.{i}.cross_attn.out_proj.weight',
F'decoder.layers.{i}.encoder_attn.out_proj.weight',
)
)
rename_keys.append(
(
F'transformer.decoder.layers.{i}.cross_attn.out_proj.bias',
F'decoder.layers.{i}.encoder_attn.out_proj.bias',
)
)
rename_keys.append((F'transformer.decoder.layers.{i}.linear1.weight', F'decoder.layers.{i}.fc1.weight'))
rename_keys.append((F'transformer.decoder.layers.{i}.linear1.bias', F'decoder.layers.{i}.fc1.bias'))
rename_keys.append((F'transformer.decoder.layers.{i}.linear2.weight', F'decoder.layers.{i}.fc2.weight'))
rename_keys.append((F'transformer.decoder.layers.{i}.linear2.bias', F'decoder.layers.{i}.fc2.bias'))
rename_keys.append(
(F'transformer.decoder.layers.{i}.norm1.weight', F'decoder.layers.{i}.self_attn_layer_norm.weight')
)
rename_keys.append((F'transformer.decoder.layers.{i}.norm1.bias', F'decoder.layers.{i}.self_attn_layer_norm.bias'))
rename_keys.append(
(F'transformer.decoder.layers.{i}.norm2.weight', F'decoder.layers.{i}.encoder_attn_layer_norm.weight')
)
rename_keys.append(
(F'transformer.decoder.layers.{i}.norm2.bias', F'decoder.layers.{i}.encoder_attn_layer_norm.bias')
)
rename_keys.append((F'transformer.decoder.layers.{i}.norm3.weight', F'decoder.layers.{i}.final_layer_norm.weight'))
rename_keys.append((F'transformer.decoder.layers.{i}.norm3.bias', F'decoder.layers.{i}.final_layer_norm.bias'))
# q, k, v projections in self/cross-attention in decoder for conditional DETR
rename_keys.append(
(F'transformer.decoder.layers.{i}.sa_qcontent_proj.weight', F'decoder.layers.{i}.sa_qcontent_proj.weight')
)
rename_keys.append(
(F'transformer.decoder.layers.{i}.sa_kcontent_proj.weight', F'decoder.layers.{i}.sa_kcontent_proj.weight')
)
rename_keys.append(
(F'transformer.decoder.layers.{i}.sa_qpos_proj.weight', F'decoder.layers.{i}.sa_qpos_proj.weight')
)
rename_keys.append(
(F'transformer.decoder.layers.{i}.sa_kpos_proj.weight', F'decoder.layers.{i}.sa_kpos_proj.weight')
)
rename_keys.append((F'transformer.decoder.layers.{i}.sa_v_proj.weight', F'decoder.layers.{i}.sa_v_proj.weight'))
rename_keys.append(
(F'transformer.decoder.layers.{i}.ca_qcontent_proj.weight', F'decoder.layers.{i}.ca_qcontent_proj.weight')
)
# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
rename_keys.append(
(F'transformer.decoder.layers.{i}.ca_kcontent_proj.weight', F'decoder.layers.{i}.ca_kcontent_proj.weight')
)
rename_keys.append(
(F'transformer.decoder.layers.{i}.ca_kpos_proj.weight', F'decoder.layers.{i}.ca_kpos_proj.weight')
)
rename_keys.append((F'transformer.decoder.layers.{i}.ca_v_proj.weight', F'decoder.layers.{i}.ca_v_proj.weight'))
rename_keys.append(
(F'transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight', F'decoder.layers.{i}.ca_qpos_sine_proj.weight')
)
rename_keys.append(
(F'transformer.decoder.layers.{i}.sa_qcontent_proj.bias', F'decoder.layers.{i}.sa_qcontent_proj.bias')
)
rename_keys.append(
(F'transformer.decoder.layers.{i}.sa_kcontent_proj.bias', F'decoder.layers.{i}.sa_kcontent_proj.bias')
)
rename_keys.append((F'transformer.decoder.layers.{i}.sa_qpos_proj.bias', F'decoder.layers.{i}.sa_qpos_proj.bias'))
rename_keys.append((F'transformer.decoder.layers.{i}.sa_kpos_proj.bias', F'decoder.layers.{i}.sa_kpos_proj.bias'))
rename_keys.append((F'transformer.decoder.layers.{i}.sa_v_proj.bias', F'decoder.layers.{i}.sa_v_proj.bias'))
rename_keys.append(
(F'transformer.decoder.layers.{i}.ca_qcontent_proj.bias', F'decoder.layers.{i}.ca_qcontent_proj.bias')
)
# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
rename_keys.append(
(F'transformer.decoder.layers.{i}.ca_kcontent_proj.bias', F'decoder.layers.{i}.ca_kcontent_proj.bias')
)
rename_keys.append((F'transformer.decoder.layers.{i}.ca_kpos_proj.bias', F'decoder.layers.{i}.ca_kpos_proj.bias'))
rename_keys.append((F'transformer.decoder.layers.{i}.ca_v_proj.bias', F'decoder.layers.{i}.ca_v_proj.bias'))
rename_keys.append(
(F'transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias', F'decoder.layers.{i}.ca_qpos_sine_proj.bias')
)
# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
rename_keys.extend(
[
('input_proj.weight', 'input_projection.weight'),
('input_proj.bias', 'input_projection.bias'),
('query_embed.weight', 'query_position_embeddings.weight'),
('transformer.decoder.norm.weight', 'decoder.layernorm.weight'),
('transformer.decoder.norm.bias', 'decoder.layernorm.bias'),
('class_embed.weight', 'class_labels_classifier.weight'),
('class_embed.bias', 'class_labels_classifier.bias'),
('bbox_embed.layers.0.weight', 'bbox_predictor.layers.0.weight'),
('bbox_embed.layers.0.bias', 'bbox_predictor.layers.0.bias'),
('bbox_embed.layers.1.weight', 'bbox_predictor.layers.1.weight'),
('bbox_embed.layers.1.bias', 'bbox_predictor.layers.1.bias'),
('bbox_embed.layers.2.weight', 'bbox_predictor.layers.2.weight'),
('bbox_embed.layers.2.bias', 'bbox_predictor.layers.2.bias'),
('transformer.decoder.ref_point_head.layers.0.weight', 'decoder.ref_point_head.layers.0.weight'),
('transformer.decoder.ref_point_head.layers.0.bias', 'decoder.ref_point_head.layers.0.bias'),
('transformer.decoder.ref_point_head.layers.1.weight', 'decoder.ref_point_head.layers.1.weight'),
('transformer.decoder.ref_point_head.layers.1.bias', 'decoder.ref_point_head.layers.1.bias'),
('transformer.decoder.query_scale.layers.0.weight', 'decoder.query_scale.layers.0.weight'),
('transformer.decoder.query_scale.layers.0.bias', 'decoder.query_scale.layers.0.bias'),
('transformer.decoder.query_scale.layers.1.weight', 'decoder.query_scale.layers.1.weight'),
('transformer.decoder.query_scale.layers.1.bias', 'decoder.query_scale.layers.1.bias'),
('transformer.decoder.layers.0.ca_qpos_proj.weight', 'decoder.layers.0.ca_qpos_proj.weight'),
('transformer.decoder.layers.0.ca_qpos_proj.bias', 'decoder.layers.0.ca_qpos_proj.bias'),
]
)
def UpperCamelCase ( __magic_name__ : str , __magic_name__ : Optional[Any] , __magic_name__ : Any ) -> List[Any]:
"""simple docstring"""
lowercase__ = state_dict.pop(__magic_name__ )
lowercase__ = val
def UpperCamelCase ( __magic_name__ : Tuple ) -> Optional[int]:
"""simple docstring"""
lowercase__ = OrderedDict()
for key, value in state_dict.items():
if "backbone.0.body" in key:
lowercase__ = key.replace("""backbone.0.body""" , """backbone.conv_encoder.model""" )
lowercase__ = value
else:
lowercase__ = value
return new_state_dict
def UpperCamelCase ( __magic_name__ : int , __magic_name__ : int=False ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = """"""
if is_panoptic:
lowercase__ = """conditional_detr."""
