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import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class a (_lowerCAmelCase ):
"""simple docstring"""
__UpperCAmelCase : List[str] = (DDIMParallelScheduler,)
__UpperCAmelCase : Tuple = (("eta", 0.0), ("num_inference_steps", 50))
def __snake_case ( self : int , **lowerCamelCase : Optional[Any] ) -> Optional[Any]:
__snake_case : Any = {
"num_train_timesteps": 1000,
"beta_start": 0.00_01,
"beta_end": 0.02,
"beta_schedule": "linear",
"clip_sample": True,
}
config.update(**lowerCamelCase )
return config
def __snake_case ( self : Optional[Any] , **lowerCamelCase : List[str] ) -> Any:
__snake_case : Optional[int] = self.scheduler_classes[0]
__snake_case : Optional[int] = self.get_scheduler_config(**lowerCamelCase )
__snake_case : Optional[int] = scheduler_class(**lowerCamelCase )
__snake_case , __snake_case : List[Any] = 10, 0.0
__snake_case : Union[str, Any] = self.dummy_model()
__snake_case : Union[str, Any] = self.dummy_sample_deter
scheduler.set_timesteps(lowerCamelCase )
for t in scheduler.timesteps:
__snake_case : List[Any] = model(lowerCamelCase , lowerCamelCase )
__snake_case : List[str] = scheduler.step(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ).prev_sample
return sample
def __snake_case ( self : Tuple ) -> Dict:
for timesteps in [100, 500, 1000]:
self.check_over_configs(num_train_timesteps=lowerCamelCase )
def __snake_case ( self : Dict ) -> int:
for steps_offset in [0, 1]:
self.check_over_configs(steps_offset=lowerCamelCase )
__snake_case : Optional[int] = self.scheduler_classes[0]
__snake_case : Optional[Any] = self.get_scheduler_config(steps_offset=1 )
__snake_case : List[str] = scheduler_class(**lowerCamelCase )
scheduler.set_timesteps(5 )
assert torch.equal(scheduler.timesteps , torch.LongTensor([801, 601, 401, 201, 1] ) )
def __snake_case ( self : List[Any] ) -> int:
for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1] , [0.0_02, 0.02, 0.2, 2] ):
self.check_over_configs(beta_start=lowerCamelCase , beta_end=lowerCamelCase )
def __snake_case ( self : List[Any] ) -> str:
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=lowerCamelCase )
def __snake_case ( self : Tuple ) -> List[str]:
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=lowerCamelCase )
def __snake_case ( self : Optional[Any] ) -> List[Any]:
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=lowerCamelCase )
def __snake_case ( self : Tuple ) -> List[Any]:
for timestep_spacing in ["trailing", "leading"]:
self.check_over_configs(timestep_spacing=lowerCamelCase )
def __snake_case ( self : Tuple ) -> Any:
for rescale_betas_zero_snr in [True, False]:
self.check_over_configs(rescale_betas_zero_snr=lowerCamelCase )
def __snake_case ( self : Dict ) -> List[str]:
self.check_over_configs(thresholding=lowerCamelCase )
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(
thresholding=lowerCamelCase , prediction_type=lowerCamelCase , sample_max_value=lowerCamelCase , )
def __snake_case ( self : int ) -> Union[str, Any]:
for t in [1, 10, 49]:
self.check_over_forward(time_step=lowerCamelCase )
def __snake_case ( self : int ) -> List[Any]:
for t, num_inference_steps in zip([1, 10, 50] , [10, 50, 500] ):
self.check_over_forward(time_step=lowerCamelCase , num_inference_steps=lowerCamelCase )
def __snake_case ( self : Dict ) -> str:
for t, eta in zip([1, 10, 49] , [0.0, 0.5, 1.0] ):
self.check_over_forward(time_step=lowerCamelCase , eta=lowerCamelCase )
def __snake_case ( self : Any ) -> Any:
__snake_case : Any = self.scheduler_classes[0]
__snake_case : Dict = self.get_scheduler_config()
__snake_case : Optional[int] = scheduler_class(**lowerCamelCase )
assert torch.sum(torch.abs(scheduler._get_variance(0 , 0 ) - 0.0 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(420 , 400 ) - 0.1_47_71 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(980 , 960 ) - 0.3_24_60 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(0 , 0 ) - 0.0 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(487 , 486 ) - 0.0_09_79 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(999 , 998 ) - 0.02 ) ) < 1E-5
def __snake_case ( self : Dict ) -> List[Any]:
__snake_case : Any = self.scheduler_classes[0]
__snake_case : Dict = self.get_scheduler_config()
__snake_case : Optional[int] = scheduler_class(**lowerCamelCase )
__snake_case , __snake_case : str = 10, 0.0
scheduler.set_timesteps(lowerCamelCase )
__snake_case : Dict = self.dummy_model()
__snake_case : List[Any] = self.dummy_sample_deter
__snake_case : int = self.dummy_sample_deter + 0.1
__snake_case : str = self.dummy_sample_deter - 0.1
__snake_case : Dict = samplea.shape[0]
__snake_case : str = torch.stack([samplea, samplea, samplea] , dim=0 )
__snake_case : int = torch.arange(lowerCamelCase )[0:3, None].repeat(1 , lowerCamelCase )
__snake_case : Union[str, Any] = model(samples.flatten(0 , 1 ) , timesteps.flatten(0 , 1 ) )
__snake_case : Any = scheduler.batch_step_no_noise(lowerCamelCase , timesteps.flatten(0 , 1 ) , samples.flatten(0 , 1 ) , lowerCamelCase )
__snake_case : Optional[Any] = torch.sum(torch.abs(lowerCamelCase ) )
__snake_case : List[Any] = torch.mean(torch.abs(lowerCamelCase ) )
assert abs(result_sum.item() - 11_47.79_04 ) < 1E-2
assert abs(result_mean.item() - 0.49_82 ) < 1E-3
def __snake_case ( self : str ) -> Optional[int]:
__snake_case : Union[str, Any] = self.full_loop()
__snake_case : Dict = torch.sum(torch.abs(lowerCamelCase ) )
__snake_case : Dict = torch.mean(torch.abs(lowerCamelCase ) )
assert abs(result_sum.item() - 1_72.00_67 ) < 1E-2
assert abs(result_mean.item() - 0.22_39_67 ) < 1E-3
def __snake_case ( self : str ) -> Dict:
__snake_case : Any = self.full_loop(prediction_type="v_prediction" )
__snake_case : Optional[int] = torch.sum(torch.abs(lowerCamelCase ) )
__snake_case : int = torch.mean(torch.abs(lowerCamelCase ) )
assert abs(result_sum.item() - 52.53_02 ) < 1E-2
assert abs(result_mean.item() - 0.06_84 ) < 1E-3
def __snake_case ( self : Dict ) -> Tuple:
# We specify different beta, so that the first alpha is 0.99
__snake_case : str = self.full_loop(set_alpha_to_one=lowerCamelCase , beta_start=0.01 )
__snake_case : Dict = torch.sum(torch.abs(lowerCamelCase ) )
__snake_case : Dict = torch.mean(torch.abs(lowerCamelCase ) )
assert abs(result_sum.item() - 1_49.82_95 ) < 1E-2
assert abs(result_mean.item() - 0.19_51 ) < 1E-3
def __snake_case ( self : Optional[Any] ) -> List[Any]:
# We specify different beta, so that the first alpha is 0.99
__snake_case : Union[str, Any] = self.full_loop(set_alpha_to_one=lowerCamelCase , beta_start=0.01 )
__snake_case : Optional[Any] = torch.sum(torch.abs(lowerCamelCase ) )
__snake_case : Dict = torch.mean(torch.abs(lowerCamelCase ) )
assert abs(result_sum.item() - 1_49.07_84 ) < 1E-2
assert abs(result_mean.item() - 0.19_41 ) < 1E-3
| 81 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json',
'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json',
'junnyu/roformer_chinese_char_small': (
'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'
),
'junnyu/roformer_chinese_char_base': (
'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'
),
'junnyu/roformer_small_discriminator': (
'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'
),
'junnyu/roformer_small_generator': (
'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'
),
# See all RoFormer models at https://huggingface.co/models?filter=roformer
}
class _UpperCamelCase ( __A ):
'''simple docstring'''
lowerCamelCase__ ='roformer'
def __init__( self : Dict , a : Any=5_0000 , a : List[Any]=None , a : str=768 , a : str=12 , a : Tuple=12 , a : Optional[Any]=3072 , a : List[str]="gelu" , a : List[Any]=0.1 , a : Union[str, Any]=0.1 , a : Tuple=1536 , a : List[str]=2 , a : Tuple=0.02 , a : Any=1e-12 , a : Optional[int]=0 , a : Union[str, Any]=False , a : int=True , **a : str , ) -> int:
"""simple docstring"""
super().__init__(pad_token_id=a , **a )
SCREAMING_SNAKE_CASE : str = vocab_size
SCREAMING_SNAKE_CASE : int = hidden_size if embedding_size is None else embedding_size
SCREAMING_SNAKE_CASE : List[str] = hidden_size
SCREAMING_SNAKE_CASE : Union[str, Any] = num_hidden_layers
SCREAMING_SNAKE_CASE : int = num_attention_heads
SCREAMING_SNAKE_CASE : Tuple = hidden_act
SCREAMING_SNAKE_CASE : int = intermediate_size
SCREAMING_SNAKE_CASE : Tuple = hidden_dropout_prob
SCREAMING_SNAKE_CASE : int = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : Optional[int] = max_position_embeddings
SCREAMING_SNAKE_CASE : Any = type_vocab_size
SCREAMING_SNAKE_CASE : Union[str, Any] = initializer_range
SCREAMING_SNAKE_CASE : List[str] = layer_norm_eps
SCREAMING_SNAKE_CASE : List[str] = rotary_value
SCREAMING_SNAKE_CASE : int = use_cache
class _UpperCamelCase ( __A ):
'''simple docstring'''
@property
def __UpperCamelCase ( self : Tuple ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
if self.task == "multiple-choice":
SCREAMING_SNAKE_CASE : Optional[Any] = {0: "batch", 1: "choice", 2: "sequence"}
else:
SCREAMING_SNAKE_CASE : str = {0: "batch", 1: "sequence"}
SCREAMING_SNAKE_CASE : List[Any] = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
("token_type_ids", dynamic_axis),
] ) | 25 | 0 |
"""simple docstring"""
def a__ ( lowerCAmelCase__ ):
if number > 0:
raise ValueError("input must be a negative integer" )
UpperCAmelCase_ = len(bin(lowerCAmelCase__ )[3:] )
UpperCAmelCase_ = bin(abs(lowerCAmelCase__ ) - (1 << binary_number_length) )[3:]
UpperCAmelCase_ = (
(
"1"
+ "0" * (binary_number_length - len(lowerCAmelCase__ ))
+ twos_complement_number
)
if number < 0
else "0"
)
return "0b" + twos_complement_number
if __name__ == "__main__":
import doctest
doctest.testmod()
| 82 |
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
a_ = logging.getLogger(__name__)
a_ = 'Hello world! cécé herlolip'
a_ = namedtuple(
'BertAbsConfig',
[
'temp_dir',
'large',
'use_bert_emb',
'finetune_bert',
'encoder',
'share_emb',
'max_pos',
'enc_layers',
'enc_hidden_size',
'enc_heads',
'enc_ff_size',
'enc_dropout',
'dec_layers',
'dec_hidden_size',
'dec_heads',
'dec_ff_size',
'dec_dropout',
],
)
def lowerCamelCase__ ( _a , _a):
SCREAMING_SNAKE_CASE : List[Any] = BertAbsConfig(
temp_dir="." , finetune_bert=_a , large=_a , share_emb=_a , use_bert_emb=_a , encoder="bert" , max_pos=512 , enc_layers=6 , enc_hidden_size=512 , enc_heads=8 , enc_ff_size=512 , enc_dropout=0.2 , dec_layers=6 , dec_hidden_size=768 , dec_heads=8 , dec_ff_size=2048 , dec_dropout=0.2 , )
SCREAMING_SNAKE_CASE : Dict = torch.load(_a , lambda _a , _a: storage)
SCREAMING_SNAKE_CASE : str = AbsSummarizer(_a , torch.device("cpu") , _a)
original.eval()
SCREAMING_SNAKE_CASE : List[str] = BertAbsSummarizer(_a , torch.device("cpu"))
new_model.eval()
# -------------------
# Convert the weights
# -------------------
logging.info("convert the model")
new_model.bert.load_state_dict(original.bert.state_dict())
new_model.decoder.load_state_dict(original.decoder.state_dict())
new_model.generator.load_state_dict(original.generator.state_dict())
# ----------------------------------
# Make sure the outpus are identical
# ----------------------------------
logging.info("Make sure that the models' outputs are identical")
SCREAMING_SNAKE_CASE : List[str] = BertTokenizer.from_pretrained("bert-base-uncased")
# prepare the model inputs
SCREAMING_SNAKE_CASE : List[str] = tokenizer.encode("This is sample éàalj'-.")
encoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(_a)))
SCREAMING_SNAKE_CASE : int = torch.tensor(_a).unsqueeze(0)
SCREAMING_SNAKE_CASE : List[Any] = tokenizer.encode("This is sample 3 éàalj'-.")
decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(_a)))
SCREAMING_SNAKE_CASE : int = torch.tensor(_a).unsqueeze(0)
# failsafe to make sure the weights reset does not affect the
# loaded weights.
assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight)) == 0
# forward pass
SCREAMING_SNAKE_CASE : List[Any] = encoder_input_ids
SCREAMING_SNAKE_CASE : List[Any] = decoder_input_ids
SCREAMING_SNAKE_CASE : Dict = None
SCREAMING_SNAKE_CASE : Optional[Any] = None
SCREAMING_SNAKE_CASE : List[Any] = None
SCREAMING_SNAKE_CASE : Optional[int] = None
SCREAMING_SNAKE_CASE : Dict = None
# The original model does not apply the geneator layer immediatly but rather in
# the beam search (where it combines softmax + linear layer). Since we already
# apply the softmax in our generation process we only apply the linear layer here.
# We make sure that the outputs of the full stack are identical
SCREAMING_SNAKE_CASE : Optional[int] = original(_a , _a , _a , _a , _a , _a , _a)[0]
SCREAMING_SNAKE_CASE : Dict = original.generator(_a)
SCREAMING_SNAKE_CASE : Any = new_model(
_a , _a , _a , _a , _a)[0]
SCREAMING_SNAKE_CASE : Tuple = new_model.generator(_a)
SCREAMING_SNAKE_CASE : List[Any] = torch.max(torch.abs(output_converted_model - output_original_model)).item()
print("Maximum absolute difference beween weights: {:.2f}".format(_a))
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.max(torch.abs(output_converted_generator - output_original_generator)).item()
print("Maximum absolute difference beween weights: {:.2f}".format(_a))
SCREAMING_SNAKE_CASE : int = torch.allclose(_a , _a , atol=1E-3)
if are_identical:
logging.info("all weights are equal up to 1e-3")
else:
raise ValueError("the weights are different. The new model is likely different from the original one.")
# The model has been saved with torch.save(model) and this is bound to the exact
# directory structure. We save the state_dict instead.
logging.info("saving the model's state dictionary")
torch.save(
new_model.state_dict() , "./bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin")
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
parser.add_argument(
'--bertabs_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_ = parser.parse_args()
convert_bertabs_checkpoints(
args.bertabs_checkpoint_path,
args.pytorch_dump_folder_path,
) | 25 | 0 |
"""simple docstring"""
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
lowerCAmelCase__ = datasets.logging.get_logger(__name__)
lowerCAmelCase__ = '''\
@InProceedings{moosavi2019minimum,
author = { Nafise Sadat Moosavi, Leo Born, Massimo Poesio and Michael Strube},
title = {Using Automatically Extracted Minimum Spans to Disentangle Coreference Evaluation from Boundary Detection},
year = {2019},
booktitle = {Proceedings of the 57th Annual Meeting of
the Association for Computational Linguistics (Volume 1: Long Papers)},
publisher = {Association for Computational Linguistics},
address = {Florence, Italy},
}
@inproceedings{10.3115/1072399.1072405,
author = {Vilain, Marc and Burger, John and Aberdeen, John and Connolly, Dennis and Hirschman, Lynette},
title = {A Model-Theoretic Coreference Scoring Scheme},
year = {1995},
isbn = {1558604022},
publisher = {Association for Computational Linguistics},
address = {USA},
url = {https://doi.org/10.3115/1072399.1072405},
doi = {10.3115/1072399.1072405},
booktitle = {Proceedings of the 6th Conference on Message Understanding},
pages = {45–52},
numpages = {8},
location = {Columbia, Maryland},
series = {MUC6 ’95}
}
@INPROCEEDINGS{Bagga98algorithmsfor,
author = {Amit Bagga and Breck Baldwin},
title = {Algorithms for Scoring Coreference Chains},
booktitle = {In The First International Conference on Language Resources and Evaluation Workshop on Linguistics Coreference},
year = {1998},
pages = {563--566}
}
@INPROCEEDINGS{Luo05oncoreference,
author = {Xiaoqiang Luo},
title = {On coreference resolution performance metrics},
booktitle = {In Proc. of HLT/EMNLP},
year = {2005},
pages = {25--32},
publisher = {URL}
}
@inproceedings{moosavi-strube-2016-coreference,
title = "Which Coreference Evaluation Metric Do You Trust? A Proposal for a Link-based Entity Aware Metric",
author = "Moosavi, Nafise Sadat and
Strube, Michael",
booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2016",
address = "Berlin, Germany",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/P16-1060",
doi = "10.18653/v1/P16-1060",
pages = "632--642",
}
'''
lowerCAmelCase__ = '''\
CoVal is a coreference evaluation tool for the CoNLL and ARRAU datasets which
implements of the common evaluation metrics including MUC [Vilain et al, 1995],
B-cubed [Bagga and Baldwin, 1998], CEAFe [Luo et al., 2005],
LEA [Moosavi and Strube, 2016] and the averaged CoNLL score
(the average of the F1 values of MUC, B-cubed and CEAFe)
[Denis and Baldridge, 2009a; Pradhan et al., 2011].
This wrapper of CoVal currently only work with CoNLL line format:
The CoNLL format has one word per line with all the annotation for this word in column separated by spaces:
Column Type Description
1 Document ID This is a variation on the document filename
2 Part number Some files are divided into multiple parts numbered as 000, 001, 002, ... etc.
3 Word number
4 Word itself This is the token as segmented/tokenized in the Treebank. Initially the *_skel file contain the placeholder [WORD] which gets replaced by the actual token from the Treebank which is part of the OntoNotes release.
5 Part-of-Speech
6 Parse bit This is the bracketed structure broken before the first open parenthesis in the parse, and the word/part-of-speech leaf replaced with a *. The full parse can be created by substituting the asterix with the "([pos] [word])" string (or leaf) and concatenating the items in the rows of that column.
7 Predicate lemma The predicate lemma is mentioned for the rows for which we have semantic role information. All other rows are marked with a "-"
8 Predicate Frameset ID This is the PropBank frameset ID of the predicate in Column 7.
9 Word sense This is the word sense of the word in Column 3.
10 Speaker/Author This is the speaker or author name where available. Mostly in Broadcast Conversation and Web Log data.
11 Named Entities These columns identifies the spans representing various named entities.
12:N Predicate Arguments There is one column each of predicate argument structure information for the predicate mentioned in Column 7.
N Coreference Coreference chain information encoded in a parenthesis structure.
More informations on the format can be found here (section "*_conll File Format"): http://www.conll.cemantix.org/2012/data.html
Details on the evaluation on CoNLL can be found here: https://github.com/ns-moosavi/coval/blob/master/conll/README.md
CoVal code was written by @ns-moosavi.
Some parts are borrowed from https://github.com/clarkkev/deep-coref/blob/master/evaluation.py
The test suite is taken from https://github.com/conll/reference-coreference-scorers/
Mention evaluation and the test suite are added by @andreasvc.
Parsing CoNLL files is developed by Leo Born.
'''
lowerCAmelCase__ = '''
Calculates coreference evaluation metrics.
Args:
predictions: list of sentences. Each sentence is a list of word predictions to score in the CoNLL format.
Each prediction is a word with its annotations as a string made of columns joined with spaces.
Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)
See the details on the format in the description of the metric.
references: list of sentences. Each sentence is a list of word reference to score in the CoNLL format.
Each reference is a word with its annotations as a string made of columns joined with spaces.
Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)
See the details on the format in the description of the metric.
keep_singletons: After extracting all mentions of key or system files,
mentions whose corresponding coreference chain is of size one,
are considered as singletons. The default evaluation mode will include
singletons in evaluations if they are included in the key or the system files.
By setting \'keep_singletons=False\', all singletons in the key and system files
will be excluded from the evaluation.
NP_only: Most of the recent coreference resolvers only resolve NP mentions and
leave out the resolution of VPs. By setting the \'NP_only\' option, the scorer will only evaluate the resolution of NPs.
min_span: By setting \'min_span\', the scorer reports the results based on automatically detected minimum spans.
Minimum spans are determined using the MINA algorithm.
Returns:
\'mentions\': mentions
\'muc\': MUC metric [Vilain et al, 1995]
\'bcub\': B-cubed [Bagga and Baldwin, 1998]
\'ceafe\': CEAFe [Luo et al., 2005]
\'lea\': LEA [Moosavi and Strube, 2016]
\'conll_score\': averaged CoNLL score (the average of the F1 values of MUC, B-cubed and CEAFe)
Examples:
>>> coval = datasets.load_metric(\'coval\')
>>> words = [\'bc/cctv/00/cctv_0005 0 0 Thank VBP (TOP(S(VP* thank 01 1 Xu_li * (V*) * -\',
... \'bc/cctv/00/cctv_0005 0 1 you PRP (NP*) - - - Xu_li * (ARG1*) (ARG0*) (116)\',
... \'bc/cctv/00/cctv_0005 0 2 everyone NN (NP*) - - - Xu_li * (ARGM-DIS*) * (116)\',
... \'bc/cctv/00/cctv_0005 0 3 for IN (PP* - - - Xu_li * (ARG2* * -\',
... \'bc/cctv/00/cctv_0005 0 4 watching VBG (S(VP*)))) watch 01 1 Xu_li * *) (V*) -\',
... \'bc/cctv/00/cctv_0005 0 5 . . *)) - - - Xu_li * * * -\']
>>> references = [words]
>>> predictions = [words]
>>> results = coval.compute(predictions=predictions, references=references)
>>> print(results) # doctest:+ELLIPSIS
{\'mentions/recall\': 1.0,[...] \'conll_score\': 100.0}
'''
def snake_case_ ( A_ : Any, A_ : str, A_ : Optional[int]=False, A_ : str=False, A_ : Dict=True, A_ : Any=False, A_ : int="dummy_doc" ):
'''simple docstring'''
_lowerCamelCase : Optional[int] = {doc: key_lines}
_lowerCamelCase : Any = {doc: sys_lines}
_lowerCamelCase : str = {}
_lowerCamelCase : Any = 0
_lowerCamelCase : List[str] = 0
_lowerCamelCase : str = 0
_lowerCamelCase : Optional[int] = 0
_lowerCamelCase : Union[str, Any] = 0
_lowerCamelCase : Optional[Any] = 0
_lowerCamelCase , _lowerCamelCase : Dict = reader.get_doc_mentions(A_, key_doc_lines[doc], A_ )
key_singletons_num += singletons_num
if NP_only or min_span:
_lowerCamelCase : Optional[int] = reader.set_annotated_parse_trees(A_, key_doc_lines[doc], A_, A_ )
_lowerCamelCase , _lowerCamelCase : Dict = reader.get_doc_mentions(A_, sys_doc_lines[doc], A_ )
sys_singletons_num += singletons_num
if NP_only or min_span:
_lowerCamelCase : Union[str, Any] = reader.set_annotated_parse_trees(A_, key_doc_lines[doc], A_, A_ )
if remove_nested:
_lowerCamelCase , _lowerCamelCase : Any = reader.remove_nested_coref_mentions(A_, A_ )
key_nested_coref_num += nested_mentions
key_removed_nested_clusters += removed_clusters
_lowerCamelCase , _lowerCamelCase : Tuple = reader.remove_nested_coref_mentions(A_, A_ )
sys_nested_coref_num += nested_mentions
sys_removed_nested_clusters += removed_clusters
_lowerCamelCase : Optional[int] = reader.get_mention_assignments(A_, A_ )
_lowerCamelCase : List[Any] = reader.get_mention_assignments(A_, A_ )
_lowerCamelCase : Tuple = (key_clusters, sys_clusters, key_mention_sys_cluster, sys_mention_key_cluster)
if remove_nested:
logger.info(
'''Number of removed nested coreferring mentions in the key '''
F'''annotation: {key_nested_coref_num}; and system annotation: {sys_nested_coref_num}''' )
logger.info(
'''Number of resulting singleton clusters in the key '''
F'''annotation: {key_removed_nested_clusters}; and system annotation: {sys_removed_nested_clusters}''' )
if not keep_singletons:
logger.info(
F'''{key_singletons_num:d} and {sys_singletons_num:d} singletons are removed from the key and system '''
'''files, respectively''' )
return doc_coref_infos
def snake_case_ ( A_ : Any, A_ : Any, A_ : Any, A_ : List[str], A_ : int, A_ : Dict, A_ : Union[str, Any] ):
'''simple docstring'''
_lowerCamelCase : str = get_coref_infos(A_, A_, A_, A_, A_, A_ )
_lowerCamelCase : List[str] = {}
_lowerCamelCase : Any = 0
_lowerCamelCase : str = 0
for name, metric in metrics:
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase : Dict = evaluator.evaluate_documents(A_, A_, beta=1 )
if name in ["muc", "bcub", "ceafe"]:
conll += fa
conll_subparts_num += 1
output_scores.update({F'''{name}/recall''': recall, F'''{name}/precision''': precision, F'''{name}/f1''': fa} )
logger.info(
name.ljust(10 ), F'''Recall: {recall * 1_00:.2f}''', F''' Precision: {precision * 1_00:.2f}''', F''' F1: {fa * 1_00:.2f}''', )
if conll_subparts_num == 3:
_lowerCamelCase : Any = (conll / 3) * 1_00
logger.info(F'''CoNLL score: {conll:.2f}''' )
output_scores.update({'''conll_score''': conll} )
return output_scores
def snake_case_ ( A_ : Optional[int] ):
'''simple docstring'''
_lowerCamelCase : int = False
for line in key_lines:
if not line.startswith('''#''' ):
if len(line.split() ) > 6:
_lowerCamelCase : int = line.split()[5]
if not parse_col == "-":
_lowerCamelCase : Optional[int] = True
break
else:
break
return has_gold_parse
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION)
class __snake_case ( datasets.Metric):
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Sequence(datasets.Value('''string''' ) ),
'''references''': datasets.Sequence(datasets.Value('''string''' ) ),
} ) , codebase_urls=['''https://github.com/ns-moosavi/coval'''] , reference_urls=[
'''https://github.com/ns-moosavi/coval''',
'''https://www.aclweb.org/anthology/P16-1060''',
'''http://www.conll.cemantix.org/2012/data.html''',
] , )
def SCREAMING_SNAKE_CASE ( self : Any , __lowerCAmelCase : Any , __lowerCAmelCase : str , __lowerCAmelCase : List[str]=True , __lowerCAmelCase : List[str]=False , __lowerCAmelCase : Optional[Any]=False , __lowerCAmelCase : int=False ):
"""simple docstring"""
_lowerCamelCase : str = [
('''mentions''', evaluator.mentions),
('''muc''', evaluator.muc),
('''bcub''', evaluator.b_cubed),
('''ceafe''', evaluator.ceafe),
('''lea''', evaluator.lea),
]
if min_span:
_lowerCamelCase : Tuple = util.check_gold_parse_annotation(__lowerCAmelCase )
if not has_gold_parse:
raise NotImplementedError('''References should have gold parse annotation to use \'min_span\'.''' )
# util.parse_key_file(key_file)
# key_file = key_file + ".parsed"
_lowerCamelCase : Dict = evaluate(
key_lines=__lowerCAmelCase , sys_lines=__lowerCAmelCase , metrics=__lowerCAmelCase , NP_only=__lowerCAmelCase , remove_nested=__lowerCAmelCase , keep_singletons=__lowerCAmelCase , min_span=__lowerCAmelCase , )
return score
| 83 |
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
parser.add_argument(
'--txt2img_unclip',
default='kakaobrain/karlo-v1-alpha',
type=str,
required=False,
help='The pretrained txt2img unclip.',
)
a_ = parser.parse_args()
a_ = UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)
a_ = CLIPImageProcessor()
a_ = CLIPVisionModelWithProjection.from_pretrained('openai/clip-vit-large-patch14')
a_ = UnCLIPImageVariationPipeline(
decoder=txtaimg.decoder,
text_encoder=txtaimg.text_encoder,
tokenizer=txtaimg.tokenizer,
text_proj=txtaimg.text_proj,
feature_extractor=feature_extractor,
image_encoder=image_encoder,
super_res_first=txtaimg.super_res_first,
super_res_last=txtaimg.super_res_last,
decoder_scheduler=txtaimg.decoder_scheduler,
super_res_scheduler=txtaimg.super_res_scheduler,
)
imgaimg.save_pretrained(args.dump_path) | 25 | 0 |
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class A_ ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = inspect.getfile(accelerate.test_utils )
lowercase = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ['scripts', 'test_script.py'] )
lowercase = os.path.sep.join(
mod_file.split(os.path.sep )[:-1] + ['scripts', 'test_distributed_data_loop.py'] )
lowercase = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ['scripts', 'test_ops.py'] )
@require_multi_gpu
def SCREAMING_SNAKE_CASE__ ( self ):
print(F'''Found {torch.cuda.device_count()} devices.''' )
lowercase = ['torchrun', F'''--nproc_per_node={torch.cuda.device_count()}''', self.test_file_path]
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(snake_case , env=os.environ.copy() )
@require_multi_gpu
def SCREAMING_SNAKE_CASE__ ( self ):
print(F'''Found {torch.cuda.device_count()} devices.''' )
lowercase = ['torchrun', F'''--nproc_per_node={torch.cuda.device_count()}''', self.operation_file_path]
print(F'''Command: {cmd}''' )
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(snake_case , env=os.environ.copy() )
@require_multi_gpu
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = ['torchrun', F'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__ )]
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(snake_case , env=os.environ.copy() )
@require_multi_gpu
def SCREAMING_SNAKE_CASE__ ( self ):
print(F'''Found {torch.cuda.device_count()} devices, using 2 devices only''' )
lowercase = ['torchrun', F'''--nproc_per_node={torch.cuda.device_count()}''', self.data_loop_file_path]
with patch_environment(omp_num_threads=1 , cuda_visible_devices='0,1' ):
execute_subprocess_async(snake_case , env=os.environ.copy() )
if __name__ == "__main__":
UpperCAmelCase = Accelerator()
UpperCAmelCase = (accelerator.state.process_index + 2, 10)
UpperCAmelCase = torch.randint(0, 10, shape).to(accelerator.device)
UpperCAmelCase = ''''''
UpperCAmelCase = accelerator.pad_across_processes(tensor)
if tensora.shape[0] != accelerator.state.num_processes + 1:
error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
error_msg += "Tensors have different values."
if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
error_msg += "Padding was not done with the right value (0)."
UpperCAmelCase = accelerator.pad_across_processes(tensor, pad_first=True)
if tensora.shape[0] != accelerator.state.num_processes + 1:
error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
UpperCAmelCase = accelerator.state.num_processes - accelerator.state.process_index - 1
if not torch.equal(tensora[index:], tensor):
error_msg += "Tensors have different values."
if not torch.all(tensora[:index] == 0):
error_msg += "Padding was not done with the right value (0)."
# Raise error at the end to make sure we don't stop at the first failure.
if len(error_msg) > 0:
raise ValueError(error_msg)
| 84 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
'configuration_swinv2': ['SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Swinv2Config'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST',
'Swinv2ForImageClassification',
'Swinv2ForMaskedImageModeling',
'Swinv2Model',
'Swinv2PreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_swinva import (
SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
SwinvaForImageClassification,
SwinvaForMaskedImageModeling,
SwinvaModel,
SwinvaPreTrainedModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 25 | 0 |
from heapq import heappop, heappush
import numpy as np
def _a ( lowercase__ : np.ndarray , lowercase__ : tuple[int, int] , lowercase__ : tuple[int, int] , lowercase__ : bool , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Union[str, Any] = grid.shape
SCREAMING_SNAKE_CASE__ : Optional[Any] = [-1, 1, 0, 0]
SCREAMING_SNAKE_CASE__ : Tuple = [0, 0, -1, 1]
if allow_diagonal:
dx += [-1, -1, 1, 1]
dy += [-1, 1, -1, 1]
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[Any] = [(0, source)], set()
SCREAMING_SNAKE_CASE__ : str = np.full((rows, cols) , np.inf )
SCREAMING_SNAKE_CASE__ : List[Any] = 0
SCREAMING_SNAKE_CASE__ : Dict = np.empty((rows, cols) , dtype=lowercase__ )
SCREAMING_SNAKE_CASE__ : List[Any] = None
while queue:
((SCREAMING_SNAKE_CASE__) , (SCREAMING_SNAKE_CASE__)) : Optional[Any] = heappop(lowercase__ )
if (x, y) in visited:
continue
visited.add((x, y) )
if (x, y) == destination:
SCREAMING_SNAKE_CASE__ : Any = []
while (x, y) != source:
path.append((x, y) )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Tuple = predecessors[x, y]
path.append(lowercase__ ) # add the source manually
path.reverse()
return matrix[destination], path
for i in range(len(lowercase__ ) ):
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Union[str, Any] = x + dx[i], y + dy[i]
if 0 <= nx < rows and 0 <= ny < cols:
SCREAMING_SNAKE_CASE__ : Optional[Any] = grid[nx][ny]
if next_node == 1 and matrix[nx, ny] > dist + 1:
heappush(lowercase__ , (dist + 1, (nx, ny)) )
SCREAMING_SNAKE_CASE__ : List[str] = dist + 1
SCREAMING_SNAKE_CASE__ : Any = (x, y)
return np.inf, []
if __name__ == "__main__":
import doctest
doctest.testmod()
| 85 |
from math import pi, sqrt, tan
def lowerCamelCase__ ( _a):
if side_length < 0:
raise ValueError("surface_area_cube() only accepts non-negative values")
return 6 * side_length**2
def lowerCamelCase__ ( _a , _a , _a):
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 lowerCamelCase__ ( _a):
if radius < 0:
raise ValueError("surface_area_sphere() only accepts non-negative values")
return 4 * pi * radius**2
def lowerCamelCase__ ( _a):
if radius < 0:
raise ValueError("surface_area_hemisphere() only accepts non-negative values")
return 3 * pi * radius**2
def lowerCamelCase__ ( _a , _a):
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 lowerCamelCase__ ( _a , _a , _a):
if radius_a < 0 or radius_a < 0 or height < 0:
raise ValueError(
"surface_area_conical_frustum() only accepts non-negative values")
SCREAMING_SNAKE_CASE : Any = (height**2 + (radius_a - radius_a) ** 2) ** 0.5
return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)
def lowerCamelCase__ ( _a , _a):
if radius < 0 or height < 0:
raise ValueError("surface_area_cylinder() only accepts non-negative values")
return 2 * pi * radius * (height + radius)
def lowerCamelCase__ ( _a , _a):
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(_a , 2) * torus_radius * tube_radius
def lowerCamelCase__ ( _a , _a):
if length < 0 or width < 0:
raise ValueError("area_rectangle() only accepts non-negative values")
return length * width
def lowerCamelCase__ ( _a):
if side_length < 0:
raise ValueError("area_square() only accepts non-negative values")
return side_length**2
def lowerCamelCase__ ( _a , _a):
if base < 0 or height < 0:
raise ValueError("area_triangle() only accepts non-negative values")
return (base * height) / 2
def lowerCamelCase__ ( _a , _a , _a):
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")
SCREAMING_SNAKE_CASE : List[str] = (sidea + sidea + sidea) / 2
SCREAMING_SNAKE_CASE : Optional[int] = sqrt(
semi_perimeter
* (semi_perimeter - sidea)
* (semi_perimeter - sidea)
* (semi_perimeter - sidea))
return area
def lowerCamelCase__ ( _a , _a):
if base < 0 or height < 0:
raise ValueError("area_parallelogram() only accepts non-negative values")
return base * height
def lowerCamelCase__ ( _a , _a , _a):
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 lowerCamelCase__ ( _a):
if radius < 0:
raise ValueError("area_circle() only accepts non-negative values")
return pi * radius**2
def lowerCamelCase__ ( _a , _a):
if radius_x < 0 or radius_y < 0:
raise ValueError("area_ellipse() only accepts non-negative values")
return pi * radius_x * radius_y
def lowerCamelCase__ ( _a , _a):
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 lowerCamelCase__ ( _a , _a):
if not isinstance(_a , _a) 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(10, 20) = }''')
print(F'''Square: {area_square(10) = }''')
print(F'''Triangle: {area_triangle(10, 10) = }''')
print(F'''Triangle: {area_triangle_three_sides(5, 12, 13) = }''')
print(F'''Parallelogram: {area_parallelogram(10, 20) = }''')
print(F'''Rhombus: {area_rhombus(10, 20) = }''')
print(F'''Trapezium: {area_trapezium(10, 20, 30) = }''')
print(F'''Circle: {area_circle(20) = }''')
print(F'''Ellipse: {area_ellipse(10, 20) = }''')
print('\nSurface Areas of various geometric shapes: \n')
print(F'''Cube: {surface_area_cube(20) = }''')
print(F'''Cuboid: {surface_area_cuboid(10, 20, 30) = }''')
print(F'''Sphere: {surface_area_sphere(20) = }''')
print(F'''Hemisphere: {surface_area_hemisphere(20) = }''')
print(F'''Cone: {surface_area_cone(10, 20) = }''')
print(F'''Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }''')
print(F'''Cylinder: {surface_area_cylinder(10, 20) = }''')
print(F'''Torus: {surface_area_torus(20, 10) = }''')
print(F'''Equilateral Triangle: {area_reg_polygon(3, 10) = }''')
print(F'''Square: {area_reg_polygon(4, 10) = }''')
print(F'''Reqular Pentagon: {area_reg_polygon(5, 10) = }''') | 25 | 0 |
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class _a ( snake_case_ , unittest.TestCase ):
"""simple docstring"""
_lowerCamelCase : int = DDIMPipeline
_lowerCamelCase : Union[str, Any] = UNCONDITIONAL_IMAGE_GENERATION_PARAMS
_lowerCamelCase : Any = PipelineTesterMixin.required_optional_params - {
'num_images_per_prompt',
'latents',
'callback',
'callback_steps',
}
_lowerCamelCase : List[Any] = UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
_lowerCamelCase : Optional[int] = False
def __A ( self : Any ):
torch.manual_seed(0 )
A_ = 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") , )
A_ = DDIMScheduler()
A_ = {"unet": unet, "scheduler": scheduler}
return components
def __A ( self : Optional[Any] , UpperCAmelCase : Optional[int] , UpperCAmelCase : Optional[int]=0 ):
if str(UpperCAmelCase ).startswith("mps" ):
A_ = torch.manual_seed(UpperCAmelCase )
else:
A_ = torch.Generator(device=UpperCAmelCase ).manual_seed(UpperCAmelCase )
A_ = {
"batch_size": 1,
"generator": generator,
"num_inference_steps": 2,
"output_type": "numpy",
}
return inputs
def __A ( self : str ):
A_ = "cpu"
A_ = self.get_dummy_components()
A_ = self.pipeline_class(**UpperCAmelCase )
pipe.to(UpperCAmelCase )
pipe.set_progress_bar_config(disable=UpperCAmelCase )
A_ = self.get_dummy_inputs(UpperCAmelCase )
A_ = pipe(**UpperCAmelCase ).images
A_ = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 32, 32, 3) )
A_ = np.array(
[1.000E00, 5.717E-01, 4.717E-01, 1.000E00, 0.000E00, 1.000E00, 3.000E-04, 0.000E00, 9.000E-04] )
A_ = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(UpperCAmelCase , 1E-3 )
def __A ( self : Any ):
super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3 )
def __A ( self : Optional[int] ):
super().test_save_load_local(expected_max_difference=3E-3 )
def __A ( self : Optional[int] ):
super().test_save_load_optional_components(expected_max_difference=3E-3 )
def __A ( self : Any ):
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
@slow
@require_torch_gpu
class _a ( unittest.TestCase ):
"""simple docstring"""
def __A ( self : Any ):
A_ = "google/ddpm-cifar10-32"
A_ = UNetaDModel.from_pretrained(UpperCAmelCase )
A_ = DDIMScheduler()
A_ = DDIMPipeline(unet=UpperCAmelCase , scheduler=UpperCAmelCase )
ddim.to(UpperCAmelCase )
ddim.set_progress_bar_config(disable=UpperCAmelCase )
A_ = torch.manual_seed(0 )
A_ = ddim(generator=UpperCAmelCase , eta=0.0 , output_type="numpy" ).images
A_ = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
A_ = np.array([0.1_723, 0.1_617, 0.1_600, 0.1_626, 0.1_497, 0.1_513, 0.1_505, 0.1_442, 0.1_453] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def __A ( self : Optional[Any] ):
A_ = "google/ddpm-ema-bedroom-256"
A_ = UNetaDModel.from_pretrained(UpperCAmelCase )
A_ = DDIMScheduler.from_pretrained(UpperCAmelCase )
A_ = DDIMPipeline(unet=UpperCAmelCase , scheduler=UpperCAmelCase )
ddpm.to(UpperCAmelCase )
ddpm.set_progress_bar_config(disable=UpperCAmelCase )
A_ = torch.manual_seed(0 )
A_ = ddpm(generator=UpperCAmelCase , output_type="numpy" ).images
A_ = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
A_ = np.array([0.0_060, 0.0_201, 0.0_344, 0.0_024, 0.0_018, 0.0_002, 0.0_022, 0.0_000, 0.0_069] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2 | 86 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
'configuration_instructblip': [
'INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'InstructBlipConfig',
'InstructBlipQFormerConfig',
'InstructBlipVisionConfig',
],
'processing_instructblip': ['InstructBlipProcessor'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
'InstructBlipQFormerModel',
'InstructBlipPreTrainedModel',
'InstructBlipForConditionalGeneration',
'InstructBlipVisionModel',
]
if TYPE_CHECKING:
from .configuration_instructblip import (
INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
InstructBlipConfig,
InstructBlipQFormerConfig,
InstructBlipVisionConfig,
)
from .processing_instructblip import InstructBlipProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_instructblip import (
INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
InstructBlipForConditionalGeneration,
InstructBlipPreTrainedModel,
InstructBlipQFormerModel,
InstructBlipVisionModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 25 | 0 |
import enum
import warnings
from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
class UpperCamelCase_ ( enum.Enum ):
'''simple docstring'''
UpperCAmelCase__ = 0
UpperCAmelCase__ = 1
UpperCAmelCase__ = 2
@add_end_docstrings(UpperCAmelCase__ )
class UpperCamelCase_ ( UpperCAmelCase__ ):
'''simple docstring'''
UpperCAmelCase__ = '''
In 1991, the remains of Russian Tsar Nicholas II and his family (except for Alexei and Maria) are discovered. The
voice of Nicholas\'s young son, Tsarevich Alexei Nikolaevich, narrates the remainder of the story. 1883 Western
Siberia, a young Grigori Rasputin is asked by his father and a group of men to perform magic. Rasputin has a vision
and denounces one of the men as a horse thief. Although his father initially slaps him for making such an
accusation, Rasputin watches as the man is chased outside and beaten. Twenty years later, Rasputin sees a vision of
the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous, with people, even a bishop,
begging for his blessing. <eod> </s> <eos>
'''
def __init__( self : int , *UpperCAmelCase__ : Any , **UpperCAmelCase__ : Dict) ->Dict:
'''simple docstring'''
super().__init__(*UpperCAmelCase__ , **UpperCAmelCase__)
self.check_model_type(
TF_MODEL_FOR_CAUSAL_LM_MAPPING if self.framework == '''tf''' else MODEL_FOR_CAUSAL_LM_MAPPING)
if "prefix" not in self._preprocess_params:
# This is very specific. The logic is quite complex and needs to be done
# as a "default".
# It also defines both some preprocess_kwargs and generate_kwargs
# which is why we cannot put them in their respective methods.
A__ = None
if self.model.config.prefix is not None:
A__ = self.model.config.prefix
if prefix is None and self.model.__class__.__name__ in [
"XLNetLMHeadModel",
"TransfoXLLMHeadModel",
"TFXLNetLMHeadModel",
"TFTransfoXLLMHeadModel",
]:
# For XLNet and TransformerXL we add an article to the prompt to give more state to the model.
A__ = self.XL_PREFIX
if prefix is not None:
# Recalculate some generate_kwargs linked to prefix.
A__ , A__ , A__ = self._sanitize_parameters(prefix=UpperCAmelCase__ , **self._forward_params)
A__ = {**self._preprocess_params, **preprocess_params}
A__ = {**self._forward_params, **forward_params}
def SCREAMING_SNAKE_CASE ( self : Tuple , UpperCAmelCase__ : Optional[int]=None , UpperCAmelCase__ : Dict=None , UpperCAmelCase__ : Tuple=None , UpperCAmelCase__ : List[Any]=None , UpperCAmelCase__ : Optional[int]=None , UpperCAmelCase__ : str=None , UpperCAmelCase__ : List[Any]=None , UpperCAmelCase__ : Dict=None , **UpperCAmelCase__ : Union[str, Any] , ) ->Dict:
'''simple docstring'''
A__ = {}
if prefix is not None:
A__ = prefix
if prefix:
A__ = self.tokenizer(
UpperCAmelCase__ , padding=UpperCAmelCase__ , add_special_tokens=UpperCAmelCase__ , return_tensors=self.framework)
A__ = prefix_inputs['''input_ids'''].shape[-1]
if handle_long_generation is not None:
if handle_long_generation not in {"hole"}:
raise ValueError(
f"""{handle_long_generation} is not a valid value for `handle_long_generation` parameter expected"""
''' [None, \'hole\']''')
A__ = handle_long_generation
preprocess_params.update(UpperCAmelCase__)
A__ = generate_kwargs
A__ = {}
if return_full_text is not None and return_type is None:
if return_text is not None:
raise ValueError('''`return_text` is mutually exclusive with `return_full_text`''')
if return_tensors is not None:
raise ValueError('''`return_full_text` is mutually exclusive with `return_tensors`''')
A__ = ReturnType.FULL_TEXT if return_full_text else ReturnType.NEW_TEXT
if return_tensors is not None and return_type is None:
if return_text is not None:
raise ValueError('''`return_text` is mutually exclusive with `return_tensors`''')
A__ = ReturnType.TENSORS
if return_type is not None:
A__ = return_type
if clean_up_tokenization_spaces is not None:
A__ = clean_up_tokenization_spaces
if stop_sequence is not None:
A__ = self.tokenizer.encode(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.''')
A__ = stop_sequence_ids[0]
return preprocess_params, forward_params, postprocess_params
def SCREAMING_SNAKE_CASE ( self : Optional[Any] , *UpperCAmelCase__ : Optional[Any] , **UpperCAmelCase__ : Optional[Any]) ->Optional[int]:
'''simple docstring'''
if self.model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
kwargs.update({'''add_space_before_punct_symbol''': True})
return super()._parse_and_tokenize(*UpperCAmelCase__ , **UpperCAmelCase__)
def __call__( self : Optional[int] , UpperCAmelCase__ : Any , **UpperCAmelCase__ : Dict) ->str:
'''simple docstring'''
return super().__call__(UpperCAmelCase__ , **UpperCAmelCase__)
def SCREAMING_SNAKE_CASE ( self : int , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : str="" , UpperCAmelCase__ : List[str]=None , **UpperCAmelCase__ : Any) ->Tuple:
'''simple docstring'''
A__ = self.tokenizer(
prefix + prompt_text , padding=UpperCAmelCase__ , add_special_tokens=UpperCAmelCase__ , return_tensors=self.framework)
A__ = prompt_text
if handle_long_generation == "hole":
A__ = inputs['''input_ids'''].shape[-1]
if "max_new_tokens" in generate_kwargs:
A__ = generate_kwargs['''max_new_tokens''']
else:
A__ = generate_kwargs.get('''max_length''' , self.model.config.max_length) - cur_len
if new_tokens < 0:
raise ValueError('''We cannot infer how many new tokens are expected''')
if cur_len + new_tokens > self.tokenizer.model_max_length:
A__ = self.tokenizer.model_max_length - new_tokens
if keep_length <= 0:
raise ValueError(
'''We cannot use `hole` to handle this generation the number of desired tokens exceeds the'''
''' models max length''')
A__ = inputs['''input_ids'''][:, -keep_length:]
if "attention_mask" in inputs:
A__ = inputs['''attention_mask'''][:, -keep_length:]
return inputs
def SCREAMING_SNAKE_CASE ( self : str , UpperCAmelCase__ : Dict , **UpperCAmelCase__ : Tuple) ->str:
'''simple docstring'''
A__ = model_inputs['''input_ids''']
A__ = model_inputs.get('''attention_mask''' , UpperCAmelCase__)
# Allow empty prompts
if input_ids.shape[1] == 0:
A__ = None
A__ = None
A__ = 1
else:
A__ = input_ids.shape[0]
A__ = model_inputs.pop('''prompt_text''')
# If there is a prefix, we may need to adjust the generation length. Do so without permanently modifying
# generate_kwargs, as some of the parameterization may come from the initialization of the pipeline.
A__ = generate_kwargs.pop('''prefix_length''' , 0)
if prefix_length > 0:
A__ = '''max_new_tokens''' in generate_kwargs or (
'''generation_config''' in generate_kwargs
and generate_kwargs['''generation_config'''].max_new_tokens is not None
)
if not has_max_new_tokens:
A__ = generate_kwargs.get('''max_length''') or self.model.config.max_length
generate_kwargs["max_length"] += prefix_length
A__ = '''min_new_tokens''' in generate_kwargs or (
'''generation_config''' in generate_kwargs
and generate_kwargs['''generation_config'''].min_new_tokens is not None
)
if not has_min_new_tokens and "min_length" in generate_kwargs:
generate_kwargs["min_length"] += prefix_length
# BS x SL
A__ = self.model.generate(input_ids=UpperCAmelCase__ , attention_mask=UpperCAmelCase__ , **UpperCAmelCase__)
A__ = generated_sequence.shape[0]
if self.framework == "pt":
A__ = generated_sequence.reshape(UpperCAmelCase__ , out_b // in_b , *generated_sequence.shape[1:])
elif self.framework == "tf":
A__ = tf.reshape(UpperCAmelCase__ , (in_b, out_b // in_b, *generated_sequence.shape[1:]))
return {"generated_sequence": generated_sequence, "input_ids": input_ids, "prompt_text": prompt_text}
def SCREAMING_SNAKE_CASE ( self : Dict , UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : str=ReturnType.FULL_TEXT , UpperCAmelCase__ : Union[str, Any]=True) ->str:
'''simple docstring'''
A__ = model_outputs['''generated_sequence'''][0]
A__ = model_outputs['''input_ids''']
A__ = model_outputs['''prompt_text''']
A__ = generated_sequence.numpy().tolist()
A__ = []
for sequence in generated_sequence:
if return_type == ReturnType.TENSORS:
A__ = {'''generated_token_ids''': sequence}
elif return_type in {ReturnType.NEW_TEXT, ReturnType.FULL_TEXT}:
# Decode text
A__ = self.tokenizer.decode(
UpperCAmelCase__ , skip_special_tokens=UpperCAmelCase__ , clean_up_tokenization_spaces=UpperCAmelCase__ , )
# Remove PADDING prompt of the sequence if XLNet or Transfo-XL model is used
if input_ids is None:
A__ = 0
else:
A__ = len(
self.tokenizer.decode(
input_ids[0] , skip_special_tokens=UpperCAmelCase__ , clean_up_tokenization_spaces=UpperCAmelCase__ , ))
if return_type == ReturnType.FULL_TEXT:
A__ = prompt_text + text[prompt_length:]
else:
A__ = text[prompt_length:]
A__ = {'''generated_text''': all_text}
records.append(UpperCAmelCase__)
return records
| 87 |
from __future__ import annotations
def lowerCamelCase__ ( _a):
SCREAMING_SNAKE_CASE : Optional[Any] = 2
SCREAMING_SNAKE_CASE : Optional[int] = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(_a)
if n > 1:
factors.append(_a)
return factors
if __name__ == "__main__":
import doctest
doctest.testmod() | 25 | 0 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowercase__ ( A_ ):
__UpperCAmelCase = ['''image_processor''', '''tokenizer''']
__UpperCAmelCase = '''AutoImageProcessor'''
__UpperCAmelCase = '''AutoTokenizer'''
def __init__( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE) -> str:
super().__init__(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE)
_lowerCamelCase : Optional[int] = self.image_processor
def __call__( self , SCREAMING_SNAKE_CASE=None , SCREAMING_SNAKE_CASE=None , SCREAMING_SNAKE_CASE=None , **SCREAMING_SNAKE_CASE) -> str:
if text is None and images is None:
raise ValueError("""You have to specify either text or images. Both cannot be none.""")
if text is not None:
_lowerCamelCase : str = self.tokenizer(SCREAMING_SNAKE_CASE , return_tensors=SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE)
if images is not None:
_lowerCamelCase : Dict = self.image_processor(SCREAMING_SNAKE_CASE , return_tensors=SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE)
if text is not None and images is not None:
_lowerCamelCase : str = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE) , tensor_type=SCREAMING_SNAKE_CASE)
def UpperCamelCase_ ( self , *SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE) -> List[str]:
return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE)
def UpperCamelCase_ ( self , *SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE) -> Tuple:
return self.tokenizer.decode(*SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE)
@property
def UpperCamelCase_ ( self) -> int:
return ["input_ids", "attention_mask", "pixel_values"]
| 88 |
from math import factorial, pi
def lowerCamelCase__ ( _a , _a = 30):
if not isinstance(_a , (int, float)):
raise ValueError("maclaurin_sin() requires either an int or float for theta")
if not isinstance(_a , _a) or accuracy <= 0:
raise ValueError("maclaurin_sin() requires a positive int for accuracy")
SCREAMING_SNAKE_CASE : int = float(_a)
SCREAMING_SNAKE_CASE : Dict = theta // (2 * pi)
theta -= 2 * div * pi
return sum(
(-1) ** r * theta ** (2 * r + 1) / factorial(2 * r + 1) for r in range(_a))
def lowerCamelCase__ ( _a , _a = 30):
if not isinstance(_a , (int, float)):
raise ValueError("maclaurin_cos() requires either an int or float for theta")
if not isinstance(_a , _a) or accuracy <= 0:
raise ValueError("maclaurin_cos() requires a positive int for accuracy")
SCREAMING_SNAKE_CASE : str = float(_a)
SCREAMING_SNAKE_CASE : Any = theta // (2 * pi)
theta -= 2 * div * pi
return sum((-1) ** r * theta ** (2 * r) / factorial(2 * r) for r in range(_a))
if __name__ == "__main__":
import doctest
doctest.testmod()
print(maclaurin_sin(10))
print(maclaurin_sin(-10))
print(maclaurin_sin(10, 15))
print(maclaurin_sin(-10, 15))
print(maclaurin_cos(5))
print(maclaurin_cos(-5))
print(maclaurin_cos(10, 15))
print(maclaurin_cos(-10, 15)) | 25 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE : Any = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Any = {"ctrl": "https://huggingface.co/ctrl/resolve/main/config.json"}
class _lowerCamelCase( _a ):
lowercase_ : int = """ctrl"""
lowercase_ : List[Any] = ["""past_key_values"""]
lowercase_ : Any = {
"""max_position_embeddings""": """n_positions""",
"""hidden_size""": """n_embd""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self, lowerCamelCase=24_65_34, lowerCamelCase=2_56, lowerCamelCase=12_80, lowerCamelCase=81_92, lowerCamelCase=48, lowerCamelCase=16, lowerCamelCase=0.1, lowerCamelCase=0.1, lowerCamelCase=1E-6, lowerCamelCase=0.0_2, lowerCamelCase=True, **lowerCamelCase, ) -> Dict:
"""simple docstring"""
_lowercase : Optional[int] = vocab_size
_lowercase : Optional[Any] = n_positions
_lowercase : int = n_embd
_lowercase : Union[str, Any] = n_layer
_lowercase : Tuple = n_head
_lowercase : Any = dff
_lowercase : Tuple = resid_pdrop
_lowercase : List[str] = embd_pdrop
_lowercase : Dict = layer_norm_epsilon
_lowercase : Optional[int] = initializer_range
_lowercase : Tuple = use_cache
super().__init__(**lowerCamelCase)
| 89 |
from __future__ import annotations
import math
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Dict , a : int ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = size
# approximate the overall size of segment tree with given value
SCREAMING_SNAKE_CASE : Any = [0 for i in range(0 , 4 * size )]
# create array to store lazy update
SCREAMING_SNAKE_CASE : Union[str, Any] = [0 for i in range(0 , 4 * size )]
SCREAMING_SNAKE_CASE : Any = [0 for i in range(0 , 4 * size )] # flag for lazy update
def __UpperCamelCase ( self : Tuple , a : int ) -> int:
"""simple docstring"""
return idx * 2
def __UpperCamelCase ( self : str , a : int ) -> int:
"""simple docstring"""
return idx * 2 + 1
def __UpperCamelCase ( self : int , a : int , a : int , a : int , a : list[int] ) -> None:
"""simple docstring"""
if left_element == right_element:
SCREAMING_SNAKE_CASE : int = a[left_element - 1]
else:
SCREAMING_SNAKE_CASE : Optional[int] = (left_element + right_element) // 2
self.build(self.left(a ) , a , a , a )
self.build(self.right(a ) , mid + 1 , a , a )
SCREAMING_SNAKE_CASE : List[Any] = max(
self.segment_tree[self.left(a )] , self.segment_tree[self.right(a )] )
def __UpperCamelCase ( self : Optional[Any] , a : int , a : int , a : int , a : int , a : int , a : int ) -> bool:
"""simple docstring"""
if self.flag[idx] is True:
SCREAMING_SNAKE_CASE : Any = self.lazy[idx]
SCREAMING_SNAKE_CASE : List[str] = False
if left_element != right_element:
SCREAMING_SNAKE_CASE : Optional[Any] = self.lazy[idx]
SCREAMING_SNAKE_CASE : int = self.lazy[idx]
SCREAMING_SNAKE_CASE : Any = True
SCREAMING_SNAKE_CASE : List[Any] = True
if right_element < a or left_element > b:
return True
if left_element >= a and right_element <= b:
SCREAMING_SNAKE_CASE : Optional[Any] = val
if left_element != right_element:
SCREAMING_SNAKE_CASE : str = val
SCREAMING_SNAKE_CASE : str = val
SCREAMING_SNAKE_CASE : Tuple = True
SCREAMING_SNAKE_CASE : Optional[Any] = True
return True
SCREAMING_SNAKE_CASE : int = (left_element + right_element) // 2
self.update(self.left(a ) , a , a , a , a , a )
self.update(self.right(a ) , mid + 1 , a , a , a , a )
SCREAMING_SNAKE_CASE : Optional[int] = max(
self.segment_tree[self.left(a )] , self.segment_tree[self.right(a )] )
return True
def __UpperCamelCase ( self : Dict , a : int , a : int , a : int , a : int , a : int ) -> int | float:
"""simple docstring"""
if self.flag[idx] is True:
SCREAMING_SNAKE_CASE : int = self.lazy[idx]
SCREAMING_SNAKE_CASE : List[Any] = False
if left_element != right_element:
SCREAMING_SNAKE_CASE : Optional[Any] = self.lazy[idx]
SCREAMING_SNAKE_CASE : Optional[Any] = self.lazy[idx]
SCREAMING_SNAKE_CASE : Optional[Any] = True
SCREAMING_SNAKE_CASE : Union[str, Any] = True
if right_element < a or left_element > b:
return -math.inf
if left_element >= a and right_element <= b:
return self.segment_tree[idx]
SCREAMING_SNAKE_CASE : Dict = (left_element + right_element) // 2
SCREAMING_SNAKE_CASE : Tuple = self.query(self.left(a ) , a , a , a , a )
SCREAMING_SNAKE_CASE : Tuple = self.query(self.right(a ) , mid + 1 , a , a , a )
return max(a , a )
def __str__( self : str ) -> str:
"""simple docstring"""
return str([self.query(1 , 1 , self.size , a , a ) for i in range(1 , self.size + 1 )] )
if __name__ == "__main__":
a_ = [1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
a_ = 15
a_ = SegmentTree(size)
segt.build(1, 1, size, A)
print(segt.query(1, 1, size, 4, 6))
print(segt.query(1, 1, size, 7, 11))
print(segt.query(1, 1, size, 7, 12))
segt.update(1, 1, size, 1, 3, 111)
print(segt.query(1, 1, size, 1, 15))
segt.update(1, 1, size, 7, 8, 235)
print(segt) | 25 | 0 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {'''vocab_file''': '''vocab.txt'''}
__UpperCAmelCase = {
'''vocab_file''': {
'''YituTech/conv-bert-base''': '''https://huggingface.co/YituTech/conv-bert-base/resolve/main/vocab.txt''',
'''YituTech/conv-bert-medium-small''': (
'''https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/vocab.txt'''
),
'''YituTech/conv-bert-small''': '''https://huggingface.co/YituTech/conv-bert-small/resolve/main/vocab.txt''',
}
}
__UpperCAmelCase = {
'''YituTech/conv-bert-base''': 512,
'''YituTech/conv-bert-medium-small''': 512,
'''YituTech/conv-bert-small''': 512,
}
__UpperCAmelCase = {
'''YituTech/conv-bert-base''': {'''do_lower_case''': True},
'''YituTech/conv-bert-medium-small''': {'''do_lower_case''': True},
'''YituTech/conv-bert-small''': {'''do_lower_case''': True},
}
class a__ ( a__ ):
'''simple docstring'''
lowercase__ : List[Any] = VOCAB_FILES_NAMES
lowercase__ : Optional[Any] = PRETRAINED_VOCAB_FILES_MAP
lowercase__ : Optional[int] = PRETRAINED_INIT_CONFIGURATION
lowercase__ : List[str] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase__ : Union[str, Any] = ConvBertTokenizer
def __init__( self , lowerCamelCase_=None , lowerCamelCase_=None , lowerCamelCase_=True , lowerCamelCase_="[UNK]" , lowerCamelCase_="[SEP]" , lowerCamelCase_="[PAD]" , lowerCamelCase_="[CLS]" , lowerCamelCase_="[MASK]" , lowerCamelCase_=True , lowerCamelCase_=None , **lowerCamelCase_ , ) -> Dict:
super().__init__(
lowerCamelCase_ , tokenizer_file=lowerCamelCase_ , do_lower_case=lowerCamelCase_ , unk_token=lowerCamelCase_ , sep_token=lowerCamelCase_ , pad_token=lowerCamelCase_ , cls_token=lowerCamelCase_ , mask_token=lowerCamelCase_ , tokenize_chinese_chars=lowerCamelCase_ , strip_accents=lowerCamelCase_ , **lowerCamelCase_ , )
lowerCAmelCase__ = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('''lowercase''' , lowerCamelCase_ ) != do_lower_case
or normalizer_state.get('''strip_accents''' , lowerCamelCase_ ) != strip_accents
or normalizer_state.get('''handle_chinese_chars''' , lowerCamelCase_ ) != tokenize_chinese_chars
):
lowerCAmelCase__ = getattr(lowerCamelCase_ , normalizer_state.pop('''type''' ) )
lowerCAmelCase__ = do_lower_case
lowerCAmelCase__ = strip_accents
lowerCAmelCase__ = tokenize_chinese_chars
lowerCAmelCase__ = normalizer_class(**lowerCamelCase_ )
lowerCAmelCase__ = do_lower_case
def __SCREAMING_SNAKE_CASE ( self , lowerCamelCase_ , lowerCamelCase_=None ) -> Optional[Any]:
lowerCAmelCase__ = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def __SCREAMING_SNAKE_CASE ( self , lowerCamelCase_ , lowerCamelCase_ = None ) -> List[int]:
lowerCAmelCase__ = [self.sep_token_id]
lowerCAmelCase__ = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def __SCREAMING_SNAKE_CASE ( self , lowerCamelCase_ , lowerCamelCase_ = None ) -> Tuple[str]:
lowerCAmelCase__ = self._tokenizer.model.save(lowerCamelCase_ , name=lowerCamelCase_ )
return tuple(lowerCamelCase_ ) | 90 |
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
@slow
@require_torch_gpu
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def __UpperCamelCase ( self : Dict ) -> Tuple:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __UpperCamelCase ( self : Optional[int] ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = StableDiffusionKDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4" )
SCREAMING_SNAKE_CASE : str = sd_pipe.to(a )
sd_pipe.set_progress_bar_config(disable=a )
sd_pipe.set_scheduler("sample_euler" )
SCREAMING_SNAKE_CASE : Optional[int] = "A painting of a squirrel eating a burger"
SCREAMING_SNAKE_CASE : Any = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : List[Any] = sd_pipe([prompt] , generator=a , guidance_scale=9.0 , num_inference_steps=20 , output_type="np" )
SCREAMING_SNAKE_CASE : Tuple = output.images
SCREAMING_SNAKE_CASE : Any = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE : Union[str, Any] = np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def __UpperCamelCase ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base" )
SCREAMING_SNAKE_CASE : Tuple = sd_pipe.to(a )
sd_pipe.set_progress_bar_config(disable=a )
sd_pipe.set_scheduler("sample_euler" )
SCREAMING_SNAKE_CASE : List[str] = "A painting of a squirrel eating a burger"
SCREAMING_SNAKE_CASE : List[str] = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Union[str, Any] = sd_pipe([prompt] , generator=a , guidance_scale=9.0 , num_inference_steps=20 , output_type="np" )
SCREAMING_SNAKE_CASE : List[Any] = output.images
SCREAMING_SNAKE_CASE : Tuple = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE : int = np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-1
def __UpperCamelCase ( self : Tuple ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base" )
SCREAMING_SNAKE_CASE : Union[str, Any] = sd_pipe.to(a )
sd_pipe.set_progress_bar_config(disable=a )
sd_pipe.set_scheduler("sample_dpmpp_2m" )
SCREAMING_SNAKE_CASE : str = "A painting of a squirrel eating a burger"
SCREAMING_SNAKE_CASE : Any = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : str = sd_pipe(
[prompt] , generator=a , guidance_scale=7.5 , num_inference_steps=15 , output_type="np" , use_karras_sigmas=a , )
SCREAMING_SNAKE_CASE : str = output.images
SCREAMING_SNAKE_CASE : Optional[int] = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE : int = np.array(
[0.1138_1689, 0.1211_2921, 0.138_9457, 0.1254_9606, 0.124_4964, 0.1083_1517, 0.1156_2866, 0.1086_7816, 0.1049_9048] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2 | 25 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_lowercase = {
'''configuration_convnext''': ['''CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ConvNextConfig''', '''ConvNextOnnxConfig''']
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = ['''ConvNextFeatureExtractor''']
_lowercase = ['''ConvNextImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
'''CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ConvNextForImageClassification''',
'''ConvNextModel''',
'''ConvNextPreTrainedModel''',
'''ConvNextBackbone''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
'''TFConvNextForImageClassification''',
'''TFConvNextModel''',
'''TFConvNextPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig, ConvNextOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_convnext import ConvNextFeatureExtractor
from .image_processing_convnext import ConvNextImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_convnext import (
CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
ConvNextBackbone,
ConvNextForImageClassification,
ConvNextModel,
ConvNextPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel
else:
import sys
_lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure) | 91 |
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class _UpperCamelCase ( __A , unittest.TestCase ):
'''simple docstring'''
pass
@nightly
@require_onnxruntime
@require_torch_gpu
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
@property
def __UpperCamelCase ( self : List[Any] ) -> List[str]:
"""simple docstring"""
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def __UpperCamelCase ( self : int ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = ort.SessionOptions()
SCREAMING_SNAKE_CASE : Union[str, Any] = False
return options
def __UpperCamelCase ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo.png" )
SCREAMING_SNAKE_CASE : Optional[Any] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo_mask.png" )
SCREAMING_SNAKE_CASE : int = OnnxStableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting" , revision="onnx" , safety_checker=a , feature_extractor=a , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=a )
SCREAMING_SNAKE_CASE : Optional[Any] = "A red cat sitting on a park bench"
SCREAMING_SNAKE_CASE : Tuple = np.random.RandomState(0 )
SCREAMING_SNAKE_CASE : Optional[int] = pipe(
prompt=a , image=a , mask_image=a , guidance_scale=7.5 , num_inference_steps=10 , generator=a , output_type="np" , )
SCREAMING_SNAKE_CASE : List[Any] = output.images
SCREAMING_SNAKE_CASE : Union[str, Any] = images[0, 255:258, 255:258, -1]
assert images.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE : int = np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def __UpperCamelCase ( self : List[Any] ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo.png" )
SCREAMING_SNAKE_CASE : Optional[Any] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo_mask.png" )
SCREAMING_SNAKE_CASE : Optional[Any] = LMSDiscreteScheduler.from_pretrained(
"runwayml/stable-diffusion-inpainting" , subfolder="scheduler" , revision="onnx" )
SCREAMING_SNAKE_CASE : Union[str, Any] = OnnxStableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting" , revision="onnx" , scheduler=a , safety_checker=a , feature_extractor=a , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=a )
SCREAMING_SNAKE_CASE : List[Any] = "A red cat sitting on a park bench"
SCREAMING_SNAKE_CASE : Dict = np.random.RandomState(0 )
SCREAMING_SNAKE_CASE : Tuple = pipe(
prompt=a , image=a , mask_image=a , guidance_scale=7.5 , num_inference_steps=20 , generator=a , output_type="np" , )
SCREAMING_SNAKE_CASE : List[str] = output.images
SCREAMING_SNAKE_CASE : Optional[int] = images[0, 255:258, 255:258, -1]
assert images.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE : Any = np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3 | 25 | 0 |
'''simple docstring'''
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
UpperCamelCase_ = pd.read_csv("""sample_data.csv""", header=None)
UpperCamelCase_ = df.shape[:1][0]
# If you're using some other dataset input the target column
UpperCamelCase_ = df.iloc[:, 1:2]
UpperCamelCase_ = actual_data.values.reshape(len_data, 1)
UpperCamelCase_ = MinMaxScaler().fit_transform(actual_data)
UpperCamelCase_ = 10
UpperCamelCase_ = 5
UpperCamelCase_ = 20
UpperCamelCase_ = len_data - periods * look_back
UpperCamelCase_ = actual_data[:division]
UpperCamelCase_ = actual_data[division - look_back :]
UpperCamelCase_ , UpperCamelCase_ = [], []
UpperCamelCase_ , UpperCamelCase_ = [], []
for i in range(0, len(train_data) - forward_days - look_back + 1):
train_x.append(train_data[i : i + look_back])
train_y.append(train_data[i + look_back : i + look_back + forward_days])
for i in range(0, len(test_data) - forward_days - look_back + 1):
test_x.append(test_data[i : i + look_back])
test_y.append(test_data[i + look_back : i + look_back + forward_days])
UpperCamelCase_ = np.array(train_x)
UpperCamelCase_ = np.array(test_x)
UpperCamelCase_ = np.array([list(i.ravel()) for i in train_y])
UpperCamelCase_ = np.array([list(i.ravel()) for i in test_y])
UpperCamelCase_ = Sequential()
model.add(LSTM(128, input_shape=(look_back, 1), return_sequences=True))
model.add(LSTM(64, input_shape=(128, 1)))
model.add(Dense(forward_days))
model.compile(loss="""mean_squared_error""", optimizer="""adam""")
UpperCamelCase_ = model.fit(
x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
)
UpperCamelCase_ = model.predict(x_test)
| 92 |
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def lowerCamelCase__ ( _a):
return getitem, k
def lowerCamelCase__ ( _a , _a):
return setitem, k, v
def lowerCamelCase__ ( _a):
return delitem, k
def lowerCamelCase__ ( _a , _a , *_a):
try:
return fun(_a , *_a), None
except Exception as e:
return None, e
a_ = (
_set('key_a', 'val_a'),
_set('key_b', 'val_b'),
)
a_ = [
_set('key_a', 'val_a'),
_set('key_a', 'val_b'),
]
a_ = [
_set('key_a', 'val_a'),
_set('key_b', 'val_b'),
_del('key_a'),
_del('key_b'),
_set('key_a', 'val_a'),
_del('key_a'),
]
a_ = [
_get('key_a'),
_del('key_a'),
_set('key_a', 'val_a'),
_del('key_a'),
_del('key_a'),
_get('key_a'),
]
a_ = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
]
a_ = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
*[_del(x) for x in range(5)],
_set('key_a', 'val_b'),
]
@pytest.mark.parametrize(
"operations" , (
pytest.param(_add_items , id="add items"),
pytest.param(_overwrite_items , id="overwrite items"),
pytest.param(_delete_items , id="delete items"),
pytest.param(_access_absent_items , id="access absent items"),
pytest.param(_add_with_resize_up , id="add with resize up"),
pytest.param(_add_with_resize_down , id="add with resize down"),
) , )
def lowerCamelCase__ ( _a):
SCREAMING_SNAKE_CASE : Dict = HashMap(initial_block_size=4)
SCREAMING_SNAKE_CASE : List[str] = {}
for _, (fun, *args) in enumerate(_a):
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Union[str, Any] = _run_operation(_a , _a , *_a)
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : List[Any] = _run_operation(_a , _a , *_a)
assert my_res == py_res
assert str(_a) == str(_a)
assert set(_a) == set(_a)
assert len(_a) == len(_a)
assert set(my.items()) == set(py.items())
def lowerCamelCase__ ( ):
def is_public(_a) -> bool:
return not name.startswith("_")
SCREAMING_SNAKE_CASE : List[str] = {name for name in dir({}) if is_public(_a)}
SCREAMING_SNAKE_CASE : Union[str, Any] = {name for name in dir(HashMap()) if is_public(_a)}
assert dict_public_names > hash_public_names | 25 | 0 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class _lowerCAmelCase ( metaclass=a ):
"""simple docstring"""
__magic_name__ :Optional[int] = ["""flax""", """transformers"""]
def __init__( self , *__UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
requires_backends(self , ['flax', 'transformers'] )
@classmethod
def snake_case ( cls , *__UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
requires_backends(cls , ['flax', 'transformers'] )
@classmethod
def snake_case ( cls , *__UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
requires_backends(cls , ['flax', 'transformers'] )
class _lowerCAmelCase ( metaclass=a ):
"""simple docstring"""
__magic_name__ :Optional[int] = ["""flax""", """transformers"""]
def __init__( self , *__UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
requires_backends(self , ['flax', 'transformers'] )
@classmethod
def snake_case ( cls , *__UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
requires_backends(cls , ['flax', 'transformers'] )
@classmethod
def snake_case ( cls , *__UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
requires_backends(cls , ['flax', 'transformers'] )
class _lowerCAmelCase ( metaclass=a ):
"""simple docstring"""
__magic_name__ :List[Any] = ["""flax""", """transformers"""]
def __init__( self , *__UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
requires_backends(self , ['flax', 'transformers'] )
@classmethod
def snake_case ( cls , *__UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
requires_backends(cls , ['flax', 'transformers'] )
@classmethod
def snake_case ( cls , *__UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
requires_backends(cls , ['flax', 'transformers'] )
class _lowerCAmelCase ( metaclass=a ):
"""simple docstring"""
__magic_name__ :Union[str, Any] = ["""flax""", """transformers"""]
def __init__( self , *__UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
requires_backends(self , ['flax', 'transformers'] )
@classmethod
def snake_case ( cls , *__UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
requires_backends(cls , ['flax', 'transformers'] )
@classmethod
def snake_case ( cls , *__UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
requires_backends(cls , ['flax', 'transformers'] )
| 93 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a_ = {'configuration_van': ['VAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VanConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'VAN_PRETRAINED_MODEL_ARCHIVE_LIST',
'VanForImageClassification',
'VanModel',
'VanPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_van import (
VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
VanForImageClassification,
VanModel,
VanPreTrainedModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure) | 25 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
SCREAMING_SNAKE_CASE = {
'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
'tokenization_biogpt': ['BioGptTokenizer'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE = [
'BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
'BioGptForCausalLM',
'BioGptForTokenClassification',
'BioGptForSequenceClassification',
'BioGptModel',
'BioGptPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
from .tokenization_biogpt import BioGptTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_biogpt import (
BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
BioGptForCausalLM,
BioGptForSequenceClassification,
BioGptForTokenClassification,
BioGptModel,
BioGptPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 94 |
from __future__ import annotations
def lowerCamelCase__ ( _a):
if len(_a) == 0:
return []
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Tuple = min(_a), max(_a)
SCREAMING_SNAKE_CASE : Dict = int(max_value - min_value) + 1
SCREAMING_SNAKE_CASE : list[list] = [[] for _ in range(_a)]
for i in my_list:
buckets[int(i - min_value)].append(_a)
return [v for bucket in buckets for v in sorted(_a)]
if __name__ == "__main__":
from doctest import testmod
testmod()
assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
assert bucket_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15] | 25 | 0 |
"""simple docstring"""
import random
def snake_case ( A__ ,A__ ):
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ : List[str] = [], [], []
for element in data:
if element < pivot:
less.append(A__ )
elif element > pivot:
greater.append(A__ )
else:
equal.append(A__ )
return less, equal, greater
def snake_case ( A__ ,A__ ):
# index = len(items) // 2 when trying to find the median
# (value of index when items is sorted)
# invalid input
if index >= len(A__ ) or index < 0:
return None
UpperCAmelCase_ : str = items[random.randint(0 ,len(A__ ) - 1 )]
UpperCAmelCase_ : Dict = 0
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ : List[Any] = _partition(A__ ,A__ )
UpperCAmelCase_ : Tuple = len(A__ )
UpperCAmelCase_ : List[str] = len(A__ )
# index is the pivot
if m <= index < m + count:
return pivot
# must be in smaller
elif m > index:
return quick_select(A__ ,A__ )
# must be in larger
else:
return quick_select(A__ ,index - (m + count) )
| 95 |
a_ = frozenset(
[
'prompt',
'height',
'width',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
'cross_attention_kwargs',
]
)
a_ = frozenset(['prompt', 'negative_prompt'])
a_ = frozenset([])
a_ = frozenset(['image'])
a_ = frozenset(
[
'image',
'height',
'width',
'guidance_scale',
]
)
a_ = frozenset(['image'])
a_ = frozenset(
[
'prompt',
'image',
'height',
'width',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
]
)
a_ = frozenset(['prompt', 'image', 'negative_prompt'])
a_ = frozenset(
[
# Text guided image variation with an image mask
'prompt',
'image',
'mask_image',
'height',
'width',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
]
)
a_ = frozenset(['prompt', 'image', 'mask_image', 'negative_prompt'])
a_ = frozenset(
[
# image variation with an image mask
'image',
'mask_image',
'height',
'width',
'guidance_scale',
]
)
a_ = frozenset(['image', 'mask_image'])
a_ = frozenset(
[
'example_image',
'image',
'mask_image',
'height',
'width',
'guidance_scale',
]
)
a_ = frozenset(['example_image', 'image', 'mask_image'])
a_ = frozenset(['class_labels'])
a_ = frozenset(['class_labels'])
a_ = frozenset(['batch_size'])
a_ = frozenset([])
a_ = frozenset(['batch_size'])
a_ = frozenset([])
a_ = frozenset(
[
'prompt',
'audio_length_in_s',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
'cross_attention_kwargs',
]
)
a_ = frozenset(['prompt', 'negative_prompt'])
a_ = frozenset(['input_tokens'])
a_ = frozenset(['input_tokens']) | 25 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = {
'sayakpaul/vit-msn-base': 'https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json',
# See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}
class __A ( SCREAMING_SNAKE_CASE_ ):
UpperCAmelCase__ = "vit_msn"
def __init__( self : Optional[int] , __snake_case : Optional[Any]=7_6_8 , __snake_case : Dict=1_2 , __snake_case : int=1_2 , __snake_case : Optional[int]=3_0_7_2 , __snake_case : Any="gelu" , __snake_case : str=0.0 , __snake_case : List[Any]=0.0 , __snake_case : str=0.02 , __snake_case : Optional[int]=1E-06 , __snake_case : List[Any]=2_2_4 , __snake_case : int=1_6 , __snake_case : List[Any]=3 , __snake_case : List[Any]=True , **__snake_case : Optional[int] , ) -> List[Any]:
super().__init__(**__snake_case )
__magic_name__: int = hidden_size
__magic_name__: int = num_hidden_layers
__magic_name__: Tuple = num_attention_heads
__magic_name__: List[str] = intermediate_size
__magic_name__: List[Any] = hidden_act
__magic_name__: Optional[int] = hidden_dropout_prob
__magic_name__: List[Any] = attention_probs_dropout_prob
__magic_name__: Optional[int] = initializer_range
__magic_name__: Tuple = layer_norm_eps
__magic_name__: Dict = image_size
__magic_name__: Union[str, Any] = patch_size
__magic_name__: Optional[Any] = num_channels
__magic_name__: str = qkv_bias
| 96 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import jax
import jaxlib
a_ = get_logger()
a_ = None
class _UpperCamelCase ( TensorFormatter[Mapping, 'jax.Array', Mapping] ):
'''simple docstring'''
def __init__( self : Optional[Any] , a : str=None , a : List[Any]=None , **a : Any ) -> Optional[Any]:
"""simple docstring"""
super().__init__(features=a )
import jax
from jaxlib.xla_client import Device
if isinstance(a , a ):
raise ValueError(
F"Expected {device} to be a `str` not {type(a )}, as `jaxlib.xla_extension.Device` "
"is not serializable neither with `pickle` nor with `dill`. Instead you can surround "
"the device with `str()` to get its string identifier that will be internally mapped "
"to the actual `jaxlib.xla_extension.Device`." )
SCREAMING_SNAKE_CASE : List[str] = device if isinstance(a , a ) else str(jax.devices()[0] )
# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
# with `pickle` nor with `dill`, so we need to use a global variable instead
global DEVICE_MAPPING
if DEVICE_MAPPING is None:
SCREAMING_SNAKE_CASE : str = self._map_devices_to_str()
if self.device not in list(DEVICE_MAPPING.keys() ):
logger.warning(
F"Device with string identifier {self.device} not listed among the available "
F"devices: {list(DEVICE_MAPPING.keys() )}, so falling back to the default "
F"device: {str(jax.devices()[0] )}." )
SCREAMING_SNAKE_CASE : Any = str(jax.devices()[0] )
SCREAMING_SNAKE_CASE : Any = jnp_array_kwargs
@staticmethod
def __UpperCamelCase ( ) -> Dict[str, "jaxlib.xla_extension.Device"]:
"""simple docstring"""
import jax
return {str(a ): device for device in jax.devices()}
def __UpperCamelCase ( self : Dict , a : Tuple ) -> str:
"""simple docstring"""
import jax
import jax.numpy as jnp
if isinstance(a , a ) and column:
if all(
isinstance(a , jax.Array ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column ):
return jnp.stack(a , axis=0 )
return column
def __UpperCamelCase ( self : Dict , a : str ) -> str:
"""simple docstring"""
import jax
import jax.numpy as jnp
if isinstance(a , (str, bytes, type(a )) ):
return value
elif isinstance(a , (np.character, np.ndarray) ) and np.issubdtype(value.dtype , np.character ):
return value.tolist()
SCREAMING_SNAKE_CASE : Union[str, Any] = {}
if isinstance(a , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.integer ):
# the default int precision depends on the jax config
# see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
if jax.config.jax_enable_xaa:
SCREAMING_SNAKE_CASE : Dict = {"dtype": jnp.intaa}
else:
SCREAMING_SNAKE_CASE : str = {"dtype": jnp.intaa}
elif isinstance(a , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.floating ):
SCREAMING_SNAKE_CASE : int = {"dtype": jnp.floataa}
elif config.PIL_AVAILABLE and "PIL" in sys.modules:
import PIL.Image
if isinstance(a , PIL.Image.Image ):
SCREAMING_SNAKE_CASE : Dict = np.asarray(a )
# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
# with `pickle` nor with `dill`, so we need to use a global variable instead
global DEVICE_MAPPING
if DEVICE_MAPPING is None:
SCREAMING_SNAKE_CASE : Optional[Any] = self._map_devices_to_str()
with jax.default_device(DEVICE_MAPPING[self.device] ):
# calling jnp.array on a np.ndarray does copy the data
# see https://github.com/google/jax/issues/4486
return jnp.array(a , **{**default_dtype, **self.jnp_array_kwargs} )
def __UpperCamelCase ( self : Any , a : List[str] ) -> Dict:
"""simple docstring"""
import jax
# support for torch, tf, jax etc.
if config.TORCH_AVAILABLE and "torch" in sys.modules:
import torch
if isinstance(a , torch.Tensor ):
return self._tensorize(data_struct.detach().cpu().numpy()[()] )
if hasattr(a , "__array__" ) and not isinstance(a , jax.Array ):
SCREAMING_SNAKE_CASE : Optional[int] = data_struct.__array__()
# support for nested types like struct of list of struct
if isinstance(a , np.ndarray ):
if data_struct.dtype == object: # jax arrays cannot be instantied from an array of objects
return self._consolidate([self.recursive_tensorize(a ) for substruct in data_struct] )
elif isinstance(a , (list, tuple) ):
return self._consolidate([self.recursive_tensorize(a ) for substruct in data_struct] )
return self._tensorize(a )
def __UpperCamelCase ( self : Optional[Any] , a : dict ) -> Dict:
"""simple docstring"""
return map_nested(self._recursive_tensorize , a , map_list=a )
def __UpperCamelCase ( self : Dict , a : pa.Table ) -> Mapping:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = self.numpy_arrow_extractor().extract_row(a )
SCREAMING_SNAKE_CASE : List[Any] = self.python_features_decoder.decode_row(a )
return self.recursive_tensorize(a )
def __UpperCamelCase ( self : Optional[int] , a : pa.Table ) -> "jax.Array":
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = self.numpy_arrow_extractor().extract_column(a )
SCREAMING_SNAKE_CASE : Optional[Any] = self.python_features_decoder.decode_column(a , pa_table.column_names[0] )
SCREAMING_SNAKE_CASE : Tuple = self.recursive_tensorize(a )
SCREAMING_SNAKE_CASE : Optional[int] = self._consolidate(a )
return column
def __UpperCamelCase ( self : List[Any] , a : pa.Table ) -> Mapping:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = self.numpy_arrow_extractor().extract_batch(a )
SCREAMING_SNAKE_CASE : str = self.python_features_decoder.decode_batch(a )
SCREAMING_SNAKE_CASE : List[Any] = self.recursive_tensorize(a )
for column_name in batch:
SCREAMING_SNAKE_CASE : List[Any] = self._consolidate(batch[column_name] )
return batch | 25 | 0 |
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
__a = get_tests_dir('fixtures/test_sentencepiece.model')
@require_sentencepiece
class lowercase__( UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
a :Optional[int] = XLMProphetNetTokenizer
a :Any = False
a :Optional[int] = True
def _lowercase ( self : Optional[int] ) -> List[str]:
super().setUp()
# We have a SentencePiece fixture for testing
lowercase_ = XLMProphetNetTokenizer(SCREAMING_SNAKE_CASE_ , keep_accents=SCREAMING_SNAKE_CASE_ )
tokenizer.save_pretrained(self.tmpdirname )
def _lowercase ( self : Any ) -> Optional[Any]:
lowercase_ = '''[PAD]'''
lowercase_ = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : Optional[Any] ) -> int:
lowercase_ = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''[PAD]''' )
self.assertEqual(vocab_keys[1] , '''[CLS]''' )
self.assertEqual(vocab_keys[-1] , '''j''' )
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , 1_0_1_2 )
def _lowercase ( self : int ) -> int:
self.assertEqual(self.get_tokenizer().vocab_size , 1_0_1_2 )
def _lowercase ( self : Union[str, Any] ) -> Union[str, Any]:
lowercase_ = XLMProphetNetTokenizer(SCREAMING_SNAKE_CASE_ , keep_accents=SCREAMING_SNAKE_CASE_ )
lowercase_ = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ ) , [value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]] , )
lowercase_ = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
SCREAMING_SNAKE_CASE_ , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''9''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''é''',
'''.''',
] , )
lowercase_ = tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
SCREAMING_SNAKE_CASE_ , [
value + tokenizer.fairseq_offset
for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, -9, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, -9, 4]
] , )
lowercase_ = tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
SCREAMING_SNAKE_CASE_ , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''[UNK]''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''[UNK]''',
'''.''',
] , )
@cached_property
def _lowercase ( self : Tuple ) -> List[Any]:
return XLMProphetNetTokenizer.from_pretrained('''microsoft/xprophetnet-large-wiki100-cased''' )
@slow
def _lowercase ( self : Optional[int] ) -> Union[str, Any]:
lowercase_ = '''Hello World!'''
