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'''simple docstring'''
import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import (
SeqaSeqDataset,
calculate_bleu,
calculate_rouge,
chunks,
lmap,
load_json,
parse_numeric_n_bool_cl_kwargs,
save_json,
use_task_specific_params,
write_txt_file,
)
_UpperCAmelCase : str = getLogger(__name__)
def _SCREAMING_SNAKE_CASE ( __snake_case : Tuple , __snake_case : str , __snake_case : str , __snake_case : int = 8 , __snake_case : int = 1_0_2_4 , __snake_case : List[str]="val" , __snake_case : List[Any]=None , __snake_case : Dict=False , __snake_case : List[str]="summarization" , __snake_case : int=None , __snake_case : int=1 , __snake_case : Dict = None , __snake_case : str="" , **__snake_case : Tuple , ):
_A = str(__snake_case )
assert local_rank is not None
torch.distributed.init_process_group(backend='nccl' , rank=__snake_case )
_A = Path(__snake_case )
_A = save_dir.joinpath(F'rank_{local_rank}_output.json' )
torch.cuda.set_device(__snake_case )
_A = AutoModelForSeqaSeqLM.from_pretrained(__snake_case ).cuda()
if fpaa:
_A = model.half()
# determine if we need to increase num_beams
use_task_specific_params(__snake_case , __snake_case ) # update config with task specific params
_A = generate_kwargs.pop('num_beams' , model.config.num_beams ) # AttributeError risk?
if num_return_sequences > num_beams:
_A = num_return_sequences
_A = AutoTokenizer.from_pretrained(__snake_case )
logger.info(F'Inferred tokenizer type: {tokenizer.__class__}' ) # if this is wrong, check config.model_type.
if max_source_length is None:
_A = tokenizer.model_max_length
if prefix is None:
_A = prefix or getattr(model.config , 'prefix' , '' ) or ''
_A = SeqaSeqDataset(
__snake_case , __snake_case , __snake_case , max_target_length=1_0_2_4 , type_path=__snake_case , n_obs=__snake_case , prefix=__snake_case , **__snake_case , )
# I set shuffle=True for a more accurate progress bar.
# If all the longest samples are first, the prog bar estimate is too high at the beginning.
_A = ds.make_sortish_sampler(__snake_case , distributed=__snake_case , add_extra_examples=__snake_case , shuffle=__snake_case )
_A = DataLoader(__snake_case , sampler=__snake_case , batch_size=__snake_case , collate_fn=ds.collate_fn )
_A = []
for batch in tqdm(__snake_case ):
_A = model.generate(
input_ids=batch['input_ids'].to(model.device ) , attention_mask=batch['attention_mask'].to(model.device ) , num_return_sequences=__snake_case , num_beams=__snake_case , **__snake_case , )
_A = tokenizer.batch_decode(__snake_case , skip_special_tokens=__snake_case , clean_up_tokenization_spaces=__snake_case )
_A = batch['ids']
if num_return_sequences > 1:
_A = chunks(__snake_case , __snake_case ) # batch size chunks, each of size num_return_seq
for i, pred in enumerate(__snake_case ):
results.append({'pred': pred, 'id': ids[i].item()} )
save_json(__snake_case , __snake_case )
return results, sampler.num_replicas
def _SCREAMING_SNAKE_CASE ( ):
_A = argparse.ArgumentParser(
epilog='Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate' )
parser.add_argument('--data_dir' , type=__snake_case , help='like cnn_dm/test.source' )
parser.add_argument(
'--model_name' , type=__snake_case , help='like facebook/bart-large-cnn,t5-base, etc.' , default='sshleifer/distilbart-xsum-12-3' , )
parser.add_argument('--save_dir' , type=__snake_case , help='where to save' , default='tmp_gen' )
parser.add_argument('--max_source_length' , type=__snake_case , default=__snake_case )
parser.add_argument(
'--type_path' , type=__snake_case , default='test' , help='which subset to evaluate typically train/val/test' )
parser.add_argument('--task' , type=__snake_case , default='summarization' , help='used for task_specific_params + metrics' )
parser.add_argument('--bs' , type=__snake_case , default=8 , required=__snake_case , help='batch size' )
parser.add_argument(
'--local_rank' , type=__snake_case , default=-1 , required=__snake_case , help='should be passed by distributed.launch' )
parser.add_argument(
'--n_obs' , type=__snake_case , default=__snake_case , required=__snake_case , help='How many observations. Defaults to all.' )
parser.add_argument(
'--num_return_sequences' , type=__snake_case , default=1 , required=__snake_case , help='How many sequences to return' )
parser.add_argument(
'--sync_timeout' , type=__snake_case , default=6_0_0 , required=__snake_case , help='How long should master process wait for other processes to finish.' , )
parser.add_argument('--src_lang' , type=__snake_case , default=__snake_case , required=__snake_case )
parser.add_argument('--tgt_lang' , type=__snake_case , default=__snake_case , required=__snake_case )
parser.add_argument(
'--prefix' , type=__snake_case , required=__snake_case , default=__snake_case , help='will be added to the begininng of src examples' )
parser.add_argument('--fp16' , action='store_true' )
parser.add_argument('--debug' , action='store_true' )
_A = time.time()
_A , _A = parser.parse_known_args()
_A = parse_numeric_n_bool_cl_kwargs(__snake_case )
if generate_kwargs and args.local_rank <= 0:
print(F'parsed the following generate kwargs: {generate_kwargs}' )
_A = Path(args.save_dir + '_tmp' )
Path(__snake_case ).mkdir(exist_ok=__snake_case ) # this handles locking.
_A = list(json_save_dir.glob('rank_*.json' ) )
if intermediate_files:
raise ValueError(F'Found files at {json_save_dir} please move or remove them.' )
# In theory, a node could finish and save before another node hits this. If this happens, we can address later.
_A = {}
if args.src_lang is not None:
_A = args.src_lang
if args.tgt_lang is not None:
_A = args.tgt_lang
Path(args.save_dir ).mkdir(exist_ok=__snake_case )
_A , _A = eval_data_dir(
args.data_dir , __snake_case , args.model_name , type_path=args.type_path , bs=args.bs , fpaa=args.fpaa , task=args.task , local_rank=args.local_rank , n_obs=args.n_obs , max_source_length=args.max_source_length , num_return_sequences=args.num_return_sequences , prefix=args.prefix , dataset_kwargs=__snake_case , **__snake_case , )
if args.local_rank <= 0:
_A = Path(args.save_dir )
save_dir.mkdir(exist_ok=__snake_case )
_A = gather_results_from_each_node(__snake_case , __snake_case , args.sync_timeout )
_A = combine_partial_results(__snake_case )
if args.num_return_sequences > 1:
_A = save_dir.joinpath('pseudolabel_results.json' )
print(F'Saving aggregated results at {save_path}, intermediate in {json_save_dir}/' )
save_json(__snake_case , __snake_case )
return
_A = Path(args.data_dir ).joinpath(args.type_path + '.target' )
with open(__snake_case ) as f:
_A = [x.rstrip() for x in f.readlines()][: len(__snake_case )]
# Calculate metrics, save metrics, and save _generations.txt
_A = 'translation' in args.task
_A = calculate_bleu if calc_bleu else calculate_rouge
_A = 'bleu' if calc_bleu else 'rouge'
_A = score_fn(__snake_case , __snake_case )
_A = len(__snake_case )
_A = time.time() - start_time
_A = round(runtime / metrics['n_obs'] , 4 )
_A = num_replicas
# TODO(@stas00): add whatever metadata to metrics
_A = save_dir.joinpath(F'{args.type_path}_{metric_name}.json' )
save_json(__snake_case , __snake_case , indent=__snake_case )
print(__snake_case )
write_txt_file(__snake_case , save_dir.joinpath(F'{args.type_path}_generations.txt' ) )
if args.debug:
write_txt_file(__snake_case , save_dir.joinpath(F'{args.type_path}.target' ) )
else:
shutil.rmtree(__snake_case )
def _SCREAMING_SNAKE_CASE ( __snake_case : int ):
_A = []
for partial_result in partial_results:
records.extend(__snake_case )
_A = sorted(__snake_case , key=lambda __snake_case : x["id"] )
_A = [x['pred'] for x in records]
return preds
def _SCREAMING_SNAKE_CASE ( __snake_case : List[str] , __snake_case : int , __snake_case : List[Any] ):
# WAIT FOR lots of .json files
_A = time.time()
logger.info('waiting for all nodes to finish' )
_A = None
while (time.time() - start_wait) < timeout:
_A = list(save_dir.glob('rank_*.json' ) )
if len(__snake_case ) < num_replicas:
continue
try:
# make sure all json files are fully saved
_A = lmap(__snake_case , __snake_case )
return json_data
except JSONDecodeError:
continue
else:
raise TimeoutError('Rank 0 gave up on waiting for other processes' )
# Unreachable
if __name__ == "__main__":
# Usage for MT:
run_generate()
| 107
|
import json
import os
from collections import Counter
import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset
_a = {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self , __lowerCAmelCase ):
'''simple docstring'''
super().__init__()
lowerCamelCase__ = torchvision.models.resnetaaa(pretrained=__lowerCAmelCase )
lowerCamelCase__ = list(model.children() )[:-2]
lowerCamelCase__ = nn.Sequential(*__lowerCAmelCase )
lowerCamelCase__ = nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds] )
def __lowerCamelCase ( self , __lowerCAmelCase ):
'''simple docstring'''
lowerCamelCase__ = self.pool(self.model(__lowerCAmelCase ) )
lowerCamelCase__ = torch.flatten(__lowerCAmelCase , start_dim=2 )
lowerCamelCase__ = out.transpose(1 , 2 ).contiguous()
return out # BxNx2048
class __A ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
'''simple docstring'''
lowerCamelCase__ = [json.loads(__lowerCAmelCase ) for l in open(__lowerCAmelCase )]
lowerCamelCase__ = os.path.dirname(__lowerCAmelCase )
lowerCamelCase__ = tokenizer
lowerCamelCase__ = labels
lowerCamelCase__ = len(__lowerCAmelCase )
lowerCamelCase__ = max_seq_length
lowerCamelCase__ = transforms
def __len__( self ):
'''simple docstring'''
return len(self.data )
def __getitem__( self , __lowerCAmelCase ):
'''simple docstring'''
lowerCamelCase__ = torch.LongTensor(self.tokenizer.encode(self.data[index]['''text'''] , add_special_tokens=__lowerCAmelCase ) )
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = sentence[0], sentence[1:-1], sentence[-1]
lowerCamelCase__ = sentence[: self.max_seq_length]
lowerCamelCase__ = torch.zeros(self.n_classes )
lowerCamelCase__ = 1
lowerCamelCase__ = Image.open(os.path.join(self.data_dir , self.data[index]['''img'''] ) ).convert('''RGB''' )
lowerCamelCase__ = self.transforms(__lowerCAmelCase )
return {
"image_start_token": start_token,
"image_end_token": end_token,
"sentence": sentence,
"image": image,
"label": label,
}
def __lowerCamelCase ( self ):
'''simple docstring'''
lowerCamelCase__ = Counter()
for row in self.data:
label_freqs.update(row['''label'''] )
return label_freqs
def lowerCAmelCase__(__snake_case ) -> Union[str, Any]:
'''simple docstring'''
lowerCamelCase__ = [len(row['''sentence'''] ) for row in batch]
lowerCamelCase__ , lowerCamelCase__ = len(__snake_case ), max(__snake_case )
lowerCamelCase__ = torch.zeros(__snake_case ,__snake_case ,dtype=torch.long )
lowerCamelCase__ = torch.zeros(__snake_case ,__snake_case ,dtype=torch.long )
for i_batch, (input_row, length) in enumerate(zip(__snake_case ,__snake_case ) ):
lowerCamelCase__ = input_row['''sentence''']
lowerCamelCase__ = 1
lowerCamelCase__ = torch.stack([row['''image'''] for row in batch] )
lowerCamelCase__ = torch.stack([row['''label'''] for row in batch] )
lowerCamelCase__ = torch.stack([row['''image_start_token'''] for row in batch] )
lowerCamelCase__ = torch.stack([row['''image_end_token'''] for row in batch] )
return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor
def lowerCAmelCase__() -> Optional[int]:
'''simple docstring'''
return [
"Crime",
"Drama",
"Thriller",
"Action",
"Comedy",
"Romance",
"Documentary",
"Short",
"Mystery",
"History",
"Family",
"Adventure",
"Fantasy",
"Sci-Fi",
"Western",
"Horror",
"Sport",
"War",
"Music",
"Musical",
"Animation",
"Biography",
"Film-Noir",
]
def lowerCAmelCase__() -> Any:
'''simple docstring'''
return transforms.Compose(
[
transforms.Resize(256 ),
transforms.CenterCrop(224 ),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.4_6_7_7_7_0_4_4, 0.4_4_5_3_1_4_2_9, 0.4_0_6_6_1_0_1_7] ,std=[0.1_2_2_2_1_9_9_4, 0.1_2_1_4_5_8_3_5, 0.1_4_3_8_0_4_6_9] ,),
] )
| 481
| 0
|
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling
def lowerCamelCase_ ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = [2, 2, 6, 2] if 'tiny' in model_name else [2, 2, 18, 2]
SCREAMING_SNAKE_CASE = True if 'large' in model_name or 'huge' in model_name else False
SCREAMING_SNAKE_CASE = True if 'large' in model_name or 'huge' in model_name else False
SCREAMING_SNAKE_CASE = True if 'large' in model_name or 'huge' in model_name else False
if "large" in model_name or "xlarge" in model_name or "huge" in model_name:
if "fl3" in model_name:
SCREAMING_SNAKE_CASE = [3, 3, 3, 3]
SCREAMING_SNAKE_CASE = [5, 5, 5, 5]
elif "fl4" in model_name:
SCREAMING_SNAKE_CASE = [4, 4, 4, 4]
SCREAMING_SNAKE_CASE = [3, 3, 3, 3]
if "tiny" in model_name or "small" in model_name or "base" in model_name:
SCREAMING_SNAKE_CASE = [3, 3, 3, 3]
if "lrf" in model_name:
SCREAMING_SNAKE_CASE = [3, 3, 3, 3]
else:
SCREAMING_SNAKE_CASE = [2, 2, 2, 2]
if "tiny" in model_name:
SCREAMING_SNAKE_CASE = 96
elif "small" in model_name:
SCREAMING_SNAKE_CASE = 96
elif "base" in model_name:
SCREAMING_SNAKE_CASE = 1_28
elif "large" in model_name:
SCREAMING_SNAKE_CASE = 1_92
elif "xlarge" in model_name:
SCREAMING_SNAKE_CASE = 2_56
elif "huge" in model_name:
SCREAMING_SNAKE_CASE = 3_52
# set label information
SCREAMING_SNAKE_CASE = 'huggingface/label-files'
if "large" in model_name or "huge" in model_name:
SCREAMING_SNAKE_CASE = 'imagenet-22k-id2label.json'
else:
SCREAMING_SNAKE_CASE = 'imagenet-1k-id2label.json'
SCREAMING_SNAKE_CASE = json.load(open(hf_hub_download(lowerCAmelCase__ , lowerCAmelCase__ , repo_type="""dataset""" ) , """r""" ) )
SCREAMING_SNAKE_CASE = {int(lowerCAmelCase__ ): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE = {v: k for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE = FocalNetConfig(
embed_dim=lowerCAmelCase__ , depths=lowerCAmelCase__ , focal_levels=lowerCAmelCase__ , focal_windows=lowerCAmelCase__ , use_conv_embed=lowerCAmelCase__ , idalabel=lowerCAmelCase__ , labelaid=lowerCAmelCase__ , use_post_layernorm=lowerCAmelCase__ , use_layerscale=lowerCAmelCase__ , )
return config
def lowerCamelCase_ ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
if "patch_embed.proj" in name:
SCREAMING_SNAKE_CASE = name.replace("""patch_embed.proj""" , """embeddings.patch_embeddings.projection""" )
if "patch_embed.norm" in name:
SCREAMING_SNAKE_CASE = name.replace("""patch_embed.norm""" , """embeddings.norm""" )
if "layers" in name:
SCREAMING_SNAKE_CASE = 'encoder.' + name
if "encoder.layers" in name:
SCREAMING_SNAKE_CASE = name.replace("""encoder.layers""" , """encoder.stages""" )
if "downsample.proj" in name:
SCREAMING_SNAKE_CASE = name.replace("""downsample.proj""" , """downsample.projection""" )
if "blocks" in name:
SCREAMING_SNAKE_CASE = name.replace("""blocks""" , """layers""" )
if "modulation.f.weight" in name or "modulation.f.bias" in name:
SCREAMING_SNAKE_CASE = name.replace("""modulation.f""" , """modulation.projection_in""" )
if "modulation.h.weight" in name or "modulation.h.bias" in name:
SCREAMING_SNAKE_CASE = name.replace("""modulation.h""" , """modulation.projection_context""" )
if "modulation.proj.weight" in name or "modulation.proj.bias" in name:
SCREAMING_SNAKE_CASE = name.replace("""modulation.proj""" , """modulation.projection_out""" )
if name == "norm.weight":
SCREAMING_SNAKE_CASE = 'layernorm.weight'
if name == "norm.bias":
SCREAMING_SNAKE_CASE = 'layernorm.bias'
if "head" in name:
SCREAMING_SNAKE_CASE = name.replace("""head""" , """classifier""" )
else:
SCREAMING_SNAKE_CASE = 'focalnet.' + name
return name
def lowerCamelCase_ ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=False ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = {
'focalnet-tiny': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_srf.pth',
'focalnet-tiny-lrf': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_lrf.pth',
'focalnet-small': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_srf.pth',
'focalnet-small-lrf': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_lrf.pth',
'focalnet-base': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_srf.pth',
'focalnet-base-lrf': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_lrf.pth',
'focalnet-large-lrf-fl3': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384.pth',
'focalnet-large-lrf-fl4': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384_fl4.pth',
'focalnet-xlarge-lrf-fl3': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384.pth',
'focalnet-xlarge-lrf-fl4': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384_fl4.pth',
}
# fmt: on
SCREAMING_SNAKE_CASE = model_name_to_url[model_name]
print("""Checkpoint URL: """ , lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = torch.hub.load_state_dict_from_url(lowerCAmelCase__ , map_location="""cpu""" )['model']
# rename keys
for key in state_dict.copy().keys():
SCREAMING_SNAKE_CASE = state_dict.pop(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = val
SCREAMING_SNAKE_CASE = get_focalnet_config(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = FocalNetForImageClassification(lowerCAmelCase__ )
model.eval()
# load state dict
model.load_state_dict(lowerCAmelCase__ )
# verify conversion
SCREAMING_SNAKE_CASE = 'http://images.cocodataset.org/val2017/000000039769.jpg'
SCREAMING_SNAKE_CASE = BitImageProcessor(
do_resize=lowerCAmelCase__ , size={"""shortest_edge""": 2_56} , resample=PILImageResampling.BILINEAR , do_center_crop=lowerCAmelCase__ , crop_size=2_24 , do_normalize=lowerCAmelCase__ , image_mean=lowerCAmelCase__ , image_std=lowerCAmelCase__ , )
SCREAMING_SNAKE_CASE = Image.open(requests.get(lowerCAmelCase__ , stream=lowerCAmelCase__ ).raw )
SCREAMING_SNAKE_CASE = processor(images=lowerCAmelCase__ , return_tensors="""pt""" )
SCREAMING_SNAKE_CASE = transforms.Compose(
[
transforms.Resize(2_56 ),
transforms.CenterCrop(2_24 ),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406] , std=[0.229, 0.224, 0.225] ),
] )
SCREAMING_SNAKE_CASE = image_transforms(lowerCAmelCase__ ).unsqueeze(0 )
# verify pixel_values
assert torch.allclose(inputs.pixel_values , lowerCAmelCase__ , atol=1E-4 )
SCREAMING_SNAKE_CASE = model(**lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = outputs.logits.argmax(-1 ).item()
print("""Predicted class:""" , model.config.idalabel[predicted_class_idx] )
print("""First values of logits:""" , outputs.logits[0, :3] )
if model_name == "focalnet-tiny":
SCREAMING_SNAKE_CASE = torch.tensor([0.2_166, -0.4_368, 0.2_191] )
elif model_name == "focalnet-tiny-lrf":
SCREAMING_SNAKE_CASE = torch.tensor([1.1_669, 0.0_125, -0.1_695] )
elif model_name == "focalnet-small":
SCREAMING_SNAKE_CASE = torch.tensor([0.4_917, -0.0_430, 0.1_341] )
elif model_name == "focalnet-small-lrf":
SCREAMING_SNAKE_CASE = torch.tensor([-0.2_588, -0.5_342, -0.2_331] )
elif model_name == "focalnet-base":
SCREAMING_SNAKE_CASE = torch.tensor([-0.1_655, -0.4_090, -0.1_730] )
elif model_name == "focalnet-base-lrf":
SCREAMING_SNAKE_CASE = torch.tensor([0.5_306, -0.0_483, -0.3_928] )
assert torch.allclose(outputs.logits[0, :3] , lowerCAmelCase__ , atol=1E-4 )
print("""Looks ok!""" )
if pytorch_dump_folder_path is not None:
print(f"""Saving model and processor of {model_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(lowerCAmelCase__ )
processor.save_pretrained(lowerCAmelCase__ )
if push_to_hub:
print(f"""Pushing model and processor of {model_name} to the hub...""" )
model.push_to_hub(f"""{model_name}""" )
processor.push_to_hub(f"""{model_name}""" )
if __name__ == "__main__":
__A : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""focalnet-tiny""",
type=str,
help="""Name of the FocalNet model you\'d like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
)
parser.add_argument(
"""--push_to_hub""",
action="""store_true""",
help="""Whether to push the model and processor to the hub.""",
)
__A : List[str] = parser.parse_args()
convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 709
|
import shutil
import tempfile
import unittest
from transformers import (
SPIECE_UNDERLINE,
AddedToken,
BatchEncoding,
NllbTokenizer,
NllbTokenizerFast,
is_torch_available,
)
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common import TokenizerTesterMixin
__A : Any = get_tests_dir("""fixtures/test_sentencepiece.model""")
if is_torch_available():
from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right
__A : Optional[int] = 2_5_6_0_4_7
__A : Dict = 2_5_6_1_4_5
@require_sentencepiece
@require_tokenizers
class UpperCAmelCase_ ( A , unittest.TestCase ):
'''simple docstring'''
a__ = NllbTokenizer
a__ = NllbTokenizerFast
a__ = True
a__ = True
a__ = {}
def _UpperCAmelCase ( self : Any ) -> int:
super().setUp()
# We have a SentencePiece fixture for testing
SCREAMING_SNAKE_CASE = NllbTokenizer(a , keep_accents=a )
tokenizer.save_pretrained(self.tmpdirname )
def _UpperCAmelCase ( self : int ) -> str:
SCREAMING_SNAKE_CASE = NllbTokenizer(a , keep_accents=a )
SCREAMING_SNAKE_CASE = tokenizer.tokenize("""This is a test""" )
self.assertListEqual(a , ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(a ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
SCREAMING_SNAKE_CASE = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
self.assertListEqual(
a , [
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""9""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""é""",
""".""",
] , )
SCREAMING_SNAKE_CASE = tokenizer.convert_tokens_to_ids(a )
self.assertListEqual(
a , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
] , )
SCREAMING_SNAKE_CASE = tokenizer.convert_ids_to_tokens(a )
self.assertListEqual(
a , [
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""<unk>""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""<unk>""",
""".""",
] , )
def _UpperCAmelCase ( self : Optional[Any] ) -> Any:
SCREAMING_SNAKE_CASE = (self.rust_tokenizer_class, """hf-internal-testing/tiny-random-nllb""", {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
SCREAMING_SNAKE_CASE = self.rust_tokenizer_class.from_pretrained(a , **a )
SCREAMING_SNAKE_CASE = self.tokenizer_class.from_pretrained(a , **a )
SCREAMING_SNAKE_CASE = tempfile.mkdtemp()
SCREAMING_SNAKE_CASE = tokenizer_r.save_pretrained(a )
SCREAMING_SNAKE_CASE = tokenizer_p.save_pretrained(a )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files ) )
SCREAMING_SNAKE_CASE = tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f )
self.assertSequenceEqual(a , a )
# Checks everything loads correctly in the same way
SCREAMING_SNAKE_CASE = tokenizer_r.from_pretrained(a )
SCREAMING_SNAKE_CASE = tokenizer_p.from_pretrained(a )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(a , a ) )
shutil.rmtree(a )
# Save tokenizer rust, legacy_format=True
SCREAMING_SNAKE_CASE = tempfile.mkdtemp()
SCREAMING_SNAKE_CASE = tokenizer_r.save_pretrained(a , legacy_format=a )
SCREAMING_SNAKE_CASE = tokenizer_p.save_pretrained(a )
# Checks it save with the same files
self.assertSequenceEqual(a , a )
# Checks everything loads correctly in the same way
SCREAMING_SNAKE_CASE = tokenizer_r.from_pretrained(a )
SCREAMING_SNAKE_CASE = tokenizer_p.from_pretrained(a )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(a , a ) )
shutil.rmtree(a )
# Save tokenizer rust, legacy_format=False
SCREAMING_SNAKE_CASE = tempfile.mkdtemp()
SCREAMING_SNAKE_CASE = tokenizer_r.save_pretrained(a , legacy_format=a )
SCREAMING_SNAKE_CASE = tokenizer_p.save_pretrained(a )
# Checks it saved the tokenizer.json file
self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
SCREAMING_SNAKE_CASE = tokenizer_r.from_pretrained(a )
SCREAMING_SNAKE_CASE = tokenizer_p.from_pretrained(a )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(a , a ) )
shutil.rmtree(a )
@require_torch
def _UpperCAmelCase ( self : Dict ) -> List[str]:
if not self.test_seqaseq:
return
SCREAMING_SNAKE_CASE = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"""{tokenizer.__class__.__name__}""" ):
# Longer text that will definitely require truncation.
SCREAMING_SNAKE_CASE = [
""" UN Chief Says There Is No Military Solution in Syria""",
""" Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for"""
""" Syria is that 'there is no military solution' to the nearly five-year conflict and more weapons"""
""" will only worsen the violence and misery for millions of people.""",
]
SCREAMING_SNAKE_CASE = [
"""Şeful ONU declară că nu există o soluţie militară în Siria""",
"""Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al"""
""" Rusiei pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi"""
""" că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.""",
]
try:
SCREAMING_SNAKE_CASE = tokenizer.prepare_seqaseq_batch(
src_texts=a , tgt_texts=a , max_length=3 , max_target_length=10 , return_tensors="""pt""" , src_lang="""eng_Latn""" , tgt_lang="""ron_Latn""" , )
except NotImplementedError:
return
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.labels.shape[1] , 10 )
# max_target_length will default to max_length if not specified
SCREAMING_SNAKE_CASE = tokenizer.prepare_seqaseq_batch(
a , tgt_texts=a , max_length=3 , return_tensors="""pt""" )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.labels.shape[1] , 3 )
SCREAMING_SNAKE_CASE = tokenizer.prepare_seqaseq_batch(
src_texts=a , max_length=3 , max_target_length=10 , return_tensors="""pt""" )
self.assertEqual(batch_encoder_only.input_ids.shape[1] , 3 )
self.assertEqual(batch_encoder_only.attention_mask.shape[1] , 3 )
self.assertNotIn("""decoder_input_ids""" , a )
@unittest.skip("""Unfortunately way too slow to build a BPE with SentencePiece.""" )
def _UpperCAmelCase ( self : int ) -> Optional[int]:
pass
def _UpperCAmelCase ( self : Optional[int] ) -> Optional[int]:
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
SCREAMING_SNAKE_CASE = [AddedToken("""<special>""" , lstrip=a )]
SCREAMING_SNAKE_CASE = self.rust_tokenizer_class.from_pretrained(
a , additional_special_tokens=a , **a )
SCREAMING_SNAKE_CASE = tokenizer_r.encode("""Hey this is a <special> token""" )
SCREAMING_SNAKE_CASE = tokenizer_r.encode("""<special>""" , add_special_tokens=a )[0]
self.assertTrue(special_token_id in r_output )
if self.test_slow_tokenizer:
SCREAMING_SNAKE_CASE = self.rust_tokenizer_class.from_pretrained(
a , additional_special_tokens=a , **a , )
SCREAMING_SNAKE_CASE = self.tokenizer_class.from_pretrained(
a , additional_special_tokens=a , **a )
SCREAMING_SNAKE_CASE = tokenizer_p.encode("""Hey this is a <special> token""" )
SCREAMING_SNAKE_CASE = tokenizer_cr.encode("""Hey this is a <special> token""" )
self.assertEqual(a , a )
self.assertEqual(a , a )
self.assertTrue(special_token_id in p_output )
self.assertTrue(special_token_id in cr_output )
@require_torch
@require_sentencepiece
@require_tokenizers
class UpperCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
a__ = '''facebook/nllb-200-distilled-600M'''
a__ = [
''' UN Chief Says There Is No Military Solution in Syria''',
''' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.''',
]
a__ = [
'''Şeful ONU declară că nu există o soluţie militară în Siria''',
'''Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'''
''' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'''
''' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.''',
]
a__ = [
2_5_6_0_4_7,
1_6_2_9_7,
1_3_4_4_0_8,
8_1_6_5,
2_4_8_0_6_6,
1_4_7_3_4,
9_5_0,
1_1_3_5,
1_0_5_7_2_1,
3_5_7_3,
8_3,
2_7_3_5_2,
1_0_8,
4_9_4_8_6,
2,
]
@classmethod
def _UpperCAmelCase ( cls : List[Any] ) -> Optional[Any]:
SCREAMING_SNAKE_CASE = NllbTokenizer.from_pretrained(
cls.checkpoint_name , src_lang="""eng_Latn""" , tgt_lang="""ron_Latn""" )
SCREAMING_SNAKE_CASE = 1
return cls
def _UpperCAmelCase ( self : Dict ) -> Dict:
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ace_Arab"""] , 256_001 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ace_Latn"""] , 256_002 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""fra_Latn"""] , 256_057 )
def _UpperCAmelCase ( self : Optional[int] ) -> Optional[Any]:
SCREAMING_SNAKE_CASE = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , a )
def _UpperCAmelCase ( self : Union[str, Any] ) -> Tuple:
self.assertIn(a , self.tokenizer.all_special_ids )
# fmt: off
SCREAMING_SNAKE_CASE = [RO_CODE, 4_254, 98_068, 112_923, 39_072, 3_909, 713, 102_767, 26, 17_314, 35_642, 14_683, 33_118, 2_022, 66_987, 2, 256_047]
# fmt: on
SCREAMING_SNAKE_CASE = self.tokenizer.decode(a , skip_special_tokens=a )
SCREAMING_SNAKE_CASE = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=a )
self.assertEqual(a , a )
self.assertNotIn(self.tokenizer.eos_token , a )
def _UpperCAmelCase ( self : Optional[Any] ) -> List[Any]:
SCREAMING_SNAKE_CASE = ["""this is gunna be a long sentence """ * 20]
assert isinstance(src_text[0] , a )
SCREAMING_SNAKE_CASE = 10
SCREAMING_SNAKE_CASE = self.tokenizer(a , max_length=a , truncation=a ).input_ids[0]
self.assertEqual(ids[-1] , 2 )
self.assertEqual(ids[0] , a )
self.assertEqual(len(a ) , a )
def _UpperCAmelCase ( self : Dict ) -> Any:
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """ar_AR"""] ) , [256_203, 3] )
def _UpperCAmelCase ( self : str ) -> Optional[int]:
SCREAMING_SNAKE_CASE = tempfile.mkdtemp()
SCREAMING_SNAKE_CASE = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(a )
SCREAMING_SNAKE_CASE = NllbTokenizer.from_pretrained(a )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , a )
@require_torch
def _UpperCAmelCase ( self : Optional[int] ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE = self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=a , truncation=a , max_length=len(self.expected_src_tokens ) , return_tensors="""pt""" , )
SCREAMING_SNAKE_CASE = shift_tokens_right(
batch["""labels"""] , self.tokenizer.pad_token_id , self.tokenizer.lang_code_to_id["""ron_Latn"""] )
self.assertIsInstance(a , a )
self.assertEqual((2, 15) , batch.input_ids.shape )
self.assertEqual((2, 15) , batch.attention_mask.shape )
SCREAMING_SNAKE_CASE = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , a )
self.assertEqual(a , batch.decoder_input_ids[0, 0] ) # EOS
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens , [EN_CODE] )
self.assertEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
def _UpperCAmelCase ( self : str ) -> Dict:
SCREAMING_SNAKE_CASE = self.tokenizer(self.src_text , padding=a , truncation=a , max_length=3 , return_tensors="""pt""" )
SCREAMING_SNAKE_CASE = self.tokenizer(
text_target=self.tgt_text , padding=a , truncation=a , max_length=10 , return_tensors="""pt""" )
SCREAMING_SNAKE_CASE = targets["""input_ids"""]
SCREAMING_SNAKE_CASE = shift_tokens_right(
a , self.tokenizer.pad_token_id , decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang] , )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.decoder_input_ids.shape[1] , 10 )
@require_torch
def _UpperCAmelCase ( self : int ) -> Optional[int]:
SCREAMING_SNAKE_CASE = self.tokenizer._build_translation_inputs(
"""A test""" , return_tensors="""pt""" , src_lang="""eng_Latn""" , tgt_lang="""fra_Latn""" )
self.assertEqual(
nested_simplify(a ) , {
# A, test, EOS, en_XX
"""input_ids""": [[256_047, 70, 7_356, 2]],
"""attention_mask""": [[1, 1, 1, 1]],
# ar_AR
"""forced_bos_token_id""": 256_057,
} , )
@require_torch
def _UpperCAmelCase ( self : str ) -> int:
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = self.tokenizer(
"""UN Chief says there is no military solution in Syria""" , src_lang="""eng_Latn""" , tgt_lang="""fra_Latn""" )
self.assertEqual(
inputs.input_ids , [16_297, 134_408, 25_653, 6_370, 248, 254, 103_929, 94_995, 108, 49_486, 2, 256_047] )
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = self.tokenizer(
"""UN Chief says there is no military solution in Syria""" , src_lang="""eng_Latn""" , tgt_lang="""fra_Latn""" )
self.assertEqual(
inputs.input_ids , [256_047, 16_297, 134_408, 25_653, 6_370, 248, 254, 103_929, 94_995, 108, 49_486, 2] )
| 450
| 0
|
import os
import unittest
from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir
from ...test_tokenization_common import TokenizerTesterMixin
lowerCAmelCase_ = get_tests_dir("""fixtures/test_sentencepiece_bpe.model""")
class _lowerCAmelCase ( UpperCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
a_ : Any =BartphoTokenizer
a_ : Optional[int] =False
a_ : List[Any] =True
def UpperCamelCase_ ( self : Tuple ):
'''simple docstring'''
super().setUp()
_snake_case : List[str] = ['▁This', '▁is', '▁a', '▁t', 'est']
_snake_case : Tuple = dict(zip(UpperCamelCase , range(len(UpperCamelCase ) ) ) )
_snake_case : Any = {'unk_token': '<unk>'}
_snake_case : Tuple = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['monolingual_vocab_file'] )
with open(self.monolingual_vocab_file , 'w' , encoding='utf-8' ) as fp:
for token in vocab_tokens:
fp.write(f"""{token} {vocab_tokens[token]}\n""" )
_snake_case : List[str] = BartphoTokenizer(UpperCamelCase , self.monolingual_vocab_file , **self.special_tokens_map )
tokenizer.save_pretrained(self.tmpdirname )
def UpperCamelCase_ ( self : List[str] , **UpperCamelCase : List[str] ):
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return BartphoTokenizer.from_pretrained(self.tmpdirname , **UpperCamelCase )
def UpperCamelCase_ ( self : str , UpperCamelCase : int ):
'''simple docstring'''
_snake_case : Dict = 'This is a là test'
_snake_case : Union[str, Any] = 'This is a<unk><unk> test'
return input_text, output_text
def UpperCamelCase_ ( self : Tuple ):
'''simple docstring'''
_snake_case : Optional[Any] = BartphoTokenizer(UpperCamelCase , self.monolingual_vocab_file , **self.special_tokens_map )
_snake_case : Tuple = 'This is a là test'
_snake_case : Any = '▁This ▁is ▁a ▁l à ▁t est'.split()
_snake_case : List[Any] = tokenizer.tokenize(UpperCamelCase )
self.assertListEqual(UpperCamelCase , UpperCamelCase )
_snake_case : Dict = tokens + [tokenizer.unk_token]
_snake_case : int = [4, 5, 6, 3, 3, 7, 8, 3]
self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase ) , UpperCamelCase )
| 411
|
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from transformers.testing_utils import require_torch
if is_torch_available():
from transformers.trainer import DEFAULT_CALLBACKS
from .test_trainer import RegressionDataset, RegressionModelConfig, RegressionPreTrainedModel
class _lowerCAmelCase ( UpperCAmelCase_ ):
'''simple docstring'''
def __init__( self : List[Any] ):
'''simple docstring'''
_snake_case : Any = []
def UpperCamelCase_ ( self : int , UpperCamelCase : Union[str, Any] , UpperCamelCase : Tuple , UpperCamelCase : int , **UpperCamelCase : Any ):
'''simple docstring'''
self.events.append('on_init_end' )
def UpperCamelCase_ ( self : Optional[Any] , UpperCamelCase : Optional[Any] , UpperCamelCase : Union[str, Any] , UpperCamelCase : Optional[Any] , **UpperCamelCase : List[Any] ):
'''simple docstring'''
self.events.append('on_train_begin' )
def UpperCamelCase_ ( self : Tuple , UpperCamelCase : Optional[Any] , UpperCamelCase : Dict , UpperCamelCase : List[str] , **UpperCamelCase : Any ):
'''simple docstring'''
self.events.append('on_train_end' )
def UpperCamelCase_ ( self : List[Any] , UpperCamelCase : List[Any] , UpperCamelCase : Any , UpperCamelCase : str , **UpperCamelCase : str ):
'''simple docstring'''
self.events.append('on_epoch_begin' )
def UpperCamelCase_ ( self : Optional[int] , UpperCamelCase : int , UpperCamelCase : List[Any] , UpperCamelCase : str , **UpperCamelCase : Optional[Any] ):
'''simple docstring'''
self.events.append('on_epoch_end' )
def UpperCamelCase_ ( self : Any , UpperCamelCase : Optional[Any] , UpperCamelCase : Optional[Any] , UpperCamelCase : Optional[int] , **UpperCamelCase : Optional[Any] ):
'''simple docstring'''
self.events.append('on_step_begin' )
def UpperCamelCase_ ( self : Tuple , UpperCamelCase : Union[str, Any] , UpperCamelCase : Optional[Any] , UpperCamelCase : Optional[int] , **UpperCamelCase : Dict ):
'''simple docstring'''
self.events.append('on_step_end' )
def UpperCamelCase_ ( self : str , UpperCamelCase : Any , UpperCamelCase : Optional[Any] , UpperCamelCase : Tuple , **UpperCamelCase : Union[str, Any] ):
'''simple docstring'''
self.events.append('on_evaluate' )
def UpperCamelCase_ ( self : List[Any] , UpperCamelCase : List[str] , UpperCamelCase : Optional[int] , UpperCamelCase : str , **UpperCamelCase : List[Any] ):
'''simple docstring'''
self.events.append('on_predict' )
def UpperCamelCase_ ( self : int , UpperCamelCase : int , UpperCamelCase : Tuple , UpperCamelCase : int , **UpperCamelCase : Optional[int] ):
'''simple docstring'''
self.events.append('on_save' )
def UpperCamelCase_ ( self : Optional[int] , UpperCamelCase : Optional[Any] , UpperCamelCase : List[str] , UpperCamelCase : Any , **UpperCamelCase : Union[str, Any] ):
'''simple docstring'''
self.events.append('on_log' )
def UpperCamelCase_ ( self : Union[str, Any] , UpperCamelCase : str , UpperCamelCase : Any , UpperCamelCase : str , **UpperCamelCase : Dict ):
'''simple docstring'''
self.events.append('on_prediction_step' )
@require_torch
class _lowerCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase_ ( self : Dict ):
'''simple docstring'''
_snake_case : str = tempfile.mkdtemp()
def UpperCamelCase_ ( self : List[str] ):
'''simple docstring'''
shutil.rmtree(self.output_dir )
def UpperCamelCase_ ( self : Union[str, Any] , UpperCamelCase : Tuple=0 , UpperCamelCase : str=0 , UpperCamelCase : str=64 , UpperCamelCase : str=64 , UpperCamelCase : List[Any]=None , UpperCamelCase : Union[str, Any]=False , **UpperCamelCase : List[str] ):
'''simple docstring'''
_snake_case : Tuple = RegressionDataset(length=UpperCamelCase )
_snake_case : int = RegressionDataset(length=UpperCamelCase )
_snake_case : Tuple = RegressionModelConfig(a=UpperCamelCase , b=UpperCamelCase )
_snake_case : Union[str, Any] = RegressionPreTrainedModel(UpperCamelCase )
_snake_case : Optional[Any] = TrainingArguments(self.output_dir , disable_tqdm=UpperCamelCase , report_to=[] , **UpperCamelCase )
return Trainer(
UpperCamelCase , UpperCamelCase , train_dataset=UpperCamelCase , eval_dataset=UpperCamelCase , callbacks=UpperCamelCase , )
def UpperCamelCase_ ( self : Tuple , UpperCamelCase : int , UpperCamelCase : Union[str, Any] ):
'''simple docstring'''
self.assertEqual(len(UpperCamelCase ) , len(UpperCamelCase ) )
# Order doesn't matter
_snake_case : Optional[Any] = sorted(UpperCamelCase , key=lambda UpperCamelCase : cb.__name__ if isinstance(UpperCamelCase , UpperCamelCase ) else cb.__class__.__name__ )
_snake_case : Dict = sorted(UpperCamelCase , key=lambda UpperCamelCase : cb.__name__ if isinstance(UpperCamelCase , UpperCamelCase ) else cb.__class__.__name__ )
for cba, cba in zip(UpperCamelCase , UpperCamelCase ):
if isinstance(UpperCamelCase , UpperCamelCase ) and isinstance(UpperCamelCase , UpperCamelCase ):
self.assertEqual(UpperCamelCase , UpperCamelCase )
elif isinstance(UpperCamelCase , UpperCamelCase ) and not isinstance(UpperCamelCase , UpperCamelCase ):
self.assertEqual(UpperCamelCase , cba.__class__ )
elif not isinstance(UpperCamelCase , UpperCamelCase ) and isinstance(UpperCamelCase , UpperCamelCase ):
self.assertEqual(cba.__class__ , UpperCamelCase )
else:
self.assertEqual(UpperCamelCase , UpperCamelCase )
def UpperCamelCase_ ( self : Dict , UpperCamelCase : Dict ):
'''simple docstring'''
_snake_case : Dict = ['on_init_end', 'on_train_begin']
_snake_case : List[str] = 0
_snake_case : str = len(trainer.get_eval_dataloader() )
_snake_case : str = ['on_prediction_step'] * len(trainer.get_eval_dataloader() ) + ['on_log', 'on_evaluate']
for _ in range(trainer.state.num_train_epochs ):
expected_events.append('on_epoch_begin' )
for _ in range(UpperCamelCase ):
step += 1
expected_events += ["on_step_begin", "on_step_end"]
if step % trainer.args.logging_steps == 0:
expected_events.append('on_log' )
if trainer.args.evaluation_strategy == IntervalStrategy.STEPS and step % trainer.args.eval_steps == 0:
expected_events += evaluation_events.copy()
if step % trainer.args.save_steps == 0:
expected_events.append('on_save' )
expected_events.append('on_epoch_end' )
if trainer.args.evaluation_strategy == IntervalStrategy.EPOCH:
expected_events += evaluation_events.copy()
expected_events += ["on_log", "on_train_end"]
return expected_events
def UpperCamelCase_ ( self : str ):
'''simple docstring'''
_snake_case : int = self.get_trainer()
_snake_case : List[Any] = DEFAULT_CALLBACKS.copy() + [ProgressCallback]
self.check_callbacks_equality(trainer.callback_handler.callbacks , UpperCamelCase )
# Callbacks passed at init are added to the default callbacks
_snake_case : Union[str, Any] = self.get_trainer(callbacks=[MyTestTrainerCallback] )
expected_callbacks.append(UpperCamelCase )
self.check_callbacks_equality(trainer.callback_handler.callbacks , UpperCamelCase )
# TrainingArguments.disable_tqdm controls if use ProgressCallback or PrinterCallback
_snake_case : Tuple = self.get_trainer(disable_tqdm=UpperCamelCase )
_snake_case : Optional[int] = DEFAULT_CALLBACKS.copy() + [PrinterCallback]
self.check_callbacks_equality(trainer.callback_handler.callbacks , UpperCamelCase )
def UpperCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
_snake_case : Tuple = DEFAULT_CALLBACKS.copy() + [ProgressCallback]
_snake_case : List[Any] = self.get_trainer()
# We can add, pop, or remove by class name
trainer.remove_callback(UpperCamelCase )
expected_callbacks.remove(UpperCamelCase )
self.check_callbacks_equality(trainer.callback_handler.callbacks , UpperCamelCase )
_snake_case : Optional[Any] = self.get_trainer()
_snake_case : Dict = trainer.pop_callback(UpperCamelCase )
self.assertEqual(cb.__class__ , UpperCamelCase )
self.check_callbacks_equality(trainer.callback_handler.callbacks , UpperCamelCase )
trainer.add_callback(UpperCamelCase )
expected_callbacks.insert(0 , UpperCamelCase )
self.check_callbacks_equality(trainer.callback_handler.callbacks , UpperCamelCase )
# We can also add, pop, or remove by instance
_snake_case : Optional[Any] = self.get_trainer()
_snake_case : Optional[int] = trainer.callback_handler.callbacks[0]
trainer.remove_callback(UpperCamelCase )
expected_callbacks.remove(UpperCamelCase )
self.check_callbacks_equality(trainer.callback_handler.callbacks , UpperCamelCase )
_snake_case : int = self.get_trainer()
_snake_case : int = trainer.callback_handler.callbacks[0]
_snake_case : int = trainer.pop_callback(UpperCamelCase )
self.assertEqual(UpperCamelCase , UpperCamelCase )
self.check_callbacks_equality(trainer.callback_handler.callbacks , UpperCamelCase )
trainer.add_callback(UpperCamelCase )
expected_callbacks.insert(0 , UpperCamelCase )
self.check_callbacks_equality(trainer.callback_handler.callbacks , UpperCamelCase )
def UpperCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
import warnings
# XXX: for now ignore scatter_gather warnings in this test since it's not relevant to what's being tested
warnings.simplefilter(action='ignore' , category=UpperCamelCase )
_snake_case : Any = self.get_trainer(callbacks=[MyTestTrainerCallback] )
trainer.train()
_snake_case : List[str] = trainer.callback_handler.callbacks[-2].events
self.assertEqual(UpperCamelCase , self.get_expected_events(UpperCamelCase ) )
# Independent log/save/eval
_snake_case : Optional[Any] = self.get_trainer(callbacks=[MyTestTrainerCallback] , logging_steps=5 )
trainer.train()
_snake_case : List[Any] = trainer.callback_handler.callbacks[-2].events
self.assertEqual(UpperCamelCase , self.get_expected_events(UpperCamelCase ) )
_snake_case : Optional[int] = self.get_trainer(callbacks=[MyTestTrainerCallback] , save_steps=5 )
trainer.train()
_snake_case : Dict = trainer.callback_handler.callbacks[-2].events
self.assertEqual(UpperCamelCase , self.get_expected_events(UpperCamelCase ) )
_snake_case : List[Any] = self.get_trainer(callbacks=[MyTestTrainerCallback] , eval_steps=5 , evaluation_strategy='steps' )
trainer.train()
_snake_case : Union[str, Any] = trainer.callback_handler.callbacks[-2].events
self.assertEqual(UpperCamelCase , self.get_expected_events(UpperCamelCase ) )
_snake_case : Tuple = self.get_trainer(callbacks=[MyTestTrainerCallback] , evaluation_strategy='epoch' )
trainer.train()
_snake_case : str = trainer.callback_handler.callbacks[-2].events
self.assertEqual(UpperCamelCase , self.get_expected_events(UpperCamelCase ) )
# A bit of everything
_snake_case : int = self.get_trainer(
callbacks=[MyTestTrainerCallback] , logging_steps=3 , save_steps=10 , eval_steps=5 , evaluation_strategy='steps' , )
trainer.train()
_snake_case : Tuple = trainer.callback_handler.callbacks[-2].events
self.assertEqual(UpperCamelCase , self.get_expected_events(UpperCamelCase ) )
# warning should be emitted for duplicated callbacks
with patch('transformers.trainer_callback.logger.warning' ) as warn_mock:
_snake_case : Dict = self.get_trainer(
callbacks=[MyTestTrainerCallback, MyTestTrainerCallback] , )
assert str(UpperCamelCase ) in warn_mock.call_args[0][0]
| 411
| 1
|
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class UpperCAmelCase_ ( UpperCamelCase ):
'''simple docstring'''
def __init__( self , __A , __A ):
"""simple docstring"""
super().__init__()
# make sure scheduler can always be converted to DDIM
lowerCamelCase : Optional[int] = DDIMScheduler.from_config(scheduler.config )
self.register_modules(unet=_SCREAMING_SNAKE_CASE , scheduler=_SCREAMING_SNAKE_CASE )
@torch.no_grad()
def __call__( self , __A = 1 , __A = None , __A = 0.0 , __A = 50 , __A = None , __A = "pil" , __A = True , ):
"""simple docstring"""
if isinstance(self.unet.config.sample_size , _SCREAMING_SNAKE_CASE ):
lowerCamelCase : Dict = (
batch_size,
self.unet.config.in_channels,
self.unet.config.sample_size,
self.unet.config.sample_size,
)
else:
lowerCamelCase : str = (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) and len(_SCREAMING_SNAKE_CASE ) != batch_size:
raise ValueError(
F"""You have passed a list of generators of length {len(_SCREAMING_SNAKE_CASE )}, but requested an effective batch"""
F""" size of {batch_size}. Make sure the batch size matches the length of the generators.""" )
lowerCamelCase : Optional[Any] = randn_tensor(_SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE , device=self.device , dtype=self.unet.dtype )
# set step values
self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE )
for t in self.progress_bar(self.scheduler.timesteps ):
# 1. predict noise model_output
lowerCamelCase : int = self.unet(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ).sample
# 2. predict previous mean of image x_t-1 and add variance depending on eta
# eta corresponds to η in paper and should be between [0, 1]
# do x_t -> x_t-1
lowerCamelCase : Union[str, Any] = self.scheduler.step(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , eta=_SCREAMING_SNAKE_CASE , use_clipped_model_output=_SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE ).prev_sample
lowerCamelCase : Dict = (image / 2 + 0.5).clamp(0 , 1 )
lowerCamelCase : str = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
lowerCamelCase : List[Any] = self.numpy_to_pil(_SCREAMING_SNAKE_CASE )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=_SCREAMING_SNAKE_CASE )
| 713
|
def lowercase_( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
if bit_count < 0:
raise ValueError("The given input must be positive" )
# get the generated string sequence
lowerCamelCase : str = gray_code_sequence_string(SCREAMING_SNAKE_CASE_ )
#
# convert them to integers
for i in range(len(SCREAMING_SNAKE_CASE_ ) ):
lowerCamelCase : Union[str, Any] = int(sequence[i] , 2 )
return sequence
def lowercase_( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
if bit_count == 0:
return ["0"]
if bit_count == 1:
return ["0", "1"]
lowerCamelCase : int = 1 << bit_count # defines the length of the sequence
# 1<< n is equivalent to 2^n
# recursive answer will generate answer for n-1 bits
lowerCamelCase : Any = gray_code_sequence_string(bit_count - 1 )
lowerCamelCase : Optional[int] = []
# append 0 to first half of the smaller sequence generated
for i in range(seq_len // 2 ):
lowerCamelCase : Union[str, Any] = "0" + smaller_sequence[i]
sequence.append(SCREAMING_SNAKE_CASE_ )
# append 1 to second half ... start from the end of the list
for i in reversed(range(seq_len // 2 ) ):
lowerCamelCase : str = "1" + smaller_sequence[i]
sequence.append(SCREAMING_SNAKE_CASE_ )
return sequence
if __name__ == "__main__":
import doctest
doctest.testmod()
| 231
| 0
|
from math import ceil
def __lowerCamelCase ( UpperCamelCase__ = 1001 ):
'''simple docstring'''
snake_case_ = 1
for i in range(1 , int(ceil(n / 2.0 ) ) ):
snake_case_ = 2 * i + 1
snake_case_ = 2 * i
snake_case_ = total + 4 * odd**2 - 6 * even
return total
if __name__ == "__main__":
import sys
if len(sys.argv) == 1:
print(solution())
else:
try:
_UpperCAmelCase : Optional[Any] = int(sys.argv[1])
print(solution(n))
except ValueError:
print("""Invalid entry - please enter a number""")
| 362
|
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class lowercase ( unittest.TestCase ):
def a ( self ):
snake_case_ = 10
def a ( self ):
snake_case_ = [1, 2, 3, 4]
snake_case_ = [1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
self.assertEqual(truncate_or_pad(snake_case , self.block_size , 0 ) , snake_case )
def a ( self ):
snake_case_ = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
snake_case_ = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
self.assertEqual(truncate_or_pad(snake_case , self.block_size , 0 ) , snake_case )
def a ( self ):
snake_case_ = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
snake_case_ = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
self.assertEqual(truncate_or_pad(snake_case , self.block_size , 0 ) , snake_case )
def a ( self ):
snake_case_ = 'It was the year of Our Lord one thousand seven hundred and\n seventy-five.\n\nSpiritual revelations were conceded to England at that\n favoured period, as at this.'
snake_case_ , snake_case_ = process_story(snake_case )
self.assertEqual(snake_case , [] )
def a ( self ):
snake_case_ = ''
snake_case_ , snake_case_ = process_story(snake_case )
self.assertEqual(snake_case , [] )
self.assertEqual(snake_case , [] )
def a ( self ):
snake_case_ = (
'It was the year of Our Lord one thousand seven hundred and '
'seventy-five\n\nSpiritual revelations were conceded to England '
'at that favoured period, as at this.\n@highlight\n\nIt was the best of times'
)
snake_case_ , snake_case_ = process_story(snake_case )
snake_case_ = [
'It was the year of Our Lord one thousand seven hundred and seventy-five.',
'Spiritual revelations were conceded to England at that favoured period, as at this.',
]
self.assertEqual(snake_case , snake_case )
snake_case_ = ['It was the best of times.']