# first: transformer encoder
for i in range(6 ):
# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
lowercase__ = state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight''' )
lowercase__ = state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) to the state dict
lowercase__ = in_proj_weight[:256, :]
lowercase__ = in_proj_bias[:256]
lowercase__ = in_proj_weight[256:512, :]
lowercase__ = in_proj_bias[256:512]
lowercase__ = in_proj_weight[-256:, :]
lowercase__ = in_proj_bias[-256:]
def UpperCamelCase ( ) -> Tuple:
"""simple docstring"""
lowercase__ = """http://images.cocodataset.org/val2017/000000039769.jpg"""
lowercase__ = Image.open(requests.get(__magic_name__ , stream=__magic_name__ ).raw )
return im
@torch.no_grad()
def UpperCamelCase ( __magic_name__ : Tuple , __magic_name__ : List[Any] ) -> List[str]:
"""simple docstring"""
lowercase__ = ConditionalDetrConfig()
# set backbone and dilation attributes
if "resnet101" in model_name:
lowercase__ = """resnet101"""
if "dc5" in model_name:
lowercase__ = True
lowercase__ = """panoptic""" in model_name
if is_panoptic:
lowercase__ = 250
else:
lowercase__ = 91
lowercase__ = """huggingface/label-files"""
lowercase__ = """coco-detection-id2label.json"""
lowercase__ = json.load(open(hf_hub_download(__magic_name__ , __magic_name__ , repo_type="""dataset""" ) , """r""" ) )
lowercase__ = {int(__magic_name__ ): v for k, v in idalabel.items()}
lowercase__ = idalabel
lowercase__ = {v: k for k, v in idalabel.items()}
# load image processor
lowercase__ = """coco_panoptic""" if is_panoptic else """coco_detection"""
lowercase__ = ConditionalDetrImageProcessor(format=__magic_name__ )
# prepare image
lowercase__ = prepare_img()
lowercase__ = image_processor(images=__magic_name__ , return_tensors="""pt""" )
lowercase__ = encoding["""pixel_values"""]
logger.info(f'''Converting model {model_name}...''' )
# load original model from torch hub
lowercase__ = torch.hub.load("""DeppMeng/ConditionalDETR""" , __magic_name__ , pretrained=__magic_name__ ).eval()
lowercase__ = conditional_detr.state_dict()
# rename keys
for src, dest in rename_keys:
if is_panoptic:
lowercase__ = """conditional_detr.""" + src
rename_key(__magic_name__ , __magic_name__ , __magic_name__ )
lowercase__ = rename_backbone_keys(__magic_name__ )
# query, key and value matrices need special treatment
read_in_q_k_v(__magic_name__ , is_panoptic=__magic_name__ )
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
lowercase__ = """conditional_detr.model.""" if is_panoptic else """model."""
for key in state_dict.copy().keys():
if is_panoptic:
if (
key.startswith("""conditional_detr""" )
and not key.startswith("""class_labels_classifier""" )
and not key.startswith("""bbox_predictor""" )
):
lowercase__ = state_dict.pop(__magic_name__ )
lowercase__ = val
elif "class_labels_classifier" in key or "bbox_predictor" in key:
lowercase__ = state_dict.pop(__magic_name__ )
lowercase__ = val
elif key.startswith("""bbox_attention""" ) or key.startswith("""mask_head""" ):
continue
else:
lowercase__ = state_dict.pop(__magic_name__ )
lowercase__ = val
else:
if not key.startswith("""class_labels_classifier""" ) and not key.startswith("""bbox_predictor""" ):
lowercase__ = state_dict.pop(__magic_name__ )
lowercase__ = val
# finally, create HuggingFace model and load state dict
lowercase__ = ConditionalDetrForSegmentation(__magic_name__ ) if is_panoptic else ConditionalDetrForObjectDetection(__magic_name__ )
model.load_state_dict(__magic_name__ )
model.eval()
model.push_to_hub(repo_id=__magic_name__ , organization="""DepuMeng""" , commit_message="""Add model""" )
# verify our conversion
lowercase__ = conditional_detr(__magic_name__ )
lowercase__ = model(__magic_name__ )
assert torch.allclose(outputs.logits , original_outputs["""pred_logits"""] , atol=1E-4 )
assert torch.allclose(outputs.pred_boxes , original_outputs["""pred_boxes"""] , atol=1E-4 )
if is_panoptic:
assert torch.allclose(outputs.pred_masks , original_outputs["""pred_masks"""] , atol=1E-4 )
# Save model and image processor
logger.info(f'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...''' )
Path(__magic_name__ ).mkdir(exist_ok=__magic_name__ )
model.save_pretrained(__magic_name__ )
image_processor.save_pretrained(__magic_name__ )
if __name__ == "__main__":
A : Dict = argparse.ArgumentParser()
parser.add_argument(
'--model_name',
default='conditional_detr_resnet50',
type=str,
help='Name of the CONDITIONAL_DETR model you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
)
A : str = parser.parse_args()
convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)
| 305
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : List[str] = logging.get_logger(__name__)
A : Any = {
'tiiuae/falcon-40b': 'https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json',
'tiiuae/falcon-7b': 'https://huggingface.co/tiiuae/falcon-7b/resolve/main/config.json',
}
class A ( UpperCAmelCase__ ):
'''simple docstring'''
A__ = '''falcon'''
A__ = ['''past_key_values''']
def __init__(self : str , _UpperCAmelCase : Dict=6_5024 , _UpperCAmelCase : Optional[Any]=4544 , _UpperCAmelCase : Optional[int]=32 , _UpperCAmelCase : Optional[Any]=71 , _UpperCAmelCase : List[Any]=1E-5 , _UpperCAmelCase : int=0.02 , _UpperCAmelCase : str=True , _UpperCAmelCase : Tuple=0.0 , _UpperCAmelCase : Any=0.0 , _UpperCAmelCase : str=None , _UpperCAmelCase : Optional[int]=False , _UpperCAmelCase : int=False , _UpperCAmelCase : Union[str, Any]=True , _UpperCAmelCase : List[Any]=True , _UpperCAmelCase : List[Any]=False , _UpperCAmelCase : Optional[int]=11 , _UpperCAmelCase : Optional[Any]=11 , **_UpperCAmelCase : Union[str, Any] , ) -> List[str]:
"""simple docstring"""
lowercase__ = vocab_size
# Backward compatibility with n_embed kwarg
lowercase__ = kwargs.pop("""n_embed""" , _UpperCAmelCase )
lowercase__ = hidden_size if n_embed is None else n_embed
lowercase__ = num_hidden_layers
lowercase__ = num_attention_heads
lowercase__ = layer_norm_epsilon
lowercase__ = initializer_range
lowercase__ = use_cache
lowercase__ = hidden_dropout
lowercase__ = attention_dropout
lowercase__ = bos_token_id
lowercase__ = eos_token_id
lowercase__ = num_attention_heads if num_kv_heads is None else num_kv_heads
lowercase__ = alibi
lowercase__ = new_decoder_architecture
lowercase__ = multi_query # Ignored when new_decoder_architecture is True
lowercase__ = parallel_attn
lowercase__ = bias
super().__init__(bos_token_id=_UpperCAmelCase , eos_token_id=_UpperCAmelCase , **_UpperCAmelCase )
@property
def lowerCamelCase__ (self : Tuple ) -> int:
"""simple docstring"""
return self.hidden_size // self.num_attention_heads
@property
def lowerCamelCase__ (self : List[str] ) -> Tuple:
"""simple docstring"""
return not self.alibi
| 305
| 1
|
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class A ( UpperCAmelCase__ ):
'''simple docstring'''
A__ = (DDPMParallelScheduler,)
def lowerCamelCase__ (self : str , **_UpperCAmelCase : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
lowercase__ = {
"""num_train_timesteps""": 1000,
"""beta_start""": 0.0_001,
"""beta_end""": 0.02,
"""beta_schedule""": """linear""",
"""variance_type""": """fixed_small""",
"""clip_sample""": True,
}
config.update(**_UpperCAmelCase )
return config
def lowerCamelCase__ (self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
for timesteps in [1, 5, 100, 1000]:
self.check_over_configs(num_train_timesteps=_UpperCAmelCase )
def lowerCamelCase__ (self : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1] , [0.002, 0.02, 0.2, 2] ):
self.check_over_configs(beta_start=_UpperCAmelCase , beta_end=_UpperCAmelCase )
def lowerCamelCase__ (self : str ) -> Optional[int]:
"""simple docstring"""
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=_UpperCAmelCase )
def lowerCamelCase__ (self : Any ) -> str:
"""simple docstring"""
for variance in ["fixed_small", "fixed_large", "other"]:
self.check_over_configs(variance_type=_UpperCAmelCase )