lowercase_ = [3_5_3_8_9, 6_6_7_2, 4_9, 2]
self.assertListEqual(SCREAMING_SNAKE_CASE_ , self.big_tokenizer.encode(SCREAMING_SNAKE_CASE_ ) )
@slow
def _lowercase ( self : Any ) -> List[Any]:
# fmt: off
lowercase_ = {'''input_ids''': [[1_1_0_7_3, 8_2_7_8_3, 1_8, 2_6, 8_2_7_8_3, 5_4_9, 5_1_5_4_0, 2_4_8, 1_7_2_0_9, 1_3_0_1, 2_1_7, 2_0, 2_1_5_1_8_6, 1_3_2_5, 1_4_7, 1_7_2_0_9, 1_3_0_1, 2_1_7, 2_0, 5_6_3_7_0, 5_3, 1_2_2_0_2_0, 2_0, 1_6_4_7_7, 2_7, 8_7_3_5_5, 4_5_4_8, 2_0, 4_7_2_8, 7_8_3_9_2, 1_7, 1_5_9_9_6_9, 1_8, 2_6, 2_4_4_9_1, 6_2_9, 1_5, 5_3_8, 2_2_7_0_4, 5_4_3_9, 1_5, 2_7_8_8, 2_4_4_9_1, 9_8_8_5, 1_5, 4_3_5_3_4, 6_0_5, 1_5, 8_1_4, 1_8_4_0_3, 3_3_2_0_0, 2_9, 1_5, 4_3_5_3_4, 2_4_4_5_8, 1_2_4_1_0, 1_1_1, 2_4_9_6_6, 8_3_6_6_9, 9_6_3_7, 1_4_4_0_6_8, 2_6, 8_5_0, 2_2_3_4_6, 2_7, 1_4_7, 2_4_9_6_6, 8_3_6_6_9, 8_3_4_9_0, 2_6, 3_9_1_1_3, 7_3_5, 2_7, 6_8_9, 6_5_6, 2_8_0_0, 1_3_3_9, 4_6_0_0, 5_3, 1_2_2_0_2_0, 1_1_5_7_8_5, 3_4, 8_1_6, 1_3_3_9, 4_6_8_8_7, 1_8, 1_4_7, 5_3_9_0_5, 1_9_5_1, 4_2_2_3_8, 4_1_1_7_0, 1_7_7_3_2, 8_3_4, 4_3_6, 1_5, 2_7_5_2_3, 9_8_7_3_3, 2_1_7, 1_4_7, 5_5_4_2, 4_9_8_1, 9_3_0, 1_7_3_4_7, 1_6, 2], [2_0_0_9_1, 6_2_9, 9_4, 8_2_7_8_6, 5_8, 4_9_0, 2_0, 1_5_2_8, 8_4, 5_3_9_0_5, 3_4_4, 8_0_5_9_2, 1_1_0_1_2_8, 1_8_8_2_2, 5_2_6_7, 1_3_0_6, 6_2, 1_5_2_5_3_7, 3_0_8, 7_9_9_7, 4_0_1, 1_2_4_4_2_7, 5_4_9, 3_5_4_4_2, 2_2_5, 1_0_9, 1_5_0_5_5, 2_5_7_4_8, 1_4_7, 7_1_1_9, 4_3_7_1_2, 3_4, 7_6_7, 1_3_5_3_6_6, 1_8, 1_6, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_9_2, 6_3_7_8_4, 1_1_9_4_6_6, 1_7, 1_4_7_8_0_8, 8_8_2_1_4, 1_8, 6_5_6, 8_1, 3_2, 3_2_9_6, 1_0_2_8_0, 1_6, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=SCREAMING_SNAKE_CASE_ , model_name='''microsoft/xprophetnet-large-wiki100-cased''' , revision='''1acad1643ddd54a44df6a1b797ada8373685d90e''' , )
| 97 |
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_pytesseract,
require_tf,
require_timm,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class _UpperCamelCase :
'''simple docstring'''
@staticmethod
def __UpperCamelCase ( *a : str , **a : int ) -> str:
"""simple docstring"""
pass
@is_pipeline_test
@require_vision
@require_timm
@require_torch
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ =MODEL_FOR_OBJECT_DETECTION_MAPPING
def __UpperCamelCase ( self : Optional[Any] , a : str , a : Optional[Any] , a : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = ObjectDetectionPipeline(model=a , image_processor=a )
return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]
def __UpperCamelCase ( self : List[Any] , a : Optional[int] , a : Optional[int] ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = object_detector("./tests/fixtures/tests_samples/COCO/000000039769.png" , threshold=0.0 )
self.assertGreater(len(a ) , 0 )
for detected_object in outputs:
self.assertEqual(
a , {
"score": ANY(a ),
"label": ANY(a ),
"box": {"xmin": ANY(a ), "ymin": ANY(a ), "xmax": ANY(a ), "ymax": ANY(a )},
} , )
import datasets
SCREAMING_SNAKE_CASE : Any = datasets.load_dataset("hf-internal-testing/fixtures_image_utils" , "image" , split="test" )
SCREAMING_SNAKE_CASE : Dict = [
Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" ),
"http://images.cocodataset.org/val2017/000000039769.jpg",
# RGBA
dataset[0]["file"],
# LA
dataset[1]["file"],
# L
dataset[2]["file"],
]
SCREAMING_SNAKE_CASE : Tuple = object_detector(a , threshold=0.0 )
self.assertEqual(len(a ) , len(a ) )
for outputs in batch_outputs:
self.assertGreater(len(a ) , 0 )
for detected_object in outputs:
self.assertEqual(
a , {
"score": ANY(a ),
"label": ANY(a ),
"box": {"xmin": ANY(a ), "ymin": ANY(a ), "xmax": ANY(a ), "ymax": ANY(a )},
} , )
@require_tf
@unittest.skip("Object detection not implemented in TF" )
def __UpperCamelCase ( self : Optional[int] ) -> str:
"""simple docstring"""
pass
@require_torch
def __UpperCamelCase ( self : Tuple ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = "hf-internal-testing/tiny-detr-mobilenetsv3"
SCREAMING_SNAKE_CASE : Dict = AutoModelForObjectDetection.from_pretrained(a )
SCREAMING_SNAKE_CASE : Any = AutoFeatureExtractor.from_pretrained(a )
SCREAMING_SNAKE_CASE : Tuple = ObjectDetectionPipeline(model=a , feature_extractor=a )
SCREAMING_SNAKE_CASE : int = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg" , threshold=0.0 )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
] , )
SCREAMING_SNAKE_CASE : Dict = object_detector(
[
"http://images.cocodataset.org/val2017/000000039769.jpg",
"http://images.cocodataset.org/val2017/000000039769.jpg",
] , threshold=0.0 , )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
[
{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
],
[
{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
],
] , )
@require_torch
@slow
def __UpperCamelCase ( self : str ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = "facebook/detr-resnet-50"
SCREAMING_SNAKE_CASE : Union[str, Any] = AutoModelForObjectDetection.from_pretrained(a )
SCREAMING_SNAKE_CASE : List[str] = AutoFeatureExtractor.from_pretrained(a )
SCREAMING_SNAKE_CASE : int = ObjectDetectionPipeline(model=a , feature_extractor=a )
SCREAMING_SNAKE_CASE : Union[str, Any] = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg" )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
] , )
SCREAMING_SNAKE_CASE : int = object_detector(
[
"http://images.cocodataset.org/val2017/000000039769.jpg",
"http://images.cocodataset.org/val2017/000000039769.jpg",
] )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
[
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
],
[
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
],
] , )
@require_torch
@slow
def __UpperCamelCase ( self : str ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = "facebook/detr-resnet-50"
SCREAMING_SNAKE_CASE : Tuple = pipeline("object-detection" , model=a )
SCREAMING_SNAKE_CASE : Tuple = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg" )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
] , )
SCREAMING_SNAKE_CASE : str = object_detector(
[
"http://images.cocodataset.org/val2017/000000039769.jpg",
"http://images.cocodataset.org/val2017/000000039769.jpg",
] )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
[
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
],
[
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
],
] , )
@require_torch
@slow
def __UpperCamelCase ( self : str ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = 0.9985
SCREAMING_SNAKE_CASE : int = "facebook/detr-resnet-50"
SCREAMING_SNAKE_CASE : List[str] = pipeline("object-detection" , model=a )
SCREAMING_SNAKE_CASE : str = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg" , threshold=a )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
] , )
@require_torch
@require_pytesseract
@slow
def __UpperCamelCase ( self : str ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = "Narsil/layoutlmv3-finetuned-funsd"
SCREAMING_SNAKE_CASE : Dict = 0.9993
SCREAMING_SNAKE_CASE : str = pipeline("object-detection" , model=a , threshold=a )
SCREAMING_SNAKE_CASE : List[Any] = object_detector(
"https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png" )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
{"score": 0.9993, "label": "I-ANSWER", "box": {"xmin": 294, "ymin": 254, "xmax": 343, "ymax": 264}},
{"score": 0.9993, "label": "I-ANSWER", "box": {"xmin": 294, "ymin": 254, "xmax": 343, "ymax": 264}},
] , ) | 25 | 0 |
'''simple docstring'''
import math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
def __init__( self : Optional[Any] , *lowerCAmelCase__ : Union[str, Any] , lowerCAmelCase__ : Any=None , lowerCAmelCase__ : Tuple=None , **lowerCAmelCase__ : Any ) -> List[str]:
'''simple docstring'''
super().__init__(*lowerCAmelCase__ , **lowerCAmelCase__ )
_UpperCamelCase = eval_examples
_UpperCamelCase = post_process_function
def snake_case__ ( self : List[Any] , lowerCAmelCase__ : Tuple=None , lowerCAmelCase__ : Any=None , lowerCAmelCase__ : str=None , lowerCAmelCase__ : str = "eval" ) -> Union[str, Any]:
'''simple docstring'''
_UpperCamelCase = self.eval_dataset if eval_dataset is None else eval_dataset
_UpperCamelCase = self.get_eval_dataloader(lowerCAmelCase__ )
_UpperCamelCase = self.eval_examples if eval_examples is None else eval_examples
# Temporarily disable metric computation, we will do it in the loop here.
_UpperCamelCase = self.compute_metrics
_UpperCamelCase = None
_UpperCamelCase = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
_UpperCamelCase = time.time()
try:
_UpperCamelCase = eval_loop(
lowerCAmelCase__ , description='''Evaluation''' , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=lowerCAmelCase__ , metric_key_prefix=lowerCAmelCase__ , )
finally:
_UpperCamelCase = compute_metrics
_UpperCamelCase = self.args.eval_batch_size * self.args.world_size
if f"""{metric_key_prefix}_jit_compilation_time""" in output.metrics:
start_time += output.metrics[f"""{metric_key_prefix}_jit_compilation_time"""]
output.metrics.update(
speed_metrics(
lowerCAmelCase__ , lowerCAmelCase__ , num_samples=output.num_samples , num_steps=math.ceil(output.num_samples / total_batch_size ) , ) )
if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
# Only the main node write the results by default
_UpperCamelCase = self.post_process_function(lowerCAmelCase__ , lowerCAmelCase__ , output.predictions )
_UpperCamelCase = self.compute_metrics(lowerCAmelCase__ )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(f"""{metric_key_prefix}_""" ):
_UpperCamelCase = metrics.pop(lowerCAmelCase__ )
metrics.update(output.metrics )
else:
_UpperCamelCase = output.metrics
if self.args.should_log:
# Only the main node log the results by default
self.log(lowerCAmelCase__ )
if self.args.tpu_metrics_debug or self.args.debug:
# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
xm.master_print(met.metrics_report() )
_UpperCamelCase = self.callback_handler.on_evaluate(self.args , self.state , self.control , lowerCAmelCase__ )
return metrics
def snake_case__ ( self : Dict , lowerCAmelCase__ : Optional[Any] , lowerCAmelCase__ : Optional[int] , lowerCAmelCase__ : int=None , lowerCAmelCase__ : str = "test" ) -> str:
'''simple docstring'''
_UpperCamelCase = self.get_test_dataloader(lowerCAmelCase__ )
# Temporarily disable metric computation, we will do it in the loop here.
_UpperCamelCase = self.compute_metrics
_UpperCamelCase = None
_UpperCamelCase = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
_UpperCamelCase = time.time()
try:
_UpperCamelCase = eval_loop(
lowerCAmelCase__ , description='''Prediction''' , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=lowerCAmelCase__ , metric_key_prefix=lowerCAmelCase__ , )
finally:
_UpperCamelCase = compute_metrics
_UpperCamelCase = self.args.eval_batch_size * self.args.world_size
if f"""{metric_key_prefix}_jit_compilation_time""" in output.metrics:
start_time += output.metrics[f"""{metric_key_prefix}_jit_compilation_time"""]
output.metrics.update(
speed_metrics(
lowerCAmelCase__ , lowerCAmelCase__ , num_samples=output.num_samples , num_steps=math.ceil(output.num_samples / total_batch_size ) , ) )
if self.post_process_function is None or self.compute_metrics is None:
return output
_UpperCamelCase = self.post_process_function(lowerCAmelCase__ , lowerCAmelCase__ , output.predictions , '''predict''' )
_UpperCamelCase = self.compute_metrics(lowerCAmelCase__ )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(f"""{metric_key_prefix}_""" ):
_UpperCamelCase = metrics.pop(lowerCAmelCase__ )
metrics.update(output.metrics )
return PredictionOutput(predictions=predictions.predictions , label_ids=predictions.label_ids , metrics=lowerCAmelCase__ )
| 98 |
def lowerCamelCase__ ( _a):
if not isinstance(_a , _a):
SCREAMING_SNAKE_CASE : Tuple = f"Input value of [number={number}] must be an integer"
raise TypeError(_a)
if number < 0:
return False
SCREAMING_SNAKE_CASE : Union[str, Any] = number * number
while number > 0:
if number % 10 != number_square % 10:
return False
number //= 10
number_square //= 10
return True
if __name__ == "__main__":
import doctest
doctest.testmod() | 25 | 0 |
def a (lowerCAmelCase__ , lowerCAmelCase__ ):
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(lowerCAmelCase__ , int(b / 2 ) ) * actual_power(lowerCAmelCase__ , int(b / 2 ) )
else:
return a * actual_power(lowerCAmelCase__ , int(b / 2 ) ) * actual_power(lowerCAmelCase__ , int(b / 2 ) )
def a (lowerCAmelCase__ , lowerCAmelCase__ ):
if b < 0:
return 1 / actual_power(lowerCAmelCase__ , lowerCAmelCase__ )
return actual_power(lowerCAmelCase__ , lowerCAmelCase__ )
if __name__ == "__main__":
print(power(-2, -3))
| 99 |
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
DistilBertModel,
)
class _UpperCamelCase ( __A ):
'''simple docstring'''
def __init__( self : Dict , a : Tuple , a : Any=13 , a : Any=7 , a : Union[str, Any]=True , a : List[Any]=True , a : List[str]=False , a : List[str]=True , a : Any=99 , a : str=32 , a : Any=5 , a : Optional[int]=4 , a : Union[str, Any]=37 , a : Dict="gelu" , a : List[Any]=0.1 , a : Optional[Any]=0.1 , a : List[str]=512 , a : Union[str, Any]=16 , a : str=2 , a : Dict=0.02 , a : Optional[int]=3 , a : Union[str, Any]=4 , a : int=None , ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = parent
SCREAMING_SNAKE_CASE : Any = batch_size
SCREAMING_SNAKE_CASE : Optional[int] = seq_length
SCREAMING_SNAKE_CASE : List[Any] = is_training
SCREAMING_SNAKE_CASE : int = use_input_mask
SCREAMING_SNAKE_CASE : Tuple = use_token_type_ids
SCREAMING_SNAKE_CASE : str = use_labels
SCREAMING_SNAKE_CASE : Any = vocab_size
SCREAMING_SNAKE_CASE : List[Any] = hidden_size
SCREAMING_SNAKE_CASE : str = num_hidden_layers
SCREAMING_SNAKE_CASE : Optional[Any] = num_attention_heads
SCREAMING_SNAKE_CASE : Tuple = intermediate_size
SCREAMING_SNAKE_CASE : Optional[int] = hidden_act
SCREAMING_SNAKE_CASE : Dict = hidden_dropout_prob
SCREAMING_SNAKE_CASE : str = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : Tuple = max_position_embeddings
SCREAMING_SNAKE_CASE : List[str] = type_vocab_size
SCREAMING_SNAKE_CASE : List[str] = type_sequence_label_size
SCREAMING_SNAKE_CASE : Optional[Any] = initializer_range
SCREAMING_SNAKE_CASE : Tuple = num_labels
SCREAMING_SNAKE_CASE : Tuple = num_choices
SCREAMING_SNAKE_CASE : Optional[Any] = scope
def __UpperCamelCase ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE : Union[str, Any] = None
if self.use_input_mask:
SCREAMING_SNAKE_CASE : str = random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE : int = None
SCREAMING_SNAKE_CASE : List[Any] = None
SCREAMING_SNAKE_CASE : List[str] = None
if self.use_labels:
SCREAMING_SNAKE_CASE : Tuple = ids_tensor([self.batch_size] , self.type_sequence_label_size )
SCREAMING_SNAKE_CASE : str = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
SCREAMING_SNAKE_CASE : Optional[Any] = ids_tensor([self.batch_size] , self.num_choices )
SCREAMING_SNAKE_CASE : int = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def __UpperCamelCase ( self : Dict ) -> str:
"""simple docstring"""
return DistilBertConfig(
vocab_size=self.vocab_size , dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , hidden_dim=self.intermediate_size , hidden_act=self.hidden_act , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , )
def __UpperCamelCase ( self : Optional[Any] , a : int , a : Optional[int] , a : Optional[int] , a : Dict , a : str , a : str ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = DistilBertModel(config=a )
model.to(a )
model.eval()
SCREAMING_SNAKE_CASE : Optional[Any] = model(a , a )
SCREAMING_SNAKE_CASE : Optional[Any] = model(a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __UpperCamelCase ( self : Tuple , a : Optional[int] , a : Dict , a : Tuple , a : int , a : int , a : Any ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = DistilBertForMaskedLM(config=a )
model.to(a )
model.eval()
SCREAMING_SNAKE_CASE : str = model(a , attention_mask=a , labels=a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __UpperCamelCase ( self : List[Any] , a : int , a : Optional[Any] , a : Optional[Any] , a : str , a : str , a : Tuple ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = DistilBertForQuestionAnswering(config=a )
model.to(a )
model.eval()
SCREAMING_SNAKE_CASE : List[str] = model(
a , attention_mask=a , start_positions=a , end_positions=a )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def __UpperCamelCase ( self : Optional[int] , a : str , a : Any , a : int , a : Optional[Any] , a : int , a : str ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = self.num_labels
SCREAMING_SNAKE_CASE : Union[str, Any] = DistilBertForSequenceClassification(a )
model.to(a )
model.eval()
SCREAMING_SNAKE_CASE : Optional[int] = model(a , attention_mask=a , labels=a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __UpperCamelCase ( self : Optional[Any] , a : List[Any] , a : Optional[int] , a : Union[str, Any] , a : Dict , a : Any , a : Optional[Any] ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = self.num_labels
SCREAMING_SNAKE_CASE : List[str] = DistilBertForTokenClassification(config=a )
model.to(a )
model.eval()
SCREAMING_SNAKE_CASE : List[str] = model(a , attention_mask=a , labels=a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __UpperCamelCase ( self : int , a : Any , a : Optional[int] , a : Union[str, Any] , a : Tuple , a : Optional[int] , a : Tuple ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = self.num_choices
SCREAMING_SNAKE_CASE : Any = DistilBertForMultipleChoice(config=a )
model.to(a )
model.eval()
SCREAMING_SNAKE_CASE : Optional[int] = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
SCREAMING_SNAKE_CASE : Dict = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
SCREAMING_SNAKE_CASE : Optional[Any] = model(
a , attention_mask=a , labels=a , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __UpperCamelCase ( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = self.prepare_config_and_inputs()
((SCREAMING_SNAKE_CASE) ,(SCREAMING_SNAKE_CASE) ,(SCREAMING_SNAKE_CASE) ,(SCREAMING_SNAKE_CASE) ,(SCREAMING_SNAKE_CASE) ,(SCREAMING_SNAKE_CASE)) : Tuple = config_and_inputs
SCREAMING_SNAKE_CASE : int = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class _UpperCamelCase ( __A , __A , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ =(
(
DistilBertModel,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
)
if is_torch_available()
else None
)
lowerCamelCase__ =(
{
'feature-extraction': DistilBertModel,
'fill-mask': DistilBertForMaskedLM,
'question-answering': DistilBertForQuestionAnswering,
'text-classification': DistilBertForSequenceClassification,
'token-classification': DistilBertForTokenClassification,
'zero-shot': DistilBertForSequenceClassification,
}
if is_torch_available()
else {}
)
lowerCamelCase__ =True
lowerCamelCase__ =True
lowerCamelCase__ =True
lowerCamelCase__ =True
def __UpperCamelCase ( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = DistilBertModelTester(self )
SCREAMING_SNAKE_CASE : List[str] = ConfigTester(self , config_class=a , dim=37 )
def __UpperCamelCase ( self : List[Any] ) -> Tuple:
"""simple docstring"""
self.config_tester.run_common_tests()
def __UpperCamelCase ( self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_model(*a )
def __UpperCamelCase ( self : Tuple ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_masked_lm(*a )
def __UpperCamelCase ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_question_answering(*a )
def __UpperCamelCase ( self : Union[str, Any] ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_sequence_classification(*a )
def __UpperCamelCase ( self : str ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_token_classification(*a )
def __UpperCamelCase ( self : List[Any] ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_multiple_choice(*a )
@slow
def __UpperCamelCase ( self : int ) -> Any:
"""simple docstring"""
for model_name in DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE : Optional[Any] = DistilBertModel.from_pretrained(a )
self.assertIsNotNone(a )
@slow
@require_torch_gpu
def __UpperCamelCase ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# BertForMultipleChoice behaves incorrectly in JIT environments.
if model_class == DistilBertForMultipleChoice:
return
SCREAMING_SNAKE_CASE : Union[str, Any] = True
SCREAMING_SNAKE_CASE : Any = model_class(config=a )
SCREAMING_SNAKE_CASE : List[Any] = self._prepare_for_class(a , a )
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.jit.trace(
a , (inputs_dict["input_ids"].to("cpu" ), inputs_dict["attention_mask"].to("cpu" )) )
with tempfile.TemporaryDirectory() as tmp:
torch.jit.save(a , os.path.join(a , "traced_model.pt" ) )
SCREAMING_SNAKE_CASE : Tuple = torch.jit.load(os.path.join(a , "traced_model.pt" ) , map_location=a )
loaded(inputs_dict["input_ids"].to(a ) , inputs_dict["attention_mask"].to(a ) )
@require_torch
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
@slow
def __UpperCamelCase ( self : int ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = DistilBertModel.from_pretrained("distilbert-base-uncased" )
SCREAMING_SNAKE_CASE : List[str] = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]] )
SCREAMING_SNAKE_CASE : Optional[int] = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
SCREAMING_SNAKE_CASE : Optional[Any] = model(a , attention_mask=a )[0]
SCREAMING_SNAKE_CASE : List[str] = torch.Size((1, 11, 768) )
self.assertEqual(output.shape , a )
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.tensor(
[[[-0.1639, 0.3299, 0.1648], [-0.1746, 0.3289, 0.1710], [-0.1884, 0.3357, 0.1810]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , a , atol=1e-4 ) ) | 25 | 0 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_A : Dict = {
"""configuration_efficientnet""": [
"""EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""EfficientNetConfig""",
"""EfficientNetOnnxConfig""",
]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A : Any = ["""EfficientNetImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A : Optional[int] = [
"""EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""EfficientNetForImageClassification""",
"""EfficientNetModel""",
"""EfficientNetPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_efficientnet import (
EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
EfficientNetConfig,
EfficientNetOnnxConfig,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_efficientnet import EfficientNetImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_efficientnet import (
EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST,
EfficientNetForImageClassification,
EfficientNetModel,
EfficientNetPreTrainedModel,
)
else:
import sys
_A : Dict = _LazyModule(__name__, globals()["""__file__"""], _import_structure)
| 100 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
a_ = {'configuration_plbart': ['PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PLBartConfig']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ['PLBartTokenizer']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'PLBART_PRETRAINED_MODEL_ARCHIVE_LIST',
'PLBartForCausalLM',
'PLBartForConditionalGeneration',
'PLBartForSequenceClassification',
'PLBartModel',
'PLBartPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_plbart import PLBartTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_plbart import (
PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
PLBartForCausalLM,
PLBartForConditionalGeneration,
PLBartForSequenceClassification,
PLBartModel,
PLBartPreTrainedModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure) | 25 | 0 |
def a__ ( A__, A__ ):
SCREAMING_SNAKE_CASE_ : Dict = len(A__ ) + 1
SCREAMING_SNAKE_CASE_ : List[str] = len(A__ ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of input_string matches with prefix string of length j of
# given pattern.
# "dp" stands for dynamic programming.
SCREAMING_SNAKE_CASE_ : Any = [[0 for i in range(A__ )] for j in range(A__ )]
# since string of zero length match pattern of zero length
SCREAMING_SNAKE_CASE_ : Union[str, Any] = 1
# since pattern of zero length will never match with string of non-zero length
for i in range(1, A__ ):
SCREAMING_SNAKE_CASE_ : List[Any] = 0
# since string of zero length will match with pattern where there
# is at least one * alternatively
for j in range(1, A__ ):
SCREAMING_SNAKE_CASE_ : List[str] = dp[0][j - 2] if pattern[j - 1] == '*' else 0
# now using bottom-up approach to find for all remaining lengths
for i in range(1, A__ ):
for j in range(1, A__ ):
if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
SCREAMING_SNAKE_CASE_ : Any = dp[i - 1][j - 1]
elif pattern[j - 1] == "*":
if dp[i][j - 2] == 1:
SCREAMING_SNAKE_CASE_ : List[str] = 1
elif pattern[j - 2] in (input_string[i - 1], "."):
SCREAMING_SNAKE_CASE_ : List[Any] = dp[i - 1][j]
else:
SCREAMING_SNAKE_CASE_ : List[str] = 0
else:
SCREAMING_SNAKE_CASE_ : int = 0
return bool(dp[-1][-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
# inputing the strings
# input_string = input("input a string :")
# pattern = input("input a pattern :")
lowerCAmelCase__ : List[Any] ='aab'
lowerCAmelCase__ : Dict ='c*a*b'
# using function to check whether given string matches the given pattern
if match_pattern(input_string, pattern):
print(F"""{input_string} matches the given pattern {pattern}""")
else:
print(F"""{input_string} does not match with the given pattern {pattern}""")
| 101 |
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
@property
def __UpperCamelCase ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : str = 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 __UpperCamelCase ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = self.dummy_uncond_unet
SCREAMING_SNAKE_CASE : Union[str, Any] = KarrasVeScheduler()
SCREAMING_SNAKE_CASE : Any = KarrasVePipeline(unet=a , scheduler=a )
pipe.to(a )
pipe.set_progress_bar_config(disable=a )
SCREAMING_SNAKE_CASE : Any = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Optional[Any] = pipe(num_inference_steps=2 , generator=a , output_type="numpy" ).images
SCREAMING_SNAKE_CASE : List[str] = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : List[Any] = pipe(num_inference_steps=2 , generator=a , output_type="numpy" , return_dict=a )[0]
SCREAMING_SNAKE_CASE : List[Any] = image[0, -3:, -3:, -1]
SCREAMING_SNAKE_CASE : Any = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
SCREAMING_SNAKE_CASE : str = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
@slow
@require_torch
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def __UpperCamelCase ( self : int ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = "google/ncsnpp-celebahq-256"
SCREAMING_SNAKE_CASE : List[Any] = UNetaDModel.from_pretrained(a )
SCREAMING_SNAKE_CASE : Any = KarrasVeScheduler()
SCREAMING_SNAKE_CASE : Optional[Any] = KarrasVePipeline(unet=a , scheduler=a )
pipe.to(a )
pipe.set_progress_bar_config(disable=a )
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Any = pipe(num_inference_steps=20 , generator=a , output_type="numpy" ).images
SCREAMING_SNAKE_CASE : List[str] = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
SCREAMING_SNAKE_CASE : str = np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2 | 25 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__magic_name__ : str = logging.get_logger(__name__)
__magic_name__ : Union[str, Any] = {
"""RWKV/rwkv-4-169m-pile""": """https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json""",
"""RWKV/rwkv-4-430m-pile""": """https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json""",
"""RWKV/rwkv-4-1b5-pile""": """https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json""",
"""RWKV/rwkv-4-3b-pile""": """https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json""",
"""RWKV/rwkv-4-7b-pile""": """https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json""",
"""RWKV/rwkv-4-14b-pile""": """https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json""",
"""RWKV/rwkv-raven-1b5""": """https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json""",
"""RWKV/rwkv-raven-3b""": """https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json""",
"""RWKV/rwkv-raven-7b""": """https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json""",
"""RWKV/rwkv-raven-14b""": """https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json""",
}
class lowercase__ ( __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
__lowerCAmelCase : Any = """rwkv"""
__lowerCAmelCase : List[str] = {"""max_position_embeddings""": """context_length"""}
def __init__( self , _A=5_0_2_7_7 , _A=1_0_2_4 , _A=4_0_9_6 , _A=3_2 , _A=None , _A=None , _A=1e-5 , _A=0 , _A=0 , _A=6 , _A=False , _A=True , **_A , ):
'''simple docstring'''
UpperCamelCase : Any = vocab_size
UpperCamelCase : Optional[Any] = context_length
UpperCamelCase : str = hidden_size
UpperCamelCase : int = num_hidden_layers
UpperCamelCase : Dict = attention_hidden_size if attention_hidden_size is not None else hidden_size
UpperCamelCase : Any = intermediate_size if intermediate_size is not None else 4 * hidden_size
UpperCamelCase : Any = layer_norm_epsilon
UpperCamelCase : Optional[int] = rescale_every
UpperCamelCase : Optional[int] = use_cache
UpperCamelCase : Union[str, Any] = bos_token_id
UpperCamelCase : Any = eos_token_id
super().__init__(
tie_word_embeddings=_A , bos_token_id=_A , eos_token_id=_A , **_A )
| 102 |
def lowerCamelCase__ ( _a , _a):
SCREAMING_SNAKE_CASE : Optional[int] = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return res
def lowerCamelCase__ ( _a , _a , _a):
SCREAMING_SNAKE_CASE : Optional[int] = 0
while b > 0:
if b & 1:
SCREAMING_SNAKE_CASE : Optional[Any] = ((res % c) + (a % c)) % c
a += a
b >>= 1
return res | 25 | 0 |
"""simple docstring"""
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_torch_available():
import torch
if is_datasets_available():
from datasets import load_dataset
snake_case = random.Random()
def snake_case ( lowerCAmelCase_ , lowerCAmelCase_=1.0 , lowerCAmelCase_=None , lowerCAmelCase_=None ) -> List[str]:
if rng is None:
_snake_case = global_rng
_snake_case = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
class UpperCAmelCase ( unittest.TestCase ):
def __init__( self : Tuple , __lowerCamelCase : List[str] , __lowerCamelCase : Optional[Any]=7 , __lowerCamelCase : List[Any]=4_0_0 , __lowerCamelCase : Any=2_0_0_0 , __lowerCamelCase : Any=2_0_4_8 , __lowerCamelCase : Any=1_2_8 , __lowerCamelCase : Any=1 , __lowerCamelCase : Optional[int]=5_1_2 , __lowerCamelCase : Tuple=3_0 , __lowerCamelCase : List[Any]=4_4_1_0_0 , ):
"""simple docstring"""
_snake_case = parent
_snake_case = batch_size
_snake_case = min_seq_length
_snake_case = max_seq_length
_snake_case = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
_snake_case = spectrogram_length
_snake_case = feature_size
_snake_case = num_audio_channels
_snake_case = hop_length
_snake_case = chunk_length
_snake_case = sampling_rate
def __UpperCAmelCase ( self : List[Any] ):
"""simple docstring"""
return {
"spectrogram_length": self.spectrogram_length,
"feature_size": self.feature_size,
"num_audio_channels": self.num_audio_channels,
"hop_length": self.hop_length,
"chunk_length": self.chunk_length,
"sampling_rate": self.sampling_rate,
}
def __UpperCAmelCase ( self : Union[str, Any] , __lowerCamelCase : Any=False , __lowerCamelCase : int=False ):
"""simple docstring"""
def _flatten(__lowerCamelCase : List[str] ):
return list(itertools.chain(*__lowerCamelCase ) )
if equal_length:
_snake_case = [floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
_snake_case = [
floats_list((x, self.feature_size) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
_snake_case = [np.asarray(__lowerCamelCase ) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE,unittest.TestCase ):
A__ : Tuple = TvltFeatureExtractor
def __UpperCAmelCase ( self : int ):
"""simple docstring"""
_snake_case = TvltFeatureExtractionTester(self )
def __UpperCAmelCase ( self : Any ):
"""simple docstring"""
_snake_case = self.feature_extraction_class(**self.feat_extract_dict )
self.assertTrue(hasattr(__lowerCamelCase , '''spectrogram_length''' ) )
self.assertTrue(hasattr(__lowerCamelCase , '''feature_size''' ) )
self.assertTrue(hasattr(__lowerCamelCase , '''num_audio_channels''' ) )
self.assertTrue(hasattr(__lowerCamelCase , '''hop_length''' ) )
self.assertTrue(hasattr(__lowerCamelCase , '''chunk_length''' ) )
self.assertTrue(hasattr(__lowerCamelCase , '''sampling_rate''' ) )
def __UpperCAmelCase ( self : int ):
"""simple docstring"""
_snake_case = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
_snake_case = feat_extract_first.save_pretrained(__lowerCamelCase )[0]
check_json_file_has_correct_format(__lowerCamelCase )
_snake_case = self.feature_extraction_class.from_pretrained(__lowerCamelCase )
_snake_case = feat_extract_first.to_dict()
_snake_case = feat_extract_second.to_dict()
_snake_case = dict_first.pop('''mel_filters''' )
_snake_case = dict_second.pop('''mel_filters''' )
self.assertTrue(np.allclose(__lowerCamelCase , __lowerCamelCase ) )
self.assertEqual(__lowerCamelCase , __lowerCamelCase )
def __UpperCAmelCase ( self : int ):
"""simple docstring"""
_snake_case = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
_snake_case = os.path.join(__lowerCamelCase , '''feat_extract.json''' )
feat_extract_first.to_json_file(__lowerCamelCase )
_snake_case = self.feature_extraction_class.from_json_file(__lowerCamelCase )
_snake_case = feat_extract_first.to_dict()
_snake_case = feat_extract_second.to_dict()
_snake_case = dict_first.pop('''mel_filters''' )
_snake_case = dict_second.pop('''mel_filters''' )
self.assertTrue(np.allclose(__lowerCamelCase , __lowerCamelCase ) )
self.assertEqual(__lowerCamelCase , __lowerCamelCase )
def __UpperCAmelCase ( self : Dict ):
"""simple docstring"""
# Initialize feature_extractor
_snake_case = self.feature_extraction_class(**self.feat_extract_dict )
# create three inputs of length 800, 1000, and 1200
_snake_case = [floats_list((1, x) )[0] for x in range(8_0_0 , 1_4_0_0 , 2_0_0 )]
_snake_case = [np.asarray(__lowerCamelCase ) for speech_input in speech_inputs]
# Test not batched input
_snake_case = feature_extractor(np_speech_inputs[0] , return_tensors='''np''' , sampling_rate=4_4_1_0_0 ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
# Test batched
_snake_case = feature_extractor(__lowerCamelCase , return_tensors='''np''' , sampling_rate=4_4_1_0_0 ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
# Test audio masking
_snake_case = feature_extractor(
__lowerCamelCase , return_tensors='''np''' , sampling_rate=4_4_1_0_0 , mask_audio=__lowerCamelCase ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
# Test 2-D numpy arrays are batched.
_snake_case = [floats_list((1, x) )[0] for x in (8_0_0, 8_0_0, 8_0_0)]
_snake_case = np.asarray(__lowerCamelCase )
_snake_case = feature_extractor(__lowerCamelCase , return_tensors='''np''' , sampling_rate=4_4_1_0_0 ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
def __UpperCAmelCase ( self : Union[str, Any] , __lowerCamelCase : Optional[int] ):
"""simple docstring"""
_snake_case = load_dataset('''hf-internal-testing/librispeech_asr_dummy''' , '''clean''' , split='''validation''' )
# automatic decoding with librispeech
_snake_case = ds.sort('''id''' ).select(range(__lowerCamelCase ) )[:num_samples]['''audio''']
return [x["array"] for x in speech_samples]
def __UpperCAmelCase ( self : Dict ):
"""simple docstring"""
_snake_case = self._load_datasamples(1 )
_snake_case = TvltFeatureExtractor()
_snake_case = feature_extractor(__lowerCamelCase , return_tensors='''pt''' ).audio_values
self.assertEquals(audio_values.shape , (1, 1, 1_9_2, 1_2_8) )
_snake_case = torch.tensor([[-0.3_0_3_2, -0.2_7_0_8], [-0.4_4_3_4, -0.4_0_0_7]] )
self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2] , __lowerCamelCase , atol=1E-4 ) )
| 103 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json',
'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json',
'junnyu/roformer_chinese_char_small': (
'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'
),
'junnyu/roformer_chinese_char_base': (
'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'
),
'junnyu/roformer_small_discriminator': (
'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'
),
'junnyu/roformer_small_generator': (
'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'
),
# See all RoFormer models at https://huggingface.co/models?filter=roformer
}
class _UpperCamelCase ( __A ):
'''simple docstring'''
lowerCamelCase__ ='roformer'
def __init__( self : Dict , a : Any=5_0000 , a : List[Any]=None , a : str=768 , a : str=12 , a : Tuple=12 , a : Optional[Any]=3072 , a : List[str]="gelu" , a : List[Any]=0.1 , a : Union[str, Any]=0.1 , a : Tuple=1536 , a : List[str]=2 , a : Tuple=0.02 , a : Any=1e-12 , a : Optional[int]=0 , a : Union[str, Any]=False , a : int=True , **a : str , ) -> int:
"""simple docstring"""
super().__init__(pad_token_id=a , **a )
SCREAMING_SNAKE_CASE : str = vocab_size
SCREAMING_SNAKE_CASE : int = hidden_size if embedding_size is None else embedding_size
SCREAMING_SNAKE_CASE : List[str] = hidden_size
SCREAMING_SNAKE_CASE : Union[str, Any] = num_hidden_layers
SCREAMING_SNAKE_CASE : int = num_attention_heads
SCREAMING_SNAKE_CASE : Tuple = hidden_act
SCREAMING_SNAKE_CASE : int = intermediate_size
SCREAMING_SNAKE_CASE : Tuple = hidden_dropout_prob
SCREAMING_SNAKE_CASE : int = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : Optional[int] = max_position_embeddings
SCREAMING_SNAKE_CASE : Any = type_vocab_size
SCREAMING_SNAKE_CASE : Union[str, Any] = initializer_range
SCREAMING_SNAKE_CASE : List[str] = layer_norm_eps
SCREAMING_SNAKE_CASE : List[str] = rotary_value
SCREAMING_SNAKE_CASE : int = use_cache
class _UpperCamelCase ( __A ):
'''simple docstring'''
@property
def __UpperCamelCase ( self : Tuple ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
if self.task == "multiple-choice":
SCREAMING_SNAKE_CASE : Optional[Any] = {0: "batch", 1: "choice", 2: "sequence"}
else:
SCREAMING_SNAKE_CASE : str = {0: "batch", 1: "sequence"}
SCREAMING_SNAKE_CASE : List[Any] = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
("token_type_ids", dynamic_axis),
] ) | 25 | 0 |
"""simple docstring"""
def _lowerCamelCase ( UpperCAmelCase_ : int = 200 ) -> int:
"""simple docstring"""
A__ = [1, 2, 5, 10, 20, 50, 100, 200]
A__ = [0] * (pence + 1)
A__ = 1 # base case: 1 way to make 0 pence
for coin in coins:
for i in range(UpperCAmelCase_, pence + 1, 1 ):
number_of_ways[i] += number_of_ways[i - coin]
return number_of_ways[pence]
if __name__ == "__main__":
assert solution(200) == 7_3682
| 104 |
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
a_ = logging.getLogger(__name__)
a_ = 'Hello world! cécé herlolip'
a_ = namedtuple(
'BertAbsConfig',
[
'temp_dir',
'large',
'use_bert_emb',
'finetune_bert',
'encoder',
'share_emb',
'max_pos',
'enc_layers',
'enc_hidden_size',
'enc_heads',
'enc_ff_size',
'enc_dropout',
'dec_layers',
'dec_hidden_size',
'dec_heads',
'dec_ff_size',
'dec_dropout',
],
)
def lowerCamelCase__ ( _a , _a):
SCREAMING_SNAKE_CASE : List[Any] = BertAbsConfig(
temp_dir="." , finetune_bert=_a , large=_a , share_emb=_a , use_bert_emb=_a , encoder="bert" , max_pos=512 , enc_layers=6 , enc_hidden_size=512 , enc_heads=8 , enc_ff_size=512 , enc_dropout=0.2 , dec_layers=6 , dec_hidden_size=768 , dec_heads=8 , dec_ff_size=2048 , dec_dropout=0.2 , )
SCREAMING_SNAKE_CASE : Dict = torch.load(_a , lambda _a , _a: storage)
SCREAMING_SNAKE_CASE : str = AbsSummarizer(_a , torch.device("cpu") , _a)
original.eval()
SCREAMING_SNAKE_CASE : List[str] = BertAbsSummarizer(_a , torch.device("cpu"))
new_model.eval()
# -------------------
# Convert the weights
# -------------------
logging.info("convert the model")
new_model.bert.load_state_dict(original.bert.state_dict())
new_model.decoder.load_state_dict(original.decoder.state_dict())
new_model.generator.load_state_dict(original.generator.state_dict())
# ----------------------------------
# Make sure the outpus are identical
# ----------------------------------
logging.info("Make sure that the models' outputs are identical")
SCREAMING_SNAKE_CASE : List[str] = BertTokenizer.from_pretrained("bert-base-uncased")
# prepare the model inputs
SCREAMING_SNAKE_CASE : List[str] = tokenizer.encode("This is sample éàalj'-.")
encoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(_a)))
SCREAMING_SNAKE_CASE : int = torch.tensor(_a).unsqueeze(0)
SCREAMING_SNAKE_CASE : List[Any] = tokenizer.encode("This is sample 3 éàalj'-.")
decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(_a)))
SCREAMING_SNAKE_CASE : int = torch.tensor(_a).unsqueeze(0)
# failsafe to make sure the weights reset does not affect the
# loaded weights.
assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight)) == 0
# forward pass
SCREAMING_SNAKE_CASE : List[Any] = encoder_input_ids
SCREAMING_SNAKE_CASE : List[Any] = decoder_input_ids
SCREAMING_SNAKE_CASE : Dict = None
SCREAMING_SNAKE_CASE : Optional[Any] = None
SCREAMING_SNAKE_CASE : List[Any] = None
SCREAMING_SNAKE_CASE : Optional[int] = None
SCREAMING_SNAKE_CASE : Dict = None
# The original model does not apply the geneator layer immediatly but rather in
# the beam search (where it combines softmax + linear layer). Since we already
# apply the softmax in our generation process we only apply the linear layer here.
# We make sure that the outputs of the full stack are identical
SCREAMING_SNAKE_CASE : Optional[int] = original(_a , _a , _a , _a , _a , _a , _a)[0]
SCREAMING_SNAKE_CASE : Dict = original.generator(_a)
SCREAMING_SNAKE_CASE : Any = new_model(
_a , _a , _a , _a , _a)[0]
SCREAMING_SNAKE_CASE : Tuple = new_model.generator(_a)
SCREAMING_SNAKE_CASE : List[Any] = torch.max(torch.abs(output_converted_model - output_original_model)).item()
print("Maximum absolute difference beween weights: {:.2f}".format(_a))
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.max(torch.abs(output_converted_generator - output_original_generator)).item()
print("Maximum absolute difference beween weights: {:.2f}".format(_a))
SCREAMING_SNAKE_CASE : int = torch.allclose(_a , _a , atol=1E-3)
if are_identical:
logging.info("all weights are equal up to 1e-3")
else:
raise ValueError("the weights are different. The new model is likely different from the original one.")
# The model has been saved with torch.save(model) and this is bound to the exact
# directory structure. We save the state_dict instead.
logging.info("saving the model's state dictionary")
torch.save(
new_model.state_dict() , "./bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin")
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
parser.add_argument(
'--bertabs_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_ = parser.parse_args()
convert_bertabs_checkpoints(
args.bertabs_checkpoint_path,
args.pytorch_dump_folder_path,
) | 25 | 0 |
import fire
from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer
def __UpperCAmelCase ( lowerCamelCase_ : str , lowerCamelCase_ : str , **lowerCamelCase_ : List[str] ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : str = AutoConfig.from_pretrained(lowerCamelCase_ , **lowerCamelCase_ )
SCREAMING_SNAKE_CASE_ : Optional[int] = AutoModelForSeqaSeqLM.from_config(lowerCamelCase_ )
model.save_pretrained(lowerCamelCase_ )
AutoTokenizer.from_pretrained(lowerCamelCase_ ).save_pretrained(lowerCamelCase_ )
return model
if __name__ == "__main__":
fire.Fire(save_randomly_initialized_version)
| 105 |
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
parser.add_argument(
'--txt2img_unclip',
default='kakaobrain/karlo-v1-alpha',
type=str,
required=False,
help='The pretrained txt2img unclip.',
)
a_ = parser.parse_args()
a_ = UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)
a_ = CLIPImageProcessor()
a_ = CLIPVisionModelWithProjection.from_pretrained('openai/clip-vit-large-patch14')
a_ = UnCLIPImageVariationPipeline(
decoder=txtaimg.decoder,
text_encoder=txtaimg.text_encoder,
tokenizer=txtaimg.tokenizer,
text_proj=txtaimg.text_proj,
feature_extractor=feature_extractor,
image_encoder=image_encoder,
super_res_first=txtaimg.super_res_first,
super_res_last=txtaimg.super_res_last,
decoder_scheduler=txtaimg.decoder_scheduler,
super_res_scheduler=txtaimg.super_res_scheduler,
)
imgaimg.save_pretrained(args.dump_path) | 25 | 0 |
from __future__ import annotations
__snake_case :Union[str, Any] =tuple[int, int, int]
__snake_case :Tuple =tuple[str, str, str]
# used alphabet --------------------------
# from string.ascii_uppercase
__snake_case :Tuple ='ABCDEFGHIJKLMNOPQRSTUVWXYZ'
# -------------------------- default selection --------------------------
# rotors --------------------------
__snake_case :List[str] ='EGZWVONAHDCLFQMSIPJBYUKXTR'
__snake_case :Any ='FOBHMDKEXQNRAULPGSJVTYICZW'
__snake_case :Union[str, Any] ='ZJXESIUQLHAVRMDOYGTNFWPBKC'
# reflector --------------------------
__snake_case :List[str] ={
'A': 'N',
'N': 'A',
'B': 'O',
'O': 'B',
'C': 'P',
'P': 'C',
'D': 'Q',
'Q': 'D',
'E': 'R',
'R': 'E',
'F': 'S',
'S': 'F',
'G': 'T',
'T': 'G',
'H': 'U',
'U': 'H',
'I': 'V',
'V': 'I',
'J': 'W',
'W': 'J',
'K': 'X',
'X': 'K',
'L': 'Y',
'Y': 'L',
'M': 'Z',
'Z': 'M',
}
# -------------------------- extra rotors --------------------------
__snake_case :List[Any] ='RMDJXFUWGISLHVTCQNKYPBEZOA'
__snake_case :Tuple ='SGLCPQWZHKXAREONTFBVIYJUDM'
__snake_case :str ='HVSICLTYKQUBXDWAJZOMFGPREN'
__snake_case :int ='RZWQHFMVDBKICJLNTUXAGYPSOE'
__snake_case :Dict ='LFKIJODBEGAMQPXVUHYSTCZRWN'
__snake_case :Any ='KOAEGVDHXPQZMLFTYWJNBRCIUS'
def lowerCamelCase_ ( lowerCAmelCase__ : RotorPositionT , lowerCAmelCase__ : RotorSelectionT , lowerCAmelCase__ : str ) -> tuple[RotorPositionT, RotorSelectionT, dict[str, str]]:
'''simple docstring'''
if (unique_rotsel := len(set(lowerCAmelCase__ ) )) < 3:
A = F'''Please use 3 unique rotors (not {unique_rotsel})'''
raise Exception(lowerCAmelCase__ )
# Checks if rotor positions are valid
A , A , A = rotpos
if not 0 < rotorposa <= len(lowerCAmelCase__ ):
A = F'''First rotor position is not within range of 1..26 ({rotorposa}'''
raise ValueError(lowerCAmelCase__ )
if not 0 < rotorposa <= len(lowerCAmelCase__ ):
A = F'''Second rotor position is not within range of 1..26 ({rotorposa})'''
raise ValueError(lowerCAmelCase__ )
if not 0 < rotorposa <= len(lowerCAmelCase__ ):
A = F'''Third rotor position is not within range of 1..26 ({rotorposa})'''
raise ValueError(lowerCAmelCase__ )
# Validates string and returns dict
A = _plugboard(lowerCAmelCase__ )
return rotpos, rotsel, pbdict
def lowerCamelCase_ ( lowerCAmelCase__ : str ) -> dict[str, str]:
'''simple docstring'''
if not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
A = F'''Plugboard setting isn\'t type string ({type(lowerCAmelCase__ )})'''
raise TypeError(lowerCAmelCase__ )
elif len(lowerCAmelCase__ ) % 2 != 0:
A = F'''Odd number of symbols ({len(lowerCAmelCase__ )})'''
raise Exception(lowerCAmelCase__ )
elif pbstring == "":
return {}
pbstring.replace(' ' , '' )
# Checks if all characters are unique
A = set()
for i in pbstring:
if i not in abc:
A = F'''\'{i}\' not in list of symbols'''
raise Exception(lowerCAmelCase__ )
elif i in tmppbl:
A = F'''Duplicate symbol ({i})'''
raise Exception(lowerCAmelCase__ )
else:
tmppbl.add(lowerCAmelCase__ )
del tmppbl
# Created the dictionary
A = {}
for j in range(0 , len(lowerCAmelCase__ ) - 1 , 2 ):
A = pbstring[j + 1]
A = pbstring[j]
return pb
def lowerCamelCase_ ( lowerCAmelCase__ : str , lowerCAmelCase__ : RotorPositionT , lowerCAmelCase__ : RotorSelectionT = (rotora, rotora, rotora) , lowerCAmelCase__ : str = "" , ) -> str:
'''simple docstring'''
A = text.upper()
A , A , A = _validator(
lowerCAmelCase__ , lowerCAmelCase__ , plugb.upper() )
A , A , A = rotor_position
A , A , A = rotor_selection
rotorposa -= 1
rotorposa -= 1
rotorposa -= 1
A = []
# encryption/decryption process --------------------------
for symbol in text:
if symbol in abc:
# 1st plugboard --------------------------
if symbol in plugboard:
A = plugboard[symbol]
# rotor ra --------------------------
A = abc.index(lowerCAmelCase__ ) + rotorposa
A = rotora[index % len(lowerCAmelCase__ )]
# rotor rb --------------------------
A = abc.index(lowerCAmelCase__ ) + rotorposa
A = rotora[index % len(lowerCAmelCase__ )]
# rotor rc --------------------------
A = abc.index(lowerCAmelCase__ ) + rotorposa
A = rotora[index % len(lowerCAmelCase__ )]
# reflector --------------------------
# this is the reason you don't need another machine to decipher
A = reflector[symbol]
# 2nd rotors
A = abc[rotora.index(lowerCAmelCase__ ) - rotorposa]
A = abc[rotora.index(lowerCAmelCase__ ) - rotorposa]
A = abc[rotora.index(lowerCAmelCase__ ) - rotorposa]
# 2nd plugboard
if symbol in plugboard:
A = plugboard[symbol]
# moves/resets rotor positions
rotorposa += 1
if rotorposa >= len(lowerCAmelCase__ ):
A = 0
rotorposa += 1
if rotorposa >= len(lowerCAmelCase__ ):
A = 0
rotorposa += 1
if rotorposa >= len(lowerCAmelCase__ ):
A = 0
# else:
# pass
# Error could be also raised
# raise ValueError(
# 'Invalid symbol('+repr(symbol)+')')
result.append(lowerCAmelCase__ )
return "".join(lowerCAmelCase__ )
if __name__ == "__main__":
__snake_case :Optional[Any] ='This is my Python script that emulates the Enigma machine from WWII.'
__snake_case :Any =(1, 1, 1)
__snake_case :Optional[int] ='pictures'
__snake_case :Dict =(rotora, rotora, rotora)
__snake_case :Union[str, Any] =enigma(message, rotor_pos, rotor_sel, pb)
print('Encrypted message:', en)
print('Decrypted message:', enigma(en, rotor_pos, rotor_sel, pb)) | 106 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
'configuration_swinv2': ['SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Swinv2Config'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST',
'Swinv2ForImageClassification',
'Swinv2ForMaskedImageModeling',
'Swinv2Model',
'Swinv2PreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_swinva import (
SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
SwinvaForImageClassification,
SwinvaForMaskedImageModeling,
SwinvaModel,
SwinvaPreTrainedModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 25 | 0 |
'''simple docstring'''
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
_UpperCAmelCase : Any = '''\
@misc{wu2016googles,
title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey
and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin
Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto
Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and
Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes
and Jeffrey Dean},
year={2016},
eprint={1609.08144},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
'''
_UpperCAmelCase : str = '''\
The BLEU score has some undesirable properties when used for single
sentences, as it was designed to be a corpus measure. We therefore
use a slightly different score for our RL experiments which we call
the \'GLEU score\'. For the GLEU score, we record all sub-sequences of
1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then
compute a recall, which is the ratio of the number of matching n-grams
to the number of total n-grams in the target (ground truth) sequence,
and a precision, which is the ratio of the number of matching n-grams
to the number of total n-grams in the generated output sequence. Then
GLEU score is simply the minimum of recall and precision. This GLEU
score\'s range is always between 0 (no matches) and 1 (all match) and
it is symmetrical when switching output and target. According to
our experiments, GLEU score correlates quite well with the BLEU
metric on a corpus level but does not have its drawbacks for our per
sentence reward objective.
'''
_UpperCAmelCase : Union[str, Any] = '''\
Computes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.
Instead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching
tokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.
Args:
predictions (list of str): list of translations to score.
Each translation should be tokenized into a list of tokens.
references (list of list of str): list of lists of references for each translation.
Each reference should be tokenized into a list of tokens.
min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.
max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.
Returns:
\'google_bleu\': google_bleu score
Examples:
Example 1:
>>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
>>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
>>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
... \'interested\', \'in\', \'world\', \'history\']
>>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
... \'because\', \'he\', \'read\', \'the\', \'book\']
>>> list_of_references = [[ref1a], [ref2a]]
>>> hypotheses = [hyp1, hyp2]
>>> google_bleu = datasets.load_metric("google_bleu")
>>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
>>> print(round(results["google_bleu"], 2))
0.44
Example 2:
>>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
>>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
>>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',
... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',
... \'heed\', \'the\', \'cat\', \'commands\']
>>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',
... \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',
... \'of\', \'the\', \'cat\']
>>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
... \'interested\', \'in\', \'world\', \'history\']
>>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
... \'because\', \'he\', \'read\', \'the\', \'book\']
>>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
>>> hypotheses = [hyp1, hyp2]
>>> google_bleu = datasets.load_metric("google_bleu")
>>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
>>> print(round(results["google_bleu"], 2))
0.61
Example 3:
>>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
>>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
>>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',
... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',
... \'heed\', \'the\', \'cat\', \'commands\']
>>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',
... \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',
... \'of\', \'the\', \'cat\']
>>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
... \'interested\', \'in\', \'world\', \'history\']
>>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
... \'because\', \'he\', \'read\', \'the\', \'book\']
>>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
>>> hypotheses = [hyp1, hyp2]
>>> google_bleu = datasets.load_metric("google_bleu")
>>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)
>>> print(round(results["google_bleu"], 2))
0.53
Example 4:
>>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
>>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
>>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',
... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',
... \'heed\', \'the\', \'cat\', \'commands\']
>>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',
... \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',
... \'of\', \'the\', \'cat\']
>>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
... \'interested\', \'in\', \'world\', \'history\']
>>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
... \'because\', \'he\', \'read\', \'the\', \'book\']
>>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
>>> hypotheses = [hyp1, hyp2]
>>> google_bleu = datasets.load_metric("google_bleu")
>>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)
>>> print(round(results["google_bleu"], 2))
0.4
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class lowercase_ ( datasets.Metric ):
"""simple docstring"""
def __UpperCAmelCase ( self : List[Any] ) -> MetricInfo:
return datasets.MetricInfo(
description=_DESCRIPTION, citation=_CITATION, inputs_description=_KWARGS_DESCRIPTION, features=datasets.Features(
{
'predictions': datasets.Sequence(datasets.Value('string', id='token' ), id='sequence' ),
'references': datasets.Sequence(
datasets.Sequence(datasets.Value('string', id='token' ), id='sequence' ), id='references' ),
} ), )
def __UpperCAmelCase ( self : Dict, UpperCamelCase__ : List[List[List[str]]], UpperCamelCase__ : List[List[str]], UpperCamelCase__ : int = 1, UpperCamelCase__ : int = 4, ) -> Dict[str, float]:
return {
"google_bleu": gleu_score.corpus_gleu(
list_of_references=UpperCamelCase__, hypotheses=UpperCamelCase__, min_len=UpperCamelCase__, max_len=UpperCamelCase__ )
}
| 107 |
from math import pi, sqrt, tan
def lowerCamelCase__ ( _a):
if side_length < 0:
raise ValueError("surface_area_cube() only accepts non-negative values")
return 6 * side_length**2
def lowerCamelCase__ ( _a , _a , _a):
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 lowerCamelCase__ ( _a):
if radius < 0:
raise ValueError("surface_area_sphere() only accepts non-negative values")
return 4 * pi * radius**2
def lowerCamelCase__ ( _a):
if radius < 0:
raise ValueError("surface_area_hemisphere() only accepts non-negative values")
return 3 * pi * radius**2
def lowerCamelCase__ ( _a , _a):
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 lowerCamelCase__ ( _a , _a , _a):
if radius_a < 0 or radius_a < 0 or height < 0:
raise ValueError(
"surface_area_conical_frustum() only accepts non-negative values")
SCREAMING_SNAKE_CASE : Any = (height**2 + (radius_a - radius_a) ** 2) ** 0.5
return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)
def lowerCamelCase__ ( _a , _a):
if radius < 0 or height < 0:
raise ValueError("surface_area_cylinder() only accepts non-negative values")
return 2 * pi * radius * (height + radius)
def lowerCamelCase__ ( _a , _a):
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(_a , 2) * torus_radius * tube_radius
def lowerCamelCase__ ( _a , _a):
if length < 0 or width < 0:
raise ValueError("area_rectangle() only accepts non-negative values")
return length * width
def lowerCamelCase__ ( _a):
if side_length < 0:
raise ValueError("area_square() only accepts non-negative values")
return side_length**2
def lowerCamelCase__ ( _a , _a):
if base < 0 or height < 0:
raise ValueError("area_triangle() only accepts non-negative values")
return (base * height) / 2
def lowerCamelCase__ ( _a , _a , _a):
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")
SCREAMING_SNAKE_CASE : List[str] = (sidea + sidea + sidea) / 2
SCREAMING_SNAKE_CASE : Optional[int] = sqrt(
semi_perimeter
* (semi_perimeter - sidea)
* (semi_perimeter - sidea)
* (semi_perimeter - sidea))
return area
def lowerCamelCase__ ( _a , _a):
if base < 0 or height < 0:
raise ValueError("area_parallelogram() only accepts non-negative values")
return base * height
def lowerCamelCase__ ( _a , _a , _a):
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 lowerCamelCase__ ( _a):
if radius < 0:
raise ValueError("area_circle() only accepts non-negative values")
return pi * radius**2
def lowerCamelCase__ ( _a , _a):
if radius_x < 0 or radius_y < 0:
raise ValueError("area_ellipse() only accepts non-negative values")
return pi * radius_x * radius_y
def lowerCamelCase__ ( _a , _a):
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 lowerCamelCase__ ( _a , _a):
if not isinstance(_a , _a) 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(10, 20) = }''')
print(F'''Square: {area_square(10) = }''')
print(F'''Triangle: {area_triangle(10, 10) = }''')
print(F'''Triangle: {area_triangle_three_sides(5, 12, 13) = }''')
print(F'''Parallelogram: {area_parallelogram(10, 20) = }''')
print(F'''Rhombus: {area_rhombus(10, 20) = }''')
print(F'''Trapezium: {area_trapezium(10, 20, 30) = }''')
print(F'''Circle: {area_circle(20) = }''')
print(F'''Ellipse: {area_ellipse(10, 20) = }''')
print('\nSurface Areas of various geometric shapes: \n')
print(F'''Cube: {surface_area_cube(20) = }''')
print(F'''Cuboid: {surface_area_cuboid(10, 20, 30) = }''')
print(F'''Sphere: {surface_area_sphere(20) = }''')
print(F'''Hemisphere: {surface_area_hemisphere(20) = }''')
print(F'''Cone: {surface_area_cone(10, 20) = }''')
print(F'''Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }''')
print(F'''Cylinder: {surface_area_cylinder(10, 20) = }''')
print(F'''Torus: {surface_area_torus(20, 10) = }''')
print(F'''Equilateral Triangle: {area_reg_polygon(3, 10) = }''')
print(F'''Square: {area_reg_polygon(4, 10) = }''')
print(F'''Reqular Pentagon: {area_reg_polygon(5, 10) = }''') | 25 | 0 |
import gc
import threading
import time
import psutil
import torch
class SCREAMING_SNAKE_CASE__ :
'''simple docstring'''
def __init__( self : Union[str, Any] ) -> str:
"""simple docstring"""
_UpperCAmelCase = psutil.Process()
_UpperCAmelCase = False
def lowerCamelCase ( self : List[Any] ) -> int:
"""simple docstring"""
_UpperCAmelCase = -1
while True:
_UpperCAmelCase = max(self.process.memory_info().rss , self.cpu_memory_peak )
# can't sleep or will not catch the peak right (this comment is here on purpose)
if not self.peak_monitoring:
break
def lowerCamelCase ( self : Optional[Any] ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase = True
_UpperCAmelCase = threading.Thread(target=self.peak_monitor )
_UpperCAmelCase = True
self.thread.start()
def lowerCamelCase ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = False
self.thread.join()
return self.cpu_memory_peak
__a: Union[str, Any] = PeakCPUMemory()
def _SCREAMING_SNAKE_CASE ( ) -> List[Any]:
# Time
_UpperCAmelCase = {"""time""": time.time()}
gc.collect()
torch.cuda.empty_cache()
# CPU mem
_UpperCAmelCase = psutil.Process().memory_info().rss
cpu_peak_tracker.start()
# GPU mem
for i in range(torch.cuda.device_count() ):
_UpperCAmelCase = torch.cuda.memory_allocated(__snake_case )
torch.cuda.reset_peak_memory_stats()
return measures
def _SCREAMING_SNAKE_CASE ( __snake_case ) -> Union[str, Any]:
# Time
_UpperCAmelCase = {"""time""": time.time() - start_measures["""time"""]}
gc.collect()
torch.cuda.empty_cache()
# CPU mem
_UpperCAmelCase = (psutil.Process().memory_info().rss - start_measures["""cpu"""]) / 2**2_0
_UpperCAmelCase = (cpu_peak_tracker.stop() - start_measures["""cpu"""]) / 2**2_0
# GPU mem
for i in range(torch.cuda.device_count() ):
_UpperCAmelCase = (torch.cuda.memory_allocated(__snake_case ) - start_measures[str(__snake_case )]) / 2**2_0
_UpperCAmelCase = (torch.cuda.max_memory_allocated(__snake_case ) - start_measures[str(__snake_case )]) / 2**2_0
return measures
def _SCREAMING_SNAKE_CASE ( __snake_case , __snake_case ) -> str:
print(f"""{description}:""" )
print(f"""- Time: {measures["time"]:.2f}s""" )
for i in range(torch.cuda.device_count() ):
print(f"""- GPU {i} allocated: {measures[str(__snake_case )]:.2f}MiB""" )
_UpperCAmelCase = measures[f"""{i}-peak"""]
print(f"""- GPU {i} peak: {peak:.2f}MiB""" )
print(f"""- CPU RAM allocated: {measures["cpu"]:.2f}MiB""" )
print(f"""- CPU RAM peak: {measures["cpu-peak"]:.2f}MiB""" ) | 108 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
'configuration_instructblip': [
'INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'InstructBlipConfig',
'InstructBlipQFormerConfig',
'InstructBlipVisionConfig',
],
'processing_instructblip': ['InstructBlipProcessor'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
'InstructBlipQFormerModel',
'InstructBlipPreTrainedModel',
'InstructBlipForConditionalGeneration',
'InstructBlipVisionModel',
]
if TYPE_CHECKING:
from .configuration_instructblip import (
INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
InstructBlipConfig,
InstructBlipQFormerConfig,
InstructBlipVisionConfig,
)
from .processing_instructblip import InstructBlipProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_instructblip import (
INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
InstructBlipForConditionalGeneration,
InstructBlipPreTrainedModel,
InstructBlipQFormerModel,
InstructBlipVisionModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 25 | 0 |
'''simple docstring'''
import unittest
from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available
from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
if is_bsa_available():
from transformers import MarkupLMFeatureExtractor
class __a ( unittest.TestCase ):
def __init__( self : Any ,lowerCamelCase : Optional[int] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = parent
def UpperCAmelCase__ ( self : Dict ):
'''simple docstring'''
return {}
def __magic_name__ ( ) -> Optional[Any]:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = """<HTML>
<HEAD>
<TITLE>sample document</TITLE>
</HEAD>
<BODY BGCOLOR=\"FFFFFF\">
<HR>
<a href=\"http://google.com\">Goog</a>
<H1>This is one header</H1>
<H2>This is a another Header</H2>
<P>Travel from
<P>
<B>SFO to JFK</B>
<BR>
<B><I>on May 2, 2015 at 2:00 pm. For details go to confirm.com </I></B>
<HR>
<div style=\"color:#0000FF\">
<h3>Traveler <b> name </b> is
<p> John Doe </p>
</div>"""
__SCREAMING_SNAKE_CASE = """
<!DOCTYPE html>
<html>
<body>
<h1>My First Heading</h1>
<p>My first paragraph.</p>
</body>
</html>
"""
return [html_string_a, html_string_a]
@require_bsa
class __a ( _snake_case, unittest.TestCase ):
__UpperCamelCase : Any = MarkupLMFeatureExtractor if is_bsa_available() else None
def UpperCAmelCase__ ( self : Optional[int] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = MarkupLMFeatureExtractionTester(self )
@property
def UpperCAmelCase__ ( self : Dict ):
'''simple docstring'''
return self.feature_extract_tester.prepare_feat_extract_dict()
def UpperCAmelCase__ ( self : Tuple ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = self.feature_extraction_class()
# Test not batched input
__SCREAMING_SNAKE_CASE = get_html_strings()[0]
__SCREAMING_SNAKE_CASE = feature_extractor(lowerCamelCase )
# fmt: off
__SCREAMING_SNAKE_CASE = [["""sample document""", """Goog""", """This is one header""", """This is a another Header""", """Travel from""", """SFO to JFK""", """on May 2, 2015 at 2:00 pm. For details go to confirm.com""", """Traveler""", """name""", """is""", """John Doe"""]]
__SCREAMING_SNAKE_CASE = [["""/html/head/title""", """/html/body/a""", """/html/body/h1""", """/html/body/h2""", """/html/body/p""", """/html/body/p/p/b[1]""", """/html/body/p/p/b[2]/i""", """/html/body/p/p/div/h3""", """/html/body/p/p/div/h3/b""", """/html/body/p/p/div/h3""", """/html/body/p/p/div/h3/p"""]]
# fmt: on
self.assertEqual(encoding.nodes ,lowerCamelCase )
self.assertEqual(encoding.xpaths ,lowerCamelCase )
# Test batched
__SCREAMING_SNAKE_CASE = get_html_strings()
__SCREAMING_SNAKE_CASE = feature_extractor(lowerCamelCase )
# fmt: off
__SCREAMING_SNAKE_CASE = expected_nodes + [["""My First Heading""", """My first paragraph."""]]
__SCREAMING_SNAKE_CASE = expected_xpaths + [["""/html/body/h1""", """/html/body/p"""]]
self.assertEqual(len(encoding.nodes ) ,2 )
self.assertEqual(len(encoding.xpaths ) ,2 )
self.assertEqual(encoding.nodes ,lowerCamelCase )
self.assertEqual(encoding.xpaths ,lowerCamelCase )
| 109 |
from __future__ import annotations
def lowerCamelCase__ ( _a):
SCREAMING_SNAKE_CASE : Optional[Any] = 2
SCREAMING_SNAKE_CASE : Optional[int] = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(_a)
if n > 1:
factors.append(_a)
return factors
if __name__ == "__main__":
import doctest
doctest.testmod() | 25 | 0 |
"""simple docstring"""
import unittest
from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available
from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
if is_bsa_available():
from transformers import MarkupLMFeatureExtractor
class a ( unittest.TestCase ):
def __init__( self , UpperCamelCase_ ):
UpperCAmelCase__ : Union[str, Any] = parent
def __snake_case ( self ):
return {}
def lowerCamelCase ( ):
UpperCAmelCase__ : Dict = '<HTML>\n\n <HEAD>\n <TITLE>sample document</TITLE>\n </HEAD>\n\n <BODY BGCOLOR="FFFFFF">\n <HR>\n <a href="http://google.com">Goog</a>\n <H1>This is one header</H1>\n <H2>This is a another Header</H2>\n <P>Travel from\n <P>\n <B>SFO to JFK</B>\n <BR>\n <B><I>on May 2, 2015 at 2:00 pm. For details go to confirm.com </I></B>\n <HR>\n <div style="color:#0000FF">\n <h3>Traveler <b> name </b> is\n <p> John Doe </p>\n </div>'
UpperCAmelCase__ : Optional[int] = '\n <!DOCTYPE html>\n <html>\n <body>\n\n <h1>My First Heading</h1>\n <p>My first paragraph.</p>\n\n </body>\n </html>\n '
return [html_string_a, html_string_a]
@require_bsa
class a ( lowercase , unittest.TestCase ):
UpperCamelCase : str = MarkupLMFeatureExtractor if is_bsa_available() else None
def __snake_case ( self ):
UpperCAmelCase__ : Optional[int] = MarkupLMFeatureExtractionTester(self )
@property
def __snake_case ( self ):
return self.feature_extract_tester.prepare_feat_extract_dict()
def __snake_case ( self ):
# Initialize feature_extractor
UpperCAmelCase__ : Any = self.feature_extraction_class()
# Test not batched input
UpperCAmelCase__ : Tuple = get_html_strings()[0]
UpperCAmelCase__ : Optional[Any] = feature_extractor(UpperCamelCase_ )
# fmt: off
UpperCAmelCase__ : Union[str, Any] = [['sample document', 'Goog', 'This is one header', 'This is a another Header', 'Travel from', 'SFO to JFK', 'on May 2, 2015 at 2:00 pm. For details go to confirm.com', 'Traveler', 'name', 'is', 'John Doe']]
UpperCAmelCase__ : int = [['/html/head/title', '/html/body/a', '/html/body/h1', '/html/body/h2', '/html/body/p', '/html/body/p/p/b[1]', '/html/body/p/p/b[2]/i', '/html/body/p/p/div/h3', '/html/body/p/p/div/h3/b', '/html/body/p/p/div/h3', '/html/body/p/p/div/h3/p']]
# fmt: on
self.assertEqual(encoding.nodes , UpperCamelCase_ )
self.assertEqual(encoding.xpaths , UpperCamelCase_ )
# Test batched
UpperCAmelCase__ : List[str] = get_html_strings()
UpperCAmelCase__ : List[Any] = feature_extractor(UpperCamelCase_ )
# fmt: off
UpperCAmelCase__ : Union[str, Any] = expected_nodes + [['My First Heading', 'My first paragraph.']]