self.assertEqual(snake_case , snake_case )
def a ( self ):
snake_case_ = torch.tensor([1, 2, 3, 4] )
snake_case_ = torch.tensor([1, 1, 1, 1] )
np.testing.assert_array_equal(build_mask(snake_case , 0 ).numpy() , expected.numpy() )
def a ( self ):
snake_case_ = torch.tensor([1, 2, 3, 4, 23, 23, 23] )
snake_case_ = torch.tensor([1, 1, 1, 1, 0, 0, 0] )
np.testing.assert_array_equal(build_mask(snake_case , 23 ).numpy() , expected.numpy() )
def a ( self ):
snake_case_ = torch.tensor([8, 2, 3, 4, 1, 1, 1] )
snake_case_ = torch.tensor([1, 1, 1, 1, 0, 0, 0] )
np.testing.assert_array_equal(build_mask(snake_case , 1 ).numpy() , expected.numpy() )
def a ( self ):
snake_case_ = 101
snake_case_ = torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 101, 5, 6], [1, 101, 3, 4, 101, 6]] )
snake_case_ = torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]] )
snake_case_ = compute_token_type_ids(snake_case , snake_case )
np.testing.assert_array_equal(snake_case , snake_case )
| 362
| 1
|
'''simple docstring'''
import torch
from diffusers import CMStochasticIterativeScheduler
from .test_schedulers import SchedulerCommonTest
class a_ ( lowerCamelCase ):
lowercase = (CMStochasticIterativeScheduler,)
lowercase = 10
def A__ ( self , **_SCREAMING_SNAKE_CASE ) -> Any:
"""simple docstring"""
UpperCamelCase = {
"""num_train_timesteps""": 201,
"""sigma_min""": 0.0_0_2,
"""sigma_max""": 8_0.0,
}
config.update(**_SCREAMING_SNAKE_CASE )
return config
def A__ ( self ) -> List[Any]:
"""simple docstring"""
UpperCamelCase = 10
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = self.scheduler_classes[0](**_SCREAMING_SNAKE_CASE )
scheduler.set_timesteps(_SCREAMING_SNAKE_CASE )
UpperCamelCase = scheduler.timesteps[0]
UpperCamelCase = scheduler.timesteps[1]
UpperCamelCase = self.dummy_sample
UpperCamelCase = 0.1 * sample
UpperCamelCase = scheduler.step(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ).prev_sample
UpperCamelCase = scheduler.step(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def A__ ( self ) -> Tuple:
"""simple docstring"""
for timesteps in [10, 50, 100, 1000]:
self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Optional[Any]:
"""simple docstring"""
for clip_denoised in [True, False]:
self.check_over_configs(clip_denoised=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> int:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = 1
scheduler.set_timesteps(_SCREAMING_SNAKE_CASE )
UpperCamelCase = scheduler.timesteps
UpperCamelCase = torch.manual_seed(0 )
UpperCamelCase = self.dummy_model()
UpperCamelCase = self.dummy_sample_deter * scheduler.init_noise_sigma
for i, t in enumerate(_SCREAMING_SNAKE_CASE ):
# 1. scale model input
UpperCamelCase = scheduler.scale_model_input(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# 2. predict noise residual
UpperCamelCase = model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# 3. predict previous sample x_t-1
UpperCamelCase = scheduler.step(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE ).prev_sample
UpperCamelCase = pred_prev_sample
UpperCamelCase = torch.sum(torch.abs(_SCREAMING_SNAKE_CASE ) )
UpperCamelCase = torch.mean(torch.abs(_SCREAMING_SNAKE_CASE ) )
assert abs(result_sum.item() - 1_9_2.7_6_1_4 ) < 1e-2
assert abs(result_mean.item() - 0.2_5_1_0 ) < 1e-3
def A__ ( self ) -> str:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = [106, 0]
scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE )
UpperCamelCase = scheduler.timesteps
UpperCamelCase = torch.manual_seed(0 )
UpperCamelCase = self.dummy_model()
UpperCamelCase = self.dummy_sample_deter * scheduler.init_noise_sigma
for t in timesteps:
# 1. scale model input
UpperCamelCase = scheduler.scale_model_input(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# 2. predict noise residual
UpperCamelCase = model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# 3. predict previous sample x_t-1
UpperCamelCase = scheduler.step(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE ).prev_sample
UpperCamelCase = pred_prev_sample
UpperCamelCase = torch.sum(torch.abs(_SCREAMING_SNAKE_CASE ) )
UpperCamelCase = torch.mean(torch.abs(_SCREAMING_SNAKE_CASE ) )
assert abs(result_sum.item() - 3_4_7.6_3_5_7 ) < 1e-2
assert abs(result_mean.item() - 0.4_5_2_7 ) < 1e-3
def A__ ( self ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = [39, 30, 12, 15, 0]
with self.assertRaises(_SCREAMING_SNAKE_CASE , msg="""`timesteps` must be in descending order.""" ):
scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> List[Any]:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = [39, 30, 12, 1, 0]
UpperCamelCase = len(_SCREAMING_SNAKE_CASE )
with self.assertRaises(_SCREAMING_SNAKE_CASE , msg="""Can only pass one of `num_inference_steps` or `timesteps`.""" ):
scheduler.set_timesteps(num_inference_steps=_SCREAMING_SNAKE_CASE , timesteps=_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> int:
"""simple docstring"""
UpperCamelCase = self.scheduler_classes[0]
UpperCamelCase = self.get_scheduler_config()
UpperCamelCase = scheduler_class(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = [scheduler.config.num_train_timesteps]
with self.assertRaises(
_SCREAMING_SNAKE_CASE , msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}""" , ):
scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE )
| 35
|
'''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def lowercase__ ( __UpperCamelCase )-> Any:
UpperCamelCase = [
"""encoder.version""",
"""decoder.version""",
"""model.encoder.version""",
"""model.decoder.version""",
"""decoder.output_projection.weight""",
"""_float_tensor""",
"""encoder.embed_positions._float_tensor""",
"""decoder.embed_positions._float_tensor""",
]
for k in ignore_keys:
state_dict.pop(__UpperCamelCase , __UpperCamelCase )
def lowercase__ ( __UpperCamelCase )-> str:
UpperCamelCase ,UpperCamelCase = emb.weight.shape
UpperCamelCase = nn.Linear(__UpperCamelCase , __UpperCamelCase , bias=__UpperCamelCase )
UpperCamelCase = emb.weight.data
return lin_layer
def lowercase__ ( __UpperCamelCase )-> str:
UpperCamelCase = torch.load(__UpperCamelCase , map_location="""cpu""" )
UpperCamelCase = mam_aaa["""args"""] or mam_aaa["""cfg"""]["""model"""]
UpperCamelCase = mam_aaa["""model"""]
remove_ignore_keys_(__UpperCamelCase )
UpperCamelCase = state_dict["""encoder.embed_tokens.weight"""].shape[0]
UpperCamelCase = MaMaaaConfig(
vocab_size=__UpperCamelCase , max_position_embeddings=1024 , encoder_layers=args.encoder_layers , decoder_layers=args.decoder_layers , encoder_attention_heads=args.encoder_attention_heads , decoder_attention_heads=args.decoder_attention_heads , encoder_ffn_dim=args.encoder_ffn_embed_dim , decoder_ffn_dim=args.decoder_ffn_embed_dim , d_model=args.encoder_embed_dim , encoder_layerdrop=args.encoder_layerdrop , decoder_layerdrop=args.decoder_layerdrop , dropout=args.dropout , attention_dropout=args.attention_dropout , activation_dropout=args.activation_dropout , activation_function="""relu""" , )
UpperCamelCase = state_dict["""decoder.embed_tokens.weight"""]
UpperCamelCase = MaMaaaForConditionalGeneration(__UpperCamelCase )
model.model.load_state_dict(__UpperCamelCase , strict=__UpperCamelCase )
UpperCamelCase = make_linear_from_emb(model.model.shared )
return model
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument('fairseq_path', type=str, help='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.')
SCREAMING_SNAKE_CASE__ = parser.parse_args()
SCREAMING_SNAKE_CASE__ = convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
model.save_pretrained(args.pytorch_dump_folder_path)
| 35
| 1
|
'''simple docstring'''
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_enabled(False)
def a__ ( lowerCAmelCase__ , lowerCAmelCase__=False ) -> Dict:
UpperCAmelCase__ : int = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((F"""module.blocks.{i}.norm1.weight""", F"""vit.encoder.layer.{i}.layernorm_before.weight""") )
rename_keys.append((F"""module.blocks.{i}.norm1.bias""", F"""vit.encoder.layer.{i}.layernorm_before.bias""") )
rename_keys.append(
(F"""module.blocks.{i}.attn.proj.weight""", F"""vit.encoder.layer.{i}.attention.output.dense.weight""") )
rename_keys.append((F"""module.blocks.{i}.attn.proj.bias""", F"""vit.encoder.layer.{i}.attention.output.dense.bias""") )
rename_keys.append((F"""module.blocks.{i}.norm2.weight""", F"""vit.encoder.layer.{i}.layernorm_after.weight""") )
rename_keys.append((F"""module.blocks.{i}.norm2.bias""", F"""vit.encoder.layer.{i}.layernorm_after.bias""") )
rename_keys.append((F"""module.blocks.{i}.mlp.fc1.weight""", F"""vit.encoder.layer.{i}.intermediate.dense.weight""") )
rename_keys.append((F"""module.blocks.{i}.mlp.fc1.bias""", F"""vit.encoder.layer.{i}.intermediate.dense.bias""") )
rename_keys.append((F"""module.blocks.{i}.mlp.fc2.weight""", F"""vit.encoder.layer.{i}.output.dense.weight""") )
rename_keys.append((F"""module.blocks.{i}.mlp.fc2.bias""", F"""vit.encoder.layer.{i}.output.dense.bias""") )
# projection layer + position embeddings
rename_keys.extend(
[
('''module.cls_token''', '''vit.embeddings.cls_token'''),
('''module.patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight'''),
('''module.patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias'''),
('''module.pos_embed''', '''vit.embeddings.position_embeddings'''),
] )
if base_model:
# layernorm + pooler
rename_keys.extend(
[
('''module.norm.weight''', '''layernorm.weight'''),
('''module.norm.bias''', '''layernorm.bias'''),
] )
# if just the base model, we should remove "vit" from all keys that start with "vit"
UpperCAmelCase__ : Dict = [(pair[0], pair[1][4:]) if pair[1].startswith('''vit''' ) else pair for pair in rename_keys]
else:
# layernorm + classification head
rename_keys.extend(
[
('''norm.weight''', '''vit.layernorm.weight'''),
('''norm.bias''', '''vit.layernorm.bias'''),
('''head.weight''', '''classifier.weight'''),
('''head.bias''', '''classifier.bias'''),
] )
return rename_keys
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__=False ) -> str:
for i in range(config.num_hidden_layers ):
if base_model:
UpperCAmelCase__ : str = ''''''
else:
UpperCAmelCase__ : Union[str, Any] = '''vit.'''
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
UpperCAmelCase__ : Optional[int] = state_dict.pop(F"""module.blocks.{i}.attn.qkv.weight""" )
UpperCAmelCase__ : Optional[int] = state_dict.pop(F"""module.blocks.{i}.attn.qkv.bias""" )
# next, add query, keys and values (in that order) to the state dict
UpperCAmelCase__ : Union[str, Any] = in_proj_weight[
: config.hidden_size, :
]
UpperCAmelCase__ : Optional[int] = in_proj_bias[: config.hidden_size]
UpperCAmelCase__ : int = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
UpperCAmelCase__ : List[Any] = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
UpperCAmelCase__ : Dict = in_proj_weight[
-config.hidden_size :, :
]
UpperCAmelCase__ : Optional[int] = in_proj_bias[-config.hidden_size :]
def a__ ( lowerCAmelCase__ ) -> Union[str, Any]:
UpperCAmelCase__ : Optional[Any] = ['''head.weight''', '''head.bias''']
for k in ignore_keys:
state_dict.pop(lowerCAmelCase__ , lowerCAmelCase__ )
def a__ ( lowerCAmelCase__ ) -> Any:
# projection head is used in the self-supervised pre-training in MSN,
# for downstream task it's not needed.
UpperCAmelCase__ : List[str] = [
'''module.fc.fc1.weight''',
'''module.fc.fc1.bias''',
'''module.fc.bn1.weight''',
'''module.fc.bn1.bias''',
'''module.fc.bn1.running_mean''',
'''module.fc.bn1.running_var''',
'''module.fc.bn1.num_batches_tracked''',
'''module.fc.fc2.weight''',
'''module.fc.fc2.bias''',
'''module.fc.bn2.weight''',
'''module.fc.bn2.bias''',
'''module.fc.bn2.running_mean''',
'''module.fc.bn2.running_var''',
'''module.fc.bn2.num_batches_tracked''',
'''module.fc.fc3.weight''',
'''module.fc.fc3.bias''',
]
for k in ignore_keys:
state_dict.pop(lowerCAmelCase__ , lowerCAmelCase__ )
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> int:
UpperCAmelCase__ : List[Any] = dct.pop(lowerCAmelCase__ )
UpperCAmelCase__ : Optional[int] = val
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ ) -> Tuple:
UpperCAmelCase__ : str = ViTMSNConfig()
UpperCAmelCase__ : Optional[int] = 10_00
UpperCAmelCase__ : List[Any] = '''datasets/huggingface/label-files'''
UpperCAmelCase__ : str = '''imagenet-1k-id2label.json'''
UpperCAmelCase__ : List[str] = json.load(open(hf_hub_download(lowerCAmelCase__ , lowerCAmelCase__ ) , '''r''' ) )
UpperCAmelCase__ : Union[str, Any] = {int(lowerCAmelCase__ ): v for k, v in idalabel.items()}
UpperCAmelCase__ : List[str] = idalabel
UpperCAmelCase__ : Optional[int] = {v: k for k, v in idalabel.items()}
if "s16" in checkpoint_url:
UpperCAmelCase__ : List[str] = 3_84
UpperCAmelCase__ : Dict = 15_36
UpperCAmelCase__ : Tuple = 6
elif "l16" in checkpoint_url:
UpperCAmelCase__ : List[str] = 10_24
UpperCAmelCase__ : str = 40_96
UpperCAmelCase__ : Tuple = 24
UpperCAmelCase__ : Optional[int] = 16
UpperCAmelCase__ : Optional[int] = 0.1
elif "b4" in checkpoint_url:
UpperCAmelCase__ : List[str] = 4
elif "l7" in checkpoint_url:
UpperCAmelCase__ : int = 7
UpperCAmelCase__ : int = 10_24
UpperCAmelCase__ : Dict = 40_96
UpperCAmelCase__ : Any = 24
UpperCAmelCase__ : str = 16
UpperCAmelCase__ : Any = 0.1
UpperCAmelCase__ : str = ViTMSNModel(lowerCAmelCase__ )
UpperCAmelCase__ : Optional[Any] = torch.hub.load_state_dict_from_url(lowerCAmelCase__ , map_location='''cpu''' )['''target_encoder''']
UpperCAmelCase__ : Union[str, Any] = ViTImageProcessor(size=config.image_size )
remove_projection_head(lowerCAmelCase__ )
UpperCAmelCase__ : List[Any] = create_rename_keys(lowerCAmelCase__ , base_model=lowerCAmelCase__ )
for src, dest in rename_keys:
rename_key(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
read_in_q_k_v(lowerCAmelCase__ , lowerCAmelCase__ , base_model=lowerCAmelCase__ )
model.load_state_dict(lowerCAmelCase__ )
model.eval()
UpperCAmelCase__ : Dict = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
UpperCAmelCase__ : Tuple = Image.open(requests.get(lowerCAmelCase__ , stream=lowerCAmelCase__ ).raw )
UpperCAmelCase__ : Tuple = ViTImageProcessor(
size=config.image_size , image_mean=lowerCAmelCase__ , image_std=lowerCAmelCase__ )
UpperCAmelCase__ : Optional[Any] = image_processor(images=lowerCAmelCase__ , return_tensors='''pt''' )
# forward pass
torch.manual_seed(2 )
UpperCAmelCase__ : Optional[Any] = model(**lowerCAmelCase__ )
UpperCAmelCase__ : Tuple = outputs.last_hidden_state
# The following Colab Notebook was used to generate these outputs:
# https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
if "s16" in checkpoint_url:
UpperCAmelCase__ : Any = torch.tensor([[-1.0_9_1_5, -1.4_8_7_6, -1.1_8_0_9]] )
elif "b16" in checkpoint_url:
UpperCAmelCase__ : Optional[int] = torch.tensor([[1_4.2_8_8_9, -1_8.9_0_4_5, 1_1.7_2_8_1]] )
elif "l16" in checkpoint_url:
UpperCAmelCase__ : int = torch.tensor([[4_1.5_0_2_8, -2_2.8_6_8_1, 4_5.6_4_7_5]] )
elif "b4" in checkpoint_url:
UpperCAmelCase__ : str = torch.tensor([[-4.3_8_6_8, 5.2_9_3_2, -0.4_1_3_7]] )
else:
UpperCAmelCase__ : Tuple = torch.tensor([[-0.1_7_9_2, -0.6_4_6_5, 2.4_2_6_3]] )
# verify logits
assert torch.allclose(last_hidden_state[:, 0, :3] , lowerCAmelCase__ , atol=1E-4 )
print(F"""Saving model to {pytorch_dump_folder_path}""" )
model.save_pretrained(lowerCAmelCase__ )
print(F"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(lowerCAmelCase__ )
if __name__ == "__main__":
UpperCamelCase__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--checkpoint_url''',
default='''https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar''',
type=str,
help='''URL of the checkpoint you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
UpperCamelCase__ = parser.parse_args()
convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
| 75
|
'''simple docstring'''
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, 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 EsmForMaskedLM, EsmForSequenceClassification, EsmForTokenClassification, EsmModel
from transformers.models.esm.modeling_esm import (
ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
EsmEmbeddings,
create_position_ids_from_input_ids,
)
class __UpperCAmelCase :
def __init__( self , lowerCAmelCase_ , lowerCAmelCase_=13 , lowerCAmelCase_=7 , lowerCAmelCase_=False , lowerCAmelCase_=True , lowerCAmelCase_=False , lowerCAmelCase_=True , lowerCAmelCase_=33 , lowerCAmelCase_=32 , lowerCAmelCase_=5 , lowerCAmelCase_=4 , lowerCAmelCase_=37 , lowerCAmelCase_="gelu" , lowerCAmelCase_=0.1 , lowerCAmelCase_=0.1 , lowerCAmelCase_=5_12 , lowerCAmelCase_=16 , lowerCAmelCase_=2 , lowerCAmelCase_=0.02 , lowerCAmelCase_=3 , lowerCAmelCase_=4 , lowerCAmelCase_=None , ):
"""simple docstring"""
_snake_case = parent
_snake_case = batch_size
_snake_case = seq_length
_snake_case = is_training
_snake_case = use_input_mask
_snake_case = use_token_type_ids
_snake_case = use_labels
_snake_case = vocab_size
_snake_case = hidden_size
_snake_case = num_hidden_layers
_snake_case = num_attention_heads
_snake_case = intermediate_size
_snake_case = hidden_act
_snake_case = hidden_dropout_prob
_snake_case = attention_probs_dropout_prob
_snake_case = max_position_embeddings
_snake_case = type_vocab_size
_snake_case = type_sequence_label_size
_snake_case = initializer_range
_snake_case = num_labels
_snake_case = num_choices
_snake_case = scope
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_snake_case = None
if self.use_input_mask:
_snake_case = random_attention_mask([self.batch_size, self.seq_length] )
_snake_case = None
_snake_case = None
_snake_case = None
if self.use_labels:
_snake_case = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_snake_case = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_snake_case = ids_tensor([self.batch_size] , self.num_choices )
_snake_case = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def lowerCamelCase ( self ):
"""simple docstring"""
return EsmConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , pad_token_id=1 , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , )
def lowerCamelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
_snake_case = EsmModel(config=lowerCAmelCase_ )
model.to(lowerCAmelCase_ )
model.eval()
_snake_case = model(lowerCAmelCase_ , attention_mask=lowerCAmelCase_ )
_snake_case = model(lowerCAmelCase_ )
_snake_case = model(lowerCAmelCase_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def lowerCamelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
_snake_case = EsmForMaskedLM(config=lowerCAmelCase_ )
model.to(lowerCAmelCase_ )
model.eval()
_snake_case = model(lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , labels=lowerCAmelCase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowerCamelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
_snake_case = self.num_labels
_snake_case = EsmForTokenClassification(config=lowerCAmelCase_ )
model.to(lowerCAmelCase_ )
model.eval()
_snake_case = model(lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , labels=lowerCAmelCase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = self.prepare_config_and_inputs()
(
(
_snake_case
) , (
_snake_case
) , (
_snake_case
) , (
_snake_case
) , (
_snake_case
) , (
_snake_case
) ,
) = config_and_inputs
_snake_case = {'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class __UpperCAmelCase ( _lowerCamelCase , _lowerCamelCase , unittest.TestCase ):
__lowercase = False
__lowercase = (
(
EsmForMaskedLM,
EsmModel,
EsmForSequenceClassification,
EsmForTokenClassification,
)
if is_torch_available()
else ()
)
__lowercase = ()
__lowercase = (
{
"""feature-extraction""": EsmModel,
"""fill-mask""": EsmForMaskedLM,
"""text-classification""": EsmForSequenceClassification,
"""token-classification""": EsmForTokenClassification,
"""zero-shot""": EsmForSequenceClassification,
}
if is_torch_available()
else {}
)
__lowercase = True
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = EsmModelTester(self )
_snake_case = ConfigTester(self , config_class=lowerCAmelCase_ , hidden_size=37 )
def lowerCamelCase ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCAmelCase_ )
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
_snake_case = type
self.model_tester.create_and_check_model(*lowerCAmelCase_ )
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*lowerCAmelCase_ )
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase_ )
@slow
def lowerCamelCase ( self ):
"""simple docstring"""
for model_name in ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_snake_case = EsmModel.from_pretrained(lowerCAmelCase_ )
self.assertIsNotNone(lowerCAmelCase_ )
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = self.model_tester.prepare_config_and_inputs()[0]
_snake_case = EsmEmbeddings(config=lowerCAmelCase_ )
_snake_case = torch.as_tensor([[12, 31, 13, model.padding_idx]] )
_snake_case = torch.as_tensor(
[
[
0 + model.padding_idx + 1,
1 + model.padding_idx + 1,
2 + model.padding_idx + 1,
model.padding_idx,
]
] )
_snake_case = create_position_ids_from_input_ids(lowerCAmelCase_ , model.padding_idx )
self.assertEqual(position_ids.shape , expected_positions.shape )
self.assertTrue(torch.all(torch.eq(lowerCAmelCase_ , lowerCAmelCase_ ) ) )
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = self.model_tester.prepare_config_and_inputs()[0]
_snake_case = EsmEmbeddings(config=lowerCAmelCase_ )
_snake_case = torch.empty(2 , 4 , 30 )
_snake_case = [
0 + embeddings.padding_idx + 1,
1 + embeddings.padding_idx + 1,
2 + embeddings.padding_idx + 1,
3 + embeddings.padding_idx + 1,
]
_snake_case = torch.as_tensor([expected_single_positions, expected_single_positions] )
_snake_case = embeddings.create_position_ids_from_inputs_embeds(lowerCAmelCase_ )
self.assertEqual(position_ids.shape , expected_positions.shape )
self.assertTrue(torch.all(torch.eq(lowerCAmelCase_ , lowerCAmelCase_ ) ) )
@unittest.skip('Esm does not support embedding resizing' )
def lowerCamelCase ( self ):
"""simple docstring"""
pass
@unittest.skip('Esm does not support embedding resizing' )
def lowerCamelCase ( self ):
"""simple docstring"""
pass
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def lowerCamelCase ( self ):
"""simple docstring"""
pass
@require_torch
class __UpperCAmelCase ( _lowerCamelCase ):
@slow
def lowerCamelCase ( self ):
"""simple docstring"""
with torch.no_grad():
_snake_case = EsmForMaskedLM.from_pretrained('facebook/esm2_t6_8M_UR50D' )
model.eval()
_snake_case = torch.tensor([[0, 1, 2, 3, 4, 5]] )
_snake_case = model(lowerCAmelCase_ )[0]
_snake_case = 33
_snake_case = torch.Size((1, 6, vocab_size) )
self.assertEqual(output.shape , lowerCAmelCase_ )
_snake_case = torch.tensor(
[[[8.9215, -10.5898, -6.4671], [-6.3967, -13.9114, -1.1212], [-7.7812, -13.9516, -3.7406]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , lowerCAmelCase_ , atol=1E-4 ) )
@slow
def lowerCamelCase ( self ):
"""simple docstring"""
with torch.no_grad():
_snake_case = EsmModel.from_pretrained('facebook/esm2_t6_8M_UR50D' )
model.eval()
_snake_case = torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]] )
_snake_case = model(lowerCAmelCase_ )[0]
# compare the actual values for a slice.
_snake_case = torch.tensor(
[[[0.1444, 0.5413, 0.3248], [0.3034, 0.0053, 0.3108], [0.3228, -0.2499, 0.3415]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , lowerCAmelCase_ , atol=1E-4 ) )
| 495
| 0
|
'''simple docstring'''
from ..utils import is_flax_available, is_torch_available
if is_torch_available():
from .autoencoder_kl import AutoencoderKL
from .controlnet import ControlNetModel
from .dual_transformer_ad import DualTransformeraDModel
from .modeling_utils import ModelMixin
from .prior_transformer import PriorTransformer
from .ta_film_transformer import TaFilmDecoder
from .transformer_ad import TransformeraDModel
from .unet_ad import UNetaDModel
from .unet_ad import UNetaDModel
from .unet_ad_condition import UNetaDConditionModel
from .unet_ad_condition import UNetaDConditionModel
from .vq_model import VQModel
if is_flax_available():
from .controlnet_flax import FlaxControlNetModel
from .unet_ad_condition_flax import FlaxUNetaDConditionModel
from .vae_flax import FlaxAutoencoderKL
| 715
|
'''simple docstring'''
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self : Tuple , lowercase : pyspark.sql.DataFrame , lowercase : Optional[NamedSplit] = None , lowercase : Optional[Features] = None , lowercase : bool = True , lowercase : str = None , lowercase : bool = False , lowercase : str = None , lowercase : bool = True , lowercase : str = "arrow" , **lowercase : Any , ) -> Optional[int]:
'''simple docstring'''
super().__init__(
split=lowercase , features=lowercase , cache_dir=lowercase , keep_in_memory=lowercase , streaming=lowercase , **lowercase , )
UpperCamelCase__ = load_from_cache_file
UpperCamelCase__ = file_format
UpperCamelCase__ = Spark(
df=lowercase , features=lowercase , cache_dir=lowercase , working_dir=lowercase , **lowercase , )
def A ( self : int ) -> Any:
'''simple docstring'''
if self.streaming:
return self.builder.as_streaming_dataset(split=self.split )
UpperCamelCase__ = None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
self.builder.download_and_prepare(
download_mode=lowercase , file_format=self._file_format , )
return self.builder.as_dataset(split=self.split )
| 265
| 0
|
def __lowerCAmelCase ( _UpperCamelCase : int = 10 ) -> str:
'''simple docstring'''
if not isinstance(_UpperCamelCase , _UpperCamelCase ) or n < 0:
raise ValueError('Invalid input' )
SCREAMING_SNAKE_CASE = 10**n
SCREAMING_SNAKE_CASE = 2_84_33 * (pow(2 , 7_83_04_57 , _UpperCamelCase )) + 1
return str(number % modulus )
if __name__ == "__main__":
from doctest import testmod
testmod()
print(F"""{solution(10) = }""")
| 439
|
from __future__ import annotations
from math import gcd
def __lowerCAmelCase ( _UpperCamelCase : int , _UpperCamelCase : int = 2 , _UpperCamelCase : int = 1 , _UpperCamelCase : int = 3 , ) -> int | None:
'''simple docstring'''
if num < 2:
raise ValueError('The input value cannot be less than 2' )
# Because of the relationship between ``f(f(x))`` and ``f(x)``, this
# algorithm struggles to find factors that are divisible by two.
# As a workaround, we specifically check for two and even inputs.
# See: https://math.stackexchange.com/a/2856214/165820
if num > 2 and num % 2 == 0:
return 2
# Pollard's Rho algorithm requires a function that returns pseudorandom
# values between 0 <= X < ``num``. It doesn't need to be random in the
# sense that the output value is cryptographically secure or difficult
# to calculate, it only needs to be random in the sense that all output
# values should be equally likely to appear.
# For this reason, Pollard suggested using ``f(x) = (x**2 - 1) % num``
# However, the success of Pollard's algorithm isn't guaranteed and is
# determined in part by the initial seed and the chosen random function.
# To make retries easier, we will instead use ``f(x) = (x**2 + C) % num``
# where ``C`` is a value that we can modify between each attempt.
def rand_fn(_UpperCamelCase : int , _UpperCamelCase : int , _UpperCamelCase : int ) -> int:
return (pow(_UpperCamelCase , 2 ) + step) % modulus
for _ in range(_UpperCamelCase ):
# These track the position within the cycle detection logic.
SCREAMING_SNAKE_CASE = seed
SCREAMING_SNAKE_CASE = seed
while True:
# At each iteration, the tortoise moves one step and the hare moves two.
SCREAMING_SNAKE_CASE = rand_fn(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
SCREAMING_SNAKE_CASE = rand_fn(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
SCREAMING_SNAKE_CASE = rand_fn(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
# At some point both the tortoise and the hare will enter a cycle whose
# length ``p`` is a divisor of ``num``. Once in that cycle, at some point
# the tortoise and hare will end up on the same value modulo ``p``.
# We can detect when this happens because the position difference between
# the tortoise and the hare will share a common divisor with ``num``.
SCREAMING_SNAKE_CASE = gcd(hare - tortoise , _UpperCamelCase )
if divisor == 1:
# No common divisor yet, just keep searching.
continue
else:
# We found a common divisor!
if divisor == num:
# Unfortunately, the divisor is ``num`` itself and is useless.
break
else:
# The divisor is a nontrivial factor of ``num``!
return divisor
# If we made it here, then this attempt failed.
# We need to pick a new starting seed for the tortoise and hare
# in addition to a new step value for the random function.
# To keep this example implementation deterministic, the
# new values will be generated based on currently available
# values instead of using something like ``random.randint``.
# We can use the hare's position as the new seed.
# This is actually what Richard Brent's the "optimized" variant does.
SCREAMING_SNAKE_CASE = hare
# The new step value for the random function can just be incremented.
# At first the results will be similar to what the old function would
# have produced, but the value will quickly diverge after a bit.
step += 1
# We haven't found a divisor within the requested number of attempts.
# We were unlucky or ``num`` itself is actually prime.
return None
if __name__ == "__main__":
import argparse
a_ : int = argparse.ArgumentParser()
parser.add_argument(
"num",
type=int,
help="The value to find a divisor of",
)
parser.add_argument(
"--attempts",
type=int,
default=3,
help="The number of attempts before giving up",
)
a_ : Tuple = parser.parse_args()
a_ : int = pollard_rho(args.num, attempts=args.attempts)
if divisor is None:
print(F"""{args.num} is probably prime""")
else:
a_ : Union[str, Any] = args.num // divisor
print(F"""{args.num} = {divisor} * {quotient}""")
| 439
| 1
|
'''simple docstring'''
from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
LineByLineTextDataset,
LineByLineWithRefDataset,
LineByLineWithSOPTextDataset,
TextDataset,
TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments
| 233
|
'''simple docstring'''
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
class __lowerCAmelCase:
def __init__( self : List[str] , SCREAMING_SNAKE_CASE : Optional[Any] , SCREAMING_SNAKE_CASE : Optional[int]=13 , SCREAMING_SNAKE_CASE : str=64 , SCREAMING_SNAKE_CASE : List[Any]=2 , SCREAMING_SNAKE_CASE : Any=3 , SCREAMING_SNAKE_CASE : Tuple=True , SCREAMING_SNAKE_CASE : List[str]=True , SCREAMING_SNAKE_CASE : Optional[Any]=32 , SCREAMING_SNAKE_CASE : Optional[int]=5 , SCREAMING_SNAKE_CASE : Dict=4 , SCREAMING_SNAKE_CASE : Optional[int]=37 , SCREAMING_SNAKE_CASE : int="gelu" , SCREAMING_SNAKE_CASE : int=0.1 , SCREAMING_SNAKE_CASE : int=0.1 , SCREAMING_SNAKE_CASE : List[Any]=10 , SCREAMING_SNAKE_CASE : Dict=0.02 , SCREAMING_SNAKE_CASE : str=[1, 16, 4, 4] , SCREAMING_SNAKE_CASE : int=None , ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ :str = parent
SCREAMING_SNAKE_CASE_ :int = batch_size
SCREAMING_SNAKE_CASE_ :Any = image_size
SCREAMING_SNAKE_CASE_ :Tuple = patch_size
SCREAMING_SNAKE_CASE_ :List[Any] = num_channels
SCREAMING_SNAKE_CASE_ :Dict = is_training
SCREAMING_SNAKE_CASE_ :Tuple = use_labels
SCREAMING_SNAKE_CASE_ :List[Any] = hidden_size
SCREAMING_SNAKE_CASE_ :Optional[Any] = num_hidden_layers
SCREAMING_SNAKE_CASE_ :Dict = num_attention_heads
SCREAMING_SNAKE_CASE_ :Optional[Any] = intermediate_size
SCREAMING_SNAKE_CASE_ :Optional[Any] = hidden_act
SCREAMING_SNAKE_CASE_ :Optional[Any] = hidden_dropout_prob
SCREAMING_SNAKE_CASE_ :Any = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE_ :List[Any] = type_sequence_label_size
SCREAMING_SNAKE_CASE_ :Optional[int] = initializer_range
SCREAMING_SNAKE_CASE_ :Dict = scope
SCREAMING_SNAKE_CASE_ :Union[str, Any] = backbone_featmap_shape
# in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
# the number of patches is based on the feature map of the backbone, which by default uses an output stride
# of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
SCREAMING_SNAKE_CASE_ :List[str] = (self.image_size // 32) ** 2
SCREAMING_SNAKE_CASE_ :Optional[int] = num_patches + 1
def _lowercase ( self : List[str] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ :Optional[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE_ :Optional[Any] = None
if self.use_labels:
SCREAMING_SNAKE_CASE_ :Optional[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
SCREAMING_SNAKE_CASE_ :Optional[int] = self.get_config()
return config, pixel_values, labels
def _lowercase ( self : Tuple ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ :List[str] = {
'global_padding': 'same',
'layer_type': 'bottleneck',
'depths': [3, 4, 9],
'out_features': ['stage1', 'stage2', 'stage3'],
'embedding_dynamic_padding': True,
'hidden_sizes': [4, 8, 16, 32],
'num_groups': 2,
}
return ViTHybridConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=SCREAMING_SNAKE_CASE , initializer_range=self.initializer_range , backbone_featmap_shape=self.backbone_featmap_shape , backbone_config=SCREAMING_SNAKE_CASE , )
def _lowercase ( self : List[str] , SCREAMING_SNAKE_CASE : List[Any] , SCREAMING_SNAKE_CASE : Optional[int] , SCREAMING_SNAKE_CASE : Optional[int] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ :Optional[int] = ViTHybridModel(config=SCREAMING_SNAKE_CASE )
model.to(SCREAMING_SNAKE_CASE )
model.eval()
SCREAMING_SNAKE_CASE_ :List[str] = model(SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _lowercase ( self : str , SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : Dict , SCREAMING_SNAKE_CASE : List[Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ :Union[str, Any] = self.type_sequence_label_size
SCREAMING_SNAKE_CASE_ :Any = ViTHybridForImageClassification(SCREAMING_SNAKE_CASE )
model.to(SCREAMING_SNAKE_CASE )
model.eval()
SCREAMING_SNAKE_CASE_ :int = model(SCREAMING_SNAKE_CASE , labels=SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def _lowercase ( self : Dict ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ :Optional[int] = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ :Tuple = config_and_inputs
SCREAMING_SNAKE_CASE_ :Optional[Any] = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class __lowerCAmelCase( lowerCAmelCase__ , lowerCAmelCase__ , unittest.TestCase ):
__snake_case : Dict = (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
__snake_case : int = (
{'feature-extraction': ViTHybridModel, 'image-classification': ViTHybridForImageClassification}
if is_torch_available()
else {}
)
__snake_case : Dict = False
__snake_case : Union[str, Any] = False
__snake_case : List[Any] = False
def _lowercase ( self : Any ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ :Tuple = ViTHybridModelTester(self )
SCREAMING_SNAKE_CASE_ :List[str] = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE , has_text_modality=SCREAMING_SNAKE_CASE , hidden_size=37 )
def _lowercase ( self : Optional[int] ):
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason='ViT does not use inputs_embeds' )
def _lowercase ( self : str ):
"""simple docstring"""
pass
def _lowercase ( self : Union[str, Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ :Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE_ :Tuple = model_class(SCREAMING_SNAKE_CASE )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
SCREAMING_SNAKE_CASE_ :Optional[int] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE , nn.Linear ) )
def _lowercase ( self : str ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ :int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE_ :int = model_class(SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE_ :List[Any] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
SCREAMING_SNAKE_CASE_ :List[Any] = [*signature.parameters.keys()]
SCREAMING_SNAKE_CASE_ :int = ['pixel_values']
self.assertListEqual(arg_names[:1] , SCREAMING_SNAKE_CASE )
def _lowercase ( self : Union[str, Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ :Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE )
def _lowercase ( self : Union[str, Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ :Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE )
def _lowercase ( self : int ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ :Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE_ :Optional[int] = _config_zero_init(SCREAMING_SNAKE_CASE )
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE_ :Union[str, Any] = model_class(config=SCREAMING_SNAKE_CASE )
# Skip the check for the backbone
for name, module in model.named_modules():
if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
SCREAMING_SNAKE_CASE_ :Any = [f'{name}.{key}' for key in module.state_dict().keys()]
break
for name, param in model.named_parameters():
if param.requires_grad:
if name in backbone_params:
continue
self.assertIn(
((param.data.mean() * 1E9).round() / 1E9).item() , [0.0, 1.0] , msg=f'Parameter {name} of model {model_class} seems not properly initialized' , )
@slow
def _lowercase ( self : Dict ):
"""simple docstring"""
for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE_ :Union[str, Any] = ViTHybridModel.from_pretrained(SCREAMING_SNAKE_CASE )
self.assertIsNotNone(SCREAMING_SNAKE_CASE )
def SCREAMING_SNAKE_CASE__ ( ):
SCREAMING_SNAKE_CASE_ :Dict = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class __lowerCAmelCase( unittest.TestCase ):
@cached_property
def _lowercase ( self : Optional[Any] ):
"""simple docstring"""
return (
ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def _lowercase ( self : str ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ :Optional[int] = ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(
SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE_ :int = self.default_image_processor
SCREAMING_SNAKE_CASE_ :Dict = prepare_img()
SCREAMING_SNAKE_CASE_ :str = image_processor(images=SCREAMING_SNAKE_CASE , return_tensors='pt' ).to(SCREAMING_SNAKE_CASE )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE_ :Union[str, Any] = model(**SCREAMING_SNAKE_CASE )
# verify the logits
SCREAMING_SNAKE_CASE_ :Any = torch.Size((1, 1_000) )
self.assertEqual(outputs.logits.shape , SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE_ :Optional[Any] = torch.tensor([-1.90_90, -0.49_93, -0.23_89] ).to(SCREAMING_SNAKE_CASE )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , SCREAMING_SNAKE_CASE , atol=1E-4 ) )
@slow
@require_accelerate
def _lowercase ( self : Optional[Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ :Tuple = ViTHybridImageProcessor.from_pretrained('google/vit-hybrid-base-bit-384' )
SCREAMING_SNAKE_CASE_ :List[Any] = ViTHybridForImageClassification.from_pretrained('google/vit-hybrid-base-bit-384' , device_map='auto' )
SCREAMING_SNAKE_CASE_ :Optional[Any] = prepare_img()
SCREAMING_SNAKE_CASE_ :Optional[Any] = image_processor(images=SCREAMING_SNAKE_CASE , return_tensors='pt' )
SCREAMING_SNAKE_CASE_ :Optional[int] = model(**SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE_ :Any = outputs.logits
# model predicts one of the 1000 ImageNet classes
SCREAMING_SNAKE_CASE_ :int = logits.argmax(-1 ).item()
self.assertTrue(model.config.idalabel[predicted_class_idx] , 'tabby, tabby cat' )
| 233
| 1
|
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer
_SCREAMING_SNAKE_CASE = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
_SCREAMING_SNAKE_CASE = {
"vocab_file": {
"google/electra-small-generator": (
"https://huggingface.co/google/electra-small-generator/resolve/main/vocab.txt"
),
"google/electra-base-generator": "https://huggingface.co/google/electra-base-generator/resolve/main/vocab.txt",
"google/electra-large-generator": (
"https://huggingface.co/google/electra-large-generator/resolve/main/vocab.txt"
),
"google/electra-small-discriminator": (
"https://huggingface.co/google/electra-small-discriminator/resolve/main/vocab.txt"
),
"google/electra-base-discriminator": (
"https://huggingface.co/google/electra-base-discriminator/resolve/main/vocab.txt"
),
"google/electra-large-discriminator": (
"https://huggingface.co/google/electra-large-discriminator/resolve/main/vocab.txt"
),
},
"tokenizer_file": {
"google/electra-small-generator": (
"https://huggingface.co/google/electra-small-generator/resolve/main/tokenizer.json"
),
"google/electra-base-generator": (
"https://huggingface.co/google/electra-base-generator/resolve/main/tokenizer.json"
),
"google/electra-large-generator": (
"https://huggingface.co/google/electra-large-generator/resolve/main/tokenizer.json"
),
"google/electra-small-discriminator": (
"https://huggingface.co/google/electra-small-discriminator/resolve/main/tokenizer.json"
),
"google/electra-base-discriminator": (
"https://huggingface.co/google/electra-base-discriminator/resolve/main/tokenizer.json"
),
"google/electra-large-discriminator": (
"https://huggingface.co/google/electra-large-discriminator/resolve/main/tokenizer.json"
),
},
}
_SCREAMING_SNAKE_CASE = {
"google/electra-small-generator": 5_12,
"google/electra-base-generator": 5_12,
"google/electra-large-generator": 5_12,
"google/electra-small-discriminator": 5_12,
"google/electra-base-discriminator": 5_12,
"google/electra-large-discriminator": 5_12,
}
_SCREAMING_SNAKE_CASE = {
"google/electra-small-generator": {"do_lower_case": True},
"google/electra-base-generator": {"do_lower_case": True},
"google/electra-large-generator": {"do_lower_case": True},
"google/electra-small-discriminator": {"do_lower_case": True},
"google/electra-base-discriminator": {"do_lower_case": True},
"google/electra-large-discriminator": {"do_lower_case": True},
}
class lowerCAmelCase_ ( __magic_name__ ):
__lowerCamelCase : Dict = VOCAB_FILES_NAMES
__lowerCamelCase : Any = PRETRAINED_VOCAB_FILES_MAP
__lowerCamelCase : List[str] = PRETRAINED_INIT_CONFIGURATION
__lowerCamelCase : List[str] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowerCamelCase : List[Any] = ElectraTokenizer
def __init__( self , _lowerCAmelCase=None , _lowerCAmelCase=None , _lowerCAmelCase=True , _lowerCAmelCase="[UNK]" , _lowerCAmelCase="[SEP]" , _lowerCAmelCase="[PAD]" , _lowerCAmelCase="[CLS]" , _lowerCAmelCase="[MASK]" , _lowerCAmelCase=True , _lowerCAmelCase=None , **_lowerCAmelCase , ) -> List[Any]:
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 _snake_case ( self , _lowerCAmelCase , _lowerCAmelCase=None ) -> str:
_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 _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 _snake_case ( self , _lowerCAmelCase , _lowerCAmelCase = None ) -> Tuple[str]:
_lowerCAmelCase = self._tokenizer.model.save(_lowerCAmelCase , name=_lowerCAmelCase )
return tuple(_lowerCAmelCase )
| 18
|
"""simple docstring"""
import os
import unittest
from tempfile import TemporaryDirectory
import torch
import torch.nn as nn
from accelerate.utils import (
OffloadedWeightsLoader,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
)
class _lowerCamelCase ( nn.Module ):
def __init__( self : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
super().__init__()
lowerCAmelCase__ : Optional[int] = nn.Linear(3 , 4 )
lowerCAmelCase__ : int = nn.BatchNormad(4 )
lowerCAmelCase__ : Optional[Any] = nn.Linear(4 , 5 )
def _lowerCAmelCase ( self : List[str] , UpperCamelCase : List[Any] ) -> Tuple:
"""simple docstring"""
return self.lineara(self.batchnorm(self.lineara(UpperCamelCase ) ) )
class _lowerCamelCase ( unittest.TestCase ):
def _lowerCAmelCase ( self : Any ) -> Optional[int]:
"""simple docstring"""
lowerCAmelCase__ : Any = ModelForTest()
with TemporaryDirectory() as tmp_dir:
offload_state_dict(UpperCamelCase , model.state_dict() )
lowerCAmelCase__ : List[str] = os.path.join(UpperCamelCase , """index.json""" )
self.assertTrue(os.path.isfile(UpperCamelCase ) )
# TODO: add tests on what is inside the index
for key in ["linear1.weight", "linear1.bias", "linear2.weight", "linear2.bias"]:
lowerCAmelCase__ : Tuple = os.path.join(UpperCamelCase , f"""{key}.dat""" )
self.assertTrue(os.path.isfile(UpperCamelCase ) )
# TODO: add tests on the fact weights are properly loaded
def _lowerCAmelCase ( self : Dict ) -> int:
"""simple docstring"""
lowerCAmelCase__ : List[str] = [torch.floataa, torch.floataa, torch.bfloataa]
for dtype in dtypes:
lowerCAmelCase__ : Union[str, Any] = torch.randn(2 , 3 , dtype=UpperCamelCase )
with TemporaryDirectory() as tmp_dir:
lowerCAmelCase__ : Optional[Any] = offload_weight(UpperCamelCase , """weight""" , UpperCamelCase , {} )
lowerCAmelCase__ : Dict = os.path.join(UpperCamelCase , """weight.dat""" )
self.assertTrue(os.path.isfile(UpperCamelCase ) )
self.assertDictEqual(UpperCamelCase , {"""weight""": {"""shape""": [2, 3], """dtype""": str(UpperCamelCase ).split(""".""" )[1]}} )
lowerCAmelCase__ : Any = load_offloaded_weight(UpperCamelCase , index["""weight"""] )
self.assertTrue(torch.equal(UpperCamelCase , UpperCamelCase ) )
def _lowerCAmelCase ( self : Optional[int] ) -> Tuple:
"""simple docstring"""
lowerCAmelCase__ : Union[str, Any] = ModelForTest()
lowerCAmelCase__ : Optional[Any] = model.state_dict()
lowerCAmelCase__ : Tuple = {k: v for k, v in state_dict.items() if """linear2""" not in k}
lowerCAmelCase__ : Any = {k: v for k, v in state_dict.items() if """linear2""" in k}
with TemporaryDirectory() as tmp_dir:
offload_state_dict(UpperCamelCase , UpperCamelCase )
lowerCAmelCase__ : str = OffloadedWeightsLoader(state_dict=UpperCamelCase , save_folder=UpperCamelCase )
# Every key is there with the right value
self.assertEqual(sorted(UpperCamelCase ) , sorted(state_dict.keys() ) )
for key, param in state_dict.items():
self.assertTrue(torch.allclose(UpperCamelCase , weight_map[key] ) )
lowerCAmelCase__ : str = {k: v for k, v in state_dict.items() if """weight""" in k}
lowerCAmelCase__ : str = {k: v for k, v in state_dict.items() if """weight""" not in k}
with TemporaryDirectory() as tmp_dir:
offload_state_dict(UpperCamelCase , UpperCamelCase )
lowerCAmelCase__ : Any = OffloadedWeightsLoader(state_dict=UpperCamelCase , save_folder=UpperCamelCase )
# Every key is there with the right value
self.assertEqual(sorted(UpperCamelCase ) , sorted(state_dict.keys() ) )
for key, param in state_dict.items():
self.assertTrue(torch.allclose(UpperCamelCase , weight_map[key] ) )
with TemporaryDirectory() as tmp_dir:
offload_state_dict(UpperCamelCase , UpperCamelCase )
# Duplicates are removed
lowerCAmelCase__ : List[str] = OffloadedWeightsLoader(state_dict=UpperCamelCase , save_folder=UpperCamelCase )
# Every key is there with the right value
self.assertEqual(sorted(UpperCamelCase ) , sorted(state_dict.keys() ) )
for key, param in state_dict.items():
self.assertTrue(torch.allclose(UpperCamelCase , weight_map[key] ) )
def _lowerCAmelCase ( self : Dict ) -> Any:
"""simple docstring"""
lowerCAmelCase__ : List[str] = {"""a.1""": 0, """a.10""": 1, """a.2""": 2}
lowerCAmelCase__ : Any = extract_submodules_state_dict(UpperCamelCase , ["""a.1""", """a.2"""] )
self.assertDictEqual(UpperCamelCase , {"""a.1""": 0, """a.2""": 2} )
lowerCAmelCase__ : str = {"""a.1.a""": 0, """a.10.a""": 1, """a.2.a""": 2}
lowerCAmelCase__ : Union[str, Any] = extract_submodules_state_dict(UpperCamelCase , ["""a.1""", """a.2"""] )
self.assertDictEqual(UpperCamelCase , {"""a.1.a""": 0, """a.2.a""": 2} )
| 299
| 0
|
import argparse
import collections
import os
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
UpperCamelCase_ = 'src/transformers'
UpperCamelCase_ = 'docs/source/en'
UpperCamelCase_ = '.'