def lowerCamelCase__ (self : List[Any] ) -> Tuple:
"""simple docstring"""
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=_UpperCAmelCase )
def lowerCamelCase__ (self : Tuple ) -> str:
"""simple docstring"""
self.check_over_configs(thresholding=_UpperCAmelCase )
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(
thresholding=_UpperCAmelCase , prediction_type=_UpperCAmelCase , sample_max_value=_UpperCAmelCase , )
def lowerCamelCase__ (self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(prediction_type=_UpperCAmelCase )
def lowerCamelCase__ (self : Optional[int] ) -> str:
"""simple docstring"""
for t in [0, 500, 999]:
self.check_over_forward(time_step=_UpperCAmelCase )
def lowerCamelCase__ (self : Any ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = self.scheduler_classes[0]
lowercase__ = self.get_scheduler_config()
lowercase__ = scheduler_class(**_UpperCAmelCase )
assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.00_979 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.02 ) ) < 1E-5
def lowerCamelCase__ (self : Optional[int] ) -> List[Any]:
"""simple docstring"""
lowercase__ = self.scheduler_classes[0]
lowercase__ = self.get_scheduler_config()
lowercase__ = scheduler_class(**_UpperCAmelCase )
lowercase__ = len(_UpperCAmelCase )
lowercase__ = self.dummy_model()
lowercase__ = self.dummy_sample_deter
lowercase__ = self.dummy_sample_deter + 0.1
lowercase__ = self.dummy_sample_deter - 0.1
lowercase__ = samplea.shape[0]
lowercase__ = torch.stack([samplea, samplea, samplea] , dim=0 )
lowercase__ = torch.arange(_UpperCAmelCase )[0:3, None].repeat(1 , _UpperCAmelCase )
lowercase__ = model(samples.flatten(0 , 1 ) , timesteps.flatten(0 , 1 ) )
lowercase__ = scheduler.batch_step_no_noise(_UpperCAmelCase , timesteps.flatten(0 , 1 ) , samples.flatten(0 , 1 ) )
lowercase__ = torch.sum(torch.abs(_UpperCAmelCase ) )
lowercase__ = torch.mean(torch.abs(_UpperCAmelCase ) )
assert abs(result_sum.item() - 1_153.1_833 ) < 1E-2
assert abs(result_mean.item() - 0.5_005 ) < 1E-3
def lowerCamelCase__ (self : Dict ) -> int:
"""simple docstring"""
lowercase__ = self.scheduler_classes[0]
lowercase__ = self.get_scheduler_config()
lowercase__ = scheduler_class(**_UpperCAmelCase )
lowercase__ = len(_UpperCAmelCase )
lowercase__ = self.dummy_model()
lowercase__ = self.dummy_sample_deter
lowercase__ = torch.manual_seed(0 )
for t in reversed(range(_UpperCAmelCase ) ):
# 1. predict noise residual
lowercase__ = model(_UpperCAmelCase , _UpperCAmelCase )
# 2. predict previous mean of sample x_t-1
lowercase__ = scheduler.step(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , generator=_UpperCAmelCase ).prev_sample
lowercase__ = pred_prev_sample
lowercase__ = torch.sum(torch.abs(_UpperCAmelCase ) )
lowercase__ = torch.mean(torch.abs(_UpperCAmelCase ) )
assert abs(result_sum.item() - 258.9_606 ) < 1E-2
assert abs(result_mean.item() - 0.3_372 ) < 1E-3
def lowerCamelCase__ (self : str ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = self.scheduler_classes[0]
lowercase__ = self.get_scheduler_config(prediction_type="""v_prediction""" )
lowercase__ = scheduler_class(**_UpperCAmelCase )
lowercase__ = len(_UpperCAmelCase )
lowercase__ = self.dummy_model()
lowercase__ = self.dummy_sample_deter
lowercase__ = torch.manual_seed(0 )
for t in reversed(range(_UpperCAmelCase ) ):
# 1. predict noise residual
lowercase__ = model(_UpperCAmelCase , _UpperCAmelCase )
# 2. predict previous mean of sample x_t-1
lowercase__ = scheduler.step(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , generator=_UpperCAmelCase ).prev_sample
lowercase__ = pred_prev_sample
lowercase__ = torch.sum(torch.abs(_UpperCAmelCase ) )
lowercase__ = torch.mean(torch.abs(_UpperCAmelCase ) )
assert abs(result_sum.item() - 202.0_296 ) < 1E-2
assert abs(result_mean.item() - 0.2_631 ) < 1E-3
def lowerCamelCase__ (self : List[str] ) -> Any:
"""simple docstring"""
lowercase__ = self.scheduler_classes[0]
lowercase__ = self.get_scheduler_config()
lowercase__ = scheduler_class(**_UpperCAmelCase )
lowercase__ = [100, 87, 50, 1, 0]
scheduler.set_timesteps(timesteps=_UpperCAmelCase )
lowercase__ = scheduler.timesteps
for i, timestep in enumerate(_UpperCAmelCase ):
if i == len(_UpperCAmelCase ) - 1:
lowercase__ = -1
else:
lowercase__ = timesteps[i + 1]
lowercase__ = scheduler.previous_timestep(_UpperCAmelCase )
lowercase__ = prev_t.item()
self.assertEqual(_UpperCAmelCase , _UpperCAmelCase )
def lowerCamelCase__ (self : Optional[Any] ) -> int:
"""simple docstring"""
lowercase__ = self.scheduler_classes[0]
lowercase__ = self.get_scheduler_config()
lowercase__ = scheduler_class(**_UpperCAmelCase )
lowercase__ = [100, 87, 50, 51, 0]
with self.assertRaises(_UpperCAmelCase , msg="""`custom_timesteps` must be in descending order.""" ):
scheduler.set_timesteps(timesteps=_UpperCAmelCase )
def lowerCamelCase__ (self : Tuple ) -> Tuple:
"""simple docstring"""
lowercase__ = self.scheduler_classes[0]
lowercase__ = self.get_scheduler_config()
lowercase__ = scheduler_class(**_UpperCAmelCase )
lowercase__ = [100, 87, 50, 1, 0]
lowercase__ = len(_UpperCAmelCase )
with self.assertRaises(_UpperCAmelCase , msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`.""" ):
scheduler.set_timesteps(num_inference_steps=_UpperCAmelCase , timesteps=_UpperCAmelCase )
def lowerCamelCase__ (self : List[Any] ) -> Optional[int]:
"""simple docstring"""
lowercase__ = self.scheduler_classes[0]
lowercase__ = self.get_scheduler_config()
lowercase__ = scheduler_class(**_UpperCAmelCase )
lowercase__ = [scheduler.config.num_train_timesteps]
with self.assertRaises(
_UpperCAmelCase , msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}""" , ):
scheduler.set_timesteps(timesteps=_UpperCAmelCase )
| 305
|
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AlignProcessor, EfficientNetImageProcessor
@require_vision
class A ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase__ (self : Union[str, Any] ) -> Any:
"""simple docstring"""
lowercase__ = tempfile.mkdtemp()
lowercase__ = [
"""[UNK]""",
"""[CLS]""",
"""[SEP]""",
"""[PAD]""",
"""[MASK]""",
"""want""",
"""##want""",
"""##ed""",
"""wa""",
"""un""",
"""runn""",
"""##ing""",
""",""",
"""low""",
"""lowest""",
]
lowercase__ = 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] ) )
lowercase__ = {
"""do_resize""": True,
"""size""": 20,
"""do_center_crop""": True,
"""crop_size""": 18,
"""do_normalize""": True,
"""image_mean""": [0.48_145_466, 0.4_578_275, 0.40_821_073],
"""image_std""": [0.26_862_954, 0.26_130_258, 0.27_577_711],
}
lowercase__ = os.path.join(self.tmpdirname , _UpperCAmelCase )
with open(self.image_processor_file , """w""" , encoding="""utf-8""" ) as fp:
json.dump(_UpperCAmelCase , _UpperCAmelCase )
def lowerCamelCase__ (self : Dict , **_UpperCAmelCase : Any ) -> Optional[Any]:
"""simple docstring"""
return BertTokenizer.from_pretrained(self.tmpdirname , **_UpperCAmelCase )
def lowerCamelCase__ (self : Union[str, Any] , **_UpperCAmelCase : Any ) -> Dict:
"""simple docstring"""
return BertTokenizerFast.from_pretrained(self.tmpdirname , **_UpperCAmelCase )
def lowerCamelCase__ (self : Optional[int] , **_UpperCAmelCase : str ) -> Dict:
"""simple docstring"""
return EfficientNetImageProcessor.from_pretrained(self.tmpdirname , **_UpperCAmelCase )
def lowerCamelCase__ (self : Optional[int] ) -> List[str]:
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def lowerCamelCase__ (self : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
lowercase__ = [Image.fromarray(np.moveaxis(_UpperCAmelCase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def lowerCamelCase__ (self : Optional[int] ) -> Tuple:
"""simple docstring"""
lowercase__ = self.get_tokenizer()
lowercase__ = self.get_rust_tokenizer()
lowercase__ = self.get_image_processor()
lowercase__ = AlignProcessor(tokenizer=_UpperCAmelCase , image_processor=_UpperCAmelCase )
processor_slow.save_pretrained(self.tmpdirname )