UpperCAmelCase__ : Dict = expected_xpaths + [['/html/body/h1', '/html/body/p']]
self.assertEqual(len(encoding.nodes ) , 2 )
self.assertEqual(len(encoding.xpaths ) , 2 )
self.assertEqual(encoding.nodes , UpperCamelCase_ )
self.assertEqual(encoding.xpaths , UpperCamelCase_ )
| 110 |
from math import factorial, pi
def lowerCamelCase__ ( _a , _a = 30):
if not isinstance(_a , (int, float)):
raise ValueError("maclaurin_sin() requires either an int or float for theta")
if not isinstance(_a , _a) or accuracy <= 0:
raise ValueError("maclaurin_sin() requires a positive int for accuracy")
SCREAMING_SNAKE_CASE : int = float(_a)
SCREAMING_SNAKE_CASE : Dict = theta // (2 * pi)
theta -= 2 * div * pi
return sum(
(-1) ** r * theta ** (2 * r + 1) / factorial(2 * r + 1) for r in range(_a))
def lowerCamelCase__ ( _a , _a = 30):
if not isinstance(_a , (int, float)):
raise ValueError("maclaurin_cos() requires either an int or float for theta")
if not isinstance(_a , _a) or accuracy <= 0:
raise ValueError("maclaurin_cos() requires a positive int for accuracy")
SCREAMING_SNAKE_CASE : str = float(_a)
SCREAMING_SNAKE_CASE : Any = theta // (2 * pi)
theta -= 2 * div * pi
return sum((-1) ** r * theta ** (2 * r) / factorial(2 * r) for r in range(_a))
if __name__ == "__main__":
import doctest
doctest.testmod()
print(maclaurin_sin(10))
print(maclaurin_sin(-10))
print(maclaurin_sin(10, 15))
print(maclaurin_sin(-10, 15))
print(maclaurin_cos(5))
print(maclaurin_cos(-5))
print(maclaurin_cos(10, 15))
print(maclaurin_cos(-10, 15)) | 25 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__SCREAMING_SNAKE_CASE :Union[str, Any] = {
'''configuration_roberta_prelayernorm''': [
'''ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''RobertaPreLayerNormConfig''',
'''RobertaPreLayerNormOnnxConfig''',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE :Optional[int] = [
'''ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''RobertaPreLayerNormForCausalLM''',
'''RobertaPreLayerNormForMaskedLM''',
'''RobertaPreLayerNormForMultipleChoice''',
'''RobertaPreLayerNormForQuestionAnswering''',
'''RobertaPreLayerNormForSequenceClassification''',
'''RobertaPreLayerNormForTokenClassification''',
'''RobertaPreLayerNormModel''',
'''RobertaPreLayerNormPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE :Optional[Any] = [
'''TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFRobertaPreLayerNormForCausalLM''',
'''TFRobertaPreLayerNormForMaskedLM''',
'''TFRobertaPreLayerNormForMultipleChoice''',
'''TFRobertaPreLayerNormForQuestionAnswering''',
'''TFRobertaPreLayerNormForSequenceClassification''',
'''TFRobertaPreLayerNormForTokenClassification''',
'''TFRobertaPreLayerNormMainLayer''',
'''TFRobertaPreLayerNormModel''',
'''TFRobertaPreLayerNormPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE :Any = [
'''FlaxRobertaPreLayerNormForCausalLM''',
'''FlaxRobertaPreLayerNormForMaskedLM''',
'''FlaxRobertaPreLayerNormForMultipleChoice''',
'''FlaxRobertaPreLayerNormForQuestionAnswering''',
'''FlaxRobertaPreLayerNormForSequenceClassification''',
'''FlaxRobertaPreLayerNormForTokenClassification''',
'''FlaxRobertaPreLayerNormModel''',
'''FlaxRobertaPreLayerNormPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_roberta_prelayernorm import (
ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
RobertaPreLayerNormConfig,
RobertaPreLayerNormOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roberta_prelayernorm import (
ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
RobertaPreLayerNormForCausalLM,
RobertaPreLayerNormForMaskedLM,
RobertaPreLayerNormForMultipleChoice,
RobertaPreLayerNormForQuestionAnswering,
RobertaPreLayerNormForSequenceClassification,
RobertaPreLayerNormForTokenClassification,
RobertaPreLayerNormModel,
RobertaPreLayerNormPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roberta_prelayernorm import (
TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRobertaPreLayerNormForCausalLM,
TFRobertaPreLayerNormForMaskedLM,
TFRobertaPreLayerNormForMultipleChoice,
TFRobertaPreLayerNormForQuestionAnswering,
TFRobertaPreLayerNormForSequenceClassification,
TFRobertaPreLayerNormForTokenClassification,
TFRobertaPreLayerNormMainLayer,
TFRobertaPreLayerNormModel,
TFRobertaPreLayerNormPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roberta_prelayernorm import (
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormModel,
FlaxRobertaPreLayerNormPreTrainedModel,
)
else:
import sys
__SCREAMING_SNAKE_CASE :Optional[int] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 236 |
from __future__ import annotations
import math
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Dict , a : int ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = size
# approximate the overall size of segment tree with given value
SCREAMING_SNAKE_CASE : Any = [0 for i in range(0 , 4 * size )]
# create array to store lazy update
SCREAMING_SNAKE_CASE : Union[str, Any] = [0 for i in range(0 , 4 * size )]
SCREAMING_SNAKE_CASE : Any = [0 for i in range(0 , 4 * size )] # flag for lazy update
def __UpperCamelCase ( self : Tuple , a : int ) -> int:
"""simple docstring"""
return idx * 2
def __UpperCamelCase ( self : str , a : int ) -> int:
"""simple docstring"""
return idx * 2 + 1
def __UpperCamelCase ( self : int , a : int , a : int , a : int , a : list[int] ) -> None:
"""simple docstring"""
if left_element == right_element:
SCREAMING_SNAKE_CASE : int = a[left_element - 1]
else:
SCREAMING_SNAKE_CASE : Optional[int] = (left_element + right_element) // 2
self.build(self.left(a ) , a , a , a )
self.build(self.right(a ) , mid + 1 , a , a )
SCREAMING_SNAKE_CASE : List[Any] = max(
self.segment_tree[self.left(a )] , self.segment_tree[self.right(a )] )
def __UpperCamelCase ( self : Optional[Any] , a : int , a : int , a : int , a : int , a : int , a : int ) -> bool:
"""simple docstring"""
if self.flag[idx] is True:
SCREAMING_SNAKE_CASE : Any = self.lazy[idx]
SCREAMING_SNAKE_CASE : List[str] = False
if left_element != right_element:
SCREAMING_SNAKE_CASE : Optional[Any] = self.lazy[idx]
SCREAMING_SNAKE_CASE : int = self.lazy[idx]
SCREAMING_SNAKE_CASE : Any = True
SCREAMING_SNAKE_CASE : List[Any] = True
if right_element < a or left_element > b:
return True
if left_element >= a and right_element <= b:
SCREAMING_SNAKE_CASE : Optional[Any] = val
if left_element != right_element:
SCREAMING_SNAKE_CASE : str = val
SCREAMING_SNAKE_CASE : str = val
SCREAMING_SNAKE_CASE : Tuple = True
SCREAMING_SNAKE_CASE : Optional[Any] = True
return True
SCREAMING_SNAKE_CASE : int = (left_element + right_element) // 2
self.update(self.left(a ) , a , a , a , a , a )
self.update(self.right(a ) , mid + 1 , a , a , a , a )
SCREAMING_SNAKE_CASE : Optional[int] = max(
self.segment_tree[self.left(a )] , self.segment_tree[self.right(a )] )
return True
def __UpperCamelCase ( self : Dict , a : int , a : int , a : int , a : int , a : int ) -> int | float:
"""simple docstring"""
if self.flag[idx] is True:
SCREAMING_SNAKE_CASE : int = self.lazy[idx]
SCREAMING_SNAKE_CASE : List[Any] = False
if left_element != right_element:
SCREAMING_SNAKE_CASE : Optional[Any] = self.lazy[idx]
SCREAMING_SNAKE_CASE : Optional[Any] = self.lazy[idx]
SCREAMING_SNAKE_CASE : Optional[Any] = True
SCREAMING_SNAKE_CASE : Union[str, Any] = True
if right_element < a or left_element > b:
return -math.inf
if left_element >= a and right_element <= b:
return self.segment_tree[idx]
SCREAMING_SNAKE_CASE : Dict = (left_element + right_element) // 2
SCREAMING_SNAKE_CASE : Tuple = self.query(self.left(a ) , a , a , a , a )
SCREAMING_SNAKE_CASE : Tuple = self.query(self.right(a ) , mid + 1 , a , a , a )
return max(a , a )
def __str__( self : str ) -> str:
"""simple docstring"""
return str([self.query(1 , 1 , self.size , a , a ) for i in range(1 , self.size + 1 )] )
if __name__ == "__main__":
a_ = [1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
a_ = 15
a_ = SegmentTree(size)
segt.build(1, 1, size, A)
print(segt.query(1, 1, size, 4, 6))
print(segt.query(1, 1, size, 7, 11))
print(segt.query(1, 1, size, 7, 12))
segt.update(1, 1, size, 1, 3, 111)
print(segt.query(1, 1, size, 1, 15))
segt.update(1, 1, size, 7, 8, 235)
print(segt) | 25 | 0 |
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
UpperCamelCase__ : Tuple = '''\\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'''
UpperCamelCase__ : List[Any] = '''\\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'''
UpperCamelCase__ : Optional[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 lowerCAmelCase_ ( datasets.Metric ):
def snake_case ( self ):
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 snake_case ( self ,snake_case__ ,snake_case__ ,snake_case__=None ,snake_case__=None ,snake_case__=None ,snake_case__=None ,snake_case__="auto" ,snake_case__=-1 ,snake_case__=0.9 ,snake_case__=5 ,snake_case__=500 ,snake_case__="gpt2-large" ,snake_case__=-1 ,snake_case__=1024 ,snake_case__=25 ,snake_case__=5 ,snake_case__=True ,snake_case__=25 ,):
SCREAMING_SNAKE_CASE_ : Union[str, Any] = compute_mauve(
p_text=snake_case__ ,q_text=snake_case__ ,p_features=snake_case__ ,q_features=snake_case__ ,p_tokens=snake_case__ ,q_tokens=snake_case__ ,num_buckets=snake_case__ ,pca_max_data=snake_case__ ,kmeans_explained_var=snake_case__ ,kmeans_num_redo=snake_case__ ,kmeans_max_iter=snake_case__ ,featurize_model_name=snake_case__ ,device_id=snake_case__ ,max_text_length=snake_case__ ,divergence_curve_discretization_size=snake_case__ ,mauve_scaling_factor=snake_case__ ,verbose=snake_case__ ,seed=snake_case__ ,)
return out
| 105 |
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
@slow
@require_torch_gpu
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def __UpperCamelCase ( self : Dict ) -> Tuple:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __UpperCamelCase ( self : Optional[int] ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = StableDiffusionKDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4" )
SCREAMING_SNAKE_CASE : str = sd_pipe.to(a )
sd_pipe.set_progress_bar_config(disable=a )
sd_pipe.set_scheduler("sample_euler" )
SCREAMING_SNAKE_CASE : Optional[int] = "A painting of a squirrel eating a burger"
SCREAMING_SNAKE_CASE : Any = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : List[Any] = sd_pipe([prompt] , generator=a , guidance_scale=9.0 , num_inference_steps=20 , output_type="np" )
SCREAMING_SNAKE_CASE : Tuple = output.images
SCREAMING_SNAKE_CASE : Any = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE : Union[str, Any] = np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def __UpperCamelCase ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base" )
SCREAMING_SNAKE_CASE : Tuple = sd_pipe.to(a )
sd_pipe.set_progress_bar_config(disable=a )
sd_pipe.set_scheduler("sample_euler" )
SCREAMING_SNAKE_CASE : List[str] = "A painting of a squirrel eating a burger"
SCREAMING_SNAKE_CASE : List[str] = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Union[str, Any] = sd_pipe([prompt] , generator=a , guidance_scale=9.0 , num_inference_steps=20 , output_type="np" )
SCREAMING_SNAKE_CASE : List[Any] = output.images
SCREAMING_SNAKE_CASE : Tuple = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE : int = np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-1
def __UpperCamelCase ( self : Tuple ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base" )
SCREAMING_SNAKE_CASE : Union[str, Any] = sd_pipe.to(a )
sd_pipe.set_progress_bar_config(disable=a )
sd_pipe.set_scheduler("sample_dpmpp_2m" )
SCREAMING_SNAKE_CASE : str = "A painting of a squirrel eating a burger"
SCREAMING_SNAKE_CASE : Any = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : str = sd_pipe(
[prompt] , generator=a , guidance_scale=7.5 , num_inference_steps=15 , output_type="np" , use_karras_sigmas=a , )
SCREAMING_SNAKE_CASE : str = output.images
SCREAMING_SNAKE_CASE : Optional[int] = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE : int = np.array(
[0.1138_1689, 0.1211_2921, 0.138_9457, 0.1254_9606, 0.124_4964, 0.1083_1517, 0.1156_2866, 0.1086_7816, 0.1049_9048] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2 | 25 | 0 |
'''simple docstring'''
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler
lowerCAmelCase_ : int = 16
lowerCAmelCase_ : Optional[Any] = 32
def UpperCAmelCase ( A : Tuple , A : Optional[int] = 16 , A : int = "bert-base-cased" ):
SCREAMING_SNAKE_CASE : List[Any] = AutoTokenizer.from_pretrained(_a )
SCREAMING_SNAKE_CASE : str = load_dataset('''glue''' , '''mrpc''' )
def tokenize_function(A : str ):
# max_length=None => use the model max length (it's actually the default)
SCREAMING_SNAKE_CASE : Any = tokenizer(examples['''sentence1'''] , examples['''sentence2'''] , truncation=_a , max_length=_a )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
SCREAMING_SNAKE_CASE : Optional[int] = datasets.map(
_a , batched=_a , remove_columns=['''idx''', '''sentence1''', '''sentence2'''] , load_from_cache_file=_a )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
SCREAMING_SNAKE_CASE : Union[str, Any] = tokenized_datasets.rename_column('''label''' , '''labels''' )
def collate_fn(A : Tuple ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(_a , padding='''max_length''' , max_length=128 , return_tensors='''pt''' )
return tokenizer.pad(_a , padding='''longest''' , return_tensors='''pt''' )
# Instantiate dataloaders.
SCREAMING_SNAKE_CASE : Any = DataLoader(
tokenized_datasets['''train'''] , shuffle=_a , collate_fn=_a , batch_size=_a )
SCREAMING_SNAKE_CASE : Optional[Any] = DataLoader(
tokenized_datasets['''validation'''] , shuffle=_a , collate_fn=_a , batch_size=_a )
return train_dataloader, eval_dataloader
def UpperCAmelCase ( A : Dict , A : int ):
# Initialize accelerator
SCREAMING_SNAKE_CASE : Dict = Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
SCREAMING_SNAKE_CASE : int = config["lr"]
SCREAMING_SNAKE_CASE : Any = int(config['''num_epochs'''] )
SCREAMING_SNAKE_CASE : Tuple = int(config['''seed'''] )
SCREAMING_SNAKE_CASE : Tuple = int(config['''batch_size'''] )
SCREAMING_SNAKE_CASE : Dict = args.model_name_or_path
set_seed(_a )
SCREAMING_SNAKE_CASE : str = get_dataloaders(_a , _a , _a )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
SCREAMING_SNAKE_CASE : Any = AutoModelForSequenceClassification.from_pretrained(_a , return_dict=_a )
# Instantiate optimizer
SCREAMING_SNAKE_CASE : Optional[Any] = (
AdamW
if accelerator.state.deepspeed_plugin is None
or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
SCREAMING_SNAKE_CASE : Dict = optimizer_cls(params=model.parameters() , lr=_a )
if accelerator.state.deepspeed_plugin is not None:
SCREAMING_SNAKE_CASE : Tuple = accelerator.state.deepspeed_plugin.deepspeed_config[
"gradient_accumulation_steps"
]
else:
SCREAMING_SNAKE_CASE : Union[str, Any] = 1
SCREAMING_SNAKE_CASE : Optional[Any] = (len(_a ) * num_epochs) // gradient_accumulation_steps
# Instantiate scheduler
if (
accelerator.state.deepspeed_plugin is None
or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
):
SCREAMING_SNAKE_CASE : Optional[int] = get_linear_schedule_with_warmup(
optimizer=_a , num_warmup_steps=0 , num_training_steps=_a , )
else:
SCREAMING_SNAKE_CASE : Dict = DummyScheduler(_a , total_num_steps=_a , warmup_num_steps=0 )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
SCREAMING_SNAKE_CASE : Dict = accelerator.prepare(
_a , _a , _a , _a , _a )
# We need to keep track of how many total steps we have iterated over
SCREAMING_SNAKE_CASE : Dict = 0
# We also need to keep track of the stating epoch so files are named properly
SCREAMING_SNAKE_CASE : Any = 0
# Now we train the model
SCREAMING_SNAKE_CASE : Optional[int] = evaluate.load('''glue''' , '''mrpc''' )
SCREAMING_SNAKE_CASE : Optional[Any] = 0
SCREAMING_SNAKE_CASE : str = {}
for epoch in range(_a , _a ):
model.train()
for step, batch in enumerate(_a ):
SCREAMING_SNAKE_CASE : Dict = model(**_a )
SCREAMING_SNAKE_CASE : int = outputs.loss
SCREAMING_SNAKE_CASE : Tuple = loss / gradient_accumulation_steps
accelerator.backward(_a )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
model.eval()
SCREAMING_SNAKE_CASE : Union[str, Any] = 0
for step, batch in enumerate(_a ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
SCREAMING_SNAKE_CASE : List[str] = model(**_a )
SCREAMING_SNAKE_CASE : Optional[int] = outputs.logits.argmax(dim=-1 )
# It is slightly faster to call this once, than multiple times
SCREAMING_SNAKE_CASE : List[str] = accelerator.gather(
(predictions, batch['''labels''']) ) # If we are in a multiprocess environment, the last batch has duplicates
if accelerator.use_distributed:
if step == len(_a ) - 1:
SCREAMING_SNAKE_CASE : str = predictions[: len(eval_dataloader.dataset ) - samples_seen]
SCREAMING_SNAKE_CASE : Tuple = references[: len(eval_dataloader.dataset ) - samples_seen]
else:
samples_seen += references.shape[0]
metric.add_batch(
predictions=_a , references=_a , )
SCREAMING_SNAKE_CASE : Tuple = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F"""epoch {epoch}:""" , _a )
SCREAMING_SNAKE_CASE : Tuple = eval_metric["accuracy"]
if best_performance < eval_metric["accuracy"]:
SCREAMING_SNAKE_CASE : str = eval_metric["accuracy"]
if args.performance_lower_bound is not None:
assert (
args.performance_lower_bound <= best_performance
), F"""Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}"""
accelerator.wait_for_everyone()
if accelerator.is_main_process:
with open(os.path.join(args.output_dir , '''all_results.json''' ) , '''w''' ) as f:
json.dump(_a , _a )
def UpperCAmelCase ( ):
SCREAMING_SNAKE_CASE : Optional[Any] = argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.''' )
parser.add_argument(
'''--model_name_or_path''' , type=_a , default='''bert-base-cased''' , help='''Path to pretrained model or model identifier from huggingface.co/models.''' , required=_a , )
parser.add_argument(
'''--output_dir''' , type=_a , default='''.''' , help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.''' , )
parser.add_argument(
'''--performance_lower_bound''' , type=_a , default=_a , help='''Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.''' , )
parser.add_argument(
'''--num_epochs''' , type=_a , default=3 , help='''Number of train epochs.''' , )
SCREAMING_SNAKE_CASE : str = parser.parse_args()
SCREAMING_SNAKE_CASE : List[Any] = {"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
training_function(_a , _a )
if __name__ == "__main__":
main()
| 527 |
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class _UpperCamelCase ( __A , unittest.TestCase ):
'''simple docstring'''
pass
@nightly
@require_onnxruntime
@require_torch_gpu
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
@property
def __UpperCamelCase ( self : List[Any] ) -> List[str]:
"""simple docstring"""
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def __UpperCamelCase ( self : int ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = ort.SessionOptions()
SCREAMING_SNAKE_CASE : Union[str, Any] = False
return options
def __UpperCamelCase ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo.png" )
SCREAMING_SNAKE_CASE : Optional[Any] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo_mask.png" )
SCREAMING_SNAKE_CASE : int = OnnxStableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting" , revision="onnx" , safety_checker=a , feature_extractor=a , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=a )
SCREAMING_SNAKE_CASE : Optional[Any] = "A red cat sitting on a park bench"
SCREAMING_SNAKE_CASE : Tuple = np.random.RandomState(0 )
SCREAMING_SNAKE_CASE : Optional[int] = pipe(
prompt=a , image=a , mask_image=a , guidance_scale=7.5 , num_inference_steps=10 , generator=a , output_type="np" , )
SCREAMING_SNAKE_CASE : List[Any] = output.images
SCREAMING_SNAKE_CASE : Union[str, Any] = images[0, 255:258, 255:258, -1]
assert images.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE : int = np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def __UpperCamelCase ( self : List[Any] ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo.png" )
SCREAMING_SNAKE_CASE : Optional[Any] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo_mask.png" )
SCREAMING_SNAKE_CASE : Optional[Any] = LMSDiscreteScheduler.from_pretrained(
"runwayml/stable-diffusion-inpainting" , subfolder="scheduler" , revision="onnx" )
SCREAMING_SNAKE_CASE : Union[str, Any] = OnnxStableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting" , revision="onnx" , scheduler=a , safety_checker=a , feature_extractor=a , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=a )
SCREAMING_SNAKE_CASE : List[Any] = "A red cat sitting on a park bench"
SCREAMING_SNAKE_CASE : Dict = np.random.RandomState(0 )
SCREAMING_SNAKE_CASE : Tuple = pipe(
prompt=a , image=a , mask_image=a , guidance_scale=7.5 , num_inference_steps=20 , generator=a , output_type="np" , )
SCREAMING_SNAKE_CASE : List[str] = output.images
SCREAMING_SNAKE_CASE : Optional[int] = images[0, 255:258, 255:258, -1]
assert images.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE : Any = np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3 | 25 | 0 |
from typing import List, Union
import numpy as np
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING
__UpperCAmelCase = logging.get_logger(__name__)
@add_end_docstrings(__A )
class SCREAMING_SNAKE_CASE ( __A ):
"""simple docstring"""
def __init__( self : List[Any] , *lowerCAmelCase : Optional[Any] , **lowerCAmelCase : Optional[Any] ) -> List[Any]:
"""simple docstring"""
super().__init__(*lowerCAmelCase , **lowerCAmelCase )
requires_backends(self , """vision""" )
self.check_model_type(lowerCAmelCase )
def __call__( self : int , lowerCAmelCase : Union[str, List[str], "Image.Image", List["Image.Image"]] , **lowerCAmelCase : Optional[int] ) -> List[Any]:
"""simple docstring"""
return super().__call__(lowerCAmelCase , **lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self : Tuple , **lowerCAmelCase : Any ) -> Optional[Any]:
"""simple docstring"""
return {}, {}, {}
def SCREAMING_SNAKE_CASE ( self : int , lowerCAmelCase : List[Any] ) -> Tuple:
"""simple docstring"""
__lowerCAmelCase : str = load_image(lowerCAmelCase )
__lowerCAmelCase : Union[str, Any] = image.size
__lowerCAmelCase : List[str] = self.image_processor(images=lowerCAmelCase , return_tensors=self.framework )
return model_inputs
def SCREAMING_SNAKE_CASE ( self : Optional[int] , lowerCAmelCase : Any ) -> Tuple:
"""simple docstring"""
__lowerCAmelCase : Dict = self.model(**lowerCAmelCase )
return model_outputs
def SCREAMING_SNAKE_CASE ( self : List[str] , lowerCAmelCase : Tuple ) -> List[Any]:
"""simple docstring"""
__lowerCAmelCase : List[str] = model_outputs.predicted_depth
__lowerCAmelCase : List[str] = torch.nn.functional.interpolate(
predicted_depth.unsqueeze(1 ) , size=self.image_size[::-1] , mode="""bicubic""" , align_corners=lowerCAmelCase )
__lowerCAmelCase : List[Any] = prediction.squeeze().cpu().numpy()
__lowerCAmelCase : List[Any] = (output * 2_55 / np.max(lowerCAmelCase )).astype("""uint8""" )
__lowerCAmelCase : Any = Image.fromarray(lowerCAmelCase )
__lowerCAmelCase : Optional[int] = {}
__lowerCAmelCase : Optional[int] = predicted_depth
__lowerCAmelCase : Optional[int] = depth
return output_dict
| 651 |
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def lowerCamelCase__ ( _a):
return getitem, k
def lowerCamelCase__ ( _a , _a):
return setitem, k, v
def lowerCamelCase__ ( _a):
return delitem, k
def lowerCamelCase__ ( _a , _a , *_a):
try:
return fun(_a , *_a), None
except Exception as e:
return None, e
a_ = (
_set('key_a', 'val_a'),
_set('key_b', 'val_b'),
)
a_ = [
_set('key_a', 'val_a'),
_set('key_a', 'val_b'),
]
a_ = [
_set('key_a', 'val_a'),
_set('key_b', 'val_b'),
_del('key_a'),
_del('key_b'),
_set('key_a', 'val_a'),
_del('key_a'),
]
a_ = [
_get('key_a'),
_del('key_a'),
_set('key_a', 'val_a'),
_del('key_a'),
_del('key_a'),
_get('key_a'),
]
a_ = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
]
a_ = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
*[_del(x) for x in range(5)],
_set('key_a', 'val_b'),
]
@pytest.mark.parametrize(
"operations" , (
pytest.param(_add_items , id="add items"),
pytest.param(_overwrite_items , id="overwrite items"),
pytest.param(_delete_items , id="delete items"),
pytest.param(_access_absent_items , id="access absent items"),
pytest.param(_add_with_resize_up , id="add with resize up"),
pytest.param(_add_with_resize_down , id="add with resize down"),
) , )
def lowerCamelCase__ ( _a):
SCREAMING_SNAKE_CASE : Dict = HashMap(initial_block_size=4)
SCREAMING_SNAKE_CASE : List[str] = {}
for _, (fun, *args) in enumerate(_a):
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Union[str, Any] = _run_operation(_a , _a , *_a)
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : List[Any] = _run_operation(_a , _a , *_a)
assert my_res == py_res
assert str(_a) == str(_a)
assert set(_a) == set(_a)
assert len(_a) == len(_a)
assert set(my.items()) == set(py.items())
def lowerCamelCase__ ( ):
def is_public(_a) -> bool:
return not name.startswith("_")
SCREAMING_SNAKE_CASE : List[str] = {name for name in dir({}) if is_public(_a)}
SCREAMING_SNAKE_CASE : Union[str, Any] = {name for name in dir(HashMap()) if is_public(_a)}
assert dict_public_names > hash_public_names | 25 | 0 |
from .imports import is_tqdm_available
if is_tqdm_available():
from tqdm.auto import tqdm as _tqdm
from ..state import PartialState
def snake_case( __magic_name__ = True , *__magic_name__ , **__magic_name__ ) -> Dict:
'''simple docstring'''
if not is_tqdm_available():
raise ImportError('''Accelerate\'s `tqdm` module requires `tqdm` to be installed. Please run `pip install tqdm`.''' )
lowercase : List[Any] = False
if main_process_only:
lowercase : Optional[int] = PartialState().local_process_index == 0
return _tqdm(*_a , **_a , disable=_a ) | 217 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a_ = {'configuration_van': ['VAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VanConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'VAN_PRETRAINED_MODEL_ARCHIVE_LIST',
'VanForImageClassification',
'VanModel',
'VanPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_van import (
VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
VanForImageClassification,
VanModel,
VanPreTrainedModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure) | 25 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE_ = {'configuration_reformer': ['REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ReformerConfig']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ = ['ReformerTokenizer']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ = ['ReformerTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ = [
'REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'ReformerAttention',
'ReformerForMaskedLM',
'ReformerForQuestionAnswering',
'ReformerForSequenceClassification',
'ReformerLayer',
'ReformerModel',
'ReformerModelWithLMHead',
'ReformerPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_reformer import ReformerTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_reformer_fast import ReformerTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_reformer import (
REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
ReformerAttention,
ReformerForMaskedLM,
ReformerForQuestionAnswering,
ReformerForSequenceClassification,
ReformerLayer,
ReformerModel,
ReformerModelWithLMHead,
ReformerPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 300 |
from __future__ import annotations
def lowerCamelCase__ ( _a):
if len(_a) == 0:
return []
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Tuple = min(_a), max(_a)
SCREAMING_SNAKE_CASE : Dict = int(max_value - min_value) + 1
SCREAMING_SNAKE_CASE : list[list] = [[] for _ in range(_a)]
for i in my_list:
buckets[int(i - min_value)].append(_a)
return [v for bucket in buckets for v in sorted(_a)]
if __name__ == "__main__":
from doctest import testmod
testmod()
assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
assert bucket_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15] | 25 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
@slow
@require_torch_gpu
class __snake_case( unittest.TestCase ):
'''simple docstring'''
def __snake_case ( self ) -> Tuple:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __snake_case ( self ) -> int:
lowerCAmelCase = StableDiffusionKDiffusionPipeline.from_pretrained("""CompVis/stable-diffusion-v1-4""" )
lowerCAmelCase = sd_pipe.to(A_ )
sd_pipe.set_progress_bar_config(disable=A_ )
sd_pipe.set_scheduler("""sample_euler""" )
lowerCAmelCase = "A painting of a squirrel eating a burger"
lowerCAmelCase = torch.manual_seed(0 )
lowerCAmelCase = sd_pipe([prompt] , generator=A_ , guidance_scale=9.0 , num_inference_steps=20 , output_type="""np""" )
lowerCAmelCase = output.images
lowerCAmelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
lowerCAmelCase = np.array([0.0_4_4_7, 0.0_4_9_2, 0.0_4_6_8, 0.0_4_0_8, 0.0_3_8_3, 0.0_4_0_8, 0.0_3_5_4, 0.0_3_8_0, 0.0_3_3_9] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def __snake_case ( self ) -> Optional[int]:
lowerCAmelCase = StableDiffusionKDiffusionPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base""" )
lowerCAmelCase = sd_pipe.to(A_ )
sd_pipe.set_progress_bar_config(disable=A_ )
sd_pipe.set_scheduler("""sample_euler""" )
lowerCAmelCase = "A painting of a squirrel eating a burger"
lowerCAmelCase = torch.manual_seed(0 )
lowerCAmelCase = sd_pipe([prompt] , generator=A_ , guidance_scale=9.0 , num_inference_steps=20 , output_type="""np""" )
lowerCAmelCase = output.images
lowerCAmelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
lowerCAmelCase = np.array([0.1_2_3_7, 0.1_3_2_0, 0.1_4_3_8, 0.1_3_5_9, 0.1_3_9_0, 0.1_1_3_2, 0.1_2_7_7, 0.1_1_7_5, 0.1_1_1_2] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-1
def __snake_case ( self ) -> List[Any]:
lowerCAmelCase = StableDiffusionKDiffusionPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base""" )
lowerCAmelCase = sd_pipe.to(A_ )
sd_pipe.set_progress_bar_config(disable=A_ )
sd_pipe.set_scheduler("""sample_dpmpp_2m""" )
lowerCAmelCase = "A painting of a squirrel eating a burger"
lowerCAmelCase = torch.manual_seed(0 )
lowerCAmelCase = sd_pipe(
[prompt] , generator=A_ , guidance_scale=7.5 , num_inference_steps=15 , output_type="""np""" , use_karras_sigmas=A_ , )
lowerCAmelCase = output.images
lowerCAmelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
lowerCAmelCase = np.array(
[0.1_1_3_8_1_6_8_9, 0.1_2_1_1_2_9_2_1, 0.1_3_8_9_4_5_7, 0.1_2_5_4_9_6_0_6, 0.1_2_4_4_9_6_4, 0.1_0_8_3_1_5_1_7, 0.1_1_5_6_2_8_6_6, 0.1_0_8_6_7_8_1_6, 0.1_0_4_9_9_0_4_8] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2 | 433 |
a_ = frozenset(
[
'prompt',
'height',
'width',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
'cross_attention_kwargs',
]
)
a_ = frozenset(['prompt', 'negative_prompt'])
a_ = frozenset([])
a_ = frozenset(['image'])
a_ = frozenset(
[
'image',
'height',
'width',
'guidance_scale',
]
)
a_ = frozenset(['image'])
a_ = frozenset(
[
'prompt',
'image',
'height',
'width',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
]
)
a_ = frozenset(['prompt', 'image', 'negative_prompt'])
a_ = frozenset(
[
# Text guided image variation with an image mask
'prompt',
'image',
'mask_image',
'height',
'width',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
]
)
a_ = frozenset(['prompt', 'image', 'mask_image', 'negative_prompt'])
a_ = frozenset(
[
# image variation with an image mask
'image',
'mask_image',
'height',
'width',
'guidance_scale',
]
)
a_ = frozenset(['image', 'mask_image'])
a_ = frozenset(
[
'example_image',
'image',
'mask_image',
'height',
'width',
'guidance_scale',
]
)
a_ = frozenset(['example_image', 'image', 'mask_image'])
a_ = frozenset(['class_labels'])
a_ = frozenset(['class_labels'])
a_ = frozenset(['batch_size'])
a_ = frozenset([])
a_ = frozenset(['batch_size'])
a_ = frozenset([])
a_ = frozenset(
[
'prompt',
'audio_length_in_s',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
'cross_attention_kwargs',
]
)
a_ = frozenset(['prompt', 'negative_prompt'])
a_ = frozenset(['input_tokens'])
a_ = frozenset(['input_tokens']) | 25 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
snake_case = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case = ['''BartphoTokenizer''']
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bartpho import BartphoTokenizer
else:
import sys
snake_case = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 103 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import jax
import jaxlib
a_ = get_logger()
a_ = None
class _UpperCamelCase ( TensorFormatter[Mapping, 'jax.Array', Mapping] ):
'''simple docstring'''
def __init__( self : Optional[Any] , a : str=None , a : List[Any]=None , **a : Any ) -> Optional[Any]:
"""simple docstring"""
super().__init__(features=a )
import jax
from jaxlib.xla_client import Device
if isinstance(a , a ):
raise ValueError(
F"Expected {device} to be a `str` not {type(a )}, as `jaxlib.xla_extension.Device` "
"is not serializable neither with `pickle` nor with `dill`. Instead you can surround "
"the device with `str()` to get its string identifier that will be internally mapped "
"to the actual `jaxlib.xla_extension.Device`." )
SCREAMING_SNAKE_CASE : List[str] = device if isinstance(a , a ) else str(jax.devices()[0] )
# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
# with `pickle` nor with `dill`, so we need to use a global variable instead
global DEVICE_MAPPING
if DEVICE_MAPPING is None:
SCREAMING_SNAKE_CASE : str = self._map_devices_to_str()
if self.device not in list(DEVICE_MAPPING.keys() ):
logger.warning(
F"Device with string identifier {self.device} not listed among the available "
F"devices: {list(DEVICE_MAPPING.keys() )}, so falling back to the default "
F"device: {str(jax.devices()[0] )}." )
SCREAMING_SNAKE_CASE : Any = str(jax.devices()[0] )
SCREAMING_SNAKE_CASE : Any = jnp_array_kwargs
@staticmethod
def __UpperCamelCase ( ) -> Dict[str, "jaxlib.xla_extension.Device"]:
"""simple docstring"""
import jax
return {str(a ): device for device in jax.devices()}
def __UpperCamelCase ( self : Dict , a : Tuple ) -> str:
"""simple docstring"""
import jax
import jax.numpy as jnp
if isinstance(a , a ) and column:
if all(
isinstance(a , jax.Array ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column ):
return jnp.stack(a , axis=0 )
return column
def __UpperCamelCase ( self : Dict , a : str ) -> str:
"""simple docstring"""
import jax
import jax.numpy as jnp
if isinstance(a , (str, bytes, type(a )) ):
return value
elif isinstance(a , (np.character, np.ndarray) ) and np.issubdtype(value.dtype , np.character ):
return value.tolist()
SCREAMING_SNAKE_CASE : Union[str, Any] = {}
if isinstance(a , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.integer ):
# the default int precision depends on the jax config
# see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
if jax.config.jax_enable_xaa:
SCREAMING_SNAKE_CASE : Dict = {"dtype": jnp.intaa}
else:
SCREAMING_SNAKE_CASE : str = {"dtype": jnp.intaa}
elif isinstance(a , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.floating ):
SCREAMING_SNAKE_CASE : int = {"dtype": jnp.floataa}
elif config.PIL_AVAILABLE and "PIL" in sys.modules:
import PIL.Image
if isinstance(a , PIL.Image.Image ):
SCREAMING_SNAKE_CASE : Dict = np.asarray(a )
# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
# with `pickle` nor with `dill`, so we need to use a global variable instead
global DEVICE_MAPPING
if DEVICE_MAPPING is None:
SCREAMING_SNAKE_CASE : Optional[Any] = self._map_devices_to_str()
with jax.default_device(DEVICE_MAPPING[self.device] ):
# calling jnp.array on a np.ndarray does copy the data
# see https://github.com/google/jax/issues/4486
return jnp.array(a , **{**default_dtype, **self.jnp_array_kwargs} )
def __UpperCamelCase ( self : Any , a : List[str] ) -> Dict:
"""simple docstring"""
import jax
# support for torch, tf, jax etc.
if config.TORCH_AVAILABLE and "torch" in sys.modules:
import torch
if isinstance(a , torch.Tensor ):
return self._tensorize(data_struct.detach().cpu().numpy()[()] )
if hasattr(a , "__array__" ) and not isinstance(a , jax.Array ):
SCREAMING_SNAKE_CASE : Optional[int] = data_struct.__array__()
# support for nested types like struct of list of struct
if isinstance(a , np.ndarray ):
if data_struct.dtype == object: # jax arrays cannot be instantied from an array of objects
return self._consolidate([self.recursive_tensorize(a ) for substruct in data_struct] )
elif isinstance(a , (list, tuple) ):
return self._consolidate([self.recursive_tensorize(a ) for substruct in data_struct] )
return self._tensorize(a )
def __UpperCamelCase ( self : Optional[Any] , a : dict ) -> Dict:
"""simple docstring"""
return map_nested(self._recursive_tensorize , a , map_list=a )
def __UpperCamelCase ( self : Dict , a : pa.Table ) -> Mapping:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = self.numpy_arrow_extractor().extract_row(a )
SCREAMING_SNAKE_CASE : List[Any] = self.python_features_decoder.decode_row(a )
return self.recursive_tensorize(a )
def __UpperCamelCase ( self : Optional[int] , a : pa.Table ) -> "jax.Array":
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = self.numpy_arrow_extractor().extract_column(a )
SCREAMING_SNAKE_CASE : Optional[Any] = self.python_features_decoder.decode_column(a , pa_table.column_names[0] )
SCREAMING_SNAKE_CASE : Tuple = self.recursive_tensorize(a )
SCREAMING_SNAKE_CASE : Optional[int] = self._consolidate(a )
return column
def __UpperCamelCase ( self : List[Any] , a : pa.Table ) -> Mapping:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = self.numpy_arrow_extractor().extract_batch(a )
SCREAMING_SNAKE_CASE : str = self.python_features_decoder.decode_batch(a )
SCREAMING_SNAKE_CASE : List[Any] = self.recursive_tensorize(a )
for column_name in batch:
SCREAMING_SNAKE_CASE : List[Any] = self._consolidate(batch[column_name] )
return batch | 25 | 0 |
import string
def UpperCAmelCase ( a_ ) -> Optional[Any]:
"""simple docstring"""
__A = ""
for i in sequence:
__A = ord(_a )
if 6_5 <= extract <= 9_0:
output += chr(1_5_5 - extract )
elif 9_7 <= extract <= 1_2_2:
output += chr(2_1_9 - extract )
else:
output += i
return output
def UpperCAmelCase ( a_ ) -> Tuple:
"""simple docstring"""
__A = string.ascii_letters
__A = string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
return "".join(
letters_reversed[letters.index(_a )] if c in letters else c for c in sequence )
def UpperCAmelCase ( ) -> Union[str, Any]:
"""simple docstring"""
from timeit import timeit
print("Running performance benchmarks..." )
__A = "from string import printable ; from __main__ import atbash, atbash_slow"
print(F'''> atbash_slow(): {timeit('atbash_slow(printable)' , setup=_a )} seconds''' )
print(F'''> atbash(): {timeit('atbash(printable)' , setup=_a )} seconds''' )
if __name__ == "__main__":
for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
print(f'''{example} encrypted in atbash: {atbash(example)}''')
benchmark()
| 55 |
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_pytesseract,
require_tf,
require_timm,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class _UpperCamelCase :
'''simple docstring'''
@staticmethod
def __UpperCamelCase ( *a : str , **a : int ) -> str:
"""simple docstring"""
pass
@is_pipeline_test
@require_vision
@require_timm
@require_torch
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ =MODEL_FOR_OBJECT_DETECTION_MAPPING
def __UpperCamelCase ( self : Optional[Any] , a : str , a : Optional[Any] , a : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = ObjectDetectionPipeline(model=a , image_processor=a )
return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]
def __UpperCamelCase ( self : List[Any] , a : Optional[int] , a : Optional[int] ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = object_detector("./tests/fixtures/tests_samples/COCO/000000039769.png" , threshold=0.0 )
self.assertGreater(len(a ) , 0 )
for detected_object in outputs:
self.assertEqual(
a , {
"score": ANY(a ),
"label": ANY(a ),
"box": {"xmin": ANY(a ), "ymin": ANY(a ), "xmax": ANY(a ), "ymax": ANY(a )},
} , )
import datasets
SCREAMING_SNAKE_CASE : Any = datasets.load_dataset("hf-internal-testing/fixtures_image_utils" , "image" , split="test" )
SCREAMING_SNAKE_CASE : Dict = [
Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" ),
"http://images.cocodataset.org/val2017/000000039769.jpg",
# RGBA
dataset[0]["file"],
# LA
dataset[1]["file"],
# L
dataset[2]["file"],
]
SCREAMING_SNAKE_CASE : Tuple = object_detector(a , threshold=0.0 )
self.assertEqual(len(a ) , len(a ) )
for outputs in batch_outputs:
self.assertGreater(len(a ) , 0 )
for detected_object in outputs:
self.assertEqual(
a , {
"score": ANY(a ),
"label": ANY(a ),
"box": {"xmin": ANY(a ), "ymin": ANY(a ), "xmax": ANY(a ), "ymax": ANY(a )},
} , )
@require_tf
@unittest.skip("Object detection not implemented in TF" )
def __UpperCamelCase ( self : Optional[int] ) -> str:
"""simple docstring"""
pass
@require_torch
def __UpperCamelCase ( self : Tuple ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = "hf-internal-testing/tiny-detr-mobilenetsv3"
SCREAMING_SNAKE_CASE : Dict = AutoModelForObjectDetection.from_pretrained(a )
SCREAMING_SNAKE_CASE : Any = AutoFeatureExtractor.from_pretrained(a )
SCREAMING_SNAKE_CASE : Tuple = ObjectDetectionPipeline(model=a , feature_extractor=a )
SCREAMING_SNAKE_CASE : int = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg" , threshold=0.0 )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
] , )
SCREAMING_SNAKE_CASE : Dict = object_detector(
[
"http://images.cocodataset.org/val2017/000000039769.jpg",
"http://images.cocodataset.org/val2017/000000039769.jpg",
] , threshold=0.0 , )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
[
{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
],
[
{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
],
] , )
@require_torch
@slow
def __UpperCamelCase ( self : str ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = "facebook/detr-resnet-50"
SCREAMING_SNAKE_CASE : Union[str, Any] = AutoModelForObjectDetection.from_pretrained(a )
SCREAMING_SNAKE_CASE : List[str] = AutoFeatureExtractor.from_pretrained(a )
SCREAMING_SNAKE_CASE : int = ObjectDetectionPipeline(model=a , feature_extractor=a )
SCREAMING_SNAKE_CASE : Union[str, Any] = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg" )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
] , )
SCREAMING_SNAKE_CASE : int = object_detector(
[
"http://images.cocodataset.org/val2017/000000039769.jpg",
"http://images.cocodataset.org/val2017/000000039769.jpg",
] )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
[
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
],
[
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
],
] , )
@require_torch
@slow
def __UpperCamelCase ( self : str ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = "facebook/detr-resnet-50"
SCREAMING_SNAKE_CASE : Tuple = pipeline("object-detection" , model=a )
SCREAMING_SNAKE_CASE : Tuple = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg" )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
] , )
SCREAMING_SNAKE_CASE : str = object_detector(
[
"http://images.cocodataset.org/val2017/000000039769.jpg",
"http://images.cocodataset.org/val2017/000000039769.jpg",
] )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
[
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
],
[
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
],
] , )
@require_torch
@slow
def __UpperCamelCase ( self : str ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = 0.9985
SCREAMING_SNAKE_CASE : int = "facebook/detr-resnet-50"
SCREAMING_SNAKE_CASE : List[str] = pipeline("object-detection" , model=a )
SCREAMING_SNAKE_CASE : str = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg" , threshold=a )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
] , )
@require_torch
@require_pytesseract
@slow
def __UpperCamelCase ( self : str ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = "Narsil/layoutlmv3-finetuned-funsd"
SCREAMING_SNAKE_CASE : Dict = 0.9993
SCREAMING_SNAKE_CASE : str = pipeline("object-detection" , model=a , threshold=a )
SCREAMING_SNAKE_CASE : List[Any] = object_detector(
"https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png" )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
{"score": 0.9993, "label": "I-ANSWER", "box": {"xmin": 294, "ymin": 254, "xmax": 343, "ymax": 264}},
{"score": 0.9993, "label": "I-ANSWER", "box": {"xmin": 294, "ymin": 254, "xmax": 343, "ymax": 264}},
] , ) | 25 | 0 |
"""simple docstring"""
import json
import os
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_UpperCamelCase : Tuple = logging.get_logger(__name__)
_UpperCamelCase : Dict = {"vocab_file": "vocab.json"}
_UpperCamelCase : Union[str, Any] = {
"vocab_file": {
"mgp-str": "https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json",
}
}
_UpperCamelCase : Optional[Any] = {"mgp-str": 27}
class UpperCAmelCase_ ( __A):
lowerCamelCase__ : Optional[int] = VOCAB_FILES_NAMES
lowerCamelCase__ : List[Any] = PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase__ : Union[str, Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__( self , a , a="[GO]" , a="[GO]" , a="[s]" , a="[GO]" , **a ) -> Optional[Any]:
super().__init__(
unk_token=a , bos_token=a , eos_token=a , pad_token=a , **a , )
with open(a , encoding='utf-8' ) as vocab_handle:
lowercase__ : Any = json.load(a )
lowercase__ : Dict = {v: k for k, v in self.vocab.items()}
@property
def _UpperCAmelCase ( self ) -> List[Any]:
return len(self.vocab )
def _UpperCAmelCase ( self ) -> Any:
return dict(self.vocab , **self.added_tokens_encoder )
def _UpperCAmelCase ( self , a ) -> List[Any]:
lowercase__ : Any = []
for s in text:
char_tokens.extend(a )
return char_tokens
def _UpperCAmelCase ( self , a ) -> int:
return self.vocab.get(a , self.vocab.get(self.unk_token ) )
def _UpperCAmelCase ( self , a ) -> List[str]:
return self.decoder.get(a )
def _UpperCAmelCase ( self , a , a = None ) -> Tuple[str]:
if not os.path.isdir(a ):
logger.error('Vocabulary path ({}) should be a directory'.format(a ) )
return
lowercase__ : List[str] = os.path.join(
a , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
with open(a , 'w' , encoding='utf-8' ) as f:
f.write(json.dumps(self.vocab , indent=2 , sort_keys=a , ensure_ascii=a ) + '\n' )
return (vocab_file,)
| 599 |
def lowerCamelCase__ ( _a):
if not isinstance(_a , _a):
SCREAMING_SNAKE_CASE : Tuple = f"Input value of [number={number}] must be an integer"
raise TypeError(_a)
if number < 0:
return False
SCREAMING_SNAKE_CASE : Union[str, Any] = number * number
while number > 0:
if number % 10 != number_square % 10:
return False
number //= 10
number_square //= 10
return True
if __name__ == "__main__":
import doctest
doctest.testmod() | 25 | 0 |
"""simple docstring"""
import argparse
import torch
from torch import nn
from transformers import MBartConfig, MBartForConditionalGeneration
def __SCREAMING_SNAKE_CASE ( lowerCamelCase_: Optional[Any] ):
"""simple docstring"""
snake_case : Optional[Any] = [
"encoder.version",
"decoder.version",
"model.encoder.version",
"model.decoder.version",
"_float_tensor",
"decoder.output_projection.weight",
]
for k in ignore_keys:
state_dict.pop(_a , _a )
def __SCREAMING_SNAKE_CASE ( lowerCamelCase_: str ):
"""simple docstring"""
snake_case : Union[str, Any] = emb.weight.shape
snake_case : int = nn.Linear(_a , _a , bias=_a )
snake_case : str = emb.weight.data
return lin_layer
def __SCREAMING_SNAKE_CASE ( lowerCamelCase_: Optional[Any] , lowerCamelCase_: str="facebook/mbart-large-en-ro" , lowerCamelCase_: Union[str, Any]=False , lowerCamelCase_: str=False ):
"""simple docstring"""
snake_case : List[str] = torch.load(_a , map_location="cpu" )["model"]
remove_ignore_keys_(_a )
snake_case : List[Any] = state_dict["encoder.embed_tokens.weight"].shape[0]
snake_case : Optional[int] = MBartConfig.from_pretrained(_a , vocab_size=_a )
if mbart_aa and finetuned:
snake_case : int = "relu"
snake_case : Optional[Any] = state_dict["decoder.embed_tokens.weight"]
snake_case : Optional[Any] = MBartForConditionalGeneration(_a )
model.model.load_state_dict(_a )
if finetuned:
snake_case : str = make_linear_from_emb(model.model.shared )
return model
if __name__ == "__main__":
A = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'fairseq_path', type=str, help='bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'
)
parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
parser.add_argument(
'--hf_config',
default='facebook/mbart-large-cc25',
type=str,
help='Which huggingface architecture to use: mbart-large',
)
parser.add_argument('--mbart_50', action='store_true', help='whether the model is mMART-50 checkpoint')
parser.add_argument('--finetuned', action='store_true', help='whether the model is a fine-tuned checkpoint')
A = parser.parse_args()
A = convert_fairseq_mbart_checkpoint_from_disk(
args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
)
model.save_pretrained(args.pytorch_dump_folder_path)
| 449 |
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
DistilBertModel,
)
class _UpperCamelCase ( __A ):
'''simple docstring'''
def __init__( self : Dict , a : Tuple , a : Any=13 , a : Any=7 , a : Union[str, Any]=True , a : List[Any]=True , a : List[str]=False , a : List[str]=True , a : Any=99 , a : str=32 , a : Any=5 , a : Optional[int]=4 , a : Union[str, Any]=37 , a : Dict="gelu" , a : List[Any]=0.1 , a : Optional[Any]=0.1 , a : List[str]=512 , a : Union[str, Any]=16 , a : str=2 , a : Dict=0.02 , a : Optional[int]=3 , a : Union[str, Any]=4 , a : int=None , ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = parent
SCREAMING_SNAKE_CASE : Any = batch_size
SCREAMING_SNAKE_CASE : Optional[int] = seq_length
SCREAMING_SNAKE_CASE : List[Any] = is_training
SCREAMING_SNAKE_CASE : int = use_input_mask
SCREAMING_SNAKE_CASE : Tuple = use_token_type_ids
SCREAMING_SNAKE_CASE : str = use_labels
SCREAMING_SNAKE_CASE : Any = vocab_size
SCREAMING_SNAKE_CASE : List[Any] = hidden_size
SCREAMING_SNAKE_CASE : str = num_hidden_layers
SCREAMING_SNAKE_CASE : Optional[Any] = num_attention_heads
SCREAMING_SNAKE_CASE : Tuple = intermediate_size
SCREAMING_SNAKE_CASE : Optional[int] = hidden_act
SCREAMING_SNAKE_CASE : Dict = hidden_dropout_prob
SCREAMING_SNAKE_CASE : str = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : Tuple = max_position_embeddings
SCREAMING_SNAKE_CASE : List[str] = type_vocab_size
SCREAMING_SNAKE_CASE : List[str] = type_sequence_label_size
SCREAMING_SNAKE_CASE : Optional[Any] = initializer_range
SCREAMING_SNAKE_CASE : Tuple = num_labels
SCREAMING_SNAKE_CASE : Tuple = num_choices
SCREAMING_SNAKE_CASE : Optional[Any] = scope
def __UpperCamelCase ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE : Union[str, Any] = None
if self.use_input_mask:
SCREAMING_SNAKE_CASE : str = random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE : int = None
SCREAMING_SNAKE_CASE : List[Any] = None
SCREAMING_SNAKE_CASE : List[str] = None
if self.use_labels:
SCREAMING_SNAKE_CASE : Tuple = ids_tensor([self.batch_size] , self.type_sequence_label_size )
SCREAMING_SNAKE_CASE : str = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
SCREAMING_SNAKE_CASE : Optional[Any] = ids_tensor([self.batch_size] , self.num_choices )
SCREAMING_SNAKE_CASE : int = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def __UpperCamelCase ( self : Dict ) -> str:
"""simple docstring"""
return DistilBertConfig(
vocab_size=self.vocab_size , dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , hidden_dim=self.intermediate_size , hidden_act=self.hidden_act , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , )
def __UpperCamelCase ( self : Optional[Any] , a : int , a : Optional[int] , a : Optional[int] , a : Dict , a : str , a : str ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = DistilBertModel(config=a )
model.to(a )
model.eval()
SCREAMING_SNAKE_CASE : Optional[Any] = model(a , a )
SCREAMING_SNAKE_CASE : Optional[Any] = model(a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __UpperCamelCase ( self : Tuple , a : Optional[int] , a : Dict , a : Tuple , a : int , a : int , a : Any ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = DistilBertForMaskedLM(config=a )
model.to(a )
model.eval()
SCREAMING_SNAKE_CASE : str = model(a , attention_mask=a , labels=a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __UpperCamelCase ( self : List[Any] , a : int , a : Optional[Any] , a : Optional[Any] , a : str , a : str , a : Tuple ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = DistilBertForQuestionAnswering(config=a )
model.to(a )
model.eval()
SCREAMING_SNAKE_CASE : List[str] = model(
a , attention_mask=a , start_positions=a , end_positions=a )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def __UpperCamelCase ( self : Optional[int] , a : str , a : Any , a : int , a : Optional[Any] , a : int , a : str ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = self.num_labels
SCREAMING_SNAKE_CASE : Union[str, Any] = DistilBertForSequenceClassification(a )
model.to(a )
model.eval()
SCREAMING_SNAKE_CASE : Optional[int] = model(a , attention_mask=a , labels=a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __UpperCamelCase ( self : Optional[Any] , a : List[Any] , a : Optional[int] , a : Union[str, Any] , a : Dict , a : Any , a : Optional[Any] ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = self.num_labels
SCREAMING_SNAKE_CASE : List[str] = DistilBertForTokenClassification(config=a )
model.to(a )
model.eval()
SCREAMING_SNAKE_CASE : List[str] = model(a , attention_mask=a , labels=a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __UpperCamelCase ( self : int , a : Any , a : Optional[int] , a : Union[str, Any] , a : Tuple , a : Optional[int] , a : Tuple ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = self.num_choices
SCREAMING_SNAKE_CASE : Any = DistilBertForMultipleChoice(config=a )
model.to(a )
model.eval()
SCREAMING_SNAKE_CASE : Optional[int] = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
SCREAMING_SNAKE_CASE : Dict = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
SCREAMING_SNAKE_CASE : Optional[Any] = model(
a , attention_mask=a , labels=a , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __UpperCamelCase ( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = self.prepare_config_and_inputs()
((SCREAMING_SNAKE_CASE) ,(SCREAMING_SNAKE_CASE) ,(SCREAMING_SNAKE_CASE) ,(SCREAMING_SNAKE_CASE) ,(SCREAMING_SNAKE_CASE) ,(SCREAMING_SNAKE_CASE)) : Tuple = config_and_inputs
SCREAMING_SNAKE_CASE : int = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class _UpperCamelCase ( __A , __A , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ =(
(
DistilBertModel,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
)
if is_torch_available()
else None
)
lowerCamelCase__ =(
{
'feature-extraction': DistilBertModel,
'fill-mask': DistilBertForMaskedLM,
'question-answering': DistilBertForQuestionAnswering,
'text-classification': DistilBertForSequenceClassification,
'token-classification': DistilBertForTokenClassification,
'zero-shot': DistilBertForSequenceClassification,
}
if is_torch_available()
else {}
)
lowerCamelCase__ =True
lowerCamelCase__ =True
lowerCamelCase__ =True
lowerCamelCase__ =True
def __UpperCamelCase ( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = DistilBertModelTester(self )
SCREAMING_SNAKE_CASE : List[str] = ConfigTester(self , config_class=a , dim=37 )
def __UpperCamelCase ( self : List[Any] ) -> Tuple:
"""simple docstring"""
self.config_tester.run_common_tests()
def __UpperCamelCase ( self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_model(*a )
def __UpperCamelCase ( self : Tuple ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_masked_lm(*a )
def __UpperCamelCase ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_question_answering(*a )
def __UpperCamelCase ( self : Union[str, Any] ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_sequence_classification(*a )
def __UpperCamelCase ( self : str ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_token_classification(*a )
def __UpperCamelCase ( self : List[Any] ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_multiple_choice(*a )
@slow
def __UpperCamelCase ( self : int ) -> Any:
"""simple docstring"""
for model_name in DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE : Optional[Any] = DistilBertModel.from_pretrained(a )
self.assertIsNotNone(a )
@slow
@require_torch_gpu
def __UpperCamelCase ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# BertForMultipleChoice behaves incorrectly in JIT environments.
if model_class == DistilBertForMultipleChoice:
return
SCREAMING_SNAKE_CASE : Union[str, Any] = True
SCREAMING_SNAKE_CASE : Any = model_class(config=a )
SCREAMING_SNAKE_CASE : List[Any] = self._prepare_for_class(a , a )
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.jit.trace(
a , (inputs_dict["input_ids"].to("cpu" ), inputs_dict["attention_mask"].to("cpu" )) )
with tempfile.TemporaryDirectory() as tmp:
torch.jit.save(a , os.path.join(a , "traced_model.pt" ) )
SCREAMING_SNAKE_CASE : Tuple = torch.jit.load(os.path.join(a , "traced_model.pt" ) , map_location=a )
loaded(inputs_dict["input_ids"].to(a ) , inputs_dict["attention_mask"].to(a ) )
@require_torch
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
@slow
def __UpperCamelCase ( self : int ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = DistilBertModel.from_pretrained("distilbert-base-uncased" )
SCREAMING_SNAKE_CASE : List[str] = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]] )
SCREAMING_SNAKE_CASE : Optional[int] = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
SCREAMING_SNAKE_CASE : Optional[Any] = model(a , attention_mask=a )[0]
SCREAMING_SNAKE_CASE : List[str] = torch.Size((1, 11, 768) )
self.assertEqual(output.shape , a )
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.tensor(
[[[-0.1639, 0.3299, 0.1648], [-0.1746, 0.3289, 0.1710], [-0.1884, 0.3357, 0.1810]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , a , atol=1e-4 ) ) | 25 | 0 |
'''simple docstring'''
from math import factorial
class A_ :
def __init__( self : Dict , snake_case_ : Optional[int] , snake_case_ : Dict ):
_UpperCAmelCase = real
if isinstance(snake_case_ , snake_case_ ):
_UpperCAmelCase = [1] * rank
else:
_UpperCAmelCase = rank
def __repr__( self : List[Any] ):
return (
f'{self.real}+'
f'{"+".join(str(snake_case_ )+"E"+str(n+1 )for n,dual in enumerate(self.duals ) )}'
)
def lowercase ( self : List[Any] ):
_UpperCAmelCase = self.duals.copy()
while cur[-1] == 0:
cur.pop(-1 )
return Dual(self.real , snake_case_ )
def __add__( self : Tuple , snake_case_ : List[str] ):
if not isinstance(snake_case_ , snake_case_ ):
return Dual(self.real + other , self.duals )
_UpperCAmelCase = self.duals.copy()
_UpperCAmelCase = other.duals.copy()
if len(snake_case_ ) > len(snake_case_ ):
o_dual.extend([1] * (len(snake_case_ ) - len(snake_case_ )) )
elif len(snake_case_ ) < len(snake_case_ ):
s_dual.extend([1] * (len(snake_case_ ) - len(snake_case_ )) )
_UpperCAmelCase = []
for i in range(len(snake_case_ ) ):
new_duals.append(s_dual[i] + o_dual[i] )
return Dual(self.real + other.real , snake_case_ )
_lowerCamelCase : Any = __add__
def __sub__( self : Optional[int] , snake_case_ : int ):
return self + other * -1
def __mul__( self : Union[str, Any] , snake_case_ : int ):
if not isinstance(snake_case_ , snake_case_ ):
_UpperCAmelCase = []
for i in self.duals:
new_duals.append(i * other )
return Dual(self.real * other , snake_case_ )
_UpperCAmelCase = [0] * (len(self.duals ) + len(other.duals ) + 1)
for i, item in enumerate(self.duals ):
for j, jtem in enumerate(other.duals ):
new_duals[i + j + 1] += item * jtem
for k in range(len(self.duals ) ):
new_duals[k] += self.duals[k] * other.real
for index in range(len(other.duals ) ):
new_duals[index] += other.duals[index] * self.real
return Dual(self.real * other.real , snake_case_ )
_lowerCamelCase : Tuple = __mul__
def __truediv__( self : int , snake_case_ : int ):
if not isinstance(snake_case_ , snake_case_ ):
_UpperCAmelCase = []
for i in self.duals:
new_duals.append(i / other )
return Dual(self.real / other , snake_case_ )
raise ValueError
def __floordiv__( self : List[Any] , snake_case_ : Optional[int] ):
if not isinstance(snake_case_ , snake_case_ ):
_UpperCAmelCase = []
for i in self.duals:
new_duals.append(i // other )
return Dual(self.real // other , snake_case_ )
raise ValueError
def __pow__( self : Optional[Any] , snake_case_ : Dict ):
if n < 0 or isinstance(snake_case_ , snake_case_ ):
raise ValueError("power must be a positive integer" )
if n == 0:
return 1
if n == 1:
return self
_UpperCAmelCase = self
for _ in range(n - 1 ):
x *= self
return x
def UpperCAmelCase_ ( __lowercase : Optional[Any] , __lowercase : str , __lowercase : List[str] ) -> Union[str, Any]:
'''simple docstring'''
if not callable(_a ):
raise ValueError("differentiate() requires a function as input for func" )
if not isinstance(_a , (float, int) ):
raise ValueError("differentiate() requires a float as input for position" )
if not isinstance(_a , _a ):
raise ValueError("differentiate() requires an int as input for order" )
_UpperCAmelCase = Dual(_a , 1 )
_UpperCAmelCase = func(_a )
if order == 0:
return result.real
return result.duals[order - 1] * factorial(_a )
if __name__ == "__main__":
import doctest
doctest.testmod()
def UpperCAmelCase_ ( __lowercase : int ) -> Tuple:
'''simple docstring'''
return y**2 * y**4
print(differentiate(f, 9, 2))
| 236 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
a_ = {'configuration_plbart': ['PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PLBartConfig']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ['PLBartTokenizer']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'PLBART_PRETRAINED_MODEL_ARCHIVE_LIST',
'PLBartForCausalLM',
'PLBartForConditionalGeneration',
'PLBartForSequenceClassification',
'PLBartModel',
'PLBartPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_plbart import PLBartTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_plbart import (
PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
PLBartForCausalLM,
PLBartForConditionalGeneration,
PLBartForSequenceClassification,
PLBartModel,
PLBartPreTrainedModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure) | 25 | 0 |
import argparse
import pathlib
import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version
from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertSelfAttention,
BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging
if version.parse(fairseq.__version__) < version.parse('''1.0.0a'''):
raise Exception('''requires fairseq >= 1.0.0a''')
logging.set_verbosity_info()
UpperCamelCase__ : Union[str, Any] = logging.get_logger(__name__)
UpperCamelCase__ : Any = '''Hello world! cécé herlolip'''
def __UpperCAmelCase ( lowerCamelCase_ : List[Any] , lowerCamelCase_ : List[Any] , lowerCamelCase_ : List[Any] ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Union[str, Any] = FairseqRobertaModel.from_pretrained(_a )
roberta.eval() # disable dropout
SCREAMING_SNAKE_CASE_ : int = roberta.model.encoder.sentence_encoder
SCREAMING_SNAKE_CASE_ : List[str] = XLMRobertaConfig(
vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings , hidden_size=roberta.cfg.model.encoder_embed_dim , num_hidden_layers=roberta.cfg.model.encoder_layers , num_attention_heads=roberta.cfg.model.encoder_attention_heads , intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim , max_position_embeddings=5_14 , type_vocab_size=1 , layer_norm_eps=1E-5 , )
if classification_head:
SCREAMING_SNAKE_CASE_ : List[str] = roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]
print('Our RoBERTa config:' , _a )
SCREAMING_SNAKE_CASE_ : Any = XLMRobertaXLForSequenceClassification(_a ) if classification_head else XLMRobertaXLForMaskedLM(_a )
model.eval()
# Now let's copy all the weights.
# Embeddings
SCREAMING_SNAKE_CASE_ : Tuple = roberta_sent_encoder.embed_tokens.weight
SCREAMING_SNAKE_CASE_ : Tuple = roberta_sent_encoder.embed_positions.weight
SCREAMING_SNAKE_CASE_ : Any = torch.zeros_like(
model.roberta.embeddings.token_type_embeddings.weight ) # just zero them out b/c RoBERTa doesn't use them.
SCREAMING_SNAKE_CASE_ : Any = roberta_sent_encoder.layer_norm.weight
SCREAMING_SNAKE_CASE_ : Union[str, Any] = roberta_sent_encoder.layer_norm.bias
for i in range(config.num_hidden_layers ):
# Encoder: start of layer
SCREAMING_SNAKE_CASE_ : BertLayer = model.roberta.encoder.layer[i]
SCREAMING_SNAKE_CASE_ : TransformerSentenceEncoderLayer = roberta_sent_encoder.layers[i]
SCREAMING_SNAKE_CASE_ : RobertaAttention = layer.attention
SCREAMING_SNAKE_CASE_ : Dict = roberta_layer.self_attn_layer_norm.weight
SCREAMING_SNAKE_CASE_ : str = roberta_layer.self_attn_layer_norm.bias
# self attention
SCREAMING_SNAKE_CASE_ : BertSelfAttention = layer.attention.self
assert (
roberta_layer.self_attn.k_proj.weight.data.shape
== roberta_layer.self_attn.q_proj.weight.data.shape
== roberta_layer.self_attn.v_proj.weight.data.shape
== torch.Size((config.hidden_size, config.hidden_size) )
)
SCREAMING_SNAKE_CASE_ : Tuple = roberta_layer.self_attn.q_proj.weight
SCREAMING_SNAKE_CASE_ : Optional[int] = roberta_layer.self_attn.q_proj.bias
SCREAMING_SNAKE_CASE_ : Dict = roberta_layer.self_attn.k_proj.weight
SCREAMING_SNAKE_CASE_ : str = roberta_layer.self_attn.k_proj.bias
SCREAMING_SNAKE_CASE_ : Union[str, Any] = roberta_layer.self_attn.v_proj.weight
SCREAMING_SNAKE_CASE_ : Optional[Any] = roberta_layer.self_attn.v_proj.bias
# self-attention output
SCREAMING_SNAKE_CASE_ : BertSelfOutput = layer.attention.output
assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
SCREAMING_SNAKE_CASE_ : Dict = roberta_layer.self_attn.out_proj.weight
SCREAMING_SNAKE_CASE_ : List[Any] = roberta_layer.self_attn.out_proj.bias
# this one is final layer norm
SCREAMING_SNAKE_CASE_ : Optional[int] = roberta_layer.final_layer_norm.weight
SCREAMING_SNAKE_CASE_ : Dict = roberta_layer.final_layer_norm.bias
# intermediate
SCREAMING_SNAKE_CASE_ : BertIntermediate = layer.intermediate
assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
SCREAMING_SNAKE_CASE_ : Optional[int] = roberta_layer.fca.weight
SCREAMING_SNAKE_CASE_ : str = roberta_layer.fca.bias
# output
SCREAMING_SNAKE_CASE_ : BertOutput = layer.output
assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
SCREAMING_SNAKE_CASE_ : Tuple = roberta_layer.fca.weight
SCREAMING_SNAKE_CASE_ : int = roberta_layer.fca.bias
# end of layer
if classification_head:
SCREAMING_SNAKE_CASE_ : Optional[Any] = roberta.model.classification_heads["mnli"].dense.weight
SCREAMING_SNAKE_CASE_ : Optional[Any] = roberta.model.classification_heads["mnli"].dense.bias
SCREAMING_SNAKE_CASE_ : List[Any] = roberta.model.classification_heads["mnli"].out_proj.weight
SCREAMING_SNAKE_CASE_ : Tuple = roberta.model.classification_heads["mnli"].out_proj.bias
else:
# LM Head
SCREAMING_SNAKE_CASE_ : Union[str, Any] = roberta.model.encoder.lm_head.dense.weight
SCREAMING_SNAKE_CASE_ : int = roberta.model.encoder.lm_head.dense.bias
SCREAMING_SNAKE_CASE_ : Dict = roberta.model.encoder.lm_head.layer_norm.weight
SCREAMING_SNAKE_CASE_ : Union[str, Any] = roberta.model.encoder.lm_head.layer_norm.bias
SCREAMING_SNAKE_CASE_ : Dict = roberta.model.encoder.lm_head.weight
SCREAMING_SNAKE_CASE_ : Tuple = roberta.model.encoder.lm_head.bias
# Let's check that we get the same results.
SCREAMING_SNAKE_CASE_ : torch.Tensor = roberta.encode(_a ).unsqueeze(0 ) # batch of size 1
SCREAMING_SNAKE_CASE_ : List[str] = model(_a )[0]
if classification_head:
SCREAMING_SNAKE_CASE_ : Optional[int] = roberta.model.classification_heads["mnli"](roberta.extract_features(_a ) )
else:
SCREAMING_SNAKE_CASE_ : Optional[int] = roberta.model(_a )[0]
print(our_output.shape , their_output.shape )
SCREAMING_SNAKE_CASE_ : str = torch.max(torch.abs(our_output - their_output ) ).item()
print(F'max_absolute_diff = {max_absolute_diff}' ) # ~ 1e-7
SCREAMING_SNAKE_CASE_ : Optional[Any] = torch.allclose(_a , _a , atol=1E-3 )
print('Do both models output the same tensors?' , '🔥' if success else '💩' )
if not success:
raise Exception('Something went wRoNg' )
pathlib.Path(_a ).mkdir(parents=_a , exist_ok=_a )
print(F'Saving model to {pytorch_dump_folder_path}' )
model.save_pretrained(_a )
if __name__ == "__main__":
UpperCamelCase__ : List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--roberta_checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
parser.add_argument(
'''--classification_head''', action='''store_true''', help='''Whether to convert a final classification head.'''