def _UpperCAmelCase ( A , A , A ):
'''simple docstring'''
with open(A , "r" , encoding="utf-8" , newline="\n" ) as f:
UpperCAmelCase__ =f.readlines()
# Find the start prompt.
UpperCAmelCase__ =0
while not lines[start_index].startswith(A ):
start_index += 1
start_index += 1
UpperCAmelCase__ =start_index
while not lines[end_index].startswith(A ):
end_index += 1
end_index -= 1
while len(lines[start_index] ) <= 1:
start_index += 1
while len(lines[end_index] ) <= 1:
end_index -= 1
end_index += 1
return "".join(lines[start_index:end_index] ), start_index, end_index, lines
# Add here suffixes that are used to identify models, separated by |
UpperCamelCase_ = 'Model|Encoder|Decoder|ForConditionalGeneration'
# Regexes that match TF/Flax/PT model names.
UpperCamelCase_ = re.compile(R'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
UpperCamelCase_ = re.compile(R'Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
UpperCamelCase_ = re.compile(R'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
# This is to make sure the transformers module imported is the one in the repo.
UpperCamelCase_ = direct_transformers_import(TRANSFORMERS_PATH)
def _UpperCAmelCase ( A ):
'''simple docstring'''
UpperCAmelCase__ =re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)" , A )
return [m.group(0 ) for m in matches]
def _UpperCAmelCase ( A , A ):
'''simple docstring'''
UpperCAmelCase__ =2 if text == "✅" or text == "❌" else len(A )
UpperCAmelCase__ =(width - text_length) // 2
UpperCAmelCase__ =width - text_length - left_indent
return " " * left_indent + text + " " * right_indent
def _UpperCAmelCase ( ):
'''simple docstring'''
UpperCAmelCase__ =transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
UpperCAmelCase__ ={
name: config_maping_names[code]
for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
if code in config_maping_names
}
UpperCAmelCase__ ={name: config.replace("Config" , "" ) for name, config in model_name_to_config.items()}
# Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
UpperCAmelCase__ =collections.defaultdict(A )
UpperCAmelCase__ =collections.defaultdict(A )
UpperCAmelCase__ =collections.defaultdict(A )
UpperCAmelCase__ =collections.defaultdict(A )
UpperCAmelCase__ =collections.defaultdict(A )
# Let's lookup through all transformers object (once).
for attr_name in dir(A ):
UpperCAmelCase__ =None
if attr_name.endswith("Tokenizer" ):
UpperCAmelCase__ =slow_tokenizers
UpperCAmelCase__ =attr_name[:-9]
elif attr_name.endswith("TokenizerFast" ):
UpperCAmelCase__ =fast_tokenizers
UpperCAmelCase__ =attr_name[:-13]
elif _re_tf_models.match(A ) is not None:
UpperCAmelCase__ =tf_models
UpperCAmelCase__ =_re_tf_models.match(A ).groups()[0]
elif _re_flax_models.match(A ) is not None:
UpperCAmelCase__ =flax_models
UpperCAmelCase__ =_re_flax_models.match(A ).groups()[0]
elif _re_pt_models.match(A ) is not None:
UpperCAmelCase__ =pt_models
UpperCAmelCase__ =_re_pt_models.match(A ).groups()[0]
if lookup_dict is not None:
while len(A ) > 0:
if attr_name in model_name_to_prefix.values():
UpperCAmelCase__ =True
break
# Try again after removing the last word in the name
UpperCAmelCase__ ="".join(camel_case_split(A )[:-1] )
# Let's build that table!
UpperCAmelCase__ =list(model_name_to_config.keys() )
model_names.sort(key=str.lower )
UpperCAmelCase__ =["Model", "Tokenizer slow", "Tokenizer fast", "PyTorch support", "TensorFlow support", "Flax Support"]
# We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
UpperCAmelCase__ =[len(A ) + 2 for c in columns]
UpperCAmelCase__ =max([len(A ) for name in model_names] ) + 2
# Build the table per se
UpperCAmelCase__ ="|" + "|".join([_center_text(A , A ) for c, w in zip(A , A )] ) + "|\n"
# Use ":-----:" format to center-aligned table cell texts
table += "|" + "|".join([":" + "-" * (w - 2) + ":" for w in widths] ) + "|\n"
UpperCAmelCase__ ={True: "✅", False: "❌"}
for name in model_names:
UpperCAmelCase__ =model_name_to_prefix[name]
UpperCAmelCase__ =[
name,
check[slow_tokenizers[prefix]],
check[fast_tokenizers[prefix]],
check[pt_models[prefix]],
check[tf_models[prefix]],
check[flax_models[prefix]],
]
table += "|" + "|".join([_center_text(A , A ) for l, w in zip(A , A )] ) + "|\n"
return table
def _UpperCAmelCase ( A=False ):
'''simple docstring'''
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ =_find_text_in_file(
filename=os.path.join(A , "index.md" ) , start_prompt="<!--This table is updated automatically from the auto modules" , end_prompt="<!-- End table-->" , )
UpperCAmelCase__ =get_model_table_from_auto_modules()
if current_table != new_table:
if overwrite:
with open(os.path.join(A , "index.md" ) , "w" , encoding="utf-8" , newline="\n" ) as f:
f.writelines(lines[:start_index] + [new_table] + lines[end_index:] )
else:
raise ValueError(
"The model table in the `index.md` has not been updated. Run `make fix-copies` to fix this." )
if __name__ == "__main__":
UpperCamelCase_ = argparse.ArgumentParser()
parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
UpperCamelCase_ = parser.parse_args()
check_model_table(args.fix_and_overwrite)
| 510
|
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class snake_case_ :
'''simple docstring'''
__UpperCamelCase = None
def __UpperCAmelCase ( self ) -> str:
UpperCAmelCase__ =self.feature_extraction_class(**self.feat_extract_dict )
UpperCAmelCase__ =json.loads(feat_extract.to_json_string() )
for key, value in self.feat_extract_dict.items():
self.assertEqual(obj[key], A_ )
def __UpperCAmelCase ( self ) -> Union[str, Any]:
UpperCAmelCase__ =self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCAmelCase__ =os.path.join(A_, "feat_extract.json" )
feat_extract_first.to_json_file(A_ )
UpperCAmelCase__ =self.feature_extraction_class.from_json_file(A_ )
self.assertEqual(feat_extract_second.to_dict(), feat_extract_first.to_dict() )
def __UpperCAmelCase ( self ) -> Union[str, Any]:
UpperCAmelCase__ =self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCAmelCase__ =feat_extract_first.save_pretrained(A_ )[0]
check_json_file_has_correct_format(A_ )
UpperCAmelCase__ =self.feature_extraction_class.from_pretrained(A_ )
self.assertEqual(feat_extract_second.to_dict(), feat_extract_first.to_dict() )
def __UpperCAmelCase ( self ) -> str:
UpperCAmelCase__ =self.feature_extraction_class()
self.assertIsNotNone(A_ )
| 510
| 1
|
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import DetrImageProcessor
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
"""simple docstring"""
def __init__( self : str , __A : List[Any] , __A : Any=7 , __A : Optional[Any]=3 , __A : Any=3_0 , __A : Optional[Any]=4_0_0 , __A : Union[str, Any]=True , __A : int=None , __A : Union[str, Any]=True , __A : Tuple=1 / 2_5_5 , __A : str=True , __A : Tuple=[0.5, 0.5, 0.5] , __A : Optional[int]=[0.5, 0.5, 0.5] , __A : Tuple=True , ):
# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
snake_case__ : int = size if size is not None else {"shortest_edge": 1_8, "longest_edge": 1_3_3_3}
snake_case__ : Union[str, Any] = parent
snake_case__ : Optional[Any] = batch_size
snake_case__ : Tuple = num_channels
snake_case__ : Any = min_resolution
snake_case__ : int = max_resolution
snake_case__ : str = do_resize
snake_case__ : Optional[int] = size
snake_case__ : List[Any] = do_rescale
snake_case__ : Optional[int] = rescale_factor
snake_case__ : Optional[Any] = do_normalize
snake_case__ : str = image_mean
snake_case__ : Union[str, Any] = image_std
snake_case__ : int = do_pad
def _lowercase ( self : List[str] ):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_pad": self.do_pad,
}
def _lowercase ( self : Optional[Any] , __A : Tuple , __A : int=False ):
if not batched:
snake_case__ : Optional[int] = image_inputs[0]
if isinstance(snake_case__ , Image.Image ):
snake_case__, snake_case__ : str = image.size
else:
snake_case__, snake_case__ : str = image.shape[1], image.shape[2]
if w < h:
snake_case__ : Optional[Any] = int(self.size["shortest_edge"] * h / w )
snake_case__ : Tuple = self.size["shortest_edge"]
elif w > h:
snake_case__ : Optional[Any] = self.size["shortest_edge"]
snake_case__ : List[str] = int(self.size["shortest_edge"] * w / h )
else:
snake_case__ : Optional[int] = self.size["shortest_edge"]
snake_case__ : List[Any] = self.size["shortest_edge"]
else:
snake_case__ : Dict = []
for image in image_inputs:
snake_case__, snake_case__ : Dict = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
snake_case__ : Tuple = max(snake_case__ , key=lambda __A : item[0] )[0]
snake_case__ : str = max(snake_case__ , key=lambda __A : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE_ , unittest.TestCase ):
"""simple docstring"""
a_ = DetrImageProcessor if is_vision_available() else None
def _lowercase ( self : Dict ):
snake_case__ : Any = DetrImageProcessingTester(self )
@property
def _lowercase ( self : Dict ):
return self.image_processor_tester.prepare_image_processor_dict()
def _lowercase ( self : Dict ):
snake_case__ : str = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(snake_case__ , "image_mean" ) )
self.assertTrue(hasattr(snake_case__ , "image_std" ) )
self.assertTrue(hasattr(snake_case__ , "do_normalize" ) )
self.assertTrue(hasattr(snake_case__ , "do_rescale" ) )
self.assertTrue(hasattr(snake_case__ , "rescale_factor" ) )
self.assertTrue(hasattr(snake_case__ , "do_resize" ) )
self.assertTrue(hasattr(snake_case__ , "size" ) )
self.assertTrue(hasattr(snake_case__ , "do_pad" ) )
def _lowercase ( self : List[str] ):
snake_case__ : Optional[int] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"shortest_edge": 1_8, "longest_edge": 1_3_3_3} )
self.assertEqual(image_processor.do_pad , snake_case__ )
snake_case__ : List[str] = self.image_processing_class.from_dict(
self.image_processor_dict , size=4_2 , max_size=8_4 , pad_and_return_pixel_mask=snake_case__ )
self.assertEqual(image_processor.size , {"shortest_edge": 4_2, "longest_edge": 8_4} )
self.assertEqual(image_processor.do_pad , snake_case__ )
def _lowercase ( self : int ):
pass
def _lowercase ( self : Union[str, Any] ):
# Initialize image_processing
snake_case__ : Tuple = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
snake_case__ : Union[str, Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=snake_case__ )
for image in image_inputs:
self.assertIsInstance(snake_case__ , Image.Image )
# Test not batched input
snake_case__ : str = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
snake_case__, snake_case__ : int = self.image_processor_tester.get_expected_values(snake_case__ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
snake_case__, snake_case__ : Any = self.image_processor_tester.get_expected_values(snake_case__ , batched=snake_case__ )
snake_case__ : Any = image_processing(snake_case__ , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def _lowercase ( self : List[str] ):
# Initialize image_processing
snake_case__ : List[Any] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
snake_case__ : int = prepare_image_inputs(self.image_processor_tester , equal_resolution=snake_case__ , numpify=snake_case__ )
for image in image_inputs:
self.assertIsInstance(snake_case__ , np.ndarray )
# Test not batched input
snake_case__ : int = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
snake_case__, snake_case__ : Tuple = self.image_processor_tester.get_expected_values(snake_case__ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
snake_case__ : Optional[Any] = image_processing(snake_case__ , return_tensors="pt" ).pixel_values
snake_case__, snake_case__ : Optional[Any] = self.image_processor_tester.get_expected_values(snake_case__ , batched=snake_case__ )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def _lowercase ( self : Dict ):
# Initialize image_processing
snake_case__ : str = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
snake_case__ : Tuple = prepare_image_inputs(self.image_processor_tester , equal_resolution=snake_case__ , torchify=snake_case__ )
for image in image_inputs:
self.assertIsInstance(snake_case__ , torch.Tensor )
# Test not batched input
snake_case__ : List[str] = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
snake_case__, snake_case__ : List[Any] = self.image_processor_tester.get_expected_values(snake_case__ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
snake_case__ : str = image_processing(snake_case__ , return_tensors="pt" ).pixel_values
snake_case__, snake_case__ : Union[str, Any] = self.image_processor_tester.get_expected_values(snake_case__ , batched=snake_case__ )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
@slow
def _lowercase ( self : List[Any] ):
# prepare image and target
snake_case__ : int = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt" , "r" ) as f:
snake_case__ : str = json.loads(f.read() )
snake_case__ : Any = {"image_id": 3_9_7_6_9, "annotations": target}
# encode them
snake_case__ : int = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50" )
snake_case__ : List[Any] = image_processing(images=snake_case__ , annotations=snake_case__ , return_tensors="pt" )
# verify pixel values
snake_case__ : Any = torch.Size([1, 3, 8_0_0, 1_0_6_6] )
self.assertEqual(encoding["pixel_values"].shape , snake_case__ )
snake_case__ : Optional[Any] = torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3] , snake_case__ , atol=1e-4 ) )
# verify area
snake_case__ : Optional[int] = torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8] )
self.assertTrue(torch.allclose(encoding["labels"][0]["area"] , snake_case__ ) )
# verify boxes
snake_case__ : int = torch.Size([6, 4] )
self.assertEqual(encoding["labels"][0]["boxes"].shape , snake_case__ )
snake_case__ : Optional[Any] = torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5] )
self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0] , snake_case__ , atol=1e-3 ) )
# verify image_id
snake_case__ : List[str] = torch.tensor([3_9_7_6_9] )
self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"] , snake_case__ ) )
# verify is_crowd
snake_case__ : Tuple = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"] , snake_case__ ) )
# verify class_labels
snake_case__ : Union[str, Any] = torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7] )
self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"] , snake_case__ ) )
# verify orig_size
snake_case__ : Union[str, Any] = torch.tensor([4_8_0, 6_4_0] )
self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"] , snake_case__ ) )
# verify size
snake_case__ : Optional[Any] = torch.tensor([8_0_0, 1_0_6_6] )
self.assertTrue(torch.allclose(encoding["labels"][0]["size"] , snake_case__ ) )
@slow
def _lowercase ( self : Any ):
# prepare image, target and masks_path
snake_case__ : Any = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt" , "r" ) as f:
snake_case__ : str = json.loads(f.read() )
snake_case__ : Union[str, Any] = {"file_name": "000000039769.png", "image_id": 3_9_7_6_9, "segments_info": target}
snake_case__ : Optional[int] = pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic" )
# encode them
snake_case__ : List[str] = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50-panoptic" )
snake_case__ : Any = image_processing(images=snake_case__ , annotations=snake_case__ , masks_path=snake_case__ , return_tensors="pt" )
# verify pixel values
snake_case__ : Union[str, Any] = torch.Size([1, 3, 8_0_0, 1_0_6_6] )
self.assertEqual(encoding["pixel_values"].shape , snake_case__ )
snake_case__ : List[str] = torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3] , snake_case__ , atol=1e-4 ) )
# verify area
snake_case__ : Tuple = torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7] )
self.assertTrue(torch.allclose(encoding["labels"][0]["area"] , snake_case__ ) )
# verify boxes
snake_case__ : Tuple = torch.Size([6, 4] )
self.assertEqual(encoding["labels"][0]["boxes"].shape , snake_case__ )
snake_case__ : Optional[int] = torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5] )
self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0] , snake_case__ , atol=1e-3 ) )
# verify image_id
snake_case__ : Optional[int] = torch.tensor([3_9_7_6_9] )
self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"] , snake_case__ ) )
# verify is_crowd
snake_case__ : Dict = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"] , snake_case__ ) )
# verify class_labels
snake_case__ : Optional[int] = torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3] )
self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"] , snake_case__ ) )
# verify masks
snake_case__ : Optional[int] = 8_2_2_8_7_3
self.assertEqual(encoding["labels"][0]["masks"].sum().item() , snake_case__ )
# verify orig_size
snake_case__ : List[Any] = torch.tensor([4_8_0, 6_4_0] )
self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"] , snake_case__ ) )
# verify size
snake_case__ : str = torch.tensor([8_0_0, 1_0_6_6] )
self.assertTrue(torch.allclose(encoding["labels"][0]["size"] , snake_case__ ) )
| 297
|
from typing import Any, Dict, Optional
import torch
import torch.nn.functional as F
from torch import nn
from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings
@maybe_allow_in_graph
class lowerCAmelCase__ ( nn.Module ):
'''simple docstring'''
def __init__( self : Dict , snake_case__ : int , snake_case__ : int , snake_case__ : int , snake_case__ : Tuple=0.0 , snake_case__ : Optional[int] = None , snake_case__ : str = "geglu" , snake_case__ : Optional[int] = None , snake_case__ : bool = False , snake_case__ : bool = False , snake_case__ : bool = False , snake_case__ : bool = False , snake_case__ : bool = True , snake_case__ : str = "layer_norm" , snake_case__ : bool = False , ) -> Dict:
super().__init__()
_lowerCamelCase = only_cross_attention
_lowerCamelCase = (num_embeds_ada_norm is not None) and norm_type == 'ada_norm_zero'
_lowerCamelCase = (num_embeds_ada_norm is not None) and norm_type == 'ada_norm'
if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
raise ValueError(
f"""`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to"""
f""" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}.""" )
# Define 3 blocks. Each block has its own normalization layer.
# 1. Self-Attn
if self.use_ada_layer_norm:
_lowerCamelCase = AdaLayerNorm(snake_case__ , snake_case__ )
elif self.use_ada_layer_norm_zero:
_lowerCamelCase = AdaLayerNormZero(snake_case__ , snake_case__ )
else:
_lowerCamelCase = nn.LayerNorm(snake_case__ , elementwise_affine=snake_case__ )
_lowerCamelCase = Attention(
query_dim=snake_case__ , heads=snake_case__ , dim_head=snake_case__ , dropout=snake_case__ , bias=snake_case__ , cross_attention_dim=cross_attention_dim if only_cross_attention else None , upcast_attention=snake_case__ , )
# 2. Cross-Attn
if cross_attention_dim is not None or double_self_attention:
# We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
# I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
# the second cross attention block.
_lowerCamelCase = (
AdaLayerNorm(snake_case__ , snake_case__ )
if self.use_ada_layer_norm
else nn.LayerNorm(snake_case__ , elementwise_affine=snake_case__ )
)
_lowerCamelCase = Attention(
query_dim=snake_case__ , cross_attention_dim=cross_attention_dim if not double_self_attention else None , heads=snake_case__ , dim_head=snake_case__ , dropout=snake_case__ , bias=snake_case__ , upcast_attention=snake_case__ , ) # is self-attn if encoder_hidden_states is none
else:
_lowerCamelCase = None
_lowerCamelCase = None
# 3. Feed-forward
_lowerCamelCase = nn.LayerNorm(snake_case__ , elementwise_affine=snake_case__ )
_lowerCamelCase = FeedForward(snake_case__ , dropout=snake_case__ , activation_fn=snake_case__ , final_dropout=snake_case__ )
# let chunk size default to None
_lowerCamelCase = None
_lowerCamelCase = 0
def _snake_case ( self : str , snake_case__ : Optional[int] , snake_case__ : int ) -> Optional[Any]:
# Sets chunk feed-forward
_lowerCamelCase = chunk_size
_lowerCamelCase = dim
def _snake_case ( self : Union[str, Any] , snake_case__ : torch.FloatTensor , snake_case__ : Optional[torch.FloatTensor] = None , snake_case__ : Optional[torch.FloatTensor] = None , snake_case__ : Optional[torch.FloatTensor] = None , snake_case__ : Optional[torch.LongTensor] = None , snake_case__ : Dict[str, Any] = None , snake_case__ : Optional[torch.LongTensor] = None , ) -> List[Any]:
# Notice that normalization is always applied before the real computation in the following blocks.
# 1. Self-Attention
if self.use_ada_layer_norm:
_lowerCamelCase = self.norma(snake_case__ , snake_case__ )
elif self.use_ada_layer_norm_zero:
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase = self.norma(
snake_case__ , snake_case__ , snake_case__ , hidden_dtype=hidden_states.dtype )
else:
_lowerCamelCase = self.norma(snake_case__ )
_lowerCamelCase = cross_attention_kwargs if cross_attention_kwargs is not None else {}
_lowerCamelCase = self.attna(
snake_case__ , encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None , attention_mask=snake_case__ , **snake_case__ , )
if self.use_ada_layer_norm_zero:
_lowerCamelCase = gate_msa.unsqueeze(1 ) * attn_output
_lowerCamelCase = attn_output + hidden_states
# 2. Cross-Attention
if self.attna is not None:
_lowerCamelCase = (
self.norma(snake_case__ , snake_case__ ) if self.use_ada_layer_norm else self.norma(snake_case__ )
)
_lowerCamelCase = self.attna(
snake_case__ , encoder_hidden_states=snake_case__ , attention_mask=snake_case__ , **snake_case__ , )
_lowerCamelCase = attn_output + hidden_states
# 3. Feed-forward
_lowerCamelCase = self.norma(snake_case__ )
if self.use_ada_layer_norm_zero:
_lowerCamelCase = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
if self._chunk_size is not None:
# "feed_forward_chunk_size" can be used to save memory
if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
raise ValueError(
f"""`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`.""" )
_lowerCamelCase = norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
_lowerCamelCase = torch.cat(
[self.ff(snake_case__ ) for hid_slice in norm_hidden_states.chunk(snake_case__ , dim=self._chunk_dim )] , dim=self._chunk_dim , )
else:
_lowerCamelCase = self.ff(snake_case__ )
if self.use_ada_layer_norm_zero:
_lowerCamelCase = gate_mlp.unsqueeze(1 ) * ff_output
_lowerCamelCase = ff_output + hidden_states
return hidden_states
class lowerCAmelCase__ ( nn.Module ):
'''simple docstring'''
def __init__( self : Union[str, Any] , snake_case__ : int , snake_case__ : Optional[int] = None , snake_case__ : int = 4 , snake_case__ : float = 0.0 , snake_case__ : str = "geglu" , snake_case__ : bool = False , ) -> Optional[int]:
super().__init__()
_lowerCamelCase = int(dim * mult )
_lowerCamelCase = dim_out if dim_out is not None else dim
if activation_fn == "gelu":
_lowerCamelCase = GELU(snake_case__ , snake_case__ )
if activation_fn == "gelu-approximate":
_lowerCamelCase = GELU(snake_case__ , snake_case__ , approximate='tanh' )
elif activation_fn == "geglu":
_lowerCamelCase = GEGLU(snake_case__ , snake_case__ )
elif activation_fn == "geglu-approximate":
_lowerCamelCase = ApproximateGELU(snake_case__ , snake_case__ )
_lowerCamelCase = nn.ModuleList([] )
# project in
self.net.append(snake_case__ )
# project dropout
self.net.append(nn.Dropout(snake_case__ ) )
# project out
self.net.append(nn.Linear(snake_case__ , snake_case__ ) )
# FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
if final_dropout:
self.net.append(nn.Dropout(snake_case__ ) )
def _snake_case ( self : Tuple , snake_case__ : List[Any] ) -> Optional[int]:
for module in self.net:
_lowerCamelCase = module(snake_case__ )
return hidden_states
class lowerCAmelCase__ ( nn.Module ):
'''simple docstring'''
def __init__( self : str , snake_case__ : int , snake_case__ : int , snake_case__ : str = "none" ) -> Any:
super().__init__()
_lowerCamelCase = nn.Linear(snake_case__ , snake_case__ )
_lowerCamelCase = approximate
def _snake_case ( self : List[Any] , snake_case__ : List[Any] ) -> int:
if gate.device.type != "mps":
return F.gelu(snake_case__ , approximate=self.approximate )
# mps: gelu is not implemented for float16
return F.gelu(gate.to(dtype=torch.floataa ) , approximate=self.approximate ).to(dtype=gate.dtype )
def _snake_case ( self : Optional[int] , snake_case__ : Union[str, Any] ) -> str:
_lowerCamelCase = self.proj(snake_case__ )
_lowerCamelCase = self.gelu(snake_case__ )
return hidden_states
class lowerCAmelCase__ ( nn.Module ):
'''simple docstring'''
def __init__( self : Dict , snake_case__ : int , snake_case__ : int ) -> List[Any]:
super().__init__()
_lowerCamelCase = nn.Linear(snake_case__ , dim_out * 2 )
def _snake_case ( self : Optional[Any] , snake_case__ : Optional[int] ) -> Optional[Any]:
if gate.device.type != "mps":
return F.gelu(snake_case__ )
# mps: gelu is not implemented for float16
return F.gelu(gate.to(dtype=torch.floataa ) ).to(dtype=gate.dtype )
def _snake_case ( self : List[str] , snake_case__ : Optional[Any] ) -> Optional[int]:
_lowerCamelCase , _lowerCamelCase = self.proj(snake_case__ ).chunk(2 , dim=-1 )
return hidden_states * self.gelu(snake_case__ )
class lowerCAmelCase__ ( nn.Module ):
'''simple docstring'''
def __init__( self : Dict , snake_case__ : int , snake_case__ : int ) -> Tuple:
super().__init__()
_lowerCamelCase = nn.Linear(snake_case__ , snake_case__ )
def _snake_case ( self : Optional[Any] , snake_case__ : List[Any] ) -> Optional[int]:
_lowerCamelCase = self.proj(snake_case__ )
return x * torch.sigmoid(1.702 * x )
class lowerCAmelCase__ ( nn.Module ):
'''simple docstring'''
def __init__( self : Optional[Any] , snake_case__ : Tuple , snake_case__ : Any ) -> Dict:
super().__init__()
_lowerCamelCase = nn.Embedding(snake_case__ , snake_case__ )
_lowerCamelCase = nn.SiLU()
_lowerCamelCase = nn.Linear(snake_case__ , embedding_dim * 2 )
_lowerCamelCase = nn.LayerNorm(snake_case__ , elementwise_affine=snake_case__ )
def _snake_case ( self : Tuple , snake_case__ : str , snake_case__ : Dict ) -> Optional[int]:
_lowerCamelCase = self.linear(self.silu(self.emb(snake_case__ ) ) )
_lowerCamelCase , _lowerCamelCase = torch.chunk(snake_case__ , 2 )
_lowerCamelCase = self.norm(snake_case__ ) * (1 + scale) + shift
return x
class lowerCAmelCase__ ( nn.Module ):
'''simple docstring'''
def __init__( self : Any , snake_case__ : Optional[Any] , snake_case__ : Union[str, Any] ) -> Dict:
super().__init__()
_lowerCamelCase = CombinedTimestepLabelEmbeddings(snake_case__ , snake_case__ )
_lowerCamelCase = nn.SiLU()
_lowerCamelCase = nn.Linear(snake_case__ , 6 * embedding_dim , bias=snake_case__ )
_lowerCamelCase = nn.LayerNorm(snake_case__ , elementwise_affine=snake_case__ , eps=1e-6 )
def _snake_case ( self : Dict , snake_case__ : Tuple , snake_case__ : str , snake_case__ : str , snake_case__ : List[Any]=None ) -> int:
_lowerCamelCase = self.linear(self.silu(self.emb(snake_case__ , snake_case__ , hidden_dtype=snake_case__ ) ) )
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase = emb.chunk(6 , dim=1 )
_lowerCamelCase = self.norm(snake_case__ ) * (1 + scale_msa[:, None]) + shift_msa[:, None]
return x, gate_msa, shift_mlp, scale_mlp, gate_mlp
class lowerCAmelCase__ ( nn.Module ):
'''simple docstring'''
def __init__( self : Tuple , snake_case__ : int , snake_case__ : int , snake_case__ : int , snake_case__ : Optional[str] = None , snake_case__ : float = 1e-5 ) -> int:
super().__init__()
_lowerCamelCase = num_groups
_lowerCamelCase = eps
if act_fn is None:
_lowerCamelCase = None
else:
_lowerCamelCase = get_activation(snake_case__ )
_lowerCamelCase = nn.Linear(snake_case__ , out_dim * 2 )
def _snake_case ( self : Union[str, Any] , snake_case__ : int , snake_case__ : Optional[Any] ) -> Union[str, Any]:
if self.act:
_lowerCamelCase = self.act(snake_case__ )
_lowerCamelCase = self.linear(snake_case__ )
_lowerCamelCase = emb[:, :, None, None]
_lowerCamelCase , _lowerCamelCase = emb.chunk(2 , dim=1 )
_lowerCamelCase = F.group_norm(snake_case__ , self.num_groups , eps=self.eps )
_lowerCamelCase = x * (1 + scale) + shift
return x
| 544
| 0
|
"""simple docstring"""
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.modeling_flax_pytorch_utils import (
convert_pytorch_state_dict_to_flax,
load_flax_weights_in_pytorch_model,
)
from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel
if is_torch_available():
import torch
class UpperCamelCase :
def __init__( self , snake_case__ , snake_case__=14 , snake_case__=7 , snake_case__=True , snake_case__=True , snake_case__=False , snake_case__=True , snake_case__=99 , snake_case__=32 , snake_case__=4 , snake_case__=4 , snake_case__=4 , snake_case__=37 , snake_case__="gelu" , snake_case__=0.1 , snake_case__=0.1 , snake_case__=512 , snake_case__=0.02 , ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : str = parent
_SCREAMING_SNAKE_CASE : List[Any] = batch_size
_SCREAMING_SNAKE_CASE : Tuple = seq_length
_SCREAMING_SNAKE_CASE : Dict = is_training
_SCREAMING_SNAKE_CASE : Optional[Any] = use_input_mask
_SCREAMING_SNAKE_CASE : Optional[int] = use_token_type_ids
_SCREAMING_SNAKE_CASE : Any = use_labels
_SCREAMING_SNAKE_CASE : List[str] = vocab_size
_SCREAMING_SNAKE_CASE : Optional[Any] = hidden_size
_SCREAMING_SNAKE_CASE : Optional[Any] = rotary_dim
_SCREAMING_SNAKE_CASE : List[str] = num_hidden_layers
_SCREAMING_SNAKE_CASE : Any = num_attention_heads
_SCREAMING_SNAKE_CASE : int = intermediate_size
_SCREAMING_SNAKE_CASE : int = hidden_act
_SCREAMING_SNAKE_CASE : str = hidden_dropout_prob
_SCREAMING_SNAKE_CASE : List[str] = attention_probs_dropout_prob
_SCREAMING_SNAKE_CASE : List[Any] = max_position_embeddings
_SCREAMING_SNAKE_CASE : List[Any] = initializer_range
_SCREAMING_SNAKE_CASE : Tuple = None
_SCREAMING_SNAKE_CASE : str = vocab_size - 1
_SCREAMING_SNAKE_CASE : Dict = vocab_size - 1
_SCREAMING_SNAKE_CASE : Dict = vocab_size - 1
def __SCREAMING_SNAKE_CASE ( self ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_SCREAMING_SNAKE_CASE : str = None
if self.use_input_mask:
_SCREAMING_SNAKE_CASE : Union[str, Any] = random_attention_mask([self.batch_size, self.seq_length] )
_SCREAMING_SNAKE_CASE : str = GPTJConfig(
vocab_size=self.vocab_size , n_embd=self.hidden_size , n_layer=self.num_hidden_layers , n_head=self.num_attention_heads , n_positions=self.max_position_embeddings , use_cache=snake_case__ , bos_token_id=self.bos_token_id , eos_token_id=self.eos_token_id , pad_token_id=self.pad_token_id , rotary_dim=self.rotary_dim , )
return (config, input_ids, input_mask)
def __SCREAMING_SNAKE_CASE ( self ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : List[Any] = self.prepare_config_and_inputs()
_SCREAMING_SNAKE_CASE : Optional[Any] = config_and_inputs
_SCREAMING_SNAKE_CASE : List[Any] = {"input_ids": input_ids, "attention_mask": attention_mask}
return config, inputs_dict
def __SCREAMING_SNAKE_CASE ( self , snake_case__ , snake_case__ , snake_case__ , snake_case__ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : int = 20
_SCREAMING_SNAKE_CASE : List[str] = model_class_name(snake_case__ )
_SCREAMING_SNAKE_CASE : Optional[Any] = model.init_cache(input_ids.shape[0] , snake_case__ )
_SCREAMING_SNAKE_CASE : int = jnp.ones((input_ids.shape[0], max_decoder_length) , dtype="i4" )
_SCREAMING_SNAKE_CASE : str = jnp.broadcast_to(
jnp.arange(input_ids.shape[-1] - 1 )[None, :] , (input_ids.shape[0], input_ids.shape[-1] - 1) )
_SCREAMING_SNAKE_CASE : int = model(
input_ids[:, :-1] , attention_mask=snake_case__ , past_key_values=snake_case__ , position_ids=snake_case__ , )
_SCREAMING_SNAKE_CASE : str = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] , dtype="i4" )
_SCREAMING_SNAKE_CASE : Optional[Any] = model(
input_ids[:, -1:] , attention_mask=snake_case__ , past_key_values=outputs_cache.past_key_values , position_ids=snake_case__ , )
_SCREAMING_SNAKE_CASE : Any = model(snake_case__ )
_SCREAMING_SNAKE_CASE : List[str] = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1E-3 , msg=F'''Max diff is {diff}''' )
def __SCREAMING_SNAKE_CASE ( self , snake_case__ , snake_case__ , snake_case__ , snake_case__ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : List[str] = 20
_SCREAMING_SNAKE_CASE : Any = model_class_name(snake_case__ )
_SCREAMING_SNAKE_CASE : Optional[Any] = jnp.concatenate(
[attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]) )] , axis=-1 , )
_SCREAMING_SNAKE_CASE : int = model.init_cache(input_ids.shape[0] , snake_case__ )
_SCREAMING_SNAKE_CASE : Any = jnp.broadcast_to(
jnp.arange(input_ids.shape[-1] - 1 )[None, :] , (input_ids.shape[0], input_ids.shape[-1] - 1) )
_SCREAMING_SNAKE_CASE : List[str] = model(
input_ids[:, :-1] , attention_mask=snake_case__ , past_key_values=snake_case__ , position_ids=snake_case__ , )
_SCREAMING_SNAKE_CASE : Optional[int] = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] , dtype="i4" )
_SCREAMING_SNAKE_CASE : Tuple = model(
input_ids[:, -1:] , past_key_values=outputs_cache.past_key_values , attention_mask=snake_case__ , position_ids=snake_case__ , )
_SCREAMING_SNAKE_CASE : Tuple = model(snake_case__ , attention_mask=snake_case__ )
_SCREAMING_SNAKE_CASE : List[Any] = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1E-3 , msg=F'''Max diff is {diff}''' )
@require_flax
class UpperCamelCase ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , unittest.TestCase ):
A__ = (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
A__ = (FlaxGPTJForCausalLM,) if is_flax_available() else ()
def __SCREAMING_SNAKE_CASE ( self ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : List[Any] = FlaxGPTJModelTester(self )
def __SCREAMING_SNAKE_CASE ( self ):
"""simple docstring"""
for model_class_name in self.all_model_classes:
_SCREAMING_SNAKE_CASE : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_use_cache_forward(snake_case__ , snake_case__ , snake_case__ , snake_case__ )
def __SCREAMING_SNAKE_CASE ( self ):
"""simple docstring"""
for model_class_name in self.all_model_classes:
_SCREAMING_SNAKE_CASE : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_use_cache_forward_with_attn_mask(
snake_case__ , snake_case__ , snake_case__ , snake_case__ )
@tooslow
def __SCREAMING_SNAKE_CASE ( self ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : Dict = GPTaTokenizer.from_pretrained("gpt2" , pad_token="<|endoftext|>" , padding_side="left" )
_SCREAMING_SNAKE_CASE : int = tokenizer(["Hello this is a long string", "Hey"] , return_tensors="np" , padding=snake_case__ , truncation=snake_case__ )
_SCREAMING_SNAKE_CASE : str = FlaxGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B" )
_SCREAMING_SNAKE_CASE : Tuple = False
_SCREAMING_SNAKE_CASE : List[str] = model.config.eos_token_id
_SCREAMING_SNAKE_CASE : Any = jax.jit(model.generate )
_SCREAMING_SNAKE_CASE : Dict = jit_generate(
inputs["input_ids"] , attention_mask=inputs["attention_mask"] , pad_token_id=tokenizer.pad_token_id ).sequences
_SCREAMING_SNAKE_CASE : List[Any] = tokenizer.batch_decode(snake_case__ , skip_special_tokens=snake_case__ )
_SCREAMING_SNAKE_CASE : Tuple = [
"Hello this is a long string of text.\n\nI'm trying to get the text of the",
"Hey, I'm a little late to the party. I'm going to",
]
self.assertListEqual(snake_case__ , snake_case__ )
@is_pt_flax_cross_test
def __SCREAMING_SNAKE_CASE ( self ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
# prepare inputs
_SCREAMING_SNAKE_CASE : Optional[int] = self._prepare_for_class(snake_case__ , snake_case__ )
_SCREAMING_SNAKE_CASE : List[str] = {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}
# load corresponding PyTorch class
_SCREAMING_SNAKE_CASE : Optional[Any] = model_class.__name__[4:] # Skip the "Flax" at the beginning
_SCREAMING_SNAKE_CASE : str = getattr(snake_case__ , snake_case__ )
_SCREAMING_SNAKE_CASE : str = pt_inputs["input_ids"].shape
_SCREAMING_SNAKE_CASE : str = np.random.randint(0 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(snake_case__ ):
_SCREAMING_SNAKE_CASE : Tuple = 0
_SCREAMING_SNAKE_CASE : Any = 1
_SCREAMING_SNAKE_CASE : Tuple = 0
_SCREAMING_SNAKE_CASE : Optional[Any] = 1
_SCREAMING_SNAKE_CASE : List[Any] = pt_model_class(snake_case__ ).eval()
_SCREAMING_SNAKE_CASE : Dict = model_class(snake_case__ , dtype=jnp.floataa )
_SCREAMING_SNAKE_CASE : Tuple = convert_pytorch_state_dict_to_flax(pt_model.state_dict() , snake_case__ )
_SCREAMING_SNAKE_CASE : Optional[Any] = fx_state
with torch.no_grad():
_SCREAMING_SNAKE_CASE : Any = pt_model(**snake_case__ ).to_tuple()
_SCREAMING_SNAKE_CASE : Tuple = fx_model(**snake_case__ ).to_tuple()
self.assertEqual(len(snake_case__ ) , len(snake_case__ ) , "Output lengths differ between Flax and PyTorch" )
for fx_output, pt_output in zip(snake_case__ , snake_case__ ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4E-2 )
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(snake_case__ )
_SCREAMING_SNAKE_CASE : List[Any] = model_class.from_pretrained(snake_case__ , from_pt=snake_case__ )
_SCREAMING_SNAKE_CASE : Tuple = fx_model_loaded(**snake_case__ ).to_tuple()
self.assertEqual(
len(snake_case__ ) , len(snake_case__ ) , "Output lengths differ between Flax and PyTorch" )
for fx_output_loaded, pt_output in zip(snake_case__ , snake_case__ ):
self.assert_almost_equals(fx_output_loaded[:, -1] , pt_output[:, -1].numpy() , 4E-2 )
@is_pt_flax_cross_test
def __SCREAMING_SNAKE_CASE ( self ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
# prepare inputs
_SCREAMING_SNAKE_CASE : Any = self._prepare_for_class(snake_case__ , snake_case__ )
_SCREAMING_SNAKE_CASE : str = {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}
# load corresponding PyTorch class
_SCREAMING_SNAKE_CASE : Dict = model_class.__name__[4:] # Skip the "Flax" at the beginning
_SCREAMING_SNAKE_CASE : List[Any] = getattr(snake_case__ , snake_case__ )
_SCREAMING_SNAKE_CASE : Optional[int] = pt_model_class(snake_case__ ).eval()
_SCREAMING_SNAKE_CASE : List[str] = model_class(snake_case__ , dtype=jnp.floataa )
_SCREAMING_SNAKE_CASE : int = load_flax_weights_in_pytorch_model(snake_case__ , fx_model.params )
_SCREAMING_SNAKE_CASE : str = pt_inputs["input_ids"].shape
_SCREAMING_SNAKE_CASE : List[Any] = np.random.randint(0 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(snake_case__ ):
_SCREAMING_SNAKE_CASE : List[str] = 0
_SCREAMING_SNAKE_CASE : List[Any] = 1
_SCREAMING_SNAKE_CASE : Optional[Any] = 0
_SCREAMING_SNAKE_CASE : str = 1
# make sure weights are tied in PyTorch
pt_model.tie_weights()
with torch.no_grad():
_SCREAMING_SNAKE_CASE : Optional[int] = pt_model(**snake_case__ ).to_tuple()
_SCREAMING_SNAKE_CASE : List[str] = fx_model(**snake_case__ ).to_tuple()
self.assertEqual(len(snake_case__ ) , len(snake_case__ ) , "Output lengths differ between Flax and PyTorch" )
for fx_output, pt_output in zip(snake_case__ , snake_case__ ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4E-2 )
with tempfile.TemporaryDirectory() as tmpdirname:
fx_model.save_pretrained(snake_case__ )
_SCREAMING_SNAKE_CASE : int = pt_model_class.from_pretrained(snake_case__ , from_flax=snake_case__ )
with torch.no_grad():
_SCREAMING_SNAKE_CASE : Optional[int] = pt_model_loaded(**snake_case__ ).to_tuple()
self.assertEqual(
len(snake_case__ ) , len(snake_case__ ) , "Output lengths differ between Flax and PyTorch" )
for fx_output, pt_output in zip(snake_case__ , snake_case__ ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4E-2 )
@tooslow
def __SCREAMING_SNAKE_CASE ( self ):
"""simple docstring"""
for model_class_name in self.all_model_classes:
_SCREAMING_SNAKE_CASE : Optional[Any] = model_class_name.from_pretrained("EleutherAI/gpt-j-6B" )
_SCREAMING_SNAKE_CASE : List[str] = model(np.ones((1, 1) ) )
self.assertIsNotNone(snake_case__ )
| 713
|
"""simple docstring"""
from typing import List
from .keymap import KEYMAP, get_character
def _lowerCAmelCase ( lowerCamelCase__ : str ) -> Optional[int]:
def decorator(lowerCamelCase__ : int ):
_SCREAMING_SNAKE_CASE : Optional[int] = getattr(lowerCamelCase__, "handle_key", [] )
handle += [key]
setattr(lowerCamelCase__, "handle_key", lowerCamelCase__ )
return func
return decorator
def _lowerCAmelCase ( *lowerCamelCase__ : List[str] ) -> Tuple:
def decorator(lowerCamelCase__ : Dict ):
_SCREAMING_SNAKE_CASE : List[Any] = getattr(lowerCamelCase__, "handle_key", [] )
handle += keys
setattr(lowerCamelCase__, "handle_key", lowerCamelCase__ )
return func
return decorator
class UpperCamelCase ( __SCREAMING_SNAKE_CASE ):
def __new__( cls , snake_case__ , snake_case__ , snake_case__ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : Tuple = super().__new__(cls , snake_case__ , snake_case__ , snake_case__ )
if not hasattr(snake_case__ , "key_handler" ):
setattr(snake_case__ , "key_handler" , {} )
setattr(snake_case__ , "handle_input" , KeyHandler.handle_input )
for value in attrs.values():
_SCREAMING_SNAKE_CASE : Union[str, Any] = getattr(snake_case__ , "handle_key" , [] )
for key in handled_keys:
_SCREAMING_SNAKE_CASE : Tuple = value
return new_cls
@staticmethod
def __SCREAMING_SNAKE_CASE ( cls ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : Any = get_character()
if char != KEYMAP["undefined"]:
_SCREAMING_SNAKE_CASE : Dict = ord(snake_case__ )
_SCREAMING_SNAKE_CASE : Union[str, Any] = cls.key_handler.get(snake_case__ )
if handler:
_SCREAMING_SNAKE_CASE : Optional[int] = char
return handler(cls )
else:
return None
def _lowerCAmelCase ( cls : List[Any] ) -> str:
return KeyHandler(cls.__name__, cls.__bases__, cls.__dict__.copy() )
| 295
| 0
|
"""simple docstring"""
import argparse
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
########################################################################
# This is a fully working simple example to use Accelerate
# and perform gradient accumulation
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
_lowerCAmelCase :Optional[int] = 16
_lowerCAmelCase :List[Any] = 32
def lowerCamelCase_ (UpperCamelCase__ : Accelerator , UpperCamelCase__ : int = 16 ):
_UpperCAmelCase : Optional[Any] = AutoTokenizer.from_pretrained('''bert-base-cased''' )
_UpperCAmelCase : Any = load_dataset('''glue''' , '''mrpc''' )
def tokenize_function(UpperCamelCase__ : Optional[int] ):
# max_length=None => use the model max length (it's actually the default)
_UpperCAmelCase : Optional[Any] = tokenizer(examples['''sentence1'''] , examples['''sentence2'''] , truncation=UpperCamelCase__ , max_length=UpperCamelCase__ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
_UpperCAmelCase : Union[str, Any] = datasets.map(
UpperCamelCase__ , batched=UpperCamelCase__ , remove_columns=['''idx''', '''sentence1''', '''sentence2'''] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
_UpperCAmelCase : List[Any] = tokenized_datasets.rename_column('''label''' , '''labels''' )
def collate_fn(UpperCamelCase__ : Dict ):
# On TPU it's best to pad everything to the same length or training will be very slow.
_UpperCAmelCase : Optional[Any] = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
_UpperCAmelCase : Tuple = 16
elif accelerator.mixed_precision != "no":
_UpperCAmelCase : Any = 8
else:
_UpperCAmelCase : int = None
return tokenizer.pad(
UpperCamelCase__ , padding='''longest''' , max_length=UpperCamelCase__ , pad_to_multiple_of=UpperCamelCase__ , return_tensors='''pt''' , )