lowercase__ = AlignProcessor.from_pretrained(self.tmpdirname , use_fast=_UpperCAmelCase )
lowercase__ = AlignProcessor(tokenizer=_UpperCAmelCase , image_processor=_UpperCAmelCase )
processor_fast.save_pretrained(self.tmpdirname )
lowercase__ = AlignProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer , _UpperCAmelCase )
self.assertIsInstance(processor_fast.tokenizer , _UpperCAmelCase )
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor , _UpperCAmelCase )
self.assertIsInstance(processor_fast.image_processor , _UpperCAmelCase )
def lowerCamelCase__ (self : Any ) -> List[str]:
"""simple docstring"""
lowercase__ = AlignProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
lowercase__ = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
lowercase__ = self.get_image_processor(do_normalize=_UpperCAmelCase , padding_value=1.0 )
lowercase__ = AlignProcessor.from_pretrained(
self.tmpdirname , bos_token="""(BOS)""" , eos_token="""(EOS)""" , do_normalize=_UpperCAmelCase , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , _UpperCAmelCase )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , _UpperCAmelCase )
def lowerCamelCase__ (self : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = self.get_image_processor()
lowercase__ = self.get_tokenizer()
lowercase__ = AlignProcessor(tokenizer=_UpperCAmelCase , image_processor=_UpperCAmelCase )
lowercase__ = self.prepare_image_inputs()
lowercase__ = image_processor(_UpperCAmelCase , return_tensors="""np""" )
lowercase__ = processor(images=_UpperCAmelCase , return_tensors="""np""" )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1E-2 )
def lowerCamelCase__ (self : Dict ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = self.get_image_processor()
lowercase__ = self.get_tokenizer()
lowercase__ = AlignProcessor(tokenizer=_UpperCAmelCase , image_processor=_UpperCAmelCase )
lowercase__ = """lower newer"""
lowercase__ = processor(text=_UpperCAmelCase )
lowercase__ = tokenizer(_UpperCAmelCase , padding="""max_length""" , max_length=64 )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def lowerCamelCase__ (self : List[Any] ) -> Tuple:
"""simple docstring"""
lowercase__ = self.get_image_processor()
lowercase__ = self.get_tokenizer()
lowercase__ = AlignProcessor(tokenizer=_UpperCAmelCase , image_processor=_UpperCAmelCase )
lowercase__ = """lower newer"""
lowercase__ = self.prepare_image_inputs()
lowercase__ = processor(text=_UpperCAmelCase , images=_UpperCAmelCase )
self.assertListEqual(list(inputs.keys() ) , ["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""] )
# test if it raises when no input is passed
with pytest.raises(_UpperCAmelCase ):
processor()
def lowerCamelCase__ (self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = self.get_image_processor()
lowercase__ = self.get_tokenizer()
lowercase__ = AlignProcessor(tokenizer=_UpperCAmelCase , image_processor=_UpperCAmelCase )
lowercase__ = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
lowercase__ = processor.batch_decode(_UpperCAmelCase )
lowercase__ = tokenizer.batch_decode(_UpperCAmelCase )
self.assertListEqual(_UpperCAmelCase , _UpperCAmelCase )
def lowerCamelCase__ (self : List[str] ) -> Tuple:
"""simple docstring"""
lowercase__ = self.get_image_processor()
lowercase__ = self.get_tokenizer()
lowercase__ = AlignProcessor(tokenizer=_UpperCAmelCase , image_processor=_UpperCAmelCase )
lowercase__ = """lower newer"""
lowercase__ = self.prepare_image_inputs()
lowercase__ = processor(text=_UpperCAmelCase , images=_UpperCAmelCase )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 305
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import math
import qiskit
def UpperCamelCase ( __magic_name__ : int = 1 , __magic_name__ : int = 1 , __magic_name__ : int = 1 ) -> qiskit.result.counts.Counts:
"""simple docstring"""
if (
isinstance(__magic_name__ , __magic_name__ )
or isinstance(__magic_name__ , __magic_name__ )
or isinstance(__magic_name__ , __magic_name__ )
):
raise TypeError("""inputs must be integers.""" )
if (input_a < 0) or (input_a < 0) or (carry_in < 0):
raise ValueError("""inputs must be positive.""" )
if (
(math.floor(__magic_name__ ) != input_a)
or (math.floor(__magic_name__ ) != input_a)
or (math.floor(__magic_name__ ) != carry_in)
):
raise ValueError("""inputs must be exact integers.""" )
if (input_a > 2) or (input_a > 2) or (carry_in > 2):
raise ValueError("""inputs must be less or equal to 2.""" )
# build registers
lowercase__ = qiskit.QuantumRegister(4 , """qr""" )
lowercase__ = qiskit.ClassicalRegister(2 , """cr""" )
# list the entries
lowercase__ = [input_a, input_a, carry_in]
lowercase__ = qiskit.QuantumCircuit(__magic_name__ , __magic_name__ )
for i in range(0 , 3 ):
if entry[i] == 2:
quantum_circuit.h(__magic_name__ ) # for hadamard entries
elif entry[i] == 1:
quantum_circuit.x(__magic_name__ ) # for 1 entries
elif entry[i] == 0:
quantum_circuit.i(__magic_name__ ) # for 0 entries
# build the circuit
quantum_circuit.ccx(0 , 1 , 3 ) # ccx = toffoli gate
quantum_circuit.cx(0 , 1 )
quantum_circuit.ccx(1 , 2 , 3 )
quantum_circuit.cx(1 , 2 )
quantum_circuit.cx(0 , 1 )
quantum_circuit.measure([2, 3] , __magic_name__ ) # measure the last two qbits
lowercase__ = qiskit.Aer.get_backend("""aer_simulator""" )
lowercase__ = qiskit.execute(__magic_name__ , __magic_name__ , shots=1000 )
return job.result().get_counts(__magic_name__ )
if __name__ == "__main__":
print(F'Total sum count for state is: {quantum_full_adder(1, 1, 1)}')
| 305
|
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def UpperCamelCase ( __magic_name__ : List[Any] ) -> Optional[int]:
"""simple docstring"""
return x + 2
class A ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase__ (self : Optional[Any] ) -> Any:
"""simple docstring"""
lowercase__ = """x = 3"""
lowercase__ = {}
lowercase__ = evaluate(_UpperCAmelCase , {} , state=_UpperCAmelCase )
assert result == 3
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3} )
lowercase__ = """x = y"""
lowercase__ = {"""y""": 5}
lowercase__ = evaluate(_UpperCAmelCase , {} , state=_UpperCAmelCase )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(_UpperCAmelCase , {"""x""": 5, """y""": 5} )
def lowerCamelCase__ (self : str ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = """y = add_two(x)"""
lowercase__ = {"""x""": 3}
lowercase__ = evaluate(_UpperCAmelCase , {"""add_two""": add_two} , state=_UpperCAmelCase )
assert result == 5
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3, """y""": 5} )
# Won't work without the tool
with CaptureStdout() as out:
lowercase__ = evaluate(_UpperCAmelCase , {} , state=_UpperCAmelCase )
assert result is None
assert "tried to execute add_two" in out.out
def lowerCamelCase__ (self : List[Any] ) -> Optional[int]:
"""simple docstring"""
lowercase__ = """x = 3"""
lowercase__ = {}
lowercase__ = evaluate(_UpperCAmelCase , {} , state=_UpperCAmelCase )
assert result == 3
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3} )
def lowerCamelCase__ (self : Optional[int] ) -> List[Any]:
"""simple docstring"""
lowercase__ = """test_dict = {'x': x, 'y': add_two(x)}"""
lowercase__ = {"""x""": 3}
lowercase__ = evaluate(_UpperCAmelCase , {"""add_two""": add_two} , state=_UpperCAmelCase )
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3, """y""": 5} )
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3, """test_dict""": {"""x""": 3, """y""": 5}} )
def lowerCamelCase__ (self : List[str] ) -> List[Any]:
"""simple docstring"""
lowercase__ = """x = 3\ny = 5"""
lowercase__ = {}
lowercase__ = evaluate(_UpperCAmelCase , {} , state=_UpperCAmelCase )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3, """y""": 5} )
def lowerCamelCase__ (self : List[Any] ) -> Dict:
"""simple docstring"""
lowercase__ = """text = f'This is x: {x}.'"""
lowercase__ = {"""x""": 3}
lowercase__ = evaluate(_UpperCAmelCase , {} , state=_UpperCAmelCase )
# evaluate returns the value of the last assignment.
assert result == "This is x: 3."