)
UpperCamelCase__ : Optional[Any] = parser.parse_args()
convert_xlm_roberta_xl_checkpoint_to_pytorch(
args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
)
| 105 |
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
@property
def __UpperCamelCase ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : str = 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 __UpperCamelCase ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = self.dummy_uncond_unet
SCREAMING_SNAKE_CASE : Union[str, Any] = KarrasVeScheduler()
SCREAMING_SNAKE_CASE : Any = KarrasVePipeline(unet=a , scheduler=a )
pipe.to(a )
pipe.set_progress_bar_config(disable=a )
SCREAMING_SNAKE_CASE : Any = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Optional[Any] = pipe(num_inference_steps=2 , generator=a , output_type="numpy" ).images
SCREAMING_SNAKE_CASE : List[str] = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : List[Any] = pipe(num_inference_steps=2 , generator=a , output_type="numpy" , return_dict=a )[0]
SCREAMING_SNAKE_CASE : List[Any] = image[0, -3:, -3:, -1]
SCREAMING_SNAKE_CASE : Any = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
SCREAMING_SNAKE_CASE : str = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
@slow
@require_torch
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def __UpperCamelCase ( self : int ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = "google/ncsnpp-celebahq-256"
SCREAMING_SNAKE_CASE : List[Any] = UNetaDModel.from_pretrained(a )
SCREAMING_SNAKE_CASE : Any = KarrasVeScheduler()
SCREAMING_SNAKE_CASE : Optional[Any] = KarrasVePipeline(unet=a , scheduler=a )
pipe.to(a )
pipe.set_progress_bar_config(disable=a )
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Any = pipe(num_inference_steps=20 , generator=a , output_type="numpy" ).images
SCREAMING_SNAKE_CASE : List[str] = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
SCREAMING_SNAKE_CASE : str = np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2 | 25 | 0 |
'''simple docstring'''
def UpperCAmelCase ( A : Optional[int] ):
if n == 1 or not isinstance(_a , _a ):
return 0
elif n == 2:
return 1
else:
SCREAMING_SNAKE_CASE : Optional[int] = [0, 1]
for i in range(2 , n + 1 ):
sequence.append(sequence[i - 1] + sequence[i - 2] )
return sequence[n]
def UpperCAmelCase ( A : List[str] ):
SCREAMING_SNAKE_CASE : str = 0
SCREAMING_SNAKE_CASE : List[str] = 2
while digits < n:
index += 1
SCREAMING_SNAKE_CASE : Dict = len(str(fibonacci(_a ) ) )
return index
def UpperCAmelCase ( A : List[Any] = 1000 ):
return fibonacci_digits_index(_a )
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 527 |
def lowerCamelCase__ ( _a , _a):
SCREAMING_SNAKE_CASE : Optional[int] = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return res
def lowerCamelCase__ ( _a , _a , _a):
SCREAMING_SNAKE_CASE : Optional[int] = 0
while b > 0:
if b & 1:
SCREAMING_SNAKE_CASE : Optional[Any] = ((res % c) + (a % c)) % c
a += a
b >>= 1
return res | 25 | 0 |
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_timm,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
else:
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
@staticmethod
def SCREAMING_SNAKE_CASE ( *lowerCAmelCase : Any , **lowerCAmelCase : str ) -> Any:
"""simple docstring"""
pass
def snake_case_ (__A : Tuple ) -> Optional[int]:
__lowerCAmelCase : str = hashlib.mda(image.tobytes() )
return m.hexdigest()
@is_pipeline_test
@require_vision
@require_timm
@require_torch
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
lowerCamelCase : int =MODEL_FOR_DEPTH_ESTIMATION_MAPPING
def SCREAMING_SNAKE_CASE ( self : Tuple , lowerCAmelCase : int , lowerCAmelCase : Any , lowerCAmelCase : Any ) -> Union[str, Any]:
"""simple docstring"""
__lowerCAmelCase : List[Any] = DepthEstimationPipeline(model=lowerCAmelCase , image_processor=lowerCAmelCase )
return depth_estimator, [
"./tests/fixtures/tests_samples/COCO/000000039769.png",
"./tests/fixtures/tests_samples/COCO/000000039769.png",
]
def SCREAMING_SNAKE_CASE ( self : int , lowerCAmelCase : List[str] , lowerCAmelCase : List[Any] ) -> str:
"""simple docstring"""
__lowerCAmelCase : Tuple = depth_estimator("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
self.assertEqual({"""predicted_depth""": ANY(torch.Tensor ), """depth""": ANY(Image.Image )} , lowerCAmelCase )
import datasets
__lowerCAmelCase : Tuple = datasets.load_dataset("""hf-internal-testing/fixtures_image_utils""" , """image""" , split="""test""" )
__lowerCAmelCase : List[Any] = depth_estimator(
[
Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" ),
"""http://images.cocodataset.org/val2017/000000039769.jpg""",
# RGBA
dataset[0]["""file"""],
# LA
dataset[1]["""file"""],
# L
dataset[2]["""file"""],
] )
self.assertEqual(
[
{"""predicted_depth""": ANY(torch.Tensor ), """depth""": ANY(Image.Image )},
{"""predicted_depth""": ANY(torch.Tensor ), """depth""": ANY(Image.Image )},
{"""predicted_depth""": ANY(torch.Tensor ), """depth""": ANY(Image.Image )},
{"""predicted_depth""": ANY(torch.Tensor ), """depth""": ANY(Image.Image )},
{"""predicted_depth""": ANY(torch.Tensor ), """depth""": ANY(Image.Image )},
] , lowerCAmelCase , )
@require_tf
@unittest.skip("""Depth estimation is not implemented in TF""" )
def SCREAMING_SNAKE_CASE ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
pass
@slow
@require_torch
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
__lowerCAmelCase : Tuple = "Intel/dpt-large"
__lowerCAmelCase : Optional[int] = pipeline("""depth-estimation""" , model=lowerCAmelCase )
__lowerCAmelCase : str = depth_estimator("""http://images.cocodataset.org/val2017/000000039769.jpg""" )
__lowerCAmelCase : Union[str, Any] = hashimage(outputs["""depth"""] )
# This seems flaky.
# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
self.assertEqual(nested_simplify(outputs["""predicted_depth"""].max().item() ) , 29.304 )
self.assertEqual(nested_simplify(outputs["""predicted_depth"""].min().item() ) , 2.662 )
@require_torch
def SCREAMING_SNAKE_CASE ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
self.skipTest("""There is not hf-internal-testing tiny model for either GLPN nor DPT""" )
| 651 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json',
'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json',
'junnyu/roformer_chinese_char_small': (
'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'
),
'junnyu/roformer_chinese_char_base': (
'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'
),
'junnyu/roformer_small_discriminator': (
'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'
),
'junnyu/roformer_small_generator': (
'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'
),
# See all RoFormer models at https://huggingface.co/models?filter=roformer
}
class _UpperCamelCase ( __A ):
'''simple docstring'''
lowerCamelCase__ ='roformer'
def __init__( self : Dict , a : Any=5_0000 , a : List[Any]=None , a : str=768 , a : str=12 , a : Tuple=12 , a : Optional[Any]=3072 , a : List[str]="gelu" , a : List[Any]=0.1 , a : Union[str, Any]=0.1 , a : Tuple=1536 , a : List[str]=2 , a : Tuple=0.02 , a : Any=1e-12 , a : Optional[int]=0 , a : Union[str, Any]=False , a : int=True , **a : str , ) -> int:
"""simple docstring"""
super().__init__(pad_token_id=a , **a )
SCREAMING_SNAKE_CASE : str = vocab_size
SCREAMING_SNAKE_CASE : int = hidden_size if embedding_size is None else embedding_size
SCREAMING_SNAKE_CASE : List[str] = hidden_size
SCREAMING_SNAKE_CASE : Union[str, Any] = num_hidden_layers
SCREAMING_SNAKE_CASE : int = num_attention_heads
SCREAMING_SNAKE_CASE : Tuple = hidden_act
SCREAMING_SNAKE_CASE : int = intermediate_size
SCREAMING_SNAKE_CASE : Tuple = hidden_dropout_prob
SCREAMING_SNAKE_CASE : int = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : Optional[int] = max_position_embeddings
SCREAMING_SNAKE_CASE : Any = type_vocab_size
SCREAMING_SNAKE_CASE : Union[str, Any] = initializer_range
SCREAMING_SNAKE_CASE : List[str] = layer_norm_eps
SCREAMING_SNAKE_CASE : List[str] = rotary_value
SCREAMING_SNAKE_CASE : int = use_cache
class _UpperCamelCase ( __A ):
'''simple docstring'''
@property
def __UpperCamelCase ( self : Tuple ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
if self.task == "multiple-choice":
SCREAMING_SNAKE_CASE : Optional[Any] = {0: "batch", 1: "choice", 2: "sequence"}
else:
SCREAMING_SNAKE_CASE : str = {0: "batch", 1: "sequence"}
SCREAMING_SNAKE_CASE : List[Any] = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
("token_type_ids", dynamic_axis),
] ) | 25 | 0 |
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 snake_case( __magic_name__ ) -> Optional[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 ):
def __init__( self : Union[str, Any] , _A : nn.Module , _A : int ) -> Dict:
"""simple docstring"""
super().__init__()
lowercase : str = module
lowercase : List[Any] = nn.Sequential(
nn.Linear(module.in_features , _A , bias=_A ) , nn.Linear(_A , module.out_features , bias=_A ) , )
lowercase : Optional[Any] = (2.0 / (5 * min(module.in_features , module.out_features ))) ** 0.5
nn.init.normal_(self.adapter[0].weight , std=_A )
nn.init.zeros_(self.adapter[1].weight )
self.adapter.to(module.weight.device )
def __a ( self : str , _A : Optional[Any] , *_A : str , **_A : List[str] ) -> Optional[int]:
"""simple docstring"""
return self.module(_A , *_A , **_A ) + self.adapter(_A )
@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class _A ( unittest.TestCase ):
_UpperCamelCase : List[str] = '''bigscience/bloom-1b7'''
# Constant values
_UpperCamelCase : List[Any] = 2.1_09_65_95_52_69_25_74
_UpperCamelCase : Dict = '''Hello my name is'''
_UpperCamelCase : Dict = 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''' )
_UpperCamelCase : int = 1_0
def __a ( self : Dict ) -> Optional[Any]:
"""simple docstring"""
lowercase : List[str] = AutoTokenizer.from_pretrained(self.model_name )
class _A ( __A ):
def __a ( self : Dict ) -> int:
"""simple docstring"""
super().setUp()
# Models and tokenizer
lowercase : Dict = AutoModelForCausalLM.from_pretrained(
self.model_name , torch_dtype=torch.floataa , device_map='''auto''' )
lowercase : int = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=_A , device_map='''auto''' )
def __a ( self : List[str] ) -> Dict:
"""simple docstring"""
del self.model_fpaa
del self.model_abit
gc.collect()
torch.cuda.empty_cache()
def __a ( self : Tuple ) -> Tuple:
"""simple docstring"""
lowercase : Optional[int] = self.model_abit.config
self.assertTrue(hasattr(_A , '''quantization_config''' ) )
lowercase : Union[str, Any] = config.to_dict()
lowercase : str = config.to_diff_dict()
lowercase : Union[str, Any] = config.to_json_string()
def __a ( self : Dict ) -> Optional[int]:
"""simple docstring"""
from bitsandbytes.nn import Paramsabit
lowercase : int = self.model_fpaa.get_memory_footprint()
lowercase : int = self.model_abit.get_memory_footprint()
self.assertAlmostEqual(mem_fpaa / mem_abit , self.EXPECTED_RELATIVE_DIFFERENCE )
lowercase : Union[str, Any] = get_some_linear_layer(self.model_abit )
self.assertTrue(linear.weight.__class__ == Paramsabit )
def __a ( self : Optional[Any] ) -> Optional[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(_A , 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 __a ( self : Dict ) -> Union[str, Any]:
"""simple docstring"""
lowercase : Optional[int] = self.tokenizer(self.input_text , return_tensors='''pt''' )
lowercase : Tuple = 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=_A ) , self.EXPECTED_OUTPUTS )
def __a ( self : Optional[int] ) -> int:
"""simple docstring"""
lowercase : Tuple = BitsAndBytesConfig()
lowercase : Optional[int] = True
lowercase : int = AutoModelForCausalLM.from_pretrained(
self.model_name , quantization_config=_A , device_map='''auto''' )
lowercase : int = self.tokenizer(self.input_text , return_tensors='''pt''' )
lowercase : Tuple = 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=_A ) , self.EXPECTED_OUTPUTS )
def __a ( self : Any ) -> str:
"""simple docstring"""
with self.assertRaises(_A ), tempfile.TemporaryDirectory() as tmpdirname:
self.model_abit.save_pretrained(_A )
def __a ( self : List[str] ) -> Tuple:
"""simple docstring"""
lowercase : int = BitsAndBytesConfig()
with self.assertRaises(_A ):
lowercase : List[str] = AutoModelForCausalLM.from_pretrained(
self.model_name , quantization_config=_A , load_in_abit=_A , device_map='''auto''' , bnb_abit_quant_type='''nf4''' , )
def __a ( self : Any ) -> Tuple:
"""simple docstring"""
with self.assertRaises(_A ):
# Tries with `str`
self.model_abit.to('''cpu''' )
with self.assertRaises(_A ):
# Tries with a `dtype``
self.model_abit.to(torch.floataa )
with self.assertRaises(_A ):
# Tries with a `device`
self.model_abit.to(torch.device('''cuda:0''' ) )
with self.assertRaises(_A ):
# Tries with a `device`
self.model_abit.float()
with self.assertRaises(_A ):
# Tries with a `device`
self.model_abit.half()
# Test if we did not break anything
lowercase : List[str] = self.tokenizer(self.input_text , return_tensors='''pt''' )
lowercase : Optional[Any] = self.model_fpaa.to(torch.floataa )
lowercase : Optional[Any] = self.model_fpaa.generate(input_ids=encoded_input['''input_ids'''].to(0 ) , max_new_tokens=10 )
# Check this does not throw an error
lowercase : Tuple = self.model_fpaa.to('''cpu''' )
# Check this does not throw an error
lowercase : Dict = self.model_fpaa.half()
# Check this does not throw an error
lowercase : str = self.model_fpaa.float()
def __a ( self : Dict ) -> Union[str, Any]:
"""simple docstring"""
lowercase : int = AutoModelForSeqaSeqLM.from_pretrained('''t5-small''' , load_in_abit=_A , 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 ):
@classmethod
def __a ( cls : str ) -> str:
"""simple docstring"""
lowercase : List[str] = "t5-small"
lowercase : Optional[Any] = "google/flan-t5-small" # flan-t5 uses dense-act instead of dense-relu-dense
lowercase : Any = AutoTokenizer.from_pretrained(cls.model_name )
lowercase : Optional[Any] = "Translate in German: Hello, my dog is cute"
def __a ( self : Union[str, Any] ) -> Any:
"""simple docstring"""
gc.collect()
torch.cuda.empty_cache()
def __a ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
from transformers import TaForConditionalGeneration
lowercase : Any = TaForConditionalGeneration._keep_in_fpaa_modules
lowercase : Dict = None
# test with `t5-small`
lowercase : List[Any] = TaForConditionalGeneration.from_pretrained(self.model_name , load_in_abit=_A , device_map='''auto''' )
lowercase : int = self.tokenizer(self.input_text , return_tensors='''pt''' ).to(0 )
lowercase : Any = model.generate(**_A )
# test with `flan-t5-small`
lowercase : Dict = TaForConditionalGeneration.from_pretrained(
self.dense_act_model_name , load_in_abit=_A , device_map='''auto''' )
lowercase : int = self.tokenizer(self.input_text , return_tensors='''pt''' ).to(0 )
lowercase : Optional[int] = model.generate(**_A )
lowercase : Tuple = modules
def __a ( self : int ) -> Dict:
"""simple docstring"""
import bitsandbytes as bnb
from transformers import TaForConditionalGeneration
# test with `t5-small`
lowercase : Optional[Any] = TaForConditionalGeneration.from_pretrained(self.model_name , load_in_abit=_A , 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 : List[Any] = self.tokenizer(self.input_text , return_tensors='''pt''' ).to(0 )
lowercase : Optional[Any] = model.generate(**_A )
# test with `flan-t5-small`
lowercase : List[str] = TaForConditionalGeneration.from_pretrained(
self.dense_act_model_name , load_in_abit=_A , device_map='''auto''' )
lowercase : Dict = self.tokenizer(self.input_text , return_tensors='''pt''' ).to(0 )
lowercase : Tuple = model.generate(**_A )
class _A ( __A ):
def __a ( self : Any ) -> Tuple:
"""simple docstring"""
super().setUp()
# model_name
lowercase : Optional[int] = "bigscience/bloom-560m"
lowercase : List[str] = "t5-small"
# Different types of model
lowercase : str = AutoModel.from_pretrained(self.model_name , load_in_abit=_A , device_map='''auto''' )
# Sequence classification model
lowercase : Union[str, Any] = AutoModelForSequenceClassification.from_pretrained(
self.model_name , load_in_abit=_A , device_map='''auto''' )
# CausalLM model
lowercase : List[Any] = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=_A , device_map='''auto''' )
# Seq2seq model
lowercase : List[str] = AutoModelForSeqaSeqLM.from_pretrained(
self.seq_to_seq_name , load_in_abit=_A , device_map='''auto''' )
def __a ( self : Optional[int] ) -> Dict:
"""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 __a ( self : List[Any] ) -> List[str]:
"""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 ( __A ):
def __a ( self : Dict ) -> Dict:
"""simple docstring"""
super().setUp()
def __a ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
del self.pipe
gc.collect()
torch.cuda.empty_cache()
def __a ( self : Dict ) -> Dict:
"""simple docstring"""
lowercase : Optional[Any] = 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 : Any = self.pipe(self.input_text )
self.assertIn(pipeline_output[0]['''generated_text'''] , self.EXPECTED_OUTPUTS )
@require_torch_multi_gpu
class _A ( __A ):
def __a ( self : List[Any] ) -> List[str]:
"""simple docstring"""
super().setUp()
def __a ( self : List[Any] ) -> Any:
"""simple docstring"""
lowercase : List[Any] = AutoModelForCausalLM.from_pretrained(
self.model_name , load_in_abit=_A , 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 : Tuple = self.tokenizer(self.input_text , return_tensors='''pt''' )
# Second real batch
lowercase : Tuple = 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=_A ) , self.EXPECTED_OUTPUTS )
class _A ( __A ):
def __a ( self : List[str] ) -> Dict:
"""simple docstring"""
lowercase : str = "facebook/opt-350m"
super().setUp()
def __a ( self : int ) -> List[Any]:
"""simple docstring"""
if version.parse(importlib.metadata.version('''bitsandbytes''' ) ) < version.parse('''0.37.0''' ):
return
# Step 1: freeze all parameters
lowercase : Dict = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=_A )
self.assertEqual(set(model.hf_device_map.values() ) , {torch.cuda.current_device()} )
for param in model.parameters():
lowercase : Any = False # freeze the model - train adapters later
if param.ndim == 1:
# cast the small parameters (e.g. layernorm) to fp32 for stability
lowercase : Union[str, Any] = param.data.to(torch.floataa )
# Step 2: add adapters
for _, module in model.named_modules():
if "OPTAttention" in repr(type(_A ) ):
lowercase : Optional[Any] = LoRALayer(module.q_proj , rank=16 )
lowercase : Optional[int] = LoRALayer(module.k_proj , rank=16 )
lowercase : Optional[int] = LoRALayer(module.v_proj , rank=16 )
# Step 3: dummy batch
lowercase : Dict = self.tokenizer('''Test batch ''' , return_tensors='''pt''' ).to(0 )
# Step 4: Check if the gradient is not None
with torch.cuda.amp.autocast():
lowercase : str = model.forward(**_A )
out.logits.norm().backward()
for module in model.modules():
if isinstance(_A , _A ):
self.assertTrue(module.adapter[1].weight.grad is not None )
self.assertTrue(module.adapter[1].weight.grad.norm().item() > 0 )
elif isinstance(_A , nn.Embedding ):
self.assertTrue(module.weight.grad is None )
class _A ( __A ):
_UpperCamelCase : int = '''gpt2-xl'''
_UpperCamelCase : Optional[Any] = 3.31_91_85_48_54_15_21_87 | 217 |
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
a_ = logging.getLogger(__name__)
a_ = 'Hello world! cécé herlolip'
a_ = namedtuple(
'BertAbsConfig',
[
'temp_dir',
'large',
'use_bert_emb',
'finetune_bert',
'encoder',
'share_emb',
'max_pos',
'enc_layers',
'enc_hidden_size',
'enc_heads',
'enc_ff_size',
'enc_dropout',
'dec_layers',
'dec_hidden_size',
'dec_heads',
'dec_ff_size',
'dec_dropout',
],
)
def lowerCamelCase__ ( _a , _a):
SCREAMING_SNAKE_CASE : List[Any] = BertAbsConfig(
temp_dir="." , finetune_bert=_a , large=_a , share_emb=_a , use_bert_emb=_a , encoder="bert" , max_pos=512 , enc_layers=6 , enc_hidden_size=512 , enc_heads=8 , enc_ff_size=512 , enc_dropout=0.2 , dec_layers=6 , dec_hidden_size=768 , dec_heads=8 , dec_ff_size=2048 , dec_dropout=0.2 , )
SCREAMING_SNAKE_CASE : Dict = torch.load(_a , lambda _a , _a: storage)
SCREAMING_SNAKE_CASE : str = AbsSummarizer(_a , torch.device("cpu") , _a)
original.eval()
SCREAMING_SNAKE_CASE : List[str] = BertAbsSummarizer(_a , torch.device("cpu"))
new_model.eval()
# -------------------
# Convert the weights
# -------------------
logging.info("convert the model")
new_model.bert.load_state_dict(original.bert.state_dict())
new_model.decoder.load_state_dict(original.decoder.state_dict())
new_model.generator.load_state_dict(original.generator.state_dict())
# ----------------------------------
# Make sure the outpus are identical
# ----------------------------------
logging.info("Make sure that the models' outputs are identical")
SCREAMING_SNAKE_CASE : List[str] = BertTokenizer.from_pretrained("bert-base-uncased")
# prepare the model inputs
SCREAMING_SNAKE_CASE : List[str] = tokenizer.encode("This is sample éàalj'-.")
encoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(_a)))
SCREAMING_SNAKE_CASE : int = torch.tensor(_a).unsqueeze(0)
SCREAMING_SNAKE_CASE : List[Any] = tokenizer.encode("This is sample 3 éàalj'-.")
decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(_a)))
SCREAMING_SNAKE_CASE : int = torch.tensor(_a).unsqueeze(0)
# failsafe to make sure the weights reset does not affect the
# loaded weights.
assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight)) == 0
# forward pass
SCREAMING_SNAKE_CASE : List[Any] = encoder_input_ids
SCREAMING_SNAKE_CASE : List[Any] = decoder_input_ids
SCREAMING_SNAKE_CASE : Dict = None
SCREAMING_SNAKE_CASE : Optional[Any] = None
SCREAMING_SNAKE_CASE : List[Any] = None
SCREAMING_SNAKE_CASE : Optional[int] = None
SCREAMING_SNAKE_CASE : Dict = None
# The original model does not apply the geneator layer immediatly but rather in
# the beam search (where it combines softmax + linear layer). Since we already
# apply the softmax in our generation process we only apply the linear layer here.
# We make sure that the outputs of the full stack are identical
SCREAMING_SNAKE_CASE : Optional[int] = original(_a , _a , _a , _a , _a , _a , _a)[0]
SCREAMING_SNAKE_CASE : Dict = original.generator(_a)
SCREAMING_SNAKE_CASE : Any = new_model(
_a , _a , _a , _a , _a)[0]
SCREAMING_SNAKE_CASE : Tuple = new_model.generator(_a)
SCREAMING_SNAKE_CASE : List[Any] = torch.max(torch.abs(output_converted_model - output_original_model)).item()
print("Maximum absolute difference beween weights: {:.2f}".format(_a))
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.max(torch.abs(output_converted_generator - output_original_generator)).item()
print("Maximum absolute difference beween weights: {:.2f}".format(_a))
SCREAMING_SNAKE_CASE : int = torch.allclose(_a , _a , atol=1E-3)
if are_identical:
logging.info("all weights are equal up to 1e-3")
else:
raise ValueError("the weights are different. The new model is likely different from the original one.")
# The model has been saved with torch.save(model) and this is bound to the exact
# directory structure. We save the state_dict instead.
logging.info("saving the model's state dictionary")
torch.save(
new_model.state_dict() , "./bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin")
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
parser.add_argument(
'--bertabs_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_ = parser.parse_args()
convert_bertabs_checkpoints(
args.bertabs_checkpoint_path,
args.pytorch_dump_folder_path,
) | 25 | 0 |
SCREAMING_SNAKE_CASE_ = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
def __SCREAMING_SNAKE_CASE ( ) -> List[Any]:
_UpperCAmelCase : Union[str, Any] = input("Enter message: " )
_UpperCAmelCase : Dict = input("Enter key [alphanumeric]: " )
_UpperCAmelCase : str = input("Encrypt/Decrypt [e/d]: " )
if mode.lower().startswith("e" ):
_UpperCAmelCase : Union[str, Any] = "encrypt"
_UpperCAmelCase : Dict = encrypt_message(_a , _a )
elif mode.lower().startswith("d" ):
_UpperCAmelCase : Dict = "decrypt"
_UpperCAmelCase : Dict = decrypt_message(_a , _a )
print(F'\n{mode.title()}ed message:' )
print(_a )
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: List[Any] , lowerCAmelCase: int ) -> str:
return translate_message(_a , _a , "encrypt" )
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: List[str] , lowerCAmelCase: str ) -> Any:
return translate_message(_a , _a , "decrypt" )
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: List[str] , lowerCAmelCase: int , lowerCAmelCase: Optional[int] ) -> Any:
_UpperCAmelCase : Tuple = []
_UpperCAmelCase : int = 0
_UpperCAmelCase : Tuple = key.upper()
for symbol in message:
_UpperCAmelCase : int = LETTERS.find(symbol.upper() )
if num != -1:
if mode == "encrypt":
num += LETTERS.find(key[key_index] )
elif mode == "decrypt":
num -= LETTERS.find(key[key_index] )
num %= len(_a )
if symbol.isupper():
translated.append(LETTERS[num] )
elif symbol.islower():
translated.append(LETTERS[num].lower() )
key_index += 1
if key_index == len(_a ):
_UpperCAmelCase : str = 0
else:
translated.append(_a )
return "".join(_a )
if __name__ == "__main__":
main()
| 300 |
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
parser.add_argument(
'--txt2img_unclip',
default='kakaobrain/karlo-v1-alpha',
type=str,
required=False,
help='The pretrained txt2img unclip.',
)
a_ = parser.parse_args()
a_ = UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)
a_ = CLIPImageProcessor()
a_ = CLIPVisionModelWithProjection.from_pretrained('openai/clip-vit-large-patch14')
a_ = UnCLIPImageVariationPipeline(
decoder=txtaimg.decoder,
text_encoder=txtaimg.text_encoder,
tokenizer=txtaimg.tokenizer,
text_proj=txtaimg.text_proj,
feature_extractor=feature_extractor,
image_encoder=image_encoder,
super_res_first=txtaimg.super_res_first,
super_res_last=txtaimg.super_res_last,
decoder_scheduler=txtaimg.decoder_scheduler,
super_res_scheduler=txtaimg.super_res_scheduler,
)
imgaimg.save_pretrained(args.dump_path) | 25 | 0 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_flax_available():
import jax
from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel
class __snake_case( unittest.TestCase ):
'''simple docstring'''
def __init__( self , A_ , A_=13 , A_=30 , A_=2 , A_=3 , A_=True , A_=True , A_=32 , A_=5 , A_=4 , A_=37 , A_="gelu" , A_=0.1 , A_=0.1 , A_=10 , A_=0.0_2 , ) -> List[Any]:
lowerCAmelCase = parent
lowerCAmelCase = batch_size
lowerCAmelCase = image_size
lowerCAmelCase = patch_size
lowerCAmelCase = num_channels
lowerCAmelCase = is_training
lowerCAmelCase = use_labels
lowerCAmelCase = hidden_size
lowerCAmelCase = num_hidden_layers
lowerCAmelCase = num_attention_heads
lowerCAmelCase = intermediate_size
lowerCAmelCase = hidden_act
lowerCAmelCase = hidden_dropout_prob
lowerCAmelCase = attention_probs_dropout_prob
lowerCAmelCase = type_sequence_label_size
lowerCAmelCase = initializer_range
# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
lowerCAmelCase = (image_size // patch_size) ** 2
lowerCAmelCase = num_patches + 1
def __snake_case ( self ) -> Optional[Any]:
lowerCAmelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowerCAmelCase = ViTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=A_ , initializer_range=self.initializer_range , )
return config, pixel_values
def __snake_case ( self , A_ , A_ ) -> List[str]:
lowerCAmelCase = FlaxViTModel(config=A_ )
lowerCAmelCase = model(A_ )
# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
lowerCAmelCase = (self.image_size, self.image_size)
lowerCAmelCase = (self.patch_size, self.patch_size)
lowerCAmelCase = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, num_patches + 1, self.hidden_size) )
def __snake_case ( self , A_ , A_ ) -> Tuple:
lowerCAmelCase = self.type_sequence_label_size
lowerCAmelCase = FlaxViTForImageClassification(config=A_ )
lowerCAmelCase = model(A_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
lowerCAmelCase = 1
lowerCAmelCase = FlaxViTForImageClassification(A_ )
lowerCAmelCase = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
lowerCAmelCase = model(A_ )
def __snake_case ( self ) -> Optional[int]:
lowerCAmelCase = self.prepare_config_and_inputs()
(
lowerCAmelCase
) = config_and_inputs
lowerCAmelCase = {"pixel_values": pixel_values}
return config, inputs_dict
@require_flax
class __snake_case( __A , unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase : str = (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()
def __snake_case ( self ) -> None:
lowerCAmelCase = FlaxViTModelTester(self )
lowerCAmelCase = ConfigTester(self , config_class=A_ , has_text_modality=A_ , hidden_size=37 )
def __snake_case ( self ) -> List[str]:
self.config_tester.run_common_tests()
def __snake_case ( self ) -> Dict:
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A_ )
def __snake_case ( self ) -> Dict:
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*A_ )
def __snake_case ( self ) -> str:
lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase = model_class(A_ )
lowerCAmelCase = inspect.signature(model.__call__ )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCAmelCase = [*signature.parameters.keys()]
lowerCAmelCase = ["pixel_values"]
self.assertListEqual(arg_names[:1] , A_ )
def __snake_case ( self ) -> Any:
lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
lowerCAmelCase = self._prepare_for_class(A_ , A_ )
lowerCAmelCase = model_class(A_ )
@jax.jit
def model_jitted(A_ , **A_ ):
return model(pixel_values=A_ , **A_ )
with self.subTest("""JIT Enabled""" ):
lowerCAmelCase = model_jitted(**A_ ).to_tuple()
with self.subTest("""JIT Disabled""" ):
with jax.disable_jit():
lowerCAmelCase = model_jitted(**A_ ).to_tuple()
self.assertEqual(len(A_ ) , len(A_ ) )
for jitted_output, output in zip(A_ , A_ ):
self.assertEqual(jitted_output.shape , output.shape )
@slow
def __snake_case ( self ) -> List[Any]:
for model_class_name in self.all_model_classes:
lowerCAmelCase = model_class_name.from_pretrained("""google/vit-base-patch16-224""" )
lowerCAmelCase = model(np.ones((1, 3, 224, 224) ) )
self.assertIsNotNone(A_ ) | 433 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
'configuration_swinv2': ['SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Swinv2Config'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST',
'Swinv2ForImageClassification',
'Swinv2ForMaskedImageModeling',
'Swinv2Model',
'Swinv2PreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_swinva import (
SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
SwinvaForImageClassification,
SwinvaForMaskedImageModeling,
SwinvaModel,
SwinvaPreTrainedModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 25 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
snake_case = {
'''configuration_falcon''': ['''FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FalconConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case = [
'''FALCON_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''FalconForCausalLM''',
'''FalconModel''',
'''FalconPreTrainedModel''',
'''FalconForSequenceClassification''',
'''FalconForTokenClassification''',
'''FalconForQuestionAnswering''',
]
if TYPE_CHECKING:
from .configuration_falcon import FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP, FalconConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_falcon import (
FALCON_PRETRAINED_MODEL_ARCHIVE_LIST,
FalconForCausalLM,
FalconForQuestionAnswering,
FalconForSequenceClassification,
FalconForTokenClassification,
FalconModel,
FalconPreTrainedModel,
)
else:
import sys
snake_case = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 103 |
from math import pi, sqrt, tan
def lowerCamelCase__ ( _a):
if side_length < 0:
raise ValueError("surface_area_cube() only accepts non-negative values")
return 6 * side_length**2
def lowerCamelCase__ ( _a , _a , _a):
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 lowerCamelCase__ ( _a):
if radius < 0:
raise ValueError("surface_area_sphere() only accepts non-negative values")
return 4 * pi * radius**2
def lowerCamelCase__ ( _a):
if radius < 0:
raise ValueError("surface_area_hemisphere() only accepts non-negative values")
return 3 * pi * radius**2
def lowerCamelCase__ ( _a , _a):
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 lowerCamelCase__ ( _a , _a , _a):
if radius_a < 0 or radius_a < 0 or height < 0:
raise ValueError(
"surface_area_conical_frustum() only accepts non-negative values")
SCREAMING_SNAKE_CASE : Any = (height**2 + (radius_a - radius_a) ** 2) ** 0.5
return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)
def lowerCamelCase__ ( _a , _a):
if radius < 0 or height < 0:
raise ValueError("surface_area_cylinder() only accepts non-negative values")
return 2 * pi * radius * (height + radius)
def lowerCamelCase__ ( _a , _a):
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(_a , 2) * torus_radius * tube_radius
def lowerCamelCase__ ( _a , _a):
if length < 0 or width < 0:
raise ValueError("area_rectangle() only accepts non-negative values")
return length * width
def lowerCamelCase__ ( _a):
if side_length < 0:
raise ValueError("area_square() only accepts non-negative values")
return side_length**2
def lowerCamelCase__ ( _a , _a):
if base < 0 or height < 0:
raise ValueError("area_triangle() only accepts non-negative values")
return (base * height) / 2
def lowerCamelCase__ ( _a , _a , _a):
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")
SCREAMING_SNAKE_CASE : List[str] = (sidea + sidea + sidea) / 2
SCREAMING_SNAKE_CASE : Optional[int] = sqrt(
semi_perimeter
* (semi_perimeter - sidea)
* (semi_perimeter - sidea)
* (semi_perimeter - sidea))
return area
def lowerCamelCase__ ( _a , _a):
if base < 0 or height < 0:
raise ValueError("area_parallelogram() only accepts non-negative values")
return base * height
def lowerCamelCase__ ( _a , _a , _a):
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 lowerCamelCase__ ( _a):
if radius < 0:
raise ValueError("area_circle() only accepts non-negative values")
return pi * radius**2
def lowerCamelCase__ ( _a , _a):
if radius_x < 0 or radius_y < 0:
raise ValueError("area_ellipse() only accepts non-negative values")
return pi * radius_x * radius_y
def lowerCamelCase__ ( _a , _a):
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 lowerCamelCase__ ( _a , _a):
if not isinstance(_a , _a) 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(10, 20) = }''')
print(F'''Square: {area_square(10) = }''')
print(F'''Triangle: {area_triangle(10, 10) = }''')
print(F'''Triangle: {area_triangle_three_sides(5, 12, 13) = }''')
print(F'''Parallelogram: {area_parallelogram(10, 20) = }''')
print(F'''Rhombus: {area_rhombus(10, 20) = }''')
print(F'''Trapezium: {area_trapezium(10, 20, 30) = }''')
print(F'''Circle: {area_circle(20) = }''')
print(F'''Ellipse: {area_ellipse(10, 20) = }''')
print('\nSurface Areas of various geometric shapes: \n')
print(F'''Cube: {surface_area_cube(20) = }''')
print(F'''Cuboid: {surface_area_cuboid(10, 20, 30) = }''')
print(F'''Sphere: {surface_area_sphere(20) = }''')
print(F'''Hemisphere: {surface_area_hemisphere(20) = }''')
print(F'''Cone: {surface_area_cone(10, 20) = }''')
print(F'''Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }''')
print(F'''Cylinder: {surface_area_cylinder(10, 20) = }''')
print(F'''Torus: {surface_area_torus(20, 10) = }''')
print(F'''Equilateral Triangle: {area_reg_polygon(3, 10) = }''')
print(F'''Square: {area_reg_polygon(4, 10) = }''')
print(F'''Reqular Pentagon: {area_reg_polygon(5, 10) = }''') | 25 | 0 |
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
"split_dict" , [
SplitDict(),
SplitDict({"train": SplitInfo(name="train" , num_bytes=1_3_3_7 , num_examples=4_2 , dataset_name="my_dataset" )} ),
SplitDict({"train": SplitInfo(name="train" , num_bytes=1_3_3_7 , num_examples=4_2 )} ),
SplitDict({"train": SplitInfo()} ),
] , )
def UpperCAmelCase ( a_ ) -> List[str]:
"""simple docstring"""
__A = split_dict._to_yaml_list()
assert len(_a ) == len(_a )
__A = SplitDict._from_yaml_list(_a )
for split_name, split_info in split_dict.items():
# dataset_name field is deprecated, and is therefore not part of the YAML dump
__A = None
# the split name of split_dict takes over the name of the split info object
__A = split_name
assert split_dict == reloaded
@pytest.mark.parametrize(
"split_info" , [SplitInfo(), SplitInfo(dataset_name=_a ), SplitInfo(dataset_name="my_dataset" )] )
def UpperCAmelCase ( a_ ) -> Tuple:
"""simple docstring"""
__A = asdict(SplitDict({"train": split_info} ) )
assert "dataset_name" in split_dict_asdict["train"]
assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name
| 55 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
'configuration_instructblip': [
'INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'InstructBlipConfig',
'InstructBlipQFormerConfig',
'InstructBlipVisionConfig',
],
'processing_instructblip': ['InstructBlipProcessor'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
'InstructBlipQFormerModel',
'InstructBlipPreTrainedModel',
'InstructBlipForConditionalGeneration',
'InstructBlipVisionModel',
]
if TYPE_CHECKING:
from .configuration_instructblip import (
INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
InstructBlipConfig,
InstructBlipQFormerConfig,
InstructBlipVisionConfig,
)
from .processing_instructblip import InstructBlipProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_instructblip import (
INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
InstructBlipForConditionalGeneration,
InstructBlipPreTrainedModel,
InstructBlipQFormerModel,
InstructBlipVisionModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 25 | 0 |
"""simple docstring"""
import importlib.metadata
from typing import Union
from packaging.version import Version, parse
from .constants import STR_OPERATION_TO_FUNC
_UpperCamelCase : List[Any] = parse(importlib.metadata.version("torch"))
def a_ ( _lowerCAmelCase : Any , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Tuple ):
'''simple docstring'''
if operation not in STR_OPERATION_TO_FUNC.keys():
raise ValueError(f"""`operation` must be one of {list(STR_OPERATION_TO_FUNC.keys() )}, received {operation}""" )
lowercase__ : Union[str, Any] = STR_OPERATION_TO_FUNC[operation]
if isinstance(_a , _a ):
lowercase__ : Any = parse(importlib.metadata.version(_a ) )
return operation(_a , parse(_a ) )
def a_ ( _lowerCAmelCase : List[Any] , _lowerCAmelCase : Tuple ):
'''simple docstring'''
return compare_versions(_a , _a , _a )
| 599 |
from __future__ import annotations
def lowerCamelCase__ ( _a):
SCREAMING_SNAKE_CASE : Optional[Any] = 2
SCREAMING_SNAKE_CASE : Optional[int] = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(_a)
if n > 1:
factors.append(_a)
return factors
if __name__ == "__main__":
import doctest
doctest.testmod() | 25 | 0 |
"""simple docstring"""
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel
class _a :
def __init__( self : List[str] , _lowercase : Optional[int] , _lowercase : str=99 , _lowercase : str=13 , _lowercase : List[Any]=7 , _lowercase : Optional[int]=9 , _lowercase : Optional[int]=True , _lowercase : Union[str, Any]=True , _lowercase : Any=False , _lowercase : Tuple=32 , _lowercase : List[str]=5 , _lowercase : Union[str, Any]=4 , _lowercase : Union[str, Any]=37 , _lowercase : str=8 , _lowercase : int=0.1 , _lowercase : Optional[int]=0.002 , _lowercase : Union[str, Any]=1 , _lowercase : Optional[int]=0 , _lowercase : Tuple=0 , _lowercase : Any=None , _lowercase : Optional[Any]=None , ) -> Tuple:
snake_case : Optional[Any] = parent
snake_case : int = batch_size
snake_case : Optional[int] = encoder_seq_length
snake_case : Tuple = decoder_seq_length
# For common tests
snake_case : int = self.decoder_seq_length
snake_case : int = is_training
snake_case : Any = use_attention_mask
snake_case : List[str] = use_labels
snake_case : Optional[Any] = vocab_size
snake_case : Union[str, Any] = hidden_size
snake_case : Tuple = num_hidden_layers
snake_case : Any = num_attention_heads
snake_case : Optional[int] = d_ff
snake_case : Tuple = relative_attention_num_buckets
snake_case : List[Any] = dropout_rate
snake_case : int = initializer_factor
snake_case : List[Any] = eos_token_id
snake_case : int = pad_token_id
snake_case : Union[str, Any] = decoder_start_token_id
snake_case : Union[str, Any] = None
snake_case : Tuple = decoder_layers
def __lowercase ( self : Tuple ) -> Optional[Any]:
return TaConfig.from_pretrained("google/umt5-base" )
def __lowercase ( self : Optional[int] , _lowercase : List[str] , _lowercase : Optional[Any] , _lowercase : Optional[int] , _lowercase : Tuple=None , _lowercase : List[Any]=None , _lowercase : int=None , _lowercase : Any=None , _lowercase : Dict=None , ) -> List[str]:
if attention_mask is None:
snake_case : int = input_ids.ne(config.pad_token_id )
if decoder_attention_mask is None:
snake_case : Any = decoder_input_ids.ne(config.pad_token_id )
if head_mask is None:
snake_case : Tuple = torch.ones(config.num_hidden_layers , config.num_attention_heads , device=_lowercase )
if decoder_head_mask is None:
snake_case : Union[str, Any] = torch.ones(config.num_decoder_layers , config.num_attention_heads , device=_lowercase )
if cross_attn_head_mask is None:
snake_case : Optional[Any] = torch.ones(
config.num_decoder_layers , config.num_attention_heads , device=_lowercase )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
def __lowercase ( self : List[Any] ) -> Union[str, Any]:
snake_case : int = ids_tensor([self.batch_size, self.encoder_seq_length] , self.vocab_size )
snake_case : Any = ids_tensor([self.batch_size, self.decoder_seq_length] , self.vocab_size )
# we need to clamp the input ids here to avoid having pad token in between
# this is because for NllbMoe the position_ids are prepared such that
# all pad tokens have pos id = 2 and rest are between 2..seq_length
# and the seq_length here is seq_length - num_pad_tokens
# but when using past, there is no way of knowing if the past input ids had
# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
# position_ids being off by num_pad_tokens in past input
snake_case : int = input_ids.clamp(self.pad_token_id + 1 )
snake_case : Tuple = decoder_input_ids.clamp(self.pad_token_id + 1 )
snake_case : Any = self.get_config()
snake_case : Union[str, Any] = config.num_attention_heads
snake_case : Dict = self.prepare_inputs_dict(_lowercase , _lowercase , _lowercase )
return config, input_dict
def __lowercase ( self : Union[str, Any] ) -> int:
snake_case : Tuple = self.prepare_config_and_inputs()
return config, inputs_dict
def __lowercase ( self : int ) -> List[Any]:
return TaConfig(
vocab_size=166 , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , )
def __lowercase ( self : List[Any] ) -> int:
return TaConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , )
def __lowercase ( self : Dict , _lowercase : List[str] , _lowercase : Dict , _lowercase : int , _lowercase : Tuple , _lowercase : List[str] , _lowercase : Union[str, Any] , ) -> Union[str, Any]:
snake_case : int = UMTaModel(config=_lowercase )
model.to(_lowercase )
model.eval()
snake_case : List[str] = model(
input_ids=_lowercase , decoder_input_ids=_lowercase , attention_mask=_lowercase , decoder_attention_mask=_lowercase , )
snake_case : Any = model(input_ids=_lowercase , decoder_input_ids=_lowercase )
snake_case : Optional[Any] = result.last_hidden_state
snake_case : List[str] = result.past_key_values
snake_case : List[str] = result.encoder_last_hidden_state
self.parent.assertEqual(encoder_output.size() , (self.batch_size, self.encoder_seq_length, self.hidden_size) )
self.parent.assertEqual(decoder_output.size() , (self.batch_size, self.decoder_seq_length, self.hidden_size) )
# There should be `num_layers` key value embeddings stored in decoder_past
self.parent.assertEqual(len(_lowercase ) , config.num_layers )
# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
self.parent.assertEqual(len(decoder_past[0] ) , 4 )
def __lowercase ( self : List[str] , _lowercase : Optional[int] , _lowercase : Tuple , _lowercase : Tuple , _lowercase : List[Any] , _lowercase : List[Any] , _lowercase : Optional[int] , ) -> Union[str, Any]:
snake_case : int = UMTaModel(config=_lowercase ).get_decoder().to(_lowercase ).eval()
# first forward pass
snake_case : int = model(_lowercase , use_cache=_lowercase )
snake_case : Dict = model(_lowercase )
snake_case : str = model(_lowercase , use_cache=_lowercase )
self.parent.assertTrue(len(_lowercase ) == len(_lowercase ) )
self.parent.assertTrue(len(_lowercase ) == len(_lowercase ) + 1 )
snake_case : Dict = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
snake_case : Optional[Any] = ids_tensor((self.batch_size, 1) , config.vocab_size )
# append to next input_ids and
snake_case : List[str] = torch.cat([input_ids, next_tokens] , dim=-1 )
snake_case : str = model(_lowercase )["last_hidden_state"]
snake_case : List[str] = model(_lowercase , past_key_values=_lowercase )["last_hidden_state"]
# select random slice
snake_case : Tuple = ids_tensor((1,) , output_from_past.shape[-1] ).item()
snake_case : List[Any] = output_from_no_past[:, -1, random_slice_idx].detach()
snake_case : Any = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(_lowercase , _lowercase , atol=1E-3 ) )
def __lowercase ( self : Tuple , _lowercase : Union[str, Any] , _lowercase : int , ) -> int:
snake_case : str = UMTaModel(config=_lowercase ).to(_lowercase ).half().eval()
snake_case : Union[str, Any] = model(**_lowercase )["last_hidden_state"]
self.parent.assertFalse(torch.isnan(_lowercase ).any().item() )
@require_torch
class _a ( __A , __A , __A , unittest.TestCase):
__magic_name__ = (
(UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
)
__magic_name__ = (UMTaForConditionalGeneration,) if is_torch_available() else ()
__magic_name__ = (
{
"""conversational""": UMTaForConditionalGeneration,
"""feature-extraction""": UMTaModel,
"""summarization""": UMTaForConditionalGeneration,
"""text2text-generation""": UMTaForConditionalGeneration,
"""translation""": UMTaForConditionalGeneration,
"""question-answering""": UMTaForQuestionAnswering,
}
if is_torch_available()
else {}
)
__magic_name__ = True
__magic_name__ = False
__magic_name__ = False
__magic_name__ = True
__magic_name__ = True
# The small UMT5 model needs higher percentages for CPU/MP tests
__magic_name__ = [0.8, 0.9]
def __lowercase ( self : Tuple ) -> Tuple:
snake_case : Dict = UMTaModelTester(self )
@unittest.skip("Test has a segmentation fault on torch 1.8.0" )
def __lowercase ( self : int ) -> Optional[int]:
snake_case : str = self.model_tester.prepare_config_and_inputs()
snake_case : int = UMTaModel(config_and_inputs[0] ).to(_lowercase )
with tempfile.TemporaryDirectory() as tmpdirname:
torch.onnx.export(
_lowercase , (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2]) , F'''{tmpdirname}/t5_test.onnx''' , export_params=_lowercase , opset_version=9 , input_names=["input_ids", "decoder_input_ids"] , )
@unittest.skipIf(torch_device == "cpu" , "Cant do half precision" )
def __lowercase ( self : Dict ) -> int:
snake_case : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model_fpaa_forward(*_lowercase )
def __lowercase ( self : Tuple ) -> str:
snake_case : int = ["encoder_attentions", "decoder_attentions", "cross_attentions"]
snake_case : int = self.model_tester.prepare_config_and_inputs()
snake_case : Union[str, Any] = config_and_inputs[0]
snake_case : Any = UMTaForConditionalGeneration(_lowercase ).eval()
model.to(_lowercase )
snake_case : List[Any] = {
"head_mask": torch.zeros(config.num_layers , config.num_heads , device=_lowercase ),
"decoder_head_mask": torch.zeros(config.num_decoder_layers , config.num_heads , device=_lowercase ),
"cross_attn_head_mask": torch.zeros(config.num_decoder_layers , config.num_heads , device=_lowercase ),
}
for attn_name, (name, mask) in zip(_lowercase , head_masking.items() ):
snake_case : Optional[int] = {name: mask}
# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
if name == "head_mask":
snake_case : List[Any] = torch.ones(
config.num_decoder_layers , config.num_heads , device=_lowercase )
snake_case : Optional[Any] = model.generate(
config_and_inputs[1]["input_ids"] , num_beams=1 , max_length=3 , output_attentions=_lowercase , return_dict_in_generate=_lowercase , **_lowercase , )
# We check the state of decoder_attentions and cross_attentions just from the last step
snake_case : int = out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
self.assertEqual(sum([w.sum().item() for w in attn_weights] ) , 0.0 )
@unittest.skip("Does not work on the tiny model as we keep hitting edge cases." )
def __lowercase ( self : Optional[int] ) -> int:
pass
@require_torch
@require_sentencepiece
@require_tokenizers
class _a ( unittest.TestCase):
@slow
@unittest.skip(
"Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged" )
def __lowercase ( self : List[str] ) -> str:
snake_case : str = UMTaForConditionalGeneration.from_pretrained("google/umt5-small" , return_dict=_lowercase ).to(_lowercase )
snake_case : int = AutoTokenizer.from_pretrained("google/umt5-small" , use_fast=_lowercase , legacy=_lowercase )
snake_case : int = [
"Bonjour monsieur <extra_id_0> bien <extra_id_1>.",
"No se como puedo <extra_id_0>.",
"This is the reason why we <extra_id_0> them.",
"The <extra_id_0> walks in <extra_id_1>, seats",
"A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.",
]
snake_case : Dict = tokenizer(_lowercase , return_tensors="pt" , padding=_lowercase ).input_ids
# fmt: off
snake_case : int = torch.tensor(
[
[ 38530, 210703, 256299, 1410, 256298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 826, 321, 671, 25922, 256299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 1460, 339, 312, 19014, 10620, 758, 256299, 2355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
[ 517, 256299, 14869, 281, 301, 256298, 275, 119983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 320, 256299, 14869, 281, 2234, 289, 2275, 333,61391, 289, 256298, 543, 256297, 168714, 329, 256296,274, 1],
] )
# fmt: on
torch.testing.assert_allclose(_lowercase , _lowercase )
snake_case : Dict = model.generate(input_ids.to(_lowercase ) )
snake_case : List[Any] = [
"<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>",
"<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
"<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
"<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
"<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
]
snake_case : Dict = tokenizer.batch_decode(_lowercase )
self.assertEqual(_lowercase , _lowercase )
| 449 |
from math import factorial, pi
def lowerCamelCase__ ( _a , _a = 30):
if not isinstance(_a , (int, float)):
raise ValueError("maclaurin_sin() requires either an int or float for theta")
if not isinstance(_a , _a) or accuracy <= 0:
raise ValueError("maclaurin_sin() requires a positive int for accuracy")
SCREAMING_SNAKE_CASE : int = float(_a)
SCREAMING_SNAKE_CASE : Dict = theta // (2 * pi)
theta -= 2 * div * pi
return sum(
(-1) ** r * theta ** (2 * r + 1) / factorial(2 * r + 1) for r in range(_a))
def lowerCamelCase__ ( _a , _a = 30):
if not isinstance(_a , (int, float)):
raise ValueError("maclaurin_cos() requires either an int or float for theta")
if not isinstance(_a , _a) or accuracy <= 0:
raise ValueError("maclaurin_cos() requires a positive int for accuracy")
SCREAMING_SNAKE_CASE : str = float(_a)
SCREAMING_SNAKE_CASE : Any = theta // (2 * pi)
theta -= 2 * div * pi
return sum((-1) ** r * theta ** (2 * r) / factorial(2 * r) for r in range(_a))
if __name__ == "__main__":
import doctest
doctest.testmod()
print(maclaurin_sin(10))
print(maclaurin_sin(-10))
print(maclaurin_sin(10, 15))
print(maclaurin_sin(-10, 15))
print(maclaurin_cos(5))
print(maclaurin_cos(-5))
print(maclaurin_cos(10, 15))
print(maclaurin_cos(-10, 15)) | 25 | 0 |
'''simple docstring'''
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
TimesformerForVideoClassification,
TimesformerModel,
)
from transformers.models.timesformer.modeling_timesformer import TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from transformers import VideoMAEImageProcessor
class A_ :
def __init__( self : Union[str, Any] , snake_case_ : Optional[int] , snake_case_ : List[str]=1_3 , snake_case_ : Optional[int]=1_0 , snake_case_ : str=3 , snake_case_ : int=2 , snake_case_ : Optional[Any]=2 , snake_case_ : Any=True , snake_case_ : Optional[int]=True , snake_case_ : List[Any]=3_2 , snake_case_ : Optional[Any]=5 , snake_case_ : str=4 , snake_case_ : Optional[int]=3_7 , snake_case_ : int="gelu" , snake_case_ : Dict=0.1 , snake_case_ : Optional[Any]=0.1 , snake_case_ : Dict=1_0 , snake_case_ : List[Any]=0.0_2 , snake_case_ : Dict="divided_space_time" , snake_case_ : Any=None , ):
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = image_size
_UpperCAmelCase = num_channels
_UpperCAmelCase = patch_size
_UpperCAmelCase = num_frames
_UpperCAmelCase = is_training
_UpperCAmelCase = use_labels
_UpperCAmelCase = hidden_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = intermediate_size
_UpperCAmelCase = hidden_act
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = attention_type
_UpperCAmelCase = initializer_range
_UpperCAmelCase = scope
_UpperCAmelCase = num_labels
# in TimeSformer, the number of spatial tokens equals num_frames * num_patches per frame + 1 CLS token
_UpperCAmelCase = (image_size // patch_size) ** 2
_UpperCAmelCase = (num_frames) * self.num_patches_per_frame + 1
def lowercase ( self : Tuple ):
_UpperCAmelCase = floats_tensor(
[self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size] )
_UpperCAmelCase = None
if self.use_labels:
_UpperCAmelCase = ids_tensor([self.batch_size] , self.num_labels )
_UpperCAmelCase = self.get_config()
return config, pixel_values, labels
def lowercase ( self : Union[str, Any] ):
_UpperCAmelCase = TimesformerConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , num_frames=self.num_frames , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , initializer_range=self.initializer_range , attention_type=self.attention_type , )
_UpperCAmelCase = self.num_labels
return config
def lowercase ( self : int , snake_case_ : Tuple , snake_case_ : List[Any] , snake_case_ : int ):
_UpperCAmelCase = TimesformerModel(config=snake_case_ )
model.to(snake_case_ )
model.eval()
_UpperCAmelCase = model(snake_case_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowercase ( self : Optional[Any] , snake_case_ : Tuple , snake_case_ : Dict , snake_case_ : Any ):
_UpperCAmelCase = TimesformerForVideoClassification(snake_case_ )
model.to(snake_case_ )
model.eval()
_UpperCAmelCase = model(snake_case_ )
# verify the logits shape
_UpperCAmelCase = torch.Size((self.batch_size, self.num_labels) )
self.parent.assertEqual(result.logits.shape , snake_case_ )
def lowercase ( self : Optional[int] ):
_UpperCAmelCase = self.prepare_config_and_inputs()
_UpperCAmelCase = config_and_inputs
_UpperCAmelCase = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class A_ ( __A , __A , unittest.TestCase ):
_lowerCamelCase : Optional[int] = (TimesformerModel, TimesformerForVideoClassification) if is_torch_available() else ()
_lowerCamelCase : List[Any] = (
{"""feature-extraction""": TimesformerModel, """video-classification""": TimesformerForVideoClassification}
if is_torch_available()
else {}
)
_lowerCamelCase : Dict = False
_lowerCamelCase : Any = False
_lowerCamelCase : List[Any] = False
_lowerCamelCase : List[str] = False
def lowercase ( self : int ):
_UpperCAmelCase = TimesformerModelTester(self )
_UpperCAmelCase = ConfigTester(
self , config_class=snake_case_ , has_text_modality=snake_case_ , hidden_size=3_7 )
def lowercase ( self : Union[str, Any] , snake_case_ : Union[str, Any] , snake_case_ : Dict , snake_case_ : Optional[Any]=False ):
_UpperCAmelCase = copy.deepcopy(snake_case_ )
if return_labels:
if model_class in get_values(snake_case_ ):
_UpperCAmelCase = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=snake_case_ )
return inputs_dict
def lowercase ( self : List[str] ):
self.config_tester.run_common_tests()
@unittest.skip(reason="TimeSformer does not use inputs_embeds" )
def lowercase ( self : Dict ):
pass
def lowercase ( self : Tuple ):
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase = model_class(snake_case_ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
_UpperCAmelCase = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(snake_case_ , nn.Linear ) )
def lowercase ( self : List[str] ):
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase = model_class(snake_case_ )
_UpperCAmelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_UpperCAmelCase = [*signature.parameters.keys()]
_UpperCAmelCase = ["pixel_values"]
self.assertListEqual(arg_names[:1] , snake_case_ )
def lowercase ( self : Union[str, Any] ):
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case_ )
def lowercase ( self : Optional[int] ):
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_video_classification(*snake_case_ )
@slow
def lowercase ( self : Any ):
for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase = TimesformerModel.from_pretrained(snake_case_ )
self.assertIsNotNone(snake_case_ )
def lowercase ( self : str ):
if not self.has_attentions:
pass
else:
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
_UpperCAmelCase = True
for model_class in self.all_model_classes:
_UpperCAmelCase = self.model_tester.seq_length
_UpperCAmelCase = self.model_tester.num_frames
_UpperCAmelCase = True
_UpperCAmelCase = False
_UpperCAmelCase = True
_UpperCAmelCase = model_class(snake_case_ )
model.to(snake_case_ )
model.eval()
with torch.no_grad():
_UpperCAmelCase = model(**self._prepare_for_class(snake_case_ , snake_case_ ) )
_UpperCAmelCase = outputs.attentions
self.assertEqual(len(snake_case_ ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
_UpperCAmelCase = True
_UpperCAmelCase = model_class(snake_case_ )
model.to(snake_case_ )
model.eval()
with torch.no_grad():
_UpperCAmelCase = model(**self._prepare_for_class(snake_case_ , snake_case_ ) )
_UpperCAmelCase = outputs.attentions
self.assertEqual(len(snake_case_ ) , self.model_tester.num_hidden_layers )
# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1] , )
_UpperCAmelCase = len(snake_case_ )
# Check attention is always last and order is fine
_UpperCAmelCase = True
_UpperCAmelCase = True
_UpperCAmelCase = model_class(snake_case_ )
model.to(snake_case_ )
model.eval()
with torch.no_grad():
_UpperCAmelCase = model(**self._prepare_for_class(snake_case_ , snake_case_ ) )
self.assertEqual(out_len + 1 , len(snake_case_ ) )
_UpperCAmelCase = outputs.attentions
self.assertEqual(len(snake_case_ ) , self.model_tester.num_hidden_layers )
# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1] , )
def lowercase ( self : List[str] ):
def check_hidden_states_output(snake_case_ : Tuple , snake_case_ : Dict , snake_case_ : Tuple ):
_UpperCAmelCase = model_class(snake_case_ )
model.to(snake_case_ )
model.eval()
with torch.no_grad():
_UpperCAmelCase = model(**self._prepare_for_class(snake_case_ , snake_case_ ) )
_UpperCAmelCase = outputs.hidden_states
_UpperCAmelCase = self.model_tester.num_hidden_layers + 1
self.assertEqual(len(snake_case_ ) , snake_case_ )
_UpperCAmelCase = self.model_tester.seq_length
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [seq_length, self.model_tester.hidden_size] , )
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase = True
check_hidden_states_output(snake_case_ , snake_case_ , snake_case_ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_UpperCAmelCase = True
check_hidden_states_output(snake_case_ , snake_case_ , snake_case_ )
def UpperCAmelCase_ ( ) -> Tuple:
'''simple docstring'''
_UpperCAmelCase = hf_hub_download(
repo_id="hf-internal-testing/spaghetti-video" , filename="eating_spaghetti.npy" , repo_type="dataset" )
_UpperCAmelCase = np.load(_a )
return list(_a )
@require_torch
@require_vision
class A_ ( unittest.TestCase ):
@cached_property
def lowercase ( self : int ):
return (
VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5] , image_std=[0.5, 0.5, 0.5] )
if is_vision_available()
else None
)
@slow
def lowercase ( self : Dict ):
_UpperCAmelCase = TimesformerForVideoClassification.from_pretrained("facebook/timesformer-base-finetuned-k400" ).to(
snake_case_ )
_UpperCAmelCase = self.default_image_processor
_UpperCAmelCase = prepare_video()
_UpperCAmelCase = image_processor(video[:8] , return_tensors="pt" ).to(snake_case_ )
# forward pass
with torch.no_grad():
_UpperCAmelCase = model(**snake_case_ )
# verify the logits
_UpperCAmelCase = torch.Size((1, 4_0_0) )
self.assertEqual(outputs.logits.shape , snake_case_ )
_UpperCAmelCase = torch.tensor([-0.3_0_1_6, -0.7_7_1_3, -0.4_2_0_5] ).to(snake_case_ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , snake_case_ , atol=1e-4 ) )
| 236 |
from __future__ import annotations
import math
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Dict , a : int ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = size
# approximate the overall size of segment tree with given value
SCREAMING_SNAKE_CASE : Any = [0 for i in range(0 , 4 * size )]
# create array to store lazy update
SCREAMING_SNAKE_CASE : Union[str, Any] = [0 for i in range(0 , 4 * size )]
SCREAMING_SNAKE_CASE : Any = [0 for i in range(0 , 4 * size )] # flag for lazy update
def __UpperCamelCase ( self : Tuple , a : int ) -> int:
"""simple docstring"""
return idx * 2
def __UpperCamelCase ( self : str , a : int ) -> int:
"""simple docstring"""
return idx * 2 + 1
def __UpperCamelCase ( self : int , a : int , a : int , a : int , a : list[int] ) -> None:
"""simple docstring"""
if left_element == right_element:
SCREAMING_SNAKE_CASE : int = a[left_element - 1]
else:
SCREAMING_SNAKE_CASE : Optional[int] = (left_element + right_element) // 2
self.build(self.left(a ) , a , a , a )
self.build(self.right(a ) , mid + 1 , a , a )
SCREAMING_SNAKE_CASE : List[Any] = max(
self.segment_tree[self.left(a )] , self.segment_tree[self.right(a )] )
def __UpperCamelCase ( self : Optional[Any] , a : int , a : int , a : int , a : int , a : int , a : int ) -> bool:
"""simple docstring"""
if self.flag[idx] is True:
SCREAMING_SNAKE_CASE : Any = self.lazy[idx]
SCREAMING_SNAKE_CASE : List[str] = False
if left_element != right_element:
SCREAMING_SNAKE_CASE : Optional[Any] = self.lazy[idx]
SCREAMING_SNAKE_CASE : int = self.lazy[idx]
SCREAMING_SNAKE_CASE : Any = True
SCREAMING_SNAKE_CASE : List[Any] = True
if right_element < a or left_element > b:
return True
if left_element >= a and right_element <= b:
SCREAMING_SNAKE_CASE : Optional[Any] = val
if left_element != right_element:
SCREAMING_SNAKE_CASE : str = val
SCREAMING_SNAKE_CASE : str = val
SCREAMING_SNAKE_CASE : Tuple = True
SCREAMING_SNAKE_CASE : Optional[Any] = True
return True
SCREAMING_SNAKE_CASE : int = (left_element + right_element) // 2
self.update(self.left(a ) , a , a , a , a , a )
self.update(self.right(a ) , mid + 1 , a , a , a , a )
SCREAMING_SNAKE_CASE : Optional[int] = max(
self.segment_tree[self.left(a )] , self.segment_tree[self.right(a )] )
return True
def __UpperCamelCase ( self : Dict , a : int , a : int , a : int , a : int , a : int ) -> int | float:
"""simple docstring"""
if self.flag[idx] is True:
SCREAMING_SNAKE_CASE : int = self.lazy[idx]
SCREAMING_SNAKE_CASE : List[Any] = False
if left_element != right_element:
SCREAMING_SNAKE_CASE : Optional[Any] = self.lazy[idx]
SCREAMING_SNAKE_CASE : Optional[Any] = self.lazy[idx]
SCREAMING_SNAKE_CASE : Optional[Any] = True
SCREAMING_SNAKE_CASE : Union[str, Any] = True
if right_element < a or left_element > b:
return -math.inf
if left_element >= a and right_element <= b:
return self.segment_tree[idx]
SCREAMING_SNAKE_CASE : Dict = (left_element + right_element) // 2
SCREAMING_SNAKE_CASE : Tuple = self.query(self.left(a ) , a , a , a , a )
SCREAMING_SNAKE_CASE : Tuple = self.query(self.right(a ) , mid + 1 , a , a , a )
return max(a , a )
def __str__( self : str ) -> str:
"""simple docstring"""
return str([self.query(1 , 1 , self.size , a , a ) for i in range(1 , self.size + 1 )] )
if __name__ == "__main__":
a_ = [1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
a_ = 15
a_ = SegmentTree(size)
segt.build(1, 1, size, A)
print(segt.query(1, 1, size, 4, 6))
print(segt.query(1, 1, size, 7, 11))
print(segt.query(1, 1, size, 7, 12))
segt.update(1, 1, size, 1, 3, 111)
print(segt.query(1, 1, size, 1, 15))
segt.update(1, 1, size, 7, 8, 235)
print(segt) | 25 | 0 |
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
UpperCamelCase__ : Union[str, Any] = '''2.13.1'''
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.parse('''3.7'''):
raise ImportWarning(
'''To use `datasets`, Python>=3.7 is required, and the current version of Python doesn\'t match this condition.'''
)
if version.parse(pyarrow.__version__).major < 8:
raise ImportWarning(
'''To use `datasets`, the module `pyarrow>=8.0.0` is required, and the current version of `pyarrow` doesn\'t match this condition.\n'''
'''If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`.'''
)
del platform
del pyarrow
del version
from .arrow_dataset import Dataset
from .arrow_reader import ReadInstruction
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
from .combine import concatenate_datasets, interleave_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .download import *
from .features import *
from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled
from .info import DatasetInfo, MetricInfo
from .inspect import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
list_datasets,
list_metrics,
)
from .iterable_dataset import IterableDataset
from .load import load_dataset, load_dataset_builder, load_from_disk, load_metric
from .metric import Metric
from .splits import (
NamedSplit,
NamedSplitAll,
Split,
SplitBase,
SplitDict,
SplitGenerator,
SplitInfo,
SubSplitInfo,
percent,
)
from .tasks import *
from .utils import *
from .utils import logging
# deprecated modules
from datasets import arrow_dataset as _arrow_dataset # isort:skip
from datasets import utils as _utils # isort:skip
from datasets.utils import download_manager as _deprecated_download_manager # isort:skip
UpperCamelCase__ : str = concatenate_datasets
UpperCamelCase__ : List[str] = DownloadConfig
UpperCamelCase__ : Optional[int] = DownloadManager
UpperCamelCase__ : Union[str, Any] = DownloadMode
UpperCamelCase__ : Optional[int] = DownloadConfig
UpperCamelCase__ : Any = DownloadMode
UpperCamelCase__ : str = DownloadManager
del _arrow_dataset, _utils, _deprecated_download_manager
| 105 |
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
@slow
@require_torch_gpu
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def __UpperCamelCase ( self : Dict ) -> Tuple:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __UpperCamelCase ( self : Optional[int] ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = StableDiffusionKDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4" )
SCREAMING_SNAKE_CASE : str = sd_pipe.to(a )
sd_pipe.set_progress_bar_config(disable=a )
sd_pipe.set_scheduler("sample_euler" )
SCREAMING_SNAKE_CASE : Optional[int] = "A painting of a squirrel eating a burger"
SCREAMING_SNAKE_CASE : Any = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : List[Any] = sd_pipe([prompt] , generator=a , guidance_scale=9.0 , num_inference_steps=20 , output_type="np" )
SCREAMING_SNAKE_CASE : Tuple = output.images
SCREAMING_SNAKE_CASE : Any = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE : Union[str, Any] = np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def __UpperCamelCase ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base" )
SCREAMING_SNAKE_CASE : Tuple = sd_pipe.to(a )
sd_pipe.set_progress_bar_config(disable=a )
sd_pipe.set_scheduler("sample_euler" )
SCREAMING_SNAKE_CASE : List[str] = "A painting of a squirrel eating a burger"
SCREAMING_SNAKE_CASE : List[str] = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Union[str, Any] = sd_pipe([prompt] , generator=a , guidance_scale=9.0 , num_inference_steps=20 , output_type="np" )
SCREAMING_SNAKE_CASE : List[Any] = output.images
SCREAMING_SNAKE_CASE : Tuple = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE : int = np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-1
def __UpperCamelCase ( self : Tuple ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base" )
SCREAMING_SNAKE_CASE : Union[str, Any] = sd_pipe.to(a )
sd_pipe.set_progress_bar_config(disable=a )
sd_pipe.set_scheduler("sample_dpmpp_2m" )
SCREAMING_SNAKE_CASE : str = "A painting of a squirrel eating a burger"
SCREAMING_SNAKE_CASE : Any = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : str = sd_pipe(
[prompt] , generator=a , guidance_scale=7.5 , num_inference_steps=15 , output_type="np" , use_karras_sigmas=a , )
SCREAMING_SNAKE_CASE : str = output.images
SCREAMING_SNAKE_CASE : Optional[int] = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE : int = np.array(
[0.1138_1689, 0.1211_2921, 0.138_9457, 0.1254_9606, 0.124_4964, 0.1083_1517, 0.1156_2866, 0.1086_7816, 0.1049_9048] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2 | 25 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowerCAmelCase_ : Optional[Any] = logging.get_logger(__name__)
lowerCAmelCase_ : Tuple = {
'microsoft/swin-tiny-patch4-window7-224': (
'https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json'
),
# See all Swin models at https://huggingface.co/models?filter=swin
}
class lowerCamelCase_ ( __A , __A ):
_lowerCAmelCase : Tuple = 'swin'
_lowerCAmelCase : str = {
'num_attention_heads': 'num_heads',
'num_hidden_layers': 'num_layers',
}
def __init__( self : Union[str, Any] , lowerCAmelCase__ : Tuple=2_24 , lowerCAmelCase__ : Optional[Any]=4 , lowerCAmelCase__ : Optional[Any]=3 , lowerCAmelCase__ : Optional[Any]=96 , lowerCAmelCase__ : Union[str, Any]=[2, 2, 6, 2] , lowerCAmelCase__ : Tuple=[3, 6, 12, 24] , lowerCAmelCase__ : Union[str, Any]=7 , lowerCAmelCase__ : List[str]=4.0 , lowerCAmelCase__ : Optional[Any]=True , lowerCAmelCase__ : Union[str, Any]=0.0 , lowerCAmelCase__ : Optional[int]=0.0 , lowerCAmelCase__ : Union[str, Any]=0.1 , lowerCAmelCase__ : List[Any]="gelu" , lowerCAmelCase__ : Union[str, Any]=False , lowerCAmelCase__ : Any=0.02 , lowerCAmelCase__ : str=1e-5 , lowerCAmelCase__ : Any=32 , lowerCAmelCase__ : Optional[int]=None , lowerCAmelCase__ : str=None , **lowerCAmelCase__ : Dict , ):
"""simple docstring"""
super().__init__(**lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : int = image_size
SCREAMING_SNAKE_CASE : Any = patch_size
SCREAMING_SNAKE_CASE : Optional[Any] = num_channels
SCREAMING_SNAKE_CASE : Any = embed_dim
SCREAMING_SNAKE_CASE : List[str] = depths
SCREAMING_SNAKE_CASE : Union[str, Any] = len(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : Dict = num_heads
SCREAMING_SNAKE_CASE : Optional[int] = window_size
SCREAMING_SNAKE_CASE : List[Any] = mlp_ratio
SCREAMING_SNAKE_CASE : int = qkv_bias
SCREAMING_SNAKE_CASE : List[Any] = hidden_dropout_prob
SCREAMING_SNAKE_CASE : Optional[Any] = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : List[Any] = drop_path_rate
SCREAMING_SNAKE_CASE : int = hidden_act
SCREAMING_SNAKE_CASE : int = use_absolute_embeddings
SCREAMING_SNAKE_CASE : Tuple = layer_norm_eps
SCREAMING_SNAKE_CASE : Optional[Any] = initializer_range
SCREAMING_SNAKE_CASE : Tuple = encoder_stride
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
SCREAMING_SNAKE_CASE : int = int(embed_dim * 2 ** (len(lowerCAmelCase__ ) - 1) )
SCREAMING_SNAKE_CASE : Union[str, Any] = ["stem"] + [F"""stage{idx}""" for idx in range(1 , len(lowerCAmelCase__ ) + 1 )]
SCREAMING_SNAKE_CASE : Any = get_aligned_output_features_output_indices(
out_features=lowerCAmelCase__ , out_indices=lowerCAmelCase__ , stage_names=self.stage_names )
class lowerCamelCase_ ( __A ):
_lowerCAmelCase : Optional[Any] = version.parse('1.11' )
@property
def __lowercase ( self : Optional[Any] ):
"""simple docstring"""
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def __lowercase ( self : Optional[int] ):
"""simple docstring"""
return 1e-4
| 527 |
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class _UpperCamelCase ( __A , unittest.TestCase ):
'''simple docstring'''
pass
@nightly
@require_onnxruntime
@require_torch_gpu
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
@property
def __UpperCamelCase ( self : List[Any] ) -> List[str]:
"""simple docstring"""
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def __UpperCamelCase ( self : int ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = ort.SessionOptions()
SCREAMING_SNAKE_CASE : Union[str, Any] = False
return options
def __UpperCamelCase ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo.png" )
SCREAMING_SNAKE_CASE : Optional[Any] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo_mask.png" )
SCREAMING_SNAKE_CASE : int = OnnxStableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting" , revision="onnx" , safety_checker=a , feature_extractor=a , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=a )
SCREAMING_SNAKE_CASE : Optional[Any] = "A red cat sitting on a park bench"
SCREAMING_SNAKE_CASE : Tuple = np.random.RandomState(0 )
SCREAMING_SNAKE_CASE : Optional[int] = pipe(
prompt=a , image=a , mask_image=a , guidance_scale=7.5 , num_inference_steps=10 , generator=a , output_type="np" , )
SCREAMING_SNAKE_CASE : List[Any] = output.images
SCREAMING_SNAKE_CASE : Union[str, Any] = images[0, 255:258, 255:258, -1]
assert images.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE : int = np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def __UpperCamelCase ( self : List[Any] ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo.png" )
SCREAMING_SNAKE_CASE : Optional[Any] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo_mask.png" )
SCREAMING_SNAKE_CASE : Optional[Any] = LMSDiscreteScheduler.from_pretrained(
"runwayml/stable-diffusion-inpainting" , subfolder="scheduler" , revision="onnx" )
SCREAMING_SNAKE_CASE : Union[str, Any] = OnnxStableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting" , revision="onnx" , scheduler=a , safety_checker=a , feature_extractor=a , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=a )
SCREAMING_SNAKE_CASE : List[Any] = "A red cat sitting on a park bench"
SCREAMING_SNAKE_CASE : Dict = np.random.RandomState(0 )
SCREAMING_SNAKE_CASE : Tuple = pipe(
prompt=a , image=a , mask_image=a , guidance_scale=7.5 , num_inference_steps=20 , generator=a , output_type="np" , )
SCREAMING_SNAKE_CASE : List[str] = output.images
SCREAMING_SNAKE_CASE : Optional[int] = images[0, 255:258, 255:258, -1]
assert images.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE : Any = np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3 | 25 | 0 |
def snake_case_ (__A : List[Any] ) -> Optional[Any]:
if a < 0:
raise ValueError("""Input value must be a positive integer""" )
elif isinstance(_a , _a ):
raise TypeError("""Input value must be a 'int' type""" )
return bin(_a ).count("""1""" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 651 |
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def lowerCamelCase__ ( _a):
return getitem, k
def lowerCamelCase__ ( _a , _a):
return setitem, k, v
def lowerCamelCase__ ( _a):
return delitem, k
def lowerCamelCase__ ( _a , _a , *_a):
try:
return fun(_a , *_a), None
except Exception as e:
return None, e
a_ = (
_set('key_a', 'val_a'),
_set('key_b', 'val_b'),
)
a_ = [
_set('key_a', 'val_a'),
_set('key_a', 'val_b'),
]
a_ = [
_set('key_a', 'val_a'),
_set('key_b', 'val_b'),
_del('key_a'),
_del('key_b'),
_set('key_a', 'val_a'),
_del('key_a'),
]
a_ = [
_get('key_a'),
_del('key_a'),
_set('key_a', 'val_a'),
_del('key_a'),
_del('key_a'),
_get('key_a'),
]
a_ = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
]
a_ = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
*[_del(x) for x in range(5)],
_set('key_a', 'val_b'),
]
@pytest.mark.parametrize(
"operations" , (
pytest.param(_add_items , id="add items"),
pytest.param(_overwrite_items , id="overwrite items"),
pytest.param(_delete_items , id="delete items"),
pytest.param(_access_absent_items , id="access absent items"),
pytest.param(_add_with_resize_up , id="add with resize up"),
pytest.param(_add_with_resize_down , id="add with resize down"),
) , )
def lowerCamelCase__ ( _a):
SCREAMING_SNAKE_CASE : Dict = HashMap(initial_block_size=4)
SCREAMING_SNAKE_CASE : List[str] = {}
for _, (fun, *args) in enumerate(_a):
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Union[str, Any] = _run_operation(_a , _a , *_a)
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : List[Any] = _run_operation(_a , _a , *_a)
assert my_res == py_res
assert str(_a) == str(_a)
assert set(_a) == set(_a)
assert len(_a) == len(_a)
assert set(my.items()) == set(py.items())
def lowerCamelCase__ ( ):
def is_public(_a) -> bool:
return not name.startswith("_")
SCREAMING_SNAKE_CASE : List[str] = {name for name in dir({}) if is_public(_a)}
SCREAMING_SNAKE_CASE : Union[str, Any] = {name for name in dir(HashMap()) if is_public(_a)}
assert dict_public_names > hash_public_names | 25 | 0 |
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SIZE
from datasets.filesystems import (
is_remote_filesystem,
rename,
)
from datasets.iterable_dataset import _BaseExamplesIterable
from datasets.utils.py_utils import convert_file_size_to_int
lowerCAmelCase_ = datasets.utils.logging.get_logger(__name__)
if TYPE_CHECKING:
import pyspark
@dataclass
class _A ( datasets.BuilderConfig ):
_UpperCamelCase : Optional[Any] = None
def snake_case( __magic_name__ , __magic_name__ , ) -> Optional[int]:
'''simple docstring'''
import pyspark
def generate_fn():
lowercase : str = df.select('''*''' , pyspark.sql.functions.spark_partition_id().alias('''part_id''' ) )
for partition_id in partition_order:
lowercase : int = df_with_partition_id.select('''*''' ).where(F"""part_id = {partition_id}""" ).drop('''part_id''' )
lowercase : Tuple = partition_df.collect()
lowercase : int = 0
for row in rows:
yield F"""{partition_id}_{row_id}""", row.asDict()
row_id += 1
return generate_fn
class _A ( _BaseExamplesIterable ):
def __init__( self : Dict , _A : "pyspark.sql.DataFrame" , _A : str=None , ) -> List[str]:
"""simple docstring"""
lowercase : List[str] = df
lowercase : Any = partition_order or range(self.df.rdd.getNumPartitions() )
lowercase : List[str] = _generate_iterable_examples(self.df , self.partition_order )
def __iter__( self : Optional[Any] ) -> List[Any]:
"""simple docstring"""
yield from self.generate_examples_fn()
def __a ( self : Optional[int] , _A : np.random.Generator ) -> "SparkExamplesIterable":
"""simple docstring"""
lowercase : str = list(range(self.df.rdd.getNumPartitions() ) )
generator.shuffle(_A )
return SparkExamplesIterable(self.df , partition_order=_A )
def __a ( self : List[Any] , _A : int , _A : int ) -> "SparkExamplesIterable":
"""simple docstring"""
lowercase : Tuple = self.split_shard_indices_by_worker(_A , _A )
return SparkExamplesIterable(self.df , partition_order=_A )
@property
def __a ( self : Optional[int] ) -> int:
"""simple docstring"""
return len(self.partition_order )
class _A ( datasets.DatasetBuilder ):
_UpperCamelCase : List[str] = SparkConfig
def __init__( self : Tuple , _A : "pyspark.sql.DataFrame" , _A : str = None , _A : str = None , **_A : Optional[int] , ) -> Tuple:
"""simple docstring"""
import pyspark
lowercase : Tuple = pyspark.sql.SparkSession.builder.getOrCreate()
lowercase : int = df
lowercase : Optional[Any] = working_dir
super().__init__(
cache_dir=_A , config_name=str(self.df.semanticHash() ) , **_A , )
def __a ( self : str ) -> Tuple:
"""simple docstring"""
def create_cache_and_write_probe(_A : Optional[int] ):
# makedirs with exist_ok will recursively create the directory. It will not throw an error if directories
# already exist.
os.makedirs(self._cache_dir , exist_ok=_A )
lowercase : Tuple = os.path.join(self._cache_dir , '''fs_test''' + uuid.uuida().hex )
# Opening the file in append mode will create a new file unless it already exists, in which case it will not
# change the file contents.
open(_A , '''a''' )
return [probe_file]
if self._spark.conf.get('''spark.master''' , '''''' ).startswith('''local''' ):