# Instantiate dataloaders.
_UpperCAmelCase : Union[str, Any] = DataLoader(
tokenized_datasets['''train'''] , shuffle=UpperCamelCase__ , collate_fn=UpperCamelCase__ , batch_size=UpperCamelCase__ )
_UpperCAmelCase : Optional[Any] = DataLoader(
tokenized_datasets['''validation'''] , shuffle=UpperCamelCase__ , collate_fn=UpperCamelCase__ , batch_size=UpperCamelCase__ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
_lowerCAmelCase :Union[str, Any] = mocked_dataloaders # noqa: F811
def lowerCamelCase_ (UpperCamelCase__ : List[Any] , UpperCamelCase__ : Optional[int] ):
# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''' , UpperCamelCase__ ) == "1":
_UpperCAmelCase : Any = 2
# New Code #
_UpperCAmelCase : str = int(args.gradient_accumulation_steps )
# Initialize accelerator
_UpperCAmelCase : Any = Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , gradient_accumulation_steps=UpperCamelCase__ )
if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
raise NotImplementedError(
'''Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`''' )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
_UpperCAmelCase : List[Any] = config['''lr''']
_UpperCAmelCase : Optional[int] = int(config['''num_epochs'''] )
_UpperCAmelCase : List[str] = int(config['''seed'''] )
_UpperCAmelCase : int = int(config['''batch_size'''] )
_UpperCAmelCase : Any = evaluate.load('''glue''' , '''mrpc''' )
set_seed(UpperCamelCase__ )
_UpperCAmelCase , _UpperCAmelCase : int = get_dataloaders(UpperCamelCase__ , UpperCamelCase__ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
_UpperCAmelCase : List[str] = AutoModelForSequenceClassification.from_pretrained('''bert-base-cased''' , return_dict=UpperCamelCase__ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
_UpperCAmelCase : Tuple = model.to(accelerator.device )
# Instantiate optimizer
_UpperCAmelCase : Tuple = AdamW(params=model.parameters() , lr=UpperCamelCase__ )
# Instantiate scheduler
_UpperCAmelCase : Union[str, Any] = get_linear_schedule_with_warmup(
optimizer=UpperCamelCase__ , num_warmup_steps=100 , num_training_steps=(len(UpperCamelCase__ ) * num_epochs) , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : int = accelerator.prepare(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
# Now we train the model
for epoch in range(UpperCamelCase__ ):
model.train()
for step, batch in enumerate(UpperCamelCase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
# New code #
# We use the new `accumulate` context manager to perform gradient accumulation
# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
with accelerator.accumulate(UpperCamelCase__ ):
_UpperCAmelCase : List[str] = model(**UpperCamelCase__ )
_UpperCAmelCase : Tuple = output.loss
accelerator.backward(UpperCamelCase__ )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(UpperCamelCase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
_UpperCAmelCase : Any = model(**UpperCamelCase__ )
_UpperCAmelCase : List[Any] = outputs.logits.argmax(dim=-1 )
_UpperCAmelCase , _UpperCAmelCase : List[str] = accelerator.gather_for_metrics((predictions, batch['''labels''']) )
metric.add_batch(
predictions=UpperCamelCase__ , references=UpperCamelCase__ , )
_UpperCAmelCase : List[str] = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F'epoch {epoch}:' , UpperCamelCase__ )
def lowerCamelCase_ ():
_UpperCAmelCase : List[Any] = argparse.ArgumentParser(description='''Simple example of training script.''' )
parser.add_argument(
'''--mixed_precision''' , type=UpperCamelCase__ , default=UpperCamelCase__ , choices=['''no''', '''fp16''', '''bf16''', '''fp8'''] , help='''Whether to use mixed precision. Choose'''
'''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
'''and an Nvidia Ampere GPU.''' , )
# New Code #
parser.add_argument(
'''--gradient_accumulation_steps''' , type=UpperCamelCase__ , default=1 , help='''The number of minibatches to be ran before gradients are accumulated.''' , )
parser.add_argument('''--cpu''' , action='''store_true''' , help='''If passed, will train on the CPU.''' )
_UpperCAmelCase : str = parser.parse_args()
_UpperCAmelCase : int = {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
training_function(UpperCamelCase__ , UpperCamelCase__ )
if __name__ == "__main__":
main()
| 506
|
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase :Any = logging.get_logger(__name__)
_lowerCAmelCase :Union[str, Any] = {
'unc-nlp/lxmert-base-uncased': 'https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json',
}
class _UpperCAmelCase ( a ):
'''simple docstring'''
a__ ='''lxmert'''
a__ ={}
def __init__( self , A=3_0_5_2_2 , A=7_6_8 , A=1_2 , A=9_5_0_0 , A=1_6_0_0 , A=4_0_0 , A=3_0_7_2 , A="gelu" , A=0.1 , A=0.1 , A=5_1_2 , A=2 , A=0.02 , A=1E-12 , A=9 , A=5 , A=5 , A=2_0_4_8 , A=4 , A=6.67 , A=True , A=True , A=True , A=True , A=True , A=True , A=True , **A , ) -> List[Any]:
_UpperCAmelCase : int = vocab_size
_UpperCAmelCase : str = hidden_size
_UpperCAmelCase : str = num_attention_heads
_UpperCAmelCase : str = hidden_act
_UpperCAmelCase : Dict = intermediate_size
_UpperCAmelCase : Union[str, Any] = hidden_dropout_prob
_UpperCAmelCase : str = attention_probs_dropout_prob
_UpperCAmelCase : Dict = max_position_embeddings
_UpperCAmelCase : str = type_vocab_size
_UpperCAmelCase : List[str] = initializer_range
_UpperCAmelCase : List[str] = layer_norm_eps
_UpperCAmelCase : Optional[int] = num_qa_labels
_UpperCAmelCase : Tuple = num_object_labels
_UpperCAmelCase : Optional[int] = num_attr_labels
_UpperCAmelCase : List[str] = l_layers
_UpperCAmelCase : Any = x_layers
_UpperCAmelCase : Tuple = r_layers
_UpperCAmelCase : Optional[Any] = visual_feat_dim
_UpperCAmelCase : Optional[int] = visual_pos_dim
_UpperCAmelCase : Optional[Any] = visual_loss_normalizer
_UpperCAmelCase : int = task_matched
_UpperCAmelCase : Optional[Any] = task_mask_lm
_UpperCAmelCase : Union[str, Any] = task_obj_predict
_UpperCAmelCase : Optional[int] = task_qa
_UpperCAmelCase : Union[str, Any] = visual_obj_loss
_UpperCAmelCase : List[str] = visual_attr_loss
_UpperCAmelCase : Optional[int] = visual_feat_loss
_UpperCAmelCase : Tuple = {'''vision''': r_layers, '''cross_encoder''': x_layers, '''language''': l_layers}
super().__init__(**A )
| 506
| 1
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available
A__ : str = {
'''configuration_ernie''': ['''ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ErnieConfig''', '''ErnieOnnxConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Union[str, Any] = [
'''ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ErnieForCausalLM''',
'''ErnieForMaskedLM''',
'''ErnieForMultipleChoice''',
'''ErnieForNextSentencePrediction''',
'''ErnieForPreTraining''',
'''ErnieForQuestionAnswering''',
'''ErnieForSequenceClassification''',
'''ErnieForTokenClassification''',
'''ErnieModel''',
'''ErniePreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig, ErnieOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_ernie import (
ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST,
ErnieForCausalLM,
ErnieForMaskedLM,
ErnieForMultipleChoice,
ErnieForNextSentencePrediction,
ErnieForPreTraining,
ErnieForQuestionAnswering,
ErnieForSequenceClassification,
ErnieForTokenClassification,
ErnieModel,
ErniePreTrainedModel,
)
else:
import sys
A__ : Union[str, Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 716
|
"""simple docstring"""
from dataclasses import dataclass
from typing import 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 .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer
@dataclass
class lowercase__ ( snake_case__ ):
_UpperCAmelCase :torch.FloatTensor
class lowercase__ ( snake_case__, snake_case__ ):
@register_to_config
def __init__( self : Any , snake_case__ : int = 3 , snake_case__ : int = 3 , snake_case__ : Tuple[str] = ("DownEncoderBlock2D",) , snake_case__ : Tuple[str] = ("UpDecoderBlock2D",) , snake_case__ : Tuple[int] = (64,) , snake_case__ : int = 1 , snake_case__ : str = "silu" , snake_case__ : int = 3 , snake_case__ : int = 32 , snake_case__ : int = 256 , snake_case__ : int = 32 , snake_case__ : Optional[int] = None , snake_case__ : float = 0.18_215 , snake_case__ : str = "group" , ):
super().__init__()
# pass init params to Encoder
lowerCamelCase_ : Union[str, Any] =Encoder(
in_channels=snake_case__ , out_channels=snake_case__ , down_block_types=snake_case__ , block_out_channels=snake_case__ , layers_per_block=snake_case__ , act_fn=snake_case__ , norm_num_groups=snake_case__ , double_z=snake_case__ , )
lowerCamelCase_ : Dict =vq_embed_dim if vq_embed_dim is not None else latent_channels
lowerCamelCase_ : List[str] =nn.Convad(snake_case__ , snake_case__ , 1 )
lowerCamelCase_ : Optional[int] =VectorQuantizer(snake_case__ , snake_case__ , beta=0.25 , remap=snake_case__ , sane_index_shape=snake_case__ )
lowerCamelCase_ : List[str] =nn.Convad(snake_case__ , snake_case__ , 1 )
# pass init params to Decoder
lowerCamelCase_ : Tuple =Decoder(
in_channels=snake_case__ , out_channels=snake_case__ , up_block_types=snake_case__ , block_out_channels=snake_case__ , layers_per_block=snake_case__ , act_fn=snake_case__ , norm_num_groups=snake_case__ , norm_type=snake_case__ , )
@apply_forward_hook
def UpperCAmelCase__ ( self : Optional[int] , snake_case__ : torch.FloatTensor , snake_case__ : bool = True ):
lowerCamelCase_ : Optional[int] =self.encoder(snake_case__ )
lowerCamelCase_ : Dict =self.quant_conv(snake_case__ )
if not return_dict:
return (h,)
return VQEncoderOutput(latents=snake_case__ )
@apply_forward_hook
def UpperCAmelCase__ ( self : Any , snake_case__ : torch.FloatTensor , snake_case__ : bool = False , snake_case__ : bool = True ):
# also go through quantization layer
if not force_not_quantize:
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ : int =self.quantize(snake_case__ )
else:
lowerCamelCase_ : Optional[int] =h
lowerCamelCase_ : Optional[Any] =self.post_quant_conv(snake_case__ )
lowerCamelCase_ : List[str] =self.decoder(snake_case__ , quant if self.config.norm_type == "spatial" else None )
if not return_dict:
return (dec,)
return DecoderOutput(sample=snake_case__ )
def UpperCAmelCase__ ( self : List[Any] , snake_case__ : torch.FloatTensor , snake_case__ : bool = True ):
lowerCamelCase_ : List[str] =sample
lowerCamelCase_ : Any =self.encode(snake_case__ ).latents
lowerCamelCase_ : List[Any] =self.decode(snake_case__ ).sample
if not return_dict:
return (dec,)
return DecoderOutput(sample=snake_case__ )
| 244
| 0
|
from math import sqrt
def lowercase_ (A : Union[str, Any] = 1_0_0_0_0_0_0 ):
snake_case__ : int = 0
snake_case__ : int = 0
snake_case__ : int
while num_cuboids <= limit:
max_cuboid_size += 1
for sum_shortest_sides in range(2 , 2 * max_cuboid_size + 1 ):
if sqrt(sum_shortest_sides**2 + max_cuboid_size**2 ).is_integer():
num_cuboids += (
min(lowercase_ , sum_shortest_sides // 2 )
- max(1 , sum_shortest_sides - max_cuboid_size )
+ 1
)
return max_cuboid_size
if __name__ == "__main__":
print(F"""{solution() = }""")
| 478
|
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = '▁'
_lowerCamelCase = {
'vocab_file': 'vocab.json',
'spm_file': 'sentencepiece.bpe.model',
}
_lowerCamelCase = {
'vocab_file': {
'facebook/s2t-small-librispeech-asr': (
'https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json'
),
},
'spm_file': {
'facebook/s2t-small-librispeech-asr': (
'https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model'
)
},
}
_lowerCamelCase = {
'facebook/s2t-small-librispeech-asr': 1024,
}
_lowerCamelCase = ['pt', 'fr', 'ru', 'nl', 'ro', 'it', 'es', 'de']
_lowerCamelCase = {'mustc': MUSTC_LANGS}
class __A ( lowerCamelCase__ ):
"""simple docstring"""
UpperCAmelCase__ = VOCAB_FILES_NAMES
UpperCAmelCase__ = PRETRAINED_VOCAB_FILES_MAP
UpperCAmelCase__ = MAX_MODEL_INPUT_SIZES
UpperCAmelCase__ = ["""input_ids""", """attention_mask"""]
UpperCAmelCase__ = []
def __init__( self , a__ , a__ , a__="<s>" , a__="</s>" , a__="<pad>" , a__="<unk>" , a__=False , a__=False , a__=None , a__=None , a__ = None , **a__ , ):
"""simple docstring"""
_lowerCamelCase : str = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=a__ , eos_token=a__ , unk_token=a__ , pad_token=a__ , do_upper_case=a__ , do_lower_case=a__ , tgt_lang=a__ , lang_codes=a__ , sp_model_kwargs=self.sp_model_kwargs , **a__ , )
_lowerCamelCase : Optional[int] = do_upper_case
_lowerCamelCase : Optional[Any] = do_lower_case
_lowerCamelCase : Tuple = load_json(a__)
_lowerCamelCase : Union[str, Any] = {v: k for k, v in self.encoder.items()}
_lowerCamelCase : Tuple = spm_file
_lowerCamelCase : Any = load_spm(a__ , self.sp_model_kwargs)
if lang_codes is not None:
_lowerCamelCase : List[Any] = lang_codes
_lowerCamelCase : List[str] = LANGUAGES[lang_codes]
_lowerCamelCase : Any = [F"""<lang:{lang}>""" for lang in self.langs]
_lowerCamelCase : Optional[Any] = {lang: self.sp_model.PieceToId(F"""<lang:{lang}>""") for lang in self.langs}
_lowerCamelCase : List[str] = self.lang_tokens
_lowerCamelCase : str = tgt_lang if tgt_lang is not None else self.langs[0]
self.set_tgt_lang_special_tokens(self._tgt_lang)
else:
_lowerCamelCase : Any = {}
@property
def __snake_case ( self):
"""simple docstring"""
return len(self.encoder)
@property
def __snake_case ( self):
"""simple docstring"""
return self._tgt_lang
@tgt_lang.setter
def __snake_case ( self , a__):
"""simple docstring"""
_lowerCamelCase : Any = new_tgt_lang
self.set_tgt_lang_special_tokens(a__)
def __snake_case ( self , a__):
"""simple docstring"""
_lowerCamelCase : Optional[Any] = self.lang_code_to_id[tgt_lang]
_lowerCamelCase : Any = [lang_code_id]
def __snake_case ( self , a__):
"""simple docstring"""
return self.sp_model.encode(a__ , out_type=a__)
def __snake_case ( self , a__):
"""simple docstring"""
return self.encoder.get(a__ , self.encoder[self.unk_token])
def __snake_case ( self , a__):
"""simple docstring"""
return self.decoder.get(a__ , self.unk_token)
def __snake_case ( self , a__):
"""simple docstring"""
_lowerCamelCase : Union[str, Any] = []
_lowerCamelCase : List[str] = ''''''
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
_lowerCamelCase : List[Any] = self.sp_model.decode(a__)
out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
_lowerCamelCase : Optional[int] = []
else:
current_sub_tokens.append(a__)
_lowerCamelCase : Tuple = self.sp_model.decode(a__)
out_string += decoded.upper() if self.do_upper_case else decoded
return out_string.strip()
def __snake_case ( self , a__ , a__=None):
"""simple docstring"""
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + [self.eos_token_id]
def __snake_case ( self , a__ , a__ = None , a__ = False):
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=a__ , token_ids_a=a__ , already_has_special_tokens=a__)
_lowerCamelCase : Tuple = [1] * len(self.prefix_tokens)
_lowerCamelCase : Tuple = [1]
if token_ids_a is None:
return prefix_ones + ([0] * len(a__)) + suffix_ones
return prefix_ones + ([0] * len(a__)) + ([0] * len(a__)) + suffix_ones
def __snake_case ( self):
"""simple docstring"""
_lowerCamelCase : Union[str, Any] = self.encoder.copy()
vocab.update(self.added_tokens_encoder)
return vocab
def __getstate__( self):
"""simple docstring"""
_lowerCamelCase : Union[str, Any] = self.__dict__.copy()
_lowerCamelCase : str = None
return state
def __setstate__( self , a__):
"""simple docstring"""
_lowerCamelCase : Optional[int] = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs'''):
_lowerCamelCase : List[Any] = {}
_lowerCamelCase : Dict = load_spm(self.spm_file , self.sp_model_kwargs)
def __snake_case ( self , a__ , a__ = None):
"""simple docstring"""
_lowerCamelCase : str = Path(a__)
assert save_dir.is_dir(), F"""{save_directory} should be a directory"""
_lowerCamelCase : Any = save_dir / (
(filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''vocab_file''']
)
_lowerCamelCase : Optional[Any] = save_dir / (
(filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''spm_file''']
)
save_json(self.encoder , a__)
if os.path.abspath(self.spm_file) != os.path.abspath(a__) and os.path.isfile(self.spm_file):
copyfile(self.spm_file , a__)
elif not os.path.isfile(self.spm_file):
with open(a__ , '''wb''') as fi:
_lowerCamelCase : List[str] = self.sp_model.serialized_model_proto()
fi.write(a__)
return (str(a__), str(a__))
def __UpperCAmelCase( lowercase_ , lowercase_ ):
_lowerCamelCase : Optional[Any] = sentencepiece.SentencePieceProcessor(**lowercase_ )
spm.Load(str(lowercase_ ) )
return spm
def __UpperCAmelCase( lowercase_ ):
with open(lowercase_ , '''r''' ) as f:
return json.load(lowercase_ )
def __UpperCAmelCase( lowercase_ , lowercase_ ):
with open(lowercase_ , '''w''' ) as f:
json.dump(lowercase_ , lowercase_ , indent=2 )
| 114
| 0
|
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'''abeja/gpt-neox-japanese-2.7b''': '''https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json''',
}
class _lowerCamelCase ( _lowercase ):
UpperCAmelCase_ = "gpt_neox_japanese"
def __init__(self , __a=3_20_00 , __a=25_60 , __a=32 , __a=32 , __a=4 , __a="gelu" , __a=1.00 , __a=1_00_00 , __a=20_48 , __a=0.02 , __a=1e-5 , __a=True , __a=3_19_96 , __a=3_19_99 , __a=0.1 , __a=0.0 , **__a , ) -> Optional[Any]:
super().__init__(bos_token_id=__a , eos_token_id=__a , **__a )
UpperCamelCase = vocab_size
UpperCamelCase = max_position_embeddings
UpperCamelCase = hidden_size
UpperCamelCase = num_hidden_layers
UpperCamelCase = num_attention_heads
UpperCamelCase = intermediate_multiple_size
UpperCamelCase = hidden_act
UpperCamelCase = rotary_pct
UpperCamelCase = rotary_emb_base
UpperCamelCase = initializer_range
UpperCamelCase = layer_norm_eps
UpperCamelCase = use_cache
UpperCamelCase = attention_dropout
UpperCamelCase = hidden_dropout
| 544
|
"""simple docstring"""
def a__ ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = 0
for ch in input_str:
UpperCamelCase = ord(_SCREAMING_SNAKE_CASE )
UpperCamelCase = pow(2 , _SCREAMING_SNAKE_CASE )
# If we already turned on bit for current character's unicode
if bitmap >> ch_unicode & 1 == 1:
return False
bitmap |= ch_bit_index_on
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
| 544
| 1
|
"""simple docstring"""
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class _lowerCAmelCase ( a ):
"""simple docstring"""
__magic_name__ :Union[str, Any] = (EulerDiscreteScheduler,)
__magic_name__ :Any = 10
def snake_case ( self , **__UpperCAmelCase ):
'''simple docstring'''
lowerCAmelCase__ :Any = {
'num_train_timesteps': 1_1_0_0,
'beta_start': 0.00_01,
'beta_end': 0.02,
'beta_schedule': 'linear',
}
config.update(**__UpperCAmelCase )
return config
def snake_case ( self ):
'''simple docstring'''
for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
self.check_over_configs(num_train_timesteps=__UpperCAmelCase )
def snake_case ( self ):
'''simple docstring'''
for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01] , [0.00_02, 0.0_02, 0.02] ):
self.check_over_configs(beta_start=__UpperCAmelCase , beta_end=__UpperCAmelCase )
def snake_case ( self ):
'''simple docstring'''
for schedule in ["linear", "scaled_linear"]:
self.check_over_configs(beta_schedule=__UpperCAmelCase )
def snake_case ( self ):
'''simple docstring'''
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=__UpperCAmelCase )
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :List[Any] = self.scheduler_classes[0]
lowerCAmelCase__ :int = self.get_scheduler_config()
lowerCAmelCase__ :Optional[Any] = scheduler_class(**__UpperCAmelCase )
scheduler.set_timesteps(self.num_inference_steps )
lowerCAmelCase__ :str = torch.manual_seed(0 )
lowerCAmelCase__ :Tuple = self.dummy_model()
lowerCAmelCase__ :Optional[Any] = self.dummy_sample_deter * scheduler.init_noise_sigma
lowerCAmelCase__ :List[Any] = sample.to(__UpperCAmelCase )
for i, t in enumerate(scheduler.timesteps ):
lowerCAmelCase__ :Tuple = scheduler.scale_model_input(__UpperCAmelCase , __UpperCAmelCase )
lowerCAmelCase__ :Dict = model(__UpperCAmelCase , __UpperCAmelCase )
lowerCAmelCase__ :Any = scheduler.step(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , generator=__UpperCAmelCase )
lowerCAmelCase__ :Any = output.prev_sample
lowerCAmelCase__ :int = torch.sum(torch.abs(__UpperCAmelCase ) )
lowerCAmelCase__ :Union[str, Any] = torch.mean(torch.abs(__UpperCAmelCase ) )
assert abs(result_sum.item() - 10.08_07 ) < 1E-2
assert abs(result_mean.item() - 0.01_31 ) < 1E-3
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :str = self.scheduler_classes[0]
lowerCAmelCase__ :Any = self.get_scheduler_config(prediction_type='v_prediction' )
lowerCAmelCase__ :int = scheduler_class(**__UpperCAmelCase )
scheduler.set_timesteps(self.num_inference_steps )
lowerCAmelCase__ :int = torch.manual_seed(0 )
lowerCAmelCase__ :int = self.dummy_model()
lowerCAmelCase__ :Optional[int] = self.dummy_sample_deter * scheduler.init_noise_sigma
lowerCAmelCase__ :Any = sample.to(__UpperCAmelCase )
for i, t in enumerate(scheduler.timesteps ):
lowerCAmelCase__ :List[Any] = scheduler.scale_model_input(__UpperCAmelCase , __UpperCAmelCase )
lowerCAmelCase__ :List[Any] = model(__UpperCAmelCase , __UpperCAmelCase )
lowerCAmelCase__ :List[Any] = scheduler.step(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , generator=__UpperCAmelCase )
lowerCAmelCase__ :Any = output.prev_sample
lowerCAmelCase__ :Tuple = torch.sum(torch.abs(__UpperCAmelCase ) )
lowerCAmelCase__ :Tuple = torch.mean(torch.abs(__UpperCAmelCase ) )
assert abs(result_sum.item() - 0.00_02 ) < 1E-2
assert abs(result_mean.item() - 2.2676E-06 ) < 1E-3
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :Any = self.scheduler_classes[0]
lowerCAmelCase__ :Tuple = self.get_scheduler_config()
lowerCAmelCase__ :Dict = scheduler_class(**__UpperCAmelCase )
scheduler.set_timesteps(self.num_inference_steps , device=__UpperCAmelCase )
lowerCAmelCase__ :int = torch.manual_seed(0 )
lowerCAmelCase__ :List[Any] = self.dummy_model()
lowerCAmelCase__ :int = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
lowerCAmelCase__ :Dict = sample.to(__UpperCAmelCase )
for t in scheduler.timesteps:
lowerCAmelCase__ :Optional[int] = scheduler.scale_model_input(__UpperCAmelCase , __UpperCAmelCase )
lowerCAmelCase__ :List[str] = model(__UpperCAmelCase , __UpperCAmelCase )
lowerCAmelCase__ :Optional[int] = scheduler.step(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , generator=__UpperCAmelCase )
lowerCAmelCase__ :List[str] = output.prev_sample
lowerCAmelCase__ :Optional[int] = torch.sum(torch.abs(__UpperCAmelCase ) )
lowerCAmelCase__ :List[Any] = torch.mean(torch.abs(__UpperCAmelCase ) )
assert abs(result_sum.item() - 10.08_07 ) < 1E-2
assert abs(result_mean.item() - 0.01_31 ) < 1E-3
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :Union[str, Any] = self.scheduler_classes[0]
lowerCAmelCase__ :str = self.get_scheduler_config()
lowerCAmelCase__ :Optional[Any] = scheduler_class(**__UpperCAmelCase , use_karras_sigmas=__UpperCAmelCase )
scheduler.set_timesteps(self.num_inference_steps , device=__UpperCAmelCase )
lowerCAmelCase__ :List[Any] = torch.manual_seed(0 )
lowerCAmelCase__ :str = self.dummy_model()
lowerCAmelCase__ :List[Any] = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
lowerCAmelCase__ :Tuple = sample.to(__UpperCAmelCase )
for t in scheduler.timesteps:
lowerCAmelCase__ :Any = scheduler.scale_model_input(__UpperCAmelCase , __UpperCAmelCase )
lowerCAmelCase__ :int = model(__UpperCAmelCase , __UpperCAmelCase )
lowerCAmelCase__ :int = scheduler.step(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , generator=__UpperCAmelCase )
lowerCAmelCase__ :Dict = output.prev_sample
lowerCAmelCase__ :str = torch.sum(torch.abs(__UpperCAmelCase ) )
lowerCAmelCase__ :str = torch.mean(torch.abs(__UpperCAmelCase ) )
assert abs(result_sum.item() - 1_24.52_29_94_99_51_17_19 ) < 1E-2
assert abs(result_mean.item() - 0.1_62_13_93_26_33_39_99_63 ) < 1E-3
| 93
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
lowerCAmelCase_ = {
'''configuration_speech_to_text''': ['''SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Speech2TextConfig'''],
'''processing_speech_to_text''': ['''Speech2TextProcessor'''],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = ['''Speech2TextTokenizer''']
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = ['''Speech2TextFeatureExtractor''']
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'''TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFSpeech2TextForConditionalGeneration''',
'''TFSpeech2TextModel''',
'''TFSpeech2TextPreTrainedModel''',
]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'''SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''Speech2TextForConditionalGeneration''',
'''Speech2TextModel''',
'''Speech2TextPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
from .processing_speech_to_text import SpeechaTextProcessor
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_speech_to_text import SpeechaTextTokenizer
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_speech_to_text import (
TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFSpeechaTextForConditionalGeneration,
TFSpeechaTextModel,
TFSpeechaTextPreTrainedModel,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speech_to_text import (
SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
SpeechaTextForConditionalGeneration,
SpeechaTextModel,
SpeechaTextPreTrainedModel,
)
else:
import sys
lowerCAmelCase_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 60
| 0
|
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils import FeatureExtractionMixin
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"""openai/whisper-base""": """https://huggingface.co/openai/whisper-base/resolve/main/config.json""",
}
# fmt: off
lowercase_ = [
1, 2, 7, 8, 9, 10, 14, 25,
26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
63, 90, 91, 92, 93, 357, 366, 438, 532, 685,
705, 796, 930, 1058, 1220, 1267, 1279, 1303, 1343, 1377,
1391, 1635, 1782, 1875, 2162, 2361, 2488, 3467, 4008, 4211,
4600, 4808, 5299, 5855, 6329, 7203, 9609, 9959, 1_0563, 1_0786,
1_1420, 1_1709, 1_1907, 1_3163, 1_3697, 1_3700, 1_4808, 1_5306, 1_6410, 1_6791,
1_7992, 1_9203, 1_9510, 2_0724, 2_2305, 2_2935, 2_7007, 3_0109, 3_0420, 3_3409,
3_4949, 4_0283, 4_0493, 4_0549, 4_7282, 4_9146, 5_0257, 5_0359, 5_0360, 5_0361
]
lowercase_ = [
1, 2, 7, 8, 9, 10, 14, 25,
26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
63, 90, 91, 92, 93, 359, 503, 522, 542, 873,
893, 902, 918, 922, 931, 1350, 1853, 1982, 2460, 2627,
3246, 3253, 3268, 3536, 3846, 3961, 4183, 4667, 6585, 6647,
7273, 9061, 9383, 1_0428, 1_0929, 1_1938, 1_2033, 1_2331, 1_2562, 1_3793,
1_4157, 1_4635, 1_5265, 1_5618, 1_6553, 1_6604, 1_8362, 1_8956, 2_0075, 2_1675,
2_2520, 2_6130, 2_6161, 2_6435, 2_8279, 2_9464, 3_1650, 3_2302, 3_2470, 3_6865,
4_2863, 4_7425, 4_9870, 5_0254, 5_0258, 5_0360, 5_0361, 5_0362
]
class _snake_case ( _UpperCamelCase):
UpperCamelCase__ : List[Any] ="whisper"
UpperCamelCase__ : Dict =["past_key_values"]
UpperCamelCase__ : List[Any] ={"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
def __init__( self : Optional[Any], __lowercase : Dict=5_1865, __lowercase : Tuple=80, __lowercase : Optional[Any]=6, __lowercase : List[Any]=4, __lowercase : Any=6, __lowercase : Tuple=4, __lowercase : List[str]=1536, __lowercase : str=1536, __lowercase : Any=0.0, __lowercase : List[str]=0.0, __lowercase : Dict=5_0257, __lowercase : str=True, __lowercase : Optional[Any]=True, __lowercase : Optional[Any]="gelu", __lowercase : Optional[int]=256, __lowercase : List[Any]=0.0, __lowercase : Dict=0.0, __lowercase : List[Any]=0.0, __lowercase : int=0.02, __lowercase : Optional[Any]=False, __lowercase : Optional[Any]=1500, __lowercase : List[str]=448, __lowercase : Union[str, Any]=5_0256, __lowercase : List[str]=5_0256, __lowercase : Any=5_0256, __lowercase : str=None, __lowercase : List[Any]=[220, 5_0256], __lowercase : int=False, __lowercase : str=256, __lowercase : Any=False, __lowercase : Optional[Any]=0.05, __lowercase : Union[str, Any]=10, __lowercase : int=2, __lowercase : int=0.0, __lowercase : Optional[int]=10, __lowercase : str=0, __lowercase : Any=7, **__lowercase : Tuple, ):
lowercase__ = vocab_size
lowercase__ = num_mel_bins
lowercase__ = d_model
lowercase__ = encoder_layers
lowercase__ = encoder_attention_heads
lowercase__ = decoder_layers
lowercase__ = decoder_attention_heads
lowercase__ = decoder_ffn_dim
lowercase__ = encoder_ffn_dim
lowercase__ = dropout
lowercase__ = attention_dropout
lowercase__ = activation_dropout
lowercase__ = activation_function
lowercase__ = init_std
lowercase__ = encoder_layerdrop
lowercase__ = decoder_layerdrop
lowercase__ = use_cache
lowercase__ = encoder_layers
lowercase__ = scale_embedding # scale factor will be sqrt(d_model) if True
lowercase__ = max_source_positions
lowercase__ = max_target_positions
# Audio Classification-specific parameters. Feel free to ignore for other classes.
lowercase__ = classifier_proj_size
lowercase__ = use_weighted_layer_sum
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
lowercase__ = apply_spec_augment
lowercase__ = mask_time_prob
lowercase__ = mask_time_length
lowercase__ = mask_time_min_masks
lowercase__ = mask_feature_prob
lowercase__ = mask_feature_length
lowercase__ = mask_feature_min_masks
lowercase__ = median_filter_width
super().__init__(
pad_token_id=__a, bos_token_id=__a, eos_token_id=__a, is_encoder_decoder=__a, decoder_start_token_id=__a, suppress_tokens=__a, begin_suppress_tokens=__a, **__a, )
class _snake_case ( _UpperCamelCase):
@property
def A__ ( self : Optional[int] ):
lowercase__ = OrderedDict(
[
("input_features", {0: "batch", 1: "feature_size", 2: "encoder_sequence"}),
] )
if self.use_past:
lowercase__ = {0: "batch"}
else:
lowercase__ = {0: "batch", 1: "decoder_sequence"}
if self.use_past:
self.fill_with_past_key_values_(__a, direction="inputs" )
return common_inputs
def A__ ( self : Dict, __lowercase : Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"], __lowercase : int = -1, __lowercase : int = -1, __lowercase : bool = False, __lowercase : Optional["TensorType"] = None, __lowercase : int = 2_2050, __lowercase : float = 5.0, __lowercase : int = 220, ):
lowercase__ = OrderedDict()
lowercase__ = OnnxConfig.generate_dummy_inputs(
self, preprocessor=preprocessor.feature_extractor, batch_size=__a, framework=__a, sampling_rate=__a, time_duration=__a, frequency=__a, )
lowercase__ = encoder_inputs["input_features"].shape[2]
lowercase__ = encoder_sequence_length // 2 if self.use_past else seq_length
lowercase__ = super().generate_dummy_inputs(
preprocessor.tokenizer, __a, __a, __a, __a )
lowercase__ = encoder_inputs.pop("input_features" )
lowercase__ = decoder_inputs.pop("decoder_input_ids" )
if "past_key_values" in decoder_inputs:
lowercase__ = decoder_inputs.pop("past_key_values" )
return dummy_inputs
@property
def A__ ( self : str ):
return 1e-3
| 706
|
import math
def __lowerCAmelCase ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
if 0 not in (x, y):
# We use the relation x^y = y*log10(x), where 10 is the base.
return y * math.logaa(SCREAMING_SNAKE_CASE_ )
else:
if x == 0: # 0 raised to any number is 0
return 0
elif y == 0:
return 1 # any number raised to 0 is 1
raise AssertionError("This should never happen" )
if __name__ == "__main__": # Main function
# Read two numbers from input and typecast them to int using map function.
# Here x is the base and y is the power.
lowercase_ = """Enter the base and the power separated by a comma: """
lowercase_ , lowercase_ = map(int, input(prompt).split(""","""))
lowercase_ , lowercase_ = map(int, input(prompt).split(""","""))
# We find the log of each number, using the function res(), which takes two
# arguments.
lowercase_ = res(xa, ya)
lowercase_ = res(xa, ya)
# We check for the largest number
if resa > resa:
print("""Largest number is""", xa, """^""", ya)
elif resa > resa:
print("""Largest number is""", xa, """^""", ya)
else:
print("""Both are equal""")
| 37
| 0
|
'''simple docstring'''
import math
def _snake_case ( A ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(A ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def _snake_case ( A = 10001 ) -> int:
try:
lowerCAmelCase__ = int(A )
except (TypeError, ValueError):
raise TypeError('''Parameter nth must be int or castable to int.''' ) from None
if nth <= 0:
raise ValueError('''Parameter nth must be greater than or equal to one.''' )
lowerCAmelCase__ = []
lowerCAmelCase__ = 2
while len(A ) < nth:
if is_prime(A ):
primes.append(A )
num += 1
else:
num += 1
return primes[len(A ) - 1]
if __name__ == "__main__":
print(f"""{solution() = }""")
| 90
|
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__: int = datasets.logging.get_logger(__name__)
lowerCAmelCase__: Optional[int] = "\\n@InProceedings{moosavi2019minimum,\n author = { Nafise Sadat Moosavi, Leo Born, Massimo Poesio and Michael Strube},\n title = {Using Automatically Extracted Minimum Spans to Disentangle Coreference Evaluation from Boundary Detection},\n year = {2019},\n booktitle = {Proceedings of the 57th Annual Meeting of\n the Association for Computational Linguistics (Volume 1: Long Papers)},\n publisher = {Association for Computational Linguistics},\n address = {Florence, Italy},\n}\n\n@inproceedings{10.3115/1072399.1072405,\nauthor = {Vilain, Marc and Burger, John and Aberdeen, John and Connolly, Dennis and Hirschman, Lynette},\ntitle = {A Model-Theoretic Coreference Scoring Scheme},\nyear = {1995},\nisbn = {1558604022},\npublisher = {Association for Computational Linguistics},\naddress = {USA},\nurl = {https://doi.org/10.3115/1072399.1072405},\ndoi = {10.3115/1072399.1072405},\nbooktitle = {Proceedings of the 6th Conference on Message Understanding},\npages = {45–52},\nnumpages = {8},\nlocation = {Columbia, Maryland},\nseries = {MUC6 ’95}\n}\n\n@INPROCEEDINGS{Bagga98algorithmsfor,\n author = {Amit Bagga and Breck Baldwin},\n title = {Algorithms for Scoring Coreference Chains},\n booktitle = {In The First International Conference on Language Resources and Evaluation Workshop on Linguistics Coreference},\n year = {1998},\n pages = {563--566}\n}\n\n@INPROCEEDINGS{Luo05oncoreference,\n author = {Xiaoqiang Luo},\n title = {On coreference resolution performance metrics},\n booktitle = {In Proc. of HLT/EMNLP},\n year = {2005},\n pages = {25--32},\n publisher = {URL}\n}\n\n@inproceedings{moosavi-strube-2016-coreference,\n title = \"Which Coreference Evaluation Metric Do You Trust? A Proposal for a Link-based Entity Aware Metric\",\n author = \"Moosavi, Nafise Sadat and\n Strube, Michael\",\n booktitle = \"Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)\",\n month = aug,\n year = \"2016\",\n address = \"Berlin, Germany\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://www.aclweb.org/anthology/P16-1060\",\n doi = \"10.18653/v1/P16-1060\",\n pages = \"632--642\",\n}\n\n"
lowerCAmelCase__: Any = "\\nCoVal is a coreference evaluation tool for the CoNLL and ARRAU datasets which\nimplements of the common evaluation metrics including MUC [Vilain et al, 1995],\nB-cubed [Bagga and Baldwin, 1998], CEAFe [Luo et al., 2005],\nLEA [Moosavi and Strube, 2016] and the averaged CoNLL score\n(the average of the F1 values of MUC, B-cubed and CEAFe)\n[Denis and Baldridge, 2009a; Pradhan et al., 2011].\n\nThis wrapper of CoVal currently only work with CoNLL line format:\nThe CoNLL format has one word per line with all the annotation for this word in column separated by spaces:\nColumn Type Description\n1 Document ID This is a variation on the document filename\n2 Part number Some files are divided into multiple parts numbered as 000, 001, 002, ... etc.\n3 Word number\n4 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.\n5 Part-of-Speech\n6 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.\n7 Predicate lemma The predicate lemma is mentioned for the rows for which we have semantic role information. All other rows are marked with a \"-\"\n8 Predicate Frameset ID This is the PropBank frameset ID of the predicate in Column 7.\n9 Word sense This is the word sense of the word in Column 3.\n10 Speaker/Author This is the speaker or author name where available. Mostly in Broadcast Conversation and Web Log data.\n11 Named Entities These columns identifies the spans representing various named entities.\n12:N Predicate Arguments There is one column each of predicate argument structure information for the predicate mentioned in Column 7.\nN Coreference Coreference chain information encoded in a parenthesis structure.\nMore informations on the format can be found here (section \"*_conll File Format\"): http://www.conll.cemantix.org/2012/data.html\n\nDetails on the evaluation on CoNLL can be found here: https://github.com/ns-moosavi/coval/blob/master/conll/README.md\n\nCoVal code was written by @ns-moosavi.\nSome parts are borrowed from https://github.com/clarkkev/deep-coref/blob/master/evaluation.py\nThe test suite is taken from https://github.com/conll/reference-coreference-scorers/\nMention evaluation and the test suite are added by @andreasvc.\nParsing CoNLL files is developed by Leo Born.\n"
lowerCAmelCase__: Tuple = "\nCalculates coreference evaluation metrics.\nArgs:\n predictions: list of sentences. Each sentence is a list of word predictions to score in the CoNLL format.\n Each prediction is a word with its annotations as a string made of columns joined with spaces.\n Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)\n See the details on the format in the description of the metric.\n references: list of sentences. Each sentence is a list of word reference to score in the CoNLL format.\n Each reference is a word with its annotations as a string made of columns joined with spaces.\n Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)\n See the details on the format in the description of the metric.\n keep_singletons: After extracting all mentions of key or system files,\n mentions whose corresponding coreference chain is of size one,\n are considered as singletons. The default evaluation mode will include\n singletons in evaluations if they are included in the key or the system files.\n By setting 'keep_singletons=False', all singletons in the key and system files\n will be excluded from the evaluation.\n NP_only: Most of the recent coreference resolvers only resolve NP mentions and\n leave out the resolution of VPs. By setting the 'NP_only' option, the scorer will only evaluate the resolution of NPs.\n min_span: By setting 'min_span', the scorer reports the results based on automatically detected minimum spans.\n Minimum spans are determined using the MINA algorithm.\n\nReturns:\n 'mentions': mentions\n 'muc': MUC metric [Vilain et al, 1995]\n 'bcub': B-cubed [Bagga and Baldwin, 1998]\n 'ceafe': CEAFe [Luo et al., 2005]\n 'lea': LEA [Moosavi and Strube, 2016]\n 'conll_score': averaged CoNLL score (the average of the F1 values of MUC, B-cubed and CEAFe)\n\nExamples:\n\n >>> coval = datasets.load_metric('coval')\n >>> words = ['bc/cctv/00/cctv_0005 0 0 Thank VBP (TOP(S(VP* thank 01 1 Xu_li * (V*) * -',\n ... 'bc/cctv/00/cctv_0005 0 1 you PRP (NP*) - - - Xu_li * (ARG1*) (ARG0*) (116)',\n ... 'bc/cctv/00/cctv_0005 0 2 everyone NN (NP*) - - - Xu_li * (ARGM-DIS*) * (116)',\n ... 'bc/cctv/00/cctv_0005 0 3 for IN (PP* - - - Xu_li * (ARG2* * -',\n ... 'bc/cctv/00/cctv_0005 0 4 watching VBG (S(VP*)))) watch 01 1 Xu_li * *) (V*) -',\n ... 'bc/cctv/00/cctv_0005 0 5 . . *)) - - - Xu_li * * * -']\n >>> references = [words]\n >>> predictions = [words]\n >>> results = coval.compute(predictions=predictions, references=references)\n >>> print(results) # doctest:+ELLIPSIS\n {'mentions/recall': 1.0,[...] 'conll_score': 100.0}\n"
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=False , SCREAMING_SNAKE_CASE=False , SCREAMING_SNAKE_CASE=True , SCREAMING_SNAKE_CASE=False , SCREAMING_SNAKE_CASE="dummy_doc" ) -> Tuple:
SCREAMING_SNAKE_CASE_ : Dict = {doc: key_lines}
SCREAMING_SNAKE_CASE_ : Dict = {doc: sys_lines}
SCREAMING_SNAKE_CASE_ : List[str] = {}
SCREAMING_SNAKE_CASE_ : int = 0
SCREAMING_SNAKE_CASE_ : int = 0
SCREAMING_SNAKE_CASE_ : Optional[Any] = 0
SCREAMING_SNAKE_CASE_ : Tuple = 0
SCREAMING_SNAKE_CASE_ : Tuple = 0
SCREAMING_SNAKE_CASE_ : Optional[Any] = 0
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : str = reader.get_doc_mentions(SCREAMING_SNAKE_CASE , key_doc_lines[doc] , SCREAMING_SNAKE_CASE )
key_singletons_num += singletons_num
if NP_only or min_span:
SCREAMING_SNAKE_CASE_ : List[str] = reader.set_annotated_parse_trees(SCREAMING_SNAKE_CASE , key_doc_lines[doc] , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : int = reader.get_doc_mentions(SCREAMING_SNAKE_CASE , sys_doc_lines[doc] , SCREAMING_SNAKE_CASE )
sys_singletons_num += singletons_num
if NP_only or min_span:
SCREAMING_SNAKE_CASE_ : Optional[Any] = reader.set_annotated_parse_trees(SCREAMING_SNAKE_CASE , key_doc_lines[doc] , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
if remove_nested:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : str = reader.remove_nested_coref_mentions(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
key_nested_coref_num += nested_mentions
key_removed_nested_clusters += removed_clusters
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Any = reader.remove_nested_coref_mentions(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
sys_nested_coref_num += nested_mentions
sys_removed_nested_clusters += removed_clusters
SCREAMING_SNAKE_CASE_ : Optional[int] = reader.get_mention_assignments(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE_ : List[str] = reader.get_mention_assignments(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE_ : Optional[Any] = (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 __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> List[str]:
SCREAMING_SNAKE_CASE_ : Optional[Any] = get_coref_infos(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE_ : Dict = {}
SCREAMING_SNAKE_CASE_ : int = 0
SCREAMING_SNAKE_CASE_ : List[str] = 0
for name, metric in metrics:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Optional[Any] = evaluator.evaluate_documents(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , 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 * 100:.2f}' , f' Precision: {precision * 100:.2f}' , f' F1: {fa * 100:.2f}' , )
if conll_subparts_num == 3:
SCREAMING_SNAKE_CASE_ : Dict = (conll / 3) * 100
logger.info(f'CoNLL score: {conll:.2f}' )
output_scores.update({'conll_score': conll} )
return output_scores
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE ) -> int:
SCREAMING_SNAKE_CASE_ : Dict = False
for line in key_lines:
if not line.startswith('#' ):
if len(line.split() ) > 6:
SCREAMING_SNAKE_CASE_ : Dict = line.split()[5]
if not parse_col == "-":
SCREAMING_SNAKE_CASE_ : 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 __A ( self ):
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 __A ( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=True , __lowerCAmelCase=False , __lowerCAmelCase=False , __lowerCAmelCase=False ):
SCREAMING_SNAKE_CASE_ : Any = [
('mentions', evaluator.mentions),
('muc', evaluator.muc),
('bcub', evaluator.b_cubed),
('ceafe', evaluator.ceafe),
('lea', evaluator.lea),
]
if min_span:
SCREAMING_SNAKE_CASE_ : Optional[int] = 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"
SCREAMING_SNAKE_CASE_ : Optional[Any] = evaluate(
key_lines=__lowerCAmelCase , sys_lines=__lowerCAmelCase , metrics=__lowerCAmelCase , NP_only=__lowerCAmelCase , remove_nested=__lowerCAmelCase , keep_singletons=__lowerCAmelCase , min_span=__lowerCAmelCase , )
return score
| 345
| 0
|
from __future__ import annotations
from collections import Counter
from random import random
class _SCREAMING_SNAKE_CASE :
def __init__( self ) -> Optional[int]:
lowerCamelCase_ = {}
def SCREAMING_SNAKE_CASE_( self , lowercase ) -> Any:
lowerCamelCase_ = {}
def SCREAMING_SNAKE_CASE_( self , lowercase , lowercase , lowercase ) -> Dict:
if nodea not in self.connections:
self.add_node(UpperCamelCase__ )
if nodea not in self.connections:
self.add_node(UpperCamelCase__ )
lowerCamelCase_ = probability
def SCREAMING_SNAKE_CASE_( self ) -> Optional[Any]:
return list(self.connections )
def SCREAMING_SNAKE_CASE_( self , lowercase ) -> List[Any]:
lowerCamelCase_ = 0
lowerCamelCase_ = random()
for dest in self.connections[node]:
current_probability += self.connections[node][dest]
if current_probability > random_value:
return dest
return ""
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
lowerCamelCase_ = MarkovChainGraphUndirectedUnweighted()
for nodea, nodea, probability in transitions:
graph.add_transition_probability(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
lowerCamelCase_ = Counter(graph.get_nodes() )
lowerCamelCase_ = start
for _ in range(lowerCamelCase__ ):
lowerCamelCase_ = graph.transition(lowerCamelCase__ )
visited[node] += 1
return visited
if __name__ == "__main__":
import doctest
doctest.testmod()
| 711
|
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_outputs import (
BaseModelOutputWithNoAttention,
BaseModelOutputWithPoolingAndNoAttention,
ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import logging
from .configuration_regnet import RegNetConfig
__A =logging.get_logger(__name__)
# General docstring
__A ='''RegNetConfig'''
# Base docstring
__A ='''facebook/regnet-y-040'''
__A =[1, 1_0_8_8, 7, 7]
# Image classification docstring
__A ='''facebook/regnet-y-040'''
__A ='''tabby, tabby cat'''
__A =[
'''facebook/regnet-y-040''',
# See all regnet models at https://huggingface.co/models?filter=regnet
]
class _SCREAMING_SNAKE_CASE ( nn.Module ):
def __init__( self , lowercase , lowercase , lowercase = 3 , lowercase = 1 , lowercase = 1 , lowercase = "relu" , ) -> Dict:
super().__init__()
lowerCamelCase_ = nn.Convad(
lowercase , lowercase , kernel_size=lowercase , stride=lowercase , padding=kernel_size // 2 , groups=lowercase , bias=lowercase , )
lowerCamelCase_ = nn.BatchNormad(lowercase )
lowerCamelCase_ = ACTaFN[activation] if activation is not None else nn.Identity()
def SCREAMING_SNAKE_CASE_( self , lowercase ) -> Optional[Any]:
lowerCamelCase_ = self.convolution(lowercase )
lowerCamelCase_ = self.normalization(lowercase )
lowerCamelCase_ = self.activation(lowercase )
return hidden_state
class _SCREAMING_SNAKE_CASE ( nn.Module ):
def __init__( self , lowercase ) -> List[Any]:
super().__init__()
lowerCamelCase_ = RegNetConvLayer(
config.num_channels , config.embedding_size , kernel_size=3 , stride=2 , activation=config.hidden_act )
lowerCamelCase_ = config.num_channels
def SCREAMING_SNAKE_CASE_( self , lowercase ) -> Union[str, Any]:
lowerCamelCase_ = pixel_values.shape[1]
if num_channels != self.num_channels:
raise ValueError(
"Make sure that the channel dimension of the pixel values match with the one set in the configuration." )
lowerCamelCase_ = self.embedder(lowercase )
return hidden_state
class _SCREAMING_SNAKE_CASE ( nn.Module ):
def __init__( self , lowercase , lowercase , lowercase = 2 ) -> List[str]:
super().__init__()
lowerCamelCase_ = nn.Convad(lowercase , lowercase , kernel_size=1 , stride=lowercase , bias=lowercase )
lowerCamelCase_ = nn.BatchNormad(lowercase )
def SCREAMING_SNAKE_CASE_( self , lowercase ) -> Tensor:
lowerCamelCase_ = self.convolution(lowercase )
lowerCamelCase_ = self.normalization(lowercase )
return hidden_state
class _SCREAMING_SNAKE_CASE ( nn.Module ):
def __init__( self , lowercase , lowercase ) -> List[Any]:
super().__init__()
lowerCamelCase_ = nn.AdaptiveAvgPoolad((1, 1) )
lowerCamelCase_ = nn.Sequential(
nn.Convad(lowercase , lowercase , kernel_size=1 ) , nn.ReLU() , nn.Convad(lowercase , lowercase , kernel_size=1 ) , nn.Sigmoid() , )
def SCREAMING_SNAKE_CASE_( self , lowercase ) -> Union[str, Any]:
# b c h w -> b c 1 1
lowerCamelCase_ = self.pooler(lowercase )
lowerCamelCase_ = self.attention(lowercase )
lowerCamelCase_ = hidden_state * attention
return hidden_state
class _SCREAMING_SNAKE_CASE ( nn.Module ):
def __init__( self , lowercase , lowercase , lowercase , lowercase = 1 ) -> int:
super().__init__()
lowerCamelCase_ = in_channels != out_channels or stride != 1
lowerCamelCase_ = max(1 , out_channels // config.groups_width )
lowerCamelCase_ = (
RegNetShortCut(lowercase , lowercase , stride=lowercase ) if should_apply_shortcut else nn.Identity()
)
lowerCamelCase_ = nn.Sequential(
RegNetConvLayer(lowercase , lowercase , kernel_size=1 , activation=config.hidden_act ) , RegNetConvLayer(lowercase , lowercase , stride=lowercase , groups=lowercase , activation=config.hidden_act ) , RegNetConvLayer(lowercase , lowercase , kernel_size=1 , activation=lowercase ) , )
lowerCamelCase_ = ACTaFN[config.hidden_act]
def SCREAMING_SNAKE_CASE_( self , lowercase ) -> Dict:
lowerCamelCase_ = hidden_state
lowerCamelCase_ = self.layer(lowercase )
lowerCamelCase_ = self.shortcut(lowercase )
hidden_state += residual
lowerCamelCase_ = self.activation(lowercase )
return hidden_state
class _SCREAMING_SNAKE_CASE ( nn.Module ):
def __init__( self , lowercase , lowercase , lowercase , lowercase = 1 ) -> Dict:
super().__init__()
lowerCamelCase_ = in_channels != out_channels or stride != 1
lowerCamelCase_ = max(1 , out_channels // config.groups_width )
lowerCamelCase_ = (
RegNetShortCut(lowercase , lowercase , stride=lowercase ) if should_apply_shortcut else nn.Identity()
)
lowerCamelCase_ = nn.Sequential(
RegNetConvLayer(lowercase , lowercase , kernel_size=1 , activation=config.hidden_act ) , RegNetConvLayer(lowercase , lowercase , stride=lowercase , groups=lowercase , activation=config.hidden_act ) , RegNetSELayer(lowercase , reduced_channels=int(round(in_channels / 4 ) ) ) , RegNetConvLayer(lowercase , lowercase , kernel_size=1 , activation=lowercase ) , )
lowerCamelCase_ = ACTaFN[config.hidden_act]
def SCREAMING_SNAKE_CASE_( self , lowercase ) -> List[str]:
lowerCamelCase_ = hidden_state
lowerCamelCase_ = self.layer(lowercase )
lowerCamelCase_ = self.shortcut(lowercase )
hidden_state += residual
lowerCamelCase_ = self.activation(lowercase )
return hidden_state
class _SCREAMING_SNAKE_CASE ( nn.Module ):
def __init__( self , lowercase , lowercase , lowercase , lowercase = 2 , lowercase = 2 , ) -> Optional[int]:
super().__init__()
lowerCamelCase_ = RegNetXLayer if config.layer_type == "x" else RegNetYLayer
lowerCamelCase_ = nn.Sequential(
# downsampling is done in the first layer with stride of 2
layer(
lowercase , lowercase , lowercase , stride=lowercase , ) , *[layer(lowercase , lowercase , lowercase ) for _ in range(depth - 1 )] , )
def SCREAMING_SNAKE_CASE_( self , lowercase ) -> int:
lowerCamelCase_ = self.layers(lowercase )
return hidden_state
class _SCREAMING_SNAKE_CASE ( nn.Module ):
def __init__( self , lowercase ) -> int:
super().__init__()
lowerCamelCase_ = nn.ModuleList([] )
# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
self.stages.append(
RegNetStage(
lowercase , config.embedding_size , config.hidden_sizes[0] , stride=2 if config.downsample_in_first_stage else 1 , depth=config.depths[0] , ) )
lowerCamelCase_ = zip(config.hidden_sizes , config.hidden_sizes[1:] )
for (in_channels, out_channels), depth in zip(lowercase , config.depths[1:] ):
self.stages.append(RegNetStage(lowercase , lowercase , lowercase , depth=lowercase ) )
def SCREAMING_SNAKE_CASE_( self , lowercase , lowercase = False , lowercase = True ) -> BaseModelOutputWithNoAttention:
lowerCamelCase_ = () if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
lowerCamelCase_ = hidden_states + (hidden_state,)
lowerCamelCase_ = stage_module(lowercase )
if output_hidden_states:
lowerCamelCase_ = hidden_states + (hidden_state,)
if not return_dict:
return tuple(v for v in [hidden_state, hidden_states] if v is not None )
return BaseModelOutputWithNoAttention(last_hidden_state=lowercase , hidden_states=lowercase )
class _SCREAMING_SNAKE_CASE ( snake_case_ ):
lowerCAmelCase__ = RegNetConfig
lowerCAmelCase__ = 'regnet'
lowerCAmelCase__ = 'pixel_values'
lowerCAmelCase__ = True
def SCREAMING_SNAKE_CASE_( self , lowercase ) -> Any:
if isinstance(lowercase , nn.Convad ):
nn.init.kaiming_normal_(module.weight , mode="fan_out" , nonlinearity="relu" )
elif isinstance(lowercase , (nn.BatchNormad, nn.GroupNorm) ):
nn.init.constant_(module.weight , 1 )
nn.init.constant_(module.bias , 0 )
def SCREAMING_SNAKE_CASE_( self , lowercase , lowercase=False ) -> Any:
if isinstance(lowercase , lowercase ):
lowerCamelCase_ = value
__A =R'''
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
'''
__A =R'''
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`ConvNextImageProcessor.__call__`] for details.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
'''
@add_start_docstrings(
'The bare RegNet model outputting raw features without any specific head on top.' , snake_case_ , )
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class _SCREAMING_SNAKE_CASE ( snake_case_ ):
def __init__( self , lowercase ) -> List[str]:
super().__init__(lowercase )
lowerCamelCase_ = config
lowerCamelCase_ = RegNetEmbeddings(lowercase )
lowerCamelCase_ = RegNetEncoder(lowercase )
lowerCamelCase_ = nn.AdaptiveAvgPoolad((1, 1) )
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(lowercase )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=lowercase , config_class=_CONFIG_FOR_DOC , modality="vision" , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def SCREAMING_SNAKE_CASE_( self , lowercase , lowercase = None , lowercase = None ) -> BaseModelOutputWithPoolingAndNoAttention:
lowerCamelCase_ = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
lowerCamelCase_ = return_dict if return_dict is not None else self.config.use_return_dict
lowerCamelCase_ = self.embedder(lowercase )
lowerCamelCase_ = self.encoder(
lowercase , output_hidden_states=lowercase , return_dict=lowercase )
lowerCamelCase_ = encoder_outputs[0]
lowerCamelCase_ = self.pooler(lowercase )
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=lowercase , pooler_output=lowercase , hidden_states=encoder_outputs.hidden_states , )
@add_start_docstrings(
'\n RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n ImageNet.\n ' , snake_case_ , )
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class _SCREAMING_SNAKE_CASE ( snake_case_ ):
def __init__( self , lowercase ) -> Any:
super().__init__(lowercase )
lowerCamelCase_ = config.num_labels
lowerCamelCase_ = RegNetModel(lowercase )
# classification head
lowerCamelCase_ = nn.Sequential(
nn.Flatten() , nn.Linear(config.hidden_sizes[-1] , config.num_labels ) if config.num_labels > 0 else nn.Identity() , )
# initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(lowercase )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=lowercase , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def SCREAMING_SNAKE_CASE_( self , lowercase = None , lowercase = None , lowercase = None , lowercase = None , ) -> ImageClassifierOutputWithNoAttention:
lowerCamelCase_ = return_dict if return_dict is not None else self.config.use_return_dict
lowerCamelCase_ = self.regnet(lowercase , output_hidden_states=lowercase , return_dict=lowercase )
lowerCamelCase_ = outputs.pooler_output if return_dict else outputs[1]
lowerCamelCase_ = self.classifier(lowercase )
lowerCamelCase_ = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
lowerCamelCase_ = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
lowerCamelCase_ = "single_label_classification"
else:
lowerCamelCase_ = "multi_label_classification"
if self.config.problem_type == "regression":
lowerCamelCase_ = MSELoss()
if self.num_labels == 1:
lowerCamelCase_ = loss_fct(logits.squeeze() , labels.squeeze() )
else:
lowerCamelCase_ = loss_fct(lowercase , lowercase )
elif self.config.problem_type == "single_label_classification":
lowerCamelCase_ = CrossEntropyLoss()
lowerCamelCase_ = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
lowerCamelCase_ = BCEWithLogitsLoss()
lowerCamelCase_ = loss_fct(lowercase , lowercase )
if not return_dict:
lowerCamelCase_ = (logits,) + outputs[2:]
return (loss,) + output if loss is not None else output
return ImageClassifierOutputWithNoAttention(loss=lowercase , logits=lowercase , hidden_states=outputs.hidden_states )
| 313
| 0
|
'''simple docstring'''
SCREAMING_SNAKE_CASE_: List[str] ={
'A': '.-', 'B': '-...', 'C': '-.-.', 'D': '-..', 'E': '.', 'F': '..-.', 'G': '--.',
'H': '....', 'I': '..', 'J': '.---', 'K': '-.-', 'L': '.-..', 'M': '--', 'N': '-.',
'O': '---', 'P': '.--.', 'Q': '--.-', 'R': '.-.', 'S': '...', 'T': '-', 'U': '..-',
'V': '...-', 'W': '.--', 'X': '-..-', 'Y': '-.--', 'Z': '--..', '1': '.----',
'2': '..---', '3': '...--', '4': '....-', '5': '.....', '6': '-....', '7': '--...',
'8': '---..', '9': '----.', '0': '-----', '&': '.-...', '@': '.--.-.',
':': '---...', ',': '--..--', '.': '.-.-.-', '\'': '.----.', '"': '.-..-.',
'?': '..--..', '/': '-..-.', '=': '-...-', '+': '.-.-.', '-': '-....-',
'(': '-.--.', ')': '-.--.-', '!': '-.-.--', ' ': '/'
} # Exclamation mark is not in ITU-R recommendation
# fmt: on
SCREAMING_SNAKE_CASE_: Optional[Any] ={value: key for key, value in MORSE_CODE_DICT.items()}
def lowerCAmelCase_ ( snake_case_ : str ) -> str:
'''simple docstring'''
return " ".join(MORSE_CODE_DICT[char] for char in message.upper() )
def lowerCAmelCase_ ( snake_case_ : str ) -> str:
'''simple docstring'''
return "".join(REVERSE_DICT[char] for char in message.split() )
def lowerCAmelCase_ ( ) -> None:
'''simple docstring'''
UpperCAmelCase_ = "Morse code here!"