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3, """text""": """This is x: 3."""} )
def lowerCamelCase__ (self : List[str] ) -> int:
"""simple docstring"""
lowercase__ = """if x <= 3:\n y = 2\nelse:\n y = 5"""
lowercase__ = {"""x""": 3}
lowercase__ = evaluate(_UpperCAmelCase , {} , state=_UpperCAmelCase )
# evaluate returns the value of the last assignment.
assert result == 2
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3, """y""": 2} )
lowercase__ = {"""x""": 8}
lowercase__ = evaluate(_UpperCAmelCase , {} , state=_UpperCAmelCase )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(_UpperCAmelCase , {"""x""": 8, """y""": 5} )
def lowerCamelCase__ (self : Dict ) -> int:
"""simple docstring"""
lowercase__ = """test_list = [x, add_two(x)]"""
lowercase__ = {"""x""": 3}
lowercase__ = evaluate(_UpperCAmelCase , {"""add_two""": add_two} , state=_UpperCAmelCase )
self.assertListEqual(_UpperCAmelCase , [3, 5] )
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3, """test_list""": [3, 5]} )
def lowerCamelCase__ (self : Any ) -> int:
"""simple docstring"""
lowercase__ = """y = x"""
lowercase__ = {"""x""": 3}
lowercase__ = evaluate(_UpperCAmelCase , {} , state=_UpperCAmelCase )
assert result == 3
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3, """y""": 3} )
def lowerCamelCase__ (self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
lowercase__ = """test_list = [x, add_two(x)]\ntest_list[1]"""
lowercase__ = {"""x""": 3}
lowercase__ = evaluate(_UpperCAmelCase , {"""add_two""": add_two} , state=_UpperCAmelCase )
assert result == 5
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3, """test_list""": [3, 5]} )
lowercase__ = """test_dict = {'x': x, 'y': add_two(x)}\ntest_dict['y']"""
lowercase__ = {"""x""": 3}
lowercase__ = evaluate(_UpperCAmelCase , {"""add_two""": add_two} , state=_UpperCAmelCase )
assert result == 5
self.assertDictEqual(_UpperCAmelCase , {"""x""": 3, """test_dict""": {"""x""": 3, """y""": 5}} )
def lowerCamelCase__ (self : Union[str, Any] ) -> Any:
"""simple docstring"""
lowercase__ = """x = 0\nfor i in range(3):\n x = i"""
lowercase__ = {}
lowercase__ = evaluate(_UpperCAmelCase , {"""range""": range} , state=_UpperCAmelCase )
assert result == 2
self.assertDictEqual(_UpperCAmelCase , {"""x""": 2, """i""": 2} )
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import requests
from bsa import BeautifulSoup
def UpperCamelCase ( __magic_name__ : str = "AAPL" ) -> str:
"""simple docstring"""
lowercase__ = f'''https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'''
lowercase__ = BeautifulSoup(requests.get(__magic_name__ ).text , """html.parser""" )
lowercase__ = """My(6px) Pos(r) smartphone_Mt(6px)"""
return soup.find("""div""" , class_=class_ ).find("""span""" ).text
if __name__ == "__main__":
for symbol in "AAPL AMZN IBM GOOG MSFT ORCL".split():
print(F'Current {symbol:<4} stock price is {stock_price(symbol):>8}')
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|
class A :
'''simple docstring'''
def __init__(self : List[str] ) -> Tuple:
"""simple docstring"""
lowercase__ = 0
lowercase__ = 0
lowercase__ = {}
def lowerCamelCase__ (self : Dict , _UpperCAmelCase : Tuple ) -> Optional[int]:
"""simple docstring"""
if vertex not in self.adjacency:
lowercase__ = {}
self.num_vertices += 1
def lowerCamelCase__ (self : List[Any] , _UpperCAmelCase : List[Any] , _UpperCAmelCase : int , _UpperCAmelCase : List[str] ) -> Tuple:
"""simple docstring"""
self.add_vertex(_UpperCAmelCase )
self.add_vertex(_UpperCAmelCase )
if head == tail:
return
lowercase__ = weight
lowercase__ = weight
def lowerCamelCase__ (self : List[str] ) -> Optional[int]:
"""simple docstring"""
lowercase__ = self.get_edges()
for edge in edges:
lowercase__ , lowercase__ , lowercase__ = edge
edges.remove((tail, head, weight) )
for i in range(len(_UpperCAmelCase ) ):
lowercase__ = list(edges[i] )
edges.sort(key=lambda _UpperCAmelCase : e[2] )
for i in range(len(_UpperCAmelCase ) - 1 ):
if edges[i][2] >= edges[i + 1][2]:
lowercase__ = edges[i][2] + 1
for edge in edges:
lowercase__ , lowercase__ , lowercase__ = edge
lowercase__ = weight
lowercase__ = weight
def __str__(self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = """"""
for tail in self.adjacency:
for head in self.adjacency[tail]:
lowercase__ = self.adjacency[head][tail]
string += f'''{head} -> {tail} == {weight}\n'''
return string.rstrip("""\n""" )
def lowerCamelCase__ (self : Any ) -> str:
"""simple docstring"""
lowercase__ = []
for tail in self.adjacency:
for head in self.adjacency[tail]:
output.append((tail, head, self.adjacency[head][tail]) )
return output
def lowerCamelCase__ (self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
return self.adjacency.keys()
@staticmethod
def lowerCamelCase__ (_UpperCAmelCase : List[str]=None , _UpperCAmelCase : Any=None ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = Graph()
if vertices is None:
lowercase__ = []
if edges is None:
lowercase__ = []
for vertex in vertices:
g.add_vertex(_UpperCAmelCase )
for edge in edges:
g.add_edge(*_UpperCAmelCase )
return g
class A :
'''simple docstring'''
def __init__(self : Optional[Any] ) -> str:
"""simple docstring"""
lowercase__ = {}
lowercase__ = {}
def __len__(self : Optional[Any] ) -> Dict:
"""simple docstring"""
return len(self.parent )
def lowerCamelCase__ (self : str , _UpperCAmelCase : Dict ) -> Any:
"""simple docstring"""
if item in self.parent:
return self.find(_UpperCAmelCase )
lowercase__ = item
lowercase__ = 0
return item
def lowerCamelCase__ (self : List[str] , _UpperCAmelCase : Dict ) -> Any:
"""simple docstring"""
if item not in self.parent:
return self.make_set(_UpperCAmelCase )
if item != self.parent[item]:
lowercase__ = self.find(self.parent[item] )
return self.parent[item]
def lowerCamelCase__ (self : List[Any] , _UpperCAmelCase : Any , _UpperCAmelCase : List[Any] ) -> Optional[int]:
"""simple docstring"""
lowercase__ = self.find(_UpperCAmelCase )
lowercase__ = self.find(_UpperCAmelCase )
if roota == roota:
return roota
if self.rank[roota] > self.rank[roota]:
lowercase__ = roota
return roota
if self.rank[roota] < self.rank[roota]:
lowercase__ = roota
return roota
if self.rank[roota] == self.rank[roota]:
self.rank[roota] += 1
lowercase__ = roota
return roota
return None
@staticmethod
def lowerCamelCase__ (_UpperCAmelCase : str ) -> Optional[int]:
"""simple docstring"""
lowercase__ = graph.num_vertices
lowercase__ = Graph.UnionFind()
lowercase__ = []
while num_components > 1:
lowercase__ = {}
for vertex in graph.get_vertices():
lowercase__ = -1
lowercase__ = graph.get_edges()
for edge in edges:
lowercase__ , lowercase__ , lowercase__ = edge
edges.remove((tail, head, weight) )
for edge in edges:
lowercase__ , lowercase__ , lowercase__ = edge
lowercase__ = union_find.find(_UpperCAmelCase )
lowercase__ = union_find.find(_UpperCAmelCase )
if seta != seta:
if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
lowercase__ = [head, tail, weight]
if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
lowercase__ = [head, tail, weight]
for vertex in cheap_edge:
if cheap_edge[vertex] != -1:
lowercase__ , lowercase__ , lowercase__ = cheap_edge[vertex]
if union_find.find(_UpperCAmelCase ) != union_find.find(_UpperCAmelCase ):
union_find.union(_UpperCAmelCase , _UpperCAmelCase )
mst_edges.append(cheap_edge[vertex] )
lowercase__ = num_components - 1
lowercase__ = Graph.build(edges=_UpperCAmelCase )
return mst
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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_big_bird import BigBirdTokenizer
else:
A : Optional[int] = None
A : str = logging.get_logger(__name__)
A : Tuple = {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}
A : Any = {
'vocab_file': {
'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model',
'google/bigbird-roberta-large': (
'https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'
),
'google/bigbird-base-trivia-itc': (
'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'
),
},
'tokenizer_file': {
'google/bigbird-roberta-base': (
'https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json'
),
'google/bigbird-roberta-large': (
'https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json'
),
'google/bigbird-base-trivia-itc': (
'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json'
),
},
}
A : List[str] = {
'google/bigbird-roberta-base': 4_0_9_6,
'google/bigbird-roberta-large': 4_0_9_6,