return
# If the cluster is multi-node, make sure that the user provided a cache_dir and that it is on an NFS
# accessible to the driver.
# TODO: Stream batches to the driver using ArrowCollectSerializer instead of throwing an error.
if self._cache_dir:
lowercase : Optional[Any] = (
self._spark.sparkContext.parallelize(range(1 ) , 1 ).mapPartitions(_A ).collect()
)
if os.path.isfile(probe[0] ):
return
raise ValueError(
'''When using Dataset.from_spark on a multi-node cluster, the driver and all workers should be able to access cache_dir''' )
def __a ( self : Any ) -> Dict:
"""simple docstring"""
return datasets.DatasetInfo(features=self.config.features )
def __a ( self : Optional[Any] , _A : datasets.download.download_manager.DownloadManager ) -> Any:
"""simple docstring"""
return [datasets.SplitGenerator(name=datasets.Split.TRAIN )]
def __a ( self : int , _A : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
import pyspark
def get_arrow_batch_size(_A : Dict ):
for batch in it:
yield pa.RecordBatch.from_pydict({'''batch_bytes''': [batch.nbytes]} )
lowercase : Tuple = self.df.count()
lowercase : int = df_num_rows if df_num_rows <= 100 else 100
# Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
lowercase : str = (
self.df.limit(_A )
.repartition(1 )
.mapInArrow(_A , '''batch_bytes: long''' )
.agg(pyspark.sql.functions.sum('''batch_bytes''' ).alias('''sample_bytes''' ) )
.collect()[0]
.sample_bytes
/ sample_num_rows
)
lowercase : Dict = approx_bytes_per_row * df_num_rows
if approx_total_size > max_shard_size:
# Make sure there is at least one row per partition.
lowercase : Tuple = min(_A , int(approx_total_size / max_shard_size ) )
lowercase : Tuple = self.df.repartition(_A )
def __a ( self : Any , _A : str , _A : str , _A : int , ) -> Iterable[Tuple[int, bool, Union[int, tuple]]]:
"""simple docstring"""
import pyspark
lowercase : Dict = ParquetWriter if file_format == "parquet" else ArrowWriter
lowercase : Union[str, Any] = os.path.join(self._working_dir , os.path.basename(_A ) ) if self._working_dir else fpath
lowercase : List[Any] = file_format == "parquet"
# Define these so that we don't reference self in write_arrow, which will result in a pickling error due to
# pickling the SparkContext.
lowercase : List[Any] = self.config.features
lowercase : Dict = self._writer_batch_size
lowercase : int = self._fs.storage_options
def write_arrow(_A : List[Any] ):
# Within the same SparkContext, no two task attempts will share the same attempt ID.
lowercase : List[str] = pyspark.TaskContext().taskAttemptId()
lowercase : List[str] = next(_A , _A )
if first_batch is None:
# Some partitions might not receive any data.
return pa.RecordBatch.from_arrays(
[[task_id], [0], [0]] , names=['''task_id''', '''num_examples''', '''num_bytes'''] , )
lowercase : Tuple = 0
lowercase : Union[str, Any] = writer_class(
features=_A , path=working_fpath.replace('''SSSSS''' , f"""{shard_id:05d}""" ).replace('''TTTTT''' , f"""{task_id:05d}""" ) , writer_batch_size=_A , storage_options=_A , embed_local_files=_A , )
lowercase : Dict = pa.Table.from_batches([first_batch] )
writer.write_table(_A )
for batch in it:
if max_shard_size is not None and writer._num_bytes >= max_shard_size:
lowercase : str = writer.finalize()
writer.close()
yield pa.RecordBatch.from_arrays(
[[task_id], [num_examples], [num_bytes]] , names=['''task_id''', '''num_examples''', '''num_bytes'''] , )
shard_id += 1
lowercase : Any = writer_class(
features=writer._features , path=working_fpath.replace('''SSSSS''' , f"""{shard_id:05d}""" ).replace('''TTTTT''' , f"""{task_id:05d}""" ) , writer_batch_size=_A , storage_options=_A , embed_local_files=_A , )
lowercase : str = pa.Table.from_batches([batch] )
writer.write_table(_A )
if writer._num_bytes > 0:
lowercase : List[Any] = writer.finalize()
writer.close()
yield pa.RecordBatch.from_arrays(
[[task_id], [num_examples], [num_bytes]] , names=['''task_id''', '''num_examples''', '''num_bytes'''] , )
if working_fpath != fpath:
for file in os.listdir(os.path.dirname(_A ) ):
lowercase : Union[str, Any] = os.path.join(os.path.dirname(_A ) , os.path.basename(_A ) )
shutil.move(_A , _A )
lowercase : Optional[int] = (
self.df.mapInArrow(_A , '''task_id: long, num_examples: long, num_bytes: long''' )
.groupBy('''task_id''' )
.agg(
pyspark.sql.functions.sum('''num_examples''' ).alias('''total_num_examples''' ) , pyspark.sql.functions.sum('''num_bytes''' ).alias('''total_num_bytes''' ) , pyspark.sql.functions.count('''num_bytes''' ).alias('''num_shards''' ) , pyspark.sql.functions.collect_list('''num_examples''' ).alias('''shard_lengths''' ) , )
.collect()
)
for row in stats:
yield row.task_id, (row.total_num_examples, row.total_num_bytes, row.num_shards, row.shard_lengths)
def __a ( self : Optional[Any] , _A : "datasets.SplitGenerator" , _A : str = "arrow" , _A : Optional[Union[str, int]] = None , _A : Optional[int] = None , **_A : str , ) -> str:
"""simple docstring"""
self._validate_cache_dir()
lowercase : int = convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE )
self._repartition_df_if_needed(_A )
lowercase : Dict = not is_remote_filesystem(self._fs )
lowercase : Optional[Any] = os.path.join if is_local else posixpath.join
lowercase : Optional[int] = "-TTTTT-SSSSS-of-NNNNN"
lowercase : Dict = f"""{self.name}-{split_generator.name}{SUFFIX}.{file_format}"""
lowercase : Dict = path_join(self._output_dir , _A )
lowercase : int = 0
lowercase : str = 0
lowercase : List[Any] = 0
lowercase : Union[str, Any] = []
lowercase : List[Any] = []
for task_id, content in self._prepare_split_single(_A , _A , _A ):
(
lowercase
) : Optional[Any] = content
if num_bytes > 0:
total_num_examples += num_examples
total_num_bytes += num_bytes
total_shards += num_shards
task_id_and_num_shards.append((task_id, num_shards) )
all_shard_lengths.extend(_A )
lowercase : List[str] = total_num_examples
lowercase : str = total_num_bytes
# should rename everything at the end
logger.debug(f"""Renaming {total_shards} shards.""" )
if total_shards > 1:
lowercase : List[Any] = all_shard_lengths
# Define fs outside of _rename_shard so that we don't reference self in the function, which will result in a
# pickling error due to pickling the SparkContext.
lowercase : Optional[Any] = self._fs
# use the -SSSSS-of-NNNNN pattern
def _rename_shard(
_A : int , _A : int , _A : int , ):
rename(
_A , fpath.replace('''SSSSS''' , f"""{shard_id:05d}""" ).replace('''TTTTT''' , f"""{task_id:05d}""" ) , fpath.replace('''TTTTT-SSSSS''' , f"""{global_shard_id:05d}""" ).replace('''NNNNN''' , f"""{total_shards:05d}""" ) , )
lowercase : Tuple = []
lowercase : str = 0
for i in range(len(_A ) ):
lowercase : Optional[int] = task_id_and_num_shards[i]
for shard_id in range(_A ):
args.append([task_id, shard_id, global_shard_id] )
global_shard_id += 1
self._spark.sparkContext.parallelize(_A , len(_A ) ).map(lambda _A : _rename_shard(*_A ) ).collect()
else:
# don't use any pattern
lowercase : Optional[int] = 0
lowercase : Optional[Any] = task_id_and_num_shards[0][0]
self._rename(
fpath.replace('''SSSSS''' , f"""{shard_id:05d}""" ).replace('''TTTTT''' , f"""{task_id:05d}""" ) , fpath.replace(_A , '''''' ) , )
def __a ( self : int , _A : "datasets.SplitGenerator" , ) -> SparkExamplesIterable:
"""simple docstring"""
return SparkExamplesIterable(self.df ) | 217 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a_ = {'configuration_van': ['VAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VanConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'VAN_PRETRAINED_MODEL_ARCHIVE_LIST',
'VanForImageClassification',
'VanModel',
'VanPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_van import (
VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
VanForImageClassification,
VanModel,
VanPreTrainedModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure) | 25 | 0 |
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
CommonSchedulerState,
FlaxKarrasDiffusionSchedulers,
FlaxSchedulerMixin,
FlaxSchedulerOutput,
add_noise_common,
get_velocity_common,
)
@flax.struct.dataclass
class a :
_lowercase = 4_2
# setable values
_lowercase = 4_2
_lowercase = 4_2
_lowercase = None
@classmethod
def _UpperCAmelCase ( cls , A_ , A_ , A_ ):
'''simple docstring'''
return cls(common=A_ , init_noise_sigma=A_ , timesteps=A_ )
@dataclass
class a ( __A ):
_lowercase = 4_2
class a ( __A , __A ):
_lowercase = [e.name for e in FlaxKarrasDiffusionSchedulers]
_lowercase = 4_2
@property
def _UpperCAmelCase ( self ):
'''simple docstring'''
return True
@register_to_config
def __init__( self , A_ = 1000 , A_ = 0.00_01 , A_ = 0.02 , A_ = "linear" , A_ = None , A_ = "fixed_small" , A_ = True , A_ = "epsilon" , A_ = jnp.floataa , ):
'''simple docstring'''
_UpperCAmelCase : Tuple = dtype
def _UpperCAmelCase ( self , A_ = None ):
'''simple docstring'''
if common is None:
_UpperCAmelCase : Optional[int] = CommonSchedulerState.create(self )
# standard deviation of the initial noise distribution
_UpperCAmelCase : Tuple = jnp.array(1.0 , dtype=self.dtype )
_UpperCAmelCase : List[Any] = jnp.arange(0 , self.config.num_train_timesteps ).round()[::-1]
return DDPMSchedulerState.create(
common=A_ , init_noise_sigma=A_ , timesteps=A_ , )
def _UpperCAmelCase ( self , A_ , A_ , A_ = None ):
'''simple docstring'''
return sample
def _UpperCAmelCase ( self , A_ , A_ , A_ = () ):
'''simple docstring'''
_UpperCAmelCase : Optional[int] = self.config.num_train_timesteps // num_inference_steps
# creates integer timesteps by multiplying by ratio
# rounding to avoid issues when num_inference_step is power of 3
_UpperCAmelCase : Dict = (jnp.arange(0 , A_ ) * step_ratio).round()[::-1]
return state.replace(
num_inference_steps=A_ , timesteps=A_ , )
def _UpperCAmelCase ( self , A_ , A_ , A_=None , A_=None ):
'''simple docstring'''
_UpperCAmelCase : Union[str, Any] = state.common.alphas_cumprod[t]
_UpperCAmelCase : Union[str, Any] = jnp.where(t > 0 , state.common.alphas_cumprod[t - 1] , jnp.array(1.0 , dtype=self.dtype ) )
# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
# and sample from it to get previous sample
# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
_UpperCAmelCase : List[str] = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]
if variance_type is None:
_UpperCAmelCase : int = self.config.variance_type
# hacks - were probably added for training stability
if variance_type == "fixed_small":
_UpperCAmelCase : str = jnp.clip(A_ , a_min=1e-20 )
# for rl-diffuser https://arxiv.org/abs/2205.09991
elif variance_type == "fixed_small_log":
_UpperCAmelCase : Any = jnp.log(jnp.clip(A_ , a_min=1e-20 ) )
elif variance_type == "fixed_large":
_UpperCAmelCase : int = state.common.betas[t]
elif variance_type == "fixed_large_log":
# Glide max_log
_UpperCAmelCase : str = jnp.log(state.common.betas[t] )
elif variance_type == "learned":
return predicted_variance
elif variance_type == "learned_range":
_UpperCAmelCase : Any = variance
_UpperCAmelCase : int = state.common.betas[t]
_UpperCAmelCase : Dict = (predicted_variance + 1) / 2
_UpperCAmelCase : List[Any] = frac * max_log + (1 - frac) * min_log
return variance
def _UpperCAmelCase ( self , A_ , A_ , A_ , A_ , A_ = None , A_ = True , ):
'''simple docstring'''
_UpperCAmelCase : Optional[Any] = timestep
if key is None:
_UpperCAmelCase : str = jax.random.PRNGKey(0 )
if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
_UpperCAmelCase : Union[str, Any] = jnp.split(A_ , sample.shape[1] , axis=1 )
else:
_UpperCAmelCase : int = None
# 1. compute alphas, betas
_UpperCAmelCase : Tuple = state.common.alphas_cumprod[t]
_UpperCAmelCase : Optional[Any] = jnp.where(t > 0 , state.common.alphas_cumprod[t - 1] , jnp.array(1.0 , dtype=self.dtype ) )
_UpperCAmelCase : str = 1 - alpha_prod_t
_UpperCAmelCase : str = 1 - alpha_prod_t_prev
# 2. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
if self.config.prediction_type == "epsilon":
_UpperCAmelCase : int = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
elif self.config.prediction_type == "sample":
_UpperCAmelCase : Union[str, Any] = model_output
elif self.config.prediction_type == "v_prediction":
_UpperCAmelCase : List[Any] = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
else:
raise ValueError(
f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` '
" for the FlaxDDPMScheduler." )
# 3. Clip "predicted x_0"
if self.config.clip_sample:
_UpperCAmelCase : Tuple = jnp.clip(A_ , -1 , 1 )
# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
_UpperCAmelCase : Optional[int] = (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
_UpperCAmelCase : int = state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t
# 5. Compute predicted previous sample µ_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
_UpperCAmelCase : Dict = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
# 6. Add noise
def random_variance():
_UpperCAmelCase : Optional[int] = jax.random.split(A_ , num=1 )
_UpperCAmelCase : Dict = jax.random.normal(A_ , shape=model_output.shape , dtype=self.dtype )
return (self._get_variance(A_ , A_ , predicted_variance=A_ ) ** 0.5) * noise
_UpperCAmelCase : str = jnp.where(t > 0 , random_variance() , jnp.zeros(model_output.shape , dtype=self.dtype ) )
_UpperCAmelCase : List[str] = pred_prev_sample + variance
if not return_dict:
return (pred_prev_sample, state)
return FlaxDDPMSchedulerOutput(prev_sample=A_ , state=A_ )
def _UpperCAmelCase ( self , A_ , A_ , A_ , A_ , ):
'''simple docstring'''
return add_noise_common(state.common , A_ , A_ , A_ )
def _UpperCAmelCase ( self , A_ , A_ , A_ , A_ , ):
'''simple docstring'''
return get_velocity_common(state.common , A_ , A_ , A_ )
def __len__( self ):
'''simple docstring'''
return self.config.num_train_timesteps
| 300 |
from __future__ import annotations
def lowerCamelCase__ ( _a):
if len(_a) == 0:
return []
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Tuple = min(_a), max(_a)
SCREAMING_SNAKE_CASE : Dict = int(max_value - min_value) + 1
SCREAMING_SNAKE_CASE : list[list] = [[] for _ in range(_a)]
for i in my_list:
buckets[int(i - min_value)].append(_a)
return [v for bucket in buckets for v in sorted(_a)]
if __name__ == "__main__":
from doctest import testmod
testmod()
assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
assert bucket_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15] | 25 | 0 |
'''simple docstring'''
def _snake_case ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : Union[str, Any] ) -> str:
"""simple docstring"""
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod() | 433 |
a_ = frozenset(
[
'prompt',
'height',
'width',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
'cross_attention_kwargs',
]
)
a_ = frozenset(['prompt', 'negative_prompt'])
a_ = frozenset([])
a_ = frozenset(['image'])
a_ = frozenset(
[
'image',
'height',
'width',
'guidance_scale',
]
)
a_ = frozenset(['image'])
a_ = frozenset(
[
'prompt',
'image',
'height',
'width',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
]
)
a_ = frozenset(['prompt', 'image', 'negative_prompt'])
a_ = frozenset(
[
# Text guided image variation with an image mask
'prompt',
'image',
'mask_image',
'height',
'width',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
]
)
a_ = frozenset(['prompt', 'image', 'mask_image', 'negative_prompt'])
a_ = frozenset(
[
# image variation with an image mask
'image',
'mask_image',
'height',
'width',
'guidance_scale',
]
)
a_ = frozenset(['image', 'mask_image'])
a_ = frozenset(
[
'example_image',
'image',
'mask_image',
'height',
'width',
'guidance_scale',
]
)
a_ = frozenset(['example_image', 'image', 'mask_image'])
a_ = frozenset(['class_labels'])
a_ = frozenset(['class_labels'])
a_ = frozenset(['batch_size'])
a_ = frozenset([])
a_ = frozenset(['batch_size'])
a_ = frozenset([])
a_ = frozenset(
[
'prompt',
'audio_length_in_s',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
'cross_attention_kwargs',
]
)
a_ = frozenset(['prompt', 'negative_prompt'])
a_ = frozenset(['input_tokens'])
a_ = frozenset(['input_tokens']) | 25 | 0 |
"""simple docstring"""
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
snake_case = logging.get_logger(__name__)
snake_case = {'''vocab_file''': '''spiece.model'''}
snake_case = {
'''vocab_file''': {
'''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/spiece.model''',
'''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/spiece.model''',
'''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model''',
'''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model''',
'''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/spiece.model''',
'''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/spiece.model''',
'''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model''',
'''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model''',
}
}
snake_case = {
'''albert-base-v1''': 5_1_2,
'''albert-large-v1''': 5_1_2,
'''albert-xlarge-v1''': 5_1_2,
'''albert-xxlarge-v1''': 5_1_2,
'''albert-base-v2''': 5_1_2,
'''albert-large-v2''': 5_1_2,
'''albert-xlarge-v2''': 5_1_2,
'''albert-xxlarge-v2''': 5_1_2,
}
snake_case = '''▁'''
class UpperCAmelCase ( __A ):
A__ : int = VOCAB_FILES_NAMES
A__ : Any = PRETRAINED_VOCAB_FILES_MAP
A__ : Dict = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__( self : Any , __lowerCamelCase : List[str] , __lowerCamelCase : List[str]=True , __lowerCamelCase : str=True , __lowerCamelCase : int=False , __lowerCamelCase : List[Any]="[CLS]" , __lowerCamelCase : str="[SEP]" , __lowerCamelCase : Tuple="<unk>" , __lowerCamelCase : Any="[SEP]" , __lowerCamelCase : List[str]="<pad>" , __lowerCamelCase : str="[CLS]" , __lowerCamelCase : Optional[int]="[MASK]" , __lowerCamelCase : Optional[Dict[str, Any]] = None , **__lowerCamelCase : Optional[int] , ):
"""simple docstring"""
_snake_case = (
AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase , normalized=__lowerCamelCase )
if isinstance(__lowerCamelCase , __lowerCamelCase )
else mask_token
)
_snake_case = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
do_lower_case=__lowerCamelCase , remove_space=__lowerCamelCase , keep_accents=__lowerCamelCase , bos_token=__lowerCamelCase , eos_token=__lowerCamelCase , unk_token=__lowerCamelCase , sep_token=__lowerCamelCase , pad_token=__lowerCamelCase , cls_token=__lowerCamelCase , mask_token=__lowerCamelCase , sp_model_kwargs=self.sp_model_kwargs , **__lowerCamelCase , )
_snake_case = do_lower_case
_snake_case = remove_space
_snake_case = keep_accents
_snake_case = vocab_file
_snake_case = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(__lowerCamelCase )
@property
def __UpperCAmelCase ( self : Optional[Any] ):
"""simple docstring"""
return len(self.sp_model )
def __UpperCAmelCase ( self : Union[str, Any] ):
"""simple docstring"""
_snake_case = {self.convert_ids_to_tokens(__lowerCamelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self : Optional[int] ):
"""simple docstring"""
_snake_case = self.__dict__.copy()
_snake_case = None
return state
def __setstate__( self : Any , __lowerCamelCase : Tuple ):
"""simple docstring"""
_snake_case = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
_snake_case = {}
_snake_case = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def __UpperCAmelCase ( self : Dict , __lowerCamelCase : List[Any] ):
"""simple docstring"""
if self.remove_space:
_snake_case = " ".join(inputs.strip().split() )
else:
_snake_case = inputs
_snake_case = outputs.replace('''``''' , '''\"''' ).replace('''\'\'''' , '''\"''' )
if not self.keep_accents:
_snake_case = unicodedata.normalize('''NFKD''' , __lowerCamelCase )
_snake_case = "".join([c for c in outputs if not unicodedata.combining(__lowerCamelCase )] )
if self.do_lower_case:
_snake_case = outputs.lower()
return outputs
def __UpperCAmelCase ( self : str , __lowerCamelCase : str ):
"""simple docstring"""
_snake_case = self.preprocess_text(__lowerCamelCase )
_snake_case = self.sp_model.encode(__lowerCamelCase , out_type=__lowerCamelCase )
_snake_case = []
for piece in pieces:
if len(__lowerCamelCase ) > 1 and piece[-1] == str(''',''' ) and piece[-2].isdigit():
_snake_case = self.sp_model.EncodeAsPieces(piece[:-1].replace(__lowerCamelCase , '''''' ) )
if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
if len(cur_pieces[0] ) == 1:
_snake_case = cur_pieces[1:]
else:
_snake_case = cur_pieces[0][1:]
cur_pieces.append(piece[-1] )
new_pieces.extend(__lowerCamelCase )
else:
new_pieces.append(__lowerCamelCase )
return new_pieces
def __UpperCAmelCase ( self : str , __lowerCamelCase : List[str] ):
"""simple docstring"""
return self.sp_model.PieceToId(__lowerCamelCase )
def __UpperCAmelCase ( self : Union[str, Any] , __lowerCamelCase : Optional[Any] ):
"""simple docstring"""
return self.sp_model.IdToPiece(__lowerCamelCase )
def __UpperCAmelCase ( self : List[str] , __lowerCamelCase : Union[str, Any] ):
"""simple docstring"""
_snake_case = []
_snake_case = ""
_snake_case = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(__lowerCamelCase ) + token
_snake_case = True
_snake_case = []
else:
current_sub_tokens.append(__lowerCamelCase )
_snake_case = False
out_string += self.sp_model.decode(__lowerCamelCase )
return out_string.strip()
def __UpperCAmelCase ( self : List[Any] , __lowerCamelCase : List[int] , __lowerCamelCase : Optional[List[int]] = None ):
"""simple docstring"""
_snake_case = [self.sep_token_id]
_snake_case = [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 __UpperCAmelCase ( self : str , __lowerCamelCase : List[int] , __lowerCamelCase : Optional[List[int]] = None , __lowerCamelCase : bool = False ):
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__lowerCamelCase , token_ids_a=__lowerCamelCase , already_has_special_tokens=__lowerCamelCase )
if token_ids_a is not None:
return [1] + ([0] * len(__lowerCamelCase )) + [1] + ([0] * len(__lowerCamelCase )) + [1]
return [1] + ([0] * len(__lowerCamelCase )) + [1]
def __UpperCAmelCase ( self : Any , __lowerCamelCase : List[int] , __lowerCamelCase : Optional[List[int]] = None ):
"""simple docstring"""
_snake_case = [self.sep_token_id]
_snake_case = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def __UpperCAmelCase ( self : Optional[int] , __lowerCamelCase : str , __lowerCamelCase : Optional[str] = None ):
"""simple docstring"""
if not os.path.isdir(__lowerCamelCase ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
_snake_case = os.path.join(
__lowerCamelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__lowerCamelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , __lowerCamelCase )
elif not os.path.isfile(self.vocab_file ):
with open(__lowerCamelCase , '''wb''' ) as fi:
_snake_case = self.sp_model.serialized_model_proto()
fi.write(__lowerCamelCase )
return (out_vocab_file,)
| 103 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import jax
import jaxlib
a_ = get_logger()
a_ = None
class _UpperCamelCase ( TensorFormatter[Mapping, 'jax.Array', Mapping] ):
'''simple docstring'''
def __init__( self : Optional[Any] , a : str=None , a : List[Any]=None , **a : Any ) -> Optional[Any]:
"""simple docstring"""
super().__init__(features=a )
import jax
from jaxlib.xla_client import Device
if isinstance(a , a ):
raise ValueError(
F"Expected {device} to be a `str` not {type(a )}, as `jaxlib.xla_extension.Device` "
"is not serializable neither with `pickle` nor with `dill`. Instead you can surround "
"the device with `str()` to get its string identifier that will be internally mapped "
"to the actual `jaxlib.xla_extension.Device`." )
SCREAMING_SNAKE_CASE : List[str] = device if isinstance(a , a ) else str(jax.devices()[0] )
# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
# with `pickle` nor with `dill`, so we need to use a global variable instead
global DEVICE_MAPPING
if DEVICE_MAPPING is None:
SCREAMING_SNAKE_CASE : str = self._map_devices_to_str()
if self.device not in list(DEVICE_MAPPING.keys() ):
logger.warning(
F"Device with string identifier {self.device} not listed among the available "
F"devices: {list(DEVICE_MAPPING.keys() )}, so falling back to the default "
F"device: {str(jax.devices()[0] )}." )
SCREAMING_SNAKE_CASE : Any = str(jax.devices()[0] )
SCREAMING_SNAKE_CASE : Any = jnp_array_kwargs
@staticmethod
def __UpperCamelCase ( ) -> Dict[str, "jaxlib.xla_extension.Device"]:
"""simple docstring"""
import jax
return {str(a ): device for device in jax.devices()}
def __UpperCamelCase ( self : Dict , a : Tuple ) -> str:
"""simple docstring"""
import jax
import jax.numpy as jnp
if isinstance(a , a ) and column:
if all(
isinstance(a , jax.Array ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column ):
return jnp.stack(a , axis=0 )
return column
def __UpperCamelCase ( self : Dict , a : str ) -> str:
"""simple docstring"""
import jax
import jax.numpy as jnp
if isinstance(a , (str, bytes, type(a )) ):
return value
elif isinstance(a , (np.character, np.ndarray) ) and np.issubdtype(value.dtype , np.character ):
return value.tolist()
SCREAMING_SNAKE_CASE : Union[str, Any] = {}
if isinstance(a , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.integer ):
# the default int precision depends on the jax config
# see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
if jax.config.jax_enable_xaa:
SCREAMING_SNAKE_CASE : Dict = {"dtype": jnp.intaa}
else:
SCREAMING_SNAKE_CASE : str = {"dtype": jnp.intaa}
elif isinstance(a , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.floating ):
SCREAMING_SNAKE_CASE : int = {"dtype": jnp.floataa}
elif config.PIL_AVAILABLE and "PIL" in sys.modules:
import PIL.Image
if isinstance(a , PIL.Image.Image ):
SCREAMING_SNAKE_CASE : Dict = np.asarray(a )
# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
# with `pickle` nor with `dill`, so we need to use a global variable instead
global DEVICE_MAPPING
if DEVICE_MAPPING is None:
SCREAMING_SNAKE_CASE : Optional[Any] = self._map_devices_to_str()
with jax.default_device(DEVICE_MAPPING[self.device] ):
# calling jnp.array on a np.ndarray does copy the data
# see https://github.com/google/jax/issues/4486
return jnp.array(a , **{**default_dtype, **self.jnp_array_kwargs} )
def __UpperCamelCase ( self : Any , a : List[str] ) -> Dict:
"""simple docstring"""
import jax
# support for torch, tf, jax etc.
if config.TORCH_AVAILABLE and "torch" in sys.modules:
import torch
if isinstance(a , torch.Tensor ):
return self._tensorize(data_struct.detach().cpu().numpy()[()] )
if hasattr(a , "__array__" ) and not isinstance(a , jax.Array ):
SCREAMING_SNAKE_CASE : Optional[int] = data_struct.__array__()
# support for nested types like struct of list of struct
if isinstance(a , np.ndarray ):
if data_struct.dtype == object: # jax arrays cannot be instantied from an array of objects
return self._consolidate([self.recursive_tensorize(a ) for substruct in data_struct] )
elif isinstance(a , (list, tuple) ):
return self._consolidate([self.recursive_tensorize(a ) for substruct in data_struct] )
return self._tensorize(a )
def __UpperCamelCase ( self : Optional[Any] , a : dict ) -> Dict:
"""simple docstring"""
return map_nested(self._recursive_tensorize , a , map_list=a )
def __UpperCamelCase ( self : Dict , a : pa.Table ) -> Mapping:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = self.numpy_arrow_extractor().extract_row(a )
SCREAMING_SNAKE_CASE : List[Any] = self.python_features_decoder.decode_row(a )
return self.recursive_tensorize(a )
def __UpperCamelCase ( self : Optional[int] , a : pa.Table ) -> "jax.Array":
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = self.numpy_arrow_extractor().extract_column(a )
SCREAMING_SNAKE_CASE : Optional[Any] = self.python_features_decoder.decode_column(a , pa_table.column_names[0] )
SCREAMING_SNAKE_CASE : Tuple = self.recursive_tensorize(a )
SCREAMING_SNAKE_CASE : Optional[int] = self._consolidate(a )
return column
def __UpperCamelCase ( self : List[Any] , a : pa.Table ) -> Mapping:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = self.numpy_arrow_extractor().extract_batch(a )
SCREAMING_SNAKE_CASE : str = self.python_features_decoder.decode_batch(a )
SCREAMING_SNAKE_CASE : List[Any] = self.recursive_tensorize(a )
for column_name in batch:
SCREAMING_SNAKE_CASE : List[Any] = self._consolidate(batch[column_name] )
return batch | 25 | 0 |
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase_ ( self : Any ):
__A = [
"safety_checker/pytorch_model.bin",
"safety_checker/model.safetensors",
"vae/diffusion_pytorch_model.bin",
"vae/diffusion_pytorch_model.safetensors",
"text_encoder/pytorch_model.bin",
"text_encoder/model.safetensors",
"unet/diffusion_pytorch_model.bin",
"unet/diffusion_pytorch_model.safetensors",
]
self.assertTrue(is_safetensors_compatible(A ) )
def UpperCamelCase_ ( self : List[Any] ):
__A = [
"unet/diffusion_pytorch_model.bin",
"unet/diffusion_pytorch_model.safetensors",
]
self.assertTrue(is_safetensors_compatible(A ) )
def UpperCamelCase_ ( self : Tuple ):
__A = [
"safety_checker/pytorch_model.bin",
"safety_checker/model.safetensors",
"vae/diffusion_pytorch_model.bin",
"vae/diffusion_pytorch_model.safetensors",
"text_encoder/pytorch_model.bin",
"text_encoder/model.safetensors",
"unet/diffusion_pytorch_model.bin",
# Removed: 'unet/diffusion_pytorch_model.safetensors',
]
self.assertFalse(is_safetensors_compatible(A ) )
def UpperCamelCase_ ( self : List[Any] ):
__A = [
"text_encoder/pytorch_model.bin",
"text_encoder/model.safetensors",
]
self.assertTrue(is_safetensors_compatible(A ) )
def UpperCamelCase_ ( self : List[Any] ):
__A = [
"safety_checker/pytorch_model.bin",
"safety_checker/model.safetensors",
"vae/diffusion_pytorch_model.bin",
"vae/diffusion_pytorch_model.safetensors",
"text_encoder/pytorch_model.bin",
# Removed: 'text_encoder/model.safetensors',
"unet/diffusion_pytorch_model.bin",
"unet/diffusion_pytorch_model.safetensors",
]
self.assertFalse(is_safetensors_compatible(A ) )
def UpperCamelCase_ ( self : Any ):
__A = [
"safety_checker/pytorch_model.fp16.bin",
"safety_checker/model.fp16.safetensors",
"vae/diffusion_pytorch_model.fp16.bin",
"vae/diffusion_pytorch_model.fp16.safetensors",
"text_encoder/pytorch_model.fp16.bin",
"text_encoder/model.fp16.safetensors",
"unet/diffusion_pytorch_model.fp16.bin",
"unet/diffusion_pytorch_model.fp16.safetensors",
]
__A = "fp16"
self.assertTrue(is_safetensors_compatible(A ,variant=A ) )
def UpperCamelCase_ ( self : Tuple ):
__A = [
"unet/diffusion_pytorch_model.fp16.bin",
"unet/diffusion_pytorch_model.fp16.safetensors",
]
__A = "fp16"
self.assertTrue(is_safetensors_compatible(A ,variant=A ) )
def UpperCamelCase_ ( self : Dict ):
__A = [
"unet/diffusion_pytorch_model.bin",
"unet/diffusion_pytorch_model.safetensors",
]
__A = "fp16"
self.assertTrue(is_safetensors_compatible(A ,variant=A ) )
def UpperCamelCase_ ( self : str ):
__A = [
"safety_checker/pytorch_model.fp16.bin",
"safety_checker/model.fp16.safetensors",
"vae/diffusion_pytorch_model.fp16.bin",
"vae/diffusion_pytorch_model.fp16.safetensors",
"text_encoder/pytorch_model.fp16.bin",
"text_encoder/model.fp16.safetensors",
"unet/diffusion_pytorch_model.fp16.bin",
# Removed: 'unet/diffusion_pytorch_model.fp16.safetensors',
]
__A = "fp16"
self.assertFalse(is_safetensors_compatible(A ,variant=A ) )
def UpperCamelCase_ ( self : List[str] ):
__A = [
"text_encoder/pytorch_model.fp16.bin",
"text_encoder/model.fp16.safetensors",
]
__A = "fp16"
self.assertTrue(is_safetensors_compatible(A ,variant=A ) )
def UpperCamelCase_ ( self : Union[str, Any] ):
__A = [
"text_encoder/pytorch_model.bin",
"text_encoder/model.safetensors",
]
__A = "fp16"
self.assertTrue(is_safetensors_compatible(A ,variant=A ) )
def UpperCamelCase_ ( self : Union[str, Any] ):
__A = [
"safety_checker/pytorch_model.fp16.bin",
"safety_checker/model.fp16.safetensors",
"vae/diffusion_pytorch_model.fp16.bin",
"vae/diffusion_pytorch_model.fp16.safetensors",
"text_encoder/pytorch_model.fp16.bin",
# 'text_encoder/model.fp16.safetensors',
"unet/diffusion_pytorch_model.fp16.bin",
"unet/diffusion_pytorch_model.fp16.safetensors",
]
__A = "fp16"
self.assertFalse(is_safetensors_compatible(A ,variant=A ) )
| 55 |
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_pytesseract,
require_tf,
require_timm,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class _UpperCamelCase :
'''simple docstring'''
@staticmethod
def __UpperCamelCase ( *a : str , **a : int ) -> str:
"""simple docstring"""
pass
@is_pipeline_test
@require_vision
@require_timm
@require_torch
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ =MODEL_FOR_OBJECT_DETECTION_MAPPING
def __UpperCamelCase ( self : Optional[Any] , a : str , a : Optional[Any] , a : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = ObjectDetectionPipeline(model=a , image_processor=a )
return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]
def __UpperCamelCase ( self : List[Any] , a : Optional[int] , a : Optional[int] ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = object_detector("./tests/fixtures/tests_samples/COCO/000000039769.png" , threshold=0.0 )
self.assertGreater(len(a ) , 0 )
for detected_object in outputs:
self.assertEqual(
a , {
"score": ANY(a ),
"label": ANY(a ),
"box": {"xmin": ANY(a ), "ymin": ANY(a ), "xmax": ANY(a ), "ymax": ANY(a )},
} , )
import datasets
SCREAMING_SNAKE_CASE : Any = datasets.load_dataset("hf-internal-testing/fixtures_image_utils" , "image" , split="test" )
SCREAMING_SNAKE_CASE : Dict = [
Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" ),
"http://images.cocodataset.org/val2017/000000039769.jpg",
# RGBA
dataset[0]["file"],
# LA
dataset[1]["file"],
# L
dataset[2]["file"],
]
SCREAMING_SNAKE_CASE : Tuple = object_detector(a , threshold=0.0 )
self.assertEqual(len(a ) , len(a ) )
for outputs in batch_outputs:
self.assertGreater(len(a ) , 0 )
for detected_object in outputs:
self.assertEqual(
a , {
"score": ANY(a ),
"label": ANY(a ),
"box": {"xmin": ANY(a ), "ymin": ANY(a ), "xmax": ANY(a ), "ymax": ANY(a )},
} , )
@require_tf
@unittest.skip("Object detection not implemented in TF" )
def __UpperCamelCase ( self : Optional[int] ) -> str:
"""simple docstring"""
pass
@require_torch
def __UpperCamelCase ( self : Tuple ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = "hf-internal-testing/tiny-detr-mobilenetsv3"
SCREAMING_SNAKE_CASE : Dict = AutoModelForObjectDetection.from_pretrained(a )
SCREAMING_SNAKE_CASE : Any = AutoFeatureExtractor.from_pretrained(a )
SCREAMING_SNAKE_CASE : Tuple = ObjectDetectionPipeline(model=a , feature_extractor=a )
SCREAMING_SNAKE_CASE : int = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg" , threshold=0.0 )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
] , )
SCREAMING_SNAKE_CASE : Dict = object_detector(
[
"http://images.cocodataset.org/val2017/000000039769.jpg",
"http://images.cocodataset.org/val2017/000000039769.jpg",
] , threshold=0.0 , )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
[
{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
],
[
{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
],
] , )
@require_torch
@slow
def __UpperCamelCase ( self : str ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = "facebook/detr-resnet-50"
SCREAMING_SNAKE_CASE : Union[str, Any] = AutoModelForObjectDetection.from_pretrained(a )
SCREAMING_SNAKE_CASE : List[str] = AutoFeatureExtractor.from_pretrained(a )
SCREAMING_SNAKE_CASE : int = ObjectDetectionPipeline(model=a , feature_extractor=a )
SCREAMING_SNAKE_CASE : Union[str, Any] = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg" )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
] , )
SCREAMING_SNAKE_CASE : int = object_detector(
[
"http://images.cocodataset.org/val2017/000000039769.jpg",
"http://images.cocodataset.org/val2017/000000039769.jpg",
] )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
[
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
],
[
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
],
] , )
@require_torch
@slow
def __UpperCamelCase ( self : str ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = "facebook/detr-resnet-50"
SCREAMING_SNAKE_CASE : Tuple = pipeline("object-detection" , model=a )
SCREAMING_SNAKE_CASE : Tuple = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg" )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
] , )
SCREAMING_SNAKE_CASE : str = object_detector(
[
"http://images.cocodataset.org/val2017/000000039769.jpg",
"http://images.cocodataset.org/val2017/000000039769.jpg",
] )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
[
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
],
[
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
],
] , )
@require_torch
@slow
def __UpperCamelCase ( self : str ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = 0.9985
SCREAMING_SNAKE_CASE : int = "facebook/detr-resnet-50"
SCREAMING_SNAKE_CASE : List[str] = pipeline("object-detection" , model=a )
SCREAMING_SNAKE_CASE : str = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg" , threshold=a )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
] , )
@require_torch
@require_pytesseract
@slow
def __UpperCamelCase ( self : str ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = "Narsil/layoutlmv3-finetuned-funsd"
SCREAMING_SNAKE_CASE : Dict = 0.9993
SCREAMING_SNAKE_CASE : str = pipeline("object-detection" , model=a , threshold=a )
SCREAMING_SNAKE_CASE : List[Any] = object_detector(
"https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png" )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
{"score": 0.9993, "label": "I-ANSWER", "box": {"xmin": 294, "ymin": 254, "xmax": 343, "ymax": 264}},
{"score": 0.9993, "label": "I-ANSWER", "box": {"xmin": 294, "ymin": 254, "xmax": 343, "ymax": 264}},
] , ) | 25 | 0 |
"""simple docstring"""
import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional
from torch.utils.data import ConcatDataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_WITH_LM_HEAD_MAPPING,
AutoConfig,
AutoModelWithLMHead,
AutoTokenizer,
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForWholeWordMask,
HfArgumentParser,
LineByLineTextDataset,
LineByLineWithRefDataset,
PreTrainedTokenizer,
TextDataset,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
_UpperCamelCase : List[Any] = logging.getLogger(__name__)
_UpperCamelCase : Union[str, Any] = list(MODEL_WITH_LM_HEAD_MAPPING.keys())
_UpperCamelCase : Optional[Any] = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class UpperCAmelCase_ :
lowerCamelCase__ : Optional[Any] = field(
default=__A , metadata={
"help": (
"The model checkpoint for weights initialization. Leave None if you want to train a model from"
" scratch."
)
} , )
lowerCamelCase__ : Optional[int] = field(
default=__A , metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(__A)} , )
lowerCamelCase__ : List[Any] = field(
default=__A , metadata={"help": "Pretrained config name or path if not the same as model_name"})
lowerCamelCase__ : Any = field(
default=__A , metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"})
lowerCamelCase__ : Any = field(
default=__A , metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"} , )
@dataclass
class UpperCAmelCase_ :
lowerCamelCase__ : Optional[Any] = field(
default=__A , metadata={"help": "The input training data file (a text file)."})
lowerCamelCase__ : Union[str, Any] = field(
default=__A , metadata={
"help": (
"The input training data files (multiple files in glob format). "
"Very often splitting large files to smaller files can prevent tokenizer going out of memory"
)
} , )
lowerCamelCase__ : Optional[Any] = field(
default=__A , metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."} , )
lowerCamelCase__ : List[str] = field(
default=__A , metadata={"help": "An optional input train ref data file for whole word mask in Chinese."} , )
lowerCamelCase__ : Tuple = field(
default=__A , metadata={"help": "An optional input eval ref data file for whole word mask in Chinese."} , )
lowerCamelCase__ : Dict = field(
default=__A , metadata={"help": "Whether distinct lines of text in the dataset are to be handled as distinct sequences."} , )
lowerCamelCase__ : Union[str, Any] = field(
default=__A , metadata={"help": "Train with masked-language modeling loss instead of language modeling."})
lowerCamelCase__ : str = field(default=__A , metadata={"help": "Whether ot not to use whole word mask."})
lowerCamelCase__ : List[str] = field(
default=0.15 , metadata={"help": "Ratio of tokens to mask for masked language modeling loss"})
lowerCamelCase__ : Optional[int] = field(
default=1 / 6 , metadata={
"help": (
"Ratio of length of a span of masked tokens to surrounding context length for permutation language"
" modeling."
)
} , )
lowerCamelCase__ : Optional[int] = field(
default=5 , metadata={"help": "Maximum length of a span of masked tokens for permutation language modeling."})
lowerCamelCase__ : int = field(
default=-1 , metadata={
"help": (
"Optional input sequence length after tokenization."
"The training dataset will be truncated in block of this size for training."
"Default to the model max input length for single sentence inputs (take into account special tokens)."
)
} , )
lowerCamelCase__ : Union[str, Any] = field(
default=__A , metadata={"help": "Overwrite the cached training and evaluation sets"})
def a_ ( _lowerCAmelCase : List[Any] , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Tuple = False , _lowerCAmelCase : Tuple = None , ):
'''simple docstring'''
def _dataset(_lowerCAmelCase : List[Any] , _lowerCAmelCase : Optional[int]=None ):
if args.line_by_line:
if ref_path is not None:
if not args.whole_word_mask or not args.mlm:
raise ValueError('You need to set world whole masking and mlm to True for Chinese Whole Word Mask' )
return LineByLineWithRefDataset(
tokenizer=_a , file_path=_a , block_size=args.block_size , ref_path=_a , )
return LineByLineTextDataset(tokenizer=_a , file_path=_a , block_size=args.block_size )
else:
return TextDataset(
tokenizer=_a , file_path=_a , block_size=args.block_size , overwrite_cache=args.overwrite_cache , cache_dir=_a , )
if evaluate:
return _dataset(args.eval_data_file , args.eval_ref_file )
elif args.train_data_files:
return ConcatDataset([_dataset(_a ) for f in glob(args.train_data_files )] )
else:
return _dataset(args.train_data_file , args.train_ref_file )
def a_ ( ):
'''simple docstring'''
lowercase__ : Dict = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
lowercase__ : str = parser.parse_args_into_dataclasses()
if data_args.eval_data_file is None and training_args.do_eval:
raise ValueError(
'Cannot do evaluation without an evaluation data file. Either supply a file to --eval_data_file '
'or remove the --do_eval argument.' )
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
f"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
' --overwrite_output_dir to overcome.' )
# Setup logging
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' , datefmt='%m/%d/%Y %H:%M:%S' , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
'Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s' , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1 ) , training_args.fpaa , )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info('Training/evaluation parameters %s' , _a )
# Set seed
set_seed(training_args.seed )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
if model_args.config_name:
lowercase__ : Optional[Any] = AutoConfig.from_pretrained(model_args.config_name , cache_dir=model_args.cache_dir )
elif model_args.model_name_or_path:
lowercase__ : List[str] = AutoConfig.from_pretrained(model_args.model_name_or_path , cache_dir=model_args.cache_dir )
else:
lowercase__ : Dict = CONFIG_MAPPING[model_args.model_type]()
logger.warning('You are instantiating a new config instance from scratch.' )
if model_args.tokenizer_name:
lowercase__ : Optional[int] = AutoTokenizer.from_pretrained(model_args.tokenizer_name , cache_dir=model_args.cache_dir )
elif model_args.model_name_or_path:
lowercase__ : List[Any] = AutoTokenizer.from_pretrained(model_args.model_name_or_path , cache_dir=model_args.cache_dir )
else:
raise ValueError(
'You are instantiating a new tokenizer from scratch. This is not supported, but you can do it from another'
' script, save it,and load it from here, using --tokenizer_name' )
if model_args.model_name_or_path:
lowercase__ : Tuple = AutoModelWithLMHead.from_pretrained(
model_args.model_name_or_path , from_tf=bool('.ckpt' in model_args.model_name_or_path ) , config=_a , cache_dir=model_args.cache_dir , )
else:
logger.info('Training new model from scratch' )
lowercase__ : Optional[Any] = AutoModelWithLMHead.from_config(_a )
model.resize_token_embeddings(len(_a ) )
if config.model_type in ["bert", "roberta", "distilbert", "camembert"] and not data_args.mlm:
raise ValueError(
'BERT and RoBERTa-like models do not have LM heads but masked LM heads. They must be run using the'
'--mlm flag (masked language modeling).' )
if data_args.block_size <= 0:
lowercase__ : str = tokenizer.max_len
# Our input block size will be the max possible for the model
else:
lowercase__ : List[str] = min(data_args.block_size , tokenizer.max_len )
# Get datasets
lowercase__ : List[Any] = (
get_dataset(_a , tokenizer=_a , cache_dir=model_args.cache_dir ) if training_args.do_train else None
)
lowercase__ : Optional[Any] = (
get_dataset(_a , tokenizer=_a , evaluate=_a , cache_dir=model_args.cache_dir )
if training_args.do_eval
else None
)
if config.model_type == "xlnet":
lowercase__ : Any = DataCollatorForPermutationLanguageModeling(
tokenizer=_a , plm_probability=data_args.plm_probability , max_span_length=data_args.max_span_length , )
else:
if data_args.mlm and data_args.whole_word_mask:
lowercase__ : Optional[int] = DataCollatorForWholeWordMask(
tokenizer=_a , mlm_probability=data_args.mlm_probability )
else:
lowercase__ : Any = DataCollatorForLanguageModeling(
tokenizer=_a , mlm=data_args.mlm , mlm_probability=data_args.mlm_probability )
# Initialize our Trainer
lowercase__ : str = Trainer(
model=_a , args=_a , data_collator=_a , train_dataset=_a , eval_dataset=_a , prediction_loss_only=_a , )
# Training
if training_args.do_train:
lowercase__ : Optional[int] = (
model_args.model_name_or_path
if model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path )
else None
)
trainer.train(model_path=_a )
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_master():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
lowercase__ : int = {}
if training_args.do_eval:
logger.info('*** Evaluate ***' )
lowercase__ : Union[str, Any] = trainer.evaluate()
lowercase__ : Any = math.exp(eval_output['eval_loss'] )
lowercase__ : Any = {"perplexity": perplexity}
lowercase__ : Dict = os.path.join(training_args.output_dir , 'eval_results_lm.txt' )
if trainer.is_world_master():
with open(_a , 'w' ) as writer:
logger.info('***** Eval results *****' )
for key in sorted(result.keys() ):
logger.info(' %s = %s' , _a , str(result[key] ) )
writer.write('%s = %s\n' % (key, str(result[key] )) )
results.update(_a )
return results
def a_ ( _lowerCAmelCase : Optional[Any] ):
'''simple docstring'''
main()
if __name__ == "__main__":
main()
| 599 |
def lowerCamelCase__ ( _a):
if not isinstance(_a , _a):
SCREAMING_SNAKE_CASE : Tuple = f"Input value of [number={number}] must be an integer"
raise TypeError(_a)
if number < 0:
return False
SCREAMING_SNAKE_CASE : Union[str, Any] = number * number
while number > 0:
if number % 10 != number_square % 10:
return False
number //= 10
number_square //= 10
return True
if __name__ == "__main__":
import doctest
doctest.testmod() | 25 | 0 |
"""simple docstring"""
import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
A = logging.get_logger(__name__)
def __SCREAMING_SNAKE_CASE ( lowerCamelCase_: str , lowerCamelCase_: List[str] , lowerCamelCase_: int ):
"""simple docstring"""
snake_case : Dict = nn.ModuleList([src_layers[i] for i in layers_to_copy] )
assert len(_a ) == len(_a ), f'''{len(_a )} != {len(_a )}'''
dest_layers.load_state_dict(layers_to_copy.state_dict() )
A = {
# maps num layers in teacher -> num_layers in student -> which teacher layers to copy.
# 12: bart, 16: pegasus, 6: marian/Helsinki-NLP
1_2: {
1: [0], # This says that if the teacher has 12 layers and the student has 1, copy layer 0 of the teacher
2: [0, 6],
3: [0, 6, 1_1],
4: [0, 4, 8, 1_1],
6: [0, 2, 4, 7, 9, 1_1],
9: [0, 1, 2, 4, 5, 7, 9, 1_0, 1_1],
1_2: list(range(1_2)),
},
1_6: { # maps num layers in student -> which teacher layers to copy
1: [0],
2: [0, 1_5],
3: [0, 8, 1_5],
4: [0, 5, 1_0, 1_5],
6: [0, 3, 6, 9, 1_2, 1_5],
8: [0, 2, 4, 6, 8, 1_0, 1_2, 1_5],
9: [0, 1, 3, 5, 7, 9, 1_1, 1_3, 1_5],
1_2: [0, 1, 2, 3, 4, 5, 6, 7, 9, 1_1, 1_3, 1_5],
1_6: list(range(1_6)),
},
6: {1: [0], 2: [0, 5], 3: [0, 2, 5], 4: [0, 1, 3, 5], 6: list(range(6))},
}
A = {
# maps num layers in student -> which teacher layers to copy.
6: {1: [5], 2: [3, 5], 3: [1, 4, 5], 4: [1, 2, 4, 5]},
1_2: {1: [1_1], 2: [5, 1_1], 3: [3, 7, 1_1], 6: [1, 3, 5, 8, 1_0, 1_1]},
1_6: {1: [1_5], 4: [4, 9, 1_2, 1_5], 8: [1, 3, 5, 7, 9, 1_1, 1_3, 1_5]},
}
def __SCREAMING_SNAKE_CASE ( lowerCamelCase_: str , lowerCamelCase_: Union[str, Any] ):
"""simple docstring"""
try:
snake_case : Any = LAYERS_TO_COPY[n_teacher][n_student]
return val
except KeyError:
if n_student != n_teacher:
warnings.warn(
f'''no hardcoded layers to copy for teacher {n_teacher} -> student {n_student}, defaulting to first'''
f''' {n_student}''' )
return list(range(_a ) )
def __SCREAMING_SNAKE_CASE ( lowerCamelCase_: Optional[Any] , lowerCamelCase_: Optional[int] ):
"""simple docstring"""
if n_student > n_teacher:
raise ValueError(f'''Cannot perform intermediate supervision for student {n_student} > teacher {n_teacher}''' )
elif n_teacher == n_student:
return list(range(_a ) )
elif n_student == 1:
return [n_teacher - 1]
else:
return LAYERS_TO_SUPERVISE[n_teacher][n_student]
def __SCREAMING_SNAKE_CASE ( lowerCamelCase_: Dict , lowerCamelCase_: Union[str, Any] = "student" , lowerCamelCase_: Dict = None , lowerCamelCase_: Dict = None , lowerCamelCase_: List[Any]=False , lowerCamelCase_: Any=None , lowerCamelCase_: int=None , **lowerCamelCase_: str , ):
"""simple docstring"""
snake_case : Dict = "encoder_layers and decoder_layers cannot be both None-- you would just have an identical teacher."
assert (e is not None) or (d is not None), _msg
if isinstance(_a , _a ):
AutoTokenizer.from_pretrained(_a ).save_pretrained(_a ) # purely for convenience
snake_case : str = AutoModelForSeqaSeqLM.from_pretrained(_a ).eval()
else:
assert isinstance(_a , _a ), f'''teacher must be a model or string got type {type(_a )}'''
snake_case : Optional[int] = teacher.config.to_diff_dict()
try:
snake_case : str = teacher.config.encoder_layers, teacher.config.decoder_layers
if e is None:
snake_case : List[str] = teacher_e
if d is None:
snake_case : Optional[Any] = teacher_d
init_kwargs.update({"encoder_layers": e, "decoder_layers": d} )
except AttributeError: # T5
if hasattr(teacher.config , "num_encoder_layers" ):
snake_case : str = teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
else:
snake_case : Tuple = teacher.config.num_layers, teacher.config.num_decoder_layers
if e is None:
snake_case : Optional[Any] = teacher_e
if d is None:
snake_case : List[Any] = teacher_d
if hasattr(teacher.config , "num_encoder_layers" ):
init_kwargs.update({"num_encoder_layers": e, "num_decoder_layers": d} )
else:
init_kwargs.update({"num_layers": e, "num_decoder_layers": d} )
# Kwargs to instantiate student: teacher kwargs with updated layer numbers + **extra_config_kwargs
init_kwargs.update(_a )
# Copy weights
snake_case : Tuple = teacher.config_class(**_a )
snake_case : Any = AutoModelForSeqaSeqLM.from_config(_a )
# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
snake_case : List[str] = student.load_state_dict(teacher.state_dict() , strict=_a )
assert info.missing_keys == [], info.missing_keys # every student key should have a teacher keys.
if copy_first_teacher_layers: # Our copying is done. We just log and save
snake_case : Optional[int] = list(range(_a ) ), list(range(_a ) )
logger.info(
f'''Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to'''
f''' {save_path}''' )
student.save_pretrained(_a )
return student, e_layers_to_copy, d_layers_to_copy
# Decide which layers of the teacher to copy. Not exactly alternating -- we try to keep first and last layer.
if e_layers_to_copy is None:
snake_case : List[int] = pick_layers_to_copy(_a , _a )
if d_layers_to_copy is None:
snake_case : List[int] = pick_layers_to_copy(_a , _a )
try:
if hasattr(
_a , "prophetnet" ): # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
copy_layers(teacher.prophetnet.encoder.layers , student.prophetnet.encoder.layers , _a )
copy_layers(teacher.prophetnet.decoder.layers , student.prophetnet.decoder.layers , _a )
else:
copy_layers(teacher.model.encoder.layers , student.model.encoder.layers , _a )
copy_layers(teacher.model.decoder.layers , student.model.decoder.layers , _a )
except AttributeError: # For t5, student.model.encoder.layers is called student.encoder.block
copy_layers(teacher.encoder.block , student.encoder.block , _a )
copy_layers(teacher.decoder.block , student.decoder.block , _a )
logger.info(
f'''Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}''' )
snake_case : Optional[int] = {
"teacher_type": teacher.config.model_type,
"copied_encoder_layers": e_layers_to_copy,
"copied_decoder_layers": d_layers_to_copy,
}
student.save_pretrained(_a )
# Save information about copying for easier reproducibility
return student, e_layers_to_copy, d_layers_to_copy
if __name__ == "__main__":
fire.Fire(create_student_by_copying_alternating_layers)
| 449 |
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
DistilBertModel,
)
class _UpperCamelCase ( __A ):
'''simple docstring'''
def __init__( self : Dict , a : Tuple , a : Any=13 , a : Any=7 , a : Union[str, Any]=True , a : List[Any]=True , a : List[str]=False , a : List[str]=True , a : Any=99 , a : str=32 , a : Any=5 , a : Optional[int]=4 , a : Union[str, Any]=37 , a : Dict="gelu" , a : List[Any]=0.1 , a : Optional[Any]=0.1 , a : List[str]=512 , a : Union[str, Any]=16 , a : str=2 , a : Dict=0.02 , a : Optional[int]=3 , a : Union[str, Any]=4 , a : int=None , ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = parent
SCREAMING_SNAKE_CASE : Any = batch_size
SCREAMING_SNAKE_CASE : Optional[int] = seq_length
SCREAMING_SNAKE_CASE : List[Any] = is_training
SCREAMING_SNAKE_CASE : int = use_input_mask
SCREAMING_SNAKE_CASE : Tuple = use_token_type_ids
SCREAMING_SNAKE_CASE : str = use_labels
SCREAMING_SNAKE_CASE : Any = vocab_size
SCREAMING_SNAKE_CASE : List[Any] = hidden_size
SCREAMING_SNAKE_CASE : str = num_hidden_layers
SCREAMING_SNAKE_CASE : Optional[Any] = num_attention_heads
SCREAMING_SNAKE_CASE : Tuple = intermediate_size
SCREAMING_SNAKE_CASE : Optional[int] = hidden_act
SCREAMING_SNAKE_CASE : Dict = hidden_dropout_prob
SCREAMING_SNAKE_CASE : str = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : Tuple = max_position_embeddings
SCREAMING_SNAKE_CASE : List[str] = type_vocab_size
SCREAMING_SNAKE_CASE : List[str] = type_sequence_label_size
SCREAMING_SNAKE_CASE : Optional[Any] = initializer_range
SCREAMING_SNAKE_CASE : Tuple = num_labels
SCREAMING_SNAKE_CASE : Tuple = num_choices
SCREAMING_SNAKE_CASE : Optional[Any] = scope
def __UpperCamelCase ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE : Union[str, Any] = None
if self.use_input_mask:
SCREAMING_SNAKE_CASE : str = random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE : int = None
SCREAMING_SNAKE_CASE : List[Any] = None
SCREAMING_SNAKE_CASE : List[str] = None
if self.use_labels:
SCREAMING_SNAKE_CASE : Tuple = ids_tensor([self.batch_size] , self.type_sequence_label_size )
SCREAMING_SNAKE_CASE : str = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
SCREAMING_SNAKE_CASE : Optional[Any] = ids_tensor([self.batch_size] , self.num_choices )
SCREAMING_SNAKE_CASE : int = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def __UpperCamelCase ( self : Dict ) -> str:
"""simple docstring"""
return DistilBertConfig(
vocab_size=self.vocab_size , dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , hidden_dim=self.intermediate_size , hidden_act=self.hidden_act , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , )
def __UpperCamelCase ( self : Optional[Any] , a : int , a : Optional[int] , a : Optional[int] , a : Dict , a : str , a : str ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = DistilBertModel(config=a )
model.to(a )
model.eval()
SCREAMING_SNAKE_CASE : Optional[Any] = model(a , a )
SCREAMING_SNAKE_CASE : Optional[Any] = model(a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __UpperCamelCase ( self : Tuple , a : Optional[int] , a : Dict , a : Tuple , a : int , a : int , a : Any ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = DistilBertForMaskedLM(config=a )
model.to(a )
model.eval()
SCREAMING_SNAKE_CASE : str = model(a , attention_mask=a , labels=a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __UpperCamelCase ( self : List[Any] , a : int , a : Optional[Any] , a : Optional[Any] , a : str , a : str , a : Tuple ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = DistilBertForQuestionAnswering(config=a )
model.to(a )
model.eval()
SCREAMING_SNAKE_CASE : List[str] = model(
a , attention_mask=a , start_positions=a , end_positions=a )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def __UpperCamelCase ( self : Optional[int] , a : str , a : Any , a : int , a : Optional[Any] , a : int , a : str ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = self.num_labels
SCREAMING_SNAKE_CASE : Union[str, Any] = DistilBertForSequenceClassification(a )
model.to(a )
model.eval()
SCREAMING_SNAKE_CASE : Optional[int] = model(a , attention_mask=a , labels=a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __UpperCamelCase ( self : Optional[Any] , a : List[Any] , a : Optional[int] , a : Union[str, Any] , a : Dict , a : Any , a : Optional[Any] ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = self.num_labels
SCREAMING_SNAKE_CASE : List[str] = DistilBertForTokenClassification(config=a )
model.to(a )
model.eval()
SCREAMING_SNAKE_CASE : List[str] = model(a , attention_mask=a , labels=a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __UpperCamelCase ( self : int , a : Any , a : Optional[int] , a : Union[str, Any] , a : Tuple , a : Optional[int] , a : Tuple ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = self.num_choices
SCREAMING_SNAKE_CASE : Any = DistilBertForMultipleChoice(config=a )
model.to(a )
model.eval()
SCREAMING_SNAKE_CASE : Optional[int] = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
SCREAMING_SNAKE_CASE : Dict = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
SCREAMING_SNAKE_CASE : Optional[Any] = model(
a , attention_mask=a , labels=a , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __UpperCamelCase ( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = self.prepare_config_and_inputs()
((SCREAMING_SNAKE_CASE) ,(SCREAMING_SNAKE_CASE) ,(SCREAMING_SNAKE_CASE) ,(SCREAMING_SNAKE_CASE) ,(SCREAMING_SNAKE_CASE) ,(SCREAMING_SNAKE_CASE)) : Tuple = config_and_inputs
SCREAMING_SNAKE_CASE : int = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class _UpperCamelCase ( __A , __A , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ =(
(
DistilBertModel,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
)
if is_torch_available()
else None
)
lowerCamelCase__ =(
{
'feature-extraction': DistilBertModel,
'fill-mask': DistilBertForMaskedLM,
'question-answering': DistilBertForQuestionAnswering,
'text-classification': DistilBertForSequenceClassification,
'token-classification': DistilBertForTokenClassification,
'zero-shot': DistilBertForSequenceClassification,
}
if is_torch_available()
else {}
)
lowerCamelCase__ =True
lowerCamelCase__ =True
lowerCamelCase__ =True
lowerCamelCase__ =True
def __UpperCamelCase ( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = DistilBertModelTester(self )
SCREAMING_SNAKE_CASE : List[str] = ConfigTester(self , config_class=a , dim=37 )
def __UpperCamelCase ( self : List[Any] ) -> Tuple:
"""simple docstring"""
self.config_tester.run_common_tests()
def __UpperCamelCase ( self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_model(*a )
def __UpperCamelCase ( self : Tuple ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_masked_lm(*a )
def __UpperCamelCase ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_question_answering(*a )
def __UpperCamelCase ( self : Union[str, Any] ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_sequence_classification(*a )
def __UpperCamelCase ( self : str ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_token_classification(*a )
def __UpperCamelCase ( self : List[Any] ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_multiple_choice(*a )
@slow
def __UpperCamelCase ( self : int ) -> Any:
"""simple docstring"""
for model_name in DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE : Optional[Any] = DistilBertModel.from_pretrained(a )
self.assertIsNotNone(a )
@slow
@require_torch_gpu
def __UpperCamelCase ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# BertForMultipleChoice behaves incorrectly in JIT environments.
if model_class == DistilBertForMultipleChoice:
return
SCREAMING_SNAKE_CASE : Union[str, Any] = True
SCREAMING_SNAKE_CASE : Any = model_class(config=a )
SCREAMING_SNAKE_CASE : List[Any] = self._prepare_for_class(a , a )
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.jit.trace(
a , (inputs_dict["input_ids"].to("cpu" ), inputs_dict["attention_mask"].to("cpu" )) )
with tempfile.TemporaryDirectory() as tmp:
torch.jit.save(a , os.path.join(a , "traced_model.pt" ) )
SCREAMING_SNAKE_CASE : Tuple = torch.jit.load(os.path.join(a , "traced_model.pt" ) , map_location=a )
loaded(inputs_dict["input_ids"].to(a ) , inputs_dict["attention_mask"].to(a ) )
@require_torch
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
@slow
def __UpperCamelCase ( self : int ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = DistilBertModel.from_pretrained("distilbert-base-uncased" )
SCREAMING_SNAKE_CASE : List[str] = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]] )
SCREAMING_SNAKE_CASE : Optional[int] = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
SCREAMING_SNAKE_CASE : Optional[Any] = model(a , attention_mask=a )[0]
SCREAMING_SNAKE_CASE : List[str] = torch.Size((1, 11, 768) )
self.assertEqual(output.shape , a )
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.tensor(
[[[-0.1639, 0.3299, 0.1648], [-0.1746, 0.3289, 0.1710], [-0.1884, 0.3357, 0.1810]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , a , atol=1e-4 ) ) | 25 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__SCREAMING_SNAKE_CASE :Optional[int] = {
'''configuration_lilt''': ['''LILT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LiltConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE :int = [
'''LILT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''LiltForQuestionAnswering''',
'''LiltForSequenceClassification''',
'''LiltForTokenClassification''',
'''LiltModel''',
'''LiltPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_lilt import (
LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
LiltForQuestionAnswering,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltModel,
LiltPreTrainedModel,
)
else:
import sys
__SCREAMING_SNAKE_CASE :Union[str, Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 236 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
a_ = {'configuration_plbart': ['PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PLBartConfig']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ['PLBartTokenizer']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'PLBART_PRETRAINED_MODEL_ARCHIVE_LIST',
'PLBartForCausalLM',
'PLBartForConditionalGeneration',
'PLBartForSequenceClassification',
'PLBartModel',
'PLBartPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_plbart import PLBartTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_plbart import (
PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
PLBartForCausalLM,
PLBartForConditionalGeneration,
PLBartForSequenceClassification,
PLBartModel,
PLBartPreTrainedModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure) | 25 | 0 |
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFFunnelBaseModel,
TFFunnelForMaskedLM,
TFFunnelForMultipleChoice,
TFFunnelForPreTraining,
TFFunnelForQuestionAnswering,
TFFunnelForSequenceClassification,
TFFunnelForTokenClassification,
TFFunnelModel,
)
class lowerCAmelCase_ :
def __init__( self ,snake_case__ ,snake_case__=13 ,snake_case__=7 ,snake_case__=True ,snake_case__=True ,snake_case__=True ,snake_case__=True ,snake_case__=99 ,snake_case__=[1, 1, 2] ,snake_case__=1 ,snake_case__=32 ,snake_case__=4 ,snake_case__=8 ,snake_case__=37 ,snake_case__="gelu_new" ,snake_case__=0.1 ,snake_case__=0.1 ,snake_case__=0.0 ,snake_case__=512 ,snake_case__=3 ,snake_case__=0.02 ,snake_case__=3 ,snake_case__=4 ,snake_case__=None ,snake_case__=False ,):
SCREAMING_SNAKE_CASE_ : Any = parent
SCREAMING_SNAKE_CASE_ : Union[str, Any] = batch_size
SCREAMING_SNAKE_CASE_ : Tuple = seq_length
SCREAMING_SNAKE_CASE_ : Optional[Any] = is_training
SCREAMING_SNAKE_CASE_ : List[Any] = use_input_mask
SCREAMING_SNAKE_CASE_ : Dict = use_token_type_ids
SCREAMING_SNAKE_CASE_ : Union[str, Any] = use_labels
SCREAMING_SNAKE_CASE_ : int = vocab_size
SCREAMING_SNAKE_CASE_ : List[str] = block_sizes
SCREAMING_SNAKE_CASE_ : List[Any] = num_decoder_layers
SCREAMING_SNAKE_CASE_ : Optional[Any] = d_model
SCREAMING_SNAKE_CASE_ : Any = n_head
SCREAMING_SNAKE_CASE_ : Tuple = d_head
SCREAMING_SNAKE_CASE_ : Dict = d_inner
SCREAMING_SNAKE_CASE_ : List[Any] = hidden_act
SCREAMING_SNAKE_CASE_ : str = hidden_dropout
SCREAMING_SNAKE_CASE_ : List[Any] = attention_dropout
SCREAMING_SNAKE_CASE_ : Optional[Any] = activation_dropout
SCREAMING_SNAKE_CASE_ : int = max_position_embeddings
SCREAMING_SNAKE_CASE_ : Optional[Any] = type_vocab_size
SCREAMING_SNAKE_CASE_ : int = 2
SCREAMING_SNAKE_CASE_ : List[Any] = num_labels
SCREAMING_SNAKE_CASE_ : Optional[int] = num_choices
SCREAMING_SNAKE_CASE_ : Dict = scope
SCREAMING_SNAKE_CASE_ : Optional[int] = initializer_std
# Used in the tests to check the size of the first attention layer
SCREAMING_SNAKE_CASE_ : Tuple = n_head
# Used in the tests to check the size of the first hidden state
SCREAMING_SNAKE_CASE_ : Any = self.d_model
# Used in the tests to check the number of output hidden states/attentions
SCREAMING_SNAKE_CASE_ : int = sum(self.block_sizes ) + (0 if base else self.num_decoder_layers)
# FunnelModel adds two hidden layers: input embeddings and the sum of the upsampled encoder hidden state with
# the last hidden state of the first block (which is the first hidden state of the decoder).
if not base:
SCREAMING_SNAKE_CASE_ : List[Any] = self.num_hidden_layers + 2
def snake_case ( self ):
SCREAMING_SNAKE_CASE_ : str = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
SCREAMING_SNAKE_CASE_ : List[str] = None
if self.use_input_mask:
SCREAMING_SNAKE_CASE_ : List[str] = random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE_ : str = None
if self.use_token_type_ids:
SCREAMING_SNAKE_CASE_ : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] ,self.type_vocab_size )
SCREAMING_SNAKE_CASE_ : int = None
SCREAMING_SNAKE_CASE_ : Optional[int] = None
SCREAMING_SNAKE_CASE_ : Optional[int] = None
if self.use_labels:
SCREAMING_SNAKE_CASE_ : List[str] = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
SCREAMING_SNAKE_CASE_ : List[str] = ids_tensor([self.batch_size, self.seq_length] ,self.num_labels )
SCREAMING_SNAKE_CASE_ : int = ids_tensor([self.batch_size] ,self.num_choices )
SCREAMING_SNAKE_CASE_ : int = FunnelConfig(
vocab_size=self.vocab_size ,block_sizes=self.block_sizes ,num_decoder_layers=self.num_decoder_layers ,d_model=self.d_model ,n_head=self.n_head ,d_head=self.d_head ,d_inner=self.d_inner ,hidden_act=self.hidden_act ,hidden_dropout=self.hidden_dropout ,attention_dropout=self.attention_dropout ,activation_dropout=self.activation_dropout ,max_position_embeddings=self.max_position_embeddings ,type_vocab_size=self.type_vocab_size ,initializer_std=self.initializer_std ,)
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
)
def snake_case ( self ,snake_case__ ,snake_case__ ,snake_case__ ,snake_case__ ,snake_case__ ,snake_case__ ,snake_case__ ,):
SCREAMING_SNAKE_CASE_ : Dict = TFFunnelModel(config=snake_case__ )
SCREAMING_SNAKE_CASE_ : Union[str, Any] = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
SCREAMING_SNAKE_CASE_ : int = model(snake_case__ )
SCREAMING_SNAKE_CASE_ : List[str] = [input_ids, input_mask]
SCREAMING_SNAKE_CASE_ : Optional[int] = model(snake_case__ )
SCREAMING_SNAKE_CASE_ : Optional[Any] = model(snake_case__ )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.d_model) )
SCREAMING_SNAKE_CASE_ : Union[str, Any] = False
SCREAMING_SNAKE_CASE_ : int = TFFunnelModel(config=snake_case__ )
SCREAMING_SNAKE_CASE_ : Tuple = model(snake_case__ )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.d_model) )
SCREAMING_SNAKE_CASE_ : Optional[int] = False
SCREAMING_SNAKE_CASE_ : List[str] = TFFunnelModel(config=snake_case__ )
SCREAMING_SNAKE_CASE_ : Union[str, Any] = model(snake_case__ )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.d_model) )
def snake_case ( self ,snake_case__ ,snake_case__ ,snake_case__ ,snake_case__ ,snake_case__ ,snake_case__ ,snake_case__ ,):
SCREAMING_SNAKE_CASE_ : Dict = TFFunnelBaseModel(config=snake_case__ )
SCREAMING_SNAKE_CASE_ : Dict = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
SCREAMING_SNAKE_CASE_ : List[Any] = model(snake_case__ )
SCREAMING_SNAKE_CASE_ : int = [input_ids, input_mask]
SCREAMING_SNAKE_CASE_ : str = model(snake_case__ )
SCREAMING_SNAKE_CASE_ : Tuple = model(snake_case__ )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, 2, self.d_model) )
SCREAMING_SNAKE_CASE_ : Optional[int] = False
SCREAMING_SNAKE_CASE_ : List[Any] = TFFunnelBaseModel(config=snake_case__ )
SCREAMING_SNAKE_CASE_ : int = model(snake_case__ )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, 3, self.d_model) )
SCREAMING_SNAKE_CASE_ : Any = False
SCREAMING_SNAKE_CASE_ : List[Any] = TFFunnelBaseModel(config=snake_case__ )
SCREAMING_SNAKE_CASE_ : List[str] = model(snake_case__ )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, 2, self.d_model) )
def snake_case ( self ,snake_case__ ,snake_case__ ,snake_case__ ,snake_case__ ,snake_case__ ,snake_case__ ,snake_case__ ,):
SCREAMING_SNAKE_CASE_ : int = TFFunnelForPreTraining(config=snake_case__ )
SCREAMING_SNAKE_CASE_ : List[Any] = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
SCREAMING_SNAKE_CASE_ : Dict = model(snake_case__ )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length) )
def snake_case ( self ,snake_case__ ,snake_case__ ,snake_case__ ,snake_case__ ,snake_case__ ,snake_case__ ,snake_case__ ,):
SCREAMING_SNAKE_CASE_ : int = TFFunnelForMaskedLM(config=snake_case__ )
SCREAMING_SNAKE_CASE_ : List[Any] = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
SCREAMING_SNAKE_CASE_ : str = model(snake_case__ )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.vocab_size) )
def snake_case ( self ,snake_case__ ,snake_case__ ,snake_case__ ,snake_case__ ,snake_case__ ,snake_case__ ,snake_case__ ,):
SCREAMING_SNAKE_CASE_ : Optional[Any] = self.num_labels
SCREAMING_SNAKE_CASE_ : Dict = TFFunnelForSequenceClassification(config=snake_case__ )
SCREAMING_SNAKE_CASE_ : int = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
SCREAMING_SNAKE_CASE_ : Union[str, Any] = model(snake_case__ )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_labels) )
def snake_case ( self ,snake_case__ ,snake_case__ ,snake_case__ ,snake_case__ ,snake_case__ ,snake_case__ ,snake_case__ ,):
SCREAMING_SNAKE_CASE_ : Optional[Any] = self.num_choices
SCREAMING_SNAKE_CASE_ : List[str] = TFFunnelForMultipleChoice(config=snake_case__ )
SCREAMING_SNAKE_CASE_ : Optional[Any] = tf.tile(tf.expand_dims(snake_case__ ,1 ) ,(1, self.num_choices, 1) )
SCREAMING_SNAKE_CASE_ : Any = tf.tile(tf.expand_dims(snake_case__ ,1 ) ,(1, self.num_choices, 1) )
SCREAMING_SNAKE_CASE_ : int = tf.tile(tf.expand_dims(snake_case__ ,1 ) ,(1, self.num_choices, 1) )
SCREAMING_SNAKE_CASE_ : Union[str, Any] = {
"input_ids": multiple_choice_inputs_ids,
"attention_mask": multiple_choice_input_mask,
"token_type_ids": multiple_choice_token_type_ids,
}
SCREAMING_SNAKE_CASE_ : List[str] = model(snake_case__ )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_choices) )
def snake_case ( self ,snake_case__ ,snake_case__ ,snake_case__ ,snake_case__ ,snake_case__ ,snake_case__ ,snake_case__ ,):
SCREAMING_SNAKE_CASE_ : Dict = self.num_labels
SCREAMING_SNAKE_CASE_ : Optional[Any] = TFFunnelForTokenClassification(config=snake_case__ )
SCREAMING_SNAKE_CASE_ : int = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
SCREAMING_SNAKE_CASE_ : Optional[Any] = model(snake_case__ )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.num_labels) )
def snake_case ( self ,snake_case__ ,snake_case__ ,snake_case__ ,snake_case__ ,snake_case__ ,snake_case__ ,snake_case__ ,):
SCREAMING_SNAKE_CASE_ : Optional[Any] = TFFunnelForQuestionAnswering(config=snake_case__ )
SCREAMING_SNAKE_CASE_ : int = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
SCREAMING_SNAKE_CASE_ : List[str] = model(snake_case__ )
self.parent.assertEqual(result.start_logits.shape ,(self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape ,(self.batch_size, self.seq_length) )
def snake_case ( self ):
SCREAMING_SNAKE_CASE_ : List[str] = self.prepare_config_and_inputs()
(
SCREAMING_SNAKE_CASE_
) : Dict = config_and_inputs
SCREAMING_SNAKE_CASE_ : List[Any] = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_tf
class lowerCAmelCase_ ( __A , __A , unittest.TestCase ):
__a : Dict = (
(
TFFunnelModel,
TFFunnelForMaskedLM,
TFFunnelForPreTraining,
TFFunnelForQuestionAnswering,
TFFunnelForTokenClassification,
)
if is_tf_available()
else ()
)
__a : Any = (
{
"feature-extraction": (TFFunnelBaseModel, TFFunnelModel),
"fill-mask": TFFunnelForMaskedLM,
"question-answering": TFFunnelForQuestionAnswering,
"text-classification": TFFunnelForSequenceClassification,
"token-classification": TFFunnelForTokenClassification,
"zero-shot": TFFunnelForSequenceClassification,
}
if is_tf_available()
else {}
)
__a : Optional[Any] = False
__a : Optional[Any] = False
def snake_case ( self ):
SCREAMING_SNAKE_CASE_ : Union[str, Any] = TFFunnelModelTester(self )
SCREAMING_SNAKE_CASE_ : Any = ConfigTester(self ,config_class=snake_case__ )
def snake_case ( self ):
self.config_tester.run_common_tests()
def snake_case ( self ):
SCREAMING_SNAKE_CASE_ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case__ )
def snake_case ( self ):
SCREAMING_SNAKE_CASE_ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*snake_case__ )
def snake_case ( self ):
SCREAMING_SNAKE_CASE_ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*snake_case__ )
def snake_case ( self ):
SCREAMING_SNAKE_CASE_ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*snake_case__ )
def snake_case ( self ):
SCREAMING_SNAKE_CASE_ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*snake_case__ )
@require_tf
class lowerCAmelCase_ ( __A , unittest.TestCase ):
__a : Dict = (
(TFFunnelBaseModel, TFFunnelForMultipleChoice, TFFunnelForSequenceClassification) if is_tf_available() else ()
)
__a : List[str] = False
__a : Union[str, Any] = False
def snake_case ( self ):
SCREAMING_SNAKE_CASE_ : Union[str, Any] = TFFunnelModelTester(self ,base=snake_case__ )
SCREAMING_SNAKE_CASE_ : int = ConfigTester(self ,config_class=snake_case__ )
def snake_case ( self ):
self.config_tester.run_common_tests()
def snake_case ( self ):
SCREAMING_SNAKE_CASE_ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_base_model(*snake_case__ )
def snake_case ( self ):
SCREAMING_SNAKE_CASE_ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*snake_case__ )
def snake_case ( self ):
SCREAMING_SNAKE_CASE_ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*snake_case__ )
| 105 |
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
@property
def __UpperCamelCase ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : str = 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 __UpperCamelCase ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = self.dummy_uncond_unet
SCREAMING_SNAKE_CASE : Union[str, Any] = KarrasVeScheduler()
SCREAMING_SNAKE_CASE : Any = KarrasVePipeline(unet=a , scheduler=a )
pipe.to(a )
pipe.set_progress_bar_config(disable=a )
SCREAMING_SNAKE_CASE : Any = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Optional[Any] = pipe(num_inference_steps=2 , generator=a , output_type="numpy" ).images
SCREAMING_SNAKE_CASE : List[str] = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : List[Any] = pipe(num_inference_steps=2 , generator=a , output_type="numpy" , return_dict=a )[0]
SCREAMING_SNAKE_CASE : List[Any] = image[0, -3:, -3:, -1]
SCREAMING_SNAKE_CASE : Any = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
SCREAMING_SNAKE_CASE : str = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
@slow
@require_torch
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def __UpperCamelCase ( self : int ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = "google/ncsnpp-celebahq-256"
SCREAMING_SNAKE_CASE : List[Any] = UNetaDModel.from_pretrained(a )
SCREAMING_SNAKE_CASE : Any = KarrasVeScheduler()
SCREAMING_SNAKE_CASE : Optional[Any] = KarrasVePipeline(unet=a , scheduler=a )
pipe.to(a )
pipe.set_progress_bar_config(disable=a )
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Any = pipe(num_inference_steps=20 , generator=a , output_type="numpy" ).images
SCREAMING_SNAKE_CASE : List[str] = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
SCREAMING_SNAKE_CASE : str = np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2 | 25 | 0 |
'''simple docstring'''
def UpperCAmelCase ( A : str , A : Dict ):
SCREAMING_SNAKE_CASE : list[list[str]] = [[] for _ in range(_a )]
SCREAMING_SNAKE_CASE : Optional[Any] = key - 1
if key <= 0:
raise ValueError('''Height of grid can\'t be 0 or negative''' )
if key == 1 or len(_a ) <= key:
return input_string
for position, character in enumerate(_a ):
SCREAMING_SNAKE_CASE : Tuple = position % (lowest * 2) # puts it in bounds
SCREAMING_SNAKE_CASE : str = min(_a , lowest * 2 - num ) # creates zigzag pattern
temp_grid[num].append(_a )
SCREAMING_SNAKE_CASE : Optional[int] = ["".join(_a ) for row in temp_grid]
SCREAMING_SNAKE_CASE : Union[str, Any] = "".join(_a )
return output_string
def UpperCAmelCase ( A : List[Any] , A : List[str] ):
SCREAMING_SNAKE_CASE : Optional[int] = []
SCREAMING_SNAKE_CASE : int = key - 1
if key <= 0:
raise ValueError('''Height of grid can\'t be 0 or negative''' )
if key == 1:
return input_string
SCREAMING_SNAKE_CASE : list[list[str]] = [[] for _ in range(_a )] # generates template
for position in range(len(_a ) ):
SCREAMING_SNAKE_CASE : Dict = position % (lowest * 2) # puts it in bounds
SCREAMING_SNAKE_CASE : int = min(_a , lowest * 2 - num ) # creates zigzag pattern
temp_grid[num].append('''*''' )
SCREAMING_SNAKE_CASE : Any = 0
for row in temp_grid: # fills in the characters
SCREAMING_SNAKE_CASE : Tuple = input_string[counter : counter + len(_a )]
grid.append(list(_a ) )
counter += len(_a )
SCREAMING_SNAKE_CASE : Optional[int] = "" # reads as zigzag
for position in range(len(_a ) ):
SCREAMING_SNAKE_CASE : int = position % (lowest * 2) # puts it in bounds
SCREAMING_SNAKE_CASE : Union[str, Any] = min(_a , lowest * 2 - num ) # creates zigzag pattern
output_string += grid[num][0]
grid[num].pop(0 )
return output_string
def UpperCAmelCase ( A : List[Any] ):
SCREAMING_SNAKE_CASE : List[str] = {}
for key_guess in range(1 , len(_a ) ): # tries every key
SCREAMING_SNAKE_CASE : Tuple = decrypt(_a , _a )
return results
if __name__ == "__main__":
import doctest
doctest.testmod()
| 527 |
def lowerCamelCase__ ( _a , _a):
SCREAMING_SNAKE_CASE : Optional[int] = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return res
def lowerCamelCase__ ( _a , _a , _a):
SCREAMING_SNAKE_CASE : Optional[int] = 0
while b > 0:
if b & 1:
SCREAMING_SNAKE_CASE : Optional[Any] = ((res % c) + (a % c)) % c
a += a
b >>= 1
return res | 25 | 0 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}
__UpperCAmelCase = {
"""vocab_file""": {
"""facebook/dpr-ctx_encoder-single-nq-base""": (
"""https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt"""
),
"""facebook/dpr-ctx_encoder-multiset-base""": (
"""https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt"""
),
},
"""tokenizer_file""": {
"""facebook/dpr-ctx_encoder-single-nq-base""": (
"""https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json"""
),
"""facebook/dpr-ctx_encoder-multiset-base""": (
"""https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json"""
),
},
}
__UpperCAmelCase = {
"""vocab_file""": {
"""facebook/dpr-question_encoder-single-nq-base""": (
"""https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt"""
),
"""facebook/dpr-question_encoder-multiset-base""": (
"""https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt"""
),
},
"""tokenizer_file""": {
"""facebook/dpr-question_encoder-single-nq-base""": (
"""https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json"""
),
"""facebook/dpr-question_encoder-multiset-base""": (
"""https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json"""
),
},
}
__UpperCAmelCase = {
"""vocab_file""": {
"""facebook/dpr-reader-single-nq-base""": (
"""https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt"""
),
"""facebook/dpr-reader-multiset-base""": (
"""https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt"""
),
},
"""tokenizer_file""": {
"""facebook/dpr-reader-single-nq-base""": (
"""https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json"""
),
"""facebook/dpr-reader-multiset-base""": (
"""https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json"""
),
},
}
__UpperCAmelCase = {
"""facebook/dpr-ctx_encoder-single-nq-base""": 512,
"""facebook/dpr-ctx_encoder-multiset-base""": 512,
}
__UpperCAmelCase = {
"""facebook/dpr-question_encoder-single-nq-base""": 512,
"""facebook/dpr-question_encoder-multiset-base""": 512,
}
__UpperCAmelCase = {
"""facebook/dpr-reader-single-nq-base""": 512,
"""facebook/dpr-reader-multiset-base""": 512,
}
__UpperCAmelCase = {
"""facebook/dpr-ctx_encoder-single-nq-base""": {"""do_lower_case""": True},
"""facebook/dpr-ctx_encoder-multiset-base""": {"""do_lower_case""": True},
}
__UpperCAmelCase = {
"""facebook/dpr-question_encoder-single-nq-base""": {"""do_lower_case""": True},
"""facebook/dpr-question_encoder-multiset-base""": {"""do_lower_case""": True},
}
__UpperCAmelCase = {
"""facebook/dpr-reader-single-nq-base""": {"""do_lower_case""": True},
"""facebook/dpr-reader-multiset-base""": {"""do_lower_case""": True},
}
class SCREAMING_SNAKE_CASE ( __A ):
"""simple docstring"""
lowerCamelCase : str =VOCAB_FILES_NAMES
lowerCamelCase : Union[str, Any] =CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase : Optional[int] =CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase : str =CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
class SCREAMING_SNAKE_CASE ( __A ):
"""simple docstring"""
lowerCamelCase : str =VOCAB_FILES_NAMES
lowerCamelCase : List[str] =QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase : Union[str, Any] =QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase : Any =QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
__UpperCAmelCase = collections.namedtuple(
"""DPRSpanPrediction""", ["""span_score""", """relevance_score""", """doc_id""", """start_index""", """end_index""", """text"""]
)
__UpperCAmelCase = collections.namedtuple("""DPRReaderOutput""", ["""start_logits""", """end_logits""", """relevance_logits"""])
__UpperCAmelCase = R"""\n Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n with the format:\n\n ```\n [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n ```\n\n Args:\n questions (`str` or `List[str]`):\n The questions to be encoded. You can specify one question for many passages. In this case, the question\n will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n `titles` or `texts`.\n titles (`str` or `List[str]`):\n The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n texts (`str` or `List[str]`):\n The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n Activates and controls padding. Accepts the following values:\n\n - `True` or `\'longest\'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n if provided).\n - `\'max_length\'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n acceptable input length for the model if that argument is not provided.\n - `False` or `\'do_not_pad\'` (default): No padding (i.e., can output a batch with sequences of different\n lengths).\n truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n Activates and controls truncation. Accepts the following values:\n\n - `True` or `\'longest_first\'`: Truncate to a maximum length specified with the argument `max_length` or to\n the maximum acceptable input length for the model if that argument is not provided. This will truncate\n token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n of pairs) is provided.\n - `\'only_first\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n acceptable input length for the model if that argument is not provided. This will only truncate the first\n sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n - `\'only_second\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n acceptable input length for the model if that argument is not provided. This will only truncate the\n second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n - `False` or `\'do_not_truncate\'` (default): No truncation (i.e., can output batch with sequence lengths\n greater than the model maximum admissible input size).\n max_length (`int`, *optional*):\n Controls the maximum length to use by one of the truncation/padding parameters.\n\n If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n is required by one of the truncation/padding parameters. If the model has no specific maximum input\n length (like XLNet) truncation/padding to a maximum length will be deactivated.\n return_tensors (`str` or [`~utils.TensorType`], *optional*):\n If set, will return tensors instead of list of python integers. Acceptable values are:\n\n - `\'tf\'`: Return TensorFlow `tf.constant` objects.\n - `\'pt\'`: Return PyTorch `torch.Tensor` objects.\n - `\'np\'`: Return Numpy `np.ndarray` objects.\n return_attention_mask (`bool`, *optional*):\n Whether or not to return the attention mask. If not set, will return the attention mask according to the\n specific tokenizer\'s default, defined by the `return_outputs` attribute.\n\n [What are attention masks?](../glossary#attention-mask)\n\n Returns:\n `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n - `input_ids`: List of token ids to be fed to a model.\n - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n """
@add_start_docstrings(__A )
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __call__( self : Union[str, Any] , lowerCAmelCase : str , lowerCAmelCase : Optional[str] = None , lowerCAmelCase : Optional[str] = None , lowerCAmelCase : Union[bool, str] = False , lowerCAmelCase : Union[bool, str] = False , lowerCAmelCase : Optional[int] = None , lowerCAmelCase : Optional[Union[str, TensorType]] = None , lowerCAmelCase : Optional[bool] = None , **lowerCAmelCase : Any , ) -> BatchEncoding:
"""simple docstring"""
if titles is None and texts is None:
return super().__call__(
lowerCAmelCase , padding=lowerCAmelCase , truncation=lowerCAmelCase , max_length=lowerCAmelCase , return_tensors=lowerCAmelCase , return_attention_mask=lowerCAmelCase , **lowerCAmelCase , )
elif titles is None or texts is None:
__lowerCAmelCase : Tuple = titles if texts is None else texts
return super().__call__(
lowerCAmelCase , lowerCAmelCase , padding=lowerCAmelCase , truncation=lowerCAmelCase , max_length=lowerCAmelCase , return_tensors=lowerCAmelCase , return_attention_mask=lowerCAmelCase , **lowerCAmelCase , )
__lowerCAmelCase : Dict = titles if not isinstance(lowerCAmelCase , lowerCAmelCase ) else [titles]
__lowerCAmelCase : Optional[int] = texts if not isinstance(lowerCAmelCase , lowerCAmelCase ) else [texts]
__lowerCAmelCase : str = len(lowerCAmelCase )
__lowerCAmelCase : Optional[Any] = questions if not isinstance(lowerCAmelCase , lowerCAmelCase ) else [questions] * n_passages
if len(lowerCAmelCase ) != len(lowerCAmelCase ):
raise ValueError(
f'''There should be as many titles than texts but got {len(lowerCAmelCase )} titles and {len(lowerCAmelCase )} texts.''' )
__lowerCAmelCase : Tuple = super().__call__(lowerCAmelCase , lowerCAmelCase , padding=lowerCAmelCase , truncation=lowerCAmelCase )["input_ids"]
__lowerCAmelCase : Optional[int] = super().__call__(lowerCAmelCase , add_special_tokens=lowerCAmelCase , padding=lowerCAmelCase , truncation=lowerCAmelCase )["input_ids"]
__lowerCAmelCase : int = {
"input_ids": [
(encoded_question_and_title + encoded_text)[:max_length]
if max_length is not None and truncation
else encoded_question_and_title + encoded_text
for encoded_question_and_title, encoded_text in zip(lowerCAmelCase , lowerCAmelCase )
]
}
if return_attention_mask is not False:
__lowerCAmelCase : Optional[Any] = []
for input_ids in encoded_inputs["input_ids"]:
attention_mask.append([int(input_id != self.pad_token_id ) for input_id in input_ids] )
__lowerCAmelCase : Dict = attention_mask
return self.pad(lowerCAmelCase , padding=lowerCAmelCase , max_length=lowerCAmelCase , return_tensors=lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self : List[str] , lowerCAmelCase : BatchEncoding , lowerCAmelCase : DPRReaderOutput , lowerCAmelCase : int = 16 , lowerCAmelCase : int = 64 , lowerCAmelCase : int = 4 , ) -> List[DPRSpanPrediction]:
"""simple docstring"""
__lowerCAmelCase : Union[str, Any] = reader_input["input_ids"]
__lowerCAmelCase : List[Any] = reader_output[:3]
__lowerCAmelCase : Any = len(lowerCAmelCase )
__lowerCAmelCase : Any = sorted(range(lowerCAmelCase ) , reverse=lowerCAmelCase , key=relevance_logits.__getitem__ )
__lowerCAmelCase : List[DPRReaderOutput] = []
for doc_id in sorted_docs:
__lowerCAmelCase : Union[str, Any] = list(input_ids[doc_id] )
# assuming question & title information is at the beginning of the sequence
__lowerCAmelCase : Tuple = sequence_ids.index(self.sep_token_id , 2 ) + 1 # second sep id
if sequence_ids[-1] == self.pad_token_id:
__lowerCAmelCase : Union[str, Any] = sequence_ids.index(self.pad_token_id )
else:
__lowerCAmelCase : Optional[int] = len(lowerCAmelCase )
__lowerCAmelCase : Union[str, Any] = self._get_best_spans(
start_logits=start_logits[doc_id][passage_offset:sequence_len] , end_logits=end_logits[doc_id][passage_offset:sequence_len] , max_answer_length=lowerCAmelCase , top_spans=lowerCAmelCase , )
for start_index, end_index in best_spans:
start_index += passage_offset
end_index += passage_offset
nbest_spans_predictions.append(
DPRSpanPrediction(
span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index] , relevance_score=relevance_logits[doc_id] , doc_id=lowerCAmelCase , start_index=lowerCAmelCase , end_index=lowerCAmelCase , text=self.decode(sequence_ids[start_index : end_index + 1] ) , ) )
if len(lowerCAmelCase ) >= num_spans:
break
return nbest_spans_predictions[:num_spans]
def SCREAMING_SNAKE_CASE ( self : str , lowerCAmelCase : List[int] , lowerCAmelCase : List[int] , lowerCAmelCase : int , lowerCAmelCase : int , ) -> List[DPRSpanPrediction]:
"""simple docstring"""
__lowerCAmelCase : Tuple = []
for start_index, start_score in enumerate(lowerCAmelCase ):
for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length] ):
scores.append(((start_index, start_index + answer_length), start_score + end_score) )
__lowerCAmelCase : Tuple = sorted(lowerCAmelCase , key=lambda lowerCAmelCase : x[1] , reverse=lowerCAmelCase )
__lowerCAmelCase : str = []
for (start_index, end_index), score in scores:
if start_index > end_index:
raise ValueError(f'''Wrong span indices: [{start_index}:{end_index}]''' )
__lowerCAmelCase : int = end_index - start_index + 1
if length > max_answer_length:
raise ValueError(f'''Span is too long: {length} > {max_answer_length}''' )
if any(
start_index <= prev_start_index <= prev_end_index <= end_index
or prev_start_index <= start_index <= end_index <= prev_end_index
for (prev_start_index, prev_end_index) in chosen_span_intervals ):
continue
chosen_span_intervals.append((start_index, end_index) )
if len(lowerCAmelCase ) == top_spans:
break
return chosen_span_intervals
@add_end_docstrings(__A )
class SCREAMING_SNAKE_CASE ( __A , __A ):
"""simple docstring"""
lowerCamelCase : Any =VOCAB_FILES_NAMES
lowerCamelCase : int =READER_PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase : Optional[int] =READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase : Union[str, Any] =READER_PRETRAINED_INIT_CONFIGURATION
lowerCamelCase : Union[str, Any] =["input_ids", "attention_mask"]
| 651 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json',
'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json',
'junnyu/roformer_chinese_char_small': (
'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'
),
'junnyu/roformer_chinese_char_base': (
'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'
),
'junnyu/roformer_small_discriminator': (
'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'
),
'junnyu/roformer_small_generator': (
'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'
),
# See all RoFormer models at https://huggingface.co/models?filter=roformer
}
class _UpperCamelCase ( __A ):
'''simple docstring'''
lowerCamelCase__ ='roformer'
def __init__( self : Dict , a : Any=5_0000 , a : List[Any]=None , a : str=768 , a : str=12 , a : Tuple=12 , a : Optional[Any]=3072 , a : List[str]="gelu" , a : List[Any]=0.1 , a : Union[str, Any]=0.1 , a : Tuple=1536 , a : List[str]=2 , a : Tuple=0.02 , a : Any=1e-12 , a : Optional[int]=0 , a : Union[str, Any]=False , a : int=True , **a : str , ) -> int:
"""simple docstring"""
super().__init__(pad_token_id=a , **a )
SCREAMING_SNAKE_CASE : str = vocab_size
SCREAMING_SNAKE_CASE : int = hidden_size if embedding_size is None else embedding_size
SCREAMING_SNAKE_CASE : List[str] = hidden_size
SCREAMING_SNAKE_CASE : Union[str, Any] = num_hidden_layers
SCREAMING_SNAKE_CASE : int = num_attention_heads
SCREAMING_SNAKE_CASE : Tuple = hidden_act
SCREAMING_SNAKE_CASE : int = intermediate_size
SCREAMING_SNAKE_CASE : Tuple = hidden_dropout_prob
SCREAMING_SNAKE_CASE : int = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : Optional[int] = max_position_embeddings
SCREAMING_SNAKE_CASE : Any = type_vocab_size
SCREAMING_SNAKE_CASE : Union[str, Any] = initializer_range
SCREAMING_SNAKE_CASE : List[str] = layer_norm_eps
SCREAMING_SNAKE_CASE : List[str] = rotary_value
SCREAMING_SNAKE_CASE : int = use_cache
class _UpperCamelCase ( __A ):
'''simple docstring'''
@property
def __UpperCamelCase ( self : Tuple ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
if self.task == "multiple-choice":
SCREAMING_SNAKE_CASE : Optional[Any] = {0: "batch", 1: "choice", 2: "sequence"}
else:
SCREAMING_SNAKE_CASE : str = {0: "batch", 1: "sequence"}
SCREAMING_SNAKE_CASE : List[Any] = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
("token_type_ids", dynamic_axis),
] ) | 25 | 0 |
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class _A :
def __init__( self : int , _A : Collection[float] | None = None ) -> None:
"""simple docstring"""
if components is None:
lowercase : str = []
lowercase : Tuple = list(_A )
def __len__( self : Optional[int] ) -> int:
"""simple docstring"""
return len(self.__components )
def __str__( self : str ) -> str:
"""simple docstring"""
return "(" + ",".join(map(_A , self.__components ) ) + ")"
def __add__( self : Union[str, Any] , _A : Vector ) -> Vector:
"""simple docstring"""
lowercase : Any = len(self )
if size == len(_A ):
lowercase : int = [self.__components[i] + other.component(_A ) for i in range(_A )]
return Vector(_A )
else:
raise Exception('''must have the same size''' )
def __sub__( self : List[str] , _A : Vector ) -> Vector:
"""simple docstring"""
lowercase : Optional[Any] = len(self )
if size == len(_A ):
lowercase : str = [self.__components[i] - other.component(_A ) for i in range(_A )]
return Vector(_A )
else: # error case
raise Exception('''must have the same size''' )
@overload
def __mul__( self : List[str] , _A : float ) -> Vector:
"""simple docstring"""
...