print(snake_case_ )
UpperCAmelCase_ = encrypt(snake_case_ )
print(snake_case_ )
UpperCAmelCase_ = decrypt(snake_case_ )
print(snake_case_ )
if __name__ == "__main__":
main()
| 78
|
import argparse
import io
import requests
import torch
from omegaconf import OmegaConf
from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
assign_to_checkpoint,
conv_attn_to_linear,
create_vae_diffusers_config,
renew_vae_attention_paths,
renew_vae_resnet_paths,
)
def A_ ( a , a ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[str] = checkpoint
SCREAMING_SNAKE_CASE_ : Optional[Any] = {}
SCREAMING_SNAKE_CASE_ : Any = vae_state_dict['encoder.conv_in.weight']
SCREAMING_SNAKE_CASE_ : str = vae_state_dict['encoder.conv_in.bias']
SCREAMING_SNAKE_CASE_ : Dict = vae_state_dict['encoder.conv_out.weight']
SCREAMING_SNAKE_CASE_ : List[str] = vae_state_dict['encoder.conv_out.bias']
SCREAMING_SNAKE_CASE_ : Tuple = vae_state_dict['encoder.norm_out.weight']
SCREAMING_SNAKE_CASE_ : Union[str, Any] = vae_state_dict['encoder.norm_out.bias']
SCREAMING_SNAKE_CASE_ : Any = vae_state_dict['decoder.conv_in.weight']
SCREAMING_SNAKE_CASE_ : Optional[Any] = vae_state_dict['decoder.conv_in.bias']
SCREAMING_SNAKE_CASE_ : Optional[Any] = vae_state_dict['decoder.conv_out.weight']
SCREAMING_SNAKE_CASE_ : Any = vae_state_dict['decoder.conv_out.bias']
SCREAMING_SNAKE_CASE_ : List[Any] = vae_state_dict['decoder.norm_out.weight']
SCREAMING_SNAKE_CASE_ : List[Any] = vae_state_dict['decoder.norm_out.bias']
SCREAMING_SNAKE_CASE_ : List[Any] = vae_state_dict['quant_conv.weight']
SCREAMING_SNAKE_CASE_ : List[str] = vae_state_dict['quant_conv.bias']
SCREAMING_SNAKE_CASE_ : str = vae_state_dict['post_quant_conv.weight']
SCREAMING_SNAKE_CASE_ : str = vae_state_dict['post_quant_conv.bias']
# Retrieves the keys for the encoder down blocks only
SCREAMING_SNAKE_CASE_ : List[Any] = len({'.'.join(layer.split('.' )[:3] ) for layer in vae_state_dict if 'encoder.down' in layer} )
SCREAMING_SNAKE_CASE_ : Optional[Any] = {
layer_id: [key for key in vae_state_dict if f"down.{layer_id}" in key] for layer_id in range(a )
}
# Retrieves the keys for the decoder up blocks only
SCREAMING_SNAKE_CASE_ : Optional[Any] = len({'.'.join(layer.split('.' )[:3] ) for layer in vae_state_dict if 'decoder.up' in layer} )
SCREAMING_SNAKE_CASE_ : int = {
layer_id: [key for key in vae_state_dict if f"up.{layer_id}" in key] for layer_id in range(a )
}
for i in range(a ):
SCREAMING_SNAKE_CASE_ : int = [key for key in down_blocks[i] if f"down.{i}" in key and f"down.{i}.downsample" not in key]
if f"encoder.down.{i}.downsample.conv.weight" in vae_state_dict:
SCREAMING_SNAKE_CASE_ : List[str] = vae_state_dict.pop(
f"encoder.down.{i}.downsample.conv.weight" )
SCREAMING_SNAKE_CASE_ : Dict = vae_state_dict.pop(
f"encoder.down.{i}.downsample.conv.bias" )
SCREAMING_SNAKE_CASE_ : Optional[Any] = renew_vae_resnet_paths(a )
SCREAMING_SNAKE_CASE_ : Any = {'old': f"down.{i}.block", 'new': f"down_blocks.{i}.resnets"}
assign_to_checkpoint(a , a , a , additional_replacements=[meta_path] , config=a )
SCREAMING_SNAKE_CASE_ : Any = [key for key in vae_state_dict if 'encoder.mid.block' in key]
SCREAMING_SNAKE_CASE_ : List[str] = 2
for i in range(1 , num_mid_res_blocks + 1 ):
SCREAMING_SNAKE_CASE_ : str = [key for key in mid_resnets if f"encoder.mid.block_{i}" in key]
SCREAMING_SNAKE_CASE_ : Tuple = renew_vae_resnet_paths(a )
SCREAMING_SNAKE_CASE_ : List[str] = {'old': f"mid.block_{i}", 'new': f"mid_block.resnets.{i - 1}"}
assign_to_checkpoint(a , a , a , additional_replacements=[meta_path] , config=a )
SCREAMING_SNAKE_CASE_ : List[str] = [key for key in vae_state_dict if 'encoder.mid.attn' in key]
SCREAMING_SNAKE_CASE_ : List[Any] = renew_vae_attention_paths(a )
SCREAMING_SNAKE_CASE_ : Union[str, Any] = {'old': 'mid.attn_1', 'new': 'mid_block.attentions.0'}
assign_to_checkpoint(a , a , a , additional_replacements=[meta_path] , config=a )
conv_attn_to_linear(a )
for i in range(a ):
SCREAMING_SNAKE_CASE_ : Any = num_up_blocks - 1 - i
SCREAMING_SNAKE_CASE_ : List[Any] = [
key for key in up_blocks[block_id] if f"up.{block_id}" in key and f"up.{block_id}.upsample" not in key
]
if f"decoder.up.{block_id}.upsample.conv.weight" in vae_state_dict:
SCREAMING_SNAKE_CASE_ : List[Any] = vae_state_dict[
f"decoder.up.{block_id}.upsample.conv.weight"
]
SCREAMING_SNAKE_CASE_ : Union[str, Any] = vae_state_dict[
f"decoder.up.{block_id}.upsample.conv.bias"
]
SCREAMING_SNAKE_CASE_ : int = renew_vae_resnet_paths(a )
SCREAMING_SNAKE_CASE_ : str = {'old': f"up.{block_id}.block", 'new': f"up_blocks.{i}.resnets"}
assign_to_checkpoint(a , a , a , additional_replacements=[meta_path] , config=a )
SCREAMING_SNAKE_CASE_ : Optional[int] = [key for key in vae_state_dict if 'decoder.mid.block' in key]
SCREAMING_SNAKE_CASE_ : Union[str, Any] = 2
for i in range(1 , num_mid_res_blocks + 1 ):
SCREAMING_SNAKE_CASE_ : Union[str, Any] = [key for key in mid_resnets if f"decoder.mid.block_{i}" in key]
SCREAMING_SNAKE_CASE_ : Any = renew_vae_resnet_paths(a )
SCREAMING_SNAKE_CASE_ : int = {'old': f"mid.block_{i}", 'new': f"mid_block.resnets.{i - 1}"}
assign_to_checkpoint(a , a , a , additional_replacements=[meta_path] , config=a )
SCREAMING_SNAKE_CASE_ : int = [key for key in vae_state_dict if 'decoder.mid.attn' in key]
SCREAMING_SNAKE_CASE_ : List[Any] = renew_vae_attention_paths(a )
SCREAMING_SNAKE_CASE_ : List[Any] = {'old': 'mid.attn_1', 'new': 'mid_block.attentions.0'}
assign_to_checkpoint(a , a , a , additional_replacements=[meta_path] , config=a )
conv_attn_to_linear(a )
return new_checkpoint
def A_ ( a , a , ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[Any] = requests.get(
' https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml' )
SCREAMING_SNAKE_CASE_ : str = io.BytesIO(r.content )
SCREAMING_SNAKE_CASE_ : Dict = OmegaConf.load(a )
SCREAMING_SNAKE_CASE_ : Union[str, Any] = 5_1_2
SCREAMING_SNAKE_CASE_ : Union[str, Any] = 'cuda' if torch.cuda.is_available() else 'cpu'
if checkpoint_path.endswith('safetensors' ):
from safetensors import safe_open
SCREAMING_SNAKE_CASE_ : Tuple = {}
with safe_open(a , framework='pt' , device='cpu' ) as f:
for key in f.keys():
SCREAMING_SNAKE_CASE_ : int = f.get_tensor(a )
else:
SCREAMING_SNAKE_CASE_ : List[str] = torch.load(a , map_location=a )['state_dict']
# Convert the VAE model.
SCREAMING_SNAKE_CASE_ : List[Any] = create_vae_diffusers_config(a , image_size=a )
SCREAMING_SNAKE_CASE_ : int = custom_convert_ldm_vae_checkpoint(a , a )
SCREAMING_SNAKE_CASE_ : List[str] = AutoencoderKL(**a )
vae.load_state_dict(a )
vae.save_pretrained(a )
if __name__ == "__main__":
lowerCAmelCase : Any = argparse.ArgumentParser()
parser.add_argument('--vae_pt_path', default=None, type=str, required=True, help='Path to the VAE.pt to convert.')
parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the VAE.pt to convert.')
lowerCAmelCase : Dict = parser.parse_args()
vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)
| 511
| 0
|
"""simple docstring"""
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
__a : List[str] = logging.get_logger(__name__)
__a : int = {
'vocab_file': 'vocab.json',
'merges_file': 'merges.txt',
'tokenizer_config_file': 'tokenizer_config.json',
}
__a : List[Any] = {
'vocab_file': {
'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'
},
'merges_file': {
'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'
},
'tokenizer_config_file': {
'facebook/blenderbot_small-90M': (
'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'
)
},
}
__a : Union[str, Any] = {
'facebook/blenderbot_small-90M': 512,
}
class _SCREAMING_SNAKE_CASE ( __snake_case ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE =VOCAB_FILES_NAMES
_SCREAMING_SNAKE_CASE =PRETRAINED_VOCAB_FILES_MAP
_SCREAMING_SNAKE_CASE =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_SCREAMING_SNAKE_CASE =BlenderbotSmallTokenizer
def __init__( self: List[Any] , __A: List[Any]=None , __A: List[str]=None , __A: Tuple="<|endoftext|>" , __A: str="<|endoftext|>" , __A: List[str]="<|endoftext|>" , __A: Tuple=False , __A: Union[str, Any]=True , **__A: Optional[Any] , ):
'''simple docstring'''
super().__init__(
ByteLevelBPETokenizer(
vocab=__A , merges=__A , add_prefix_space=__A , trim_offsets=__A , ) , bos_token=__A , eos_token=__A , unk_token=__A , **__A , )
a__ = add_prefix_space
def lowercase ( self: Tuple , __A: str , __A: Union[str, Any]=None ):
'''simple docstring'''
a__ = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def lowercase ( self: Any , __A: List[int] , __A: Optional[List[int]] = None ):
'''simple docstring'''
a__ = [self.sep_token_id]
a__ = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
| 200
|
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__a : str = logging.get_logger(__name__)
__a : Dict = {
'xlm-mlm-en-2048': 'https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json',
'xlm-mlm-ende-1024': 'https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json',
'xlm-mlm-enfr-1024': 'https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json',
'xlm-mlm-enro-1024': 'https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json',
'xlm-mlm-tlm-xnli15-1024': 'https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json',
'xlm-mlm-xnli15-1024': 'https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json',
'xlm-clm-enfr-1024': 'https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json',
'xlm-clm-ende-1024': 'https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json',
'xlm-mlm-17-1280': 'https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json',
'xlm-mlm-100-1280': 'https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json',
}
class _SCREAMING_SNAKE_CASE ( __snake_case ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE ='xlm'
_SCREAMING_SNAKE_CASE ={
'hidden_size': 'emb_dim',
'num_attention_heads': 'n_heads',
'num_hidden_layers': 'n_layers',
'n_words': 'vocab_size', # For backward compatibility
}
def __init__( self: Optional[Any] , __A: str=30145 , __A: Union[str, Any]=2048 , __A: Union[str, Any]=12 , __A: List[Any]=16 , __A: List[Any]=0.1 , __A: int=0.1 , __A: str=True , __A: int=False , __A: Optional[int]=False , __A: Union[str, Any]=False , __A: List[Any]=1 , __A: Dict=True , __A: List[Any]=512 , __A: Optional[int]=2048**-0.5 , __A: Tuple=1e-12 , __A: Optional[int]=0.0_2 , __A: Optional[int]=0 , __A: Union[str, Any]=1 , __A: List[Any]=2 , __A: Union[str, Any]=3 , __A: Tuple=5 , __A: Tuple=True , __A: Dict="first" , __A: Optional[int]=True , __A: int=None , __A: Optional[Any]=True , __A: Dict=0.1 , __A: Tuple=5 , __A: Union[str, Any]=5 , __A: Tuple=0 , __A: List[str]=0 , __A: int=2 , __A: Optional[int]=0 , **__A: Union[str, Any] , ):
'''simple docstring'''
a__ = vocab_size
a__ = emb_dim
a__ = n_layers
a__ = n_heads
a__ = dropout
a__ = attention_dropout
a__ = gelu_activation
a__ = sinusoidal_embeddings
a__ = causal
a__ = asm
a__ = n_langs
a__ = use_lang_emb
a__ = layer_norm_eps
a__ = bos_index
a__ = eos_index
a__ = pad_index
a__ = unk_index
a__ = mask_index
a__ = is_encoder
a__ = max_position_embeddings
a__ = embed_init_std
a__ = init_std
a__ = summary_type
a__ = summary_use_proj
a__ = summary_activation
a__ = summary_proj_to_labels
a__ = summary_first_dropout
a__ = start_n_top
a__ = end_n_top
a__ = mask_token_id
a__ = lang_id
if "n_words" in kwargs:
a__ = kwargs['''n_words''']
super().__init__(pad_token_id=__A , bos_token_id=__A , **__A )
class _SCREAMING_SNAKE_CASE ( __snake_case ):
"""simple docstring"""
@property
def lowercase ( self: int ):
'''simple docstring'''
if self.task == "multiple-choice":
a__ = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
a__ = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
('''token_type_ids''', dynamic_axis),
] )
| 200
| 1
|
'''simple docstring'''
import os
import sys
import unittest
UpperCamelCase_ = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get_model_to_test_mapping,
get_model_to_tester_mapping,
get_test_to_tester_mapping,
)
UpperCamelCase_ = os.path.join("""tests""", """models""", """bert""", """test_modeling_bert.py""")
UpperCamelCase_ = os.path.join("""tests""", """models""", """blip""", """test_modeling_blip.py""")
class a_ (unittest.TestCase ):
def __UpperCamelCase ( self ):
_lowerCAmelCase : str = get_test_to_tester_mapping(snake_case_ )
_lowerCAmelCase : str = get_test_to_tester_mapping(snake_case_ )
_lowerCAmelCase : Optional[Any] = {"""BertModelTest""": """BertModelTester"""}
_lowerCAmelCase : Union[str, Any] = {
"""BlipModelTest""": """BlipModelTester""",
"""BlipTextImageModelTest""": """BlipTextImageModelsModelTester""",
"""BlipTextModelTest""": """BlipTextModelTester""",
"""BlipTextRetrievalModelTest""": """BlipTextRetrievalModelTester""",
"""BlipVQAModelTest""": """BlipVQAModelTester""",
"""BlipVisionModelTest""": """BlipVisionModelTester""",
}
self.assertEqual(get_test_info.to_json(snake_case_ ) , snake_case_ )
self.assertEqual(get_test_info.to_json(snake_case_ ) , snake_case_ )
def __UpperCamelCase ( self ):
_lowerCAmelCase : Optional[Any] = get_model_to_test_mapping(snake_case_ )
_lowerCAmelCase : List[str] = get_model_to_test_mapping(snake_case_ )
_lowerCAmelCase : Union[str, Any] = {
"""BertForMaskedLM""": ["""BertModelTest"""],
"""BertForMultipleChoice""": ["""BertModelTest"""],
"""BertForNextSentencePrediction""": ["""BertModelTest"""],
"""BertForPreTraining""": ["""BertModelTest"""],
"""BertForQuestionAnswering""": ["""BertModelTest"""],
"""BertForSequenceClassification""": ["""BertModelTest"""],
"""BertForTokenClassification""": ["""BertModelTest"""],
"""BertLMHeadModel""": ["""BertModelTest"""],
"""BertModel""": ["""BertModelTest"""],
}
_lowerCAmelCase : str = {
"""BlipForConditionalGeneration""": ["""BlipTextImageModelTest"""],
"""BlipForImageTextRetrieval""": ["""BlipTextRetrievalModelTest"""],
"""BlipForQuestionAnswering""": ["""BlipVQAModelTest"""],
"""BlipModel""": ["""BlipModelTest"""],
"""BlipTextModel""": ["""BlipTextModelTest"""],
"""BlipVisionModel""": ["""BlipVisionModelTest"""],
}
self.assertEqual(get_test_info.to_json(snake_case_ ) , snake_case_ )
self.assertEqual(get_test_info.to_json(snake_case_ ) , snake_case_ )
def __UpperCamelCase ( self ):
_lowerCAmelCase : Any = get_model_to_tester_mapping(snake_case_ )
_lowerCAmelCase : str = get_model_to_tester_mapping(snake_case_ )
_lowerCAmelCase : List[str] = {
"""BertForMaskedLM""": ["""BertModelTester"""],
"""BertForMultipleChoice""": ["""BertModelTester"""],
"""BertForNextSentencePrediction""": ["""BertModelTester"""],
"""BertForPreTraining""": ["""BertModelTester"""],
"""BertForQuestionAnswering""": ["""BertModelTester"""],
"""BertForSequenceClassification""": ["""BertModelTester"""],
"""BertForTokenClassification""": ["""BertModelTester"""],
"""BertLMHeadModel""": ["""BertModelTester"""],
"""BertModel""": ["""BertModelTester"""],
}
_lowerCAmelCase : int = {
"""BlipForConditionalGeneration""": ["""BlipTextImageModelsModelTester"""],
"""BlipForImageTextRetrieval""": ["""BlipTextRetrievalModelTester"""],
"""BlipForQuestionAnswering""": ["""BlipVQAModelTester"""],
"""BlipModel""": ["""BlipModelTester"""],
"""BlipTextModel""": ["""BlipTextModelTester"""],
"""BlipVisionModel""": ["""BlipVisionModelTester"""],
}
self.assertEqual(get_test_info.to_json(snake_case_ ) , snake_case_ )
self.assertEqual(get_test_info.to_json(snake_case_ ) , snake_case_ )
| 384
|
'''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
UpperCamelCase_ = [
"""good first issue""",
"""good second issue""",
"""good difficult issue""",
"""enhancement""",
"""new pipeline/model""",
"""new scheduler""",
"""wip""",
]
def _UpperCAmelCase ( ) -> List[Any]:
_lowerCAmelCase : Dict = Github(os.environ["""GITHUB_TOKEN"""] )
_lowerCAmelCase : Any = g.get_repo("""huggingface/diffusers""" )
_lowerCAmelCase : Tuple = repo.get_issues(state="""open""" )
for issue in open_issues:
_lowerCAmelCase : Union[str, Any] = sorted(issue.get_comments() , key=lambda _lowerCamelCase : i.created_at , reverse=_lowerCamelCase )
_lowerCAmelCase : List[Any] = comments[0] if len(_lowerCamelCase ) > 0 else None
if (
last_comment is not None
and last_comment.user.login == "github-actions[bot]"
and (dt.utcnow() - issue.updated_at).days > 7
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Closes the issue after 7 days of inactivity since the Stalebot notification.
issue.edit(state="""closed""" )
elif (
"stale" in issue.get_labels()
and last_comment is not None
and last_comment.user.login != "github-actions[bot]"
):
# Opens the issue if someone other than Stalebot commented.
issue.edit(state="""open""" )
issue.remove_from_labels("""stale""" )
elif (
(dt.utcnow() - issue.updated_at).days > 23
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Post a Stalebot notification after 23 days of inactivity.
issue.create_comment(
"""This issue has been automatically marked as stale because it has not had """
"""recent activity. If you think this still needs to be addressed """
"""please comment on this thread.\n\nPlease note that issues that do not follow the """
"""[contributing guidelines](https://github.com/huggingface/diffusers/blob/main/CONTRIBUTING.md) """
"""are likely to be ignored.""" )
issue.add_to_labels("""stale""" )
if __name__ == "__main__":
main()
| 384
| 1
|
"""simple docstring"""
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
a_ = datasets.utils.logging.get_logger(__name__)
if TYPE_CHECKING:
import pyspark
@dataclass
class snake_case ( datasets.BuilderConfig):
__UpperCamelCase = None
def a__ ( __lowercase , __lowercase , ) -> str:
import pyspark
def generate_fn():
_A = df.select("*" , pyspark.sql.functions.spark_partition_id().alias("part_id" ) )
for partition_id in partition_order:
_A = df_with_partition_id.select("*" ).where(f"""part_id = {partition_id}""" ).drop("part_id" )
_A = partition_df.collect()
_A = 0
for row in rows:
yield f"""{partition_id}_{row_id}""", row.asDict()
row_id += 1
return generate_fn
class snake_case ( _BaseExamplesIterable):
def __init__( self : int , a__ : "pyspark.sql.DataFrame" , a__ : Optional[int]=None , ) -> str:
'''simple docstring'''
_A = df
_A = partition_order or range(self.df.rdd.getNumPartitions() )
_A = _generate_iterable_examples(self.df , self.partition_order )
def __iter__( self : List[Any] ) -> Union[str, Any]:
'''simple docstring'''
yield from self.generate_examples_fn()
def a_ ( self : Optional[int] , a__ : np.random.Generator ) -> int:
'''simple docstring'''
_A = list(range(self.df.rdd.getNumPartitions() ) )
generator.shuffle(lowercase_ )
return SparkExamplesIterable(self.df , partition_order=lowercase_ )
def a_ ( self : Optional[int] , a__ : int , a__ : int ) -> int:
'''simple docstring'''
_A = self.split_shard_indices_by_worker(lowercase_ , lowercase_ )
return SparkExamplesIterable(self.df , partition_order=lowercase_ )
@property
def a_ ( self : List[str] ) -> Dict:
'''simple docstring'''
return len(self.partition_order )
class snake_case ( datasets.DatasetBuilder):
__UpperCamelCase = SparkConfig
def __init__( self : Tuple , a__ : "pyspark.sql.DataFrame" , a__ : str = None , a__ : str = None , **a__ : str , ) -> str:
'''simple docstring'''
import pyspark
_A = pyspark.sql.SparkSession.builder.getOrCreate()
_A = df
_A = working_dir
super().__init__(
cache_dir=lowercase_ , config_name=str(self.df.semanticHash() ) , **lowercase_ , )
def a_ ( self : str ) -> Any:
'''simple docstring'''
def create_cache_and_write_probe(a__ : str ):
# 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=lowercase_ )
_A = 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(lowercase_ , "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:
_A = (
self._spark.sparkContext.parallelize(range(1 ) , 1 ).mapPartitions(lowercase_ ).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 : Union[str, Any] ) -> Dict:
'''simple docstring'''
return datasets.DatasetInfo(features=self.config.features )
def a_ ( self : List[Any] , a__ : datasets.download.download_manager.DownloadManager ) -> Union[str, Any]:
'''simple docstring'''
return [datasets.SplitGenerator(name=datasets.Split.TRAIN )]
def a_ ( self : List[str] , a__ : Union[str, Any] ) -> str:
'''simple docstring'''
import pyspark
def get_arrow_batch_size(a__ : Any ):
for batch in it:
yield pa.RecordBatch.from_pydict({"batch_bytes": [batch.nbytes]} )
_A = self.df.count()
_A = df_num_rows if df_num_rows <= 1_00 else 1_00
# Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
_A = (
self.df.limit(lowercase_ )
.repartition(1 )
.mapInArrow(lowercase_ , "batch_bytes: long" )
.agg(pyspark.sql.functions.sum("batch_bytes" ).alias("sample_bytes" ) )
.collect()[0]
.sample_bytes
/ sample_num_rows
)
_A = approx_bytes_per_row * df_num_rows
if approx_total_size > max_shard_size:
# Make sure there is at least one row per partition.
_A = min(lowercase_ , int(approx_total_size / max_shard_size ) )
_A = self.df.repartition(lowercase_ )
def a_ ( self : Any , a__ : str , a__ : str , a__ : int , ) -> Any:
'''simple docstring'''
import pyspark
_A = ParquetWriter if file_format == """parquet""" else ArrowWriter
_A = os.path.join(self._working_dir , os.path.basename(lowercase_ ) ) if self._working_dir else fpath
_A = 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.
_A = self.config.features
_A = self._writer_batch_size
_A = self._fs.storage_options
def write_arrow(a__ : str ):
# Within the same SparkContext, no two task attempts will share the same attempt ID.
_A = pyspark.TaskContext().taskAttemptId()
_A = next(lowercase_ , lowercase_ )
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"] , )
_A = 0
_A = writer_class(
features=lowercase_ , path=working_fpath.replace("SSSSS" , F"""{shard_id:05d}""" ).replace("TTTTT" , F"""{task_id:05d}""" ) , writer_batch_size=lowercase_ , storage_options=lowercase_ , embed_local_files=lowercase_ , )
_A = pa.Table.from_batches([first_batch] )
writer.write_table(lowercase_ )
for batch in it:
if max_shard_size is not None and writer._num_bytes >= max_shard_size:
_A = 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
_A = writer_class(
features=writer._features , path=working_fpath.replace("SSSSS" , F"""{shard_id:05d}""" ).replace("TTTTT" , F"""{task_id:05d}""" ) , writer_batch_size=lowercase_ , storage_options=lowercase_ , embed_local_files=lowercase_ , )
_A = pa.Table.from_batches([batch] )
writer.write_table(lowercase_ )
if writer._num_bytes > 0:
_A = 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(lowercase_ ) ):
_A = os.path.join(os.path.dirname(lowercase_ ) , os.path.basename(lowercase_ ) )
shutil.move(lowercase_ , lowercase_ )
_A = (
self.df.mapInArrow(lowercase_ , "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 : Dict , a__ : "datasets.SplitGenerator" , a__ : str = "arrow" , a__ : Optional[Union[str, int]] = None , a__ : Optional[int] = None , **a__ : List[str] , ) -> Union[str, Any]:
'''simple docstring'''
self._validate_cache_dir()
_A = convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE )
self._repartition_df_if_needed(lowercase_ )
_A = not is_remote_filesystem(self._fs )
_A = os.path.join if is_local else posixpath.join
_A = """-TTTTT-SSSSS-of-NNNNN"""
_A = F"""{self.name}-{split_generator.name}{SUFFIX}.{file_format}"""
_A = path_join(self._output_dir , lowercase_ )
_A = 0
_A = 0
_A = 0
_A = []
_A = []
for task_id, content in self._prepare_split_single(lowercase_ , lowercase_ , lowercase_ ):
(
_A
) = 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(lowercase_ )
_A = total_num_examples
_A = total_num_bytes
# should rename everything at the end
logger.debug(F"""Renaming {total_shards} shards.""" )
if total_shards > 1:
_A = 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.
_A = self._fs
# use the -SSSSS-of-NNNNN pattern
def _rename_shard(
a__ : int , a__ : int , a__ : int , ):
rename(
lowercase_ , 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}""" ) , )
_A = []
_A = 0
for i in range(len(lowercase_ ) ):
_A = task_id_and_num_shards[i]
for shard_id in range(lowercase_ ):
args.append([task_id, shard_id, global_shard_id] )
global_shard_id += 1
self._spark.sparkContext.parallelize(lowercase_ , len(lowercase_ ) ).map(lambda a__ : _rename_shard(*lowercase_ ) ).collect()
else:
# don't use any pattern
_A = 0
_A = task_id_and_num_shards[0][0]
self._rename(
fpath.replace("SSSSS" , F"""{shard_id:05d}""" ).replace("TTTTT" , F"""{task_id:05d}""" ) , fpath.replace(lowercase_ , "" ) , )
def a_ ( self : Union[str, Any] , a__ : "datasets.SplitGenerator" , ) -> Any:
'''simple docstring'''
return SparkExamplesIterable(self.df )
| 708
|
"""simple docstring"""
from __future__ import annotations
def a__ ( __lowercase , __lowercase ) -> float:
_A = sorted(numsa + numsa )
_A , _A = divmod(len(__lowercase ) , 2 )
if mod == 1:
return all_numbers[div]
else:
return (all_numbers[div] + all_numbers[div - 1]) / 2
if __name__ == "__main__":
import doctest
doctest.testmod()
a_ = [float(x) for x in input("Enter the elements of first array: ").split()]
a_ = [float(x) for x in input("Enter the elements of second array: ").split()]
print(f'''The median of two arrays is: {median_of_two_arrays(array_a, array_a)}''')
| 621
| 0
|
def lowercase ( __A : List[Any] ) -> int:
'''simple docstring'''
if not isinstance(A__ , A__ ):
snake_case : Optional[Any] = f"""Input value of [number={number}] must be an integer"""
raise TypeError(A__ )
if number < 1:
snake_case : str = f"""Input value of [number={number}] must be > 0"""
raise ValueError(A__ )
snake_case : Optional[Any] = 1
for i in range(1 , A__ ):
current_number *= 4 * i - 2
current_number //= i + 1
return current_number
if __name__ == "__main__":
import doctest
doctest.testmod()
| 36
|
'''simple docstring'''
import os
lowercase : Tuple = {'I': 1, 'V': 5, 'X': 1_0, 'L': 5_0, 'C': 1_0_0, 'D': 5_0_0, 'M': 1_0_0_0}
def __a ( A__ ) -> int:
lowerCAmelCase = 0
lowerCAmelCase = 0
while index < len(A__ ) - 1:
lowerCAmelCase = SYMBOLS[numerals[index]]
lowerCAmelCase = SYMBOLS[numerals[index + 1]]
if current_value < next_value:
total_value -= current_value
else:
total_value += current_value
index += 1
total_value += SYMBOLS[numerals[index]]
return total_value
def __a ( A__ ) -> str:
lowerCAmelCase = ""
lowerCAmelCase = num // 1000
numerals += m_count * "M"
num %= 1000
lowerCAmelCase = num // 100
if c_count == 9:
numerals += "CM"
c_count -= 9
elif c_count == 4:
numerals += "CD"
c_count -= 4
if c_count >= 5:
numerals += "D"
c_count -= 5
numerals += c_count * "C"
num %= 100
lowerCAmelCase = num // 10
if x_count == 9:
numerals += "XC"
x_count -= 9
elif x_count == 4:
numerals += "XL"
x_count -= 4
if x_count >= 5:
numerals += "L"
x_count -= 5
numerals += x_count * "X"
num %= 10
if num == 9:
numerals += "IX"
num -= 9
elif num == 4:
numerals += "IV"
num -= 4
if num >= 5:
numerals += "V"
num -= 5
numerals += num * "I"
return numerals
def __a ( A__ = "/p089_roman.txt" ) -> int:
lowerCAmelCase = 0
with open(os.path.dirname(A__ ) + roman_numerals_filename ) as filea:
lowerCAmelCase = filea.readlines()
for line in lines:
lowerCAmelCase = line.strip()
lowerCAmelCase = parse_roman_numerals(A__ )
lowerCAmelCase = generate_roman_numerals(A__ )
savings += len(A__ ) - len(A__ )
return savings
if __name__ == "__main__":
print(f"{solution() = }")
| 649
| 0
|
"""simple docstring"""
import logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
A : int = logging.getLogger(__name__)
@dataclass
class _UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
__UpperCAmelCase : Optional[float] =field(
default=0.0 ,metadata={"""help""": """The label smoothing epsilon to apply (if not zero)."""} )
__UpperCAmelCase : bool =field(default=lowerCAmelCase__ ,metadata={"""help""": """Whether to SortishSamler or not."""} )
__UpperCAmelCase : bool =field(
default=lowerCAmelCase__ ,metadata={"""help""": """Whether to use generate to calculate generative metrics (ROUGE, BLEU)."""} )
__UpperCAmelCase : bool =field(default=lowerCAmelCase__ ,metadata={"""help""": """whether to use adafactor"""} )
__UpperCAmelCase : Optional[float] =field(
default=lowerCAmelCase__ ,metadata={"""help""": """Encoder layer dropout probability. Goes into model.config."""} )
__UpperCAmelCase : Optional[float] =field(
default=lowerCAmelCase__ ,metadata={"""help""": """Decoder layer dropout probability. Goes into model.config."""} )
__UpperCAmelCase : Optional[float] =field(default=lowerCAmelCase__ ,metadata={"""help""": """Dropout probability. Goes into model.config."""} )
__UpperCAmelCase : Optional[float] =field(
default=lowerCAmelCase__ ,metadata={"""help""": """Attention dropout probability. Goes into model.config."""} )
__UpperCAmelCase : Optional[str] =field(
default="""linear""" ,metadata={"""help""": F'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys() )}'''} ,)
| 282
|
"""simple docstring"""
import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel
from ...models.attention import BasicTransformerBlock
from ...utils import logging
A : Dict = logging.get_logger(__name__) # pylint: disable=invalid-name
class _UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
def __init__( self , __a , __a=7_68 ):
super().__init__(__a )
__lowerCAmelCase = proj_size
__lowerCAmelCase = CLIPVisionModel(__a )
__lowerCAmelCase = PaintByExampleMapper(__a )
__lowerCAmelCase = nn.LayerNorm(config.hidden_size )
__lowerCAmelCase = nn.Linear(config.hidden_size , self.proj_size )
# uncondition for scaling
__lowerCAmelCase = nn.Parameter(torch.randn((1, 1, self.proj_size) ) )
def snake_case ( self , __a , __a=False ):
__lowerCAmelCase = self.model(pixel_values=__a )
__lowerCAmelCase = clip_output.pooler_output
__lowerCAmelCase = self.mapper(latent_states[:, None] )
__lowerCAmelCase = self.final_layer_norm(__a )
__lowerCAmelCase = self.proj_out(__a )
if return_uncond_vector:
return latent_states, self.uncond_vector
return latent_states
class _UpperCamelCase ( nn.Module ):
'''simple docstring'''
def __init__( self , __a ):
super().__init__()
__lowerCAmelCase = (config.num_hidden_layers + 1) // 5
__lowerCAmelCase = config.hidden_size
__lowerCAmelCase = 1
__lowerCAmelCase = nn.ModuleList(
[
BasicTransformerBlock(__a , __a , __a , activation_fn="gelu" , attention_bias=__a )
for _ in range(__a )
] )
def snake_case ( self , __a ):
for block in self.blocks:
__lowerCAmelCase = block(__a )
return hidden_states
| 282
| 1
|
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
snake_case__ : Tuple = logging.get_logger(__name__)
def __lowerCamelCase ( A__ : Union[str, Any] ) -> Tuple:
lowerCamelCase_ : int = OrderedDict()
for key, value in state_dict.items():
if key.startswith("""module.encoder""" ):
lowerCamelCase_ : Tuple = key.replace("""module.encoder""" , """glpn.encoder""" )
if key.startswith("""module.decoder""" ):
lowerCamelCase_ : str = key.replace("""module.decoder""" , """decoder.stages""" )
if "patch_embed" in key:
# replace for example patch_embed1 by patch_embeddings.0
lowerCamelCase_ : List[Any] = key[key.find("""patch_embed""" ) + len("""patch_embed""" )]
lowerCamelCase_ : List[str] = key.replace(f'''patch_embed{idx}''' , f'''patch_embeddings.{int(A__ )-1}''' )
if "norm" in key:
lowerCamelCase_ : str = key.replace("""norm""" , """layer_norm""" )
if "glpn.encoder.layer_norm" in key:
# replace for example layer_norm1 by layer_norm.0
lowerCamelCase_ : List[Any] = key[key.find("""glpn.encoder.layer_norm""" ) + len("""glpn.encoder.layer_norm""" )]
lowerCamelCase_ : Any = key.replace(f'''layer_norm{idx}''' , f'''layer_norm.{int(A__ )-1}''' )
if "layer_norm1" in key:
lowerCamelCase_ : Dict = key.replace("""layer_norm1""" , """layer_norm_1""" )
if "layer_norm2" in key:
lowerCamelCase_ : Optional[Any] = key.replace("""layer_norm2""" , """layer_norm_2""" )
if "block" in key:
# replace for example block1 by block.0
lowerCamelCase_ : int = key[key.find("""block""" ) + len("""block""" )]
lowerCamelCase_ : List[Any] = key.replace(f'''block{idx}''' , f'''block.{int(A__ )-1}''' )
if "attn.q" in key:
lowerCamelCase_ : Union[str, Any] = key.replace("""attn.q""" , """attention.self.query""" )
if "attn.proj" in key:
lowerCamelCase_ : Optional[Any] = key.replace("""attn.proj""" , """attention.output.dense""" )
if "attn" in key:
lowerCamelCase_ : str = key.replace("""attn""" , """attention.self""" )
if "fc1" in key:
lowerCamelCase_ : List[Any] = key.replace("""fc1""" , """dense1""" )
if "fc2" in key:
lowerCamelCase_ : List[str] = key.replace("""fc2""" , """dense2""" )
if "linear_pred" in key:
lowerCamelCase_ : Dict = key.replace("""linear_pred""" , """classifier""" )
if "linear_fuse" in key:
lowerCamelCase_ : int = key.replace("""linear_fuse.conv""" , """linear_fuse""" )
lowerCamelCase_ : str = key.replace("""linear_fuse.bn""" , """batch_norm""" )
if "linear_c" in key:
# replace for example linear_c4 by linear_c.3
lowerCamelCase_ : List[Any] = key[key.find("""linear_c""" ) + len("""linear_c""" )]
lowerCamelCase_ : Dict = key.replace(f'''linear_c{idx}''' , f'''linear_c.{int(A__ )-1}''' )
if "bot_conv" in key:
lowerCamelCase_ : Optional[Any] = key.replace("""bot_conv""" , """0.convolution""" )
if "skip_conv1" in key:
lowerCamelCase_ : List[Any] = key.replace("""skip_conv1""" , """1.convolution""" )
if "skip_conv2" in key:
lowerCamelCase_ : Union[str, Any] = key.replace("""skip_conv2""" , """2.convolution""" )
if "fusion1" in key:
lowerCamelCase_ : str = key.replace("""fusion1""" , """1.fusion""" )
if "fusion2" in key:
lowerCamelCase_ : Any = key.replace("""fusion2""" , """2.fusion""" )
if "fusion3" in key:
lowerCamelCase_ : Dict = key.replace("""fusion3""" , """3.fusion""" )
if "fusion" in key and "conv" in key:
lowerCamelCase_ : Optional[Any] = key.replace("""conv""" , """convolutional_layer""" )
if key.startswith("""module.last_layer_depth""" ):
lowerCamelCase_ : List[Any] = key.replace("""module.last_layer_depth""" , """head.head""" )
lowerCamelCase_ : Dict = value
return new_state_dict
def __lowerCamelCase ( A__ : Optional[Any] , A__ : List[Any] ) -> List[str]:
# for each of the encoder blocks:
for i in range(config.num_encoder_blocks ):
for j in range(config.depths[i] ):
# read in weights + bias of keys and values (which is a single matrix in the original implementation)
lowerCamelCase_ : Tuple = state_dict.pop(f'''glpn.encoder.block.{i}.{j}.attention.self.kv.weight''' )
lowerCamelCase_ : Optional[int] = state_dict.pop(f'''glpn.encoder.block.{i}.{j}.attention.self.kv.bias''' )
# next, add keys and values (in that order) to the state dict
lowerCamelCase_ : Any = kv_weight[
: config.hidden_sizes[i], :
]
lowerCamelCase_ : Optional[Any] = kv_bias[: config.hidden_sizes[i]]
lowerCamelCase_ : Optional[Any] = kv_weight[
config.hidden_sizes[i] :, :
]
lowerCamelCase_ : Dict = kv_bias[config.hidden_sizes[i] :]
def __lowerCamelCase ( ) -> Dict:
lowerCamelCase_ : Optional[Any] = """http://images.cocodataset.org/val2017/000000039769.jpg"""
lowerCamelCase_ : List[str] = Image.open(requests.get(A__ , stream=A__ ).raw )
return image
@torch.no_grad()
def __lowerCamelCase ( A__ : Union[str, Any] , A__ : Optional[int] , A__ : int=False , A__ : List[Any]=None ) -> str:
lowerCamelCase_ : Any = GLPNConfig(hidden_sizes=[64, 128, 320, 512] , decoder_hidden_size=64 , depths=[3, 8, 27, 3] )
# load image processor (only resize + rescale)
lowerCamelCase_ : str = GLPNImageProcessor()
# prepare image
lowerCamelCase_ : List[str] = prepare_img()
lowerCamelCase_ : List[Any] = image_processor(images=A__ , return_tensors="""pt""" ).pixel_values
logger.info("""Converting model...""" )
# load original state dict
lowerCamelCase_ : Optional[int] = torch.load(A__ , map_location=torch.device("""cpu""" ) )
# rename keys
lowerCamelCase_ : List[Any] = rename_keys(A__ )
# key and value matrices need special treatment
read_in_k_v(A__ , A__ )
# create HuggingFace model and load state dict
lowerCamelCase_ : int = GLPNForDepthEstimation(A__ )
model.load_state_dict(A__ )
model.eval()
# forward pass
lowerCamelCase_ : Any = model(A__ )
lowerCamelCase_ : List[str] = outputs.predicted_depth
# verify output
if model_name is not None:
if "nyu" in model_name:
lowerCamelCase_ : Union[str, Any] = torch.tensor(
[[4.4147, 4.0873, 4.0673], [3.7890, 3.2881, 3.1525], [3.7674, 3.5423, 3.4913]] )
elif "kitti" in model_name:
lowerCamelCase_ : Optional[int] = torch.tensor(
[[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]] )
else:
raise ValueError(f'''Unknown model name: {model_name}''' )
lowerCamelCase_ : Tuple = torch.Size([1, 480, 640] )
assert predicted_depth.shape == expected_shape
assert torch.allclose(predicted_depth[0, :3, :3] , A__ , atol=1e-4 )
print("""Looks ok!""" )
# finally, push to hub if required
if push_to_hub:
logger.info("""Pushing model and image processor to the hub...""" )
model.push_to_hub(
repo_path_or_name=Path(A__ , A__ ) , organization="""nielsr""" , commit_message="""Add model""" , use_temp_dir=A__ , )
image_processor.push_to_hub(
repo_path_or_name=Path(A__ , A__ ) , organization="""nielsr""" , commit_message="""Add image processor""" , use_temp_dir=A__ , )
if __name__ == "__main__":
snake_case__ : str = argparse.ArgumentParser()
parser.add_argument(
'--checkpoint_path',
default=None,
type=str,
help='Path to the original PyTorch checkpoint (.pth file).',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
)
parser.add_argument(
'--push_to_hub', action='store_true', help='Whether to upload the model to the HuggingFace hub.'
)
parser.add_argument(
'--model_name',
default='glpn-kitti',
type=str,
help='Name of the model in case you\'re pushing to the hub.',
)
snake_case__ : Any = parser.parse_args()
convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)
| 278
|
snake_case__ : List[Any] = [
999,
800,
799,
600,
599,
500,
400,
399,
377,
355,
333,
311,
288,
266,
244,
222,
200,
199,
177,
155,
133,
111,
88,
66,
44,
22,
0,
]
snake_case__ : Tuple = [
999,
976,
952,
928,
905,
882,
858,
857,
810,
762,
715,
714,
572,
429,
428,
286,
285,
238,
190,
143,
142,
118,
95,
71,
47,
24,
0,
]
snake_case__ : List[Any] = [
999,
988,
977,
966,
955,
944,
933,
922,
911,
900,
899,
879,
859,
840,
820,
800,
799,
766,
733,
700,
699,
650,
600,
599,
500,
499,
400,
399,
350,
300,
299,
266,
233,
200,
199,
179,
159,
140,
120,
100,
99,
88,
77,
66,
55,
44,
33,
22,
11,
0,
]
snake_case__ : List[str] = [
999,
995,
992,
989,
985,
981,
978,
975,
971,
967,
964,
961,
957,
956,
951,
947,
942,
937,
933,
928,
923,
919,
914,
913,
908,
903,
897,
892,
887,
881,
876,
871,
870,
864,
858,
852,
846,
840,
834,
828,
827,
820,
813,
806,
799,
792,
785,
784,
777,
770,
763,
756,
749,
742,
741,
733,
724,
716,
707,
699,
698,
688,
677,
666,
656,
655,
645,
634,
623,
613,
612,
598,
584,
570,
569,
555,
541,
527,
526,
505,
484,
483,
462,
440,
439,
396,
395,
352,
351,
308,
307,
264,
263,
220,
219,
176,
132,
88,
44,
0,
]
snake_case__ : Any = [
999,
997,
995,
992,
990,
988,
986,
984,
981,
979,
977,
975,
972,
970,
968,
966,
964,
961,
959,
957,
956,
954,
951,
949,
946,
944,
941,
939,
936,
934,
931,
929,
926,
924,
921,
919,
916,
914,
913,
910,
907,
905,
902,
899,
896,
893,
891,
888,
885,
882,
879,
877,
874,
871,
870,
867,
864,
861,
858,
855,
852,
849,
846,
843,
840,
837,
834,
831,
828,
827,
824,
821,
817,
814,
811,
808,
804,
801,
798,
795,
791,
788,
785,
784,
780,
777,
774,
770,
766,
763,
760,
756,
752,
749,
746,
742,
741,
737,
733,
730,
726,
722,
718,
714,
710,
707,
703,
699,
698,
694,
690,
685,
681,
677,
673,
669,
664,
660,
656,
655,
650,
646,
641,
636,
632,
627,
622,
618,
613,
612,
607,
602,
596,
591,
586,
580,
575,
570,
569,
563,
557,
551,
545,
539,
533,
527,
526,
519,
512,
505,
498,
491,
484,
483,
474,
466,
457,
449,
440,
439,
428,
418,
407,
396,
395,
381,
366,
352,
351,
330,
308,
307,
286,
264,
263,
242,
220,
219,
176,
175,
132,
131,
88,
44,
0,
]
snake_case__ : int = [
999,
991,
982,
974,
966,
958,
950,
941,
933,
925,
916,
908,
900,
899,
874,
850,
825,
800,
799,
700,
600,
500,
400,
300,
200,
100,
0,
]
snake_case__ : Any = [
999,
992,
985,
978,
971,
964,
957,
949,
942,
935,
928,
921,
914,
907,
900,
899,
879,
859,
840,
820,
800,
799,
766,
733,
700,
699,
650,
600,
599,
500,
499,
400,
399,
300,
299,
200,
199,
100,
99,
0,
]
snake_case__ : Union[str, Any] = [
999,
996,
992,
989,
985,
982,
979,
975,
972,
968,
965,
961,
958,
955,
951,
948,
944,
941,
938,
934,
931,
927,
924,
920,
917,
914,
910,
907,
903,
900,
899,
891,
884,
876,
869,
861,
853,
846,
838,
830,
823,
815,
808,
800,
799,
788,
777,
766,
755,
744,
733,
722,
711,
700,
699,
688,
677,
666,
655,
644,
633,
622,
611,
600,
599,
585,
571,
557,
542,
528,
514,
500,
499,
485,
471,
457,
442,
428,
414,
400,
399,
379,
359,
340,
320,
300,
299,
279,
259,
240,
220,
200,
199,
166,
133,
100,
99,
66,
33,
0,
]
| 278
| 1
|
"""simple docstring"""
import argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logging.set_verbosity_info()
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {name: getattr(transformers, name + """Fast""") for name in SLOW_TO_FAST_CONVERTERS}
def A( snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
"""simple docstring"""
if tokenizer_name is not None and tokenizer_name not in TOKENIZER_CLASSES:
raise ValueError(F"""Unrecognized tokenizer name, should be one of {list(TOKENIZER_CLASSES.keys() )}.""" )
if tokenizer_name is None:
lowercase__: str = TOKENIZER_CLASSES
else:
lowercase__: Optional[Any] = {tokenizer_name: getattr(snake_case_ , tokenizer_name + "Fast" )}
logger.info(F"""Loading tokenizer classes: {tokenizer_names}""" )
for tokenizer_name in tokenizer_names:
lowercase__: Union[str, Any] = TOKENIZER_CLASSES[tokenizer_name]
lowercase__: Tuple = True
if checkpoint_name is None:
lowercase__: List[Any] = list(tokenizer_class.max_model_input_sizes.keys() )
else:
lowercase__: Any = [checkpoint_name]
logger.info(F"""For tokenizer {tokenizer_class.__class__.__name__} loading checkpoints: {checkpoint_names}""" )
for checkpoint in checkpoint_names:
logger.info(F"""Loading {tokenizer_class.__class__.__name__} {checkpoint}""" )
# Load tokenizer
lowercase__: Tuple = tokenizer_class.from_pretrained(snake_case_ , force_download=snake_case_ )
# Save fast tokenizer
logger.info(F"""Save fast tokenizer to {dump_path} with prefix {checkpoint} add_prefix {add_prefix}""" )
# For organization names we create sub-directories
if "/" in checkpoint:
lowercase__: Optional[Any] = checkpoint.split("/" )
lowercase__: Dict = os.path.join(snake_case_ , snake_case_ )
elif add_prefix:
lowercase__: Tuple = checkpoint
lowercase__: Any = dump_path
else:
lowercase__: List[Any] = None
lowercase__: int = dump_path
logger.info(F"""=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}""" )
if checkpoint in list(tokenizer.pretrained_vocab_files_map.values() )[0]:
lowercase__: List[Any] = list(tokenizer.pretrained_vocab_files_map.values() )[0][checkpoint]
lowercase__: Union[str, Any] = file_path.split(snake_case_ )[-1][0]
if next_char == "/":
lowercase__: Any = os.path.join(snake_case_ , snake_case_ )
lowercase__: str = None
logger.info(F"""=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}""" )
lowercase__: List[str] = tokenizer.save_pretrained(
snake_case_ , legacy_format=snake_case_ , filename_prefix=snake_case_ )
logger.info(F"""=> File names {file_names}""" )
for file_name in file_names:
if not file_name.endswith("tokenizer.json" ):
os.remove(snake_case_ )
logger.info(F"""=> removing {file_name}""" )
if __name__ == "__main__":
UpperCamelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--dump_path""", default=None, type=str, required=True, help="""Path to output generated fast tokenizer files."""
)
parser.add_argument(
"""--tokenizer_name""",
default=None,
type=str,
help=(
F"Optional tokenizer type selected in the list of {list(TOKENIZER_CLASSES.keys())}. If not given, will "
"""download and convert all the checkpoints from AWS."""
),
)
parser.add_argument(
"""--checkpoint_name""",
default=None,
type=str,
help="""Optional checkpoint name. If not given, will download and convert the canonical checkpoints from AWS.""",
)
parser.add_argument(
"""--force_download""",
action="""store_true""",
help="""Re-download checkpoints.""",
)
UpperCamelCase = parser.parse_args()
convert_slow_checkpoint_to_fast(args.tokenizer_name, args.checkpoint_name, args.dump_path, args.force_download)
| 716
|
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class _a ( metaclass=lowercase_ ):
'''simple docstring'''
UpperCamelCase__ = ["""torch""", """transformers""", """onnx"""]
def __init__( self , *UpperCAmelCase_ , **UpperCAmelCase_) -> str:
'''simple docstring'''
requires_backends(self , ["torch", "transformers", "onnx"])
@classmethod
def __lowercase ( cls , *UpperCAmelCase_ , **UpperCAmelCase_) -> List[Any]:
'''simple docstring'''
requires_backends(cls , ["torch", "transformers", "onnx"])
@classmethod
def __lowercase ( cls , *UpperCAmelCase_ , **UpperCAmelCase_) -> Tuple:
'''simple docstring'''
requires_backends(cls , ["torch", "transformers", "onnx"])
class _a ( metaclass=lowercase_ ):
'''simple docstring'''
UpperCamelCase__ = ["""torch""", """transformers""", """onnx"""]
def __init__( self , *UpperCAmelCase_ , **UpperCAmelCase_) -> Optional[Any]:
'''simple docstring'''
requires_backends(self , ["torch", "transformers", "onnx"])
@classmethod
def __lowercase ( cls , *UpperCAmelCase_ , **UpperCAmelCase_) -> Optional[Any]:
'''simple docstring'''
requires_backends(cls , ["torch", "transformers", "onnx"])
@classmethod
def __lowercase ( cls , *UpperCAmelCase_ , **UpperCAmelCase_) -> int:
'''simple docstring'''
requires_backends(cls , ["torch", "transformers", "onnx"])
class _a ( metaclass=lowercase_ ):
'''simple docstring'''
UpperCamelCase__ = ["""torch""", """transformers""", """onnx"""]
def __init__( self , *UpperCAmelCase_ , **UpperCAmelCase_) -> int:
'''simple docstring'''
requires_backends(self , ["torch", "transformers", "onnx"])
@classmethod
def __lowercase ( cls , *UpperCAmelCase_ , **UpperCAmelCase_) -> Any:
'''simple docstring'''
requires_backends(cls , ["torch", "transformers", "onnx"])
@classmethod
def __lowercase ( cls , *UpperCAmelCase_ , **UpperCAmelCase_) -> int:
'''simple docstring'''
requires_backends(cls , ["torch", "transformers", "onnx"])
class _a ( metaclass=lowercase_ ):
'''simple docstring'''
UpperCamelCase__ = ["""torch""", """transformers""", """onnx"""]
def __init__( self , *UpperCAmelCase_ , **UpperCAmelCase_) -> Tuple:
'''simple docstring'''
requires_backends(self , ["torch", "transformers", "onnx"])
@classmethod
def __lowercase ( cls , *UpperCAmelCase_ , **UpperCAmelCase_) -> str:
'''simple docstring'''
requires_backends(cls , ["torch", "transformers", "onnx"])
@classmethod
def __lowercase ( cls , *UpperCAmelCase_ , **UpperCAmelCase_) -> Union[str, Any]:
'''simple docstring'''
requires_backends(cls , ["torch", "transformers", "onnx"])
class _a ( metaclass=lowercase_ ):
'''simple docstring'''
UpperCamelCase__ = ["""torch""", """transformers""", """onnx"""]
def __init__( self , *UpperCAmelCase_ , **UpperCAmelCase_) -> Union[str, Any]:
'''simple docstring'''
requires_backends(self , ["torch", "transformers", "onnx"])
@classmethod
def __lowercase ( cls , *UpperCAmelCase_ , **UpperCAmelCase_) -> Union[str, Any]:
'''simple docstring'''
requires_backends(cls , ["torch", "transformers", "onnx"])
@classmethod
def __lowercase ( cls , *UpperCAmelCase_ , **UpperCAmelCase_) -> List[str]:
'''simple docstring'''
requires_backends(cls , ["torch", "transformers", "onnx"])
class _a ( metaclass=lowercase_ ):
'''simple docstring'''
UpperCamelCase__ = ["""torch""", """transformers""", """onnx"""]
def __init__( self , *UpperCAmelCase_ , **UpperCAmelCase_) -> str:
'''simple docstring'''
requires_backends(self , ["torch", "transformers", "onnx"])
@classmethod
def __lowercase ( cls , *UpperCAmelCase_ , **UpperCAmelCase_) -> List[str]:
'''simple docstring'''
requires_backends(cls , ["torch", "transformers", "onnx"])
@classmethod
def __lowercase ( cls , *UpperCAmelCase_ , **UpperCAmelCase_) -> str:
'''simple docstring'''
requires_backends(cls , ["torch", "transformers", "onnx"])
| 120
| 0
|
"""simple docstring"""
import numpy as np
import torch
import tqdm
from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class SCREAMING_SNAKE_CASE ( __lowerCamelCase ):
"""simple docstring"""
def __init__( self : Union[str, Any] , _snake_case : UNetaDModel , _snake_case : UNetaDModel , _snake_case : DDPMScheduler , _snake_case : List[str] , ) -> Optional[Any]:
'''simple docstring'''
super().__init__()
a__ = value_function
a__ = unet
a__ = scheduler
a__ = env
a__ = env.get_dataset()
a__ = {}
for key in self.data.keys():
try:
a__ = self.data[key].mean()
except: # noqa: E722
pass
a__ = {}
for key in self.data.keys():
try:
a__ = self.data[key].std()
except: # noqa: E722
pass
a__ = env.observation_space.shape[0]
a__ = env.action_space.shape[0]
def _lowerCAmelCase ( self : int , _snake_case : Union[str, Any] , _snake_case : Tuple ) -> str:
'''simple docstring'''
return (x_in - self.means[key]) / self.stds[key]
def _lowerCAmelCase ( self : Any , _snake_case : Optional[Any] , _snake_case : str ) -> List[Any]:
'''simple docstring'''
return x_in * self.stds[key] + self.means[key]
def _lowerCAmelCase ( self : int , _snake_case : List[Any] ) -> Any:
'''simple docstring'''
if type(__lowerCamelCase ) is dict:
return {k: self.to_torch(__lowerCamelCase ) for k, v in x_in.items()}
elif torch.is_tensor(__lowerCamelCase ):
return x_in.to(self.unet.device )
return torch.tensor(__lowerCamelCase , device=self.unet.device )
def _lowerCAmelCase ( self : List[str] , _snake_case : str , _snake_case : Any , _snake_case : List[str] ) -> Optional[Any]:
'''simple docstring'''
for key, val in cond.items():
a__ = val.clone()
return x_in
def _lowerCAmelCase ( self : Union[str, Any] , _snake_case : Tuple , _snake_case : Any , _snake_case : Optional[Any] , _snake_case : Any ) -> Optional[Any]:
'''simple docstring'''
a__ = x.shape[0]
a__ = None
for i in tqdm.tqdm(self.scheduler.timesteps ):
# create batch of timesteps to pass into model
a__ = torch.full((batch_size,) , __lowerCamelCase , device=self.unet.device , dtype=torch.long )
for _ in range(__lowerCamelCase ):
with torch.enable_grad():
x.requires_grad_()
# permute to match dimension for pre-trained models
a__ = self.value_function(x.permute(0 , 2 , 1 ) , __lowerCamelCase ).sample
a__ = torch.autograd.grad([y.sum()] , [x] )[0]
a__ = self.scheduler._get_variance(__lowerCamelCase )
a__ = torch.exp(0.5 * posterior_variance )
a__ = model_std * grad
a__ = 0
a__ = x.detach()
a__ = x + scale * grad
a__ = self.reset_xa(__lowerCamelCase , __lowerCamelCase , self.action_dim )
a__ = self.unet(x.permute(0 , 2 , 1 ) , __lowerCamelCase ).sample.permute(0 , 2 , 1 )
# TODO: verify deprecation of this kwarg
a__ = self.scheduler.step(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , predict_epsilon=__lowerCamelCase )['''prev_sample''']
# apply conditions to the trajectory (set the initial state)
a__ = self.reset_xa(__lowerCamelCase , __lowerCamelCase , self.action_dim )
a__ = self.to_torch(__lowerCamelCase )
return x, y
def __call__( self : Union[str, Any] , _snake_case : Optional[int] , _snake_case : Optional[int]=64 , _snake_case : List[str]=32 , _snake_case : Optional[int]=2 , _snake_case : Tuple=0.1 ) -> int:
'''simple docstring'''
a__ = self.normalize(__lowerCamelCase , 'observations' )
a__ = obs[None].repeat(__lowerCamelCase , axis=0 )
a__ = {0: self.to_torch(__lowerCamelCase )}
a__ = (batch_size, planning_horizon, self.state_dim + self.action_dim)
# generate initial noise and apply our conditions (to make the trajectories start at current state)
a__ = randn_tensor(__lowerCamelCase , device=self.unet.device )
a__ = self.reset_xa(__lowerCamelCase , __lowerCamelCase , self.action_dim )
a__ = self.to_torch(__lowerCamelCase )
# run the diffusion process
a__ = self.run_diffusion(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
# sort output trajectories by value
a__ = y.argsort(0 , descending=__lowerCamelCase ).squeeze()
a__ = x[sorted_idx]
a__ = sorted_values[:, :, : self.action_dim]
a__ = actions.detach().cpu().numpy()
a__ = self.de_normalize(__lowerCamelCase , key='actions' )
# select the action with the highest value
if y is not None:
a__ = 0
else:
# if we didn't run value guiding, select a random action
a__ = np.random.randint(0 , __lowerCamelCase )
a__ = denorm_actions[selected_index, 0]
return denorm_actions
| 232
|
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool
class UpperCamelCase__ ( __lowerCamelCase ):
a__ : List[str] = 'philschmid/bart-large-cnn-samsum'
a__ : List[Any] = (
'This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, '
'and returns a summary of the text.'