'google/bigbird-base-trivia-itc': 4_0_9_6,
}
A : List[str] = '▁'
class A ( UpperCAmelCase__ ):
'''simple docstring'''
A__ = VOCAB_FILES_NAMES
A__ = PRETRAINED_VOCAB_FILES_MAP
A__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A__ = BigBirdTokenizer
A__ = ['''input_ids''', '''attention_mask''']
A__ = []
def __init__(self : Tuple , _UpperCAmelCase : Any=None , _UpperCAmelCase : Union[str, Any]=None , _UpperCAmelCase : Tuple="<unk>" , _UpperCAmelCase : Tuple="<s>" , _UpperCAmelCase : Optional[Any]="</s>" , _UpperCAmelCase : str="<pad>" , _UpperCAmelCase : List[str]="[SEP]" , _UpperCAmelCase : Union[str, Any]="[MASK]" , _UpperCAmelCase : Optional[int]="[CLS]" , **_UpperCAmelCase : Tuple , ) -> Tuple:
"""simple docstring"""
lowercase__ = AddedToken(_UpperCAmelCase , lstrip=_UpperCAmelCase , rstrip=_UpperCAmelCase ) if isinstance(_UpperCAmelCase , _UpperCAmelCase ) else bos_token
lowercase__ = AddedToken(_UpperCAmelCase , lstrip=_UpperCAmelCase , rstrip=_UpperCAmelCase ) if isinstance(_UpperCAmelCase , _UpperCAmelCase ) else eos_token
lowercase__ = AddedToken(_UpperCAmelCase , lstrip=_UpperCAmelCase , rstrip=_UpperCAmelCase ) if isinstance(_UpperCAmelCase , _UpperCAmelCase ) else unk_token
lowercase__ = AddedToken(_UpperCAmelCase , lstrip=_UpperCAmelCase , rstrip=_UpperCAmelCase ) if isinstance(_UpperCAmelCase , _UpperCAmelCase ) else pad_token
lowercase__ = AddedToken(_UpperCAmelCase , lstrip=_UpperCAmelCase , rstrip=_UpperCAmelCase ) if isinstance(_UpperCAmelCase , _UpperCAmelCase ) else cls_token
lowercase__ = AddedToken(_UpperCAmelCase , lstrip=_UpperCAmelCase , rstrip=_UpperCAmelCase ) if isinstance(_UpperCAmelCase , _UpperCAmelCase ) else sep_token
# Mask token behave like a normal word, i.e. include the space before it
lowercase__ = AddedToken(_UpperCAmelCase , lstrip=_UpperCAmelCase , rstrip=_UpperCAmelCase ) if isinstance(_UpperCAmelCase , _UpperCAmelCase ) else mask_token
super().__init__(
_UpperCAmelCase , tokenizer_file=_UpperCAmelCase , bos_token=_UpperCAmelCase , eos_token=_UpperCAmelCase , unk_token=_UpperCAmelCase , sep_token=_UpperCAmelCase , pad_token=_UpperCAmelCase , cls_token=_UpperCAmelCase , mask_token=_UpperCAmelCase , **_UpperCAmelCase , )
lowercase__ = vocab_file
lowercase__ = False if not self.vocab_file else True
def lowerCamelCase__ (self : int , _UpperCAmelCase : List[int] , _UpperCAmelCase : Optional[List[int]] = None ) -> List[int]:
"""simple docstring"""
lowercase__ = [self.sep_token_id]
lowercase__ = [self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def lowerCamelCase__ (self : Any , _UpperCAmelCase : List[int] , _UpperCAmelCase : Optional[List[int]] = None , _UpperCAmelCase : bool = False ) -> List[int]:
"""simple docstring"""
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
"""You should not supply a second sequence if the provided sequence of """
"""ids is already formatted with special tokens for the model.""" )
return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]
if token_ids_a is None:
return [1] + ([0] * len(_UpperCAmelCase )) + [1]
return [1] + ([0] * len(_UpperCAmelCase )) + [1] + ([0] * len(_UpperCAmelCase )) + [1]
def lowerCamelCase__ (self : int , _UpperCAmelCase : List[int] , _UpperCAmelCase : Optional[List[int]] = None ) -> List[int]:
"""simple docstring"""
lowercase__ = [self.sep_token_id]
lowercase__ = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def lowerCamelCase__ (self : List[Any] , _UpperCAmelCase : str , _UpperCAmelCase : Optional[str] = None ) -> Tuple[str]:
"""simple docstring"""
if not self.can_save_slow_tokenizer:
raise ValueError(
"""Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
"""tokenizer.""" )
if not os.path.isdir(_UpperCAmelCase ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
lowercase__ = os.path.join(
_UpperCAmelCase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_UpperCAmelCase ):
copyfile(self.vocab_file , _UpperCAmelCase )
return (out_vocab_file,)
| 305
|
def UpperCamelCase ( __magic_name__ : int , __magic_name__ : int ) -> int:
"""simple docstring"""
return int((input_a, input_a).count(1 ) != 0 )
def UpperCamelCase ( ) -> None:
"""simple docstring"""
assert or_gate(0 , 0 ) == 0
assert or_gate(0 , 1 ) == 1
assert or_gate(1 , 0 ) == 1
assert or_gate(1 , 1 ) == 1
if __name__ == "__main__":
print(or_gate(0, 1))
print(or_gate(1, 0))
print(or_gate(0, 0))
print(or_gate(1, 1))
| 305
| 1
|
def UpperCamelCase ( ) -> Optional[int]:
"""simple docstring"""
for n in range(1 , 100_0000 ):
yield n * (n + 1) // 2
def UpperCamelCase ( __magic_name__ : List[str] ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = 1
lowercase__ = 2
while i * i <= n:
lowercase__ = 0
while n % i == 0:
n //= i
multiplicity += 1
divisors_count *= multiplicity + 1
i += 1
if n > 1:
divisors_count *= 2
return divisors_count
def UpperCamelCase ( ) -> Tuple:
"""simple docstring"""
return next(i for i in triangle_number_generator() if count_divisors(__magic_name__ ) > 500 )
if __name__ == "__main__":
print(solution())
| 305
|
import argparse
from torch import nn
# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)
from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging
A : Any = logging.get_logger(__name__)
logging.set_verbosity_info()
def UpperCamelCase ( __magic_name__ : str , __magic_name__ : str ) -> List[str]:
"""simple docstring"""
if "xprophetnet" in prophetnet_checkpoint_path:
lowercase__ = XLMProphetNetForConditionalGenerationOld.from_pretrained(__magic_name__ )
lowercase__ , lowercase__ = XLMProphetNetForConditionalGeneration.from_pretrained(
__magic_name__ , output_loading_info=__magic_name__ )
else:
lowercase__ = ProphetNetForConditionalGenerationOld.from_pretrained(__magic_name__ )
lowercase__ , lowercase__ = ProphetNetForConditionalGeneration.from_pretrained(
__magic_name__ , output_loading_info=__magic_name__ )
lowercase__ = ["""key_proj""", """value_proj""", """query_proj"""]
lowercase__ = {
"""self_attn""": """ngram_self_attn""",
"""cross_attn""": """encoder_attn""",
"""cross_attn_layer_norm""": """encoder_attn_layer_norm""",
"""feed_forward_layer_norm""": """final_layer_norm""",
"""feed_forward""": """""",
"""intermediate""": """fc1""",
"""output""": """fc2""",
"""key_proj""": """k_proj""",
"""query_proj""": """q_proj""",
"""value_proj""": """v_proj""",
"""word_embeddings""": """embed_tokens""",
"""embeddings_layer_norm""": """emb_layer_norm""",
"""relative_pos_embeddings""": """relative_linear""",
"""ngram_embeddings""": """ngram_input_embed""",
"""position_embeddings""": """embed_positions""",
}
for key in loading_info["missing_keys"]:
lowercase__ = key.split(""".""" )
if attributes[0] == "lm_head":
lowercase__ = prophet
lowercase__ = prophet_old
else:
lowercase__ = prophet.prophetnet
lowercase__ = prophet_old.model
lowercase__ = False
for attribute in attributes:
if attribute in mapping:
lowercase__ = mapping[attribute]
if not hasattr(__magic_name__ , __magic_name__ ) and len(__magic_name__ ) > 0:
lowercase__ = attribute
elif hasattr(__magic_name__ , __magic_name__ ):
lowercase__ = attribute
if attribute == "weight":
assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
lowercase__ = old_model.weight
logger.info(f'''{attribute} is initialized.''' )
lowercase__ = True
break
elif attribute == "bias":
assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
lowercase__ = old_model.bias
logger.info(f'''{attribute} is initialized''' )
lowercase__ = True
break
elif attribute in special_keys and hasattr(__magic_name__ , """in_proj_weight""" ):
lowercase__ = old_model.in_proj_weight.shape[0] // 3
lowercase__ = getattr(__magic_name__ , __magic_name__ )
param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
if attribute == "query_proj":
lowercase__ = nn.Parameter(old_model.in_proj_weight[:embed_dim, :] )
lowercase__ = nn.Parameter(old_model.in_proj_bias[:embed_dim] )
elif attribute == "key_proj":
lowercase__ = nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :] )
lowercase__ = nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim] )
elif attribute == "value_proj":
lowercase__ = nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :] )
lowercase__ = nn.Parameter(old_model.in_proj_bias[2 * embed_dim :] )
lowercase__ = True
break
elif attribute == "position_embeddings":
assert (
model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
), "Hidden size has to match"
assert model.position_embeddings.weight.shape[0] == 512, "We want 512 position_embeddings."