@overload
def __mul__( self : List[Any] , _A : Vector ) -> float:
"""simple docstring"""
...
def __mul__( self : Optional[int] , _A : float | Vector ) -> float | Vector:
"""simple docstring"""
if isinstance(_A , (float, int) ):
lowercase : Union[str, Any] = [c * other for c in self.__components]
return Vector(_A )
elif isinstance(_A , _A ) and len(self ) == len(_A ):
lowercase : List[Any] = len(self )
lowercase : str = [self.__components[i] * other.component(_A ) for i in range(_A )]
return sum(_A )
else: # error case
raise Exception('''invalid operand!''' )
def __a ( self : Tuple ) -> Vector:
"""simple docstring"""
return Vector(self.__components )
def __a ( self : Union[str, Any] , _A : int ) -> float:
"""simple docstring"""
if isinstance(_A , _A ) and -len(self.__components ) <= i < len(self.__components ):
return self.__components[i]
else:
raise Exception('''index out of range''' )
def __a ( self : str , _A : int , _A : float ) -> None:
"""simple docstring"""
assert -len(self.__components ) <= pos < len(self.__components )
lowercase : Union[str, Any] = value
def __a ( self : str ) -> float:
"""simple docstring"""
if len(self.__components ) == 0:
raise Exception('''Vector is empty''' )
lowercase : Optional[int] = [c**2 for c in self.__components]
return math.sqrt(sum(_A ) )
def __a ( self : int , _A : Vector , _A : bool = False ) -> float:
"""simple docstring"""
lowercase : Union[str, Any] = self * other
lowercase : Tuple = self.euclidean_length() * other.euclidean_length()
if deg:
return math.degrees(math.acos(num / den ) )
else:
return math.acos(num / den )
def snake_case( __magic_name__ ) -> str:
'''simple docstring'''
assert isinstance(_a , _a )
return Vector([0] * dimension )
def snake_case( __magic_name__ , __magic_name__ ) -> Optional[int]:
'''simple docstring'''
assert isinstance(_a , _a ) and (isinstance(_a , _a ))
lowercase : Union[str, Any] = [0] * dimension
lowercase : Any = 1
return Vector(_a )
def snake_case( __magic_name__ , __magic_name__ , __magic_name__ ) -> int:
'''simple docstring'''
assert (
isinstance(_a , _a )
and isinstance(_a , _a )
and (isinstance(_a , (int, float) ))
)
return x * scalar + y
def snake_case( __magic_name__ , __magic_name__ , __magic_name__ ) -> Dict:
'''simple docstring'''
random.seed(_a )
lowercase : Union[str, Any] = [random.randint(_a , _a ) for _ in range(_a )]
return Vector(_a )
class _A :
def __init__( self : int , _A : list[list[float]] , _A : int , _A : int ) -> None:
"""simple docstring"""
lowercase : List[str] = matrix
lowercase : Optional[int] = w
lowercase : Optional[Any] = h
def __str__( self : Any ) -> str:
"""simple docstring"""
lowercase : Dict = ""
for i in range(self.__height ):
ans += "|"
for j in range(self.__width ):
if j < self.__width - 1:
ans += str(self.__matrix[i][j] ) + ","
else:
ans += str(self.__matrix[i][j] ) + "|\n"
return ans
def __add__( self : List[Any] , _A : Matrix ) -> Matrix:
"""simple docstring"""
if self.__width == other.width() and self.__height == other.height():
lowercase : Dict = []
for i in range(self.__height ):
lowercase : Any = [
self.__matrix[i][j] + other.component(_A , _A )
for j in range(self.__width )
]
matrix.append(_A )
return Matrix(_A , self.__width , self.__height )
else:
raise Exception('''matrix must have the same dimension!''' )
def __sub__( self : Optional[Any] , _A : Matrix ) -> Matrix:
"""simple docstring"""
if self.__width == other.width() and self.__height == other.height():
lowercase : Union[str, Any] = []
for i in range(self.__height ):
lowercase : Union[str, Any] = [
self.__matrix[i][j] - other.component(_A , _A )
for j in range(self.__width )
]
matrix.append(_A )
return Matrix(_A , self.__width , self.__height )
else:
raise Exception('''matrices must have the same dimension!''' )
@overload
def __mul__( self : List[str] , _A : float ) -> Matrix:
"""simple docstring"""
...
@overload
def __mul__( self : Any , _A : Vector ) -> Vector:
"""simple docstring"""
...
def __mul__( self : str , _A : float | Vector ) -> Vector | Matrix:
"""simple docstring"""
if isinstance(_A , _A ): # matrix-vector
if len(_A ) == self.__width:
lowercase : Union[str, Any] = zero_vector(self.__height )
for i in range(self.__height ):
lowercase : Optional[int] = [
self.__matrix[i][j] * other.component(_A )
for j in range(self.__width )
]
ans.change_component(_A , sum(_A ) )
return ans
else:
raise Exception(
'''vector must have the same size as the '''
'''number of columns of the matrix!''' )
elif isinstance(_A , (int, float) ): # matrix-scalar
lowercase : str = [
[self.__matrix[i][j] * other for j in range(self.__width )]
for i in range(self.__height )
]
return Matrix(_A , self.__width , self.__height )
return None
def __a ( self : Optional[int] ) -> int:
"""simple docstring"""
return self.__height
def __a ( self : Tuple ) -> int:
"""simple docstring"""
return self.__width
def __a ( self : Any , _A : int , _A : int ) -> float:
"""simple docstring"""
if 0 <= x < self.__height and 0 <= y < self.__width:
return self.__matrix[x][y]
else:
raise Exception('''change_component: indices out of bounds''' )
def __a ( self : Tuple , _A : int , _A : int , _A : float ) -> None:
"""simple docstring"""
if 0 <= x < self.__height and 0 <= y < self.__width:
lowercase : str = value
else:
raise Exception('''change_component: indices out of bounds''' )
def __a ( self : int , _A : int , _A : int ) -> float:
"""simple docstring"""
if self.__height != self.__width:
raise Exception('''Matrix is not square''' )
lowercase : List[str] = self.__matrix[:x] + self.__matrix[x + 1 :]
for i in range(len(_A ) ):
lowercase : Optional[int] = minor[i][:y] + minor[i][y + 1 :]
return Matrix(_A , self.__width - 1 , self.__height - 1 ).determinant()
def __a ( self : int , _A : int , _A : int ) -> float:
"""simple docstring"""
if self.__height != self.__width:
raise Exception('''Matrix is not square''' )
if 0 <= x < self.__height and 0 <= y < self.__width:
return (-1) ** (x + y) * self.minor(_A , _A )
else:
raise Exception('''Indices out of bounds''' )
def __a ( self : Dict ) -> float:
"""simple docstring"""
if self.__height != self.__width:
raise Exception('''Matrix is not square''' )
if self.__height < 1:
raise Exception('''Matrix has no element''' )
elif self.__height == 1:
return self.__matrix[0][0]
elif self.__height == 2:
return (
self.__matrix[0][0] * self.__matrix[1][1]
- self.__matrix[0][1] * self.__matrix[1][0]
)
else:
lowercase : List[Any] = [
self.__matrix[0][y] * self.cofactor(0 , _A ) for y in range(self.__width )
]
return sum(_A )
def snake_case( __magic_name__ ) -> str:
'''simple docstring'''
lowercase : list[list[float]] = [[0] * n for _ in range(_a )]
return Matrix(_a , _a , _a )
def snake_case( __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ ) -> List[str]:
'''simple docstring'''
random.seed(_a )
lowercase : list[list[float]] = [
[random.randint(_a , _a ) for _ in range(_a )] for _ in range(_a )
]
return Matrix(_a , _a , _a ) | 217 |
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
a_ = logging.getLogger(__name__)
a_ = 'Hello world! cécé herlolip'
a_ = namedtuple(
'BertAbsConfig',
[
'temp_dir',
'large',
'use_bert_emb',
'finetune_bert',
'encoder',
'share_emb',
'max_pos',
'enc_layers',
'enc_hidden_size',
'enc_heads',
'enc_ff_size',
'enc_dropout',
'dec_layers',
'dec_hidden_size',
'dec_heads',
'dec_ff_size',
'dec_dropout',
],
)
def lowerCamelCase__ ( _a , _a):
SCREAMING_SNAKE_CASE : List[Any] = BertAbsConfig(
temp_dir="." , finetune_bert=_a , large=_a , share_emb=_a , use_bert_emb=_a , encoder="bert" , max_pos=512 , enc_layers=6 , enc_hidden_size=512 , enc_heads=8 , enc_ff_size=512 , enc_dropout=0.2 , dec_layers=6 , dec_hidden_size=768 , dec_heads=8 , dec_ff_size=2048 , dec_dropout=0.2 , )
SCREAMING_SNAKE_CASE : Dict = torch.load(_a , lambda _a , _a: storage)
SCREAMING_SNAKE_CASE : str = AbsSummarizer(_a , torch.device("cpu") , _a)
original.eval()
SCREAMING_SNAKE_CASE : List[str] = BertAbsSummarizer(_a , torch.device("cpu"))
new_model.eval()
# -------------------
# Convert the weights
# -------------------
logging.info("convert the model")
new_model.bert.load_state_dict(original.bert.state_dict())
new_model.decoder.load_state_dict(original.decoder.state_dict())
new_model.generator.load_state_dict(original.generator.state_dict())
# ----------------------------------
# Make sure the outpus are identical
# ----------------------------------
logging.info("Make sure that the models' outputs are identical")
SCREAMING_SNAKE_CASE : List[str] = BertTokenizer.from_pretrained("bert-base-uncased")
# prepare the model inputs
SCREAMING_SNAKE_CASE : List[str] = tokenizer.encode("This is sample éàalj'-.")
encoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(_a)))
SCREAMING_SNAKE_CASE : int = torch.tensor(_a).unsqueeze(0)
SCREAMING_SNAKE_CASE : List[Any] = tokenizer.encode("This is sample 3 éàalj'-.")
decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(_a)))
SCREAMING_SNAKE_CASE : int = torch.tensor(_a).unsqueeze(0)
# failsafe to make sure the weights reset does not affect the
# loaded weights.
assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight)) == 0
# forward pass
SCREAMING_SNAKE_CASE : List[Any] = encoder_input_ids
SCREAMING_SNAKE_CASE : List[Any] = decoder_input_ids
SCREAMING_SNAKE_CASE : Dict = None
SCREAMING_SNAKE_CASE : Optional[Any] = None
SCREAMING_SNAKE_CASE : List[Any] = None
SCREAMING_SNAKE_CASE : Optional[int] = None
SCREAMING_SNAKE_CASE : Dict = None
# The original model does not apply the geneator layer immediatly but rather in
# the beam search (where it combines softmax + linear layer). Since we already
# apply the softmax in our generation process we only apply the linear layer here.
# We make sure that the outputs of the full stack are identical
SCREAMING_SNAKE_CASE : Optional[int] = original(_a , _a , _a , _a , _a , _a , _a)[0]
SCREAMING_SNAKE_CASE : Dict = original.generator(_a)
SCREAMING_SNAKE_CASE : Any = new_model(
_a , _a , _a , _a , _a)[0]
SCREAMING_SNAKE_CASE : Tuple = new_model.generator(_a)
SCREAMING_SNAKE_CASE : List[Any] = torch.max(torch.abs(output_converted_model - output_original_model)).item()
print("Maximum absolute difference beween weights: {:.2f}".format(_a))
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.max(torch.abs(output_converted_generator - output_original_generator)).item()
print("Maximum absolute difference beween weights: {:.2f}".format(_a))
SCREAMING_SNAKE_CASE : int = torch.allclose(_a , _a , atol=1E-3)
if are_identical:
logging.info("all weights are equal up to 1e-3")
else:
raise ValueError("the weights are different. The new model is likely different from the original one.")
# The model has been saved with torch.save(model) and this is bound to the exact
# directory structure. We save the state_dict instead.
logging.info("saving the model's state dictionary")
torch.save(
new_model.state_dict() , "./bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin")
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
parser.add_argument(
'--bertabs_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_ = parser.parse_args()
convert_bertabs_checkpoints(
args.bertabs_checkpoint_path,
args.pytorch_dump_folder_path,
) | 25 | 0 |
from __future__ import annotations
from cmath import sqrt
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: List[Any] , lowerCAmelCase: Tuple , lowerCAmelCase: List[str] ) -> Tuple:
if a == 0:
raise ValueError("Coefficient 'a' must not be zero." )
_UpperCAmelCase : Tuple = b * b - 4 * a * c
_UpperCAmelCase : Dict = (-b + sqrt(_a )) / (2 * a)
_UpperCAmelCase : List[Any] = (-b - sqrt(_a )) / (2 * a)
return (
root_a.real if not root_a.imag else root_a,
root_a.real if not root_a.imag else root_a,
)
def __SCREAMING_SNAKE_CASE ( ) -> int:
_UpperCAmelCase : Optional[int] = quadratic_roots(a=5 , b=6 , c=1 )
print(F'The solutions are: {solutiona} and {solutiona}' )
if __name__ == "__main__":
main()
| 300 |
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
parser.add_argument(
'--txt2img_unclip',
default='kakaobrain/karlo-v1-alpha',
type=str,
required=False,
help='The pretrained txt2img unclip.',
)
a_ = parser.parse_args()
a_ = UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)
a_ = CLIPImageProcessor()
a_ = CLIPVisionModelWithProjection.from_pretrained('openai/clip-vit-large-patch14')
a_ = UnCLIPImageVariationPipeline(
decoder=txtaimg.decoder,
text_encoder=txtaimg.text_encoder,
tokenizer=txtaimg.tokenizer,
text_proj=txtaimg.text_proj,
feature_extractor=feature_extractor,
image_encoder=image_encoder,
super_res_first=txtaimg.super_res_first,
super_res_last=txtaimg.super_res_last,
decoder_scheduler=txtaimg.decoder_scheduler,
super_res_scheduler=txtaimg.super_res_scheduler,
)
imgaimg.save_pretrained(args.dump_path) | 25 | 0 |
'''simple docstring'''
from __future__ import annotations
from math import pi
def _snake_case ( _SCREAMING_SNAKE_CASE : Any , _SCREAMING_SNAKE_CASE : Union[str, Any] , _SCREAMING_SNAKE_CASE : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError("""One and only one argument must be 0""" )
if inductance < 0:
raise ValueError("""Inductance cannot be negative""" )
if frequency < 0:
raise ValueError("""Frequency cannot be negative""" )
if reactance < 0:
raise ValueError("""Inductive reactance cannot be negative""" )
if inductance == 0:
return {"inductance": reactance / (2 * pi * frequency)}
elif frequency == 0:
return {"frequency": reactance / (2 * pi * inductance)}
elif reactance == 0:
return {"reactance": 2 * pi * frequency * inductance}
else:
raise ValueError("""Exactly one argument must be 0""" )
if __name__ == "__main__":
import doctest
doctest.testmod() | 433 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
'configuration_swinv2': ['SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Swinv2Config'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST',
'Swinv2ForImageClassification',
'Swinv2ForMaskedImageModeling',
'Swinv2Model',
'Swinv2PreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_swinva import (
SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
SwinvaForImageClassification,
SwinvaForMaskedImageModeling,
SwinvaModel,
SwinvaPreTrainedModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 25 | 0 |
"""simple docstring"""
import contextlib
from multiprocessing import Pool, RLock
from tqdm.auto import tqdm
from ..utils import experimental, logging
snake_case = logging.get_logger(__name__)
class UpperCAmelCase :
A__ : List[Any] = None
@experimental
def snake_case ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) -> int:
if ParallelBackendConfig.backend_name is None:
return _map_with_multiprocessing_pool(
_a , _a , _a , _a , _a , _a , _a )
return _map_with_joblib(_a , _a , _a , _a , _a , _a , _a )
def snake_case ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) -> Optional[Any]:
_snake_case = num_proc if num_proc <= len(_a ) else len(_a )
_snake_case = [] # We organize the splits ourselve (contiguous splits)
for index in range(_a ):
_snake_case = len(_a ) // num_proc
_snake_case = len(_a ) % num_proc
_snake_case = div * index + min(_a , _a )
_snake_case = start + div + (1 if index < mod else 0)
split_kwds.append((function, iterable[start:end], types, index, disable_tqdm, desc) )
if len(_a ) != sum(len(i[1] ) for i in split_kwds ):
raise ValueError(
f"""Error dividing inputs iterable among processes. """
f"""Total number of objects {len(_a )}, """
f"""length: {sum(len(i[1] ) for i in split_kwds )}""" )
logger.info(
f"""Spawning {num_proc} processes for {len(_a )} objects in slices of {[len(i[1] ) for i in split_kwds]}""" )
_snake_case = None, None
if not disable_tqdm:
_snake_case = (RLock(),), tqdm.set_lock
with Pool(_a , initargs=_a , initializer=_a ) as pool:
_snake_case = pool.map(_a , _a )
logger.info(f"""Finished {num_proc} processes""" )
_snake_case = [obj for proc_res in mapped for obj in proc_res]
logger.info(f"""Unpacked {len(_a )} objects""" )
return mapped
def snake_case ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) -> List[Any]:
# progress bar is not yet supported for _map_with_joblib, because tqdm couldn't accurately be applied to joblib,
# and it requires monkey-patching joblib internal classes which is subject to change
import joblib
with joblib.parallel_backend(ParallelBackendConfig.backend_name , n_jobs=_a ):
return joblib.Parallel()(
joblib.delayed(_a )((function, obj, types, None, True, None) ) for obj in iterable )
@experimental
@contextlib.contextmanager
def snake_case ( lowerCAmelCase_ ) -> List[Any]:
_snake_case = backend_name
if backend_name == "spark":
from joblibspark import register_spark
register_spark()
# TODO: call create_cache_and_write_probe if "download" in steps
# TODO: raise NotImplementedError when Dataset.map etc is called
try:
yield
finally:
_snake_case = None
| 103 |
from math import pi, sqrt, tan
def lowerCamelCase__ ( _a):
if side_length < 0:
raise ValueError("surface_area_cube() only accepts non-negative values")
return 6 * side_length**2
def lowerCamelCase__ ( _a , _a , _a):
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 lowerCamelCase__ ( _a):
if radius < 0:
raise ValueError("surface_area_sphere() only accepts non-negative values")
return 4 * pi * radius**2
def lowerCamelCase__ ( _a):
if radius < 0:
raise ValueError("surface_area_hemisphere() only accepts non-negative values")
return 3 * pi * radius**2
def lowerCamelCase__ ( _a , _a):
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 lowerCamelCase__ ( _a , _a , _a):
if radius_a < 0 or radius_a < 0 or height < 0:
raise ValueError(
"surface_area_conical_frustum() only accepts non-negative values")
SCREAMING_SNAKE_CASE : Any = (height**2 + (radius_a - radius_a) ** 2) ** 0.5
return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)
def lowerCamelCase__ ( _a , _a):
if radius < 0 or height < 0:
raise ValueError("surface_area_cylinder() only accepts non-negative values")
return 2 * pi * radius * (height + radius)
def lowerCamelCase__ ( _a , _a):
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(_a , 2) * torus_radius * tube_radius
def lowerCamelCase__ ( _a , _a):
if length < 0 or width < 0:
raise ValueError("area_rectangle() only accepts non-negative values")
return length * width
def lowerCamelCase__ ( _a):
if side_length < 0:
raise ValueError("area_square() only accepts non-negative values")
return side_length**2
def lowerCamelCase__ ( _a , _a):
if base < 0 or height < 0:
raise ValueError("area_triangle() only accepts non-negative values")
return (base * height) / 2
def lowerCamelCase__ ( _a , _a , _a):
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")
SCREAMING_SNAKE_CASE : List[str] = (sidea + sidea + sidea) / 2
SCREAMING_SNAKE_CASE : Optional[int] = sqrt(
semi_perimeter
* (semi_perimeter - sidea)
* (semi_perimeter - sidea)
* (semi_perimeter - sidea))
return area
def lowerCamelCase__ ( _a , _a):
if base < 0 or height < 0:
raise ValueError("area_parallelogram() only accepts non-negative values")
return base * height
def lowerCamelCase__ ( _a , _a , _a):
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 lowerCamelCase__ ( _a):
if radius < 0:
raise ValueError("area_circle() only accepts non-negative values")
return pi * radius**2
def lowerCamelCase__ ( _a , _a):
if radius_x < 0 or radius_y < 0:
raise ValueError("area_ellipse() only accepts non-negative values")
return pi * radius_x * radius_y
def lowerCamelCase__ ( _a , _a):
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 lowerCamelCase__ ( _a , _a):
if not isinstance(_a , _a) 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(10, 20) = }''')
print(F'''Square: {area_square(10) = }''')
print(F'''Triangle: {area_triangle(10, 10) = }''')
print(F'''Triangle: {area_triangle_three_sides(5, 12, 13) = }''')
print(F'''Parallelogram: {area_parallelogram(10, 20) = }''')
print(F'''Rhombus: {area_rhombus(10, 20) = }''')
print(F'''Trapezium: {area_trapezium(10, 20, 30) = }''')
print(F'''Circle: {area_circle(20) = }''')
print(F'''Ellipse: {area_ellipse(10, 20) = }''')
print('\nSurface Areas of various geometric shapes: \n')
print(F'''Cube: {surface_area_cube(20) = }''')
print(F'''Cuboid: {surface_area_cuboid(10, 20, 30) = }''')
print(F'''Sphere: {surface_area_sphere(20) = }''')
print(F'''Hemisphere: {surface_area_hemisphere(20) = }''')
print(F'''Cone: {surface_area_cone(10, 20) = }''')
print(F'''Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }''')
print(F'''Cylinder: {surface_area_cylinder(10, 20) = }''')
print(F'''Torus: {surface_area_torus(20, 10) = }''')
print(F'''Equilateral Triangle: {area_reg_polygon(3, 10) = }''')
print(F'''Square: {area_reg_polygon(4, 10) = }''')
print(F'''Reqular Pentagon: {area_reg_polygon(5, 10) = }''') | 25 | 0 |
from cva import destroyAllWindows, imread, imshow, waitKey
def UpperCAmelCase ( a_ ) -> Optional[int]:
"""simple docstring"""
__A = img.shape[0], img.shape[1]
# converting each pixel's color to its negative
for i in range(_a ):
for j in range(_a ):
__A = [2_5_5, 2_5_5, 2_5_5] - img[i][j]
return img
if __name__ == "__main__":
# read original image
SCREAMING_SNAKE_CASE :List[str] = imread('image_data/lena.jpg', 1)
# convert to its negative
SCREAMING_SNAKE_CASE :Dict = convert_to_negative(img)
# show result image
imshow('negative of original image', img)
waitKey(0)
destroyAllWindows()
| 55 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
'configuration_instructblip': [
'INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'InstructBlipConfig',
'InstructBlipQFormerConfig',
'InstructBlipVisionConfig',
],
'processing_instructblip': ['InstructBlipProcessor'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
'InstructBlipQFormerModel',
'InstructBlipPreTrainedModel',
'InstructBlipForConditionalGeneration',
'InstructBlipVisionModel',
]
if TYPE_CHECKING:
from .configuration_instructblip import (
INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
InstructBlipConfig,
InstructBlipQFormerConfig,
InstructBlipVisionConfig,
)
from .processing_instructblip import InstructBlipProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_instructblip import (
INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
InstructBlipForConditionalGeneration,
InstructBlipPreTrainedModel,
InstructBlipQFormerModel,
InstructBlipVisionModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 25 | 0 |
"""simple docstring"""
from __future__ import annotations
def a_ ( _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Dict ):
'''simple docstring'''
lowercase__ : str = 0
lowercase__ : Tuple = len(_a ) - 1
while i < j:
if nums[i] + nums[j] == target:
return [i, j]
elif nums[i] + nums[j] < target:
lowercase__ : Optional[int] = i + 1
else:
lowercase__ : List[str] = j - 1
return []
if __name__ == "__main__":
import doctest
doctest.testmod()
print(f'''{two_pointer([2, 7, 11, 15], 9) = }''')
| 599 |
from __future__ import annotations
def lowerCamelCase__ ( _a):
SCREAMING_SNAKE_CASE : Optional[Any] = 2
SCREAMING_SNAKE_CASE : Optional[int] = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(_a)
if n > 1:
factors.append(_a)
return factors
if __name__ == "__main__":
import doctest
doctest.testmod() | 25 | 0 |
"""simple docstring"""
class _a :
def __init__( self : Union[str, Any] , _lowercase : int ) -> Dict:
snake_case : Optional[Any] = n
snake_case : str = [None] * self.n
snake_case : List[str] = 0 # index of the first element
snake_case : Tuple = 0
snake_case : str = 0
def __len__( self : Union[str, Any] ) -> int:
return self.size
def __lowercase ( self : Any ) -> bool:
return self.size == 0
def __lowercase ( self : Union[str, Any] ) -> Dict:
return False if self.is_empty() else self.array[self.front]
def __lowercase ( self : Tuple , _lowercase : Union[str, Any] ) -> Union[str, Any]:
if self.size >= self.n:
raise Exception("QUEUE IS FULL" )
snake_case : Union[str, Any] = data
snake_case : Optional[Any] = (self.rear + 1) % self.n
self.size += 1
return self
def __lowercase ( self : Union[str, Any] ) -> Dict:
if self.size == 0:
raise Exception("UNDERFLOW" )
snake_case : Tuple = self.array[self.front]
snake_case : List[str] = None
snake_case : Any = (self.front + 1) % self.n
self.size -= 1
return temp
| 449 |
from math import factorial, pi
def lowerCamelCase__ ( _a , _a = 30):
if not isinstance(_a , (int, float)):
raise ValueError("maclaurin_sin() requires either an int or float for theta")
if not isinstance(_a , _a) or accuracy <= 0:
raise ValueError("maclaurin_sin() requires a positive int for accuracy")
SCREAMING_SNAKE_CASE : int = float(_a)
SCREAMING_SNAKE_CASE : Dict = theta // (2 * pi)
theta -= 2 * div * pi
return sum(
(-1) ** r * theta ** (2 * r + 1) / factorial(2 * r + 1) for r in range(_a))
def lowerCamelCase__ ( _a , _a = 30):
if not isinstance(_a , (int, float)):
raise ValueError("maclaurin_cos() requires either an int or float for theta")
if not isinstance(_a , _a) or accuracy <= 0:
raise ValueError("maclaurin_cos() requires a positive int for accuracy")
SCREAMING_SNAKE_CASE : str = float(_a)
SCREAMING_SNAKE_CASE : Any = theta // (2 * pi)
theta -= 2 * div * pi
return sum((-1) ** r * theta ** (2 * r) / factorial(2 * r) for r in range(_a))
if __name__ == "__main__":
import doctest
doctest.testmod()
print(maclaurin_sin(10))
print(maclaurin_sin(-10))
print(maclaurin_sin(10, 15))
print(maclaurin_sin(-10, 15))
print(maclaurin_cos(5))
print(maclaurin_cos(-5))
print(maclaurin_cos(10, 15))
print(maclaurin_cos(-10, 15)) | 25 | 0 |
'''simple docstring'''
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Value
from .base import TaskTemplate
@dataclass(frozen=__A )
class A_ ( __A ):
_lowerCamelCase : List[str] = field(default="""text-classification""" , metadata={"""include_in_asdict_even_if_is_default""": True} )
_lowerCamelCase : Optional[int] = Features({"""text""": Value("""string""" )} )
_lowerCamelCase : Optional[Any] = Features({"""labels""": ClassLabel} )
_lowerCamelCase : Union[str, Any] = """text"""
_lowerCamelCase : str = """labels"""
def lowercase ( self : List[Any] , snake_case_ : Optional[Any] ):
if self.label_column not in features:
raise ValueError(f'Column {self.label_column} is not present in features.' )
if not isinstance(features[self.label_column] , snake_case_ ):
raise ValueError(f'Column {self.label_column} is not a ClassLabel.' )
_UpperCAmelCase = copy.deepcopy(self )
_UpperCAmelCase = self.label_schema.copy()
_UpperCAmelCase = features[self.label_column]
_UpperCAmelCase = label_schema
return task_template
@property
def lowercase ( self : Optional[int] ):
return {
self.text_column: "text",
self.label_column: "labels",
}
| 236 |
from __future__ import annotations
import math
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Dict , a : int ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = size
# approximate the overall size of segment tree with given value
SCREAMING_SNAKE_CASE : Any = [0 for i in range(0 , 4 * size )]
# create array to store lazy update
SCREAMING_SNAKE_CASE : Union[str, Any] = [0 for i in range(0 , 4 * size )]
SCREAMING_SNAKE_CASE : Any = [0 for i in range(0 , 4 * size )] # flag for lazy update
def __UpperCamelCase ( self : Tuple , a : int ) -> int:
"""simple docstring"""
return idx * 2
def __UpperCamelCase ( self : str , a : int ) -> int:
"""simple docstring"""
return idx * 2 + 1
def __UpperCamelCase ( self : int , a : int , a : int , a : int , a : list[int] ) -> None:
"""simple docstring"""
if left_element == right_element:
SCREAMING_SNAKE_CASE : int = a[left_element - 1]
else:
SCREAMING_SNAKE_CASE : Optional[int] = (left_element + right_element) // 2
self.build(self.left(a ) , a , a , a )
self.build(self.right(a ) , mid + 1 , a , a )
SCREAMING_SNAKE_CASE : List[Any] = max(
self.segment_tree[self.left(a )] , self.segment_tree[self.right(a )] )
def __UpperCamelCase ( self : Optional[Any] , a : int , a : int , a : int , a : int , a : int , a : int ) -> bool:
"""simple docstring"""
if self.flag[idx] is True:
SCREAMING_SNAKE_CASE : Any = self.lazy[idx]
SCREAMING_SNAKE_CASE : List[str] = False
if left_element != right_element:
SCREAMING_SNAKE_CASE : Optional[Any] = self.lazy[idx]
SCREAMING_SNAKE_CASE : int = self.lazy[idx]
SCREAMING_SNAKE_CASE : Any = True
SCREAMING_SNAKE_CASE : List[Any] = True
if right_element < a or left_element > b:
return True
if left_element >= a and right_element <= b:
SCREAMING_SNAKE_CASE : Optional[Any] = val
if left_element != right_element:
SCREAMING_SNAKE_CASE : str = val
SCREAMING_SNAKE_CASE : str = val
SCREAMING_SNAKE_CASE : Tuple = True
SCREAMING_SNAKE_CASE : Optional[Any] = True
return True
SCREAMING_SNAKE_CASE : int = (left_element + right_element) // 2
self.update(self.left(a ) , a , a , a , a , a )
self.update(self.right(a ) , mid + 1 , a , a , a , a )
SCREAMING_SNAKE_CASE : Optional[int] = max(
self.segment_tree[self.left(a )] , self.segment_tree[self.right(a )] )
return True
def __UpperCamelCase ( self : Dict , a : int , a : int , a : int , a : int , a : int ) -> int | float:
"""simple docstring"""
if self.flag[idx] is True:
SCREAMING_SNAKE_CASE : int = self.lazy[idx]
SCREAMING_SNAKE_CASE : List[Any] = False
if left_element != right_element:
SCREAMING_SNAKE_CASE : Optional[Any] = self.lazy[idx]
SCREAMING_SNAKE_CASE : Optional[Any] = self.lazy[idx]
SCREAMING_SNAKE_CASE : Optional[Any] = True
SCREAMING_SNAKE_CASE : Union[str, Any] = True
if right_element < a or left_element > b:
return -math.inf
if left_element >= a and right_element <= b:
return self.segment_tree[idx]
SCREAMING_SNAKE_CASE : Dict = (left_element + right_element) // 2
SCREAMING_SNAKE_CASE : Tuple = self.query(self.left(a ) , a , a , a , a )
SCREAMING_SNAKE_CASE : Tuple = self.query(self.right(a ) , mid + 1 , a , a , a )
return max(a , a )
def __str__( self : str ) -> str:
"""simple docstring"""
return str([self.query(1 , 1 , self.size , a , a ) for i in range(1 , self.size + 1 )] )
if __name__ == "__main__":
a_ = [1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
a_ = 15
a_ = SegmentTree(size)
segt.build(1, 1, size, A)
print(segt.query(1, 1, size, 4, 6))
print(segt.query(1, 1, size, 7, 11))
print(segt.query(1, 1, size, 7, 12))
segt.update(1, 1, size, 1, 3, 111)
print(segt.query(1, 1, size, 1, 15))
segt.update(1, 1, size, 7, 8, 235)
print(segt) | 25 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase__ : Optional[Any] = {'''configuration_plbart''': ['''PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PLBartConfig''']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ : Tuple = ['''PLBartTokenizer''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ : int = [
'''PLBART_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''PLBartForCausalLM''',
'''PLBartForConditionalGeneration''',
'''PLBartForSequenceClassification''',
'''PLBartModel''',
'''PLBartPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_plbart import PLBartTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_plbart import (
PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
PLBartForCausalLM,
PLBartForConditionalGeneration,
PLBartForSequenceClassification,
PLBartModel,
PLBartPreTrainedModel,
)
else:
import sys
UpperCamelCase__ : Optional[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure)
| 105 |
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
@slow
@require_torch_gpu
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def __UpperCamelCase ( self : Dict ) -> Tuple:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __UpperCamelCase ( self : Optional[int] ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = StableDiffusionKDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4" )
SCREAMING_SNAKE_CASE : str = sd_pipe.to(a )
sd_pipe.set_progress_bar_config(disable=a )
sd_pipe.set_scheduler("sample_euler" )
SCREAMING_SNAKE_CASE : Optional[int] = "A painting of a squirrel eating a burger"
SCREAMING_SNAKE_CASE : Any = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : List[Any] = sd_pipe([prompt] , generator=a , guidance_scale=9.0 , num_inference_steps=20 , output_type="np" )
SCREAMING_SNAKE_CASE : Tuple = output.images
SCREAMING_SNAKE_CASE : Any = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE : Union[str, Any] = np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def __UpperCamelCase ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base" )
SCREAMING_SNAKE_CASE : Tuple = sd_pipe.to(a )
sd_pipe.set_progress_bar_config(disable=a )
sd_pipe.set_scheduler("sample_euler" )
SCREAMING_SNAKE_CASE : List[str] = "A painting of a squirrel eating a burger"
SCREAMING_SNAKE_CASE : List[str] = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Union[str, Any] = sd_pipe([prompt] , generator=a , guidance_scale=9.0 , num_inference_steps=20 , output_type="np" )
SCREAMING_SNAKE_CASE : List[Any] = output.images
SCREAMING_SNAKE_CASE : Tuple = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE : int = np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-1
def __UpperCamelCase ( self : Tuple ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base" )
SCREAMING_SNAKE_CASE : Union[str, Any] = sd_pipe.to(a )
sd_pipe.set_progress_bar_config(disable=a )
sd_pipe.set_scheduler("sample_dpmpp_2m" )
SCREAMING_SNAKE_CASE : str = "A painting of a squirrel eating a burger"
SCREAMING_SNAKE_CASE : Any = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : str = sd_pipe(
[prompt] , generator=a , guidance_scale=7.5 , num_inference_steps=15 , output_type="np" , use_karras_sigmas=a , )
SCREAMING_SNAKE_CASE : str = output.images
SCREAMING_SNAKE_CASE : Optional[int] = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE : int = np.array(
[0.1138_1689, 0.1211_2921, 0.138_9457, 0.1254_9606, 0.124_4964, 0.1083_1517, 0.1156_2866, 0.1086_7816, 0.1049_9048] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2 | 25 | 0 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class lowerCamelCase_ ( metaclass=__A ):
_lowerCAmelCase : Union[str, Any] = ['torch', 'torchsde']
def __init__( self : Optional[int] , *lowerCAmelCase__ : Optional[Any] , **lowerCAmelCase__ : Any ):
"""simple docstring"""
requires_backends(self , ['''torch''', '''torchsde'''] )
@classmethod
def __lowercase ( cls : List[str] , *lowerCAmelCase__ : int , **lowerCAmelCase__ : Tuple ):
"""simple docstring"""
requires_backends(cls , ['''torch''', '''torchsde'''] )
@classmethod
def __lowercase ( cls : Optional[Any] , *lowerCAmelCase__ : Dict , **lowerCAmelCase__ : Optional[int] ):
"""simple docstring"""
requires_backends(cls , ['''torch''', '''torchsde'''] )
| 527 |
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class _UpperCamelCase ( __A , unittest.TestCase ):
'''simple docstring'''
pass
@nightly
@require_onnxruntime
@require_torch_gpu
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
@property
def __UpperCamelCase ( self : List[Any] ) -> List[str]:
"""simple docstring"""
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def __UpperCamelCase ( self : int ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = ort.SessionOptions()
SCREAMING_SNAKE_CASE : Union[str, Any] = False
return options
def __UpperCamelCase ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo.png" )
SCREAMING_SNAKE_CASE : Optional[Any] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo_mask.png" )
SCREAMING_SNAKE_CASE : int = OnnxStableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting" , revision="onnx" , safety_checker=a , feature_extractor=a , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=a )
SCREAMING_SNAKE_CASE : Optional[Any] = "A red cat sitting on a park bench"
SCREAMING_SNAKE_CASE : Tuple = np.random.RandomState(0 )
SCREAMING_SNAKE_CASE : Optional[int] = pipe(
prompt=a , image=a , mask_image=a , guidance_scale=7.5 , num_inference_steps=10 , generator=a , output_type="np" , )
SCREAMING_SNAKE_CASE : List[Any] = output.images
SCREAMING_SNAKE_CASE : Union[str, Any] = images[0, 255:258, 255:258, -1]
assert images.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE : int = np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def __UpperCamelCase ( self : List[Any] ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo.png" )
SCREAMING_SNAKE_CASE : Optional[Any] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo_mask.png" )
SCREAMING_SNAKE_CASE : Optional[Any] = LMSDiscreteScheduler.from_pretrained(
"runwayml/stable-diffusion-inpainting" , subfolder="scheduler" , revision="onnx" )
SCREAMING_SNAKE_CASE : Union[str, Any] = OnnxStableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting" , revision="onnx" , scheduler=a , safety_checker=a , feature_extractor=a , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=a )
SCREAMING_SNAKE_CASE : List[Any] = "A red cat sitting on a park bench"
SCREAMING_SNAKE_CASE : Dict = np.random.RandomState(0 )
SCREAMING_SNAKE_CASE : Tuple = pipe(
prompt=a , image=a , mask_image=a , guidance_scale=7.5 , num_inference_steps=20 , generator=a , output_type="np" , )
SCREAMING_SNAKE_CASE : List[str] = output.images
SCREAMING_SNAKE_CASE : Optional[int] = images[0, 255:258, 255:258, -1]
assert images.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE : Any = np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3 | 25 | 0 |
from __future__ import annotations
import os
import tempfile
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow
from ..test_modeling_tf_common import floats_tensor
from .test_framework_agnostic import GenerationIntegrationTestsMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
AutoTokenizer,
TFAutoModelForCausalLM,
TFAutoModelForSeqaSeqLM,
TFAutoModelForSpeechSeqaSeq,
TFAutoModelForVisionaSeq,
TFBartForConditionalGeneration,
TFLogitsProcessorList,
TFMinLengthLogitsProcessor,
tf_top_k_top_p_filtering,
)
if is_tensorflow_text_available():
import tensorflow_text as text
@require_tf
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( self : Dict ) -> Optional[Any]:
"""simple docstring"""
__lowerCAmelCase : List[str] = tf.convert_to_tensor(
[
[
8.222_0991, # 3rd highest value; idx. 0
-0.562_0044,
5.2322_9752,
4.038_6393,
-6.879_8378,
-0.5478_5802,
-3.201_2153,
2.9277_7176,
1.8817_1953,
7.3534_1276, # 5th highest value; idx. 9
8.4320_7833, # 2nd highest value; idx. 10
-9.8571_1836,
-5.9620_9236,
-1.1303_9161,
-7.111_5294,
-0.836_9633,
-5.318_6408,
7.0642_7407,
0.8136_9344,
-0.8202_3817,
-5.917_9796,
0.5881_3443,
-6.9977_8438,
4.7155_1189,
-0.1877_1637,
7.4402_0759, # 4th highest value; idx. 25
9.3845_0987, # 1st highest value; idx. 26
2.1266_2941,
-9.3256_2038,
2.3565_2522,
], # cummulative prob of 5 highest values <= 0.6
[
0.5842_5518,
4.5313_9238,
-5.5751_0464,
-6.2803_0699,
-7.1952_9503,
-4.0212_2551,
1.3933_7037,
-6.0670_7057,
1.5948_0517,
-9.64_3119,
0.0390_7799,
0.6723_1762,
-8.8820_6726,
6.2711_5922, # 4th highest value; idx. 13
2.2852_0723,
4.8276_7506,
4.3042_1368,
8.827_5313, # 2nd highest value; idx. 17
5.4402_9958, # 5th highest value; idx. 18
-4.473_5794,
7.3857_9536, # 3rd highest value; idx. 20
-2.9105_1663,
2.6194_6077,
-2.567_4762,
-9.4895_9302,
-4.0292_2645,
-1.3541_6918,
9.6770_2323, # 1st highest value; idx. 27
-5.8947_8553,
1.8537_0467,
], # cummulative prob of 5 highest values <= 0.6
] , dtype=tf.floataa , )
__lowerCAmelCase : Optional[int] = tf.convert_to_tensor(
[[0, 0], [0, 9], [0, 10], [0, 25], [0, 26], [1, 13], [1, 17], [1, 18], [1, 20], [1, 27]] , dtype=tf.intaa , ) # expected non filtered idx as noted above
__lowerCAmelCase : List[Any] = tf.convert_to_tensor(
[8.22_2099, 7.353_4126, 8.43_2078, 7.440_2075, 9.3_8451, 6.27_1159, 8.82_7531, 5.440_2995, 7.385_7956, 9.67_7023] , dtype=tf.floataa , ) # expected non filtered values as noted above
__lowerCAmelCase : Union[str, Any] = tf_top_k_top_p_filtering(lowerCAmelCase , top_k=10 , top_p=0.6 , min_tokens_to_keep=4 )
__lowerCAmelCase : List[Any] = output[output != -float("""inf""" )]
__lowerCAmelCase : Tuple = tf.cast(
tf.where(tf.not_equal(lowerCAmelCase , tf.constant(-float("""inf""" ) , dtype=tf.floataa ) ) ) , dtype=tf.intaa , )
tf.debugging.assert_near(lowerCAmelCase , lowerCAmelCase , rtol=1e-12 )
tf.debugging.assert_equal(lowerCAmelCase , lowerCAmelCase )
@require_tf
class SCREAMING_SNAKE_CASE ( unittest.TestCase , __A ):
"""simple docstring"""
if is_tf_available():
lowerCamelCase : List[Any] ={
"AutoModelForCausalLM": TFAutoModelForCausalLM,
"AutoModelForSpeechSeq2Seq": TFAutoModelForSpeechSeqaSeq,
"AutoModelForSeq2SeqLM": TFAutoModelForSeqaSeqLM,
"AutoModelForVision2Seq": TFAutoModelForVisionaSeq,
"LogitsProcessorList": TFLogitsProcessorList,
"MinLengthLogitsProcessor": TFMinLengthLogitsProcessor,
"create_tensor_fn": tf.convert_to_tensor,
"floats_tensor": floats_tensor,
"return_tensors": "tf",
}
@slow
def SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> Dict:
"""simple docstring"""
__lowerCAmelCase : Tuple = TFAutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2""" )
__lowerCAmelCase : str = 2
__lowerCAmelCase : List[str] = 2
class SCREAMING_SNAKE_CASE ( tf.Module ):
"""simple docstring"""
def __init__( self : List[str] , lowerCAmelCase : Optional[int] ) -> str:
"""simple docstring"""
super(lowerCAmelCase , self ).__init__()
__lowerCAmelCase : Dict = model
@tf.function(
input_signature=(
tf.TensorSpec((None, input_length) , tf.intaa , name="""input_ids""" ),
tf.TensorSpec((None, input_length) , tf.intaa , name="""attention_mask""" ),
) , jit_compile=lowerCAmelCase , )
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] , lowerCAmelCase : List[str] , lowerCAmelCase : Any ) -> Tuple:
"""simple docstring"""
__lowerCAmelCase : List[Any] = self.model.generate(
input_ids=lowerCAmelCase , attention_mask=lowerCAmelCase , max_new_tokens=lowerCAmelCase , return_dict_in_generate=lowerCAmelCase , )
return {"sequences": outputs["sequences"]}
__lowerCAmelCase : Dict = [[2, 0], [1_02, 1_03]]
__lowerCAmelCase : Optional[int] = [[1, 0], [1, 1]]
__lowerCAmelCase : Any = DummyModel(model=lowerCAmelCase )
with tempfile.TemporaryDirectory() as tmp_dir:
tf.saved_model.save(lowerCAmelCase , lowerCAmelCase , signatures={"""serving_default""": dummy_model.serving} )
__lowerCAmelCase : Tuple = tf.saved_model.load(lowerCAmelCase ).signatures["serving_default"]
for batch_size in range(1 , len(lowerCAmelCase ) + 1 ):
__lowerCAmelCase : Optional[int] = {
"input_ids": tf.constant(dummy_input_ids[:batch_size] ),
"attention_mask": tf.constant(dummy_attention_masks[:batch_size] ),
}
__lowerCAmelCase : Dict = serving_func(**lowerCAmelCase )["sequences"]
__lowerCAmelCase : Union[str, Any] = test_model.generate(**lowerCAmelCase , max_new_tokens=lowerCAmelCase )
tf.debugging.assert_equal(lowerCAmelCase , lowerCAmelCase )
@slow
def SCREAMING_SNAKE_CASE ( self : Any ) -> List[str]:
"""simple docstring"""
__lowerCAmelCase : List[Any] = TFAutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2""" )
__lowerCAmelCase : int = 1
__lowerCAmelCase : Union[str, Any] = 2
class SCREAMING_SNAKE_CASE ( tf.Module ):
"""simple docstring"""
def __init__( self : List[str] , lowerCAmelCase : List[Any] ) -> Optional[int]:
"""simple docstring"""
super(lowerCAmelCase , self ).__init__()
__lowerCAmelCase : List[Any] = model
@tf.function(
input_signature=(
tf.TensorSpec((batch_size, None) , tf.intaa , name="""input_ids""" ),
tf.TensorSpec((batch_size, None) , tf.intaa , name="""attention_mask""" ),
) , jit_compile=lowerCAmelCase , )
def SCREAMING_SNAKE_CASE ( self : str , lowerCAmelCase : List[str] , lowerCAmelCase : str ) -> Any:
"""simple docstring"""
__lowerCAmelCase : Any = self.model.generate(
input_ids=lowerCAmelCase , attention_mask=lowerCAmelCase , max_new_tokens=lowerCAmelCase , return_dict_in_generate=lowerCAmelCase , )
return {"sequences": outputs["sequences"]}
__lowerCAmelCase : str = [[2], [1_02, 1_03]]
__lowerCAmelCase : str = [[1], [1, 1]]
__lowerCAmelCase : Optional[Any] = DummyModel(model=lowerCAmelCase )
with tempfile.TemporaryDirectory() as tmp_dir:
tf.saved_model.save(lowerCAmelCase , lowerCAmelCase , signatures={"""serving_default""": dummy_model.serving} )
__lowerCAmelCase : List[Any] = tf.saved_model.load(lowerCAmelCase ).signatures["serving_default"]
for input_row in range(len(lowerCAmelCase ) ):
__lowerCAmelCase : List[str] = {
"input_ids": tf.constant([dummy_input_ids[input_row]] ),
"attention_mask": tf.constant([dummy_attention_masks[input_row]] ),
}
__lowerCAmelCase : Union[str, Any] = serving_func(**lowerCAmelCase )["sequences"]
__lowerCAmelCase : str = test_model.generate(**lowerCAmelCase , max_new_tokens=lowerCAmelCase )
tf.debugging.assert_equal(lowerCAmelCase , lowerCAmelCase )
@slow
@require_tensorflow_text
def SCREAMING_SNAKE_CASE ( self : Tuple ) -> str:
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmp_dir:
# file needed to load the TF tokenizer
hf_hub_download(repo_id="""google/flan-t5-small""" , filename="""spiece.model""" , local_dir=lowerCAmelCase )
class SCREAMING_SNAKE_CASE ( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
super().__init__()
__lowerCAmelCase : List[str] = text.SentencepieceTokenizer(
model=tf.io.gfile.GFile(os.path.join(lowerCAmelCase , """spiece.model""" ) , """rb""" ).read() )
__lowerCAmelCase : str = TFAutoModelForSeqaSeqLM.from_pretrained("""hf-internal-testing/tiny-random-t5""" )
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] , lowerCAmelCase : Optional[Any] , *lowerCAmelCase : Union[str, Any] , **lowerCAmelCase : int ) -> List[str]:
"""simple docstring"""
__lowerCAmelCase : List[Any] = self.tokenizer.tokenize(lowerCAmelCase )
__lowerCAmelCase : Optional[int] = text.pad_model_inputs(
lowerCAmelCase , max_seq_length=64 , pad_value=self.model.config.pad_token_id )
__lowerCAmelCase : str = self.model.generate(input_ids=lowerCAmelCase , attention_mask=lowerCAmelCase )
return self.tokenizer.detokenize(lowerCAmelCase )
__lowerCAmelCase : Union[str, Any] = CompleteSentenceTransformer()
__lowerCAmelCase : Optional[Any] = tf.keras.layers.Input(shape=(1,) , dtype=tf.string , name="""inputs""" )
__lowerCAmelCase : Dict = complete_model(lowerCAmelCase )
__lowerCAmelCase : Union[str, Any] = tf.keras.Model(lowerCAmelCase , lowerCAmelCase )
keras_model.save(lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self : int ) -> Dict:
"""simple docstring"""
__lowerCAmelCase : List[str] = {
"do_sample": True,
"num_beams": 1,
"top_p": 0.7,
"top_k": 10,
"temperature": 0.7,
}
__lowerCAmelCase : Dict = 14
__lowerCAmelCase : int = AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2""" )
__lowerCAmelCase : int = "Hello, my dog is cute and"
__lowerCAmelCase : Optional[Any] = tokenizer(lowerCAmelCase , return_tensors="""tf""" )
__lowerCAmelCase : str = TFAutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2""" )
__lowerCAmelCase : Dict = 6_38
# forces the generation to happen on CPU, to avoid GPU-related quirks
with tf.device(""":/CPU:0""" ):
tf.random.set_seed(0 )
__lowerCAmelCase : Union[str, Any] = model.generate(**lowerCAmelCase , eos_token_id=lowerCAmelCase , **lowerCAmelCase )
self.assertTrue(expectation == len(generated_tokens[0] ) )
__lowerCAmelCase : Union[str, Any] = [6_38, 1_98]
with tf.device(""":/CPU:0""" ):
tf.random.set_seed(0 )
__lowerCAmelCase : Optional[Any] = model.generate(**lowerCAmelCase , eos_token_id=lowerCAmelCase , **lowerCAmelCase )
self.assertTrue(expectation == len(generated_tokens[0] ) )
def SCREAMING_SNAKE_CASE ( self : Tuple ) -> str:
"""simple docstring"""
__lowerCAmelCase : Tuple = AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-bart""" )
__lowerCAmelCase : Optional[int] = "Hugging Face is a technology company based in New York and Paris."
__lowerCAmelCase : List[Any] = bart_tokenizer(lowerCAmelCase , return_tensors="""tf""" ).input_ids
__lowerCAmelCase : Dict = TFBartForConditionalGeneration.from_pretrained("""hf-internal-testing/tiny-random-bart""" )
__lowerCAmelCase : Dict = bart_model.generate(lowerCAmelCase ).numpy()
class SCREAMING_SNAKE_CASE ( __A ):
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( self : Optional[int] , lowerCAmelCase : int , lowerCAmelCase : List[str]=None , **lowerCAmelCase : List[str] ) -> Optional[int]:
"""simple docstring"""
return super().call(lowerCAmelCase , **lowerCAmelCase )
__lowerCAmelCase : Optional[int] = FakeBart.from_pretrained("""hf-internal-testing/tiny-random-bart""" )
__lowerCAmelCase : Union[str, Any] = bart_model.generate(lowerCAmelCase , foo="""bar""" ).numpy()
self.assertTrue(np.array_equal(lowerCAmelCase , lowerCAmelCase ) )
class SCREAMING_SNAKE_CASE ( bart_model.model.encoder.__class__ ):
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( self : Tuple , lowerCAmelCase : Any , **lowerCAmelCase : List[Any] ) -> Optional[Any]:
"""simple docstring"""
return super().call(lowerCAmelCase , **lowerCAmelCase )
__lowerCAmelCase : int = FakeEncoder(bart_model.config , bart_model.model.shared )
__lowerCAmelCase : List[str] = fake_encoder
# Normal generation still works (the output will be different because the encoder weights are different)
__lowerCAmelCase : Union[str, Any] = bart_model.generate(lowerCAmelCase ).numpy()
with self.assertRaises(lowerCAmelCase ):
# FakeEncoder.call() accepts **kwargs -> no filtering -> value error due to unexpected input "foo"
bart_model.generate(lowerCAmelCase , foo="""bar""" )
| 651 |
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def lowerCamelCase__ ( _a):
return getitem, k
def lowerCamelCase__ ( _a , _a):
return setitem, k, v
def lowerCamelCase__ ( _a):
return delitem, k
def lowerCamelCase__ ( _a , _a , *_a):
try:
return fun(_a , *_a), None
except Exception as e:
return None, e
a_ = (
_set('key_a', 'val_a'),
_set('key_b', 'val_b'),
)
a_ = [
_set('key_a', 'val_a'),
_set('key_a', 'val_b'),
]
a_ = [
_set('key_a', 'val_a'),
_set('key_b', 'val_b'),
_del('key_a'),
_del('key_b'),
_set('key_a', 'val_a'),
_del('key_a'),
]
a_ = [
_get('key_a'),
_del('key_a'),
_set('key_a', 'val_a'),
_del('key_a'),
_del('key_a'),
_get('key_a'),
]
a_ = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
]
a_ = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
*[_del(x) for x in range(5)],
_set('key_a', 'val_b'),
]
@pytest.mark.parametrize(
"operations" , (
pytest.param(_add_items , id="add items"),
pytest.param(_overwrite_items , id="overwrite items"),
pytest.param(_delete_items , id="delete items"),
pytest.param(_access_absent_items , id="access absent items"),
pytest.param(_add_with_resize_up , id="add with resize up"),
pytest.param(_add_with_resize_down , id="add with resize down"),
) , )
def lowerCamelCase__ ( _a):
SCREAMING_SNAKE_CASE : Dict = HashMap(initial_block_size=4)
SCREAMING_SNAKE_CASE : List[str] = {}
for _, (fun, *args) in enumerate(_a):
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Union[str, Any] = _run_operation(_a , _a , *_a)
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : List[Any] = _run_operation(_a , _a , *_a)
assert my_res == py_res
assert str(_a) == str(_a)
assert set(_a) == set(_a)
assert len(_a) == len(_a)
assert set(my.items()) == set(py.items())
def lowerCamelCase__ ( ):
def is_public(_a) -> bool:
return not name.startswith("_")
SCREAMING_SNAKE_CASE : List[str] = {name for name in dir({}) if is_public(_a)}
SCREAMING_SNAKE_CASE : Union[str, Any] = {name for name in dir(HashMap()) if is_public(_a)}
assert dict_public_names > hash_public_names | 25 | 0 |
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
lowerCAmelCase_ = logging.get_logger(__name__)
class _A ( __A ):
_UpperCamelCase : Optional[Any] = ['''pixel_values''']
def __init__( self : Tuple , _A : bool = True , _A : Optional[Dict[str, int]] = None , _A : PILImageResampling = PILImageResampling.BILINEAR , _A : bool = True , _A : Dict[str, int] = None , _A : bool = True , _A : Union[int, float] = 1 / 255 , _A : bool = True , _A : Optional[Union[float, List[float]]] = None , _A : Optional[Union[float, List[float]]] = None , **_A : List[str] , ) -> None:
"""simple docstring"""
super().__init__(**_A )
lowercase : Union[str, Any] = size if size is not None else {"shortest_edge": 256}
lowercase : Tuple = get_size_dict(_A , default_to_square=_A )
lowercase : Any = crop_size if crop_size is not None else {"height": 224, "width": 224}
lowercase : Optional[Any] = get_size_dict(_A )
lowercase : str = do_resize
lowercase : List[str] = size
lowercase : List[str] = resample
lowercase : Union[str, Any] = do_center_crop
lowercase : int = crop_size
lowercase : Tuple = do_rescale
lowercase : str = rescale_factor
lowercase : List[str] = do_normalize
lowercase : Union[str, Any] = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
lowercase : int = image_std if image_std is not None else IMAGENET_STANDARD_STD
def __a ( self : int , _A : np.ndarray , _A : Dict[str, int] , _A : PILImageResampling = PILImageResampling.BICUBIC , _A : Optional[Union[str, ChannelDimension]] = None , **_A : str , ) -> np.ndarray:
"""simple docstring"""
lowercase : Optional[int] = get_size_dict(_A , default_to_square=_A )
if "shortest_edge" not in size:
raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}""" )
lowercase : str = get_resize_output_image_size(_A , size=size['''shortest_edge'''] , default_to_square=_A )
return resize(_A , size=_A , resample=_A , data_format=_A , **_A )
def __a ( self : Tuple , _A : np.ndarray , _A : Dict[str, int] , _A : Optional[Union[str, ChannelDimension]] = None , **_A : str , ) -> np.ndarray:
"""simple docstring"""
lowercase : str = get_size_dict(_A )
return center_crop(_A , size=(size['''height'''], size['''width''']) , data_format=_A , **_A )
def __a ( self : Optional[int] , _A : np.ndarray , _A : float , _A : Optional[Union[str, ChannelDimension]] = None , **_A : List[str] ) -> np.ndarray:
"""simple docstring"""
return rescale(_A , scale=_A , data_format=_A , **_A )
def __a ( self : Optional[int] , _A : np.ndarray , _A : Union[float, List[float]] , _A : Union[float, List[float]] , _A : Optional[Union[str, ChannelDimension]] = None , **_A : int , ) -> np.ndarray:
"""simple docstring"""
return normalize(_A , mean=_A , std=_A , data_format=_A , **_A )
def __a ( self : Any , _A : ImageInput , _A : Optional[bool] = None , _A : Dict[str, int] = None , _A : PILImageResampling = None , _A : bool = None , _A : Dict[str, int] = None , _A : Optional[bool] = None , _A : Optional[float] = None , _A : Optional[bool] = None , _A : Optional[Union[float, List[float]]] = None , _A : Optional[Union[float, List[float]]] = None , _A : Optional[Union[str, TensorType]] = None , _A : Union[str, ChannelDimension] = ChannelDimension.FIRST , **_A : Any , ) -> Union[str, Any]:
"""simple docstring"""
lowercase : List[Any] = do_resize if do_resize is not None else self.do_resize
lowercase : Any = size if size is not None else self.size
lowercase : Union[str, Any] = get_size_dict(_A , default_to_square=_A )
lowercase : Tuple = resample if resample is not None else self.resample
lowercase : Optional[Any] = do_center_crop if do_center_crop is not None else self.do_center_crop
lowercase : Optional[Any] = crop_size if crop_size is not None else self.crop_size
lowercase : Union[str, Any] = get_size_dict(_A )
lowercase : List[str] = do_rescale if do_rescale is not None else self.do_rescale
lowercase : Dict = rescale_factor if rescale_factor is not None else self.rescale_factor
lowercase : List[Any] = do_normalize if do_normalize is not None else self.do_normalize
lowercase : Optional[Any] = image_mean if image_mean is not None else self.image_mean
lowercase : Optional[Any] = image_std if image_std is not None else self.image_std
lowercase : Union[str, Any] = make_list_of_images(_A )
if not valid_images(_A ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
if do_resize and size is None:
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 : Dict = [to_numpy_array(_A ) for image in images]
if do_resize:
lowercase : Dict = [self.resize(image=_A , size=_A , resample=_A ) for image in images]
if do_center_crop:
lowercase : List[Any] = [self.center_crop(image=_A , size=_A ) for image in images]
if do_rescale:
lowercase : List[str] = [self.rescale(image=_A , scale=_A ) for image in images]
if do_normalize:
lowercase : str = [self.normalize(image=_A , mean=_A , std=_A ) for image in images]
lowercase : Union[str, Any] = [to_channel_dimension_format(_A , _A ) for image in images]
lowercase : Tuple = {"pixel_values": images}
return BatchFeature(data=_A , tensor_type=_A ) | 217 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a_ = {'configuration_van': ['VAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VanConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'VAN_PRETRAINED_MODEL_ARCHIVE_LIST',
'VanForImageClassification',
'VanModel',
'VanPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_van import (
VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
VanForImageClassification,
VanModel,
VanPreTrainedModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure) | 25 | 0 |
import warnings
from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum
from ...processing_utils import ProcessorMixin
if is_torch_available():
import torch
class a ( __A ):
_lowercase = "char"
_lowercase = "bpe"
_lowercase = "wp"
SCREAMING_SNAKE_CASE_ = (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)
class a ( __A ):
_lowercase = ["image_processor", "char_tokenizer"]
_lowercase = "ViTImageProcessor"
_lowercase = "MgpstrTokenizer"
def __init__( self , A_=None , A_=None , **A_ ):
'''simple docstring'''
_UpperCAmelCase : str = None
if "feature_extractor" in kwargs:
warnings.warn(
"The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
" instead." , A_ , )
_UpperCAmelCase : Dict = kwargs.pop("feature_extractor" )
_UpperCAmelCase : int = 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`." )
_UpperCAmelCase : str = tokenizer
_UpperCAmelCase : List[str] = AutoTokenizer.from_pretrained("gpt2" )
_UpperCAmelCase : List[Any] = AutoTokenizer.from_pretrained("bert-base-uncased" )
super().__init__(A_ , A_ )
def __call__( self , A_=None , A_=None , A_=None , **A_ ):
'''simple docstring'''
if images is None and text is None:
raise ValueError("You need to specify either an `images` or `text` input to process." )
if images is not None:
_UpperCAmelCase : Tuple = self.image_processor(A_ , return_tensors=A_ , **A_ )
if text is not None:
_UpperCAmelCase : List[str] = self.char_tokenizer(A_ , return_tensors=A_ , **A_ )
if text is None:
return inputs
elif images is None:
return encodings
else:
_UpperCAmelCase : Tuple = encodings["input_ids"]
return inputs
def _UpperCAmelCase ( self , A_ ):
'''simple docstring'''
_UpperCAmelCase : List[str] = sequences
_UpperCAmelCase : Optional[int] = char_preds.size(0 )
_UpperCAmelCase : Tuple = self._decode_helper(A_ , "char" )
_UpperCAmelCase : Optional[int] = self._decode_helper(A_ , "bpe" )
_UpperCAmelCase : Union[str, Any] = self._decode_helper(A_ , "wp" )
_UpperCAmelCase : Tuple = []
_UpperCAmelCase : List[str] = []
for i in range(A_ ):
_UpperCAmelCase : Dict = [char_scores[i], bpe_scores[i], wp_scores[i]]
_UpperCAmelCase : int = [char_strs[i], bpe_strs[i], wp_strs[i]]
_UpperCAmelCase : Optional[Any] = scores.index(max(A_ ) )
final_strs.append(strs[max_score_index] )
final_scores.append(scores[max_score_index] )
_UpperCAmelCase : int = {}
_UpperCAmelCase : List[str] = final_strs
_UpperCAmelCase : Union[str, Any] = final_scores
_UpperCAmelCase : Any = char_strs
_UpperCAmelCase : Tuple = bpe_strs
_UpperCAmelCase : Union[str, Any] = wp_strs
return out
def _UpperCAmelCase ( self , A_ , A_ ):
'''simple docstring'''
if format == DecodeType.CHARACTER:
_UpperCAmelCase : Union[str, Any] = self.char_decode
_UpperCAmelCase : int = 1
_UpperCAmelCase : Tuple = "[s]"
elif format == DecodeType.BPE:
_UpperCAmelCase : Any = self.bpe_decode
_UpperCAmelCase : Union[str, Any] = 2
_UpperCAmelCase : Optional[Any] = "#"
elif format == DecodeType.WORDPIECE:
_UpperCAmelCase : Any = self.wp_decode
_UpperCAmelCase : Tuple = 102
_UpperCAmelCase : List[str] = "[SEP]"
else:
raise ValueError(f'Format {format} is not supported.' )
_UpperCAmelCase : Dict = [], []
_UpperCAmelCase : Union[str, Any] = pred_logits.size(0 )
_UpperCAmelCase : Optional[Any] = pred_logits.size(1 )
_UpperCAmelCase : Union[str, Any] = pred_logits.topk(1 , dim=-1 , largest=A_ , sorted=A_ )
_UpperCAmelCase : Dict = preds_index.view(-1 , A_ )[:, 1:]
_UpperCAmelCase : Any = decoder(A_ )
_UpperCAmelCase : Any = torch.nn.functional.softmax(A_ , dim=2 ).max(dim=2 )
_UpperCAmelCase : Tuple = preds_max_prob[:, 1:]
for index in range(A_ ):
_UpperCAmelCase : List[Any] = preds_str[index].find(A_ )
_UpperCAmelCase : Tuple = preds_str[index][:pred_eos]
_UpperCAmelCase : int = preds_index[index].cpu().tolist()
_UpperCAmelCase : Union[str, Any] = pred_index.index(A_ ) if eos_token in pred_index else -1
_UpperCAmelCase : int = preds_max_prob[index][: pred_eos_index + 1]
_UpperCAmelCase : List[Any] = pred_max_prob.cumprod(dim=0 )[-1] if pred_max_prob.nelement() != 0 else 0.0
dec_strs.append(A_ )
conf_scores.append(A_ )
return dec_strs, conf_scores
def _UpperCAmelCase ( self , A_ ):
'''simple docstring'''
_UpperCAmelCase : Union[str, Any] = [seq.replace(" " , "" ) for seq in self.char_tokenizer.batch_decode(A_ )]
return decode_strs
def _UpperCAmelCase ( self , A_ ):
'''simple docstring'''
return self.bpe_tokenizer.batch_decode(A_ )
def _UpperCAmelCase ( self , A_ ):
'''simple docstring'''
_UpperCAmelCase : Optional[Any] = [seq.replace(" " , "" ) for seq in self.wp_tokenizer.batch_decode(A_ )]
return decode_strs
| 300 |
from __future__ import annotations
def lowerCamelCase__ ( _a):
if len(_a) == 0:
return []
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Tuple = min(_a), max(_a)
SCREAMING_SNAKE_CASE : Dict = int(max_value - min_value) + 1
SCREAMING_SNAKE_CASE : list[list] = [[] for _ in range(_a)]
for i in my_list:
buckets[int(i - min_value)].append(_a)
return [v for bucket in buckets for v in sorted(_a)]
if __name__ == "__main__":
from doctest import testmod
testmod()
assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
assert bucket_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15] | 25 | 0 |
'''simple docstring'''
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version
UpperCAmelCase = get_logger(__name__)
class __snake_case:
'''simple docstring'''
UpperCAmelCase : Optional[Any] = "dummy_data"
UpperCAmelCase : Optional[int] = "datasets"
UpperCAmelCase : int = False
def __init__( self , A_ , A_ , A_ , A_ = None , A_ = False , A_ = True , A_ = None , ) -> Optional[Any]:
lowerCAmelCase = 0
lowerCAmelCase = dataset_name
lowerCAmelCase = cache_dir
lowerCAmelCase = use_local_dummy_data
lowerCAmelCase = config
# download_callbacks take a single url as input
lowerCAmelCase = download_callbacks or []
# if False, it doesn't load existing files and it returns the paths of the dummy files relative
# to the dummy_data zip file root
lowerCAmelCase = load_existing_dummy_data
# TODO(PVP, QL) might need to make this more general
lowerCAmelCase = str(A_ )
# to be downloaded
lowerCAmelCase = None
lowerCAmelCase = None
@property
def __snake_case ( self ) -> Union[str, Any]:
if self._dummy_file is None:
lowerCAmelCase = self.download_dummy_data()
return self._dummy_file
@property
def __snake_case ( self ) -> Optional[Any]:
if self.config is not None:
# structure is dummy / config_name / version_name
return os.path.join("""dummy""" , self.config.name , self.version_name )
# structure is dummy / version_name
return os.path.join("""dummy""" , self.version_name )
@property
def __snake_case ( self ) -> Union[str, Any]:
return os.path.join(self.dummy_data_folder , """dummy_data.zip""" )
def __snake_case ( self ) -> List[str]:
lowerCAmelCase = (
self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
)
lowerCAmelCase = cached_path(
A_ , cache_dir=self.cache_dir , extract_compressed_file=A_ , force_extract=A_ )
return os.path.join(A_ , self.dummy_file_name )
@property
def __snake_case ( self ) -> Union[str, Any]:
return os.path.join(self.datasets_scripts_dir , self.dataset_name , self.dummy_zip_file )
@property
def __snake_case ( self ) -> Optional[int]:
if self._bucket_url is None:
lowerCAmelCase = hf_github_url(self.dataset_name , self.dummy_zip_file.replace(os.sep , """/""" ) )
return self._bucket_url
@property
def __snake_case ( self ) -> Tuple:
if os.path.isdir(self.dummy_file ):
return self.dummy_file
# else cut off path to file -> example `xsum`.
return "/".join(self.dummy_file.replace(os.sep , """/""" ).split("""/""" )[:-1] )
def __snake_case ( self , A_ , *A_ ) -> Dict:
if self.load_existing_dummy_data:
# dummy data is downloaded and tested
lowerCAmelCase = self.dummy_file
else:
# dummy data cannot be downloaded and only the path to dummy file is returned
lowerCAmelCase = self.dummy_file_name
# special case when data_url is a dict
if isinstance(A_ , A_ ):
return self.create_dummy_data_dict(A_ , A_ )
elif isinstance(A_ , (list, tuple) ):
return self.create_dummy_data_list(A_ , A_ )
else:
return self.create_dummy_data_single(A_ , A_ )
def __snake_case ( self , A_ , *A_ ) -> Optional[Any]:
return self.download_and_extract(A_ )
def __snake_case ( self , A_ , A_ ) -> List[Any]:
return self.download_and_extract(A_ )
def __snake_case ( self , A_ , *A_ , **A_ ) -> Optional[int]:
return path
def __snake_case ( self ) -> Optional[int]:
return {}
def __snake_case ( self , A_ , A_ ) -> Dict:
lowerCAmelCase = {}
for key, single_urls in data_url.items():
for download_callback in self.download_callbacks:
if isinstance(A_ , A_ ):
for single_url in single_urls:
download_callback(A_ )
else:
lowerCAmelCase = single_urls
download_callback(A_ )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
if isinstance(A_ , A_ ):
lowerCAmelCase = [os.path.join(A_ , urllib.parse.quote_plus(Path(A_ ).name ) ) for x in single_urls]
else:
lowerCAmelCase = single_urls
lowerCAmelCase = os.path.join(A_ , urllib.parse.quote_plus(Path(A_ ).name ) )
lowerCAmelCase = value
# make sure that values are unique
if all(isinstance(A_ , A_ ) for i in dummy_data_dict.values() ) and len(set(dummy_data_dict.values() ) ) < len(
dummy_data_dict.values() ):
# append key to value to make its name unique
lowerCAmelCase = {key: value + key for key, value in dummy_data_dict.items()}
return dummy_data_dict
def __snake_case ( self , A_ , A_ ) -> Tuple:
lowerCAmelCase = []
# trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
lowerCAmelCase = all(bool(re.findall("""[0-9]{3,}-of-[0-9]{3,}""" , A_ ) ) for url in data_url )
lowerCAmelCase = all(
url.startswith("""https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed""" ) for url in data_url )
if data_url and (is_tf_records or is_pubmed_records):
lowerCAmelCase = [data_url[0]] * len(A_ )
for single_url in data_url:
for download_callback in self.download_callbacks:
download_callback(A_ )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
lowerCAmelCase = os.path.join(A_ , urllib.parse.quote_plus(single_url.split("""/""" )[-1] ) )
dummy_data_list.append(A_ )
return dummy_data_list
def __snake_case ( self , A_ , A_ ) -> int:
for download_callback in self.download_callbacks:
download_callback(A_ )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
lowerCAmelCase = os.path.join(A_ , urllib.parse.quote_plus(data_url.split("""/""" )[-1] ) )
if os.path.exists(A_ ) or not self.load_existing_dummy_data:
return value
else:
# Backward compatibility, maybe deprecate at one point.
# For many datasets with single url calls to dl_manager.download_and_extract,
# the dummy_data.zip file is actually the zipped downloaded file
# while now we expected the dummy_data.zip file to be a directory containing
# the downloaded file.
return path_to_dummy_data
def __snake_case ( self ) -> Union[str, Any]:
pass
def __snake_case ( self ) -> Optional[Any]:
pass
def __snake_case ( self , A_ ) -> List[Any]:
def _iter_archive_members(A_ ):
# this preserves the order of the members inside the ZIP archive
lowerCAmelCase = Path(self.dummy_file ).parent
lowerCAmelCase = path.relative_to(A_ )
with ZipFile(self.local_path_to_dummy_data ) as zip_file:
lowerCAmelCase = zip_file.namelist()
for member in members:
if member.startswith(relative_path.as_posix() ):
yield dummy_parent_path.joinpath(A_ )
lowerCAmelCase = Path(A_ )
lowerCAmelCase = _iter_archive_members(A_ ) if self.use_local_dummy_data else path.rglob("""*""" )
for file_path in file_paths:
if file_path.is_file() and not file_path.name.startswith((""".""", """__""") ):
yield file_path.relative_to(A_ ).as_posix(), file_path.open("""rb""" )
def __snake_case ( self , A_ ) -> List[str]:
if not isinstance(A_ , A_ ):
lowerCAmelCase = [paths]
for path in paths:
if os.path.isfile(A_ ):
if os.path.basename(A_ ).startswith((""".""", """__""") ):
return
yield path
else:
for dirpath, dirnames, filenames in os.walk(A_ ):
if os.path.basename(A_ ).startswith((""".""", """__""") ):
continue
dirnames.sort()
for filename in sorted(A_ ):
if filename.startswith((""".""", """__""") ):
continue
yield os.path.join(A_ , A_ ) | 433 |
a_ = frozenset(
[
'prompt',
'height',
'width',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
'cross_attention_kwargs',
]
)
a_ = frozenset(['prompt', 'negative_prompt'])
a_ = frozenset([])
a_ = frozenset(['image'])
a_ = frozenset(
[
'image',
'height',
'width',
'guidance_scale',
]
)
a_ = frozenset(['image'])
a_ = frozenset(
[
'prompt',
'image',
'height',
'width',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
]
)
a_ = frozenset(['prompt', 'image', 'negative_prompt'])
a_ = frozenset(
[
# Text guided image variation with an image mask
'prompt',
'image',
'mask_image',
'height',
'width',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
]
)
a_ = frozenset(['prompt', 'image', 'mask_image', 'negative_prompt'])
a_ = frozenset(
[
# image variation with an image mask
'image',
'mask_image',
'height',
'width',
'guidance_scale',
]
)
a_ = frozenset(['image', 'mask_image'])
a_ = frozenset(
[
'example_image',
'image',
'mask_image',
'height',
'width',
'guidance_scale',
]
)
a_ = frozenset(['example_image', 'image', 'mask_image'])
a_ = frozenset(['class_labels'])
a_ = frozenset(['class_labels'])
a_ = frozenset(['batch_size'])
a_ = frozenset([])
a_ = frozenset(['batch_size'])
a_ = frozenset([])
a_ = frozenset(
[
'prompt',
'audio_length_in_s',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
'cross_attention_kwargs',
]
)
a_ = frozenset(['prompt', 'negative_prompt'])
a_ = frozenset(['input_tokens'])
a_ = frozenset(['input_tokens']) | 25 | 0 |
"""simple docstring"""
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_space_optuna,
default_hp_space_ray,
default_hp_space_sigopt,
default_hp_space_wandb,
)
from .utils import logging
snake_case = logging.get_logger(__name__)
class UpperCAmelCase :
A__ : int = 42
A__ : Tuple = None
@staticmethod
def __UpperCAmelCase ( ):
"""simple docstring"""
raise NotImplementedError
def __UpperCAmelCase ( self : str , __lowerCamelCase : Optional[Any] , __lowerCamelCase : int , __lowerCamelCase : str , **__lowerCamelCase : int ):
"""simple docstring"""
raise NotImplementedError
def __UpperCAmelCase ( self : Any , __lowerCamelCase : List[Any] ):
"""simple docstring"""
raise NotImplementedError
def __UpperCAmelCase ( self : Tuple ):
"""simple docstring"""
if not self.is_available():
raise RuntimeError(
f"""You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}.""" )
@classmethod
def __UpperCAmelCase ( cls : str ):
"""simple docstring"""
return f"""`pip install {cls.pip_package or cls.name}`"""
class UpperCAmelCase ( __A ):
A__ : List[str] = '''optuna'''
@staticmethod
def __UpperCAmelCase ( ):
"""simple docstring"""
return is_optuna_available()
def __UpperCAmelCase ( self : List[str] , __lowerCamelCase : Tuple , __lowerCamelCase : int , __lowerCamelCase : str , **__lowerCamelCase : Optional[Any] ):
"""simple docstring"""
return run_hp_search_optuna(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , **__lowerCamelCase )
def __UpperCAmelCase ( self : Optional[int] , __lowerCamelCase : Optional[Any] ):
"""simple docstring"""
return default_hp_space_optuna(__lowerCamelCase )
class UpperCAmelCase ( __A ):
A__ : Tuple = '''ray'''
A__ : int = '''\'ray[tune]\''''
@staticmethod
def __UpperCAmelCase ( ):
"""simple docstring"""
return is_ray_available()
def __UpperCAmelCase ( self : Tuple , __lowerCamelCase : str , __lowerCamelCase : int , __lowerCamelCase : str , **__lowerCamelCase : str ):
"""simple docstring"""
return run_hp_search_ray(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , **__lowerCamelCase )
def __UpperCAmelCase ( self : int , __lowerCamelCase : Optional[int] ):
"""simple docstring"""
return default_hp_space_ray(__lowerCamelCase )
class UpperCAmelCase ( __A ):
A__ : Tuple = '''sigopt'''
@staticmethod
def __UpperCAmelCase ( ):
"""simple docstring"""
return is_sigopt_available()
def __UpperCAmelCase ( self : List[Any] , __lowerCamelCase : Any , __lowerCamelCase : int , __lowerCamelCase : str , **__lowerCamelCase : int ):
"""simple docstring"""
return run_hp_search_sigopt(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , **__lowerCamelCase )
def __UpperCAmelCase ( self : Optional[Any] , __lowerCamelCase : Dict ):
"""simple docstring"""
return default_hp_space_sigopt(__lowerCamelCase )
class UpperCAmelCase ( __A ):
A__ : Optional[int] = '''wandb'''
@staticmethod
def __UpperCAmelCase ( ):
"""simple docstring"""
return is_wandb_available()
def __UpperCAmelCase ( self : List[str] , __lowerCamelCase : Optional[Any] , __lowerCamelCase : int , __lowerCamelCase : str , **__lowerCamelCase : List[Any] ):
"""simple docstring"""
return run_hp_search_wandb(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , **__lowerCamelCase )
def __UpperCAmelCase ( self : Tuple , __lowerCamelCase : Optional[int] ):
"""simple docstring"""
return default_hp_space_wandb(__lowerCamelCase )
snake_case = {
HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}
def snake_case ( ) -> int:
_snake_case = [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
if len(_a ) > 0:
_snake_case = available_backends[0].name
if len(_a ) > 1:
logger.info(
f"""{len(_a )} hyperparameter search backends available. Using {name} as the default.""" )
return name
raise RuntimeError(
'''No hyperparameter search backend available.\n'''
+ '''\n'''.join(
f""" - To install {backend.name} run {backend.pip_install()}"""
for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() ) )
| 103 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import jax
import jaxlib
a_ = get_logger()
a_ = None
class _UpperCamelCase ( TensorFormatter[Mapping, 'jax.Array', Mapping] ):
'''simple docstring'''
def __init__( self : Optional[Any] , a : str=None , a : List[Any]=None , **a : Any ) -> Optional[Any]:
"""simple docstring"""
super().__init__(features=a )
import jax
from jaxlib.xla_client import Device
if isinstance(a , a ):
raise ValueError(
F"Expected {device} to be a `str` not {type(a )}, as `jaxlib.xla_extension.Device` "
"is not serializable neither with `pickle` nor with `dill`. Instead you can surround "
"the device with `str()` to get its string identifier that will be internally mapped "
"to the actual `jaxlib.xla_extension.Device`." )
SCREAMING_SNAKE_CASE : List[str] = device if isinstance(a , a ) else str(jax.devices()[0] )
# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
# with `pickle` nor with `dill`, so we need to use a global variable instead
global DEVICE_MAPPING
if DEVICE_MAPPING is None:
SCREAMING_SNAKE_CASE : str = self._map_devices_to_str()
if self.device not in list(DEVICE_MAPPING.keys() ):
logger.warning(
F"Device with string identifier {self.device} not listed among the available "
F"devices: {list(DEVICE_MAPPING.keys() )}, so falling back to the default "
F"device: {str(jax.devices()[0] )}." )
SCREAMING_SNAKE_CASE : Any = str(jax.devices()[0] )
SCREAMING_SNAKE_CASE : Any = jnp_array_kwargs
@staticmethod
def __UpperCamelCase ( ) -> Dict[str, "jaxlib.xla_extension.Device"]:
"""simple docstring"""
import jax
return {str(a ): device for device in jax.devices()}
def __UpperCamelCase ( self : Dict , a : Tuple ) -> str:
"""simple docstring"""
import jax
import jax.numpy as jnp
if isinstance(a , a ) and column:
if all(
isinstance(a , jax.Array ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column ):
return jnp.stack(a , axis=0 )
return column
def __UpperCamelCase ( self : Dict , a : str ) -> str:
"""simple docstring"""
import jax
import jax.numpy as jnp
if isinstance(a , (str, bytes, type(a )) ):
return value
elif isinstance(a , (np.character, np.ndarray) ) and np.issubdtype(value.dtype , np.character ):
return value.tolist()
SCREAMING_SNAKE_CASE : Union[str, Any] = {}
if isinstance(a , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.integer ):
# the default int precision depends on the jax config
# see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
if jax.config.jax_enable_xaa:
SCREAMING_SNAKE_CASE : Dict = {"dtype": jnp.intaa}
else:
SCREAMING_SNAKE_CASE : str = {"dtype": jnp.intaa}
elif isinstance(a , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.floating ):
SCREAMING_SNAKE_CASE : int = {"dtype": jnp.floataa}
elif config.PIL_AVAILABLE and "PIL" in sys.modules:
import PIL.Image
if isinstance(a , PIL.Image.Image ):
SCREAMING_SNAKE_CASE : Dict = np.asarray(a )
# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
# with `pickle` nor with `dill`, so we need to use a global variable instead
global DEVICE_MAPPING
if DEVICE_MAPPING is None:
SCREAMING_SNAKE_CASE : Optional[Any] = self._map_devices_to_str()
with jax.default_device(DEVICE_MAPPING[self.device] ):
# calling jnp.array on a np.ndarray does copy the data
# see https://github.com/google/jax/issues/4486
return jnp.array(a , **{**default_dtype, **self.jnp_array_kwargs} )
def __UpperCamelCase ( self : Any , a : List[str] ) -> Dict:
"""simple docstring"""
import jax
# support for torch, tf, jax etc.
if config.TORCH_AVAILABLE and "torch" in sys.modules:
import torch
if isinstance(a , torch.Tensor ):
return self._tensorize(data_struct.detach().cpu().numpy()[()] )
if hasattr(a , "__array__" ) and not isinstance(a , jax.Array ):
SCREAMING_SNAKE_CASE : Optional[int] = data_struct.__array__()
# support for nested types like struct of list of struct
if isinstance(a , np.ndarray ):
if data_struct.dtype == object: # jax arrays cannot be instantied from an array of objects
return self._consolidate([self.recursive_tensorize(a ) for substruct in data_struct] )
elif isinstance(a , (list, tuple) ):
return self._consolidate([self.recursive_tensorize(a ) for substruct in data_struct] )
return self._tensorize(a )
def __UpperCamelCase ( self : Optional[Any] , a : dict ) -> Dict:
"""simple docstring"""
return map_nested(self._recursive_tensorize , a , map_list=a )
def __UpperCamelCase ( self : Dict , a : pa.Table ) -> Mapping:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = self.numpy_arrow_extractor().extract_row(a )
SCREAMING_SNAKE_CASE : List[Any] = self.python_features_decoder.decode_row(a )
return self.recursive_tensorize(a )
def __UpperCamelCase ( self : Optional[int] , a : pa.Table ) -> "jax.Array":
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = self.numpy_arrow_extractor().extract_column(a )
SCREAMING_SNAKE_CASE : Optional[Any] = self.python_features_decoder.decode_column(a , pa_table.column_names[0] )
SCREAMING_SNAKE_CASE : Tuple = self.recursive_tensorize(a )
SCREAMING_SNAKE_CASE : Optional[int] = self._consolidate(a )
return column
def __UpperCamelCase ( self : List[Any] , a : pa.Table ) -> Mapping:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = self.numpy_arrow_extractor().extract_batch(a )
SCREAMING_SNAKE_CASE : str = self.python_features_decoder.decode_batch(a )
SCREAMING_SNAKE_CASE : List[Any] = self.recursive_tensorize(a )
for column_name in batch:
SCREAMING_SNAKE_CASE : List[Any] = self._consolidate(batch[column_name] )
return batch | 25 | 0 |
from math import pi, sqrt, tan
def UpperCAmelCase ( a_ ) -> Optional[Any]:
"""simple docstring"""
if side_length < 0:
raise ValueError("surface_area_cube() only accepts non-negative values" )
return 6 * side_length**2
def UpperCAmelCase ( a_ , a_ , a_ ) -> Any:
"""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 ( a_ ) -> Optional[Any]:
"""simple docstring"""
if radius < 0:
raise ValueError("surface_area_sphere() only accepts non-negative values" )
return 4 * pi * radius**2
def UpperCAmelCase ( a_ ) -> Tuple:
"""simple docstring"""
if radius < 0:
raise ValueError("surface_area_hemisphere() only accepts non-negative values" )
return 3 * pi * radius**2
def UpperCAmelCase ( a_ , a_ ) -> Tuple:
"""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 ( a_ , a_ , a_ ) -> Optional[int]:
"""simple docstring"""
if radius_a < 0 or radius_a < 0 or height < 0:
raise ValueError(
"surface_area_conical_frustum() only accepts non-negative values" )
__A = (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 ( a_ , a_ ) -> Optional[int]:
"""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 ( a_ , a_ ) -> int:
"""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(_a , 2 ) * torus_radius * tube_radius
def UpperCAmelCase ( a_ , a_ ) -> Dict:
"""simple docstring"""
if length < 0 or width < 0:
raise ValueError("area_rectangle() only accepts non-negative values" )
return length * width
def UpperCAmelCase ( a_ ) -> List[Any]:
"""simple docstring"""
if side_length < 0:
raise ValueError("area_square() only accepts non-negative values" )
return side_length**2
def UpperCAmelCase ( a_ , a_ ) -> Union[str, Any]:
"""simple docstring"""
if base < 0 or height < 0:
raise ValueError("area_triangle() only accepts non-negative values" )
return (base * height) / 2
def UpperCAmelCase ( a_ , a_ , a_ ) -> str:
"""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" )
__A = (sidea + sidea + sidea) / 2
__A = sqrt(
semi_perimeter
* (semi_perimeter - sidea)
* (semi_perimeter - sidea)
* (semi_perimeter - sidea) )
return area
def UpperCAmelCase ( a_ , a_ ) -> int:
"""simple docstring"""
if base < 0 or height < 0:
raise ValueError("area_parallelogram() only accepts non-negative values" )
return base * height
def UpperCAmelCase ( a_ , a_ , a_ ) -> Union[str, Any]:
"""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 ( a_ ) -> Union[str, Any]:
"""simple docstring"""
if radius < 0:
raise ValueError("area_circle() only accepts non-negative values" )
return pi * radius**2
def UpperCAmelCase ( a_ , a_ ) -> int:
"""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 ( a_ , a_ ) -> Dict:
"""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 ( a_ , a_ ) -> Dict:
"""simple docstring"""
if not isinstance(_a , _a ) 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(10, 20) = }''')
print(f'''Square: {area_square(10) = }''')
print(f'''Triangle: {area_triangle(10, 10) = }''')
print(f'''Triangle: {area_triangle_three_sides(5, 12, 13) = }''')
print(f'''Parallelogram: {area_parallelogram(10, 20) = }''')
print(f'''Rhombus: {area_rhombus(10, 20) = }''')
print(f'''Trapezium: {area_trapezium(10, 20, 30) = }''')
print(f'''Circle: {area_circle(20) = }''')
print(f'''Ellipse: {area_ellipse(10, 20) = }''')
print('\nSurface Areas of various geometric shapes: \n')
print(f'''Cube: {surface_area_cube(20) = }''')
print(f'''Cuboid: {surface_area_cuboid(10, 20, 30) = }''')
print(f'''Sphere: {surface_area_sphere(20) = }''')
print(f'''Hemisphere: {surface_area_hemisphere(20) = }''')
print(f'''Cone: {surface_area_cone(10, 20) = }''')
print(f'''Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }''')
print(f'''Cylinder: {surface_area_cylinder(10, 20) = }''')
print(f'''Torus: {surface_area_torus(20, 10) = }''')
print(f'''Equilateral Triangle: {area_reg_polygon(3, 10) = }''')
print(f'''Square: {area_reg_polygon(4, 10) = }''')
print(f'''Reqular Pentagon: {area_reg_polygon(5, 10) = }''')
| 55 |
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_pytesseract,
require_tf,
require_timm,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class _UpperCamelCase :
'''simple docstring'''
@staticmethod
def __UpperCamelCase ( *a : str , **a : int ) -> str:
"""simple docstring"""
pass
@is_pipeline_test
@require_vision
@require_timm
@require_torch
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ =MODEL_FOR_OBJECT_DETECTION_MAPPING
def __UpperCamelCase ( self : Optional[Any] , a : str , a : Optional[Any] , a : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = ObjectDetectionPipeline(model=a , image_processor=a )
return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]
def __UpperCamelCase ( self : List[Any] , a : Optional[int] , a : Optional[int] ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = object_detector("./tests/fixtures/tests_samples/COCO/000000039769.png" , threshold=0.0 )
self.assertGreater(len(a ) , 0 )
for detected_object in outputs:
self.assertEqual(
a , {
"score": ANY(a ),
"label": ANY(a ),
"box": {"xmin": ANY(a ), "ymin": ANY(a ), "xmax": ANY(a ), "ymax": ANY(a )},
} , )
import datasets
SCREAMING_SNAKE_CASE : Any = datasets.load_dataset("hf-internal-testing/fixtures_image_utils" , "image" , split="test" )
SCREAMING_SNAKE_CASE : Dict = [
Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" ),
"http://images.cocodataset.org/val2017/000000039769.jpg",
# RGBA
dataset[0]["file"],
# LA
dataset[1]["file"],
# L
dataset[2]["file"],
]
SCREAMING_SNAKE_CASE : Tuple = object_detector(a , threshold=0.0 )
self.assertEqual(len(a ) , len(a ) )
for outputs in batch_outputs:
self.assertGreater(len(a ) , 0 )
for detected_object in outputs:
self.assertEqual(
a , {
"score": ANY(a ),
"label": ANY(a ),
"box": {"xmin": ANY(a ), "ymin": ANY(a ), "xmax": ANY(a ), "ymax": ANY(a )},
} , )
@require_tf
@unittest.skip("Object detection not implemented in TF" )
def __UpperCamelCase ( self : Optional[int] ) -> str:
"""simple docstring"""
pass
@require_torch
def __UpperCamelCase ( self : Tuple ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = "hf-internal-testing/tiny-detr-mobilenetsv3"
SCREAMING_SNAKE_CASE : Dict = AutoModelForObjectDetection.from_pretrained(a )
SCREAMING_SNAKE_CASE : Any = AutoFeatureExtractor.from_pretrained(a )
SCREAMING_SNAKE_CASE : Tuple = ObjectDetectionPipeline(model=a , feature_extractor=a )
SCREAMING_SNAKE_CASE : int = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg" , threshold=0.0 )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
] , )
SCREAMING_SNAKE_CASE : Dict = object_detector(
[
"http://images.cocodataset.org/val2017/000000039769.jpg",
"http://images.cocodataset.org/val2017/000000039769.jpg",
] , threshold=0.0 , )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
[
{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
],
[
{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
],
] , )
@require_torch
@slow
def __UpperCamelCase ( self : str ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = "facebook/detr-resnet-50"
SCREAMING_SNAKE_CASE : Union[str, Any] = AutoModelForObjectDetection.from_pretrained(a )
SCREAMING_SNAKE_CASE : List[str] = AutoFeatureExtractor.from_pretrained(a )
SCREAMING_SNAKE_CASE : int = ObjectDetectionPipeline(model=a , feature_extractor=a )
SCREAMING_SNAKE_CASE : Union[str, Any] = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg" )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
] , )
SCREAMING_SNAKE_CASE : int = object_detector(
[
"http://images.cocodataset.org/val2017/000000039769.jpg",
"http://images.cocodataset.org/val2017/000000039769.jpg",
] )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
[
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
],
[
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
],
] , )
@require_torch
@slow
def __UpperCamelCase ( self : str ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = "facebook/detr-resnet-50"
SCREAMING_SNAKE_CASE : Tuple = pipeline("object-detection" , model=a )
SCREAMING_SNAKE_CASE : Tuple = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg" )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
] , )
SCREAMING_SNAKE_CASE : str = object_detector(
[
"http://images.cocodataset.org/val2017/000000039769.jpg",
"http://images.cocodataset.org/val2017/000000039769.jpg",
] )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
[
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
],
[
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
],
] , )
@require_torch
@slow
def __UpperCamelCase ( self : str ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = 0.9985
SCREAMING_SNAKE_CASE : int = "facebook/detr-resnet-50"
SCREAMING_SNAKE_CASE : List[str] = pipeline("object-detection" , model=a )
SCREAMING_SNAKE_CASE : str = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg" , threshold=a )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
] , )
@require_torch
@require_pytesseract
@slow
def __UpperCamelCase ( self : str ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = "Narsil/layoutlmv3-finetuned-funsd"
SCREAMING_SNAKE_CASE : Dict = 0.9993
SCREAMING_SNAKE_CASE : str = pipeline("object-detection" , model=a , threshold=a )
SCREAMING_SNAKE_CASE : List[Any] = object_detector(
"https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png" )
self.assertEqual(
nested_simplify(a , decimals=4 ) , [
{"score": 0.9993, "label": "I-ANSWER", "box": {"xmin": 294, "ymin": 254, "xmax": 343, "ymax": 264}},
{"score": 0.9993, "label": "I-ANSWER", "box": {"xmin": 294, "ymin": 254, "xmax": 343, "ymax": 264}},
] , ) | 25 | 0 |
"""simple docstring"""
import unittest
from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MPNetForMaskedLM,
MPNetForMultipleChoice,
MPNetForQuestionAnswering,
MPNetForSequenceClassification,
MPNetForTokenClassification,
MPNetModel,
)
class UpperCAmelCase_ :
def __init__( self , a , a=1_3 , a=7 , a=True , a=True , a=False , a=True , a=9_9 , a=6_4 , a=5 , a=4 , a=6_4 , a="gelu" , a=0.1 , a=0.1 , a=5_1_2 , a=1_6 , a=2 , a=0.02 , a=3 , a=4 , a=None , ) -> Optional[Any]:
lowercase__ : Dict = parent
lowercase__ : List[Any] = batch_size
lowercase__ : Any = seq_length
lowercase__ : Tuple = is_training
lowercase__ : List[Any] = use_input_mask
lowercase__ : Union[str, Any] = use_token_type_ids
lowercase__ : Union[str, Any] = use_labels
lowercase__ : str = vocab_size
lowercase__ : int = hidden_size
lowercase__ : Tuple = num_hidden_layers
lowercase__ : List[Any] = num_attention_heads
lowercase__ : Union[str, Any] = intermediate_size
lowercase__ : Optional[Any] = hidden_act
lowercase__ : Dict = hidden_dropout_prob
lowercase__ : Union[str, Any] = attention_probs_dropout_prob
lowercase__ : Dict = max_position_embeddings
lowercase__ : List[str] = type_vocab_size
lowercase__ : str = type_sequence_label_size
lowercase__ : List[Any] = initializer_range
lowercase__ : Dict = num_labels
lowercase__ : Optional[int] = num_choices
lowercase__ : Optional[int] = scope
def _UpperCAmelCase ( self ) -> List[str]:
return MPNetConfig.from_pretrained('microsoft/mpnet-base' )
def _UpperCAmelCase ( self ) -> int:
lowercase__ : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase__ : Union[str, Any] = None
if self.use_input_mask:
lowercase__ : List[Any] = random_attention_mask([self.batch_size, self.seq_length] )
lowercase__ : Tuple = None
lowercase__ : Any = None
lowercase__ : Tuple = None
if self.use_labels:
lowercase__ : List[str] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase__ : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowercase__ : Optional[int] = ids_tensor([self.batch_size] , self.num_choices )
lowercase__ : Union[str, Any] = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def _UpperCAmelCase ( self ) -> Dict:
return MPNetConfig(
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 , initializer_range=self.initializer_range , )
def _UpperCAmelCase ( self , a , a , a , a , a , a ) -> Optional[int]:
lowercase__ : Optional[int] = MPNetModel(config=a )
model.to(a )
model.eval()
lowercase__ : Tuple = model(a , a )
lowercase__ : int = model(a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def _UpperCAmelCase ( self , a , a , a , a , a , a ) -> int:
lowercase__ : Optional[Any] = MPNetForQuestionAnswering(config=a )
model.to(a )
model.eval()
lowercase__ : Optional[int] = model(
a , attention_mask=a , start_positions=a , end_positions=a , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def _UpperCAmelCase ( self , a , a , a , a , a , a ) -> int:
lowercase__ : Optional[Any] = self.num_labels
lowercase__ : Dict = MPNetForSequenceClassification(a )
model.to(a )
model.eval()
lowercase__ : List[Any] = model(a , attention_mask=a , labels=a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _UpperCAmelCase ( self , a , a , a , a , a , a ) -> List[str]:
lowercase__ : Optional[Any] = self.num_choices
lowercase__ : Optional[int] = MPNetForMultipleChoice(config=a )
model.to(a )
model.eval()
lowercase__ : Optional[Any] = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowercase__ : Tuple = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowercase__ : List[Any] = model(
a , attention_mask=a , labels=a , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def _UpperCAmelCase ( self , a , a , a , a , a , a ) -> List[str]:
lowercase__ : Tuple = self.num_labels
lowercase__ : List[str] = MPNetForTokenClassification(config=a )
model.to(a )
model.eval()
lowercase__ : List[Any] = model(a , attention_mask=a , labels=a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def _UpperCAmelCase ( self ) -> Optional[Any]:
lowercase__ : Any = self.prepare_config_and_inputs()
(lowercase__) : Optional[Any] = config_and_inputs
lowercase__ : List[str] = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class UpperCAmelCase_ ( __A , __A , unittest.TestCase):
lowerCamelCase__ : Tuple = (
(
MPNetForMaskedLM,
MPNetForMultipleChoice,
MPNetForQuestionAnswering,
MPNetForSequenceClassification,
MPNetForTokenClassification,
MPNetModel,
)
if is_torch_available()
else ()
)
lowerCamelCase__ : Tuple = (
{
"feature-extraction": MPNetModel,
"fill-mask": MPNetForMaskedLM,
"question-answering": MPNetForQuestionAnswering,
"text-classification": MPNetForSequenceClassification,
"token-classification": MPNetForTokenClassification,
"zero-shot": MPNetForSequenceClassification,
}
if is_torch_available()
else {}
)
lowerCamelCase__ : List[Any] = False
lowerCamelCase__ : Optional[int] = True
def _UpperCAmelCase ( self ) -> Tuple:
lowercase__ : List[str] = MPNetModelTester(self )
lowercase__ : str = ConfigTester(self , config_class=a , hidden_size=3_7 )
def _UpperCAmelCase ( self ) -> List[str]:
self.config_tester.run_common_tests()
def _UpperCAmelCase ( self ) -> Optional[int]:
lowercase__ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_model(*a )
def _UpperCAmelCase ( self ) -> str:
lowercase__ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_for_sequence_classification(*a )
def _UpperCAmelCase ( self ) -> List[Any]:
lowercase__ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_for_multiple_choice(*a )
def _UpperCAmelCase ( self ) -> int:
lowercase__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_for_token_classification(*a )
def _UpperCAmelCase ( self ) -> List[str]:
lowercase__ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_for_question_answering(*a )
@require_torch
class UpperCAmelCase_ ( unittest.TestCase):
@slow
def _UpperCAmelCase ( self ) -> Optional[Any]:
lowercase__ : List[str] = MPNetModel.from_pretrained('microsoft/mpnet-base' )
lowercase__ : Optional[int] = torch.tensor([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]] )
lowercase__ : List[str] = model(a )[0]
lowercase__ : Tuple = torch.Size((1, 1_1, 7_6_8) )
self.assertEqual(output.shape , a )
lowercase__ : List[Any] = torch.tensor(
[[[-0.0_550, 0.1_943, -0.0_740], [-0.0_562, 0.2_211, -0.0_579], [-0.0_437, 0.3_337, -0.0_641]]] )
# compare the actual values for a slice.
self.assertTrue(torch.allclose(output[:, :3, :3] , a , atol=1e-4 ) )
| 599 |
def lowerCamelCase__ ( _a):
if not isinstance(_a , _a):
SCREAMING_SNAKE_CASE : Tuple = f"Input value of [number={number}] must be an integer"
raise TypeError(_a)
if number < 0:
return False
SCREAMING_SNAKE_CASE : Union[str, Any] = number * number
while number > 0:
if number % 10 != number_square % 10:
return False
number //= 10
number_square //= 10
return True
if __name__ == "__main__":
import doctest
doctest.testmod() | 25 | 0 |
"""simple docstring"""
def __SCREAMING_SNAKE_CASE ( lowerCamelCase_: Any , lowerCamelCase_: str , lowerCamelCase_: List[str]=False ):
"""simple docstring"""
if isinstance(_a , _a ) and isinstance(_a , _a ):
snake_case : Tuple = len(set_a.intersection(_a ) )
if alternative_union:
snake_case : Dict = len(_a ) + len(_a )
else:
snake_case : str = len(set_a.union(_a ) )
return intersection / union
if isinstance(_a , (list, tuple) ) and isinstance(_a , (list, tuple) ):
snake_case : Dict = [element for element in set_a if element in set_b]
if alternative_union:
snake_case : List[Any] = len(_a ) + len(_a )
return len(_a ) / union
else:
snake_case : Optional[Any] = set_a + [element for element in set_b if element not in set_a]
return len(_a ) / len(_a )
return len(_a ) / len(_a )
return None
if __name__ == "__main__":
A = {'a', 'b', 'c', 'd', 'e'}
A = {'c', 'd', 'e', 'f', 'h', 'i'}
print(jaccard_similarity(set_a, set_b))
| 449 |
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
DistilBertModel,
)
class _UpperCamelCase ( __A ):
'''simple docstring'''
def __init__( self : Dict , a : Tuple , a : Any=13 , a : Any=7 , a : Union[str, Any]=True , a : List[Any]=True , a : List[str]=False , a : List[str]=True , a : Any=99 , a : str=32 , a : Any=5 , a : Optional[int]=4 , a : Union[str, Any]=37 , a : Dict="gelu" , a : List[Any]=0.1 , a : Optional[Any]=0.1 , a : List[str]=512 , a : Union[str, Any]=16 , a : str=2 , a : Dict=0.02 , a : Optional[int]=3 , a : Union[str, Any]=4 , a : int=None , ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = parent
SCREAMING_SNAKE_CASE : Any = batch_size
SCREAMING_SNAKE_CASE : Optional[int] = seq_length
SCREAMING_SNAKE_CASE : List[Any] = is_training
SCREAMING_SNAKE_CASE : int = use_input_mask
SCREAMING_SNAKE_CASE : Tuple = use_token_type_ids
SCREAMING_SNAKE_CASE : str = use_labels
SCREAMING_SNAKE_CASE : Any = vocab_size
SCREAMING_SNAKE_CASE : List[Any] = hidden_size
SCREAMING_SNAKE_CASE : str = num_hidden_layers
SCREAMING_SNAKE_CASE : Optional[Any] = num_attention_heads
SCREAMING_SNAKE_CASE : Tuple = intermediate_size
SCREAMING_SNAKE_CASE : Optional[int] = hidden_act
SCREAMING_SNAKE_CASE : Dict = hidden_dropout_prob
SCREAMING_SNAKE_CASE : str = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : Tuple = max_position_embeddings
SCREAMING_SNAKE_CASE : List[str] = type_vocab_size
SCREAMING_SNAKE_CASE : List[str] = type_sequence_label_size
SCREAMING_SNAKE_CASE : Optional[Any] = initializer_range
SCREAMING_SNAKE_CASE : Tuple = num_labels
SCREAMING_SNAKE_CASE : Tuple = num_choices
SCREAMING_SNAKE_CASE : Optional[Any] = scope
def __UpperCamelCase ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE : Union[str, Any] = None
if self.use_input_mask:
SCREAMING_SNAKE_CASE : str = random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE : int = None
SCREAMING_SNAKE_CASE : List[Any] = None
SCREAMING_SNAKE_CASE : List[str] = None
if self.use_labels:
SCREAMING_SNAKE_CASE : Tuple = ids_tensor([self.batch_size] , self.type_sequence_label_size )
SCREAMING_SNAKE_CASE : str = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
SCREAMING_SNAKE_CASE : Optional[Any] = ids_tensor([self.batch_size] , self.num_choices )
SCREAMING_SNAKE_CASE : int = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def __UpperCamelCase ( self : Dict ) -> str:
"""simple docstring"""
return DistilBertConfig(
vocab_size=self.vocab_size , dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , hidden_dim=self.intermediate_size , hidden_act=self.hidden_act , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , )
def __UpperCamelCase ( self : Optional[Any] , a : int , a : Optional[int] , a : Optional[int] , a : Dict , a : str , a : str ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = DistilBertModel(config=a )
model.to(a )
model.eval()
SCREAMING_SNAKE_CASE : Optional[Any] = model(a , a )
SCREAMING_SNAKE_CASE : Optional[Any] = model(a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __UpperCamelCase ( self : Tuple , a : Optional[int] , a : Dict , a : Tuple , a : int , a : int , a : Any ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = DistilBertForMaskedLM(config=a )
model.to(a )
model.eval()
SCREAMING_SNAKE_CASE : str = model(a , attention_mask=a , labels=a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __UpperCamelCase ( self : List[Any] , a : int , a : Optional[Any] , a : Optional[Any] , a : str , a : str , a : Tuple ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = DistilBertForQuestionAnswering(config=a )
model.to(a )
model.eval()
SCREAMING_SNAKE_CASE : List[str] = model(
a , attention_mask=a , start_positions=a , end_positions=a )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def __UpperCamelCase ( self : Optional[int] , a : str , a : Any , a : int , a : Optional[Any] , a : int , a : str ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = self.num_labels
SCREAMING_SNAKE_CASE : Union[str, Any] = DistilBertForSequenceClassification(a )
model.to(a )
model.eval()
SCREAMING_SNAKE_CASE : Optional[int] = model(a , attention_mask=a , labels=a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __UpperCamelCase ( self : Optional[Any] , a : List[Any] , a : Optional[int] , a : Union[str, Any] , a : Dict , a : Any , a : Optional[Any] ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = self.num_labels
SCREAMING_SNAKE_CASE : List[str] = DistilBertForTokenClassification(config=a )
model.to(a )
model.eval()
SCREAMING_SNAKE_CASE : List[str] = model(a , attention_mask=a , labels=a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __UpperCamelCase ( self : int , a : Any , a : Optional[int] , a : Union[str, Any] , a : Tuple , a : Optional[int] , a : Tuple ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = self.num_choices
SCREAMING_SNAKE_CASE : Any = DistilBertForMultipleChoice(config=a )
model.to(a )
model.eval()
SCREAMING_SNAKE_CASE : Optional[int] = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
SCREAMING_SNAKE_CASE : Dict = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
SCREAMING_SNAKE_CASE : Optional[Any] = model(
a , attention_mask=a , labels=a , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __UpperCamelCase ( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = self.prepare_config_and_inputs()
((SCREAMING_SNAKE_CASE) ,(SCREAMING_SNAKE_CASE) ,(SCREAMING_SNAKE_CASE) ,(SCREAMING_SNAKE_CASE) ,(SCREAMING_SNAKE_CASE) ,(SCREAMING_SNAKE_CASE)) : Tuple = config_and_inputs
SCREAMING_SNAKE_CASE : int = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class _UpperCamelCase ( __A , __A , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ =(
(
DistilBertModel,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
)
if is_torch_available()
else None
)
lowerCamelCase__ =(
{
'feature-extraction': DistilBertModel,
'fill-mask': DistilBertForMaskedLM,
'question-answering': DistilBertForQuestionAnswering,
'text-classification': DistilBertForSequenceClassification,
'token-classification': DistilBertForTokenClassification,
'zero-shot': DistilBertForSequenceClassification,
}
if is_torch_available()
else {}
)
lowerCamelCase__ =True
lowerCamelCase__ =True
lowerCamelCase__ =True
lowerCamelCase__ =True
def __UpperCamelCase ( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = DistilBertModelTester(self )
SCREAMING_SNAKE_CASE : List[str] = ConfigTester(self , config_class=a , dim=37 )
def __UpperCamelCase ( self : List[Any] ) -> Tuple:
"""simple docstring"""
self.config_tester.run_common_tests()
def __UpperCamelCase ( self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_model(*a )
def __UpperCamelCase ( self : Tuple ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_masked_lm(*a )
def __UpperCamelCase ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_question_answering(*a )
def __UpperCamelCase ( self : Union[str, Any] ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_sequence_classification(*a )
def __UpperCamelCase ( self : str ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_token_classification(*a )
def __UpperCamelCase ( self : List[Any] ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_multiple_choice(*a )
@slow
def __UpperCamelCase ( self : int ) -> Any:
"""simple docstring"""
for model_name in DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE : Optional[Any] = DistilBertModel.from_pretrained(a )
self.assertIsNotNone(a )
@slow
@require_torch_gpu
def __UpperCamelCase ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# BertForMultipleChoice behaves incorrectly in JIT environments.
if model_class == DistilBertForMultipleChoice:
return
SCREAMING_SNAKE_CASE : Union[str, Any] = True
SCREAMING_SNAKE_CASE : Any = model_class(config=a )
SCREAMING_SNAKE_CASE : List[Any] = self._prepare_for_class(a , a )
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.jit.trace(
a , (inputs_dict["input_ids"].to("cpu" ), inputs_dict["attention_mask"].to("cpu" )) )
with tempfile.TemporaryDirectory() as tmp:
torch.jit.save(a , os.path.join(a , "traced_model.pt" ) )
SCREAMING_SNAKE_CASE : Tuple = torch.jit.load(os.path.join(a , "traced_model.pt" ) , map_location=a )
loaded(inputs_dict["input_ids"].to(a ) , inputs_dict["attention_mask"].to(a ) )
@require_torch
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
@slow
def __UpperCamelCase ( self : int ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = DistilBertModel.from_pretrained("distilbert-base-uncased" )
SCREAMING_SNAKE_CASE : List[str] = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]] )
SCREAMING_SNAKE_CASE : Optional[int] = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
SCREAMING_SNAKE_CASE : Optional[Any] = model(a , attention_mask=a )[0]
SCREAMING_SNAKE_CASE : List[str] = torch.Size((1, 11, 768) )
self.assertEqual(output.shape , a )
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.tensor(
[[[-0.1639, 0.3299, 0.1648], [-0.1746, 0.3289, 0.1710], [-0.1884, 0.3357, 0.1810]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , a , atol=1e-4 ) ) | 25 | 0 |
'''simple docstring'''
import argparse
import os
import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints
from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion import SpectrogramContEncoder, SpectrogramNotesEncoder, TaFilmDecoder
__SCREAMING_SNAKE_CASE :int = '''base_with_context'''
def UpperCAmelCase_ ( __lowercase : str , __lowercase : str ) -> Tuple:
'''simple docstring'''
_UpperCAmelCase = nn.Parameter(torch.FloatTensor(weights["token_embedder"]["embedding"] ) )
_UpperCAmelCase = nn.Parameter(
torch.FloatTensor(weights["Embed_0"]["embedding"] ) , requires_grad=_a )
for lyr_num, lyr in enumerate(model.encoders ):
_UpperCAmelCase = weights[f'layers_{lyr_num}']
_UpperCAmelCase = nn.Parameter(
torch.FloatTensor(ly_weight["pre_attention_layer_norm"]["scale"] ) )
_UpperCAmelCase = ly_weight["attention"]
_UpperCAmelCase = nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T ) )
_UpperCAmelCase = nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T ) )
_UpperCAmelCase = nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T ) )
_UpperCAmelCase = nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T ) )
_UpperCAmelCase = nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"] ) )
_UpperCAmelCase = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T ) )
_UpperCAmelCase = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T ) )
_UpperCAmelCase = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T ) )
_UpperCAmelCase = nn.Parameter(torch.FloatTensor(weights["encoder_norm"]["scale"] ) )
return model
def UpperCAmelCase_ ( __lowercase : List[str] , __lowercase : Optional[Any] ) -> int:
'''simple docstring'''
_UpperCAmelCase = nn.Parameter(torch.FloatTensor(weights["input_proj"]["kernel"].T ) )
_UpperCAmelCase = nn.Parameter(
torch.FloatTensor(weights["Embed_0"]["embedding"] ) , requires_grad=_a )
for lyr_num, lyr in enumerate(model.encoders ):
_UpperCAmelCase = weights[f'layers_{lyr_num}']
_UpperCAmelCase = ly_weight["attention"]
_UpperCAmelCase = nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T ) )
_UpperCAmelCase = nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T ) )
_UpperCAmelCase = nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T ) )
_UpperCAmelCase = nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T ) )
_UpperCAmelCase = nn.Parameter(
torch.FloatTensor(ly_weight["pre_attention_layer_norm"]["scale"] ) )
_UpperCAmelCase = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T ) )
_UpperCAmelCase = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T ) )
_UpperCAmelCase = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T ) )
_UpperCAmelCase = nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"] ) )
_UpperCAmelCase = nn.Parameter(torch.FloatTensor(weights["encoder_norm"]["scale"] ) )
return model
def UpperCAmelCase_ ( __lowercase : Tuple , __lowercase : List[Any] ) -> int:
'''simple docstring'''
_UpperCAmelCase = nn.Parameter(torch.FloatTensor(weights["time_emb_dense0"]["kernel"].T ) )
_UpperCAmelCase = nn.Parameter(torch.FloatTensor(weights["time_emb_dense1"]["kernel"].T ) )
_UpperCAmelCase = nn.Parameter(
torch.FloatTensor(weights["Embed_0"]["embedding"] ) , requires_grad=_a )
_UpperCAmelCase = nn.Parameter(
torch.FloatTensor(weights["continuous_inputs_projection"]["kernel"].T ) )
for lyr_num, lyr in enumerate(model.decoders ):
_UpperCAmelCase = weights[f'layers_{lyr_num}']
_UpperCAmelCase = nn.Parameter(
torch.FloatTensor(ly_weight["pre_self_attention_layer_norm"]["scale"] ) )
_UpperCAmelCase = nn.Parameter(
torch.FloatTensor(ly_weight["FiLMLayer_0"]["DenseGeneral_0"]["kernel"].T ) )
_UpperCAmelCase = ly_weight["self_attention"]
_UpperCAmelCase = nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T ) )
_UpperCAmelCase = nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T ) )
_UpperCAmelCase = nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T ) )
_UpperCAmelCase = nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T ) )
_UpperCAmelCase = ly_weight["MultiHeadDotProductAttention_0"]
_UpperCAmelCase = nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T ) )
_UpperCAmelCase = nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T ) )
_UpperCAmelCase = nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T ) )
_UpperCAmelCase = nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T ) )
_UpperCAmelCase = nn.Parameter(
torch.FloatTensor(ly_weight["pre_cross_attention_layer_norm"]["scale"] ) )
_UpperCAmelCase = nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"] ) )
_UpperCAmelCase = nn.Parameter(
torch.FloatTensor(ly_weight["FiLMLayer_1"]["DenseGeneral_0"]["kernel"].T ) )
_UpperCAmelCase = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T ) )
_UpperCAmelCase = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T ) )
_UpperCAmelCase = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T ) )
_UpperCAmelCase = nn.Parameter(torch.FloatTensor(weights["decoder_norm"]["scale"] ) )
_UpperCAmelCase = nn.Parameter(torch.FloatTensor(weights["spec_out_dense"]["kernel"].T ) )
return model
def UpperCAmelCase_ ( __lowercase : Optional[Any] ) -> List[Any]:
'''simple docstring'''
_UpperCAmelCase = checkpoints.load_tax_checkpoint(args.checkpoint_path )
_UpperCAmelCase = jnp.tree_util.tree_map(onp.array , _a )
_UpperCAmelCase = [
"from __gin__ import dynamic_registration",
"from music_spectrogram_diffusion.models.diffusion import diffusion_utils",
"diffusion_utils.ClassifierFreeGuidanceConfig.eval_condition_weight = 2.0",
"diffusion_utils.DiffusionConfig.classifier_free_guidance = @diffusion_utils.ClassifierFreeGuidanceConfig()",
]
_UpperCAmelCase = os.path.join(args.checkpoint_path , ".." , "config.gin" )
_UpperCAmelCase = inference.parse_training_gin_file(_a , _a )
_UpperCAmelCase = inference.InferenceModel(args.checkpoint_path , _a )
_UpperCAmelCase = DDPMScheduler(beta_schedule="squaredcos_cap_v2" , variance_type="fixed_large" )
_UpperCAmelCase = SpectrogramNotesEncoder(
max_length=synth_model.sequence_length["inputs"] , vocab_size=synth_model.model.module.config.vocab_size , d_model=synth_model.model.module.config.emb_dim , dropout_rate=synth_model.model.module.config.dropout_rate , num_layers=synth_model.model.module.config.num_encoder_layers , num_heads=synth_model.model.module.config.num_heads , d_kv=synth_model.model.module.config.head_dim , d_ff=synth_model.model.module.config.mlp_dim , feed_forward_proj="gated-gelu" , )
_UpperCAmelCase = SpectrogramContEncoder(
input_dims=synth_model.audio_codec.n_dims , targets_context_length=synth_model.sequence_length["targets_context"] , d_model=synth_model.model.module.config.emb_dim , dropout_rate=synth_model.model.module.config.dropout_rate , num_layers=synth_model.model.module.config.num_encoder_layers , num_heads=synth_model.model.module.config.num_heads , d_kv=synth_model.model.module.config.head_dim , d_ff=synth_model.model.module.config.mlp_dim , feed_forward_proj="gated-gelu" , )
_UpperCAmelCase = TaFilmDecoder(
input_dims=synth_model.audio_codec.n_dims , targets_length=synth_model.sequence_length["targets_context"] , max_decoder_noise_time=synth_model.model.module.config.max_decoder_noise_time , d_model=synth_model.model.module.config.emb_dim , num_layers=synth_model.model.module.config.num_decoder_layers , num_heads=synth_model.model.module.config.num_heads , d_kv=synth_model.model.module.config.head_dim , d_ff=synth_model.model.module.config.mlp_dim , dropout_rate=synth_model.model.module.config.dropout_rate , )
_UpperCAmelCase = load_notes_encoder(ta_checkpoint["target"]["token_encoder"] , _a )
_UpperCAmelCase = load_continuous_encoder(ta_checkpoint["target"]["continuous_encoder"] , _a )
_UpperCAmelCase = load_decoder(ta_checkpoint["target"]["decoder"] , _a )
_UpperCAmelCase = OnnxRuntimeModel.from_pretrained("kashif/soundstream_mel_decoder" )
_UpperCAmelCase = SpectrogramDiffusionPipeline(
notes_encoder=_a , continuous_encoder=_a , decoder=_a , scheduler=_a , melgan=_a , )
if args.save:
pipe.save_pretrained(args.output_path )
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE :int = argparse.ArgumentParser()
parser.add_argument('''--output_path''', default=None, type=str, required=True, help='''Path to the converted model.''')
parser.add_argument(
'''--save''', default=True, type=bool, required=False, help='''Whether to save the converted model or not.'''
)
parser.add_argument(
'''--checkpoint_path''',
default=F"{MODEL}/checkpoint_500000",
type=str,
required=False,
help='''Path to the original jax model checkpoint.''',
)
__SCREAMING_SNAKE_CASE :Tuple = parser.parse_args()
main(args)
| 236 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
a_ = {'configuration_plbart': ['PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PLBartConfig']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ['PLBartTokenizer']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'PLBART_PRETRAINED_MODEL_ARCHIVE_LIST',
'PLBartForCausalLM',
'PLBartForConditionalGeneration',
'PLBartForSequenceClassification',
'PLBartModel',
'PLBartPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_plbart import PLBartTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_plbart import (
PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
PLBartForCausalLM,
PLBartForConditionalGeneration,
PLBartForSequenceClassification,
PLBartModel,
PLBartPreTrainedModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure) | 25 | 0 |
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def __UpperCAmelCase ( lowerCamelCase_ : List[Any] , lowerCamelCase_ : Optional[int] ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[Any] = args.log_outputs
SCREAMING_SNAKE_CASE_ : str = "_".join(args.dataset.split('/' ) + [args.config, args.split] )
# load metric
SCREAMING_SNAKE_CASE_ : List[Any] = load_metric('wer' )
SCREAMING_SNAKE_CASE_ : Union[str, Any] = load_metric('cer' )
# compute metrics
SCREAMING_SNAKE_CASE_ : Dict = wer.compute(references=result['target'] , predictions=result['prediction'] )
SCREAMING_SNAKE_CASE_ : Optional[Any] = cer.compute(references=result['target'] , predictions=result['prediction'] )
# print & log results
SCREAMING_SNAKE_CASE_ : Optional[Any] = F'WER: {wer_result}\nCER: {cer_result}'
print(_a )
with open(F'{dataset_id}_eval_results.txt' , 'w' ) as f:
f.write(_a )
# log all results in text file. Possibly interesting for analysis
if log_outputs is not None:
SCREAMING_SNAKE_CASE_ : str = F'log_{dataset_id}_predictions.txt'
SCREAMING_SNAKE_CASE_ : str = F'log_{dataset_id}_targets.txt'
with open(_a , 'w' ) as p, open(_a , 'w' ) as t:
# mapping function to write output
def write_to_file(lowerCamelCase_ : Optional[Any] , lowerCamelCase_ : Dict ):
p.write(F'{i}' + '\n' )
p.write(batch['prediction'] + '\n' )
t.write(F'{i}' + '\n' )
t.write(batch['target'] + '\n' )
result.map(_a , with_indices=_a )
def __UpperCAmelCase ( lowerCamelCase_ : Dict ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Union[str, Any] = "[,?.!\-\;\:\"“%‘”�—’…–]" # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training
SCREAMING_SNAKE_CASE_ : Tuple = re.sub(_a , '' , text.lower() )
# In addition, we can normalize the target text, e.g. removing new lines characters etc...
# note that order is important here!
SCREAMING_SNAKE_CASE_ : Tuple = ["\n\n", "\n", " ", " "]
for t in token_sequences_to_ignore:
SCREAMING_SNAKE_CASE_ : List[Any] = " ".join(text.split(_a ) )
return text
def __UpperCAmelCase ( lowerCamelCase_ : int ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[Any] = load_dataset(args.dataset , args.config , split=args.split , use_auth_token=_a )
# for testing: only process the first two examples as a test
# dataset = dataset.select(range(10))
# load processor
SCREAMING_SNAKE_CASE_ : Dict = AutoFeatureExtractor.from_pretrained(args.model_id )
SCREAMING_SNAKE_CASE_ : Optional[int] = feature_extractor.sampling_rate
# resample audio
SCREAMING_SNAKE_CASE_ : List[str] = dataset.cast_column('audio' , Audio(sampling_rate=_a ) )
# load eval pipeline
if args.device is None:
SCREAMING_SNAKE_CASE_ : Tuple = 0 if torch.cuda.is_available() else -1
SCREAMING_SNAKE_CASE_ : int = pipeline('automatic-speech-recognition' , model=args.model_id , device=args.device )
# map function to decode audio
def map_to_pred(lowerCamelCase_ : Optional[int] ):
SCREAMING_SNAKE_CASE_ : Optional[int] = asr(
batch['audio']['array'] , chunk_length_s=args.chunk_length_s , stride_length_s=args.stride_length_s )
SCREAMING_SNAKE_CASE_ : Optional[Any] = prediction["text"]
SCREAMING_SNAKE_CASE_ : Union[str, Any] = normalize_text(batch['sentence'] )
return batch
# run inference on all examples
SCREAMING_SNAKE_CASE_ : Optional[Any] = dataset.map(_a , remove_columns=dataset.column_names )
# compute and log_results
# do not change function below
log_results(_a , _a )
if __name__ == "__main__":
UpperCamelCase__ : List[Any] = argparse.ArgumentParser()
parser.add_argument(
'''--model_id''', type=str, required=True, help='''Model identifier. Should be loadable with 🤗 Transformers'''
)
parser.add_argument(
'''--dataset''',
type=str,
required=True,
help='''Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets''',
)
parser.add_argument(
'''--config''', type=str, required=True, help='''Config of the dataset. *E.g.* `\'en\'` for Common Voice'''
)
parser.add_argument('''--split''', type=str, required=True, help='''Split of the dataset. *E.g.* `\'test\'`''')
parser.add_argument(
'''--chunk_length_s''', type=float, default=None, help='''Chunk length in seconds. Defaults to 5 seconds.'''
)
parser.add_argument(
'''--stride_length_s''', type=float, default=None, help='''Stride of the audio chunks. Defaults to 1 second.'''
)
parser.add_argument(
'''--log_outputs''', action='''store_true''', help='''If defined, write outputs to log file for analysis.'''
)
parser.add_argument(
'''--device''',
type=int,
default=None,
help='''The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.''',
)
UpperCamelCase__ : Dict = parser.parse_args()
main(args)
| 105 |
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
@property
def __UpperCamelCase ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : str = 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 __UpperCamelCase ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = self.dummy_uncond_unet
SCREAMING_SNAKE_CASE : Union[str, Any] = KarrasVeScheduler()
SCREAMING_SNAKE_CASE : Any = KarrasVePipeline(unet=a , scheduler=a )
pipe.to(a )
pipe.set_progress_bar_config(disable=a )
SCREAMING_SNAKE_CASE : Any = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Optional[Any] = pipe(num_inference_steps=2 , generator=a , output_type="numpy" ).images
SCREAMING_SNAKE_CASE : List[str] = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : List[Any] = pipe(num_inference_steps=2 , generator=a , output_type="numpy" , return_dict=a )[0]
SCREAMING_SNAKE_CASE : List[Any] = image[0, -3:, -3:, -1]
SCREAMING_SNAKE_CASE : Any = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
SCREAMING_SNAKE_CASE : str = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
@slow
@require_torch
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def __UpperCamelCase ( self : int ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = "google/ncsnpp-celebahq-256"
SCREAMING_SNAKE_CASE : List[Any] = UNetaDModel.from_pretrained(a )
SCREAMING_SNAKE_CASE : Any = KarrasVeScheduler()
SCREAMING_SNAKE_CASE : Optional[Any] = KarrasVePipeline(unet=a , scheduler=a )
pipe.to(a )
pipe.set_progress_bar_config(disable=a )
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Any = pipe(num_inference_steps=20 , generator=a , output_type="numpy" ).images
SCREAMING_SNAKE_CASE : List[str] = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
SCREAMING_SNAKE_CASE : str = np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2 | 25 | 0 |
'''simple docstring'''
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput
@dataclass
class lowerCamelCase_ ( __A ):
_lowerCAmelCase : Union[str, Any] = 4_2
_lowerCAmelCase : Any = 4_2
class lowerCamelCase_ ( __A , __A ):
_lowerCAmelCase : int = 1
@register_to_config
def __init__( self : Dict , lowerCAmelCase__ : int = 20_00 , lowerCAmelCase__ : float = 0.15 , lowerCAmelCase__ : float = 0.01 , lowerCAmelCase__ : float = 1348.0 , lowerCAmelCase__ : float = 1e-5 , lowerCAmelCase__ : int = 1 , ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = sigma_max
# setable values
SCREAMING_SNAKE_CASE : str = None
self.set_sigmas(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
def __lowercase ( self : Optional[int] , lowerCAmelCase__ : torch.FloatTensor , lowerCAmelCase__ : Optional[int] = None ):
"""simple docstring"""
return sample
def __lowercase ( self : Optional[int] , lowerCAmelCase__ : int , lowerCAmelCase__ : float = None , lowerCAmelCase__ : Union[str, torch.device] = None ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = sampling_eps if sampling_eps is not None else self.config.sampling_eps
SCREAMING_SNAKE_CASE : List[Any] = torch.linspace(1 , lowerCAmelCase__ , lowerCAmelCase__ , device=lowerCAmelCase__ )
def __lowercase ( self : str , lowerCAmelCase__ : int , lowerCAmelCase__ : float = None , lowerCAmelCase__ : float = None , lowerCAmelCase__ : float = None ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = sigma_min if sigma_min is not None else self.config.sigma_min
SCREAMING_SNAKE_CASE : List[str] = sigma_max if sigma_max is not None else self.config.sigma_max
SCREAMING_SNAKE_CASE : Tuple = sampling_eps if sampling_eps is not None else self.config.sampling_eps
if self.timesteps is None:
self.set_timesteps(lowerCAmelCase__ , lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : Tuple = sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
SCREAMING_SNAKE_CASE : Optional[Any] = torch.exp(torch.linspace(math.log(lowerCAmelCase__ ) , math.log(lowerCAmelCase__ ) , lowerCAmelCase__ ) )
SCREAMING_SNAKE_CASE : List[str] = torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps] )
def __lowercase ( self : Dict , lowerCAmelCase__ : int , lowerCAmelCase__ : Dict ):
"""simple docstring"""
return torch.where(
timesteps == 0 , torch.zeros_like(t.to(timesteps.device ) ) , self.discrete_sigmas[timesteps - 1].to(timesteps.device ) , )
def __lowercase ( self : str , lowerCAmelCase__ : torch.FloatTensor , lowerCAmelCase__ : int , lowerCAmelCase__ : torch.FloatTensor , lowerCAmelCase__ : Optional[torch.Generator] = None , lowerCAmelCase__ : bool = True , ):
"""simple docstring"""
if self.timesteps is None:
raise ValueError(
'''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler''' )
SCREAMING_SNAKE_CASE : int = timestep * torch.ones(
sample.shape[0] , device=sample.device ) # torch.repeat_interleave(timestep, sample.shape[0])
SCREAMING_SNAKE_CASE : Tuple = (timestep * (len(self.timesteps ) - 1)).long()
# mps requires indices to be in the same device, so we use cpu as is the default with cuda
SCREAMING_SNAKE_CASE : int = timesteps.to(self.discrete_sigmas.device )
SCREAMING_SNAKE_CASE : Optional[Any] = self.discrete_sigmas[timesteps].to(sample.device )
SCREAMING_SNAKE_CASE : Optional[Any] = self.get_adjacent_sigma(lowerCAmelCase__ , lowerCAmelCase__ ).to(sample.device )
SCREAMING_SNAKE_CASE : Tuple = torch.zeros_like(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : List[Any] = (sigma**2 - adjacent_sigma**2) ** 0.5
# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
# also equation 47 shows the analog from SDE models to ancestral sampling methods
SCREAMING_SNAKE_CASE : int = diffusion.flatten()
while len(diffusion.shape ) < len(sample.shape ):
SCREAMING_SNAKE_CASE : List[Any] = diffusion.unsqueeze(-1 )
SCREAMING_SNAKE_CASE : List[str] = drift - diffusion**2 * model_output
# equation 6: sample noise for the diffusion term of
SCREAMING_SNAKE_CASE : Optional[Any] = randn_tensor(
sample.shape , layout=sample.layout , generator=lowerCAmelCase__ , device=sample.device , dtype=sample.dtype )
SCREAMING_SNAKE_CASE : Optional[Any] = sample - drift # subtract because `dt` is a small negative timestep
# TODO is the variable diffusion the correct scaling term for the noise?
SCREAMING_SNAKE_CASE : Any = prev_sample_mean + diffusion * noise # add impact of diffusion field g
if not return_dict:
return (prev_sample, prev_sample_mean)
return SdeVeOutput(prev_sample=lowerCAmelCase__ , prev_sample_mean=lowerCAmelCase__ )
def __lowercase ( self : str , lowerCAmelCase__ : torch.FloatTensor , lowerCAmelCase__ : torch.FloatTensor , lowerCAmelCase__ : Optional[torch.Generator] = None , lowerCAmelCase__ : bool = True , ):
"""simple docstring"""
if self.timesteps is None:
raise ValueError(
'''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler''' )
# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
# sample noise for correction
SCREAMING_SNAKE_CASE : List[str] = randn_tensor(sample.shape , layout=sample.layout , generator=lowerCAmelCase__ ).to(sample.device )
# compute step size from the model_output, the noise, and the snr
SCREAMING_SNAKE_CASE : Dict = torch.norm(model_output.reshape(model_output.shape[0] , -1 ) , dim=-1 ).mean()
SCREAMING_SNAKE_CASE : str = torch.norm(noise.reshape(noise.shape[0] , -1 ) , dim=-1 ).mean()
SCREAMING_SNAKE_CASE : int = (self.config.snr * noise_norm / grad_norm) ** 2 * 2
SCREAMING_SNAKE_CASE : Dict = step_size * torch.ones(sample.shape[0] ).to(sample.device )
# self.repeat_scalar(step_size, sample.shape[0])
# compute corrected sample: model_output term and noise term
SCREAMING_SNAKE_CASE : Optional[int] = step_size.flatten()
while len(step_size.shape ) < len(sample.shape ):
SCREAMING_SNAKE_CASE : Union[str, Any] = step_size.unsqueeze(-1 )
SCREAMING_SNAKE_CASE : str = sample + step_size * model_output
SCREAMING_SNAKE_CASE : List[Any] = prev_sample_mean + ((step_size * 2) ** 0.5) * noise
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=lowerCAmelCase__ )
def __lowercase ( self : int , lowerCAmelCase__ : torch.FloatTensor , lowerCAmelCase__ : torch.FloatTensor , lowerCAmelCase__ : torch.FloatTensor , ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = timesteps.to(original_samples.device )
SCREAMING_SNAKE_CASE : Optional[int] = self.discrete_sigmas.to(original_samples.device )[timesteps]
SCREAMING_SNAKE_CASE : str = (
noise * sigmas[:, None, None, None]
if noise is not None
else torch.randn_like(lowerCAmelCase__ ) * sigmas[:, None, None, None]
)
SCREAMING_SNAKE_CASE : Optional[Any] = noise + original_samples
return noisy_samples
def __len__( self : Optional[Any] ):
"""simple docstring"""
return self.config.num_train_timesteps
| 527 |
def lowerCamelCase__ ( _a , _a):
SCREAMING_SNAKE_CASE : Optional[int] = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return res
def lowerCamelCase__ ( _a , _a , _a):
SCREAMING_SNAKE_CASE : Optional[int] = 0
while b > 0:
if b & 1:
SCREAMING_SNAKE_CASE : Optional[Any] = ((res % c) + (a % c)) % c
a += a
b >>= 1
return res | 25 | 0 |
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def snake_case_ (__A : Optional[int] , __A : Dict ) -> List[Any]:
__lowerCAmelCase : Optional[int] = []
for part_id in partition_order:
__lowerCAmelCase : str = df.where(f'''SPARK_PARTITION_ID() = {part_id}''' ).collect()
for row_idx, row in enumerate(_a ):
expected_row_ids_and_row_dicts.append((f'''{part_id}_{row_idx}''', row.asDict()) )
return expected_row_ids_and_row_dicts
@require_not_windows
@require_dill_gt_0_3_2
def snake_case_ () -> List[str]:
__lowerCAmelCase : str = pyspark.sql.SparkSession.builder.master("""local[*]""" ).appName("""pyspark""" ).getOrCreate()
__lowerCAmelCase : Union[str, Any] = spark.range(1_0_0 ).repartition(1 )
__lowerCAmelCase : Dict = Spark(_a )
# The id ints will be converted to Pyarrow int64s, so each row will be 8 bytes. Setting a max_shard_size of 16 means
# that each partition can hold 2 rows.
spark_builder._repartition_df_if_needed(max_shard_size=1_6 )
# Given that the dataframe has 100 rows and each partition has 2 rows, we expect 50 partitions.
assert spark_builder.df.rdd.getNumPartitions() == 5_0
@require_not_windows
@require_dill_gt_0_3_2
def snake_case_ () -> Dict:
__lowerCAmelCase : Tuple = pyspark.sql.SparkSession.builder.master("""local[*]""" ).appName("""pyspark""" ).getOrCreate()
__lowerCAmelCase : Any = spark.range(1_0 ).repartition(2 )
__lowerCAmelCase : Tuple = [1, 0]
__lowerCAmelCase : Union[str, Any] = _generate_iterable_examples(_a , _a ) # Reverse the partitions.
__lowerCAmelCase : Optional[Any] = _get_expected_row_ids_and_row_dicts_for_partition_order(_a , _a )
for i, (row_id, row_dict) in enumerate(generate_fn() ):
__lowerCAmelCase : Optional[Any] = expected_row_ids_and_row_dicts[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def snake_case_ () -> Optional[Any]:
__lowerCAmelCase : Optional[int] = pyspark.sql.SparkSession.builder.master("""local[*]""" ).appName("""pyspark""" ).getOrCreate()
__lowerCAmelCase : List[Any] = spark.range(1_0 ).repartition(1 )
__lowerCAmelCase : Tuple = SparkExamplesIterable(_a )
assert it.n_shards == 1
for i, (row_id, row_dict) in enumerate(_a ):
assert row_id == f'''0_{i}'''
assert row_dict == {"id": i}
@require_not_windows
@require_dill_gt_0_3_2
def snake_case_ () -> Tuple:
__lowerCAmelCase : int = pyspark.sql.SparkSession.builder.master("""local[*]""" ).appName("""pyspark""" ).getOrCreate()
__lowerCAmelCase : int = spark.range(3_0 ).repartition(3 )
# Mock the generator so that shuffle reverses the partition indices.
with patch("""numpy.random.Generator""" ) as generator_mock:
__lowerCAmelCase : int = lambda __A : x.reverse()
__lowerCAmelCase : List[Any] = _get_expected_row_ids_and_row_dicts_for_partition_order(_a , [2, 1, 0] )
__lowerCAmelCase : str = SparkExamplesIterable(_a ).shuffle_data_sources(_a )
assert shuffled_it.n_shards == 3
for i, (row_id, row_dict) in enumerate(_a ):
__lowerCAmelCase : Optional[int] = expected_row_ids_and_row_dicts[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def snake_case_ () -> Tuple:
__lowerCAmelCase : Optional[int] = pyspark.sql.SparkSession.builder.master("""local[*]""" ).appName("""pyspark""" ).getOrCreate()
__lowerCAmelCase : Optional[Any] = spark.range(2_0 ).repartition(4 )
# Partitions 0 and 2
__lowerCAmelCase : int = SparkExamplesIterable(_a ).shard_data_sources(worker_id=0 , num_workers=2 )
assert shard_it_a.n_shards == 2
__lowerCAmelCase : str = _get_expected_row_ids_and_row_dicts_for_partition_order(_a , [0, 2] )
for i, (row_id, row_dict) in enumerate(_a ):
__lowerCAmelCase : Optional[Any] = expected_row_ids_and_row_dicts_a[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
# Partitions 1 and 3
__lowerCAmelCase : Any = SparkExamplesIterable(_a ).shard_data_sources(worker_id=1 , num_workers=2 )
assert shard_it_a.n_shards == 2
__lowerCAmelCase : List[Any] = _get_expected_row_ids_and_row_dicts_for_partition_order(_a , [1, 3] )
for i, (row_id, row_dict) in enumerate(_a ):
__lowerCAmelCase : Union[str, Any] = expected_row_ids_and_row_dicts_a[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def snake_case_ () -> Dict:
__lowerCAmelCase : str = pyspark.sql.SparkSession.builder.master("""local[*]""" ).appName("""pyspark""" ).getOrCreate()
__lowerCAmelCase : List[str] = spark.range(1_0_0 ).repartition(1 )
__lowerCAmelCase : int = Spark(_a )
# Choose a small max_shard_size for maximum partitioning.
spark_builder._repartition_df_if_needed(max_shard_size=1 )
# The new number of partitions should not be greater than the number of rows.
assert spark_builder.df.rdd.getNumPartitions() == 1_0_0
| 651 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json',
'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json',
'junnyu/roformer_chinese_char_small': (
'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'
),
'junnyu/roformer_chinese_char_base': (
'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'
),
'junnyu/roformer_small_discriminator': (
'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'
),
'junnyu/roformer_small_generator': (
'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'
),
# See all RoFormer models at https://huggingface.co/models?filter=roformer
}
class _UpperCamelCase ( __A ):
'''simple docstring'''
lowerCamelCase__ ='roformer'
def __init__( self : Dict , a : Any=5_0000 , a : List[Any]=None , a : str=768 , a : str=12 , a : Tuple=12 , a : Optional[Any]=3072 , a : List[str]="gelu" , a : List[Any]=0.1 , a : Union[str, Any]=0.1 , a : Tuple=1536 , a : List[str]=2 , a : Tuple=0.02 , a : Any=1e-12 , a : Optional[int]=0 , a : Union[str, Any]=False , a : int=True , **a : str , ) -> int:
"""simple docstring"""
super().__init__(pad_token_id=a , **a )
SCREAMING_SNAKE_CASE : str = vocab_size
SCREAMING_SNAKE_CASE : int = hidden_size if embedding_size is None else embedding_size
SCREAMING_SNAKE_CASE : List[str] = hidden_size
SCREAMING_SNAKE_CASE : Union[str, Any] = num_hidden_layers
SCREAMING_SNAKE_CASE : int = num_attention_heads
SCREAMING_SNAKE_CASE : Tuple = hidden_act
SCREAMING_SNAKE_CASE : int = intermediate_size
SCREAMING_SNAKE_CASE : Tuple = hidden_dropout_prob
SCREAMING_SNAKE_CASE : int = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : Optional[int] = max_position_embeddings
SCREAMING_SNAKE_CASE : Any = type_vocab_size
SCREAMING_SNAKE_CASE : Union[str, Any] = initializer_range
SCREAMING_SNAKE_CASE : List[str] = layer_norm_eps
SCREAMING_SNAKE_CASE : List[str] = rotary_value
SCREAMING_SNAKE_CASE : int = use_cache
class _UpperCamelCase ( __A ):
'''simple docstring'''
@property
def __UpperCamelCase ( self : Tuple ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
if self.task == "multiple-choice":
SCREAMING_SNAKE_CASE : Optional[Any] = {0: "batch", 1: "choice", 2: "sequence"}
else:
SCREAMING_SNAKE_CASE : str = {0: "batch", 1: "sequence"}
SCREAMING_SNAKE_CASE : List[Any] = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
("token_type_ids", dynamic_axis),
] ) | 25 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase_ = {
'configuration_clipseg': [
'CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP',
'CLIPSegConfig',
'CLIPSegTextConfig',
'CLIPSegVisionConfig',
],
'processing_clipseg': ['CLIPSegProcessor'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST',
'CLIPSegModel',
'CLIPSegPreTrainedModel',
'CLIPSegTextModel',
'CLIPSegVisionModel',
'CLIPSegForImageSegmentation',
]
if TYPE_CHECKING:
from .configuration_clipseg import (
CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
CLIPSegConfig,
CLIPSegTextConfig,
CLIPSegVisionConfig,
)
from .processing_clipseg import CLIPSegProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_clipseg import (
CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
CLIPSegForImageSegmentation,
CLIPSegModel,
CLIPSegPreTrainedModel,
CLIPSegTextModel,
CLIPSegVisionModel,
)
else:
import sys
lowerCAmelCase_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 217 |
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
a_ = logging.getLogger(__name__)
a_ = 'Hello world! cécé herlolip'
a_ = namedtuple(
'BertAbsConfig',
[
'temp_dir',
'large',
'use_bert_emb',
'finetune_bert',
'encoder',
'share_emb',
'max_pos',
'enc_layers',
'enc_hidden_size',
'enc_heads',
'enc_ff_size',
'enc_dropout',
'dec_layers',
'dec_hidden_size',
'dec_heads',
'dec_ff_size',
'dec_dropout',
],
)
def lowerCamelCase__ ( _a , _a):
SCREAMING_SNAKE_CASE : List[Any] = BertAbsConfig(
temp_dir="." , finetune_bert=_a , large=_a , share_emb=_a , use_bert_emb=_a , encoder="bert" , max_pos=512 , enc_layers=6 , enc_hidden_size=512 , enc_heads=8 , enc_ff_size=512 , enc_dropout=0.2 , dec_layers=6 , dec_hidden_size=768 , dec_heads=8 , dec_ff_size=2048 , dec_dropout=0.2 , )
SCREAMING_SNAKE_CASE : Dict = torch.load(_a , lambda _a , _a: storage)
SCREAMING_SNAKE_CASE : str = AbsSummarizer(_a , torch.device("cpu") , _a)
original.eval()
SCREAMING_SNAKE_CASE : List[str] = BertAbsSummarizer(_a , torch.device("cpu"))
new_model.eval()
# -------------------
# Convert the weights
# -------------------
logging.info("convert the model")
new_model.bert.load_state_dict(original.bert.state_dict())
new_model.decoder.load_state_dict(original.decoder.state_dict())
new_model.generator.load_state_dict(original.generator.state_dict())
# ----------------------------------
# Make sure the outpus are identical
# ----------------------------------
logging.info("Make sure that the models' outputs are identical")
SCREAMING_SNAKE_CASE : List[str] = BertTokenizer.from_pretrained("bert-base-uncased")
# prepare the model inputs
SCREAMING_SNAKE_CASE : List[str] = tokenizer.encode("This is sample éàalj'-.")
encoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(_a)))
SCREAMING_SNAKE_CASE : int = torch.tensor(_a).unsqueeze(0)
SCREAMING_SNAKE_CASE : List[Any] = tokenizer.encode("This is sample 3 éàalj'-.")
decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(_a)))
SCREAMING_SNAKE_CASE : int = torch.tensor(_a).unsqueeze(0)
# failsafe to make sure the weights reset does not affect the
# loaded weights.
assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight)) == 0
# forward pass
SCREAMING_SNAKE_CASE : List[Any] = encoder_input_ids
SCREAMING_SNAKE_CASE : List[Any] = decoder_input_ids
SCREAMING_SNAKE_CASE : Dict = None
SCREAMING_SNAKE_CASE : Optional[Any] = None
SCREAMING_SNAKE_CASE : List[Any] = None
SCREAMING_SNAKE_CASE : Optional[int] = None
SCREAMING_SNAKE_CASE : Dict = None
# The original model does not apply the geneator layer immediatly but rather in
# the beam search (where it combines softmax + linear layer). Since we already
# apply the softmax in our generation process we only apply the linear layer here.
# We make sure that the outputs of the full stack are identical
SCREAMING_SNAKE_CASE : Optional[int] = original(_a , _a , _a , _a , _a , _a , _a)[0]
SCREAMING_SNAKE_CASE : Dict = original.generator(_a)
SCREAMING_SNAKE_CASE : Any = new_model(
_a , _a , _a , _a , _a)[0]
SCREAMING_SNAKE_CASE : Tuple = new_model.generator(_a)
SCREAMING_SNAKE_CASE : List[Any] = torch.max(torch.abs(output_converted_model - output_original_model)).item()
print("Maximum absolute difference beween weights: {:.2f}".format(_a))
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.max(torch.abs(output_converted_generator - output_original_generator)).item()
print("Maximum absolute difference beween weights: {:.2f}".format(_a))
SCREAMING_SNAKE_CASE : int = torch.allclose(_a , _a , atol=1E-3)
if are_identical:
logging.info("all weights are equal up to 1e-3")
else:
raise ValueError("the weights are different. The new model is likely different from the original one.")