)
a__ : int = 'summarizer'
a__ : int = AutoTokenizer
a__ : Any = AutoModelForSeqaSeqLM
a__ : Optional[int] = ['text']
a__ : Optional[int] = ['text']
def __lowercase( self : int, __lowerCamelCase : List[str] ) -> List[Any]:
return self.pre_processor(__lowerCamelCase, return_tensors='''pt''', truncation=__lowerCamelCase )
def __lowercase( self : int, __lowerCamelCase : Union[str, Any] ) -> Union[str, Any]:
return self.model.generate(**__lowerCamelCase )[0]
def __lowercase( self : Optional[Any], __lowerCamelCase : Optional[int] ) -> Any:
return self.pre_processor.decode(__lowerCamelCase, skip_special_tokens=__lowerCamelCase, clean_up_tokenization_spaces=__lowerCamelCase )
| 344
| 0
|
import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch
from parameterized import parameterized
from transformers.testing_utils import (
CaptureStderr,
ExtendSysPath,
TestCasePlus,
execute_subprocess_async,
get_gpu_count,
get_torch_dist_unique_port,
require_apex,
require_bitsandbytes,
require_fairscale,
require_torch,
require_torch_gpu,
require_torch_multi_gpu,
require_torch_non_multi_gpu,
slow,
)
from transformers.trainer_callback import TrainerState
from transformers.trainer_utils import set_seed
__lowerCAmelCase : Tuple = os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(F'''{bindir}/../../examples/pytorch/translation'''):
from run_translation import main # noqa
set_seed(42)
__lowerCAmelCase : Union[str, Any] = "sshleifer/student_marian_en_ro_6_1"
__lowerCAmelCase : str = "sshleifer/tiny-mbart"
@require_torch
class UpperCAmelCase_ ( _lowerCamelCase ):
'''simple docstring'''
def _lowercase ( self : List[Any] , UpperCamelCase__ : Tuple=False , UpperCamelCase__ : Tuple=None , UpperCamelCase__ : Any=True , UpperCamelCase__ : str=True , UpperCamelCase__ : Union[str, Any]=True , UpperCamelCase__ : Optional[int]=True , ) -> Optional[Any]:
"""simple docstring"""
__magic_name__ = self.run_trainer(
eval_steps=1 , max_len=12 , model_name=A__ , num_train_epochs=1 , distributed=A__ , extra_args_str=A__ , predict_with_generate=A__ , do_train=A__ , do_eval=A__ , do_predict=A__ , )
__magic_name__ = TrainerState.load_from_json(os.path.join(A__ , """trainer_state.json""" ) ).log_history
if not do_eval:
return
__magic_name__ = [log for log in logs if """eval_loss""" in log.keys()]
__magic_name__ = eval_metrics[0]
if predict_with_generate:
assert "eval_bleu" in first_step_stats
__magic_name__ = eval_metrics[-1]
assert isinstance(last_step_stats["""eval_bleu"""] , A__ )
assert not math.isnan(float(last_step_stats["""eval_loss"""] ) ), "eval_loss must not be `nan`"
@require_torch_non_multi_gpu
def _lowercase ( self : str ) -> Optional[int]:
"""simple docstring"""
self.run_seqaseq_quick()
@require_torch_multi_gpu
def _lowercase ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
self.run_seqaseq_quick(distributed=A__ )
@require_torch_multi_gpu
def _lowercase ( self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
self.run_seqaseq_quick(distributed=A__ )
@unittest.skip("""Requires an update of the env running those tests""" )
@require_torch_multi_gpu
@require_fairscale
def _lowercase ( self : Union[str, Any] ) -> Any:
"""simple docstring"""
self.run_seqaseq_quick(distributed=A__ , extra_args_str="""--sharded_ddp simple""" )
@unittest.skip("""Requires an update of the env running those tests""" )
@require_torch_multi_gpu
@require_fairscale
def _lowercase ( self : int ) -> Tuple:
"""simple docstring"""
self.run_seqaseq_quick(distributed=A__ , extra_args_str="""--sharded_ddp simple --fp16""" )
@unittest.skip("""Requires an update of the env running those tests""" )
@require_torch_multi_gpu
@require_fairscale
def _lowercase ( self : int ) -> str:
"""simple docstring"""
self.run_seqaseq_quick(distributed=A__ , extra_args_str="""--sharded_ddp zero_dp_2""" , predict_with_generate=A__ )
@unittest.skip("""Requires an update of the env running those tests""" )
@require_torch_multi_gpu
@require_fairscale
def _lowercase ( self : Any ) -> List[str]:
"""simple docstring"""
self.run_seqaseq_quick(
distributed=A__ , extra_args_str="""--sharded_ddp zero_dp_2 --fp16""" , predict_with_generate=A__ )
@require_apex
@require_torch_gpu
def _lowercase ( self : Dict ) -> Optional[Any]:
"""simple docstring"""
self.run_seqaseq_quick(distributed=A__ , extra_args_str="""--fp16 --fp16_backend=apex""" )
# test 2nd time - was getting eval_loss': nan'
# to reproduce the problem set distributed=False
self.run_seqaseq_quick(distributed=A__ , extra_args_str="""--fp16 --fp16_backend=apex""" )
@parameterized.expand(["""base""", """low""", """high""", """mixed"""] )
@require_torch_multi_gpu
def _lowercase ( self : List[str] , UpperCamelCase__ : int ) -> List[Any]:
"""simple docstring"""
__magic_name__ = {
# test with the default log_level - should be info and thus log info once
"""base""": {"""extra_args_str""": """""", """n_matches""": 1},
# test with low log_level and log_level_replica - should be noisy on all processes
# now the info string should appear twice on 2 processes
"""low""": {"""extra_args_str""": """--log_level debug --log_level_replica debug""", """n_matches""": 2},
# test with high log_level and low log_level_replica
# now the info string should appear once only on the replica
"""high""": {"""extra_args_str""": """--log_level error --log_level_replica debug""", """n_matches""": 1},
# test with high log_level and log_level_replica - should be quiet on all processes
"""mixed""": {"""extra_args_str""": """--log_level error --log_level_replica error""", """n_matches""": 0},
}
__magic_name__ = experiments[experiment_id]
__magic_name__ = {"""distributed""": True, """predict_with_generate""": False, """do_eval""": False, """do_predict""": False}
__magic_name__ = """Running training"""
with CaptureStderr() as cl:
self.run_seqaseq_quick(**A__ , extra_args_str=data["""extra_args_str"""] )
__magic_name__ = len(re.findall(A__ , cl.err ) )
self.assertEqual(A__ , data["""n_matches"""] )
@slow
def _lowercase ( self : Tuple ) -> Any:
"""simple docstring"""
__magic_name__ = self.run_trainer(
eval_steps=2 , max_len=128 , model_name=A__ , learning_rate=3E-4 , num_train_epochs=10 , distributed=A__ , )
# Check metrics
__magic_name__ = TrainerState.load_from_json(os.path.join(A__ , """trainer_state.json""" ) ).log_history
__magic_name__ = [log for log in logs if """eval_loss""" in log.keys()]
__magic_name__ = eval_metrics[0]
__magic_name__ = eval_metrics[-1]
assert first_step_stats["eval_loss"] > last_step_stats["eval_loss"], "model learned nothing"
assert isinstance(last_step_stats["""eval_bleu"""] , A__ )
# test if do_predict saves generations and metrics
__magic_name__ = os.listdir(A__ )
__magic_name__ = {os.path.basename(A__ ) for p in contents}
assert "generated_predictions.txt" in contents
assert "predict_results.json" in contents
@slow
@require_bitsandbytes
def _lowercase ( self : Tuple ) -> Dict:
"""simple docstring"""
from transformers.training_args import OptimizerNames
def train_and_return_metrics(UpperCamelCase__ : str ) -> Tuple[int, float]:
__magic_name__ = """--skip_memory_metrics 0"""
__magic_name__ = self.run_trainer(
max_len=128 , model_name=A__ , learning_rate=3E-4 , num_train_epochs=1 , optim=A__ , distributed=A__ , extra_args_str=A__ , do_eval=A__ , do_predict=A__ , n_gpus_to_use=1 , )
# Check metrics
__magic_name__ = TrainerState.load_from_json(Path(A__ , """trainer_state.json""" ) ).log_history
__magic_name__ = int(logs[0]["""train_mem_gpu_peaked_delta"""] / 2**20 )
__magic_name__ = int(logs[0]["""train_mem_gpu_alloc_delta"""] / 2**20 )
__magic_name__ = logs[0]["""train_loss"""]
return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss
__magic_name__ , __magic_name__ , __magic_name__ = train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value )
__magic_name__ , __magic_name__ , __magic_name__ = train_and_return_metrics(OptimizerNames.ADAMW_BNB.value )
__magic_name__ = gpu_alloc_mem_orig - gpu_alloc_mem_bnb
__magic_name__ = gpu_peak_mem_orig + gpu_alloc_mem_orig
__magic_name__ = gpu_peak_mem_bnb + gpu_alloc_mem_bnb
__magic_name__ = gpu_total_mem_orig - gpu_total_mem_bnb
# sshleifer/student_marian_en_ro_6_1 has 54M parameter, 29M of which is `nn.Embedding` which
# doesn't get quantized and remains in fp32. Therefore we only have 25M parameters quantized
# in 2 bytes and the diff in optim memory usage is derived as so:
#
# - normal 25*8=~200MB (8 bytes per param)
# - bnb 25*2= ~50MB (2 bytes per param)
#
# Thus we should expect ~150MB total memory saved.
#
# Peak memory should be the same - the total should be different by about that same margin
#
# After leaving a small margin to accommodate for differences between gpus let's check
# that we have at least 120MB in savings
__magic_name__ = 120
# uncomment the following if this test starts failing - requires py38 for a new print feature
# gpu_peak_mem_diff = gpu_peak_mem_orig - gpu_peak_mem_bnb
# print(f"{gpu_alloc_mem_orig=}MB {gpu_peak_mem_orig=}MB {gpu_alloc_mem_orig+gpu_peak_mem_orig=}MB")
# print(f" {gpu_alloc_mem_bnb=}MB {gpu_peak_mem_bnb=}MB {gpu_alloc_mem_bnb+gpu_peak_mem_bnb=}MB")
# print(f"{gpu_alloc_mem_diff=}MB")
# print(f"{gpu_peak_mem_diff=}MB")
# print(f"{gpu_total_mem_orig=}MB, {gpu_total_mem_bnb=}MB")
# print(f"{gpu_total_mem_diff=}MB, {gpu_total_mem_diff=}MB")
self.assertGreater(
A__ , A__ , """should use ~150MB less alloc gpu memory with BNB, compared to without it for this model but got"""
F''' a difference of {gpu_alloc_mem_diff}MB, with gpu_alloc_mem_orig={gpu_alloc_mem_orig}MB and'''
F''' gpu_alloc_mem_bnb={gpu_alloc_mem_bnb}MB''' , )
self.assertGreater(
A__ , A__ , """should use ~150MB less total gpu memory with BNB, compared to without it for this model but got"""
F''' a difference of {gpu_total_mem_diff}MB, with gpu_total_mem_orig={gpu_total_mem_orig}MB and'''
F''' gpu_total_mem_bnb={gpu_total_mem_bnb}MB''' , )
self.assertEqual(
A__ , A__ , F'''loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}''' )
def _lowercase ( self : int , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : List[Any] , UpperCamelCase__ : List[Any] , UpperCamelCase__ : Dict = 3E-3 , UpperCamelCase__ : Optional[int] = "adafactor" , UpperCamelCase__ : str = False , UpperCamelCase__ : str = None , UpperCamelCase__ : List[Any] = 0 , UpperCamelCase__ : List[Any] = True , UpperCamelCase__ : Optional[Any] = True , UpperCamelCase__ : Dict = True , UpperCamelCase__ : Union[str, Any] = True , UpperCamelCase__ : Optional[Any] = None , ) -> Dict:
"""simple docstring"""
__magic_name__ = self.test_file_dir / """../fixtures/tests_samples/wmt_en_ro"""
__magic_name__ = self.get_auto_remove_tmp_dir()
__magic_name__ = F'''\n --model_name_or_path {model_name}\n --train_file {data_dir}/train.json\n --validation_file {data_dir}/val.json\n --test_file {data_dir}/test.json\n --output_dir {output_dir}\n --overwrite_output_dir\n --max_train_samples 8\n --max_source_length {max_len}\n --max_target_length {max_len}\n --do_train\n --num_train_epochs {str(A__ )}\n --per_device_train_batch_size 4\n --learning_rate {learning_rate}\n --warmup_steps 8\n --logging_steps 0\n --logging_strategy no\n --save_steps {str(A__ )}\n --group_by_length\n --label_smoothing_factor 0.1\n --target_lang ro_RO\n --source_lang en_XX\n '''.split()
__magic_name__ = F'''\n --do_eval\n --per_device_eval_batch_size 4\n --max_eval_samples 8\n --val_max_target_length {max_len}\n --evaluation_strategy steps\n --eval_steps {str(A__ )}\n '''.split()
__magic_name__ = """
--do_predict
""".split()
__magic_name__ = []
if do_train:
args += args_train
if do_eval:
args += args_eval
if do_predict:
args += args_predict
if predict_with_generate:
args += "--predict_with_generate".split()
if do_train:
if optim == "adafactor":
args += "--adafactor".split()
else:
args += F'''--optim {optim}'''.split()
if extra_args_str is not None:
args += extra_args_str.split()
if distributed:
if n_gpus_to_use is None:
__magic_name__ = get_gpu_count()
__magic_name__ = get_torch_dist_unique_port()
__magic_name__ = F'''\n -m torch.distributed.run\n --nproc_per_node={n_gpus_to_use}\n --master_port={master_port}\n {self.examples_dir_str}/pytorch/translation/run_translation.py\n '''.split()
__magic_name__ = [sys.executable] + distributed_args + args
# keep for quick debug
# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
execute_subprocess_async(A__ , env=self.get_env() )
else:
__magic_name__ = ["""run_translation.py"""] + args
with patch.object(A__ , """argv""" , A__ ):
main()
return output_dir
| 700
|
import unittest
from transformers import AutoTokenizer, NystromformerConfig, 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 (
NystromformerForMaskedLM,
NystromformerForMultipleChoice,
NystromformerForQuestionAnswering,
NystromformerForSequenceClassification,
NystromformerForTokenClassification,
NystromformerModel,
)
from transformers.models.nystromformer.modeling_nystromformer import NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
class UpperCAmelCase_ :
'''simple docstring'''
def __init__( self : Any , UpperCamelCase__ : Any , UpperCamelCase__ : Tuple=13 , UpperCamelCase__ : Dict=7 , UpperCamelCase__ : Dict=True , UpperCamelCase__ : List[str]=True , UpperCamelCase__ : Optional[int]=True , UpperCamelCase__ : int=True , UpperCamelCase__ : Optional[int]=99 , UpperCamelCase__ : List[Any]=32 , UpperCamelCase__ : Any=5 , UpperCamelCase__ : List[Any]=4 , UpperCamelCase__ : str=37 , UpperCamelCase__ : Any="gelu" , UpperCamelCase__ : int=0.1 , UpperCamelCase__ : Tuple=0.1 , UpperCamelCase__ : Dict=512 , UpperCamelCase__ : Union[str, Any]=16 , UpperCamelCase__ : Dict=2 , UpperCamelCase__ : Any=0.02 , UpperCamelCase__ : Any=3 , UpperCamelCase__ : Dict=4 , UpperCamelCase__ : List[Any]=None , ) -> Union[str, Any]:
"""simple docstring"""
__magic_name__ = parent
__magic_name__ = batch_size
__magic_name__ = seq_length
__magic_name__ = is_training
__magic_name__ = use_input_mask
__magic_name__ = use_token_type_ids
__magic_name__ = use_labels
__magic_name__ = vocab_size
__magic_name__ = hidden_size
__magic_name__ = num_hidden_layers
__magic_name__ = num_attention_heads
__magic_name__ = intermediate_size
__magic_name__ = hidden_act
__magic_name__ = hidden_dropout_prob
__magic_name__ = attention_probs_dropout_prob
__magic_name__ = max_position_embeddings
__magic_name__ = type_vocab_size
__magic_name__ = type_sequence_label_size
__magic_name__ = initializer_range
__magic_name__ = num_labels
__magic_name__ = num_choices
__magic_name__ = scope
def _lowercase ( self : Any ) -> Any:
"""simple docstring"""
__magic_name__ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__magic_name__ = None
if self.use_input_mask:
__magic_name__ = random_attention_mask([self.batch_size, self.seq_length] )
__magic_name__ = None
if self.use_token_type_ids:
__magic_name__ = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__magic_name__ = None
__magic_name__ = None
__magic_name__ = None
if self.use_labels:
__magic_name__ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__magic_name__ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__magic_name__ = ids_tensor([self.batch_size] , self.num_choices )
__magic_name__ = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def _lowercase ( self : Tuple ) -> Any:
"""simple docstring"""
return NystromformerConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=UpperCamelCase__ , initializer_range=self.initializer_range , )
def _lowercase ( self : Any , UpperCamelCase__ : Tuple , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : str , UpperCamelCase__ : int , UpperCamelCase__ : str , UpperCamelCase__ : Dict , UpperCamelCase__ : str ) -> Tuple:
"""simple docstring"""
__magic_name__ = NystromformerModel(config=UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
__magic_name__ = model(UpperCamelCase__ , attention_mask=UpperCamelCase__ , token_type_ids=UpperCamelCase__ )
__magic_name__ = model(UpperCamelCase__ , token_type_ids=UpperCamelCase__ )
__magic_name__ = model(UpperCamelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _lowercase ( self : Optional[int] , UpperCamelCase__ : List[str] , UpperCamelCase__ : Tuple , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : List[str] , UpperCamelCase__ : str , UpperCamelCase__ : List[str] , UpperCamelCase__ : List[str] ) -> str:
"""simple docstring"""
__magic_name__ = NystromformerForMaskedLM(config=UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
__magic_name__ = model(UpperCamelCase__ , attention_mask=UpperCamelCase__ , token_type_ids=UpperCamelCase__ , labels=UpperCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _lowercase ( self : Tuple , UpperCamelCase__ : Any , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : List[str] , UpperCamelCase__ : Dict , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : Any , UpperCamelCase__ : Any ) -> Optional[Any]:
"""simple docstring"""
__magic_name__ = NystromformerForQuestionAnswering(config=UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
__magic_name__ = model(
UpperCamelCase__ , attention_mask=UpperCamelCase__ , token_type_ids=UpperCamelCase__ , start_positions=UpperCamelCase__ , end_positions=UpperCamelCase__ , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def _lowercase ( self : Optional[Any] , UpperCamelCase__ : Dict , UpperCamelCase__ : List[str] , UpperCamelCase__ : List[str] , UpperCamelCase__ : List[str] , UpperCamelCase__ : Dict , UpperCamelCase__ : Dict , UpperCamelCase__ : Any ) -> Optional[int]:
"""simple docstring"""
__magic_name__ = self.num_labels
__magic_name__ = NystromformerForSequenceClassification(UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
__magic_name__ = model(UpperCamelCase__ , attention_mask=UpperCamelCase__ , token_type_ids=UpperCamelCase__ , labels=UpperCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _lowercase ( self : Any , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : List[str] , UpperCamelCase__ : List[Any] , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : List[str] , UpperCamelCase__ : str , UpperCamelCase__ : Any ) -> Dict:
"""simple docstring"""
__magic_name__ = self.num_labels
__magic_name__ = NystromformerForTokenClassification(config=UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
__magic_name__ = model(UpperCamelCase__ , attention_mask=UpperCamelCase__ , token_type_ids=UpperCamelCase__ , labels=UpperCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def _lowercase ( self : Union[str, Any] , UpperCamelCase__ : Tuple , UpperCamelCase__ : Tuple , UpperCamelCase__ : int , UpperCamelCase__ : List[Any] , UpperCamelCase__ : Tuple , UpperCamelCase__ : int , UpperCamelCase__ : int ) -> Optional[Any]:
"""simple docstring"""
__magic_name__ = self.num_choices
__magic_name__ = NystromformerForMultipleChoice(config=UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
__magic_name__ = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__magic_name__ = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__magic_name__ = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__magic_name__ = model(
UpperCamelCase__ , attention_mask=UpperCamelCase__ , token_type_ids=UpperCamelCase__ , labels=UpperCamelCase__ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def _lowercase ( self : int ) -> List[Any]:
"""simple docstring"""
__magic_name__ = self.prepare_config_and_inputs()
(
(
__magic_name__
) , (
__magic_name__
) , (
__magic_name__
) , (
__magic_name__
) , (
__magic_name__
) , (
__magic_name__
) , (
__magic_name__
) ,
) = config_and_inputs
__magic_name__ = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class UpperCAmelCase_ ( _A , _A , unittest.TestCase ):
'''simple docstring'''
a__ = (
(
NystromformerModel,
NystromformerForMaskedLM,
NystromformerForMultipleChoice,
NystromformerForQuestionAnswering,
NystromformerForSequenceClassification,
NystromformerForTokenClassification,
)
if is_torch_available()
else ()
)
a__ = (
{
"""feature-extraction""": NystromformerModel,
"""fill-mask""": NystromformerForMaskedLM,
"""question-answering""": NystromformerForQuestionAnswering,
"""text-classification""": NystromformerForSequenceClassification,
"""token-classification""": NystromformerForTokenClassification,
"""zero-shot""": NystromformerForSequenceClassification,
}
if is_torch_available()
else {}
)
a__ = False
a__ = False
def _lowercase ( self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
__magic_name__ = NystromformerModelTester(self )
__magic_name__ = ConfigTester(self , config_class=UpperCamelCase__ , hidden_size=37 )
def _lowercase ( self : Tuple ) -> Any:
"""simple docstring"""
self.config_tester.run_common_tests()
def _lowercase ( self : Optional[Any] ) -> Any:
"""simple docstring"""
__magic_name__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCamelCase__ )
def _lowercase ( self : Optional[Any] ) -> int:
"""simple docstring"""
__magic_name__ = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
__magic_name__ = type
self.model_tester.create_and_check_model(*UpperCamelCase__ )
def _lowercase ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
__magic_name__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*UpperCamelCase__ )
def _lowercase ( self : Union[str, Any] ) -> str:
"""simple docstring"""
__magic_name__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*UpperCamelCase__ )
def _lowercase ( self : Dict ) -> List[Any]:
"""simple docstring"""
__magic_name__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*UpperCamelCase__ )
def _lowercase ( self : str ) -> int:
"""simple docstring"""
__magic_name__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*UpperCamelCase__ )
def _lowercase ( self : List[Any] ) -> List[str]:
"""simple docstring"""
__magic_name__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*UpperCamelCase__ )
@slow
def _lowercase ( self : str ) -> Tuple:
"""simple docstring"""
for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__magic_name__ = NystromformerModel.from_pretrained(UpperCamelCase__ )
self.assertIsNotNone(UpperCamelCase__ )
@require_torch
class UpperCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
@slow
def _lowercase ( self : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
__magic_name__ = NystromformerModel.from_pretrained("""uw-madison/nystromformer-512""" )
__magic_name__ = torch.tensor([[0, 1, 2, 3, 4, 5]] )
with torch.no_grad():
__magic_name__ = model(UpperCamelCase__ )[0]
__magic_name__ = torch.Size((1, 6, 768) )
self.assertEqual(output.shape , UpperCamelCase__ )
__magic_name__ = torch.tensor(
[[[-0.4532, -0.0936, 0.5137], [-0.2676, 0.0628, 0.6186], [-0.3629, -0.1726, 0.4716]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , UpperCamelCase__ , atol=1E-4 ) )
@slow
def _lowercase ( self : int ) -> str:
"""simple docstring"""
__magic_name__ = """the [MASK] of Belgium is Brussels"""
__magic_name__ = AutoTokenizer.from_pretrained("""uw-madison/nystromformer-512""" )
__magic_name__ = NystromformerForMaskedLM.from_pretrained("""uw-madison/nystromformer-512""" )
__magic_name__ = tokenizer(UpperCamelCase__ , return_tensors="""pt""" )
with torch.no_grad():
__magic_name__ = model(encoding.input_ids ).logits
__magic_name__ = token_logits[:, 2, :].argmax(-1 )[0]
self.assertEqual(tokenizer.decode(UpperCamelCase__ ) , """capital""" )
| 76
| 0
|
'''simple docstring'''
UpperCamelCase_ = {
"A": ["B", "C", "E"],
"B": ["A", "D", "E"],
"C": ["A", "F", "G"],
"D": ["B"],
"E": ["A", "B", "D"],
"F": ["C"],
"G": ["C"],
}
def lowercase__( __UpperCamelCase: dict ,__UpperCamelCase: Dict ,__UpperCamelCase: List[Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = set()
# keep track of all the paths to be checked
SCREAMING_SNAKE_CASE : Any = [[start]]
# return path if start is goal
if start == goal:
return [start]
# keeps looping until all possible paths have been checked
while queue:
# pop the first path from the queue
SCREAMING_SNAKE_CASE : List[Any] = queue.pop(0 )
# get the last node from the path
SCREAMING_SNAKE_CASE : Optional[Any] = path[-1]
if node not in explored:
SCREAMING_SNAKE_CASE : int = graph[node]
# go through all neighbour nodes, construct a new path and
# push it into the queue
for neighbour in neighbours:
SCREAMING_SNAKE_CASE : Tuple = list(__UpperCamelCase )
new_path.append(__UpperCamelCase )
queue.append(__UpperCamelCase )
# return path if neighbour is goal
if neighbour == goal:
return new_path
# mark node as explored
explored.add(__UpperCamelCase )
# in case there's no path between the 2 nodes
return []
def lowercase__( __UpperCamelCase: dict ,__UpperCamelCase: Dict ,__UpperCamelCase: List[str] ):
"""simple docstring"""
if not graph or start not in graph or target not in graph:
return -1
if start == target:
return 0
SCREAMING_SNAKE_CASE : str = [start]
SCREAMING_SNAKE_CASE : List[Any] = set(__UpperCamelCase )
# Keep tab on distances from `start` node.
SCREAMING_SNAKE_CASE : int = {start: 0, target: -1}
while queue:
SCREAMING_SNAKE_CASE : Optional[Any] = queue.pop(0 )
if node == target:
SCREAMING_SNAKE_CASE : Optional[Any] = (
dist[node] if dist[target] == -1 else min(dist[target] ,dist[node] )
)
for adjacent in graph[node]:
if adjacent not in visited:
visited.add(__UpperCamelCase )
queue.append(__UpperCamelCase )
SCREAMING_SNAKE_CASE : List[Any] = dist[node] + 1
return dist[target]
if __name__ == "__main__":
print(bfs_shortest_path(demo_graph, "G", "D")) # returns ['G', 'C', 'A', 'B', 'D']
print(bfs_shortest_path_distance(demo_graph, "G", "D")) # returns 4
| 28
|
'''simple docstring'''
# Algorithm for the pigeonhole sorting
def UpperCamelCase__ ( _lowercase : Any ) -> List[Any]:
__UpperCAmelCase: List[Any] = min(_lowercase ) # min() finds the minimum value
__UpperCAmelCase: List[str] = max(_lowercase ) # max() finds the maximum value
__UpperCAmelCase: Dict = max_val - min_val + 1 # size is difference of max and min values plus one
# list of pigeonholes of size equal to the variable size
__UpperCAmelCase: str = [0] * size
# Populate the pigeonholes.
for x in a:
assert isinstance(_lowercase , _lowercase ), "integers only please"
holes[x - min_val] += 1
# Putting the elements back into the array in an order.
__UpperCAmelCase: List[str] = 0
for count in range(_lowercase ):
while holes[count] > 0:
holes[count] -= 1
__UpperCAmelCase: Optional[int] = count + min_val
i += 1
def UpperCamelCase__ ( ) -> Optional[int]:
__UpperCAmelCase: Union[str, Any] = [8, 3, 2, 7, 4, 6, 8]
pigeonhole_sort(_lowercase )
print("""Sorted order is:""" , """ """.join(_lowercase ) )
if __name__ == "__main__":
main()
| 523
| 0
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowercase_ = {
'''configuration_wav2vec2''': ['''WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Wav2Vec2Config'''],
'''feature_extraction_wav2vec2''': ['''Wav2Vec2FeatureExtractor'''],
'''processing_wav2vec2''': ['''Wav2Vec2Processor'''],
'''tokenization_wav2vec2''': ['''Wav2Vec2CTCTokenizer''', '''Wav2Vec2Tokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
'''WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''Wav2Vec2ForAudioFrameClassification''',
'''Wav2Vec2ForCTC''',
'''Wav2Vec2ForMaskedLM''',
'''Wav2Vec2ForPreTraining''',
'''Wav2Vec2ForSequenceClassification''',
'''Wav2Vec2ForXVector''',
'''Wav2Vec2Model''',
'''Wav2Vec2PreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
'''TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFWav2Vec2ForCTC''',
'''TFWav2Vec2Model''',
'''TFWav2Vec2PreTrainedModel''',
'''TFWav2Vec2ForSequenceClassification''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
'''FlaxWav2Vec2ForCTC''',
'''FlaxWav2Vec2ForPreTraining''',
'''FlaxWav2Vec2Model''',
'''FlaxWav2Vec2PreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .processing_wavaveca import WavaVecaProcessor
from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_wavaveca import (
WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
WavaVecaForAudioFrameClassification,
WavaVecaForCTC,
WavaVecaForMaskedLM,
WavaVecaForPreTraining,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
WavaVecaModel,
WavaVecaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_wavaveca import (
TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
TFWavaVecaForCTC,
TFWavaVecaForSequenceClassification,
TFWavaVecaModel,
TFWavaVecaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_wavaveca import (
FlaxWavaVecaForCTC,
FlaxWavaVecaForPreTraining,
FlaxWavaVecaModel,
FlaxWavaVecaPreTrainedModel,
)
else:
import sys
lowercase_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 702
|
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def __lowerCAmelCase ( __lowerCamelCase : str = "laptop" ) -> DataFrame:
__lowerCAmelCase =f"""https://www.amazon.in/laptop/s?k={product}"""
__lowerCAmelCase ={
"""User-Agent""": """Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36
(KHTML, like Gecko)Chrome/44.0.2403.157 Safari/537.36""",
"""Accept-Language""": """en-US, en;q=0.5""",
}
__lowerCAmelCase =BeautifulSoup(requests.get(__lowerCamelCase , headers=__lowerCamelCase ).text )
# Initialize a Pandas dataframe with the column titles
__lowerCAmelCase =DataFrame(
columns=[
"""Product Title""",
"""Product Link""",
"""Current Price of the product""",
"""Product Rating""",
"""MRP of the product""",
"""Discount""",
] )
# Loop through each entry and store them in the dataframe
for item, _ in zip_longest(
soup.find_all(
"""div""" , attrs={"""class""": """s-result-item""", """data-component-type""": """s-search-result"""} , ) , soup.find_all("""div""" , attrs={"""class""": """a-row a-size-base a-color-base"""} ) , ):
try:
__lowerCAmelCase =item.ha.text
__lowerCAmelCase ="""https://www.amazon.in/""" + item.ha.a["""href"""]
__lowerCAmelCase =item.find("""span""" , attrs={"""class""": """a-offscreen"""} ).text
try:
__lowerCAmelCase =item.find("""span""" , attrs={"""class""": """a-icon-alt"""} ).text
except AttributeError:
__lowerCAmelCase ="""Not available"""
try:
__lowerCAmelCase =(
"""₹"""
+ item.find(
"""span""" , attrs={"""class""": """a-price a-text-price"""} ).text.split("""₹""" )[1]
)
except AttributeError:
__lowerCAmelCase =""""""
try:
__lowerCAmelCase =float(
(
(
float(product_mrp.strip("""₹""" ).replace(""",""" , """""" ) )
- float(product_price.strip("""₹""" ).replace(""",""" , """""" ) )
)
/ float(product_mrp.strip("""₹""" ).replace(""",""" , """""" ) )
)
* 100 )
except ValueError:
__lowerCAmelCase =float("""nan""" )
except AttributeError:
pass
__lowerCAmelCase =[
product_title,
product_link,
product_price,
product_rating,
product_mrp,
discount,
]
__lowerCAmelCase =""" """
__lowerCAmelCase =""" """
data_frame.index += 1
return data_frame
if __name__ == "__main__":
lowercase_ = '''headphones'''
get_amazon_product_data(product).to_csv(F"Amazon Product Data for {product}.csv")
| 456
| 0
|
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class _SCREAMING_SNAKE_CASE :
@staticmethod
def __snake_case( *A_ , **A_ ):
pass
@is_pipeline_test
@require_vision
@require_torch
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
__SCREAMING_SNAKE_CASE = MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING
def __snake_case( self , A_ , A_ , A_ ):
_UpperCAmelCase : List[Any] = pipeline(
"""zero-shot-object-detection""" , model="""hf-internal-testing/tiny-random-owlvit-object-detection""" )
_UpperCAmelCase : Tuple = [
{
"""image""": """./tests/fixtures/tests_samples/COCO/000000039769.png""",
"""candidate_labels""": ["""cat""", """remote""", """couch"""],
}
]
return object_detector, examples
def __snake_case( self , A_ , A_ ):
_UpperCAmelCase : Union[str, Any] = object_detector(examples[0] , threshold=0.0 )
_UpperCAmelCase : Dict = len(A_ )
self.assertGreater(A_ , 0 )
self.assertEqual(
A_ , [
{
"""score""": ANY(A_ ),
"""label""": ANY(A_ ),
"""box""": {"""xmin""": ANY(A_ ), """ymin""": ANY(A_ ), """xmax""": ANY(A_ ), """ymax""": ANY(A_ )},
}
for i in range(A_ )
] , )
@require_tf
@unittest.skip("""Zero Shot Object Detection not implemented in TF""" )
def __snake_case( self ):
pass
@require_torch
def __snake_case( self ):
_UpperCAmelCase : str = pipeline(
"""zero-shot-object-detection""" , model="""hf-internal-testing/tiny-random-owlvit-object-detection""" )
_UpperCAmelCase : Optional[Any] = object_detector(
"""./tests/fixtures/tests_samples/COCO/000000039769.png""" , candidate_labels=["""cat""", """remote""", """couch"""] , threshold=0.6_4 , )
self.assertEqual(
nested_simplify(A_ , decimals=4 ) , [
{"""score""": 0.7_2_3_5, """label""": """cat""", """box""": {"""xmin""": 2_04, """ymin""": 1_67, """xmax""": 2_32, """ymax""": 1_90}},
{"""score""": 0.7_2_1_8, """label""": """remote""", """box""": {"""xmin""": 2_04, """ymin""": 1_67, """xmax""": 2_32, """ymax""": 1_90}},
{"""score""": 0.7_1_8_4, """label""": """couch""", """box""": {"""xmin""": 2_04, """ymin""": 1_67, """xmax""": 2_32, """ymax""": 1_90}},
{"""score""": 0.6_7_4_8, """label""": """remote""", """box""": {"""xmin""": 5_71, """ymin""": 83, """xmax""": 5_98, """ymax""": 1_03}},
{"""score""": 0.6_6_5_6, """label""": """cat""", """box""": {"""xmin""": 5_71, """ymin""": 83, """xmax""": 5_98, """ymax""": 1_03}},
{"""score""": 0.6_6_1_4, """label""": """couch""", """box""": {"""xmin""": 5_71, """ymin""": 83, """xmax""": 5_98, """ymax""": 1_03}},
{"""score""": 0.6_4_5_6, """label""": """remote""", """box""": {"""xmin""": 4_94, """ymin""": 1_05, """xmax""": 5_21, """ymax""": 1_27}},
{"""score""": 0.6_4_2, """label""": """remote""", """box""": {"""xmin""": 67, """ymin""": 2_74, """xmax""": 93, """ymax""": 2_97}},
{"""score""": 0.6_4_1_9, """label""": """cat""", """box""": {"""xmin""": 4_94, """ymin""": 1_05, """xmax""": 5_21, """ymax""": 1_27}},
] , )
_UpperCAmelCase : List[str] = object_detector(
[
{
"""image""": """./tests/fixtures/tests_samples/COCO/000000039769.png""",
"""candidate_labels""": ["""cat""", """remote""", """couch"""],
}
] , threshold=0.6_4 , )
self.assertEqual(
nested_simplify(A_ , decimals=4 ) , [
[
{"""score""": 0.7_2_3_5, """label""": """cat""", """box""": {"""xmin""": 2_04, """ymin""": 1_67, """xmax""": 2_32, """ymax""": 1_90}},
{"""score""": 0.7_2_1_8, """label""": """remote""", """box""": {"""xmin""": 2_04, """ymin""": 1_67, """xmax""": 2_32, """ymax""": 1_90}},
{"""score""": 0.7_1_8_4, """label""": """couch""", """box""": {"""xmin""": 2_04, """ymin""": 1_67, """xmax""": 2_32, """ymax""": 1_90}},
{"""score""": 0.6_7_4_8, """label""": """remote""", """box""": {"""xmin""": 5_71, """ymin""": 83, """xmax""": 5_98, """ymax""": 1_03}},
{"""score""": 0.6_6_5_6, """label""": """cat""", """box""": {"""xmin""": 5_71, """ymin""": 83, """xmax""": 5_98, """ymax""": 1_03}},
{"""score""": 0.6_6_1_4, """label""": """couch""", """box""": {"""xmin""": 5_71, """ymin""": 83, """xmax""": 5_98, """ymax""": 1_03}},
{"""score""": 0.6_4_5_6, """label""": """remote""", """box""": {"""xmin""": 4_94, """ymin""": 1_05, """xmax""": 5_21, """ymax""": 1_27}},
{"""score""": 0.6_4_2, """label""": """remote""", """box""": {"""xmin""": 67, """ymin""": 2_74, """xmax""": 93, """ymax""": 2_97}},
{"""score""": 0.6_4_1_9, """label""": """cat""", """box""": {"""xmin""": 4_94, """ymin""": 1_05, """xmax""": 5_21, """ymax""": 1_27}},
]
] , )
@require_torch
@slow
def __snake_case( self ):
_UpperCAmelCase : List[Any] = pipeline("""zero-shot-object-detection""" )
_UpperCAmelCase : Union[str, Any] = object_detector(
"""http://images.cocodataset.org/val2017/000000039769.jpg""" , candidate_labels=["""cat""", """remote""", """couch"""] , )
self.assertEqual(
nested_simplify(A_ , decimals=4 ) , [
{"""score""": 0.2_8_6_8, """label""": """cat""", """box""": {"""xmin""": 3_24, """ymin""": 20, """xmax""": 6_40, """ymax""": 3_73}},
{"""score""": 0.2_7_7, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 72, """xmax""": 1_77, """ymax""": 1_15}},
{"""score""": 0.2_5_3_7, """label""": """cat""", """box""": {"""xmin""": 1, """ymin""": 55, """xmax""": 3_15, """ymax""": 4_72}},
{"""score""": 0.1_4_7_4, """label""": """remote""", """box""": {"""xmin""": 3_35, """ymin""": 74, """xmax""": 3_71, """ymax""": 1_87}},
{"""score""": 0.1_2_0_8, """label""": """couch""", """box""": {"""xmin""": 4, """ymin""": 0, """xmax""": 6_42, """ymax""": 4_76}},
] , )
_UpperCAmelCase : str = object_detector(
[
{
"""image""": """http://images.cocodataset.org/val2017/000000039769.jpg""",
"""candidate_labels""": ["""cat""", """remote""", """couch"""],
},
{
"""image""": """http://images.cocodataset.org/val2017/000000039769.jpg""",
"""candidate_labels""": ["""cat""", """remote""", """couch"""],
},
] , )
self.assertEqual(
nested_simplify(A_ , decimals=4 ) , [
[
{"""score""": 0.2_8_6_8, """label""": """cat""", """box""": {"""xmin""": 3_24, """ymin""": 20, """xmax""": 6_40, """ymax""": 3_73}},
{"""score""": 0.2_7_7, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 72, """xmax""": 1_77, """ymax""": 1_15}},
{"""score""": 0.2_5_3_7, """label""": """cat""", """box""": {"""xmin""": 1, """ymin""": 55, """xmax""": 3_15, """ymax""": 4_72}},
{"""score""": 0.1_4_7_4, """label""": """remote""", """box""": {"""xmin""": 3_35, """ymin""": 74, """xmax""": 3_71, """ymax""": 1_87}},
{"""score""": 0.1_2_0_8, """label""": """couch""", """box""": {"""xmin""": 4, """ymin""": 0, """xmax""": 6_42, """ymax""": 4_76}},
],
[
{"""score""": 0.2_8_6_8, """label""": """cat""", """box""": {"""xmin""": 3_24, """ymin""": 20, """xmax""": 6_40, """ymax""": 3_73}},
{"""score""": 0.2_7_7, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 72, """xmax""": 1_77, """ymax""": 1_15}},
{"""score""": 0.2_5_3_7, """label""": """cat""", """box""": {"""xmin""": 1, """ymin""": 55, """xmax""": 3_15, """ymax""": 4_72}},
{"""score""": 0.1_4_7_4, """label""": """remote""", """box""": {"""xmin""": 3_35, """ymin""": 74, """xmax""": 3_71, """ymax""": 1_87}},
{"""score""": 0.1_2_0_8, """label""": """couch""", """box""": {"""xmin""": 4, """ymin""": 0, """xmax""": 6_42, """ymax""": 4_76}},
],
] , )
@require_tf
@unittest.skip("""Zero Shot Object Detection not implemented in TF""" )
def __snake_case( self ):
pass
@require_torch
@slow
def __snake_case( self ):
_UpperCAmelCase : Tuple = 0.2
_UpperCAmelCase : Union[str, Any] = pipeline("""zero-shot-object-detection""" )
_UpperCAmelCase : Optional[int] = object_detector(
"""http://images.cocodataset.org/val2017/000000039769.jpg""" , candidate_labels=["""cat""", """remote""", """couch"""] , threshold=A_ , )
self.assertEqual(
nested_simplify(A_ , decimals=4 ) , [
{"""score""": 0.2_8_6_8, """label""": """cat""", """box""": {"""xmin""": 3_24, """ymin""": 20, """xmax""": 6_40, """ymax""": 3_73}},
{"""score""": 0.2_7_7, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 72, """xmax""": 1_77, """ymax""": 1_15}},
{"""score""": 0.2_5_3_7, """label""": """cat""", """box""": {"""xmin""": 1, """ymin""": 55, """xmax""": 3_15, """ymax""": 4_72}},
] , )
@require_torch
@slow
def __snake_case( self ):
_UpperCAmelCase : Dict = 2
_UpperCAmelCase : Union[str, Any] = pipeline("""zero-shot-object-detection""" )
_UpperCAmelCase : Any = object_detector(
"""http://images.cocodataset.org/val2017/000000039769.jpg""" , candidate_labels=["""cat""", """remote""", """couch"""] , top_k=A_ , )
self.assertEqual(
nested_simplify(A_ , decimals=4 ) , [
{"""score""": 0.2_8_6_8, """label""": """cat""", """box""": {"""xmin""": 3_24, """ymin""": 20, """xmax""": 6_40, """ymax""": 3_73}},
{"""score""": 0.2_7_7, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 72, """xmax""": 1_77, """ymax""": 1_15}},
] , )
| 643
|
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.
def a__ ( snake_case__ : Dict ):
_UpperCAmelCase : str = [False] * len(snake_case__ )
_UpperCAmelCase : str = [-1] * len(snake_case__ )
def dfs(snake_case__ : Dict , snake_case__ : Optional[Any] ):
_UpperCAmelCase : str = True
_UpperCAmelCase : Optional[Any] = c
for u in graph[v]:
if not visited[u]:
dfs(snake_case__ , 1 - c )
for i in range(len(snake_case__ ) ):
if not visited[i]:
dfs(snake_case__ , 0 )
for i in range(len(snake_case__ ) ):
for j in graph[i]:
if color[i] == color[j]:
return False
return True
# Adjacency list of graph
SCREAMING_SNAKE_CASE__ : Optional[Any] = {0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}
print(check_bipartite_dfs(graph))
| 643
| 1
|
'''simple docstring'''
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
lowerCAmelCase_ : int = get_tests_dir('fixtures/test_sentencepiece.model')
@require_sentencepiece
@require_tokenizers
class lowerCamelCase_ ( snake_case_ , unittest.TestCase ):
_lowerCAmelCase : Optional[int] = XLMRobertaTokenizer
_lowerCAmelCase : Optional[Any] = XLMRobertaTokenizerFast
_lowerCAmelCase : List[str] = True
_lowerCAmelCase : Union[str, Any] = True
def __lowercase ( self : int ):
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
SCREAMING_SNAKE_CASE : str = XLMRobertaTokenizer(lowerCAmelCase__ , keep_accents=lowerCAmelCase__ )
tokenizer.save_pretrained(self.tmpdirname )
def __lowercase ( self : Dict ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = '''<pad>'''
SCREAMING_SNAKE_CASE : Tuple = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase__ ) , lowerCAmelCase__ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase__ ) , lowerCAmelCase__ )
def __lowercase ( self : int ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<s>''' )
self.assertEqual(vocab_keys[1] , '''<pad>''' )
self.assertEqual(vocab_keys[-1] , '''<mask>''' )
self.assertEqual(len(lowerCAmelCase__ ) , 10_02 )
def __lowercase ( self : List[Any] ):
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 10_02 )
def __lowercase ( self : Dict ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = XLMRobertaTokenizer(lowerCAmelCase__ , keep_accents=lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : List[str] = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(lowerCAmelCase__ , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(lowerCAmelCase__ ) , [value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]] , )
SCREAMING_SNAKE_CASE : Dict = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
lowerCAmelCase__ , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''9''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''é''',
'''.''',
] , )
SCREAMING_SNAKE_CASE : Dict = tokenizer.convert_tokens_to_ids(lowerCAmelCase__ )
self.assertListEqual(
lowerCAmelCase__ , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
] , )
SCREAMING_SNAKE_CASE : Any = tokenizer.convert_ids_to_tokens(lowerCAmelCase__ )
self.assertListEqual(
lowerCAmelCase__ , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''<unk>''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''<unk>''',
'''.''',
] , )
def __lowercase ( self : Tuple ):
"""simple docstring"""
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
SCREAMING_SNAKE_CASE : Tuple = (self.rust_tokenizer_class, '''hf-internal-testing/tiny-xlm-roberta''', {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
SCREAMING_SNAKE_CASE : int = self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__ , **lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : Dict = self.tokenizer_class.from_pretrained(lowerCAmelCase__ , **lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : int = tempfile.mkdtemp()
SCREAMING_SNAKE_CASE : Union[str, Any] = tokenizer_r.save_pretrained(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : Dict = tokenizer_p.save_pretrained(lowerCAmelCase__ )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files ) )
SCREAMING_SNAKE_CASE : Optional[Any] = tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f )
self.assertSequenceEqual(lowerCAmelCase__ , lowerCAmelCase__ )
# Checks everything loads correctly in the same way
SCREAMING_SNAKE_CASE : Dict = tokenizer_r.from_pretrained(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : int = tokenizer_p.from_pretrained(lowerCAmelCase__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowerCAmelCase__ , lowerCAmelCase__ ) )
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(lowerCAmelCase__ )
# Save tokenizer rust, legacy_format=True
SCREAMING_SNAKE_CASE : Tuple = tempfile.mkdtemp()
SCREAMING_SNAKE_CASE : List[Any] = tokenizer_r.save_pretrained(lowerCAmelCase__ , legacy_format=lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : Optional[int] = tokenizer_p.save_pretrained(lowerCAmelCase__ )
# Checks it save with the same files
self.assertSequenceEqual(lowerCAmelCase__ , lowerCAmelCase__ )
# Checks everything loads correctly in the same way
SCREAMING_SNAKE_CASE : List[Any] = tokenizer_r.from_pretrained(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : str = tokenizer_p.from_pretrained(lowerCAmelCase__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowerCAmelCase__ , lowerCAmelCase__ ) )
shutil.rmtree(lowerCAmelCase__ )
# Save tokenizer rust, legacy_format=False
SCREAMING_SNAKE_CASE : List[Any] = tempfile.mkdtemp()
SCREAMING_SNAKE_CASE : Dict = tokenizer_r.save_pretrained(lowerCAmelCase__ , legacy_format=lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : Optional[int] = tokenizer_p.save_pretrained(lowerCAmelCase__ )
# Checks it saved the tokenizer.json file
self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
SCREAMING_SNAKE_CASE : Any = tokenizer_r.from_pretrained(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : Any = tokenizer_p.from_pretrained(lowerCAmelCase__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowerCAmelCase__ , lowerCAmelCase__ ) )
shutil.rmtree(lowerCAmelCase__ )
@cached_property
def __lowercase ( self : Tuple ):
"""simple docstring"""
return XLMRobertaTokenizer.from_pretrained('''xlm-roberta-base''' )
def __lowercase ( self : Optional[int] ):
"""simple docstring"""
with tempfile.NamedTemporaryFile() as f:
shutil.copyfile(lowerCAmelCase__ , f.name )
SCREAMING_SNAKE_CASE : str = XLMRobertaTokenizer(f.name , keep_accents=lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : str = pickle.dumps(lowerCAmelCase__ )
pickle.loads(lowerCAmelCase__ )
def __lowercase ( self : Dict ):
"""simple docstring"""
if not self.test_rust_tokenizer:
return
SCREAMING_SNAKE_CASE : int = self.get_tokenizer()
SCREAMING_SNAKE_CASE : int = self.get_rust_tokenizer()
SCREAMING_SNAKE_CASE : List[Any] = '''I was born in 92000, and this is falsé.'''
SCREAMING_SNAKE_CASE : int = tokenizer.tokenize(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : Optional[Any] = rust_tokenizer.tokenize(lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : Any = tokenizer.encode(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : Union[str, Any] = rust_tokenizer.encode(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : str = self.get_rust_tokenizer()
SCREAMING_SNAKE_CASE : Dict = tokenizer.encode(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : List[Any] = rust_tokenizer.encode(lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
@slow
def __lowercase ( self : int ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = '''Hello World!'''