lowercase__ = nn.Parameter(old_model.embed_positions.weight[:512, :] )
lowercase__ = True
break
if attribute.isdigit():
lowercase__ = model[int(__magic_name__ )]
lowercase__ = old_model[int(__magic_name__ )]
else:
lowercase__ = getattr(__magic_name__ , __magic_name__ )
if old_attribute == "":
lowercase__ = old_model
else:
if not hasattr(__magic_name__ , __magic_name__ ):
raise ValueError(f'''{old_model} does not have {old_attribute}''' )
lowercase__ = getattr(__magic_name__ , __magic_name__ )
if not is_key_init:
raise ValueError(f'''{key} was not correctly initialized!''' )
print(f'''Saving model to {pytorch_dump_folder_path}''' )
prophet.save_pretrained(__magic_name__ )
if __name__ == "__main__":
A : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--prophetnet_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
A : str = parser.parse_args()
convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)
| 305
| 1
|
import baseaa
import io
import json
import os
from copy import deepcopy
from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler
class A :
'''simple docstring'''
def __init__(self : Optional[Any] , _UpperCAmelCase : Dict ) -> Tuple:
"""simple docstring"""
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
# Don't modify user's data should they want to reuse it (e.g. in tests), because once we
# modified it, it will not be accepted here again, since `auto` values would have been overridden
lowercase__ = deepcopy(_UpperCAmelCase )
elif os.path.exists(_UpperCAmelCase ):
with io.open(_UpperCAmelCase , """r""" , encoding="""utf-8""" ) as f:
lowercase__ = json.load(_UpperCAmelCase )
else:
try:
lowercase__ = baseaa.urlsafe_baadecode(_UpperCAmelCase ).decode("""utf-8""" )
lowercase__ = json.loads(_UpperCAmelCase )
except (UnicodeDecodeError, AttributeError, ValueError):
raise ValueError(
f'''Expected a string path to an existing deepspeed config, or a dictionary, or a base64 encoded string. Received: {config_file_or_dict}''' )
lowercase__ = config
self.set_stage_and_offload()
def lowerCamelCase__ (self : Optional[Any] ) -> Tuple:
"""simple docstring"""
lowercase__ = self.get_value("""zero_optimization.stage""" , -1 )
# offload
lowercase__ = False
if self.is_zeroa() or self.is_zeroa():
lowercase__ = set(["""cpu""", """nvme"""] )
lowercase__ = set(
[
self.get_value("""zero_optimization.offload_optimizer.device""" ),
self.get_value("""zero_optimization.offload_param.device""" ),
] )
if len(offload_devices & offload_devices_valid ) > 0:
lowercase__ = True
def lowerCamelCase__ (self : Optional[int] , _UpperCAmelCase : Optional[int] ) -> List[str]:
"""simple docstring"""
lowercase__ = self.config
# find the config node of interest if it exists
lowercase__ = ds_key_long.split(""".""" )
lowercase__ = nodes.pop()
for node in nodes:
lowercase__ = config.get(_UpperCAmelCase )
if config is None:
return None, ds_key
return config, ds_key
def lowerCamelCase__ (self : Optional[Any] , _UpperCAmelCase : Any , _UpperCAmelCase : Optional[int]=None ) -> List[str]:
"""simple docstring"""
lowercase__ , lowercase__ = self.find_config_node(_UpperCAmelCase )
if config is None:
return default
return config.get(_UpperCAmelCase , _UpperCAmelCase )
def lowerCamelCase__ (self : Any , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Tuple=False ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = self.config
# find the config node of interest if it exists
lowercase__ = ds_key_long.split(""".""" )
for node in nodes:
lowercase__ = config
lowercase__ = config.get(_UpperCAmelCase )
if config is None:
if must_exist:
raise ValueError(f'''Can\'t find {ds_key_long} entry in the config: {self.config}''' )
else:
return
# if found remove it
if parent_config is not None:
parent_config.pop(_UpperCAmelCase )
def lowerCamelCase__ (self : str , _UpperCAmelCase : Optional[int] ) -> Any:
"""simple docstring"""
lowercase__ = self.get_value(_UpperCAmelCase )
return False if value is None else bool(_UpperCAmelCase )
def lowerCamelCase__ (self : int , _UpperCAmelCase : str ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = self.get_value(_UpperCAmelCase )
return False if value is None else not bool(_UpperCAmelCase )
def lowerCamelCase__ (self : Optional[Any] ) -> Tuple:
"""simple docstring"""
return self._stage == 2
def lowerCamelCase__ (self : List[Any] ) -> Tuple:
"""simple docstring"""
return self._stage == 3
def lowerCamelCase__ (self : Dict ) -> Any:
"""simple docstring"""
return self._offload
class A :
'''simple docstring'''
def __init__(self : Union[str, Any] , _UpperCAmelCase : Tuple ) -> Tuple:
"""simple docstring"""
lowercase__ = engine
def lowerCamelCase__ (self : int , _UpperCAmelCase : Dict , **_UpperCAmelCase : Optional[int] ) -> Tuple:
"""simple docstring"""
self.engine.backward(_UpperCAmelCase , **_UpperCAmelCase )
# Deepspeed's `engine.step` performs the following operations:
# - gradient accumulation check
# - gradient clipping
# - optimizer step
# - zero grad
# - checking overflow
# - lr_scheduler step (only if engine.lr_scheduler is not None)
self.engine.step()