# The model has been saved with torch.save(model) and this is bound to the exact
# directory structure. We save the state_dict instead.
logging.info("saving the model's state dictionary")
torch.save(
new_model.state_dict() , "./bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin")
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
parser.add_argument(
'--bertabs_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_ = parser.parse_args()
convert_bertabs_checkpoints(
args.bertabs_checkpoint_path,
args.pytorch_dump_folder_path,
) | 25 | 0 |
from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnProcessor
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder
@dataclass
class a ( __A ):
_lowercase = 4_2
class a ( __A , __A ):
_lowercase = True
@register_to_config
def __init__( self , A_ = 3 , A_ = 3 , A_ = ("DownEncoderBlock2D",) , A_ = ("UpDecoderBlock2D",) , A_ = (64,) , A_ = 1 , A_ = "silu" , A_ = 4 , A_ = 32 , A_ = 32 , A_ = 0.1_82_15 , ):
'''simple docstring'''
super().__init__()
# pass init params to Encoder
_UpperCAmelCase : List[Any] = Encoder(
in_channels=A_ , out_channels=A_ , down_block_types=A_ , block_out_channels=A_ , layers_per_block=A_ , act_fn=A_ , norm_num_groups=A_ , double_z=A_ , )
# pass init params to Decoder
_UpperCAmelCase : Dict = Decoder(
in_channels=A_ , out_channels=A_ , up_block_types=A_ , block_out_channels=A_ , layers_per_block=A_ , norm_num_groups=A_ , act_fn=A_ , )
_UpperCAmelCase : Any = nn.Convad(2 * latent_channels , 2 * latent_channels , 1 )
_UpperCAmelCase : Dict = nn.Convad(A_ , A_ , 1 )
_UpperCAmelCase : Any = False
_UpperCAmelCase : Optional[Any] = False
# only relevant if vae tiling is enabled
_UpperCAmelCase : Dict = self.config.sample_size
_UpperCAmelCase : int = (
self.config.sample_size[0]
if isinstance(self.config.sample_size , (list, tuple) )
else self.config.sample_size
)
_UpperCAmelCase : Union[str, Any] = int(sample_size / (2 ** (len(self.config.block_out_channels ) - 1)) )
_UpperCAmelCase : Union[str, Any] = 0.25
def _UpperCAmelCase ( self , A_ , A_=False ):
'''simple docstring'''
if isinstance(A_ , (Encoder, Decoder) ):
_UpperCAmelCase : Any = value
def _UpperCAmelCase ( self , A_ = True ):
'''simple docstring'''
_UpperCAmelCase : Optional[int] = use_tiling
def _UpperCAmelCase ( self ):
'''simple docstring'''
self.enable_tiling(A_ )
def _UpperCAmelCase ( self ):
'''simple docstring'''
_UpperCAmelCase : Tuple = True
def _UpperCAmelCase ( self ):
'''simple docstring'''
_UpperCAmelCase : str = False
@property
# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
def _UpperCAmelCase ( self ):
'''simple docstring'''
_UpperCAmelCase : Any = {}
def fn_recursive_add_processors(A_ , A_ , A_ ):
if hasattr(A_ , "set_processor" ):
_UpperCAmelCase : Any = module.processor
for sub_name, child in module.named_children():
fn_recursive_add_processors(f'{name}.{sub_name}' , A_ , A_ )
return processors
for name, module in self.named_children():
fn_recursive_add_processors(A_ , A_ , A_ )
return processors
def _UpperCAmelCase ( self , A_ ):
'''simple docstring'''
_UpperCAmelCase : Optional[Any] = len(self.attn_processors.keys() )
if isinstance(A_ , A_ ) and len(A_ ) != count:
raise ValueError(
f'A dict of processors was passed, but the number of processors {len(A_ )} does not match the'
f' number of attention layers: {count}. Please make sure to pass {count} processor classes.' )
def fn_recursive_attn_processor(A_ , A_ , A_ ):
if hasattr(A_ , "set_processor" ):
if not isinstance(A_ , A_ ):
module.set_processor(A_ )
else:
module.set_processor(processor.pop(f'{name}.processor' ) )
for sub_name, child in module.named_children():
fn_recursive_attn_processor(f'{name}.{sub_name}' , A_ , A_ )
for name, module in self.named_children():
fn_recursive_attn_processor(A_ , A_ , A_ )
def _UpperCAmelCase ( self ):
'''simple docstring'''
self.set_attn_processor(AttnProcessor() )
@apply_forward_hook
def _UpperCAmelCase ( self , A_ , A_ = True ):
'''simple docstring'''
if self.use_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size):
return self.tiled_encode(A_ , return_dict=A_ )
if self.use_slicing and x.shape[0] > 1:
_UpperCAmelCase : str = [self.encoder(A_ ) for x_slice in x.split(1 )]
_UpperCAmelCase : Any = torch.cat(A_ )
else:
_UpperCAmelCase : Optional[Any] = self.encoder(A_ )
_UpperCAmelCase : Any = self.quant_conv(A_ )
_UpperCAmelCase : Optional[Any] = DiagonalGaussianDistribution(A_ )
if not return_dict:
return (posterior,)
return AutoencoderKLOutput(latent_dist=A_ )
def _UpperCAmelCase ( self , A_ , A_ = True ):
'''simple docstring'''
if self.use_tiling and (z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > self.tile_latent_min_size):
return self.tiled_decode(A_ , return_dict=A_ )
_UpperCAmelCase : int = self.post_quant_conv(A_ )
_UpperCAmelCase : List[Any] = self.decoder(A_ )
if not return_dict:
return (dec,)
return DecoderOutput(sample=A_ )
@apply_forward_hook
def _UpperCAmelCase ( self , A_ , A_ = True ):
'''simple docstring'''
if self.use_slicing and z.shape[0] > 1:
_UpperCAmelCase : List[str] = [self._decode(A_ ).sample for z_slice in z.split(1 )]
_UpperCAmelCase : Any = torch.cat(A_ )
else:
_UpperCAmelCase : Optional[int] = self._decode(A_ ).sample
if not return_dict:
return (decoded,)
return DecoderOutput(sample=A_ )
def _UpperCAmelCase ( self , A_ , A_ , A_ ):
'''simple docstring'''
_UpperCAmelCase : List[str] = min(a.shape[2] , b.shape[2] , A_ )
for y in range(A_ ):
_UpperCAmelCase : List[str] = a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
return b
def _UpperCAmelCase ( self , A_ , A_ , A_ ):
'''simple docstring'''
_UpperCAmelCase : List[str] = min(a.shape[3] , b.shape[3] , A_ )
for x in range(A_ ):
_UpperCAmelCase : Tuple = a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
return b
def _UpperCAmelCase ( self , A_ , A_ = True ):
'''simple docstring'''
_UpperCAmelCase : Dict = int(self.tile_sample_min_size * (1 - self.tile_overlap_factor) )
_UpperCAmelCase : Optional[Any] = int(self.tile_latent_min_size * self.tile_overlap_factor )
_UpperCAmelCase : Dict = self.tile_latent_min_size - blend_extent
# Split the image into 512x512 tiles and encode them separately.
_UpperCAmelCase : Tuple = []
for i in range(0 , x.shape[2] , A_ ):
_UpperCAmelCase : Dict = []
for j in range(0 , x.shape[3] , A_ ):
_UpperCAmelCase : Dict = x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
_UpperCAmelCase : Union[str, Any] = self.encoder(A_ )
_UpperCAmelCase : Optional[int] = self.quant_conv(A_ )
row.append(A_ )
rows.append(A_ )
_UpperCAmelCase : Tuple = []
for i, row in enumerate(A_ ):
_UpperCAmelCase : List[str] = []
for j, tile in enumerate(A_ ):
# blend the above tile and the left tile
# to the current tile and add the current tile to the result row
if i > 0:
_UpperCAmelCase : Optional[Any] = self.blend_v(rows[i - 1][j] , A_ , A_ )
if j > 0:
_UpperCAmelCase : str = self.blend_h(row[j - 1] , A_ , A_ )
result_row.append(tile[:, :, :row_limit, :row_limit] )
result_rows.append(torch.cat(A_ , dim=3 ) )
_UpperCAmelCase : List[str] = torch.cat(A_ , dim=2 )
_UpperCAmelCase : List[str] = DiagonalGaussianDistribution(A_ )
if not return_dict:
return (posterior,)
return AutoencoderKLOutput(latent_dist=A_ )
def _UpperCAmelCase ( self , A_ , A_ = True ):
'''simple docstring'''
_UpperCAmelCase : Dict = int(self.tile_latent_min_size * (1 - self.tile_overlap_factor) )
_UpperCAmelCase : List[str] = int(self.tile_sample_min_size * self.tile_overlap_factor )
_UpperCAmelCase : Dict = self.tile_sample_min_size - blend_extent
# Split z into overlapping 64x64 tiles and decode them separately.
# The tiles have an overlap to avoid seams between tiles.
_UpperCAmelCase : str = []
for i in range(0 , z.shape[2] , A_ ):
_UpperCAmelCase : Optional[Any] = []
for j in range(0 , z.shape[3] , A_ ):
_UpperCAmelCase : str = z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
_UpperCAmelCase : Optional[int] = self.post_quant_conv(A_ )
_UpperCAmelCase : List[Any] = self.decoder(A_ )
row.append(A_ )
rows.append(A_ )
_UpperCAmelCase : Any = []
for i, row in enumerate(A_ ):
_UpperCAmelCase : Optional[int] = []
for j, tile in enumerate(A_ ):
# blend the above tile and the left tile
# to the current tile and add the current tile to the result row
if i > 0:
_UpperCAmelCase : List[str] = self.blend_v(rows[i - 1][j] , A_ , A_ )
if j > 0:
_UpperCAmelCase : List[str] = self.blend_h(row[j - 1] , A_ , A_ )
result_row.append(tile[:, :, :row_limit, :row_limit] )
result_rows.append(torch.cat(A_ , dim=3 ) )
_UpperCAmelCase : Dict = torch.cat(A_ , dim=2 )
if not return_dict:
return (dec,)
return DecoderOutput(sample=A_ )
def _UpperCAmelCase ( self , A_ , A_ = False , A_ = True , A_ = None , ):
'''simple docstring'''
_UpperCAmelCase : Dict = sample
_UpperCAmelCase : Optional[int] = self.encode(A_ ).latent_dist
if sample_posterior:
_UpperCAmelCase : Optional[int] = posterior.sample(generator=A_ )
else:
_UpperCAmelCase : Tuple = posterior.mode()
_UpperCAmelCase : List[str] = self.decode(A_ ).sample
if not return_dict:
return (dec,)
return DecoderOutput(sample=A_ )
| 300 |
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
parser.add_argument(
'--txt2img_unclip',
default='kakaobrain/karlo-v1-alpha',
type=str,
required=False,
help='The pretrained txt2img unclip.',
)
a_ = parser.parse_args()
a_ = UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)
a_ = CLIPImageProcessor()
a_ = CLIPVisionModelWithProjection.from_pretrained('openai/clip-vit-large-patch14')
a_ = UnCLIPImageVariationPipeline(
decoder=txtaimg.decoder,
text_encoder=txtaimg.text_encoder,
tokenizer=txtaimg.tokenizer,
text_proj=txtaimg.text_proj,
feature_extractor=feature_extractor,
image_encoder=image_encoder,
super_res_first=txtaimg.super_res_first,
super_res_last=txtaimg.super_res_last,
decoder_scheduler=txtaimg.decoder_scheduler,
super_res_scheduler=txtaimg.super_res_scheduler,
)
imgaimg.save_pretrained(args.dump_path) | 25 | 0 |
'''simple docstring'''
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class __snake_case( __A , unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase : Any = "hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"
def __snake_case ( self , A_=0 ) -> Dict:
lowerCAmelCase = floats_tensor((1, 3, 128, 128) , rng=random.Random(A_ ) )
lowerCAmelCase = np.random.RandomState(A_ )
lowerCAmelCase = {
"prompt": "A painting of a squirrel eating a burger",
"image": image,
"generator": generator,
"num_inference_steps": 3,
"strength": 0.7_5,
"guidance_scale": 7.5,
"output_type": "numpy",
}
return inputs
def __snake_case ( self ) -> Any:
lowerCAmelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
pipe.set_progress_bar_config(disable=A_ )
lowerCAmelCase = self.get_dummy_inputs()
lowerCAmelCase = pipe(**A_ ).images
lowerCAmelCase = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 128, 128, 3)
lowerCAmelCase = np.array([0.6_9_6_4_3, 0.5_8_4_8_4, 0.5_0_3_1_4, 0.5_8_7_6_0, 0.5_5_3_6_8, 0.5_9_6_4_3, 0.5_1_5_2_9, 0.4_1_2_1_7, 0.4_9_0_8_7] )
assert np.abs(image_slice - expected_slice ).max() < 1e-1
def __snake_case ( self ) -> List[Any]:
lowerCAmelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
lowerCAmelCase = PNDMScheduler.from_config(pipe.scheduler.config , skip_prk_steps=A_ )
pipe.set_progress_bar_config(disable=A_ )
lowerCAmelCase = self.get_dummy_inputs()
lowerCAmelCase = pipe(**A_ ).images
lowerCAmelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
lowerCAmelCase = np.array([0.6_1_7_3_7, 0.5_4_6_4_2, 0.5_3_1_8_3, 0.5_4_4_6_5, 0.5_2_7_4_2, 0.6_0_5_2_5, 0.4_9_9_6_9, 0.4_0_6_5_5, 0.4_8_1_5_4] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def __snake_case ( self ) -> Union[str, Any]:
lowerCAmelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
lowerCAmelCase = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=A_ )
# warmup pass to apply optimizations
lowerCAmelCase = pipe(**self.get_dummy_inputs() )
lowerCAmelCase = self.get_dummy_inputs()
lowerCAmelCase = pipe(**A_ ).images
lowerCAmelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
lowerCAmelCase = np.array([0.5_2_7_6_1, 0.5_9_9_7_7, 0.4_9_0_3_3, 0.4_9_6_1_9, 0.5_4_2_8_2, 0.5_0_3_1_1, 0.4_7_6_0_0, 0.4_0_9_1_8, 0.4_5_2_0_3] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def __snake_case ( self ) -> List[Any]:
lowerCAmelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
lowerCAmelCase = EulerDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=A_ )
lowerCAmelCase = self.get_dummy_inputs()
lowerCAmelCase = pipe(**A_ ).images
lowerCAmelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
lowerCAmelCase = np.array([0.5_2_9_1_1, 0.6_0_0_0_4, 0.4_9_2_2_9, 0.4_9_8_0_5, 0.5_4_5_0_2, 0.5_0_6_8_0, 0.4_7_7_7_7, 0.4_1_0_2_8, 0.4_5_3_0_4] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def __snake_case ( self ) -> Dict:
lowerCAmelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
lowerCAmelCase = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=A_ )
lowerCAmelCase = self.get_dummy_inputs()
lowerCAmelCase = pipe(**A_ ).images
lowerCAmelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
lowerCAmelCase = np.array([0.5_2_9_1_1, 0.6_0_0_0_4, 0.4_9_2_2_9, 0.4_9_8_0_5, 0.5_4_5_0_2, 0.5_0_6_8_0, 0.4_7_7_7_7, 0.4_1_0_2_8, 0.4_5_3_0_4] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def __snake_case ( self ) -> List[str]:
lowerCAmelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
lowerCAmelCase = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=A_ )
lowerCAmelCase = self.get_dummy_inputs()
lowerCAmelCase = pipe(**A_ ).images
lowerCAmelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
lowerCAmelCase = np.array([0.6_5_3_3_1, 0.5_8_2_7_7, 0.4_8_2_0_4, 0.5_6_0_5_9, 0.5_3_6_6_5, 0.5_6_2_3_5, 0.5_0_9_6_9, 0.4_0_0_0_9, 0.4_6_5_5_2] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
@nightly
@require_onnxruntime
@require_torch_gpu
class __snake_case( unittest.TestCase ):
'''simple docstring'''
@property
def __snake_case ( self ) -> Optional[int]:
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def __snake_case ( self ) -> Dict:
lowerCAmelCase = ort.SessionOptions()
lowerCAmelCase = False
return options
def __snake_case ( self ) -> Dict:
lowerCAmelCase = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/img2img/sketch-mountains-input.jpg""" )
lowerCAmelCase = init_image.resize((768, 512) )
# using the PNDM scheduler by default
lowerCAmelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(
"""CompVis/stable-diffusion-v1-4""" , revision="""onnx""" , safety_checker=A_ , feature_extractor=A_ , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=A_ )
lowerCAmelCase = "A fantasy landscape, trending on artstation"
lowerCAmelCase = np.random.RandomState(0 )
lowerCAmelCase = pipe(
prompt=A_ , image=A_ , strength=0.7_5 , guidance_scale=7.5 , num_inference_steps=10 , generator=A_ , output_type="""np""" , )
lowerCAmelCase = output.images
lowerCAmelCase = images[0, 255:258, 383:386, -1]
assert images.shape == (1, 512, 768, 3)
lowerCAmelCase = np.array([0.4_9_0_9, 0.5_0_5_9, 0.5_3_7_2, 0.4_6_2_3, 0.4_8_7_6, 0.5_0_4_9, 0.4_8_2_0, 0.4_9_5_6, 0.5_0_1_9] )
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2e-2
def __snake_case ( self ) -> List[str]:
lowerCAmelCase = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/img2img/sketch-mountains-input.jpg""" )
lowerCAmelCase = init_image.resize((768, 512) )
lowerCAmelCase = LMSDiscreteScheduler.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , subfolder="""scheduler""" , revision="""onnx""" )
lowerCAmelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , revision="""onnx""" , scheduler=A_ , safety_checker=A_ , feature_extractor=A_ , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=A_ )
lowerCAmelCase = "A fantasy landscape, trending on artstation"
lowerCAmelCase = np.random.RandomState(0 )
lowerCAmelCase = pipe(
prompt=A_ , image=A_ , strength=0.7_5 , guidance_scale=7.5 , num_inference_steps=20 , generator=A_ , output_type="""np""" , )
lowerCAmelCase = output.images
lowerCAmelCase = images[0, 255:258, 383:386, -1]
assert images.shape == (1, 512, 768, 3)
lowerCAmelCase = np.array([0.8_0_4_3, 0.9_2_6, 0.9_5_8_1, 0.8_1_1_9, 0.8_9_5_4, 0.9_1_3, 0.7_2_0_9, 0.7_4_6_3, 0.7_4_3_1] )
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2e-2 | 433 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
'configuration_swinv2': ['SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Swinv2Config'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST',
'Swinv2ForImageClassification',
'Swinv2ForMaskedImageModeling',
'Swinv2Model',
'Swinv2PreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_swinva import (
SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
SwinvaForImageClassification,
SwinvaForMaskedImageModeling,
SwinvaModel,
SwinvaPreTrainedModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 25 | 0 |
"""simple docstring"""
import argparse
import torch
from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging
logging.set_verbosity_info()
def snake_case ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) -> str:
# Initialise PyTorch model
_snake_case = LxmertConfig.from_json_file(_a )
print(f"""Building PyTorch model from configuration: {config}""" )
_snake_case = LxmertForPreTraining(_a )
# Load weights from tf checkpoint
load_tf_weights_in_lxmert(_a , _a , _a )
# Save pytorch-model
print(f"""Save PyTorch model to {pytorch_dump_path}""" )
torch.save(model.state_dict() , _a )
if __name__ == "__main__":
snake_case = 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 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.'''
)
snake_case = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
| 103 |
from math import pi, sqrt, tan
def lowerCamelCase__ ( _a):
if side_length < 0:
raise ValueError("surface_area_cube() only accepts non-negative values")
return 6 * side_length**2
def lowerCamelCase__ ( _a , _a , _a):
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 lowerCamelCase__ ( _a):
if radius < 0:
raise ValueError("surface_area_sphere() only accepts non-negative values")
return 4 * pi * radius**2
def lowerCamelCase__ ( _a):
if radius < 0:
raise ValueError("surface_area_hemisphere() only accepts non-negative values")
return 3 * pi * radius**2
def lowerCamelCase__ ( _a , _a):
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 lowerCamelCase__ ( _a , _a , _a):
if radius_a < 0 or radius_a < 0 or height < 0:
raise ValueError(
"surface_area_conical_frustum() only accepts non-negative values")
SCREAMING_SNAKE_CASE : Any = (height**2 + (radius_a - radius_a) ** 2) ** 0.5
return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)
def lowerCamelCase__ ( _a , _a):
if radius < 0 or height < 0:
raise ValueError("surface_area_cylinder() only accepts non-negative values")
return 2 * pi * radius * (height + radius)
def lowerCamelCase__ ( _a , _a):
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(_a , 2) * torus_radius * tube_radius
def lowerCamelCase__ ( _a , _a):
if length < 0 or width < 0:
raise ValueError("area_rectangle() only accepts non-negative values")
return length * width
def lowerCamelCase__ ( _a):
if side_length < 0:
raise ValueError("area_square() only accepts non-negative values")
return side_length**2
def lowerCamelCase__ ( _a , _a):
if base < 0 or height < 0:
raise ValueError("area_triangle() only accepts non-negative values")
return (base * height) / 2
def lowerCamelCase__ ( _a , _a , _a):
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")
SCREAMING_SNAKE_CASE : List[str] = (sidea + sidea + sidea) / 2
SCREAMING_SNAKE_CASE : Optional[int] = sqrt(
semi_perimeter
* (semi_perimeter - sidea)
* (semi_perimeter - sidea)
* (semi_perimeter - sidea))
return area
def lowerCamelCase__ ( _a , _a):
if base < 0 or height < 0:
raise ValueError("area_parallelogram() only accepts non-negative values")
return base * height
def lowerCamelCase__ ( _a , _a , _a):
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 lowerCamelCase__ ( _a):
if radius < 0:
raise ValueError("area_circle() only accepts non-negative values")
return pi * radius**2
def lowerCamelCase__ ( _a , _a):
if radius_x < 0 or radius_y < 0:
raise ValueError("area_ellipse() only accepts non-negative values")
return pi * radius_x * radius_y
def lowerCamelCase__ ( _a , _a):
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 lowerCamelCase__ ( _a , _a):
if not isinstance(_a , _a) 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(10, 20) = }''')
print(F'''Square: {area_square(10) = }''')
print(F'''Triangle: {area_triangle(10, 10) = }''')
print(F'''Triangle: {area_triangle_three_sides(5, 12, 13) = }''')
print(F'''Parallelogram: {area_parallelogram(10, 20) = }''')
print(F'''Rhombus: {area_rhombus(10, 20) = }''')
print(F'''Trapezium: {area_trapezium(10, 20, 30) = }''')
print(F'''Circle: {area_circle(20) = }''')
print(F'''Ellipse: {area_ellipse(10, 20) = }''')
print('\nSurface Areas of various geometric shapes: \n')
print(F'''Cube: {surface_area_cube(20) = }''')
print(F'''Cuboid: {surface_area_cuboid(10, 20, 30) = }''')
print(F'''Sphere: {surface_area_sphere(20) = }''')
print(F'''Hemisphere: {surface_area_hemisphere(20) = }''')
print(F'''Cone: {surface_area_cone(10, 20) = }''')
print(F'''Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }''')
print(F'''Cylinder: {surface_area_cylinder(10, 20) = }''')
print(F'''Torus: {surface_area_torus(20, 10) = }''')
print(F'''Equilateral Triangle: {area_reg_polygon(3, 10) = }''')
print(F'''Square: {area_reg_polygon(4, 10) = }''')
print(F'''Reqular Pentagon: {area_reg_polygon(5, 10) = }''') | 25 | 0 |
class UpperCAmelCase :
'''simple docstring'''
def __init__( self : Tuple ,A : Union[str, Any] ):
__A = arr.split("," )
def UpperCamelCase_ ( self : Any ):
__A = [int(self.array[0] )] * len(self.array )
__A = [int(self.array[0] )] * len(self.array )
for i in range(1 ,len(self.array ) ):
__A = max(
int(self.array[i] ) + sum_value[i - 1] ,int(self.array[i] ) )
__A = max(sum_value[i] ,rear[i - 1] )
return rear[len(self.array ) - 1]
if __name__ == "__main__":
SCREAMING_SNAKE_CASE :Dict = input('please input some numbers:')
SCREAMING_SNAKE_CASE :List[Any] = SubArray(whole_array)
SCREAMING_SNAKE_CASE :List[Any] = array.solve_sub_array()
print(('the results is:', re))
| 55 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
'configuration_instructblip': [
'INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'InstructBlipConfig',
'InstructBlipQFormerConfig',
'InstructBlipVisionConfig',
],
'processing_instructblip': ['InstructBlipProcessor'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
'InstructBlipQFormerModel',
'InstructBlipPreTrainedModel',
'InstructBlipForConditionalGeneration',
'InstructBlipVisionModel',
]
if TYPE_CHECKING:
from .configuration_instructblip import (
INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
InstructBlipConfig,
InstructBlipQFormerConfig,
InstructBlipVisionConfig,
)
from .processing_instructblip import InstructBlipProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_instructblip import (
INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
InstructBlipForConditionalGeneration,
InstructBlipPreTrainedModel,
InstructBlipQFormerModel,
InstructBlipVisionModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 25 | 0 |
"""simple docstring"""
import gc
import threading
import time
import psutil
import torch
class UpperCAmelCase_ :
def __init__( self ) -> List[Any]:
lowercase__ : int = psutil.Process()
lowercase__ : int = False
def _UpperCAmelCase ( self ) -> List[str]:
lowercase__ : Dict = -1
while True:
lowercase__ : Tuple = max(self.process.memory_info().rss , self.cpu_memory_peak )
# can't sleep or will not catch the peak right (this comment is here on purpose)
if not self.peak_monitoring:
break
def _UpperCAmelCase ( self ) -> Optional[int]:
lowercase__ : List[Any] = True
lowercase__ : Tuple = threading.Thread(target=self.peak_monitor )
lowercase__ : Union[str, Any] = True
self.thread.start()
def _UpperCAmelCase ( self ) -> List[str]:
lowercase__ : List[str] = False
self.thread.join()
return self.cpu_memory_peak
_UpperCamelCase : Optional[int] = PeakCPUMemory()
def a_ ( ):
'''simple docstring'''
lowercase__ : Optional[int] = {"time": time.time()}
gc.collect()
torch.cuda.empty_cache()
# CPU mem
lowercase__ : List[Any] = psutil.Process().memory_info().rss
cpu_peak_tracker.start()
# GPU mem
for i in range(torch.cuda.device_count() ):
lowercase__ : Dict = torch.cuda.memory_allocated(_a )
torch.cuda.reset_peak_memory_stats()
return measures
def a_ ( _lowerCAmelCase : int ):
'''simple docstring'''
lowercase__ : Optional[int] = {"time": time.time() - start_measures["time"]}
gc.collect()
torch.cuda.empty_cache()
# CPU mem
lowercase__ : Tuple = (psutil.Process().memory_info().rss - start_measures["cpu"]) / 2**20
lowercase__ : Optional[Any] = (cpu_peak_tracker.stop() - start_measures["cpu"]) / 2**20
# GPU mem
for i in range(torch.cuda.device_count() ):
lowercase__ : Tuple = (torch.cuda.memory_allocated(_a ) - start_measures[str(_a )]) / 2**20
lowercase__ : Optional[Any] = (torch.cuda.max_memory_allocated(_a ) - start_measures[str(_a )]) / 2**20
return measures
def a_ ( _lowerCAmelCase : int , _lowerCAmelCase : str ):
'''simple docstring'''
print(f"""{description}:""" )
print(f"""- Time: {measures["time"]:.2f}s""" )
for i in range(torch.cuda.device_count() ):
print(f"""- GPU {i} allocated: {measures[str(_a )]:.2f}MiB""" )
lowercase__ : Tuple = measures[f"""{i}-peak"""]
print(f"""- GPU {i} peak: {peak:.2f}MiB""" )
print(f"""- CPU RAM allocated: {measures["cpu"]:.2f}MiB""" )
print(f"""- CPU RAM peak: {measures["cpu-peak"]:.2f}MiB""" )
| 599 |
from __future__ import annotations
def lowerCamelCase__ ( _a):
SCREAMING_SNAKE_CASE : Optional[Any] = 2
SCREAMING_SNAKE_CASE : Optional[int] = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(_a)
if n > 1:
factors.append(_a)
return factors
if __name__ == "__main__":
import doctest
doctest.testmod() | 25 | 0 |
"""simple docstring"""
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
A = {
'facebook/mask2former-swin-small-coco-instance': (
'https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json'
)
# See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}
A = logging.get_logger(__name__)
class _a ( __A):
__magic_name__ = """mask2former"""
__magic_name__ = ["""swin"""]
__magic_name__ = {"""hidden_size""": """hidden_dim"""}
def __init__( self : Dict , _lowercase : Optional[Dict] = None , _lowercase : int = 256 , _lowercase : int = 256 , _lowercase : int = 256 , _lowercase : int = 1024 , _lowercase : str = "relu" , _lowercase : int = 6 , _lowercase : int = 10 , _lowercase : int = 8 , _lowercase : float = 0.0 , _lowercase : int = 2048 , _lowercase : bool = False , _lowercase : bool = False , _lowercase : int = 4 , _lowercase : int = 255 , _lowercase : int = 100 , _lowercase : float = 0.1 , _lowercase : float = 2.0 , _lowercase : float = 5.0 , _lowercase : float = 5.0 , _lowercase : int = 12544 , _lowercase : float = 3.0 , _lowercase : float = 0.75 , _lowercase : float = 0.02 , _lowercase : float = 1.0 , _lowercase : bool = True , _lowercase : List[int] = [4, 8, 16, 32] , _lowercase : bool = None , **_lowercase : Optional[int] , ) -> List[Any]:
if backbone_config is None:
logger.info("`backbone_config` is `None`. Initializing the config with the default `Swin` backbone." )
snake_case : Tuple = CONFIG_MAPPING["swin"](
image_size=224 , in_channels=3 , patch_size=4 , embed_dim=96 , depths=[2, 2, 18, 2] , num_heads=[3, 6, 12, 24] , window_size=7 , drop_path_rate=0.3 , use_absolute_embeddings=_lowercase , out_features=["stage1", "stage2", "stage3", "stage4"] , )
if isinstance(_lowercase , _lowercase ):
snake_case : int = backbone_config.pop("model_type" )
snake_case : List[Any] = CONFIG_MAPPING[backbone_model_type]
snake_case : Tuple = config_class.from_dict(_lowercase )
# verify that the backbone is supported
if backbone_config.model_type not in self.backbones_supported:
logger.warning_once(
F'''Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. '''
F'''Supported model types: {",".join(self.backbones_supported )}''' )
snake_case : Optional[Any] = backbone_config
snake_case : int = feature_size
snake_case : Dict = mask_feature_size
snake_case : Any = hidden_dim
snake_case : Any = encoder_feedforward_dim
snake_case : Tuple = activation_function
snake_case : Tuple = encoder_layers
snake_case : Dict = decoder_layers
snake_case : List[Any] = num_attention_heads
snake_case : List[Any] = dropout
snake_case : int = dim_feedforward
snake_case : int = pre_norm
snake_case : Union[str, Any] = enforce_input_projection
snake_case : int = common_stride
snake_case : Optional[Any] = ignore_value
snake_case : Tuple = num_queries
snake_case : List[str] = no_object_weight
snake_case : Optional[int] = class_weight
snake_case : int = mask_weight
snake_case : Tuple = dice_weight
snake_case : Union[str, Any] = train_num_points
snake_case : Tuple = oversample_ratio
snake_case : int = importance_sample_ratio
snake_case : Any = init_std
snake_case : Union[str, Any] = init_xavier_std
snake_case : List[Any] = use_auxiliary_loss
snake_case : Optional[int] = feature_strides
snake_case : Tuple = output_auxiliary_logits
snake_case : str = decoder_layers
super().__init__(**_lowercase )
@classmethod
def __lowercase ( cls : Any , _lowercase : PretrainedConfig , **_lowercase : str ) -> Optional[int]:
return cls(
backbone_config=_lowercase , **_lowercase , )
def __lowercase ( self : Optional[Any] ) -> Dict[str, any]:
snake_case : Any = copy.deepcopy(self.__dict__ )
snake_case : int = self.backbone_config.to_dict()
snake_case : str = self.__class__.model_type
return output
| 449 |
from math import factorial, pi
def lowerCamelCase__ ( _a , _a = 30):
if not isinstance(_a , (int, float)):
raise ValueError("maclaurin_sin() requires either an int or float for theta")
if not isinstance(_a , _a) or accuracy <= 0:
raise ValueError("maclaurin_sin() requires a positive int for accuracy")
SCREAMING_SNAKE_CASE : int = float(_a)
SCREAMING_SNAKE_CASE : Dict = theta // (2 * pi)
theta -= 2 * div * pi
return sum(
(-1) ** r * theta ** (2 * r + 1) / factorial(2 * r + 1) for r in range(_a))
def lowerCamelCase__ ( _a , _a = 30):
if not isinstance(_a , (int, float)):
raise ValueError("maclaurin_cos() requires either an int or float for theta")
if not isinstance(_a , _a) or accuracy <= 0:
raise ValueError("maclaurin_cos() requires a positive int for accuracy")
SCREAMING_SNAKE_CASE : str = float(_a)
SCREAMING_SNAKE_CASE : Any = theta // (2 * pi)
theta -= 2 * div * pi
return sum((-1) ** r * theta ** (2 * r) / factorial(2 * r) for r in range(_a))
if __name__ == "__main__":
import doctest
doctest.testmod()
print(maclaurin_sin(10))
print(maclaurin_sin(-10))
print(maclaurin_sin(10, 15))
print(maclaurin_sin(-10, 15))
print(maclaurin_cos(5))
print(maclaurin_cos(-5))
print(maclaurin_cos(10, 15))
print(maclaurin_cos(-10, 15)) | 25 | 0 |
'''simple docstring'''
import os
from typing import BinaryIO, Optional, Union
import numpy as np
import pyarrow.parquet as pq
from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
def UpperCAmelCase_ ( __lowercase : Tuple ) -> List[str]:
'''simple docstring'''
_UpperCAmelCase = np.inf
def set_batch_size(__lowercase : Tuple ) -> None:
nonlocal batch_size
if isinstance(_a , _a ):
_UpperCAmelCase = min(_a , config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS )
elif isinstance(_a , _a ):
_UpperCAmelCase = min(_a , config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS )
elif isinstance(_a , _a ) and feature.dtype == "binary":
_UpperCAmelCase = min(_a , config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS )
_visit(_a , _a )
return None if batch_size is np.inf else batch_size
class A_ ( __A ):
def __init__( self : Union[str, Any] , snake_case_ : NestedDataStructureLike[PathLike] , snake_case_ : Optional[NamedSplit] = None , snake_case_ : Optional[Features] = None , snake_case_ : str = None , snake_case_ : bool = False , snake_case_ : bool = False , snake_case_ : Optional[int] = None , **snake_case_ : str , ):
super().__init__(
snake_case_ , split=snake_case_ , features=snake_case_ , cache_dir=snake_case_ , keep_in_memory=snake_case_ , streaming=snake_case_ , num_proc=snake_case_ , **snake_case_ , )
_UpperCAmelCase = path_or_paths if isinstance(snake_case_ , snake_case_ ) else {self.split: path_or_paths}
_UpperCAmelCase = _PACKAGED_DATASETS_MODULES["parquet"][1]
_UpperCAmelCase = Parquet(
cache_dir=snake_case_ , data_files=snake_case_ , features=snake_case_ , hash=snake_case_ , **snake_case_ , )
def lowercase ( self : int ):
if self.streaming:
_UpperCAmelCase = self.builder.as_streaming_dataset(split=self.split )
# Build regular (map-style) dataset
else:
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
self.builder.download_and_prepare(
download_config=snake_case_ , download_mode=snake_case_ , verification_mode=snake_case_ , base_path=snake_case_ , num_proc=self.num_proc , )
_UpperCAmelCase = self.builder.as_dataset(
split=self.split , verification_mode=snake_case_ , in_memory=self.keep_in_memory )
return dataset
class A_ :
def __init__( self : Union[str, Any] , snake_case_ : Dataset , snake_case_ : Union[PathLike, BinaryIO] , snake_case_ : Optional[int] = None , **snake_case_ : Optional[Any] , ):
_UpperCAmelCase = dataset
_UpperCAmelCase = path_or_buf
_UpperCAmelCase = batch_size or get_writer_batch_size(dataset.features )
_UpperCAmelCase = parquet_writer_kwargs
def lowercase ( self : Optional[int] ):
_UpperCAmelCase = self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE
if isinstance(self.path_or_buf , (str, bytes, os.PathLike) ):
with open(self.path_or_buf , "wb+" ) as buffer:
_UpperCAmelCase = self._write(file_obj=snake_case_ , batch_size=snake_case_ , **self.parquet_writer_kwargs )
else:
_UpperCAmelCase = self._write(file_obj=self.path_or_buf , batch_size=snake_case_ , **self.parquet_writer_kwargs )
return written
def lowercase ( self : Optional[Any] , snake_case_ : BinaryIO , snake_case_ : int , **snake_case_ : str ):
_UpperCAmelCase = 0
_UpperCAmelCase = parquet_writer_kwargs.pop("path_or_buf" , snake_case_ )
_UpperCAmelCase = self.dataset.features.arrow_schema
_UpperCAmelCase = pq.ParquetWriter(snake_case_ , schema=snake_case_ , **snake_case_ )
for offset in logging.tqdm(
range(0 , len(self.dataset ) , snake_case_ ) , unit="ba" , disable=not logging.is_progress_bar_enabled() , desc="Creating parquet from Arrow format" , ):
_UpperCAmelCase = query_table(
table=self.dataset._data , key=slice(snake_case_ , offset + batch_size ) , indices=self.dataset._indices if self.dataset._indices is not None else None , )
writer.write_table(snake_case_ )
written += batch.nbytes
writer.close()
return written
| 236 |
from __future__ import annotations
import math
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Dict , a : int ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = size
# approximate the overall size of segment tree with given value
SCREAMING_SNAKE_CASE : Any = [0 for i in range(0 , 4 * size )]
# create array to store lazy update
SCREAMING_SNAKE_CASE : Union[str, Any] = [0 for i in range(0 , 4 * size )]
SCREAMING_SNAKE_CASE : Any = [0 for i in range(0 , 4 * size )] # flag for lazy update
def __UpperCamelCase ( self : Tuple , a : int ) -> int:
"""simple docstring"""
return idx * 2
def __UpperCamelCase ( self : str , a : int ) -> int:
"""simple docstring"""
return idx * 2 + 1
def __UpperCamelCase ( self : int , a : int , a : int , a : int , a : list[int] ) -> None:
"""simple docstring"""
if left_element == right_element:
SCREAMING_SNAKE_CASE : int = a[left_element - 1]
else:
SCREAMING_SNAKE_CASE : Optional[int] = (left_element + right_element) // 2
self.build(self.left(a ) , a , a , a )
self.build(self.right(a ) , mid + 1 , a , a )
SCREAMING_SNAKE_CASE : List[Any] = max(
self.segment_tree[self.left(a )] , self.segment_tree[self.right(a )] )
def __UpperCamelCase ( self : Optional[Any] , a : int , a : int , a : int , a : int , a : int , a : int ) -> bool:
"""simple docstring"""
if self.flag[idx] is True:
SCREAMING_SNAKE_CASE : Any = self.lazy[idx]
SCREAMING_SNAKE_CASE : List[str] = False
if left_element != right_element:
SCREAMING_SNAKE_CASE : Optional[Any] = self.lazy[idx]
SCREAMING_SNAKE_CASE : int = self.lazy[idx]
SCREAMING_SNAKE_CASE : Any = True
SCREAMING_SNAKE_CASE : List[Any] = True
if right_element < a or left_element > b:
return True
if left_element >= a and right_element <= b:
SCREAMING_SNAKE_CASE : Optional[Any] = val
if left_element != right_element:
SCREAMING_SNAKE_CASE : str = val
SCREAMING_SNAKE_CASE : str = val
SCREAMING_SNAKE_CASE : Tuple = True
SCREAMING_SNAKE_CASE : Optional[Any] = True
return True
SCREAMING_SNAKE_CASE : int = (left_element + right_element) // 2
self.update(self.left(a ) , a , a , a , a , a )
self.update(self.right(a ) , mid + 1 , a , a , a , a )
SCREAMING_SNAKE_CASE : Optional[int] = max(
self.segment_tree[self.left(a )] , self.segment_tree[self.right(a )] )
return True
def __UpperCamelCase ( self : Dict , a : int , a : int , a : int , a : int , a : int ) -> int | float:
"""simple docstring"""
if self.flag[idx] is True:
SCREAMING_SNAKE_CASE : int = self.lazy[idx]
SCREAMING_SNAKE_CASE : List[Any] = False
if left_element != right_element:
SCREAMING_SNAKE_CASE : Optional[Any] = self.lazy[idx]
SCREAMING_SNAKE_CASE : Optional[Any] = self.lazy[idx]
SCREAMING_SNAKE_CASE : Optional[Any] = True
SCREAMING_SNAKE_CASE : Union[str, Any] = True
if right_element < a or left_element > b:
return -math.inf
if left_element >= a and right_element <= b:
return self.segment_tree[idx]
SCREAMING_SNAKE_CASE : Dict = (left_element + right_element) // 2
SCREAMING_SNAKE_CASE : Tuple = self.query(self.left(a ) , a , a , a , a )
SCREAMING_SNAKE_CASE : Tuple = self.query(self.right(a ) , mid + 1 , a , a , a )
return max(a , a )
def __str__( self : str ) -> str:
"""simple docstring"""
return str([self.query(1 , 1 , self.size , a , a ) for i in range(1 , self.size + 1 )] )
if __name__ == "__main__":
a_ = [1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
a_ = 15
a_ = SegmentTree(size)
segt.build(1, 1, size, A)
print(segt.query(1, 1, size, 4, 6))
print(segt.query(1, 1, size, 7, 11))
print(segt.query(1, 1, size, 7, 12))
segt.update(1, 1, size, 1, 3, 111)
print(segt.query(1, 1, size, 1, 15))
segt.update(1, 1, size, 7, 8, 235)
print(segt) | 25 | 0 |
UpperCamelCase__ : str = range(2, 20 + 1)
UpperCamelCase__ : Dict = [10**k for k in range(ks[-1] + 1)]
UpperCamelCase__ : Tuple = {}
def __UpperCAmelCase ( lowerCamelCase_ : str , lowerCamelCase_ : List[str] , lowerCamelCase_ : Optional[int] , lowerCamelCase_ : Optional[int] ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Dict = sum(a_i[j] for j in range(_a , len(_a ) ) )
SCREAMING_SNAKE_CASE_ : str = sum(a_i[j] * base[j] for j in range(min(len(_a ) , _a ) ) )
SCREAMING_SNAKE_CASE_ : Any = 0, 0
SCREAMING_SNAKE_CASE_ : List[Any] = n - i
SCREAMING_SNAKE_CASE_ : List[Any] = memo.get(_a )
if sub_memo is not None:
SCREAMING_SNAKE_CASE_ : Dict = sub_memo.get(_a )
if jumps is not None and len(_a ) > 0:
# find and make the largest jump without going over
SCREAMING_SNAKE_CASE_ : Dict = -1
for _k in range(len(_a ) - 1 , -1 , -1 ):
if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
SCREAMING_SNAKE_CASE_ : str = _k
break
if max_jump >= 0:
SCREAMING_SNAKE_CASE_ : Dict = jumps[max_jump]
# since the difference between jumps is cached, add c
SCREAMING_SNAKE_CASE_ : List[str] = diff + c
for j in range(min(_a , len(_a ) ) ):
SCREAMING_SNAKE_CASE_ : Optional[Any] = divmod(_a , 10 )
if new_c > 0:
add(_a , _a , _a )
else:
SCREAMING_SNAKE_CASE_ : Optional[Any] = []
else:
SCREAMING_SNAKE_CASE_ : str = {c: []}
SCREAMING_SNAKE_CASE_ : Optional[Any] = sub_memo
if dn >= max_dn or c + diff >= base[k]:
return diff, dn
if k > ks[0]:
while True:
# keep doing smaller jumps
SCREAMING_SNAKE_CASE_ : List[Any] = next_term(_a , k - 1 , i + dn , _a )
diff += _diff
dn += terms_jumped
if dn >= max_dn or c + diff >= base[k]:
break
else:
# would be too small a jump, just compute sequential terms instead
SCREAMING_SNAKE_CASE_ : int = compute(_a , _a , i + dn , _a )
diff += _diff
dn += terms_jumped
SCREAMING_SNAKE_CASE_ : Union[str, Any] = sub_memo[c]
# keep jumps sorted by # of terms skipped
SCREAMING_SNAKE_CASE_ : int = 0
while j < len(_a ):
if jumps[j][1] > dn:
break
j += 1
# cache the jump for this value digitsum(b) and c
sub_memo[c].insert(_a , (diff, dn, k) )
return (diff, dn)
def __UpperCAmelCase ( lowerCamelCase_ : str , lowerCamelCase_ : Tuple , lowerCamelCase_ : Tuple , lowerCamelCase_ : Optional[Any] ) -> str:
"""simple docstring"""
if i >= n:
return 0, i
if k > len(_a ):
a_i.extend([0 for _ in range(k - len(_a ) )] )
# note: a_i -> b * 10^k + c
# ds_b -> digitsum(b)
# ds_c -> digitsum(c)
SCREAMING_SNAKE_CASE_ : Tuple = i
SCREAMING_SNAKE_CASE_ : Any = 0, 0, 0
for j in range(len(_a ) ):
if j >= k:
ds_b += a_i[j]
else:
ds_c += a_i[j]
while i < n:
i += 1
SCREAMING_SNAKE_CASE_ : str = ds_c + ds_b
diff += addend
SCREAMING_SNAKE_CASE_ : str = 0
for j in range(_a ):
SCREAMING_SNAKE_CASE_ : str = a_i[j] + addend
SCREAMING_SNAKE_CASE_ : Union[str, Any] = divmod(_a , 10 )
ds_c += a_i[j]
if addend > 0:
break
if addend > 0:
add(_a , _a , _a )
return diff, i - start_i
def __UpperCAmelCase ( lowerCamelCase_ : Dict , lowerCamelCase_ : Optional[Any] , lowerCamelCase_ : Optional[int] ) -> int:
"""simple docstring"""
for j in range(_a , len(_a ) ):
SCREAMING_SNAKE_CASE_ : Tuple = digits[j] + addend
if s >= 10:
SCREAMING_SNAKE_CASE_ : List[Any] = divmod(_a , 10 )
SCREAMING_SNAKE_CASE_ : int = addend // 10 + quotient
else:
SCREAMING_SNAKE_CASE_ : Union[str, Any] = s
SCREAMING_SNAKE_CASE_ : List[Any] = addend // 10
if addend == 0:
break
while addend > 0:
SCREAMING_SNAKE_CASE_ : str = divmod(_a , 10 )
digits.append(_a )
def __UpperCAmelCase ( lowerCamelCase_ : Any = 10**15 ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[str] = [1]
SCREAMING_SNAKE_CASE_ : Any = 1
SCREAMING_SNAKE_CASE_ : List[Any] = 0
while True:
SCREAMING_SNAKE_CASE_ : Optional[Any] = next_term(_a , 20 , i + dn , _a )
dn += terms_jumped
if dn == n - i:
break
SCREAMING_SNAKE_CASE_ : int = 0
for j in range(len(_a ) ):
a_n += digits[j] * 10**j
return a_n
if __name__ == "__main__":
print(F"""{solution() = }""")
| 105 |
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
@slow
@require_torch_gpu
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def __UpperCamelCase ( self : Dict ) -> Tuple:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __UpperCamelCase ( self : Optional[int] ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = StableDiffusionKDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4" )
SCREAMING_SNAKE_CASE : str = sd_pipe.to(a )
sd_pipe.set_progress_bar_config(disable=a )
sd_pipe.set_scheduler("sample_euler" )
SCREAMING_SNAKE_CASE : Optional[int] = "A painting of a squirrel eating a burger"
SCREAMING_SNAKE_CASE : Any = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : List[Any] = sd_pipe([prompt] , generator=a , guidance_scale=9.0 , num_inference_steps=20 , output_type="np" )
SCREAMING_SNAKE_CASE : Tuple = output.images
SCREAMING_SNAKE_CASE : Any = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE : Union[str, Any] = np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def __UpperCamelCase ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base" )
SCREAMING_SNAKE_CASE : Tuple = sd_pipe.to(a )
sd_pipe.set_progress_bar_config(disable=a )
sd_pipe.set_scheduler("sample_euler" )
SCREAMING_SNAKE_CASE : List[str] = "A painting of a squirrel eating a burger"
SCREAMING_SNAKE_CASE : List[str] = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Union[str, Any] = sd_pipe([prompt] , generator=a , guidance_scale=9.0 , num_inference_steps=20 , output_type="np" )
SCREAMING_SNAKE_CASE : List[Any] = output.images
SCREAMING_SNAKE_CASE : Tuple = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE : int = np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-1
def __UpperCamelCase ( self : Tuple ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base" )
SCREAMING_SNAKE_CASE : Union[str, Any] = sd_pipe.to(a )
sd_pipe.set_progress_bar_config(disable=a )
sd_pipe.set_scheduler("sample_dpmpp_2m" )
SCREAMING_SNAKE_CASE : str = "A painting of a squirrel eating a burger"
SCREAMING_SNAKE_CASE : Any = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : str = sd_pipe(
[prompt] , generator=a , guidance_scale=7.5 , num_inference_steps=15 , output_type="np" , use_karras_sigmas=a , )
SCREAMING_SNAKE_CASE : str = output.images
SCREAMING_SNAKE_CASE : Optional[int] = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE : int = np.array(
[0.1138_1689, 0.1211_2921, 0.138_9457, 0.1254_9606, 0.124_4964, 0.1083_1517, 0.1156_2866, 0.1086_7816, 0.1049_9048] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2 | 25 | 0 |
'''simple docstring'''
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class lowerCamelCase_ ( __A ):
_lowerCAmelCase : Union[str, Any] = (PNDMScheduler,)
_lowerCAmelCase : List[str] = (('num_inference_steps', 5_0),)
def __lowercase ( self : List[Any] , **lowerCAmelCase__ : Optional[int] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = {
"num_train_timesteps": 10_00,
"beta_start": 0.0001,
"beta_end": 0.02,
"beta_schedule": "linear",
}
config.update(**lowerCAmelCase__ )
return config
def __lowercase ( self : Optional[int] , lowerCAmelCase__ : List[str]=0 , **lowerCAmelCase__ : int ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = dict(self.forward_default_kwargs )
SCREAMING_SNAKE_CASE : Union[str, Any] = kwargs.pop('''num_inference_steps''' , lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : str = self.dummy_sample
SCREAMING_SNAKE_CASE : Union[str, Any] = 0.1 * sample
SCREAMING_SNAKE_CASE : Any = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
for scheduler_class in self.scheduler_classes:
SCREAMING_SNAKE_CASE : Optional[Any] = self.get_scheduler_config(**lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : Any = scheduler_class(**lowerCAmelCase__ )
scheduler.set_timesteps(lowerCAmelCase__ )
# copy over dummy past residuals
SCREAMING_SNAKE_CASE : str = dummy_past_residuals[:]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : List[Any] = scheduler_class.from_pretrained(lowerCAmelCase__ )
new_scheduler.set_timesteps(lowerCAmelCase__ )
# copy over dummy past residuals
SCREAMING_SNAKE_CASE : Optional[Any] = dummy_past_residuals[:]
SCREAMING_SNAKE_CASE : Dict = scheduler.step_prk(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , **lowerCAmelCase__ ).prev_sample
SCREAMING_SNAKE_CASE : int = new_scheduler.step_prk(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , **lowerCAmelCase__ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
SCREAMING_SNAKE_CASE : List[Any] = scheduler.step_plms(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , **lowerCAmelCase__ ).prev_sample
SCREAMING_SNAKE_CASE : Optional[Any] = new_scheduler.step_plms(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , **lowerCAmelCase__ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def __lowercase ( self : Optional[int] ):
"""simple docstring"""
pass
def __lowercase ( self : List[str] , lowerCAmelCase__ : Optional[int]=0 , **lowerCAmelCase__ : Dict ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = dict(self.forward_default_kwargs )
SCREAMING_SNAKE_CASE : List[Any] = kwargs.pop('''num_inference_steps''' , lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : Optional[int] = self.dummy_sample
SCREAMING_SNAKE_CASE : Union[str, Any] = 0.1 * sample
SCREAMING_SNAKE_CASE : Any = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
for scheduler_class in self.scheduler_classes:
SCREAMING_SNAKE_CASE : Optional[Any] = self.get_scheduler_config()
SCREAMING_SNAKE_CASE : Optional[int] = scheduler_class(**lowerCAmelCase__ )
scheduler.set_timesteps(lowerCAmelCase__ )
# copy over dummy past residuals (must be after setting timesteps)
SCREAMING_SNAKE_CASE : Dict = dummy_past_residuals[:]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : Any = scheduler_class.from_pretrained(lowerCAmelCase__ )
# copy over dummy past residuals
new_scheduler.set_timesteps(lowerCAmelCase__ )
# copy over dummy past residual (must be after setting timesteps)
SCREAMING_SNAKE_CASE : int = dummy_past_residuals[:]
SCREAMING_SNAKE_CASE : Optional[Any] = scheduler.step_prk(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , **lowerCAmelCase__ ).prev_sample
SCREAMING_SNAKE_CASE : Union[str, Any] = new_scheduler.step_prk(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , **lowerCAmelCase__ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
SCREAMING_SNAKE_CASE : str = scheduler.step_plms(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , **lowerCAmelCase__ ).prev_sample
SCREAMING_SNAKE_CASE : List[str] = new_scheduler.step_plms(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , **lowerCAmelCase__ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def __lowercase ( self : Optional[int] , **lowerCAmelCase__ : int ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE : List[str] = self.get_scheduler_config(**lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : str = scheduler_class(**lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : List[Any] = 10
SCREAMING_SNAKE_CASE : List[Any] = self.dummy_model()
SCREAMING_SNAKE_CASE : Optional[int] = self.dummy_sample_deter
scheduler.set_timesteps(lowerCAmelCase__ )
for i, t in enumerate(scheduler.prk_timesteps ):
SCREAMING_SNAKE_CASE : Union[str, Any] = model(lowerCAmelCase__ , lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : Any = scheduler.step_prk(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ).prev_sample
for i, t in enumerate(scheduler.plms_timesteps ):
SCREAMING_SNAKE_CASE : Dict = model(lowerCAmelCase__ , lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : List[str] = scheduler.step_plms(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ).prev_sample
return sample
def __lowercase ( self : List[str] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = dict(self.forward_default_kwargs )
SCREAMING_SNAKE_CASE : int = kwargs.pop('''num_inference_steps''' , lowerCAmelCase__ )
for scheduler_class in self.scheduler_classes:
SCREAMING_SNAKE_CASE : Dict = self.get_scheduler_config()
SCREAMING_SNAKE_CASE : str = scheduler_class(**lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : List[str] = self.dummy_sample
SCREAMING_SNAKE_CASE : Any = 0.1 * sample
if num_inference_steps is not None and hasattr(lowerCAmelCase__ , '''set_timesteps''' ):
scheduler.set_timesteps(lowerCAmelCase__ )
elif num_inference_steps is not None and not hasattr(lowerCAmelCase__ , '''set_timesteps''' ):
SCREAMING_SNAKE_CASE : str = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
SCREAMING_SNAKE_CASE : List[Any] = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
SCREAMING_SNAKE_CASE : Dict = dummy_past_residuals[:]
SCREAMING_SNAKE_CASE : Any = scheduler.step_prk(lowerCAmelCase__ , 0 , lowerCAmelCase__ , **lowerCAmelCase__ ).prev_sample
SCREAMING_SNAKE_CASE : str = scheduler.step_prk(lowerCAmelCase__ , 1 , lowerCAmelCase__ , **lowerCAmelCase__ ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
SCREAMING_SNAKE_CASE : List[str] = scheduler.step_plms(lowerCAmelCase__ , 0 , lowerCAmelCase__ , **lowerCAmelCase__ ).prev_sample
SCREAMING_SNAKE_CASE : int = scheduler.step_plms(lowerCAmelCase__ , 1 , lowerCAmelCase__ , **lowerCAmelCase__ ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def __lowercase ( self : Dict ):
"""simple docstring"""
for timesteps in [1_00, 10_00]:
self.check_over_configs(num_train_timesteps=lowerCAmelCase__ )
def __lowercase ( self : List[Any] ):
"""simple docstring"""
for steps_offset in [0, 1]:
self.check_over_configs(steps_offset=lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : Tuple = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE : List[Any] = self.get_scheduler_config(steps_offset=1 )
SCREAMING_SNAKE_CASE : List[str] = scheduler_class(**lowerCAmelCase__ )
scheduler.set_timesteps(10 )
assert torch.equal(
scheduler.timesteps , torch.LongTensor(
[9_01, 8_51, 8_51, 8_01, 8_01, 7_51, 7_51, 7_01, 7_01, 6_51, 6_51, 6_01, 6_01, 5_01, 4_01, 3_01, 2_01, 1_01, 1] ) , )
def __lowercase ( self : Dict ):
"""simple docstring"""
for beta_start, beta_end in zip([0.0001, 0.001] , [0.002, 0.02] ):
self.check_over_configs(beta_start=lowerCAmelCase__ , beta_end=lowerCAmelCase__ )
def __lowercase ( self : Optional[Any] ):
"""simple docstring"""
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=lowerCAmelCase__ )
def __lowercase ( self : Optional[Any] ):
"""simple docstring"""
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=lowerCAmelCase__ )
def __lowercase ( self : Any ):
"""simple docstring"""
for t in [1, 5, 10]:
self.check_over_forward(time_step=lowerCAmelCase__ )
def __lowercase ( self : Optional[Any] ):
"""simple docstring"""
for t, num_inference_steps in zip([1, 5, 10] , [10, 50, 1_00] ):
self.check_over_forward(num_inference_steps=lowerCAmelCase__ )
def __lowercase ( self : Union[str, Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = 27
for scheduler_class in self.scheduler_classes:
SCREAMING_SNAKE_CASE : str = self.dummy_sample
SCREAMING_SNAKE_CASE : Tuple = 0.1 * sample
SCREAMING_SNAKE_CASE : Optional[Any] = self.get_scheduler_config()
SCREAMING_SNAKE_CASE : Union[str, Any] = scheduler_class(**lowerCAmelCase__ )
scheduler.set_timesteps(lowerCAmelCase__ )
# before power of 3 fix, would error on first step, so we only need to do two
for i, t in enumerate(scheduler.prk_timesteps[:2] ):
SCREAMING_SNAKE_CASE : Dict = scheduler.step_prk(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ).prev_sample
def __lowercase ( self : Optional[int] ):
"""simple docstring"""
with self.assertRaises(lowerCAmelCase__ ):
SCREAMING_SNAKE_CASE : List[Any] = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE : List[str] = self.get_scheduler_config()
SCREAMING_SNAKE_CASE : Optional[Any] = scheduler_class(**lowerCAmelCase__ )
scheduler.step_plms(self.dummy_sample , 1 , self.dummy_sample ).prev_sample
def __lowercase ( self : List[Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = self.full_loop()
SCREAMING_SNAKE_CASE : List[Any] = torch.sum(torch.abs(lowerCAmelCase__ ) )
SCREAMING_SNAKE_CASE : Optional[Any] = torch.mean(torch.abs(lowerCAmelCase__ ) )
assert abs(result_sum.item() - 198.1318 ) < 1e-2
assert abs(result_mean.item() - 0.2580 ) < 1e-3
def __lowercase ( self : Any ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = self.full_loop(prediction_type='''v_prediction''' )
SCREAMING_SNAKE_CASE : List[str] = torch.sum(torch.abs(lowerCAmelCase__ ) )
SCREAMING_SNAKE_CASE : List[str] = torch.mean(torch.abs(lowerCAmelCase__ ) )
assert abs(result_sum.item() - 67.3986 ) < 1e-2
assert abs(result_mean.item() - 0.0878 ) < 1e-3
def __lowercase ( self : List[str] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = self.full_loop(set_alpha_to_one=lowerCAmelCase__ , beta_start=0.01 )
SCREAMING_SNAKE_CASE : List[str] = torch.sum(torch.abs(lowerCAmelCase__ ) )
SCREAMING_SNAKE_CASE : Optional[int] = torch.mean(torch.abs(lowerCAmelCase__ ) )
assert abs(result_sum.item() - 230.0399 ) < 1e-2
assert abs(result_mean.item() - 0.2995 ) < 1e-3
def __lowercase ( self : int ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = self.full_loop(set_alpha_to_one=lowerCAmelCase__ , beta_start=0.01 )
SCREAMING_SNAKE_CASE : Optional[int] = torch.sum(torch.abs(lowerCAmelCase__ ) )
SCREAMING_SNAKE_CASE : Dict = torch.mean(torch.abs(lowerCAmelCase__ ) )
assert abs(result_sum.item() - 186.9482 ) < 1e-2
assert abs(result_mean.item() - 0.2434 ) < 1e-3
| 527 |
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class _UpperCamelCase ( __A , unittest.TestCase ):
'''simple docstring'''
pass
@nightly
@require_onnxruntime
@require_torch_gpu
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
@property
def __UpperCamelCase ( self : List[Any] ) -> List[str]:
"""simple docstring"""
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def __UpperCamelCase ( self : int ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = ort.SessionOptions()
SCREAMING_SNAKE_CASE : Union[str, Any] = False
return options
def __UpperCamelCase ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo.png" )
SCREAMING_SNAKE_CASE : Optional[Any] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo_mask.png" )
SCREAMING_SNAKE_CASE : int = OnnxStableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting" , revision="onnx" , safety_checker=a , feature_extractor=a , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=a )
SCREAMING_SNAKE_CASE : Optional[Any] = "A red cat sitting on a park bench"
SCREAMING_SNAKE_CASE : Tuple = np.random.RandomState(0 )
SCREAMING_SNAKE_CASE : Optional[int] = pipe(
prompt=a , image=a , mask_image=a , guidance_scale=7.5 , num_inference_steps=10 , generator=a , output_type="np" , )
SCREAMING_SNAKE_CASE : List[Any] = output.images
SCREAMING_SNAKE_CASE : Union[str, Any] = images[0, 255:258, 255:258, -1]
assert images.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE : int = np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def __UpperCamelCase ( self : List[Any] ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo.png" )
SCREAMING_SNAKE_CASE : Optional[Any] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo_mask.png" )
SCREAMING_SNAKE_CASE : Optional[Any] = LMSDiscreteScheduler.from_pretrained(
"runwayml/stable-diffusion-inpainting" , subfolder="scheduler" , revision="onnx" )
SCREAMING_SNAKE_CASE : Union[str, Any] = OnnxStableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting" , revision="onnx" , scheduler=a , safety_checker=a , feature_extractor=a , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=a )
SCREAMING_SNAKE_CASE : List[Any] = "A red cat sitting on a park bench"
SCREAMING_SNAKE_CASE : Dict = np.random.RandomState(0 )
SCREAMING_SNAKE_CASE : Tuple = pipe(
prompt=a , image=a , mask_image=a , guidance_scale=7.5 , num_inference_steps=20 , generator=a , output_type="np" , )
SCREAMING_SNAKE_CASE : List[str] = output.images
SCREAMING_SNAKE_CASE : Optional[int] = images[0, 255:258, 255:258, -1]
assert images.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE : Any = np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3 | 25 | 0 |
import inspect
import unittest
from transformers import MobileViTConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MobileViTForImageClassification, MobileViTForSemanticSegmentation, MobileViTModel
from transformers.models.mobilevit.modeling_mobilevit import MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import MobileViTImageProcessor
class SCREAMING_SNAKE_CASE ( __A ):
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( self : List[str] ) -> Dict:
"""simple docstring"""
__lowerCAmelCase : Optional[int] = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(lowerCAmelCase , """hidden_sizes""" ) )
self.parent.assertTrue(hasattr(lowerCAmelCase , """neck_hidden_sizes""" ) )
self.parent.assertTrue(hasattr(lowerCAmelCase , """num_attention_heads""" ) )
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self : int , lowerCAmelCase : str , lowerCAmelCase : Union[str, Any]=13 , lowerCAmelCase : Dict=32 , lowerCAmelCase : Optional[Any]=2 , lowerCAmelCase : str=3 , lowerCAmelCase : Optional[Any]=6_40 , lowerCAmelCase : List[str]=4 , lowerCAmelCase : Optional[int]="silu" , lowerCAmelCase : Optional[int]=3 , lowerCAmelCase : str=32 , lowerCAmelCase : List[Any]=0.1 , lowerCAmelCase : Optional[Any]=0.1 , lowerCAmelCase : Optional[int]=0.1 , lowerCAmelCase : Any=0.02 , lowerCAmelCase : int=True , lowerCAmelCase : Dict=True , lowerCAmelCase : Dict=10 , lowerCAmelCase : Optional[int]=None , ) -> Dict:
"""simple docstring"""
__lowerCAmelCase : Dict = parent
__lowerCAmelCase : Union[str, Any] = batch_size
__lowerCAmelCase : Any = image_size
__lowerCAmelCase : Dict = patch_size
__lowerCAmelCase : Optional[Any] = num_channels
__lowerCAmelCase : int = last_hidden_size
__lowerCAmelCase : str = num_attention_heads
__lowerCAmelCase : Tuple = hidden_act
__lowerCAmelCase : Tuple = conv_kernel_size
__lowerCAmelCase : List[Any] = output_stride
__lowerCAmelCase : List[str] = hidden_dropout_prob
__lowerCAmelCase : List[str] = attention_probs_dropout_prob
__lowerCAmelCase : List[Any] = classifier_dropout_prob
__lowerCAmelCase : Optional[int] = use_labels
__lowerCAmelCase : Optional[int] = is_training
__lowerCAmelCase : Any = num_labels
__lowerCAmelCase : Dict = initializer_range
__lowerCAmelCase : List[Any] = scope
def SCREAMING_SNAKE_CASE ( self : Dict ) -> Optional[Any]:
"""simple docstring"""
__lowerCAmelCase : Optional[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__lowerCAmelCase : Any = None
__lowerCAmelCase : Any = None
if self.use_labels:
__lowerCAmelCase : Dict = ids_tensor([self.batch_size] , self.num_labels )
__lowerCAmelCase : Optional[int] = ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels )
__lowerCAmelCase : List[str] = self.get_config()
return config, pixel_values, labels, pixel_labels
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
return MobileViTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , num_attention_heads=self.num_attention_heads , hidden_act=self.hidden_act , conv_kernel_size=self.conv_kernel_size , output_stride=self.output_stride , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , classifier_dropout_prob=self.classifier_dropout_prob , initializer_range=self.initializer_range , )
def SCREAMING_SNAKE_CASE ( self : Optional[Any] , lowerCAmelCase : Optional[Any] , lowerCAmelCase : Optional[int] , lowerCAmelCase : Optional[int] , lowerCAmelCase : Tuple ) -> Any:
"""simple docstring"""
__lowerCAmelCase : Optional[int] = MobileViTModel(config=lowerCAmelCase )
model.to(lowerCAmelCase )
model.eval()
__lowerCAmelCase : Optional[Any] = model(lowerCAmelCase )
self.parent.assertEqual(
result.last_hidden_state.shape , (
self.batch_size,
self.last_hidden_size,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
def SCREAMING_SNAKE_CASE ( self : Any , lowerCAmelCase : Tuple , lowerCAmelCase : Any , lowerCAmelCase : List[Any] , lowerCAmelCase : Union[str, Any] ) -> Tuple:
"""simple docstring"""
__lowerCAmelCase : str = self.num_labels
__lowerCAmelCase : Union[str, Any] = MobileViTForImageClassification(lowerCAmelCase )
model.to(lowerCAmelCase )
model.eval()
__lowerCAmelCase : Optional[int] = model(lowerCAmelCase , labels=lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def SCREAMING_SNAKE_CASE ( self : Optional[int] , lowerCAmelCase : List[str] , lowerCAmelCase : Tuple , lowerCAmelCase : Optional[int] , lowerCAmelCase : Dict ) -> int:
"""simple docstring"""
__lowerCAmelCase : Union[str, Any] = self.num_labels
__lowerCAmelCase : Union[str, Any] = MobileViTForSemanticSegmentation(lowerCAmelCase )
model.to(lowerCAmelCase )
model.eval()
__lowerCAmelCase : Tuple = model(lowerCAmelCase )
self.parent.assertEqual(
result.logits.shape , (
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
__lowerCAmelCase : Tuple = model(lowerCAmelCase , labels=lowerCAmelCase )
self.parent.assertEqual(
result.logits.shape , (
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
__lowerCAmelCase : int = self.prepare_config_and_inputs()
__lowerCAmelCase : List[str] = config_and_inputs
__lowerCAmelCase : Any = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class SCREAMING_SNAKE_CASE ( __A , __A , unittest.TestCase ):
"""simple docstring"""
lowerCamelCase : List[str] =(
(MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
if is_torch_available()
else ()
)
lowerCamelCase : str =(
{
"feature-extraction": MobileViTModel,
"image-classification": MobileViTForImageClassification,
"image-segmentation": MobileViTForSemanticSegmentation,
}
if is_torch_available()
else {}
)
lowerCamelCase : Tuple =False
lowerCamelCase : Optional[Any] =False
lowerCamelCase : str =False
lowerCamelCase : List[Any] =False
def SCREAMING_SNAKE_CASE ( self : str ) -> List[Any]:
"""simple docstring"""
__lowerCAmelCase : Optional[int] = MobileViTModelTester(self )
__lowerCAmelCase : Tuple = MobileViTConfigTester(self , config_class=lowerCAmelCase , has_text_modality=lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self : Tuple ) -> Dict:
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason="""MobileViT does not use inputs_embeds""" )
def SCREAMING_SNAKE_CASE ( self : List[Any] ) -> int:
"""simple docstring"""
pass
@unittest.skip(reason="""MobileViT does not support input and output embeddings""" )
def SCREAMING_SNAKE_CASE ( self : Any ) -> Tuple:
"""simple docstring"""
pass
@unittest.skip(reason="""MobileViT does not output attentions""" )
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> str:
"""simple docstring"""
pass
def SCREAMING_SNAKE_CASE ( self : Any ) -> List[Any]:
"""simple docstring"""
__lowerCAmelCase : Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowerCAmelCase : Tuple = model_class(lowerCAmelCase )
__lowerCAmelCase : str = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__lowerCAmelCase : Dict = [*signature.parameters.keys()]
__lowerCAmelCase : Union[str, Any] = ["pixel_values"]
self.assertListEqual(arg_names[:1] , lowerCAmelCase )
@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> str:
"""simple docstring"""
pass
def SCREAMING_SNAKE_CASE ( self : Tuple ) -> List[str]:
"""simple docstring"""
__lowerCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self : str ) -> Tuple:
"""simple docstring"""
def check_hidden_states_output(lowerCAmelCase : Tuple , lowerCAmelCase : Tuple , lowerCAmelCase : Optional[Any] ):
__lowerCAmelCase : str = model_class(lowerCAmelCase )
model.to(lowerCAmelCase )
model.eval()
with torch.no_grad():
__lowerCAmelCase : List[Any] = model(**self._prepare_for_class(lowerCAmelCase , lowerCAmelCase ) )
__lowerCAmelCase : List[str] = outputs.hidden_states
__lowerCAmelCase : Optional[Any] = 5
self.assertEqual(len(lowerCAmelCase ) , lowerCAmelCase )
# MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
# with the width and height being successively divided by 2.
__lowerCAmelCase : Union[str, Any] = 2
for i in range(len(lowerCAmelCase ) ):
self.assertListEqual(
list(hidden_states[i].shape[-2:] ) , [self.model_tester.image_size // divisor, self.model_tester.image_size // divisor] , )
divisor *= 2
self.assertEqual(self.model_tester.output_stride , divisor // 2 )
__lowerCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowerCAmelCase : Dict = True
check_hidden_states_output(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__lowerCAmelCase : List[Any] = True
check_hidden_states_output(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self : Tuple ) -> int:
"""simple docstring"""
__lowerCAmelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self : str ) -> Tuple:
"""simple docstring"""
__lowerCAmelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*lowerCAmelCase )
@slow
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> List[Any]:
"""simple docstring"""
for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowerCAmelCase : List[Any] = MobileViTModel.from_pretrained(lowerCAmelCase )
self.assertIsNotNone(lowerCAmelCase )
def snake_case_ () -> Tuple:
__lowerCAmelCase : Tuple = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
@require_vision
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def SCREAMING_SNAKE_CASE ( self : str ) -> List[Any]:
"""simple docstring"""
return MobileViTImageProcessor.from_pretrained("""apple/mobilevit-xx-small""" ) if is_vision_available() else None
@slow
def SCREAMING_SNAKE_CASE ( self : Dict ) -> Tuple:
"""simple docstring"""
__lowerCAmelCase : int = MobileViTForImageClassification.from_pretrained("""apple/mobilevit-xx-small""" ).to(lowerCAmelCase )
__lowerCAmelCase : Dict = self.default_image_processor
__lowerCAmelCase : List[Any] = prepare_img()
__lowerCAmelCase : str = image_processor(images=lowerCAmelCase , return_tensors="""pt""" ).to(lowerCAmelCase )
# forward pass
with torch.no_grad():
__lowerCAmelCase : List[str] = model(**lowerCAmelCase )
# verify the logits
__lowerCAmelCase : Tuple = torch.Size((1, 10_00) )
self.assertEqual(outputs.logits.shape , lowerCAmelCase )
__lowerCAmelCase : Union[str, Any] = torch.tensor([-1.9364, -1.2327, -0.4653] ).to(lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , lowerCAmelCase , atol=1e-4 ) )
@slow
def SCREAMING_SNAKE_CASE ( self : int ) -> Dict:
"""simple docstring"""
__lowerCAmelCase : List[Any] = MobileViTForSemanticSegmentation.from_pretrained("""apple/deeplabv3-mobilevit-xx-small""" )
__lowerCAmelCase : Optional[int] = model.to(lowerCAmelCase )
__lowerCAmelCase : Union[str, Any] = MobileViTImageProcessor.from_pretrained("""apple/deeplabv3-mobilevit-xx-small""" )
__lowerCAmelCase : Tuple = prepare_img()
__lowerCAmelCase : Tuple = image_processor(images=lowerCAmelCase , return_tensors="""pt""" ).to(lowerCAmelCase )
# forward pass
with torch.no_grad():
__lowerCAmelCase : Optional[int] = model(**lowerCAmelCase )
__lowerCAmelCase : Optional[int] = outputs.logits
# verify the logits
__lowerCAmelCase : List[Any] = torch.Size((1, 21, 32, 32) )
self.assertEqual(logits.shape , lowerCAmelCase )
__lowerCAmelCase : Any = torch.tensor(
[
[[6.9713, 6.9786, 7.2422], [7.2893, 7.2825, 7.4446], [7.6580, 7.8797, 7.9420]],
[[-10.6869, -10.3250, -10.3471], [-10.4228, -9.9868, -9.7132], [-11.0405, -11.0221, -10.7318]],
[[-3.3089, -2.8539, -2.6740], [-3.2706, -2.5621, -2.5108], [-3.2534, -2.6615, -2.6651]],
] , device=lowerCAmelCase , )
self.assertTrue(torch.allclose(logits[0, :3, :3, :3] , lowerCAmelCase , atol=1e-4 ) )
@slow
def SCREAMING_SNAKE_CASE ( self : Dict ) -> List[str]:
"""simple docstring"""
__lowerCAmelCase : Optional[int] = MobileViTForSemanticSegmentation.from_pretrained("""apple/deeplabv3-mobilevit-xx-small""" )
__lowerCAmelCase : List[str] = model.to(lowerCAmelCase )
__lowerCAmelCase : Dict = MobileViTImageProcessor.from_pretrained("""apple/deeplabv3-mobilevit-xx-small""" )
__lowerCAmelCase : Optional[int] = prepare_img()
__lowerCAmelCase : List[str] = image_processor(images=lowerCAmelCase , return_tensors="""pt""" ).to(lowerCAmelCase )
# forward pass
with torch.no_grad():
__lowerCAmelCase : Optional[int] = model(**lowerCAmelCase )
__lowerCAmelCase : int = outputs.logits.detach().cpu()
__lowerCAmelCase : int = image_processor.post_process_semantic_segmentation(outputs=lowerCAmelCase , target_sizes=[(50, 60)] )
__lowerCAmelCase : List[str] = torch.Size((50, 60) )
self.assertEqual(segmentation[0].shape , lowerCAmelCase )
__lowerCAmelCase : int = image_processor.post_process_semantic_segmentation(outputs=lowerCAmelCase )
__lowerCAmelCase : Tuple = torch.Size((32, 32) )
self.assertEqual(segmentation[0].shape , lowerCAmelCase )
| 651 |
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def lowerCamelCase__ ( _a):
return getitem, k
def lowerCamelCase__ ( _a , _a):
return setitem, k, v
def lowerCamelCase__ ( _a):
return delitem, k
def lowerCamelCase__ ( _a , _a , *_a):
try:
return fun(_a , *_a), None
except Exception as e:
return None, e
a_ = (
_set('key_a', 'val_a'),
_set('key_b', 'val_b'),
)
a_ = [
_set('key_a', 'val_a'),
_set('key_a', 'val_b'),
]
a_ = [
_set('key_a', 'val_a'),
_set('key_b', 'val_b'),
_del('key_a'),
_del('key_b'),
_set('key_a', 'val_a'),
_del('key_a'),
]
a_ = [
_get('key_a'),
_del('key_a'),
_set('key_a', 'val_a'),
_del('key_a'),
_del('key_a'),
_get('key_a'),
]
a_ = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
]
a_ = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
*[_del(x) for x in range(5)],
_set('key_a', 'val_b'),
]
@pytest.mark.parametrize(
"operations" , (
pytest.param(_add_items , id="add items"),
pytest.param(_overwrite_items , id="overwrite items"),
pytest.param(_delete_items , id="delete items"),
pytest.param(_access_absent_items , id="access absent items"),
pytest.param(_add_with_resize_up , id="add with resize up"),
pytest.param(_add_with_resize_down , id="add with resize down"),
) , )
def lowerCamelCase__ ( _a):
SCREAMING_SNAKE_CASE : Dict = HashMap(initial_block_size=4)
SCREAMING_SNAKE_CASE : List[str] = {}
for _, (fun, *args) in enumerate(_a):
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Union[str, Any] = _run_operation(_a , _a , *_a)
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : List[Any] = _run_operation(_a , _a , *_a)
assert my_res == py_res
assert str(_a) == str(_a)
assert set(_a) == set(_a)
assert len(_a) == len(_a)
assert set(my.items()) == set(py.items())
def lowerCamelCase__ ( ):
def is_public(_a) -> bool:
return not name.startswith("_")
SCREAMING_SNAKE_CASE : List[str] = {name for name in dir({}) if is_public(_a)}
SCREAMING_SNAKE_CASE : Union[str, Any] = {name for name in dir(HashMap()) if is_public(_a)}
assert dict_public_names > hash_public_names | 25 | 0 |
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