SCREAMING_SNAKE_CASE : Union[str, Any] = [0, 3_53_78, 66_61, 38, 2]
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer
# xlmr.eval()
# xlmr.encode(symbols)
self.assertListEqual(lowerCAmelCase__ , self.big_tokenizer.encode(lowerCAmelCase__ ) )
@slow
def __lowercase ( self : Optional[int] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = (
'''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
)
SCREAMING_SNAKE_CASE : Any = [
0,
32_93,
83,
10,
45_52,
49_89,
79_86,
6_78,
10,
59_15,
1_11,
17_94_59,
12_48_50,
4,
60_44,
2_37,
12,
6,
5,
6,
4,
67_80,
7_05,
15,
13_88,
44,
3_78,
1_01_14,
7_11,
1_52,
20,
6,
5,
2_23_76,
6_42,
12_21,
1_51_90,
3_41_53,
4_50,
56_08,
9_59,
11_19,
5_77_02,
1_36,
1_86,
47,
10_98,
2_93_67,
47,
# 4426, # What fairseq tokenizes from "<unk>": "_<"
# 3678, # What fairseq tokenizes from "<unk>": "unk"
# 2740, # What fairseq tokenizes from "<unk>": ">"
3, # What we tokenize from "<unk>": "<unk>"
6, # Residue from the tokenization: an extra sentencepiece underline
4,
60_44,
2_37,
62_84,
5_09_01,
5_28,
31,
90,
34,
9_27,
2,
]
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer
# xlmr.eval()
# xlmr.encode(symbols)
self.assertListEqual(lowerCAmelCase__ , self.big_tokenizer.encode(lowerCAmelCase__ ) )
@slow
def __lowercase ( self : List[str] ):
"""simple docstring"""
# fmt: off
SCREAMING_SNAKE_CASE : Union[str, Any] = {'''input_ids''': [[0, 1_10_62, 8_27_72, 7, 15, 8_27_72, 5_38, 5_15_29, 2_37, 1_71_98, 12_90, 2_06, 9, 21_51_75, 13_14, 1_36, 1_71_98, 12_90, 2_06, 9, 5_63_59, 42, 12_20_09, 9, 1_64_66, 16, 8_73_44, 45_37, 9, 47_17, 7_83_81, 6, 15_99_58, 7, 15, 2_44_80, 6_18, 4, 5_27, 2_26_93, 54_28, 4, 27_77, 2_44_80, 98_74, 4, 4_35_23, 5_94, 4, 8_03, 1_83_92, 3_31_89, 18, 4, 4_35_23, 2_44_47, 1_23_99, 1_00, 2_49_55, 8_36_58, 96_26, 14_40_57, 15, 8_39, 2_23_35, 16, 1_36, 2_49_55, 8_36_58, 8_34_79, 15, 3_91_02, 7_24, 16, 6_78, 6_45, 27_89, 13_28, 45_89, 42, 12_20_09, 11_57_74, 23, 8_05, 13_28, 4_68_76, 7, 1_36, 5_38_94, 19_40, 4_22_27, 4_11_59, 1_77_21, 8_23, 4_25, 4, 2_75_12, 9_87_22, 2_06, 1_36, 55_31, 49_70, 9_19, 1_73_36, 5, 2], [0, 2_00_80, 6_18, 83, 8_27_75, 47, 4_79, 9, 15_17, 73, 5_38_94, 3_33, 8_05_81, 11_01_17, 1_88_11, 52_56, 12_95, 51, 15_25_26, 2_97, 79_86, 3_90, 12_44_16, 5_38, 3_54_31, 2_14, 98, 1_50_44, 2_57_37, 1_36, 71_08, 4_37_01, 23, 7_56, 13_53_55, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 5_81, 6_37_73, 11_94_55, 6, 14_77_97, 8_82_03, 7, 6_45, 70, 21, 32_85, 1_02_69, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=lowerCAmelCase__ , model_name='''xlm-roberta-base''' , revision='''d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3''' , )
| 464
|
'''simple docstring'''
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...image_processing_utils import ImageProcessingMixin
from ...utils import CONFIG_NAME, IMAGE_PROCESSOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
CONFIG_MAPPING_NAMES,
AutoConfig,
model_type_to_module_name,
replace_list_option_in_docstrings,
)
lowerCAmelCase_ : str = logging.get_logger(__name__)
lowerCAmelCase_ : List[Any] = OrderedDict(
[
('align', 'EfficientNetImageProcessor'),
('beit', 'BeitImageProcessor'),
('bit', 'BitImageProcessor'),
('blip', 'BlipImageProcessor'),
('blip-2', 'BlipImageProcessor'),
('bridgetower', 'BridgeTowerImageProcessor'),
('chinese_clip', 'ChineseCLIPImageProcessor'),
('clip', 'CLIPImageProcessor'),
('clipseg', 'ViTImageProcessor'),
('conditional_detr', 'ConditionalDetrImageProcessor'),
('convnext', 'ConvNextImageProcessor'),
('convnextv2', 'ConvNextImageProcessor'),
('cvt', 'ConvNextImageProcessor'),
('data2vec-vision', 'BeitImageProcessor'),
('deformable_detr', 'DeformableDetrImageProcessor'),
('deit', 'DeiTImageProcessor'),
('deta', 'DetaImageProcessor'),
('detr', 'DetrImageProcessor'),
('dinat', 'ViTImageProcessor'),
('donut-swin', 'DonutImageProcessor'),
('dpt', 'DPTImageProcessor'),
('efficientformer', 'EfficientFormerImageProcessor'),
('efficientnet', 'EfficientNetImageProcessor'),
('flava', 'FlavaImageProcessor'),
('focalnet', 'BitImageProcessor'),
('git', 'CLIPImageProcessor'),
('glpn', 'GLPNImageProcessor'),
('groupvit', 'CLIPImageProcessor'),
('imagegpt', 'ImageGPTImageProcessor'),
('instructblip', 'BlipImageProcessor'),
('layoutlmv2', 'LayoutLMv2ImageProcessor'),
('layoutlmv3', 'LayoutLMv3ImageProcessor'),
('levit', 'LevitImageProcessor'),
('mask2former', 'Mask2FormerImageProcessor'),
('maskformer', 'MaskFormerImageProcessor'),
('mgp-str', 'ViTImageProcessor'),
('mobilenet_v1', 'MobileNetV1ImageProcessor'),
('mobilenet_v2', 'MobileNetV2ImageProcessor'),
('mobilevit', 'MobileViTImageProcessor'),
('mobilevit', 'MobileViTImageProcessor'),
('mobilevitv2', 'MobileViTImageProcessor'),
('nat', 'ViTImageProcessor'),
('oneformer', 'OneFormerImageProcessor'),
('owlvit', 'OwlViTImageProcessor'),
('perceiver', 'PerceiverImageProcessor'),
('pix2struct', 'Pix2StructImageProcessor'),
('poolformer', 'PoolFormerImageProcessor'),
('regnet', 'ConvNextImageProcessor'),
('resnet', 'ConvNextImageProcessor'),
('sam', 'SamImageProcessor'),
('segformer', 'SegformerImageProcessor'),
('swiftformer', 'ViTImageProcessor'),
('swin', 'ViTImageProcessor'),
('swin2sr', 'Swin2SRImageProcessor'),
('swinv2', 'ViTImageProcessor'),
('table-transformer', 'DetrImageProcessor'),
('timesformer', 'VideoMAEImageProcessor'),
('tvlt', 'TvltImageProcessor'),
('upernet', 'SegformerImageProcessor'),
('van', 'ConvNextImageProcessor'),
('videomae', 'VideoMAEImageProcessor'),
('vilt', 'ViltImageProcessor'),
('vit', 'ViTImageProcessor'),
('vit_hybrid', 'ViTHybridImageProcessor'),
('vit_mae', 'ViTImageProcessor'),
('vit_msn', 'ViTImageProcessor'),
('xclip', 'CLIPImageProcessor'),
('yolos', 'YolosImageProcessor'),
]
)
lowerCAmelCase_ : List[str] = _LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)
def UpperCAmelCase ( A : str ):
for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
if class_name in extractors:
SCREAMING_SNAKE_CASE : Tuple = model_type_to_module_name(A )
SCREAMING_SNAKE_CASE : List[Any] = importlib.import_module(F""".{module_name}""" , '''transformers.models''' )
try:
return getattr(A , A )
except AttributeError:
continue
for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
if getattr(A , '''__name__''' , A ) == class_name:
return extractor
# We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
# init and we return the proper dummy to get an appropriate error message.
SCREAMING_SNAKE_CASE : List[str] = importlib.import_module('''transformers''' )
if hasattr(A , A ):
return getattr(A , A )
return None
def UpperCAmelCase ( A : Union[str, os.PathLike] , A : Optional[Union[str, os.PathLike]] = None , A : bool = False , A : bool = False , A : Optional[Dict[str, str]] = None , A : Optional[Union[bool, str]] = None , A : Optional[str] = None , A : bool = False , **A : List[str] , ):
SCREAMING_SNAKE_CASE : List[Any] = get_file_from_repo(
A , A , cache_dir=A , force_download=A , resume_download=A , proxies=A , use_auth_token=A , revision=A , local_files_only=A , )
if resolved_config_file is None:
logger.info(
'''Could not locate the image processor configuration file, will try to use the model config instead.''' )
return {}
with open(A , encoding='''utf-8''' ) as reader:
return json.load(A )
class lowerCamelCase_ :
def __init__( self : Tuple ):
"""simple docstring"""
raise EnvironmentError(
'''AutoImageProcessor is designed to be instantiated '''
'''using the `AutoImageProcessor.from_pretrained(pretrained_model_name_or_path)` method.''' )
@classmethod
@replace_list_option_in_docstrings(lowerCAmelCase__ )
def __lowercase ( cls : int , lowerCAmelCase__ : Optional[int] , **lowerCAmelCase__ : List[Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = kwargs.pop('''config''' , lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : Tuple = kwargs.pop('''trust_remote_code''' , lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : List[str] = True
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : str = ImageProcessingMixin.get_image_processor_dict(lowerCAmelCase__ , **lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : Tuple = config_dict.get('''image_processor_type''' , lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : str = None
if "AutoImageProcessor" in config_dict.get('''auto_map''' , {} ):
SCREAMING_SNAKE_CASE : List[Any] = config_dict['''auto_map''']['''AutoImageProcessor''']
# If we still don't have the image processor class, check if we're loading from a previous feature extractor config
# and if so, infer the image processor class from there.
if image_processor_class is None and image_processor_auto_map is None:
SCREAMING_SNAKE_CASE : Optional[Any] = config_dict.pop('''feature_extractor_type''' , lowerCAmelCase__ )
if feature_extractor_class is not None:
logger.warning(
'''Could not find image processor class in the image processor config or the model config. Loading'''
''' based on pattern matching with the model\'s feature extractor configuration.''' )
SCREAMING_SNAKE_CASE : Optional[Any] = feature_extractor_class.replace('''FeatureExtractor''' , '''ImageProcessor''' )
if "AutoFeatureExtractor" in config_dict.get('''auto_map''' , {} ):
SCREAMING_SNAKE_CASE : Union[str, Any] = config_dict['''auto_map''']['''AutoFeatureExtractor''']
SCREAMING_SNAKE_CASE : str = feature_extractor_auto_map.replace('''FeatureExtractor''' , '''ImageProcessor''' )
logger.warning(
'''Could not find image processor auto map in the image processor config or the model config.'''
''' Loading based on pattern matching with the model\'s feature extractor configuration.''' )
# If we don't find the image processor class in the image processor config, let's try the model config.
if image_processor_class is None and image_processor_auto_map is None:
if not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
SCREAMING_SNAKE_CASE : List[str] = AutoConfig.from_pretrained(lowerCAmelCase__ , **lowerCAmelCase__ )
# It could be in `config.image_processor_type``
SCREAMING_SNAKE_CASE : Any = getattr(lowerCAmelCase__ , '''image_processor_type''' , lowerCAmelCase__ )
if hasattr(lowerCAmelCase__ , '''auto_map''' ) and "AutoImageProcessor" in config.auto_map:
SCREAMING_SNAKE_CASE : Tuple = config.auto_map['''AutoImageProcessor''']
if image_processor_class is not None:
SCREAMING_SNAKE_CASE : List[str] = image_processor_class_from_name(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : Tuple = image_processor_auto_map is not None
SCREAMING_SNAKE_CASE : str = image_processor_class is not None or type(lowerCAmelCase__ ) in IMAGE_PROCESSOR_MAPPING
SCREAMING_SNAKE_CASE : Union[str, Any] = resolve_trust_remote_code(
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
if has_remote_code and trust_remote_code:
SCREAMING_SNAKE_CASE : List[Any] = get_class_from_dynamic_module(
lowerCAmelCase__ , lowerCAmelCase__ , **lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : Optional[int] = kwargs.pop('''code_revision''' , lowerCAmelCase__ )
if os.path.isdir(lowerCAmelCase__ ):
image_processor_class.register_for_auto_class()
return image_processor_class.from_dict(lowerCAmelCase__ , **lowerCAmelCase__ )
elif image_processor_class is not None:
return image_processor_class.from_dict(lowerCAmelCase__ , **lowerCAmelCase__ )
# Last try: we use the IMAGE_PROCESSOR_MAPPING.
elif type(lowerCAmelCase__ ) in IMAGE_PROCESSOR_MAPPING:
SCREAMING_SNAKE_CASE : List[str] = IMAGE_PROCESSOR_MAPPING[type(lowerCAmelCase__ )]
return image_processor_class.from_dict(lowerCAmelCase__ , **lowerCAmelCase__ )
raise ValueError(
F"""Unrecognized image processor in {pretrained_model_name_or_path}. Should have a """
F"""`image_processor_type` key in its {IMAGE_PROCESSOR_NAME} of {CONFIG_NAME}, or one of the following """
F"""`model_type` keys in its {CONFIG_NAME}: {", ".join(c for c in IMAGE_PROCESSOR_MAPPING_NAMES.keys() )}""" )
@staticmethod
def __lowercase ( lowerCAmelCase__ : Optional[int] , lowerCAmelCase__ : Optional[int] ):
"""simple docstring"""
IMAGE_PROCESSOR_MAPPING.register(lowerCAmelCase__ , lowerCAmelCase__ )
| 464
| 1
|
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def _UpperCAmelCase ( a__):
'''simple docstring'''
if "cls_token" in name:
a_ : int = name.replace("""cls_token""" , """vit.embeddings.cls_token""")
if "mask_token" in name:
a_ : List[str] = name.replace("""mask_token""" , """decoder.mask_token""")
if "decoder_pos_embed" in name:
a_ : Optional[int] = name.replace("""decoder_pos_embed""" , """decoder.decoder_pos_embed""")
if "pos_embed" in name and "decoder" not in name:
a_ : Union[str, Any] = name.replace("""pos_embed""" , """vit.embeddings.position_embeddings""")
if "patch_embed.proj" in name:
a_ : Union[str, Any] = name.replace("""patch_embed.proj""" , """vit.embeddings.patch_embeddings.projection""")
if "patch_embed.norm" in name:
a_ : str = name.replace("""patch_embed.norm""" , """vit.embeddings.norm""")
if "decoder_blocks" in name:
a_ : Optional[int] = name.replace("""decoder_blocks""" , """decoder.decoder_layers""")
if "blocks" in name:
a_ : Dict = name.replace("""blocks""" , """vit.encoder.layer""")
if "attn.proj" in name:
a_ : Optional[Any] = name.replace("""attn.proj""" , """attention.output.dense""")
if "attn" in name:
a_ : Any = name.replace("""attn""" , """attention.self""")
if "norm1" in name:
a_ : int = name.replace("""norm1""" , """layernorm_before""")
if "norm2" in name:
a_ : List[Any] = name.replace("""norm2""" , """layernorm_after""")
if "mlp.fc1" in name:
a_ : List[Any] = name.replace("""mlp.fc1""" , """intermediate.dense""")
if "mlp.fc2" in name:
a_ : Optional[int] = name.replace("""mlp.fc2""" , """output.dense""")
if "decoder_embed" in name:
a_ : List[Any] = name.replace("""decoder_embed""" , """decoder.decoder_embed""")
if "decoder_norm" in name:
a_ : Dict = name.replace("""decoder_norm""" , """decoder.decoder_norm""")
if "decoder_pred" in name:
a_ : str = name.replace("""decoder_pred""" , """decoder.decoder_pred""")
if "norm.weight" in name and "decoder" not in name:
a_ : Optional[int] = name.replace("""norm.weight""" , """vit.layernorm.weight""")
if "norm.bias" in name and "decoder" not in name:
a_ : str = name.replace("""norm.bias""" , """vit.layernorm.bias""")
return name
def _UpperCAmelCase ( a__ , a__):
'''simple docstring'''
for key in orig_state_dict.copy().keys():
a_ : Optional[Any] = orig_state_dict.pop(a__)
if "qkv" in key:
a_ : List[Any] = key.split(""".""")
a_ : str = int(key_split[1])
if "decoder_blocks" in key:
a_ : Optional[int] = config.decoder_hidden_size
a_ : Dict = """decoder.decoder_layers."""
if "weight" in key:
a_ : Dict = val[:dim, :]
a_ : List[Any] = val[dim : dim * 2, :]
a_ : Optional[Any] = val[-dim:, :]
elif "bias" in key:
a_ : Optional[int] = val[:dim]
a_ : Optional[int] = val[dim : dim * 2]
a_ : int = val[-dim:]
else:
a_ : Tuple = config.hidden_size
a_ : List[Any] = """vit.encoder.layer."""
if "weight" in key:
a_ : Tuple = val[:dim, :]
a_ : Any = val[dim : dim * 2, :]
a_ : List[str] = val[-dim:, :]
elif "bias" in key:
a_ : int = val[:dim]
a_ : str = val[dim : dim * 2]
a_ : int = val[-dim:]
else:
a_ : int = val
return orig_state_dict
def _UpperCAmelCase ( a__ , a__):
'''simple docstring'''
a_ : List[Any] = ViTMAEConfig()
if "large" in checkpoint_url:
a_ : Tuple = 1_0_2_4
a_ : Optional[Any] = 4_0_9_6
a_ : List[str] = 2_4
a_ : str = 1_6
elif "huge" in checkpoint_url:
a_ : int = 1_4
a_ : int = 1_2_8_0
a_ : Optional[Any] = 5_1_2_0
a_ : Optional[int] = 3_2
a_ : Any = 1_6
a_ : Tuple = ViTMAEForPreTraining(a__)
a_ : Optional[Any] = torch.hub.load_state_dict_from_url(a__ , map_location="""cpu""")["""model"""]
a_ : Optional[Any] = ViTMAEImageProcessor(size=config.image_size)
a_ : List[str] = convert_state_dict(a__ , a__)
model.load_state_dict(a__)
model.eval()
a_ : Optional[int] = """https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg"""
a_ : str = Image.open(requests.get(a__ , stream=a__).raw)
a_ : int = ViTMAEImageProcessor(size=config.image_size)
a_ : Optional[Any] = image_processor(images=a__ , return_tensors="""pt""")
# forward pass
torch.manual_seed(2)
a_ : Any = model(**a__)
a_ : Union[str, Any] = outputs.logits
if "large" in checkpoint_url:
a_ : Optional[Any] = torch.tensor(
[[-0.7309, -0.7128, -1.0169], [-1.0161, -0.9058, -1.1878], [-1.0478, -0.9411, -1.1911]])
elif "huge" in checkpoint_url:
a_ : Tuple = torch.tensor(
[[-1.1599, -0.9199, -1.2221], [-1.1952, -0.9269, -1.2307], [-1.2143, -0.9337, -1.2262]])
else:
a_ : Dict = torch.tensor(
[[-0.9192, -0.8481, -1.1259], [-1.1349, -1.0034, -1.2599], [-1.1757, -1.0429, -1.2726]])
# verify logits
assert torch.allclose(logits[0, :3, :3] , a__ , atol=1e-4)
print(f'''Saving model to {pytorch_dump_folder_path}''')
model.save_pretrained(a__)
print(f'''Saving image processor to {pytorch_dump_folder_path}''')
image_processor.save_pretrained(a__)
if __name__ == "__main__":
__snake_case : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--checkpoint_url""",
default="""https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth""",
type=str,
help="""URL of the checkpoint you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
)
__snake_case : List[Any] = parser.parse_args()
convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
| 540
|
def _UpperCAmelCase ( a__ , a__):
'''simple docstring'''
return x if y == 0 else greatest_common_divisor(a__ , x % y)
def _UpperCAmelCase ( a__ , a__):
'''simple docstring'''
return (x * y) // greatest_common_divisor(a__ , a__)
def _UpperCAmelCase ( a__ = 2_0):
'''simple docstring'''
a_ : Any = 1
for i in range(1 , n + 1):
a_ : Tuple = lcm(a__ , a__)
return g
if __name__ == "__main__":
print(F"""{solution() = }""")
| 540
| 1
|
'''simple docstring'''
def _UpperCamelCase ( __UpperCamelCase = 50 ) -> int:
lowerCamelCase_ = [1] * (length + 1)
for row_length in range(3 ,length + 1 ):
for block_length in range(3 ,row_length + 1 ):
for block_start in range(row_length - block_length ):
ways_number[row_length] += ways_number[
row_length - block_start - block_length - 1
]
ways_number[row_length] += 1
return ways_number[length]
if __name__ == "__main__":
print(f'''{solution() = }''')
| 384
|
'''simple docstring'''
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
if is_vision_available():
from PIL import Image
else:
class UpperCAmelCase :
'''simple docstring'''
@staticmethod
def UpperCamelCase( *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) -> List[str]:
'''simple docstring'''
pass
def _UpperCamelCase ( __UpperCamelCase ) -> str:
lowerCamelCase_ = hashlib.mda(image.tobytes() )
return m.hexdigest()[:10]
def _UpperCamelCase ( __UpperCamelCase ) -> Dict:
lowerCamelCase_ = np.array(__UpperCamelCase )
lowerCamelCase_ = npimg.shape
return {"hash": hashimage(__UpperCamelCase ), "shape": shape}
@is_pipeline_test
@require_vision
@require_torch
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = dict(
(list(MODEL_FOR_MASK_GENERATION_MAPPING.items() ) if MODEL_FOR_MASK_GENERATION_MAPPING else []) )
SCREAMING_SNAKE_CASE_ = dict(
(list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items() ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else []) )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[Any]:
'''simple docstring'''
lowerCamelCase_ = MaskGenerationPipeline(model=SCREAMING_SNAKE_CASE_ , image_processor=SCREAMING_SNAKE_CASE_ )
return image_segmenter, [
"./tests/fixtures/tests_samples/COCO/000000039769.png",
"./tests/fixtures/tests_samples/COCO/000000039769.png",
]
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> str:
'''simple docstring'''
pass
@require_tf
@unittest.skip('Image segmentation not implemented in TF' )
def UpperCamelCase( self ) -> int:
'''simple docstring'''
pass
@slow
@require_torch
def UpperCamelCase( self ) -> Optional[int]:
'''simple docstring'''
lowerCamelCase_ = pipeline('mask-generation' , model='facebook/sam-vit-huge' )
lowerCamelCase_ = image_segmenter('http://images.cocodataset.org/val2017/000000039769.jpg' , points_per_batch=256 )
# Shortening by hashing
lowerCamelCase_ = []
for i, o in enumerate(outputs['masks'] ):
new_outupt += [{"mask": mask_to_test_readable(SCREAMING_SNAKE_CASE_ ), "scores": outputs["scores"][i]}]
# fmt: off
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE_ , decimals=4 ) , [
{'mask': {'hash': '115ad19f5f', 'shape': (480, 640)}, 'scores': 1.0_444},
{'mask': {'hash': '6affa964c6', 'shape': (480, 640)}, 'scores': 1.021},
{'mask': {'hash': 'dfe28a0388', 'shape': (480, 640)}, 'scores': 1.0_167},
{'mask': {'hash': 'c0a5f4a318', 'shape': (480, 640)}, 'scores': 1.0_132},
{'mask': {'hash': 'fe8065c197', 'shape': (480, 640)}, 'scores': 1.0_053},
{'mask': {'hash': 'e2d0b7a0b7', 'shape': (480, 640)}, 'scores': 0.9_967},
{'mask': {'hash': '453c7844bd', 'shape': (480, 640)}, 'scores': 0.993},
{'mask': {'hash': '3d44f2926d', 'shape': (480, 640)}, 'scores': 0.9_909},
{'mask': {'hash': '64033ddc3f', 'shape': (480, 640)}, 'scores': 0.9_879},
{'mask': {'hash': '801064ff79', 'shape': (480, 640)}, 'scores': 0.9_834},
{'mask': {'hash': '6172f276ef', 'shape': (480, 640)}, 'scores': 0.9_716},
{'mask': {'hash': 'b49e60e084', 'shape': (480, 640)}, 'scores': 0.9_612},
{'mask': {'hash': 'a811e775fd', 'shape': (480, 640)}, 'scores': 0.9_599},
{'mask': {'hash': 'a6a8ebcf4b', 'shape': (480, 640)}, 'scores': 0.9_552},
{'mask': {'hash': '9d8257e080', 'shape': (480, 640)}, 'scores': 0.9_532},
{'mask': {'hash': '32de6454a8', 'shape': (480, 640)}, 'scores': 0.9_516},
{'mask': {'hash': 'af3d4af2c8', 'shape': (480, 640)}, 'scores': 0.9_499},
{'mask': {'hash': '3c6db475fb', 'shape': (480, 640)}, 'scores': 0.9_483},
{'mask': {'hash': 'c290813fb9', 'shape': (480, 640)}, 'scores': 0.9_464},
{'mask': {'hash': 'b6f0b8f606', 'shape': (480, 640)}, 'scores': 0.943},
{'mask': {'hash': '92ce16bfdf', 'shape': (480, 640)}, 'scores': 0.943},
{'mask': {'hash': 'c749b25868', 'shape': (480, 640)}, 'scores': 0.9_408},
{'mask': {'hash': 'efb6cab859', 'shape': (480, 640)}, 'scores': 0.9_335},
{'mask': {'hash': '1ff2eafb30', 'shape': (480, 640)}, 'scores': 0.9_326},
{'mask': {'hash': '788b798e24', 'shape': (480, 640)}, 'scores': 0.9_262},
{'mask': {'hash': 'abea804f0e', 'shape': (480, 640)}, 'scores': 0.8_999},
{'mask': {'hash': '7b9e8ddb73', 'shape': (480, 640)}, 'scores': 0.8_986},
{'mask': {'hash': 'cd24047c8a', 'shape': (480, 640)}, 'scores': 0.8_984},
{'mask': {'hash': '6943e6bcbd', 'shape': (480, 640)}, 'scores': 0.8_873},
{'mask': {'hash': 'b5f47c9191', 'shape': (480, 640)}, 'scores': 0.8_871}
] , )
# fmt: on
@require_torch
@slow
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
lowerCamelCase_ = 'facebook/sam-vit-huge'
lowerCamelCase_ = pipeline('mask-generation' , model=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = image_segmenter(
'http://images.cocodataset.org/val2017/000000039769.jpg' , pred_iou_thresh=1 , points_per_batch=256 )
# Shortening by hashing
lowerCamelCase_ = []
for i, o in enumerate(outputs['masks'] ):
new_outupt += [{"mask": mask_to_test_readable(SCREAMING_SNAKE_CASE_ ), "scores": outputs["scores"][i]}]
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE_ , decimals=4 ) , [
{'mask': {'hash': '115ad19f5f', 'shape': (480, 640)}, 'scores': 1.0_444},
{'mask': {'hash': '6affa964c6', 'shape': (480, 640)}, 'scores': 1.0_210},
{'mask': {'hash': 'dfe28a0388', 'shape': (480, 640)}, 'scores': 1.0_167},
{'mask': {'hash': 'c0a5f4a318', 'shape': (480, 640)}, 'scores': 1.0_132},
{'mask': {'hash': 'fe8065c197', 'shape': (480, 640)}, 'scores': 1.0_053},
] , )
| 384
| 1
|
"""simple docstring"""
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
a : int = logging.getLogger(__name__)
class __UpperCamelCase ( a__ ):
def __init__( self , lowerCAmelCase__=-1 ) -> Union[str, Any]:
# in NER datasets, the last column is usually reserved for NER label
a : Optional[Any] = label_idx
def __a ( self , lowerCAmelCase__ , lowerCAmelCase__ ) -> List[InputExample]:
if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
a : Optional[Any] = mode.value
a : Any = os.path.join(lowerCAmelCase__ , f"""{mode}.txt""" )
a : Union[str, Any] = 1
a : str = []
with open(lowerCAmelCase__ , encoding="utf-8" ) as f:
a : Optional[Any] = []
a : str = []
for line in f:
if line.startswith("-DOCSTART-" ) or line == "" or line == "\n":
if words:
examples.append(InputExample(guid=f"""{mode}-{guid_index}""" , words=lowerCAmelCase__ , labels=lowerCAmelCase__ ) )
guid_index += 1
a : Union[str, Any] = []
a : Union[str, Any] = []
else:
a : List[str] = line.split(" " )
words.append(splits[0] )
if len(lowerCAmelCase__ ) > 1:
labels.append(splits[self.label_idx].replace("\n" , "" ) )
else:
# Examples could have no label for mode = "test"
labels.append("O" )
if words:
examples.append(InputExample(guid=f"""{mode}-{guid_index}""" , words=lowerCAmelCase__ , labels=lowerCAmelCase__ ) )
return examples
def __a ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> Dict:
a : Any = 0
for line in test_input_reader:
if line.startswith("-DOCSTART-" ) or line == "" or line == "\n":
writer.write(lowerCAmelCase__ )
if not preds_list[example_id]:
example_id += 1
elif preds_list[example_id]:
a : int = line.split()[0] + " " + preds_list[example_id].pop(0 ) + "\n"
writer.write(lowerCAmelCase__ )
else:
logger.warning("Maximum sequence length exceeded: No prediction for '%s'." , line.split()[0] )
def __a ( self , lowerCAmelCase__ ) -> List[str]:
if path:
with open(lowerCAmelCase__ , "r" ) as f:
a : Any = f.read().splitlines()
if "O" not in labels:
a : Optional[Any] = ["O"] + labels
return labels
else:
return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]
class __UpperCamelCase ( a__ ):
def __init__( self ) -> Optional[int]:
# in CONLL2003 dataset chunk column is second-to-last
super().__init__(label_idx=-2 )
def __a ( self , lowerCAmelCase__ ) -> List[str]:
if path:
with open(lowerCAmelCase__ , "r" ) as f:
a : List[Any] = f.read().splitlines()
if "O" not in labels:
a : List[Any] = ["O"] + labels
return labels
else:
return [
"O",
"B-ADVP",
"B-INTJ",
"B-LST",
"B-PRT",
"B-NP",
"B-SBAR",
"B-VP",
"B-ADJP",
"B-CONJP",
"B-PP",
"I-ADVP",
"I-INTJ",
"I-LST",
"I-PRT",
"I-NP",
"I-SBAR",
"I-VP",
"I-ADJP",
"I-CONJP",
"I-PP",
]
class __UpperCamelCase ( a__ ):
def __a ( self , lowerCAmelCase__ , lowerCAmelCase__ ) -> List[InputExample]:
if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
a : Optional[Any] = mode.value
a : Any = os.path.join(lowerCAmelCase__ , f"""{mode}.txt""" )
a : Optional[int] = 1
a : str = []
with open(lowerCAmelCase__ , encoding="utf-8" ) as f:
for sentence in parse_incr(lowerCAmelCase__ ):
a : List[str] = []
a : Optional[int] = []
for token in sentence:
words.append(token["form"] )
labels.append(token["upos"] )
assert len(lowerCAmelCase__ ) == len(lowerCAmelCase__ )
if words:
examples.append(InputExample(guid=f"""{mode}-{guid_index}""" , words=lowerCAmelCase__ , labels=lowerCAmelCase__ ) )
guid_index += 1
return examples
def __a ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> Dict:
a : int = 0
for sentence in parse_incr(lowerCAmelCase__ ):
a : Any = preds_list[example_id]
a : Any = ""
for token in sentence:
out += f"""{token['form']} ({token['upos']}|{s_p.pop(0 )}) """
out += "\n"
writer.write(lowerCAmelCase__ )
example_id += 1
def __a ( self , lowerCAmelCase__ ) -> List[str]:
if path:
with open(lowerCAmelCase__ , "r" ) as f:
return f.read().splitlines()
else:
return [
"ADJ",
"ADP",
"ADV",
"AUX",
"CCONJ",
"DET",
"INTJ",
"NOUN",
"NUM",
"PART",
"PRON",
"PROPN",
"PUNCT",
"SCONJ",
"SYM",
"VERB",
"X",
]
| 633
|
"""simple docstring"""
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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
a : int = logging.get_logger(__name__)
class __UpperCamelCase ( a__ ):
lowerCamelCase : Optional[int] =["""pixel_values"""]
def __init__( self , lowerCAmelCase__ = True , lowerCAmelCase__ = None , lowerCAmelCase__ = PIL.Image.BICUBIC , lowerCAmelCase__ = True , lowerCAmelCase__ = None , lowerCAmelCase__ = 1 / 255 , lowerCAmelCase__ = True , lowerCAmelCase__ = True , lowerCAmelCase__ = None , lowerCAmelCase__ = None , **lowerCAmelCase__ , ) -> None:
super().__init__(**lowerCAmelCase__ )
a : Optional[int] = size if size is not None else {"height": 256, "width": 256}
a : Tuple = get_size_dict(lowerCAmelCase__ )
a : Any = crop_size if crop_size is not None else {"height": 224, "width": 224}
a : Dict = get_size_dict(lowerCAmelCase__ , param_name="crop_size" )
a : Tuple = do_resize
a : int = size
a : List[str] = resample
a : List[Any] = do_center_crop
a : List[Any] = crop_size
a : Union[str, Any] = do_rescale
a : List[Any] = rescale_factor
a : Union[str, Any] = do_normalize
a : Union[str, Any] = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
a : List[str] = image_std if image_std is not None else IMAGENET_STANDARD_STD
def __a ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = PIL.Image.BICUBIC , lowerCAmelCase__ = None , **lowerCAmelCase__ , ) -> np.ndarray:
a : Optional[int] = get_size_dict(lowerCAmelCase__ )
if "height" not in size or "width" not in size:
raise ValueError(f"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}""" )
return resize(
lowerCAmelCase__ , size=(size["height"], size["width"]) , resample=lowerCAmelCase__ , data_format=lowerCAmelCase__ , **lowerCAmelCase__ )
def __a ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = None , **lowerCAmelCase__ , ) -> np.ndarray:
a : List[str] = get_size_dict(lowerCAmelCase__ )
if "height" not in size or "width" not in size:
raise ValueError(f"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}""" )
return center_crop(lowerCAmelCase__ , size=(size["height"], size["width"]) , data_format=lowerCAmelCase__ , **lowerCAmelCase__ )
def __a ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = None , **lowerCAmelCase__ , ) -> Union[str, Any]:
return rescale(lowerCAmelCase__ , scale=lowerCAmelCase__ , data_format=lowerCAmelCase__ , **lowerCAmelCase__ )
def __a ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = None , **lowerCAmelCase__ , ) -> np.ndarray:
return normalize(lowerCAmelCase__ , mean=lowerCAmelCase__ , std=lowerCAmelCase__ , data_format=lowerCAmelCase__ , **lowerCAmelCase__ )
def __a ( self , lowerCAmelCase__ , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__=None , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = ChannelDimension.FIRST , **lowerCAmelCase__ , ) -> PIL.Image.Image:
a : List[Any] = do_resize if do_resize is not None else self.do_resize
a : str = resample if resample is not None else self.resample
a : Dict = do_center_crop if do_center_crop is not None else self.do_center_crop
a : Union[str, Any] = do_rescale if do_rescale is not None else self.do_rescale
a : str = rescale_factor if rescale_factor is not None else self.rescale_factor
a : Dict = do_normalize if do_normalize is not None else self.do_normalize
a : List[str] = image_mean if image_mean is not None else self.image_mean
a : Union[str, Any] = image_std if image_std is not None else self.image_std
a : str = size if size is not None else self.size
a : Any = get_size_dict(lowerCAmelCase__ )
a : Dict = crop_size if crop_size is not None else self.crop_size
a : List[str] = get_size_dict(lowerCAmelCase__ , param_name="crop_size" )
a : str = make_list_of_images(lowerCAmelCase__ )
if not valid_images(lowerCAmelCase__ ):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray." )
if do_resize and size is None or resample is None:
raise ValueError("Size and resample must be specified if do_resize is True." )
if do_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.
a : Optional[Any] = [to_numpy_array(lowerCAmelCase__ ) for image in images]
if do_resize:
a : Optional[int] = [self.resize(image=lowerCAmelCase__ , size=lowerCAmelCase__ , resample=lowerCAmelCase__ ) for image in images]
if do_center_crop:
a : Dict = [self.center_crop(image=lowerCAmelCase__ , size=lowerCAmelCase__ ) for image in images]
if do_rescale:
a : Any = [self.rescale(image=lowerCAmelCase__ , scale=lowerCAmelCase__ ) for image in images]
if do_normalize:
a : Optional[Any] = [self.normalize(image=lowerCAmelCase__ , mean=lowerCAmelCase__ , std=lowerCAmelCase__ ) for image in images]
a : Optional[Any] = [to_channel_dimension_format(lowerCAmelCase__ , lowerCAmelCase__ ) for image in images]
a : Tuple = {"pixel_values": images}
return BatchFeature(data=lowerCAmelCase__ , tensor_type=lowerCAmelCase__ )
| 633
| 1
|
"""simple docstring"""
import sys
from typing import Tuple
import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class _lowercase :
def __init__( self , UpperCamelCase_ , UpperCamelCase_=sys.maxsize ):
__magic_name__ = '''bilinear'''
__magic_name__ = max_size
__magic_name__ = short_edge_length
def __call__( self , UpperCamelCase_ ):
__magic_name__ = []
for img in imgs:
__magic_name__ , __magic_name__ = img.shape[:2]
# later: provide list and randomly choose index for resize
__magic_name__ = np.random.randint(self.short_edge_length[0] , self.short_edge_length[1] + 1 )
if size == 0:
return img
__magic_name__ = size * 1.0 / min(UpperCamelCase_ , UpperCamelCase_ )
if h < w:
__magic_name__ , __magic_name__ = size, scale * w
else:
__magic_name__ , __magic_name__ = scale * h, size
if max(UpperCamelCase_ , UpperCamelCase_ ) > self.max_size:
__magic_name__ = self.max_size * 1.0 / max(UpperCamelCase_ , UpperCamelCase_ )
__magic_name__ = newh * scale
__magic_name__ = neww * scale
__magic_name__ = int(neww + 0.5 )
__magic_name__ = int(newh + 0.5 )
if img.dtype == np.uinta:
__magic_name__ = Image.fromarray(UpperCamelCase_ )
__magic_name__ = pil_image.resize((neww, newh) , PILImageResampling.BILINEAR )
__magic_name__ = np.asarray(UpperCamelCase_ )
else:
__magic_name__ = img.permute(2 , 0 , 1 ).unsqueeze(0 ) # 3, 0, 1) # hw(c) -> nchw
__magic_name__ = nn.functional.interpolate(
UpperCamelCase_ , (newh, neww) , mode=self.interp_method , align_corners=UpperCamelCase_ ).squeeze(0 )
img_augs.append(UpperCamelCase_ )
return img_augs
class _lowercase :
def __init__( self , UpperCamelCase_ ):
__magic_name__ = ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST] , cfg.INPUT.MAX_SIZE_TEST )
__magic_name__ = cfg.INPUT.FORMAT
__magic_name__ = cfg.SIZE_DIVISIBILITY
__magic_name__ = cfg.PAD_VALUE
__magic_name__ = cfg.INPUT.MAX_SIZE_TEST
__magic_name__ = cfg.MODEL.DEVICE
__magic_name__ = torch.tensor(cfg.MODEL.PIXEL_STD ).to(self.device ).view(len(cfg.MODEL.PIXEL_STD ) , 1 , 1 )
__magic_name__ = torch.tensor(cfg.MODEL.PIXEL_MEAN ).to(self.device ).view(len(cfg.MODEL.PIXEL_STD ) , 1 , 1 )
__magic_name__ = lambda UpperCamelCase_ : (x - self.pixel_mean) / self.pixel_std
def lowerCAmelCase__ ( self , UpperCamelCase_ ):
__magic_name__ = tuple(max(UpperCamelCase_ ) for s in zip(*[img.shape for img in images] ) )
__magic_name__ = [im.shape[-2:] for im in images]
__magic_name__ = [
nn.functional.pad(
UpperCamelCase_ , [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]] , value=self.pad_value , )
for size, im in zip(UpperCamelCase_ , UpperCamelCase_ )
]
return torch.stack(UpperCamelCase_ ), torch.tensor(UpperCamelCase_ )
def __call__( self , UpperCamelCase_ , UpperCamelCase_=False ):
with torch.no_grad():
if not isinstance(UpperCamelCase_ , UpperCamelCase_ ):
__magic_name__ = [images]
if single_image:
assert len(UpperCamelCase_ ) == 1
for i in range(len(UpperCamelCase_ ) ):
if isinstance(images[i] , torch.Tensor ):
images.insert(UpperCamelCase_ , images.pop(UpperCamelCase_ ).to(self.device ).float() )
elif not isinstance(images[i] , torch.Tensor ):
images.insert(
UpperCamelCase_ , torch.as_tensor(img_tensorize(images.pop(UpperCamelCase_ ) , input_format=self.input_format ) )
.to(self.device )
.float() , )
# resize smallest edge
__magic_name__ = torch.tensor([im.shape[:2] for im in images] )
__magic_name__ = self.aug(UpperCamelCase_ )
# transpose images and convert to torch tensors
# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
# now normalize before pad to avoid useless arithmetic
__magic_name__ = [self.normalizer(UpperCamelCase_ ) for x in images]
# now pad them to do the following operations
__magic_name__ , __magic_name__ = self.pad(UpperCamelCase_ )
# Normalize
if self.size_divisibility > 0:
raise NotImplementedError()
# pad
__magic_name__ = torch.true_divide(UpperCamelCase_ , UpperCamelCase_ )
if single_image:
return images[0], sizes[0], scales_yx[0]
else:
return images, sizes, scales_yx
def lowercase ( __UpperCamelCase , __UpperCamelCase ) -> List[str]:
boxes[:, 0::2] *= scale_yx[:, 1]
boxes[:, 1::2] *= scale_yx[:, 0]
return boxes
def lowercase ( __UpperCamelCase , __UpperCamelCase ) -> int:
assert torch.isfinite(__UpperCamelCase ).all(), "Box tensor contains infinite or NaN!"
__magic_name__ , __magic_name__ = box_size
tensor[:, 0].clamp_(min=0 , max=__UpperCamelCase )
tensor[:, 1].clamp_(min=0 , max=__UpperCamelCase )
tensor[:, 2].clamp_(min=0 , max=__UpperCamelCase )
tensor[:, 3].clamp_(min=0 , max=__UpperCamelCase )
| 708
|
"""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_bert import BertTokenizer
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
__lowerCamelCase = {
"vocab_file": {
"bert-base-uncased": "https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt",
"bert-large-uncased": "https://huggingface.co/bert-large-uncased/resolve/main/vocab.txt",
"bert-base-cased": "https://huggingface.co/bert-base-cased/resolve/main/vocab.txt",
"bert-large-cased": "https://huggingface.co/bert-large-cased/resolve/main/vocab.txt",
"bert-base-multilingual-uncased": (
"https://huggingface.co/bert-base-multilingual-uncased/resolve/main/vocab.txt"
),
"bert-base-multilingual-cased": "https://huggingface.co/bert-base-multilingual-cased/resolve/main/vocab.txt",
"bert-base-chinese": "https://huggingface.co/bert-base-chinese/resolve/main/vocab.txt",
"bert-base-german-cased": "https://huggingface.co/bert-base-german-cased/resolve/main/vocab.txt",
"bert-large-uncased-whole-word-masking": (
"https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/vocab.txt"
),
"bert-large-cased-whole-word-masking": (
"https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/vocab.txt"
),
"bert-large-uncased-whole-word-masking-finetuned-squad": (
"https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt"
),
"bert-large-cased-whole-word-masking-finetuned-squad": (
"https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt"
),
"bert-base-cased-finetuned-mrpc": (
"https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/vocab.txt"
),
"bert-base-german-dbmdz-cased": "https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/vocab.txt",
"bert-base-german-dbmdz-uncased": (
"https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/vocab.txt"
),
"TurkuNLP/bert-base-finnish-cased-v1": (
"https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/vocab.txt"
),
"TurkuNLP/bert-base-finnish-uncased-v1": (
"https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/vocab.txt"
),
"wietsedv/bert-base-dutch-cased": (
"https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/vocab.txt"
),
},
"tokenizer_file": {
"bert-base-uncased": "https://huggingface.co/bert-base-uncased/resolve/main/tokenizer.json",
"bert-large-uncased": "https://huggingface.co/bert-large-uncased/resolve/main/tokenizer.json",
"bert-base-cased": "https://huggingface.co/bert-base-cased/resolve/main/tokenizer.json",
"bert-large-cased": "https://huggingface.co/bert-large-cased/resolve/main/tokenizer.json",
"bert-base-multilingual-uncased": (
"https://huggingface.co/bert-base-multilingual-uncased/resolve/main/tokenizer.json"
),
"bert-base-multilingual-cased": (
"https://huggingface.co/bert-base-multilingual-cased/resolve/main/tokenizer.json"
),
"bert-base-chinese": "https://huggingface.co/bert-base-chinese/resolve/main/tokenizer.json",
"bert-base-german-cased": "https://huggingface.co/bert-base-german-cased/resolve/main/tokenizer.json",
"bert-large-uncased-whole-word-masking": (
"https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/tokenizer.json"
),
"bert-large-cased-whole-word-masking": (
"https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/tokenizer.json"
),
"bert-large-uncased-whole-word-masking-finetuned-squad": (
"https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json"
),
"bert-large-cased-whole-word-masking-finetuned-squad": (
"https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json"
),
"bert-base-cased-finetuned-mrpc": (
"https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/tokenizer.json"
),
"bert-base-german-dbmdz-cased": (
"https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/tokenizer.json"
),
"bert-base-german-dbmdz-uncased": (
"https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/tokenizer.json"
),
"TurkuNLP/bert-base-finnish-cased-v1": (
"https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/tokenizer.json"
),
"TurkuNLP/bert-base-finnish-uncased-v1": (
"https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/tokenizer.json"
),
"wietsedv/bert-base-dutch-cased": (
"https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/tokenizer.json"
),
},
}
__lowerCamelCase = {
"bert-base-uncased": 5_12,
"bert-large-uncased": 5_12,
"bert-base-cased": 5_12,
"bert-large-cased": 5_12,
"bert-base-multilingual-uncased": 5_12,
"bert-base-multilingual-cased": 5_12,
"bert-base-chinese": 5_12,
"bert-base-german-cased": 5_12,
"bert-large-uncased-whole-word-masking": 5_12,
"bert-large-cased-whole-word-masking": 5_12,
"bert-large-uncased-whole-word-masking-finetuned-squad": 5_12,
"bert-large-cased-whole-word-masking-finetuned-squad": 5_12,
"bert-base-cased-finetuned-mrpc": 5_12,
"bert-base-german-dbmdz-cased": 5_12,
"bert-base-german-dbmdz-uncased": 5_12,
"TurkuNLP/bert-base-finnish-cased-v1": 5_12,
"TurkuNLP/bert-base-finnish-uncased-v1": 5_12,
"wietsedv/bert-base-dutch-cased": 5_12,
}
__lowerCamelCase = {
"bert-base-uncased": {"do_lower_case": True},
"bert-large-uncased": {"do_lower_case": True},
"bert-base-cased": {"do_lower_case": False},
"bert-large-cased": {"do_lower_case": False},
"bert-base-multilingual-uncased": {"do_lower_case": True},
"bert-base-multilingual-cased": {"do_lower_case": False},
"bert-base-chinese": {"do_lower_case": False},
"bert-base-german-cased": {"do_lower_case": False},
"bert-large-uncased-whole-word-masking": {"do_lower_case": True},
"bert-large-cased-whole-word-masking": {"do_lower_case": False},
"bert-large-uncased-whole-word-masking-finetuned-squad": {"do_lower_case": True},
"bert-large-cased-whole-word-masking-finetuned-squad": {"do_lower_case": False},
"bert-base-cased-finetuned-mrpc": {"do_lower_case": False},
"bert-base-german-dbmdz-cased": {"do_lower_case": False},
"bert-base-german-dbmdz-uncased": {"do_lower_case": True},
"TurkuNLP/bert-base-finnish-cased-v1": {"do_lower_case": False},
"TurkuNLP/bert-base-finnish-uncased-v1": {"do_lower_case": True},
"wietsedv/bert-base-dutch-cased": {"do_lower_case": False},
}
class _lowercase ( __UpperCAmelCase ):
_lowerCamelCase = VOCAB_FILES_NAMES
_lowerCamelCase = PRETRAINED_VOCAB_FILES_MAP
_lowerCamelCase = PRETRAINED_INIT_CONFIGURATION
_lowerCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_lowerCamelCase = BertTokenizer
def __init__( self , UpperCamelCase_=None , UpperCamelCase_=None , UpperCamelCase_=True , UpperCamelCase_="[UNK]" , UpperCamelCase_="[SEP]" , UpperCamelCase_="[PAD]" , UpperCamelCase_="[CLS]" , UpperCamelCase_="[MASK]" , UpperCamelCase_=True , UpperCamelCase_=None , **UpperCamelCase_ , ):
super().__init__(
UpperCamelCase_ , tokenizer_file=UpperCamelCase_ , do_lower_case=UpperCamelCase_ , unk_token=UpperCamelCase_ , sep_token=UpperCamelCase_ , pad_token=UpperCamelCase_ , cls_token=UpperCamelCase_ , mask_token=UpperCamelCase_ , tokenize_chinese_chars=UpperCamelCase_ , strip_accents=UpperCamelCase_ , **UpperCamelCase_ , )
__magic_name__ = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('''lowercase''' , UpperCamelCase_ ) != do_lower_case
or normalizer_state.get('''strip_accents''' , UpperCamelCase_ ) != strip_accents
or normalizer_state.get('''handle_chinese_chars''' , UpperCamelCase_ ) != tokenize_chinese_chars
):
__magic_name__ = getattr(UpperCamelCase_ , normalizer_state.pop('''type''' ) )
__magic_name__ = do_lower_case
__magic_name__ = strip_accents
__magic_name__ = tokenize_chinese_chars
__magic_name__ = normalizer_class(**UpperCamelCase_ )
__magic_name__ = do_lower_case
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_=None ):
__magic_name__ = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_ = None ):
__magic_name__ = [self.sep_token_id]
__magic_name__ = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_ = None ):
__magic_name__ = self._tokenizer.model.save(UpperCamelCase_ , name=UpperCamelCase_ )
return tuple(UpperCamelCase_ )
| 190
| 0
|
'''simple docstring'''
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 _UpperCamelCase (_lowerCamelCase : Union[str, Any] , _lowerCamelCase : List[Any] )-> Optional[Any]:
'''simple docstring'''
__snake_case = []
for part_id in partition_order:
__snake_case = df.where(f'''SPARK_PARTITION_ID() = {part_id}''' ).collect()
for row_idx, row in enumerate(_lowerCamelCase ):
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 _UpperCamelCase ()-> Any:
'''simple docstring'''
__snake_case = pyspark.sql.SparkSession.builder.master('''local[*]''' ).appName('''pyspark''' ).getOrCreate()
__snake_case = spark.range(1_00 ).repartition(1 )
__snake_case = Spark(_lowerCamelCase )
# 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=16 )
# Given that the dataframe has 100 rows and each partition has 2 rows, we expect 50 partitions.
assert spark_builder.df.rdd.getNumPartitions() == 50
@require_not_windows
@require_dill_gt_0_3_2
def _UpperCamelCase ()-> Tuple:
'''simple docstring'''