# and this plugin overrides the above calls with no-ops when Accelerate runs under
# Deepspeed, but allows normal functionality for non-Deepspeed cases thus enabling a simple
# training loop that works transparently under many training regimes.
class A ( UpperCAmelCase__ ):
'''simple docstring'''
def __init__(self : List[str] , _UpperCAmelCase : List[str] ) -> List[str]:
"""simple docstring"""
super().__init__(_UpperCAmelCase , device_placement=_UpperCAmelCase , scaler=_UpperCAmelCase )
lowercase__ = hasattr(self.optimizer , """overflow""" )
def lowerCamelCase__ (self : Dict , _UpperCAmelCase : str=None ) -> Union[str, Any]:
"""simple docstring"""
pass # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed
def lowerCamelCase__ (self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
pass # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed
@property
def lowerCamelCase__ (self : Optional[Any] ) -> int:
"""simple docstring"""
if self.__has_overflow__:
return self.optimizer.overflow
return False
class A ( UpperCAmelCase__ ):
'''simple docstring'''
def __init__(self : Optional[int] , _UpperCAmelCase : Tuple , _UpperCAmelCase : Optional[Any] ) -> str:
"""simple docstring"""
super().__init__(_UpperCAmelCase , _UpperCAmelCase )
def lowerCamelCase__ (self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
pass # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed
class A :
'''simple docstring'''
def __init__(self : int , _UpperCAmelCase : int , _UpperCAmelCase : int=0.001 , _UpperCAmelCase : str=0 , **_UpperCAmelCase : List[Any] ) -> str:
"""simple docstring"""
lowercase__ = params
lowercase__ = lr
lowercase__ = weight_decay
lowercase__ = kwargs
class A :
'''simple docstring'''
def __init__(self : Any , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Union[str, Any]=None , _UpperCAmelCase : int=0 , **_UpperCAmelCase : Tuple ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = optimizer
lowercase__ = total_num_steps
lowercase__ = warmup_num_steps
lowercase__ = kwargs
| 305
|
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class A ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__(self : Any , _UpperCAmelCase : Dict[str, int] , _UpperCAmelCase : List[str] , _UpperCAmelCase : int = None , _UpperCAmelCase : int = None ) -> Dict:
"""simple docstring"""
super().__init__()
lowercase__ = pad_token_id
lowercase__ = max_length
lowercase__ = vocab
lowercase__ = merges
lowercase__ = BytePairTokenizer(_UpperCAmelCase , _UpperCAmelCase , sequence_length=_UpperCAmelCase )
@classmethod
def lowerCamelCase__ (cls : Optional[int] , _UpperCAmelCase : GPTaTokenizer , *_UpperCAmelCase : List[Any] , **_UpperCAmelCase : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = [""" """.join(_UpperCAmelCase ) for m in tokenizer.bpe_ranks.keys()]
lowercase__ = tokenizer.get_vocab()
return cls(_UpperCAmelCase , _UpperCAmelCase , *_UpperCAmelCase , **_UpperCAmelCase )
@classmethod
def lowerCamelCase__ (cls : Union[str, Any] , _UpperCAmelCase : Union[str, os.PathLike] , *_UpperCAmelCase : str , **_UpperCAmelCase : List[Any] ) -> Any:
"""simple docstring"""
lowercase__ = GPTaTokenizer.from_pretrained(_UpperCAmelCase , *_UpperCAmelCase , **_UpperCAmelCase )
return cls.from_tokenizer(_UpperCAmelCase , *_UpperCAmelCase , **_UpperCAmelCase )
@classmethod
def lowerCamelCase__ (cls : Any , _UpperCAmelCase : Tuple ) -> Union[str, Any]:
"""simple docstring"""
return cls(**_UpperCAmelCase )
def lowerCamelCase__ (self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
return {
"vocab": self.vocab,
"merges": self.merges,
"max_length": self.max_length,
"pad_token_id": self.pad_token_id,
}
def lowerCamelCase__ (self : str , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : int = None ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = self.tf_tokenizer(_UpperCAmelCase )
lowercase__ = tf.ones_like(_UpperCAmelCase )
if self.pad_token_id is not None:
# pad the tokens up to max length
lowercase__ = max_length if max_length is not None else self.max_length
if max_length is not None:
lowercase__ , lowercase__ = pad_model_inputs(
_UpperCAmelCase , max_seq_length=_UpperCAmelCase , pad_value=self.pad_token_id )
return {"attention_mask": attention_mask, "input_ids": input_ids}
| 305
| 1
|
def UpperCamelCase ( __magic_name__ : List[Any] ) -> Optional[int]:
"""simple docstring"""
lowercase__ = [0] * len(__magic_name__ )
lowercase__ = []
lowercase__ = [1] * len(__magic_name__ )
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(__magic_name__ ) ):
if indegree[i] == 0:
queue.append(__magic_name__ )
while queue:
lowercase__ = queue.pop(0 )
for x in graph[vertex]:
indegree[x] -= 1
if long_dist[vertex] + 1 > long_dist[x]:
lowercase__ = long_dist[vertex] + 1
if indegree[x] == 0:
queue.append(__magic_name__ )
print(max(__magic_name__ ) )
# Adjacency list of Graph
A : Union[str, Any] = {0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}
longest_distance(graph)
| 305
|
from __future__ import annotations
from functools import lru_cache
from math import ceil
A : Optional[int] = 1_0_0
A : int = set(range(3, NUM_PRIMES, 2))
primes.add(2)
A : int
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prime not in primes:
continue
primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime)))
@lru_cache(maxsize=100 )
def UpperCamelCase ( __magic_name__ : int ) -> set[int]:
"""simple docstring"""
if number_to_partition < 0:
return set()
elif number_to_partition == 0:
return {1}
lowercase__ = set()
lowercase__ = 42
lowercase__ = 42
for prime in primes:
if prime > number_to_partition:
continue
for sub in partition(number_to_partition - prime ):
ret.add(sub * prime )
return ret
def UpperCamelCase ( __magic_name__ : int = 5000 ) -> int | None:
"""simple docstring"""
for number_to_partition in range(1 , __magic_name__ ):
if len(partition(__magic_name__ ) ) > number_unique_partitions:
return number_to_partition
return None
if __name__ == "__main__":
print(F'{solution() = }')
| 305
| 1
|
import unittest
import numpy as np
import torch
from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class A ( unittest.TestCase ):
'''simple docstring'''
@property
def lowerCamelCase__ (self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
torch.manual_seed(0 )
lowercase__ = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=("""DownBlock2D""", """AttnDownBlock2D""") , up_block_types=("""AttnUpBlock2D""", """UpBlock2D""") , )
return model
def lowerCamelCase__ (self : List[str] ) -> Any:
"""simple docstring"""
lowercase__ = self.dummy_uncond_unet
lowercase__ = PNDMScheduler()
lowercase__ = PNDMPipeline(unet=_UpperCAmelCase , scheduler=_UpperCAmelCase )
pndm.to(_UpperCAmelCase )
pndm.set_progress_bar_config(disable=_UpperCAmelCase )
lowercase__ = torch.manual_seed(0 )
lowercase__ = pndm(generator=_UpperCAmelCase , num_inference_steps=20 , output_type="""numpy""" ).images
lowercase__ = torch.manual_seed(0 )
lowercase__ = pndm(generator=_UpperCAmelCase , num_inference_steps=20 , output_type="""numpy""" , return_dict=_UpperCAmelCase )[0]
lowercase__ = image[0, -3:, -3:, -1]
lowercase__ = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
lowercase__ = np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
@slow
@require_torch
class A ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase__ (self : Dict ) -> List[str]:
"""simple docstring"""
lowercase__ = """google/ddpm-cifar10-32"""
lowercase__ = UNetaDModel.from_pretrained(_UpperCAmelCase )
lowercase__ = PNDMScheduler()
lowercase__ = PNDMPipeline(unet=_UpperCAmelCase , scheduler=_UpperCAmelCase )
pndm.to(_UpperCAmelCase )
pndm.set_progress_bar_config(disable=_UpperCAmelCase )
lowercase__ = torch.manual_seed(0 )
lowercase__ = pndm(generator=_UpperCAmelCase , output_type="""numpy""" ).images
lowercase__ = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
lowercase__ = np.array([0.1_564, 0.14_645, 0.1_406, 0.14_715, 0.12_425, 0.14_045, 0.13_115, 0.12_175, 0.125] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 305
|
def UpperCamelCase ( __magic_name__ : List[Any] ) -> Optional[int]:
"""simple docstring"""
lowercase__ = [0] * len(__magic_name__ )
lowercase__ = []
lowercase__ = [1] * len(__magic_name__ )
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(__magic_name__ ) ):
if indegree[i] == 0:
queue.append(__magic_name__ )
while queue:
lowercase__ = queue.pop(0 )
for x in graph[vertex]:
indegree[x] -= 1
if long_dist[vertex] + 1 > long_dist[x]:
lowercase__ = long_dist[vertex] + 1
if indegree[x] == 0:
queue.append(__magic_name__ )
print(max(__magic_name__ ) )
# Adjacency list of Graph
A : Union[str, Any] = {0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}
longest_distance(graph)
| 305
| 1
|
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