__snake_case = pyspark.sql.SparkSession.builder.master('''local[*]''' ).appName('''pyspark''' ).getOrCreate()
__snake_case = spark.range(10 ).repartition(2 )
__snake_case = [1, 0]
__snake_case = _generate_iterable_examples(_lowerCamelCase , _lowerCamelCase ) # Reverse the partitions.
__snake_case = _get_expected_row_ids_and_row_dicts_for_partition_order(_lowerCamelCase , _lowerCamelCase )
for i, (row_id, row_dict) in enumerate(generate_fn() ):
__snake_case , __snake_case = 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 _UpperCamelCase ()-> int:
'''simple docstring'''
__snake_case = pyspark.sql.SparkSession.builder.master('''local[*]''' ).appName('''pyspark''' ).getOrCreate()
__snake_case = spark.range(10 ).repartition(1 )
__snake_case = SparkExamplesIterable(_lowerCamelCase )
assert it.n_shards == 1
for i, (row_id, row_dict) in enumerate(_lowerCamelCase ):
assert row_id == f'''0_{i}'''
assert row_dict == {"id": i}
@require_not_windows
@require_dill_gt_0_3_2
def _UpperCamelCase ()-> Union[str, Any]:
'''simple docstring'''
__snake_case = pyspark.sql.SparkSession.builder.master('''local[*]''' ).appName('''pyspark''' ).getOrCreate()
__snake_case = spark.range(30 ).repartition(3 )
# Mock the generator so that shuffle reverses the partition indices.
with patch('''numpy.random.Generator''' ) as generator_mock:
__snake_case = lambda _lowerCamelCase : x.reverse()
__snake_case = _get_expected_row_ids_and_row_dicts_for_partition_order(_lowerCamelCase , [2, 1, 0] )
__snake_case = SparkExamplesIterable(_lowerCamelCase ).shuffle_data_sources(_lowerCamelCase )
assert shuffled_it.n_shards == 3
for i, (row_id, row_dict) in enumerate(_lowerCamelCase ):
__snake_case , __snake_case = 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 _UpperCamelCase ()-> Tuple:
'''simple docstring'''
__snake_case = pyspark.sql.SparkSession.builder.master('''local[*]''' ).appName('''pyspark''' ).getOrCreate()
__snake_case = spark.range(20 ).repartition(4 )
# Partitions 0 and 2
__snake_case = SparkExamplesIterable(_lowerCamelCase ).shard_data_sources(worker_id=0 , num_workers=2 )
assert shard_it_a.n_shards == 2
__snake_case = _get_expected_row_ids_and_row_dicts_for_partition_order(_lowerCamelCase , [0, 2] )
for i, (row_id, row_dict) in enumerate(_lowerCamelCase ):
__snake_case , __snake_case = expected_row_ids_and_row_dicts_a[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
# Partitions 1 and 3
__snake_case = SparkExamplesIterable(_lowerCamelCase ).shard_data_sources(worker_id=1 , num_workers=2 )
assert shard_it_a.n_shards == 2
__snake_case = _get_expected_row_ids_and_row_dicts_for_partition_order(_lowerCamelCase , [1, 3] )
for i, (row_id, row_dict) in enumerate(_lowerCamelCase ):
__snake_case , __snake_case = 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 _UpperCamelCase ()-> Optional[int]:
'''simple docstring'''
__snake_case = pyspark.sql.SparkSession.builder.master('''local[*]''' ).appName('''pyspark''' ).getOrCreate()
__snake_case = spark.range(1_00 ).repartition(1 )
__snake_case = Spark(_lowerCamelCase )
# 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_00
| 24
|
SCREAMING_SNAKE_CASE :List[Any] = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
SCREAMING_SNAKE_CASE :Union[str, Any] = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
SCREAMING_SNAKE_CASE :int = {
0: 'Sunday',
1: 'Monday',
2: 'Tuesday',
3: 'Wednesday',
4: 'Thursday',
5: 'Friday',
6: 'Saturday',
}
def UpperCAmelCase ( a_ , a_ , a_ ) -> str:
"""simple docstring"""
assert len(str(a_ ) ) > 2, "year should be in YYYY format"
assert 1 <= month <= 1_2, "month should be between 1 to 12"
assert 1 <= day <= 3_1, "day should be between 1 to 31"
# Doomsday algorithm:
__A = year // 1_0_0
__A = (5 * (century % 4) + 2) % 7
__A = year % 1_0_0
__A = centurian % 1_2
__A = (
(centurian // 1_2) + centurian_m + (centurian_m // 4) + century_anchor
) % 7
__A = (
DOOMSDAY_NOT_LEAP[month - 1]
if (year % 4 != 0) or (centurian == 0 and (year % 4_0_0) == 0)
else DOOMSDAY_LEAP[month - 1]
)
__A = (dooms_day + day - day_anchor) % 7
return WEEK_DAY_NAMES[week_day]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 55
| 0
|
'''simple docstring'''
import unittest
import numpy as np
from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
load_numpy,
nightly,
require_onnxruntime,
require_torch_gpu,
)
if is_onnx_available():
import onnxruntime as ort
@nightly
@require_onnxruntime
@require_torch_gpu
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
@property
def a ( self ):
'''simple docstring'''
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def a ( self ):
'''simple docstring'''
_lowerCAmelCase : List[str] = ort.SessionOptions()
_lowerCAmelCase : str = False
return options
def a ( self ):
'''simple docstring'''
_lowerCAmelCase : Dict = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/in_paint/overture-creations-5sI6fQgYIuo.png' )
_lowerCAmelCase : int = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/in_paint/overture-creations-5sI6fQgYIuo_mask.png' )
_lowerCAmelCase : List[str] = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy' )
# using the PNDM scheduler by default
_lowerCAmelCase : Optional[Any] = OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
'CompVis/stable-diffusion-v1-4' , revision='onnx' , safety_checker=snake_case__ , feature_extractor=snake_case__ , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=snake_case__ )
_lowerCAmelCase : Union[str, Any] = 'A red cat sitting on a park bench'
_lowerCAmelCase : Tuple = np.random.RandomState(0 )
_lowerCAmelCase : Tuple = pipe(
prompt=snake_case__ , image=snake_case__ , mask_image=snake_case__ , strength=0.75 , guidance_scale=7.5 , num_inference_steps=15 , generator=snake_case__ , output_type='np' , )
_lowerCAmelCase : Optional[int] = output.images[0]
assert image.shape == (512, 512, 3)
assert np.abs(expected_image - image ).max() < 1E-2
| 630
|
'''simple docstring'''
import pytest
import datasets
# Import fixture modules as plugins
lowerCAmelCase : List[str] = ["""tests.fixtures.files""", """tests.fixtures.hub""", """tests.fixtures.fsspec"""]
def lowercase (_A , _A ):
"""simple docstring"""
for item in items:
if any(marker in item.keywords for marker in ['integration', 'unit'] ):
continue
item.add_marker(pytest.mark.unit )
def lowercase (_A ):
"""simple docstring"""
config.addinivalue_line('markers' , 'torchaudio_latest: mark test to run with torchaudio>=0.12' )
@pytest.fixture(autouse=_A )
def lowercase (_A , _A ):
"""simple docstring"""
_lowerCAmelCase : str = tmp_path_factory.getbasetemp() / 'cache'
_lowerCAmelCase : Dict = test_hf_cache_home / 'datasets'
_lowerCAmelCase : List[Any] = test_hf_cache_home / 'metrics'
_lowerCAmelCase : List[Any] = test_hf_cache_home / 'modules'
monkeypatch.setattr('datasets.config.HF_DATASETS_CACHE' , str(_A ) )
monkeypatch.setattr('datasets.config.HF_METRICS_CACHE' , str(_A ) )
monkeypatch.setattr('datasets.config.HF_MODULES_CACHE' , str(_A ) )
_lowerCAmelCase : Dict = test_hf_datasets_cache / 'downloads'
monkeypatch.setattr('datasets.config.DOWNLOADED_DATASETS_PATH' , str(_A ) )
_lowerCAmelCase : Union[str, Any] = test_hf_datasets_cache / 'downloads' / 'extracted'
monkeypatch.setattr('datasets.config.EXTRACTED_DATASETS_PATH' , str(_A ) )
@pytest.fixture(autouse=_A , scope='session' )
def lowercase ():
"""simple docstring"""
datasets.disable_progress_bar()
@pytest.fixture(autouse=_A )
def lowercase (_A ):
"""simple docstring"""
monkeypatch.setattr('datasets.config.HF_UPDATE_DOWNLOAD_COUNTS' , _A )
@pytest.fixture
def lowercase (_A ):
"""simple docstring"""
monkeypatch.setattr('sqlalchemy.util.deprecations.SILENCE_UBER_WARNING' , _A )
| 630
| 1
|
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase ={
"configuration_informer": [
"INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"InformerConfig",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase =[
"INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"InformerForPrediction",
"InformerModel",
"InformerPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_informer import (
INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
InformerForPrediction,
InformerModel,
InformerPreTrainedModel,
)
else:
import sys
UpperCAmelCase =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 617
|
"""simple docstring"""
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
HfArgumentParser,
Trainer,
TrainingArguments,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForPreTraining,
is_apex_available,
trainer_utils,
)
from transformers.models.wavaveca.modeling_wavaveca import _compute_mask_indices
if is_apex_available():
from apex import amp
if version.parse(version.parse(torch.__version__).base_version) >= version.parse("1.6"):
UpperCAmelCase =True
from torch.cuda.amp import autocast
UpperCAmelCase =logging.getLogger(__name__)
@dataclass
class lowerCamelCase__ :
'''simple docstring'''
_lowerCamelCase = field(
metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
_lowerCamelCase = field(
default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} , )
_lowerCamelCase = field(
default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''Whether to freeze the feature extractor layers of the model.'''} )
_lowerCamelCase = field(
default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''Whether to log verbose messages or not.'''} , )
_lowerCamelCase = field(
default=2.0 , metadata={'''help''': '''Maximum temperature for gumbel softmax.'''} )
_lowerCamelCase = field(
default=0.5 , metadata={'''help''': '''Minimum temperature for gumbel softmax.'''} )
_lowerCamelCase = field(
default=0.9_9_9_9_9_5 , metadata={'''help''': '''Decay of gumbel temperature during training.'''} )
def _A ( _a : ModelArguments , _a : TrainingArguments ):
"""simple docstring"""
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , handlers=[logging.StreamHandler(sys.stdout )] , )
A = logging.WARNING
if model_args.verbose_logging:
A = logging.DEBUG
elif trainer_utils.is_main_process(training_args.local_rank ):
A = logging.INFO
logger.setLevel(_a )
@dataclass
class lowerCamelCase__ :
'''simple docstring'''
_lowerCamelCase = field(
default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''} )
_lowerCamelCase = field(
default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''} )
_lowerCamelCase = field(
default='''train''' , metadata={
'''help''': '''The name of the training data set split to use (via the datasets library). Defaults to \'train\''''
} , )
_lowerCamelCase = field(
default='''validation''' , metadata={
'''help''': (
'''The name of the validation data set split to use (via the datasets library). Defaults to \'validation\''''
)
} , )
_lowerCamelCase = field(
default='''file''' , metadata={'''help''': '''Column in the dataset that contains speech file path. Defaults to \'file\''''} , )
_lowerCamelCase = field(
default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''} )
_lowerCamelCase = field(
default=1 , metadata={
'''help''': '''The percentage of the train set used as validation set in case there\'s no validation split'''
} , )
_lowerCamelCase = field(
default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''The number of processes to use for the preprocessing.'''} , )
_lowerCamelCase = field(
default=2_0.0 , metadata={'''help''': '''Filter audio files that are longer than `max_duration_in_seconds` seconds'''} )
@dataclass
class lowerCamelCase__ :
'''simple docstring'''
_lowerCamelCase = 42
_lowerCamelCase = 42
_lowerCamelCase = "longest"
_lowerCamelCase = None
_lowerCamelCase = None
def __call__( self ,lowerCamelCase_ ) -> Dict[str, torch.Tensor]:
# reformat list to dict and set to pytorch format
A = self.feature_extractor.pad(
lowerCamelCase_ ,max_length=self.max_length ,padding=self.padding ,pad_to_multiple_of=self.pad_to_multiple_of ,return_tensors="""pt""" ,)
A = self.model._get_feat_extract_output_lengths(batch["""input_values"""].shape[-1] )
A = batch["""input_values"""].shape[0]
# make sure that no loss is computed on padded inputs
if batch["attention_mask"] is not None:
# compute real output lengths according to convolution formula
A = self.model._get_feat_extract_output_lengths(batch["""attention_mask"""].sum(-1 ) ).to(
torch.long )
A = torch.zeros(
(batch_size, mask_indices_seq_length) ,dtype=torch.long ,device=batch["""input_values"""].device )
# these two operations makes sure that all values
# before the output lengths indices are attended to
A = 1
A = attention_mask.flip([-1] ).cumsum(-1 ).flip([-1] ).bool()
# sample randomly masked indices
A = _compute_mask_indices(
(batch_size, mask_indices_seq_length) ,self.model.config.mask_time_prob ,self.model.config.mask_time_length ,attention_mask=lowerCamelCase_ ,min_masks=2 ,)
return batch
class lowerCamelCase__ ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self ,*lowerCamelCase_ ,lowerCamelCase_=1 ,lowerCamelCase_=0 ,lowerCamelCase_=1.0 ,**lowerCamelCase_ ) -> Union[str, Any]:
super().__init__(*lowerCamelCase_ ,**lowerCamelCase_ )
A = 0
A = max_gumbel_temp
A = min_gumbel_temp
A = gumbel_temp_decay
def UpperCamelCase__ ( self ,lowerCamelCase_ ,lowerCamelCase_ ) -> torch.Tensor:
model.train()
A = self._prepare_inputs(lowerCamelCase_ )
if self.use_amp:
with autocast():
A = self.compute_loss(lowerCamelCase_ ,lowerCamelCase_ )
else:
A = self.compute_loss(lowerCamelCase_ ,lowerCamelCase_ )
if self.args.n_gpu > 1 or self.deepspeed:
if model.module.config.ctc_loss_reduction == "mean":
A = loss.mean()
elif model.module.config.ctc_loss_reduction == "sum":
A = loss.sum() / (inputs["""mask_time_indices"""]).sum()
else:
raise ValueError(f'{model.config.ctc_loss_reduction} is not valid. Choose one of [\'mean\', \'sum\']' )
if self.args.gradient_accumulation_steps > 1:
A = loss / self.args.gradient_accumulation_steps
if self.use_amp:
self.scaler.scale(lowerCamelCase_ ).backward()
elif self.use_apex:
with amp.scale_loss(lowerCamelCase_ ,self.optimizer ) as scaled_loss:
scaled_loss.backward()
elif self.deepspeed:
self.deepspeed.backward(lowerCamelCase_ )
else:
loss.backward()
self.num_update_step += 1
# make sure gumbel softmax temperature is decayed
if self.args.n_gpu > 1 or self.deepspeed:
model.module.set_gumbel_temperature(
max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step ,self.min_gumbel_temp ) )
else:
model.set_gumbel_temperature(
max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step ,self.min_gumbel_temp ) )
return loss.detach()
def _A ( ):
"""simple docstring"""
A = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
A , A , A = parser.parse_args_into_dataclasses()
configure_logger(_a , _a )
# Downloading and loading a dataset from the hub.
A = load_dataset(data_args.dataset_name , data_args.dataset_config_name , cache_dir=model_args.cache_dir )
if "validation" not in datasets.keys():
# make sure only "validation" and "train" keys remain"
A = DatasetDict()
A = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=f'{data_args.train_split_name}[:{data_args.validation_split_percentage}%]' , cache_dir=model_args.cache_dir , )
A = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=f'{data_args.train_split_name}[{data_args.validation_split_percentage}%:]' , cache_dir=model_args.cache_dir , )
else:
# make sure only "validation" and "train" keys remain"
A = DatasetDict()
A = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split="""validation""" , cache_dir=model_args.cache_dir , )
A = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=f'{data_args.train_split_name}' , cache_dir=model_args.cache_dir , )
# only normalized-inputs-training is supported
A = WavaVecaFeatureExtractor.from_pretrained(
model_args.model_name_or_path , cache_dir=model_args.cache_dir , do_normalize=_a )
def prepare_dataset(_a : Dict ):
# check that all files have the correct sampling rate
A , A = librosa.load(batch[data_args.speech_file_column] , sr=feature_extractor.sampling_rate )
return batch
# load audio files into numpy arrays
A = datasets.map(
_a , num_proc=data_args.preprocessing_num_workers , remove_columns=datasets["""train"""].column_names )
# filter audio files that are too long
A = vectorized_datasets.filter(
lambda _a : len(data["""speech"""] ) < int(data_args.max_duration_in_seconds * feature_extractor.sampling_rate ) )
def normalize(_a : Optional[Any] ):
return feature_extractor(batch["""speech"""] , sampling_rate=feature_extractor.sampling_rate )
# normalize and transform to `BatchFeatures`
A = vectorized_datasets.map(
_a , batched=_a , num_proc=data_args.preprocessing_num_workers , load_from_cache_file=not data_args.overwrite_cache , remove_columns=vectorized_datasets["""train"""].column_names , )
# pretraining is only supported for "newer" stable layer norm architecture
# apply_spec_augment has to be True, mask_feature_prob has to be 0.0
A = WavaVecaConfig.from_pretrained(
model_args.model_name_or_path , cache_dir=model_args.cache_dir , gradient_checkpointing=training_args.gradient_checkpointing , )
if not config.do_stable_layer_norm or config.feat_extract_norm != "layer":
raise ValueError(
"""PreTraining is only supported for ``config.do_stable_layer_norm=True`` and"""
""" ``config.feat_extract_norm='layer'""" )
A = WavaVecaForPreTraining(_a )
A = DataCollatorForWavaVecaPretraining(model=_a , feature_extractor=_a )
A = WavaVecaPreTrainer(
model=_a , data_collator=_a , args=_a , train_dataset=vectorized_datasets["""train"""] , eval_dataset=vectorized_datasets["""validation"""] , tokenizer=_a , max_gumbel_temp=model_args.max_gumbel_temperature , min_gumbel_temp=model_args.min_gumbel_temperature , gumbel_temp_decay=model_args.gumbel_temperature_decay , )
trainer.train()
if __name__ == "__main__":
main()
| 617
| 1
|
import unittest
from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
LiltForQuestionAnswering,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltModel,
)
from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST
class a :
"""simple docstring"""
def __init__( self : Optional[Any] , lowerCamelCase : Optional[Any] , lowerCamelCase : List[str]=13 , lowerCamelCase : Tuple=7 , lowerCamelCase : int=True , lowerCamelCase : Any=True , lowerCamelCase : Optional[int]=True , lowerCamelCase : Union[str, Any]=True , lowerCamelCase : Optional[Any]=99 , lowerCamelCase : Optional[int]=24 , lowerCamelCase : Union[str, Any]=2 , lowerCamelCase : Any=6 , lowerCamelCase : Optional[int]=37 , lowerCamelCase : Union[str, Any]="gelu" , lowerCamelCase : Dict=0.1 , lowerCamelCase : Optional[Any]=0.1 , lowerCamelCase : Dict=512 , lowerCamelCase : List[str]=16 , lowerCamelCase : Optional[int]=2 , lowerCamelCase : Union[str, Any]=0.02 , lowerCamelCase : Any=3 , lowerCamelCase : Optional[Any]=None , lowerCamelCase : Optional[Any]=1000 , ) -> Dict:
__snake_case : Optional[Any] = parent
__snake_case : Tuple = batch_size
__snake_case : int = seq_length
__snake_case : List[str] = is_training
__snake_case : Tuple = use_input_mask
__snake_case : Union[str, Any] = use_token_type_ids
__snake_case : List[Any] = use_labels
__snake_case : Union[str, Any] = vocab_size
__snake_case : Tuple = hidden_size
__snake_case : Tuple = num_hidden_layers
__snake_case : Optional[int] = num_attention_heads
__snake_case : Tuple = intermediate_size
__snake_case : Tuple = hidden_act
__snake_case : Optional[Any] = hidden_dropout_prob
__snake_case : Optional[Any] = attention_probs_dropout_prob
__snake_case : Any = max_position_embeddings
__snake_case : Optional[int] = type_vocab_size
__snake_case : List[Any] = type_sequence_label_size
__snake_case : int = initializer_range
__snake_case : str = num_labels
__snake_case : List[Any] = scope
__snake_case : str = range_bbox
def __snake_case ( self : Optional[Any] ) -> Optional[int]:
__snake_case : int = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__snake_case : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length, 4] , self.range_bbox )
# Ensure that bbox is legal
for i in range(bbox.shape[0] ):
for j in range(bbox.shape[1] ):
if bbox[i, j, 3] < bbox[i, j, 1]:
__snake_case : Optional[Any] = bbox[i, j, 3]
__snake_case : str = bbox[i, j, 1]
__snake_case : int = t
if bbox[i, j, 2] < bbox[i, j, 0]:
__snake_case : Dict = bbox[i, j, 2]
__snake_case : Union[str, Any] = bbox[i, j, 0]
__snake_case : Optional[Any] = t
__snake_case : Union[str, Any] = None
if self.use_input_mask:
__snake_case : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
__snake_case : Union[str, Any] = None
if self.use_token_type_ids:
__snake_case : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__snake_case : List[Any] = None
__snake_case : Optional[Any] = None
if self.use_labels:
__snake_case : Optional[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__snake_case : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__snake_case : List[str] = self.get_config()
return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels
def __snake_case ( self : Union[str, Any] ) -> str:
return LiltConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , )
def __snake_case ( self : List[str] , lowerCamelCase : Dict , lowerCamelCase : Dict , lowerCamelCase : List[str] , lowerCamelCase : Optional[Any] , lowerCamelCase : Dict , lowerCamelCase : List[str] , lowerCamelCase : Dict , ) -> List[str]:
__snake_case : Dict = LiltModel(config=lowerCamelCase )
model.to(lowerCamelCase )
model.eval()
__snake_case : Optional[Any] = model(lowerCamelCase , bbox=lowerCamelCase , attention_mask=lowerCamelCase , token_type_ids=lowerCamelCase )
__snake_case : int = model(lowerCamelCase , bbox=lowerCamelCase , token_type_ids=lowerCamelCase )
__snake_case : Dict = model(lowerCamelCase , bbox=lowerCamelCase )
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 __snake_case ( self : Union[str, Any] , lowerCamelCase : str , lowerCamelCase : Optional[Any] , lowerCamelCase : Dict , lowerCamelCase : Tuple , lowerCamelCase : Dict , lowerCamelCase : Dict , lowerCamelCase : Dict , ) -> str:
__snake_case : List[Any] = self.num_labels
__snake_case : List[str] = LiltForTokenClassification(config=lowerCamelCase )
model.to(lowerCamelCase )
model.eval()
__snake_case : Optional[Any] = model(
lowerCamelCase , bbox=lowerCamelCase , attention_mask=lowerCamelCase , token_type_ids=lowerCamelCase , labels=lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __snake_case ( self : str , lowerCamelCase : Dict , lowerCamelCase : List[Any] , lowerCamelCase : Dict , lowerCamelCase : Dict , lowerCamelCase : Tuple , lowerCamelCase : List[str] , lowerCamelCase : Any , ) -> Any:
__snake_case : Optional[int] = LiltForQuestionAnswering(config=lowerCamelCase )
model.to(lowerCamelCase )
model.eval()
__snake_case : Optional[int] = model(
lowerCamelCase , bbox=lowerCamelCase , attention_mask=lowerCamelCase , token_type_ids=lowerCamelCase , start_positions=lowerCamelCase , end_positions=lowerCamelCase , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def __snake_case ( self : str ) -> Union[str, Any]:
__snake_case : List[str] = self.prepare_config_and_inputs()
(
__snake_case
) : Optional[Any] = config_and_inputs
__snake_case : Optional[Any] = {
"input_ids": input_ids,
"bbox": bbox,
"token_type_ids": token_type_ids,
"attention_mask": input_mask,
}
return config, inputs_dict
@require_torch
class a (_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , unittest.TestCase ):
"""simple docstring"""
__UpperCAmelCase : Tuple = (
(
LiltModel,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltForQuestionAnswering,
)
if is_torch_available()
else ()
)
__UpperCAmelCase : List[str] = (
{
"feature-extraction": LiltModel,
"question-answering": LiltForQuestionAnswering,
"text-classification": LiltForSequenceClassification,
"token-classification": LiltForTokenClassification,
"zero-shot": LiltForSequenceClassification,
}
if is_torch_available()
else {}
)
__UpperCAmelCase : int = False
__UpperCAmelCase : str = False
def __snake_case ( self : List[str] , lowerCamelCase : Optional[int] , lowerCamelCase : List[Any] , lowerCamelCase : List[Any] , lowerCamelCase : Dict , lowerCamelCase : Optional[int] ) -> List[Any]:
return True
def __snake_case ( self : str ) -> List[str]:
__snake_case : str = LiltModelTester(self )
__snake_case : int = ConfigTester(self , config_class=lowerCamelCase , hidden_size=37 )
def __snake_case ( self : List[Any] ) -> Any:
self.config_tester.run_common_tests()
def __snake_case ( self : str ) -> Tuple:
__snake_case : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCamelCase )
def __snake_case ( self : List[str] ) -> str:
__snake_case : List[str] = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
__snake_case : Dict = type
self.model_tester.create_and_check_model(*lowerCamelCase )
def __snake_case ( self : Union[str, Any] ) -> Optional[Any]:
__snake_case : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*lowerCamelCase )
def __snake_case ( self : Any ) -> int:
__snake_case : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*lowerCamelCase )
@slow
def __snake_case ( self : Dict ) -> int:
for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__snake_case : List[Any] = LiltModel.from_pretrained(lowerCamelCase )
self.assertIsNotNone(lowerCamelCase )
@require_torch
@slow
class a (unittest.TestCase ):
"""simple docstring"""
def __snake_case ( self : Tuple ) -> List[str]:
__snake_case : Optional[int] = LiltModel.from_pretrained("SCUT-DLVCLab/lilt-roberta-en-base" ).to(lowerCamelCase )
__snake_case : List[str] = torch.tensor([[1, 2]] , device=lowerCamelCase )
__snake_case : List[str] = torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]] , device=lowerCamelCase )
# forward pass
with torch.no_grad():
__snake_case : List[str] = model(input_ids=lowerCamelCase , bbox=lowerCamelCase )
__snake_case : List[str] = torch.Size([1, 2, 768] )
__snake_case : str = torch.tensor(
[[-0.06_53, 0.09_50, -0.00_61], [-0.05_45, 0.09_26, -0.03_24]] , device=lowerCamelCase , )
self.assertTrue(outputs.last_hidden_state.shape , lowerCamelCase )
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3] , lowerCamelCase , atol=1E-3 ) )
| 704
|
import os
from pathlib import Path
import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader
from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset
_snake_case : Any = "bert-base-cased"
_snake_case : List[Any] = "google/pegasus-xsum"
_snake_case : Dict = [" Sam ate lunch today.", "Sams lunch ingredients."]
_snake_case : Dict = ["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
_snake_case : List[str] = "patrickvonplaten/t5-tiny-random"
_snake_case : Optional[Any] = "sshleifer/bart-tiny-random"
_snake_case : Optional[Any] = "sshleifer/tiny-mbart"
_snake_case : Tuple = "sshleifer/tiny-marian-en-de"
def lowerCAmelCase_ ( __lowerCamelCase , __lowerCamelCase ):
__snake_case : Union[str, Any] = "\n".join(__lowerCamelCase )
Path(__lowerCamelCase ).open("w" ).writelines(__lowerCamelCase )
def lowerCAmelCase_ ( __lowerCamelCase ):
for split in ["train", "val", "test"]:
_dump_articles(os.path.join(__lowerCamelCase , F'{split}.source' ) , __lowerCamelCase )
_dump_articles(os.path.join(__lowerCamelCase , F'{split}.target' ) , __lowerCamelCase )
return tmp_dir
class a (_lowerCAmelCase ):
"""simple docstring"""
@parameterized.expand(
[
MBART_TINY,
MARIAN_TINY,
T5_TINY,
BART_TINY,
PEGASUS_XSUM,
] , )
@slow
def __snake_case ( self : List[Any] , lowerCamelCase : int ) -> Union[str, Any]:
__snake_case : Optional[Any] = AutoTokenizer.from_pretrained(lowerCamelCase )
__snake_case : List[Any] = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
__snake_case : Optional[int] = max(len(tokenizer.encode(lowerCamelCase ) ) for a in ARTICLES )
__snake_case : Optional[int] = max(len(tokenizer.encode(lowerCamelCase ) ) for a in SUMMARIES )
__snake_case : str = 4
__snake_case : Optional[int] = 8
assert max_len_target > max_src_len # Will be truncated
assert max_len_source > max_src_len # Will be truncated
__snake_case , __snake_case : Optional[int] = "ro_RO", "de_DE" # ignored for all but mbart, but never causes error.
__snake_case : List[str] = SeqaSeqDataset(
lowerCamelCase , data_dir=lowerCamelCase , type_path="train" , max_source_length=lowerCamelCase , max_target_length=lowerCamelCase , src_lang=lowerCamelCase , tgt_lang=lowerCamelCase , )
__snake_case : str = DataLoader(lowerCamelCase , batch_size=2 , collate_fn=train_dataset.collate_fn )
for batch in dataloader:
assert isinstance(lowerCamelCase , lowerCamelCase )
assert batch["attention_mask"].shape == batch["input_ids"].shape
# show that articles were trimmed.
assert batch["input_ids"].shape[1] == max_src_len
# show that targets are the same len
assert batch["labels"].shape[1] == max_tgt_len
if tok_name != MBART_TINY:
continue
# check language codes in correct place
__snake_case : int = shift_tokens_right(batch["labels"] , tokenizer.pad_token_id )
assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]
break # No need to test every batch
@parameterized.expand([BART_TINY, BERT_BASE_CASED] )
def __snake_case ( self : Union[str, Any] , lowerCamelCase : List[Any] ) -> Optional[Any]:
__snake_case : Any = AutoTokenizer.from_pretrained(lowerCamelCase )
__snake_case : List[str] = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
__snake_case : Union[str, Any] = max(len(tokenizer.encode(lowerCamelCase ) ) for a in ARTICLES )
__snake_case : Tuple = max(len(tokenizer.encode(lowerCamelCase ) ) for a in SUMMARIES )
__snake_case : List[str] = 4
__snake_case : List[str] = LegacySeqaSeqDataset(
lowerCamelCase , data_dir=lowerCamelCase , type_path="train" , max_source_length=20 , max_target_length=lowerCamelCase , )
__snake_case : int = DataLoader(lowerCamelCase , batch_size=2 , collate_fn=train_dataset.collate_fn )
for batch in dataloader:
assert batch["attention_mask"].shape == batch["input_ids"].shape
# show that articles were trimmed.
assert batch["input_ids"].shape[1] == max_len_source
assert 20 >= batch["input_ids"].shape[1] # trimmed significantly
# show that targets were truncated
assert batch["labels"].shape[1] == trunc_target # Truncated
assert max_len_target > trunc_target # Truncated
break # No need to test every batch
def __snake_case ( self : List[str] ) -> int:
__snake_case : str = AutoTokenizer.from_pretrained("facebook/mbart-large-cc25" )
__snake_case : int = Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) )
__snake_case : str = tmp_dir.joinpath("train.source" ).open().readlines()
__snake_case : Any = Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) )
pack_data_dir(lowerCamelCase , lowerCamelCase , 128 , lowerCamelCase )
__snake_case : str = {x.name for x in tmp_dir.iterdir()}
__snake_case : Optional[int] = {x.name for x in save_dir.iterdir()}
__snake_case : Optional[int] = save_dir.joinpath("train.source" ).open().readlines()
# orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
# desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
assert len(lowerCamelCase ) < len(lowerCamelCase )
assert len(lowerCamelCase ) == 1
assert len(packed_examples[0] ) == sum(len(lowerCamelCase ) for x in orig_examples )
assert orig_paths == new_paths
@pytest.mark.skipif(not FAIRSEQ_AVAILABLE , reason="This test requires fairseq" )
def __snake_case ( self : Tuple ) -> str:
if not FAIRSEQ_AVAILABLE:
return
__snake_case , __snake_case , __snake_case : Optional[Any] = self._get_dataset(max_len=64 )
__snake_case : Optional[int] = 64
__snake_case : List[Any] = ds.make_dynamic_sampler(lowerCamelCase , required_batch_size_multiple=lowerCamelCase )
__snake_case : str = [len(lowerCamelCase ) for x in batch_sampler]
assert len(set(lowerCamelCase ) ) > 1 # it's not dynamic batch size if every batch is the same length
assert sum(lowerCamelCase ) == len(lowerCamelCase ) # no dropped or added examples
__snake_case : Optional[Any] = DataLoader(lowerCamelCase , batch_sampler=lowerCamelCase , collate_fn=ds.collate_fn , num_workers=2 )
__snake_case : Union[str, Any] = []
__snake_case : str = []
for batch in data_loader:
__snake_case : str = batch["input_ids"].shape
__snake_case : Dict = src_shape[0]
assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
__snake_case : str = np.product(batch["input_ids"].shape )
num_src_per_batch.append(lowerCamelCase )
if num_src_tokens > (max_tokens * 1.1):
failures.append(lowerCamelCase )
assert num_src_per_batch[0] == max(lowerCamelCase )
if failures:
raise AssertionError(F'too many tokens in {len(lowerCamelCase )} batches' )
def __snake_case ( self : int ) -> Any:
__snake_case , __snake_case , __snake_case : Union[str, Any] = self._get_dataset(max_len=512 )
__snake_case : Union[str, Any] = 2
__snake_case : List[str] = ds.make_sortish_sampler(lowerCamelCase , shuffle=lowerCamelCase )
__snake_case : Dict = DataLoader(lowerCamelCase , batch_size=lowerCamelCase , collate_fn=ds.collate_fn , num_workers=2 )
__snake_case : List[Any] = DataLoader(lowerCamelCase , batch_size=lowerCamelCase , collate_fn=ds.collate_fn , num_workers=2 , sampler=lowerCamelCase )
__snake_case : List[Any] = tokenizer.pad_token_id
def count_pad_tokens(lowerCamelCase : List[Any] , lowerCamelCase : Optional[int]="input_ids" ):
return [batch[k].eq(lowerCamelCase ).sum().item() for batch in data_loader]
assert sum(count_pad_tokens(lowerCamelCase , k="labels" ) ) < sum(count_pad_tokens(lowerCamelCase , k="labels" ) )
assert sum(count_pad_tokens(lowerCamelCase ) ) < sum(count_pad_tokens(lowerCamelCase ) )
assert len(lowerCamelCase ) == len(lowerCamelCase )
def __snake_case ( self : Any , lowerCamelCase : List[Any]=1000 , lowerCamelCase : Union[str, Any]=128 ) -> Any:
if os.getenv("USE_REAL_DATA" , lowerCamelCase ):
__snake_case : int = "examples/seq2seq/wmt_en_ro"
__snake_case : Union[str, Any] = max_len * 2 * 64
if not Path(lowerCamelCase ).joinpath("train.len" ).exists():
save_len_file(lowerCamelCase , lowerCamelCase )
else:
__snake_case : List[str] = "examples/seq2seq/test_data/wmt_en_ro"
__snake_case : List[Any] = max_len * 4
save_len_file(lowerCamelCase , lowerCamelCase )
__snake_case : Dict = AutoTokenizer.from_pretrained(lowerCamelCase )
__snake_case : int = SeqaSeqDataset(
lowerCamelCase , data_dir=lowerCamelCase , type_path="train" , max_source_length=lowerCamelCase , max_target_length=lowerCamelCase , n_obs=lowerCamelCase , )
return ds, max_tokens, tokenizer
def __snake_case ( self : Tuple ) -> Dict:
__snake_case , __snake_case , __snake_case : Any = self._get_dataset()
__snake_case : Optional[Any] = set(DistributedSortishSampler(lowerCamelCase , 256 , num_replicas=2 , rank=0 , add_extra_examples=lowerCamelCase ) )
__snake_case : int = set(DistributedSortishSampler(lowerCamelCase , 256 , num_replicas=2 , rank=1 , add_extra_examples=lowerCamelCase ) )
assert idsa.intersection(lowerCamelCase ) == set()
@parameterized.expand(
[
MBART_TINY,
MARIAN_TINY,
T5_TINY,
BART_TINY,
PEGASUS_XSUM,
] , )
def __snake_case ( self : List[str] , lowerCamelCase : Union[str, Any] ) -> str:
__snake_case : Tuple = AutoTokenizer.from_pretrained(lowerCamelCase , use_fast=lowerCamelCase )
if tok_name == MBART_TINY:
__snake_case : Dict = SeqaSeqDataset(
lowerCamelCase , data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) , type_path="train" , max_source_length=4 , max_target_length=8 , src_lang="EN" , tgt_lang="FR" , )
__snake_case : Any = train_dataset.dataset_kwargs
assert "src_lang" in kwargs and "tgt_lang" in kwargs
else:
__snake_case : Union[str, Any] = SeqaSeqDataset(
lowerCamelCase , data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) , type_path="train" , max_source_length=4 , max_target_length=8 , )
__snake_case : Optional[int] = train_dataset.dataset_kwargs
assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
assert len(lowerCamelCase ) == 1 if tok_name == BART_TINY else len(lowerCamelCase ) == 0
| 203
| 0
|
'''simple docstring'''
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
a__ : int = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
a__ : str = typing.Union[np.floataa, int, float] # noqa: UP007
def __lowerCamelCase ( UpperCAmelCase_ , UpperCAmelCase_ ) ->VectorOut:
return np.sqrt(np.sum((np.asarray(UpperCAmelCase_ ) - np.asarray(UpperCAmelCase_ )) ** 2 ) )
def __lowerCamelCase ( UpperCAmelCase_ , UpperCAmelCase_ ) ->VectorOut:
return sum((va - va) ** 2 for va, va in zip(UpperCAmelCase_ , UpperCAmelCase_ ) ) ** (1 / 2)
if __name__ == "__main__":
def __lowerCamelCase ( ) ->None:
from timeit import timeit
print('Without Numpy' )
print(
timeit(
'euclidean_distance_no_np([1, 2, 3], [4, 5, 6])' , number=1_00_00 , globals=globals() , ) )
print('With Numpy' )
print(
timeit(
'euclidean_distance([1, 2, 3], [4, 5, 6])' , number=1_00_00 , globals=globals() , ) )
benchmark()
| 368
|
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
a__ : int = logging.get_logger(__name__)
class __snake_case ( __magic_name__ ):
def __init__( self , *UpperCamelCase_ , **UpperCamelCase_ ) -> None:
warnings.warn(
'The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
' Please use PerceiverImageProcessor instead.' , UpperCamelCase_ , )
super().__init__(*UpperCamelCase_ , **UpperCamelCase_ )
| 368
| 1
|
import numpy as np
class __A:
"""simple docstring"""
def __init__(self ):
UpperCamelCase__ = (0, 0)
UpperCamelCase__ = None
UpperCamelCase__ = 0
UpperCamelCase__ = 0
UpperCamelCase__ = 0
def __eq__(self , SCREAMING_SNAKE_CASE_ ):
return self.position == cell.position
def UpperCAmelCase_ (self ):
print(self.position )
class __A:
"""simple docstring"""
def __init__(self , SCREAMING_SNAKE_CASE_=(5, 5) ):
UpperCamelCase__ = np.zeros(SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = world_size[0]
UpperCamelCase__ = world_size[1]
def UpperCAmelCase_ (self ):
print(self.w )
def UpperCAmelCase_ (self , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase__ = [
(-1, -1),
(-1, 0),
(-1, 1),
(0, -1),
(0, 1),
(1, -1),
(1, 0),
(1, 1),
]
UpperCamelCase__ = cell.position[0]
UpperCamelCase__ = cell.position[1]
UpperCamelCase__ = []
for n in neughbour_cord:
UpperCamelCase__ = current_x + n[0]
UpperCamelCase__ = current_y + n[1]
if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
UpperCamelCase__ = Cell()
UpperCamelCase__ = (x, y)
UpperCamelCase__ = cell
neighbours.append(SCREAMING_SNAKE_CASE_ )
return neighbours
def __magic_name__ ( __a : Optional[Any] , __a : str , __a : Union[str, Any] ):
'''simple docstring'''
UpperCamelCase__ = []
UpperCamelCase__ = []
_open.append(__a )
while _open:
UpperCamelCase__ = np.argmin([n.f for n in _open] )
UpperCamelCase__ = _open[min_f]
_closed.append(_open.pop(__a ) )
if current == goal:
break
for n in world.get_neigbours(__a ):
for c in _closed:
if c == n:
continue
UpperCamelCase__ = current.g + 1
UpperCamelCase__ , UpperCamelCase__ = n.position
UpperCamelCase__ , UpperCamelCase__ = goal.position
UpperCamelCase__ = (ya - ya) ** 2 + (xa - xa) ** 2
UpperCamelCase__ = n.h + n.g
for c in _open:
if c == n and c.f < n.f:
continue
_open.append(__a )
UpperCamelCase__ = []
while current.parent is not None:
path.append(current.position )
UpperCamelCase__ = current.parent
path.append(current.position )
return path[::-1]
if __name__ == "__main__":
lowerCamelCase_ = Gridworld()
# Start position and goal
lowerCamelCase_ = Cell()
lowerCamelCase_ = (0, 0)
lowerCamelCase_ = Cell()
lowerCamelCase_ = (4, 4)
print(f'path from {start.position} to {goal.position}')
lowerCamelCase_ = astar(world, start, goal)
# Just for visual reasons.
for i in s:
lowerCamelCase_ = 1
print(world.w)
| 86
|
from __future__ import annotations
lowerCamelCase_ = '''#'''
class __A:
"""simple docstring"""
def __init__(self ):
UpperCamelCase__ = {}
def UpperCAmelCase_ (self , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase__ = self._trie
for char in text:
if char not in trie:
UpperCamelCase__ = {}
UpperCamelCase__ = trie[char]
UpperCamelCase__ = True
def UpperCAmelCase_ (self , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase__ = self._trie
for char in prefix:
if char in trie:
UpperCamelCase__ = trie[char]
else:
return []
return self._elements(SCREAMING_SNAKE_CASE_ )
def UpperCAmelCase_ (self , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase__ = []
for c, v in d.items():
UpperCamelCase__ = [""" """] if c == END else [(c + s) for s in self._elements(SCREAMING_SNAKE_CASE_ )]
result.extend(SCREAMING_SNAKE_CASE_ )
return tuple(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = Trie()
lowerCamelCase_ = ('''depart''', '''detergent''', '''daring''', '''dog''', '''deer''', '''deal''')
for word in words:
trie.insert_word(word)
def __magic_name__ ( __a : str ):
'''simple docstring'''
UpperCamelCase__ = trie.find_word(__a )
return tuple(string + word for word in suffixes )
def __magic_name__ ( ):
'''simple docstring'''
print(autocomplete_using_trie("""de""" ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 86
| 1
|
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
snake_case__ : Optional[Any] = logging.get_logger(__name__)
class _a ( UpperCAmelCase__ ):
"""simple docstring"""
def __init__( self , *_UpperCAmelCase , **_UpperCAmelCase ) -> None:
warnings.warn(
'The class LayoutLMv2FeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
' Please use LayoutLMv2ImageProcessor instead.' , _UpperCAmelCase , )
super().__init__(*_UpperCAmelCase , **_UpperCAmelCase )
| 23
|
'''simple docstring'''
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
import sqlitea
import sqlalchemy
class SCREAMING_SNAKE_CASE ( __a ):
"""simple docstring"""
def __init__( self : List[Any] , __lowerCAmelCase : Union[str, "sqlalchemy.sql.Selectable"] , __lowerCAmelCase : Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"] , __lowerCAmelCase : Optional[Features] = None , __lowerCAmelCase : str = None , __lowerCAmelCase : bool = False , **__lowerCAmelCase : Optional[int] , ):
"""simple docstring"""
super().__init__(features=__lowerCAmelCase , cache_dir=__lowerCAmelCase , keep_in_memory=__lowerCAmelCase , **__lowerCAmelCase )
_lowerCAmelCase = Sql(
cache_dir=__lowerCAmelCase , features=__lowerCAmelCase , sql=__lowerCAmelCase , con=__lowerCAmelCase , **__lowerCAmelCase , )
def a ( self : str ):
"""simple docstring"""
_lowerCAmelCase = None
_lowerCAmelCase = None
_lowerCAmelCase = None
_lowerCAmelCase = None
self.builder.download_and_prepare(
download_config=__lowerCAmelCase , download_mode=__lowerCAmelCase , verification_mode=__lowerCAmelCase , base_path=__lowerCAmelCase , )
# Build dataset for splits
_lowerCAmelCase = self.builder.as_dataset(
split='train' , verification_mode=__lowerCAmelCase , in_memory=self.keep_in_memory )
return dataset
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self : Dict , __lowerCAmelCase : Dataset , __lowerCAmelCase : str , __lowerCAmelCase : Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"] , __lowerCAmelCase : Optional[int] = None , __lowerCAmelCase : Optional[int] = None , **__lowerCAmelCase : Tuple , ):
"""simple docstring"""
if num_proc is not None and num_proc <= 0:
raise ValueError(F"num_proc {num_proc} must be an integer > 0." )
_lowerCAmelCase = dataset
_lowerCAmelCase = name
_lowerCAmelCase = con
_lowerCAmelCase = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
_lowerCAmelCase = num_proc
_lowerCAmelCase = to_sql_kwargs
def a ( self : Optional[int] ):
"""simple docstring"""
_lowerCAmelCase = self.to_sql_kwargs.pop('sql' , __lowerCAmelCase )
_lowerCAmelCase = self.to_sql_kwargs.pop('con' , __lowerCAmelCase )
_lowerCAmelCase = self.to_sql_kwargs.pop('index' , __lowerCAmelCase )
_lowerCAmelCase = self._write(index=__lowerCAmelCase , **self.to_sql_kwargs )
return written
def a ( self : Any , __lowerCAmelCase : int ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = args
_lowerCAmelCase = {**to_sql_kwargs, 'if_exists': 'append'} if offset > 0 else to_sql_kwargs
_lowerCAmelCase = query_table(
table=self.dataset.data , key=slice(__lowerCAmelCase , offset + self.batch_size ) , indices=self.dataset._indices , )
_lowerCAmelCase = batch.to_pandas()
_lowerCAmelCase = df.to_sql(self.name , self.con , index=__lowerCAmelCase , **__lowerCAmelCase )
return num_rows or len(__lowerCAmelCase )
def a ( self : Dict , __lowerCAmelCase : List[Any] , **__lowerCAmelCase : Dict ):
"""simple docstring"""
_lowerCAmelCase = 0
if self.num_proc is None or self.num_proc == 1:
for offset in logging.tqdm(
range(0 , len(self.dataset ) , self.batch_size ) , unit='ba' , disable=not logging.is_progress_bar_enabled() , desc='Creating SQL from Arrow format' , ):
written += self._batch_sql((offset, index, to_sql_kwargs) )
else:
_lowerCAmelCase , _lowerCAmelCase = len(self.dataset ), self.batch_size
with multiprocessing.Pool(self.num_proc ) as pool:
for num_rows in logging.tqdm(
pool.imap(
self._batch_sql , [(offset, index, to_sql_kwargs) for offset in range(0 , __lowerCAmelCase , __lowerCAmelCase )] , ) , total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size , unit='ba' , disable=not logging.is_progress_bar_enabled() , desc='Creating SQL from Arrow format' , ):
written += num_rows
return written
| 309
| 0
|
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class __magic_name__ ( A__ ):
lowercase : Dict =(IPNDMScheduler,)
lowercase : Optional[int] =(('''num_inference_steps''', 50),)
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , **UpperCamelCase__ : Optional[Any] ) -> str:
'''simple docstring'''
UpperCAmelCase = {"num_train_timesteps": 10_00}
config.update(**UpperCamelCase__ )
return config
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , UpperCamelCase__ : Optional[int]=0 , **UpperCamelCase__ : Any ) -> Tuple:
'''simple docstring'''
UpperCAmelCase = dict(self.forward_default_kwargs )
UpperCAmelCase = kwargs.pop("num_inference_steps" , UpperCamelCase__ )
UpperCAmelCase = self.dummy_sample
UpperCAmelCase = 0.1 * sample
UpperCAmelCase = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
for scheduler_class in self.scheduler_classes:
UpperCAmelCase = self.get_scheduler_config(**UpperCamelCase__ )
UpperCAmelCase = scheduler_class(**UpperCamelCase__ )
scheduler.set_timesteps(UpperCamelCase__ )
# copy over dummy past residuals
UpperCAmelCase = dummy_past_residuals[:]
if time_step is None:
UpperCAmelCase = scheduler.timesteps[len(scheduler.timesteps ) // 2]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(UpperCamelCase__ )
UpperCAmelCase = scheduler_class.from_pretrained(UpperCamelCase__ )
new_scheduler.set_timesteps(UpperCamelCase__ )
# copy over dummy past residuals
UpperCAmelCase = dummy_past_residuals[:]
UpperCAmelCase = scheduler.step(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , **UpperCamelCase__ ).prev_sample
UpperCAmelCase = new_scheduler.step(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , **UpperCamelCase__ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
UpperCAmelCase = scheduler.step(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , **UpperCamelCase__ ).prev_sample
UpperCAmelCase = new_scheduler.step(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , **UpperCamelCase__ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def SCREAMING_SNAKE_CASE_ ( self : List[str] ) -> int:
'''simple docstring'''
pass
def SCREAMING_SNAKE_CASE_ ( self : Dict , UpperCamelCase__ : Any=0 , **UpperCamelCase__ : Union[str, Any] ) -> int:
'''simple docstring'''
UpperCAmelCase = dict(self.forward_default_kwargs )
UpperCAmelCase = kwargs.pop("num_inference_steps" , UpperCamelCase__ )
UpperCAmelCase = self.dummy_sample
UpperCAmelCase = 0.1 * sample
UpperCAmelCase = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
for scheduler_class in self.scheduler_classes:
UpperCAmelCase = self.get_scheduler_config()
UpperCAmelCase = scheduler_class(**UpperCamelCase__ )
scheduler.set_timesteps(UpperCamelCase__ )
# copy over dummy past residuals (must be after setting timesteps)
UpperCAmelCase = dummy_past_residuals[:]
if time_step is None:
UpperCAmelCase = scheduler.timesteps[len(scheduler.timesteps ) // 2]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(UpperCamelCase__ )
UpperCAmelCase = scheduler_class.from_pretrained(UpperCamelCase__ )
# copy over dummy past residuals
new_scheduler.set_timesteps(UpperCamelCase__ )
# copy over dummy past residual (must be after setting timesteps)
UpperCAmelCase = dummy_past_residuals[:]
UpperCAmelCase = scheduler.step(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , **UpperCamelCase__ ).prev_sample
UpperCAmelCase = new_scheduler.step(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , **UpperCamelCase__ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
UpperCAmelCase = scheduler.step(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , **UpperCamelCase__ ).prev_sample
UpperCAmelCase = new_scheduler.step(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , **UpperCamelCase__ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def SCREAMING_SNAKE_CASE_ ( self : Dict , **UpperCamelCase__ : Any ) -> str:
'''simple docstring'''
UpperCAmelCase = self.scheduler_classes[0]
UpperCAmelCase = self.get_scheduler_config(**UpperCamelCase__ )
UpperCAmelCase = scheduler_class(**UpperCamelCase__ )
UpperCAmelCase = 10
UpperCAmelCase = self.dummy_model()
UpperCAmelCase = self.dummy_sample_deter
scheduler.set_timesteps(UpperCamelCase__ )
for i, t in enumerate(scheduler.timesteps ):
UpperCAmelCase = model(UpperCamelCase__ , UpperCamelCase__ )
UpperCAmelCase = scheduler.step(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ).prev_sample
for i, t in enumerate(scheduler.timesteps ):
UpperCAmelCase = model(UpperCamelCase__ , UpperCamelCase__ )
UpperCAmelCase = scheduler.step(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ).prev_sample
return sample
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ) -> int:
'''simple docstring'''
UpperCAmelCase = dict(self.forward_default_kwargs )
UpperCAmelCase = kwargs.pop("num_inference_steps" , UpperCamelCase__ )
for scheduler_class in self.scheduler_classes:
UpperCAmelCase = self.get_scheduler_config()
UpperCAmelCase = scheduler_class(**UpperCamelCase__ )
UpperCAmelCase = self.dummy_sample
UpperCAmelCase = 0.1 * sample
if num_inference_steps is not None and hasattr(UpperCamelCase__ , "set_timesteps" ):
scheduler.set_timesteps(UpperCamelCase__ )
elif num_inference_steps is not None and not hasattr(UpperCamelCase__ , "set_timesteps" ):
UpperCAmelCase = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
UpperCAmelCase = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
UpperCAmelCase = dummy_past_residuals[:]
UpperCAmelCase = scheduler.timesteps[5]
UpperCAmelCase = scheduler.timesteps[6]
UpperCAmelCase = scheduler.step(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , **UpperCamelCase__ ).prev_sample
UpperCAmelCase = scheduler.step(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , **UpperCamelCase__ ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
UpperCAmelCase = scheduler.step(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , **UpperCamelCase__ ).prev_sample
UpperCAmelCase = scheduler.step(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , **UpperCamelCase__ ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ) -> Optional[int]:
'''simple docstring'''
for timesteps in [1_00, 10_00]:
self.check_over_configs(num_train_timesteps=UpperCamelCase__ , time_step=UpperCamelCase__ )
def SCREAMING_SNAKE_CASE_ ( self : Dict ) -> Dict:
'''simple docstring'''
for t, num_inference_steps in zip([1, 5, 10] , [10, 50, 1_00] ):
self.check_over_forward(num_inference_steps=UpperCamelCase__ , time_step=UpperCamelCase__ )
def SCREAMING_SNAKE_CASE_ ( self : Tuple ) -> Tuple:
'''simple docstring'''
UpperCAmelCase = self.full_loop()
UpperCAmelCase = torch.mean(torch.abs(UpperCamelCase__ ) )
assert abs(result_mean.item() - 2_54_05_29 ) < 10
| 457
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__lowerCamelCase : Optional[int] = {
"configuration_roc_bert": ["ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoCBertConfig"],
"tokenization_roc_bert": ["RoCBertTokenizer"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
pass
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : List[Any] = [
"ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"RoCBertForCausalLM",
"RoCBertForMaskedLM",
"RoCBertForMultipleChoice",
"RoCBertForPreTraining",
"RoCBertForQuestionAnswering",
"RoCBertForSequenceClassification",
"RoCBertForTokenClassification",
"RoCBertLayer",
"RoCBertModel",
"RoCBertPreTrainedModel",
"load_tf_weights_in_roc_bert",
]
if TYPE_CHECKING:
from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
from .tokenization_roc_bert import RoCBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
raise OptionalDependencyNotAvailable()
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roc_bert import (
ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
RoCBertForCausalLM,
RoCBertForMaskedLM,
RoCBertForMultipleChoice,
RoCBertForPreTraining,
RoCBertForQuestionAnswering,
RoCBertForSequenceClassification,
RoCBertForTokenClassification,
RoCBertLayer,
RoCBertModel,
RoCBertPreTrainedModel,
load_tf_weights_in_roc_bert,
)
else:
import sys
__lowerCamelCase : Optional[int] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 457
| 1
|
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
from .pipeline_unclip import UnCLIPPipeline
from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
from .text_proj import UnCLIPTextProjModel
| 424
|
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
from accelerate.utils import ComputeEnvironment
from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment # noqa: F401
from .sagemaker import get_sagemaker_input
_SCREAMING_SNAKE_CASE : Optional[Any] = 'Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine'
def __lowerCAmelCase ( ):
_lowercase: List[Any] = _ask_options(
"In which compute environment are you running?" , ["This machine", "AWS (Amazon SageMaker)"] , _convert_compute_environment , )
if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
_lowercase: Any = get_sagemaker_input()
else:
_lowercase: Tuple = get_cluster_input()
return config
def __lowerCAmelCase ( __magic_name__=None ):
if subparsers is not None:
_lowercase: List[Any] = subparsers.add_parser("config" , description=__magic_name__ )
else:
_lowercase: List[Any] = argparse.ArgumentParser("Accelerate config command" , description=__magic_name__ )
parser.add_argument(
"--config_file" , default=__magic_name__ , help=(
"The path to use to store the config file. Will default to a file named default_config.yaml in the cache "
"location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have "
"such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed "
"with 'huggingface'."
) , )
if subparsers is not None:
parser.set_defaults(func=__magic_name__ )
return parser
def __lowerCAmelCase ( __magic_name__ ):
_lowercase: Union[str, Any] = get_user_input()
if args.config_file is not None:
_lowercase: Dict = args.config_file
else:
if not os.path.isdir(__magic_name__ ):
os.makedirs(__magic_name__ )
_lowercase: Tuple = default_yaml_config_file
if config_file.endswith(".json" ):
config.to_json_file(__magic_name__ )
else:
config.to_yaml_file(__magic_name__ )
print(f"accelerate configuration saved at {config_file}" )
def __lowerCAmelCase ( ):
_lowercase: int = config_command_parser()
_lowercase: List[Any] = parser.parse_args()
config_command(__magic_name__ )
if __name__ == "__main__":
main()
| 226
| 0
|
def A__ ( lowerCamelCase , lowerCamelCase ) -> List[Any]:
return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def A__ ( lowerCamelCase , lowerCamelCase=0 ) -> Optional[Any]:
return sorted(lowerCamelCase , key=lambda lowerCamelCase : x[column] )
def A__ ( lowerCamelCase , lowerCamelCase , lowerCamelCase=float("""inf""" ) ) -> Optional[int]:
for i in range(points_counts - 1 ):
for j in range(i + 1 , lowerCamelCase ):
UpperCamelCase_: Any = euclidean_distance_sqr(points[i] , points[j] )
if current_dis < min_dis:
UpperCamelCase_: Any = current_dis
return min_dis
def A__ ( lowerCamelCase , lowerCamelCase , lowerCamelCase=float("""inf""" ) ) -> Tuple:
for i in range(min(6 , points_counts - 1 ) , lowerCamelCase ):
for j in range(max(0 , i - 6 ) , lowerCamelCase ):
UpperCamelCase_: Optional[int] = euclidean_distance_sqr(points[i] , points[j] )
if current_dis < min_dis:
UpperCamelCase_: Dict = current_dis
return min_dis
def A__ ( lowerCamelCase , lowerCamelCase , lowerCamelCase ) -> int:
# base case
if points_counts <= 3:
return dis_between_closest_pair(lowerCamelCase , lowerCamelCase )
# recursion
UpperCamelCase_: int = points_counts // 2
UpperCamelCase_: Optional[Any] = closest_pair_of_points_sqr(
lowerCamelCase , points_sorted_on_y[:mid] , lowerCamelCase )
UpperCamelCase_: str = closest_pair_of_points_sqr(
lowerCamelCase , points_sorted_on_y[mid:] , points_counts - mid )
UpperCamelCase_: Dict = min(lowerCamelCase , lowerCamelCase )
UpperCamelCase_: List[str] = []
for point in points_sorted_on_x:
if abs(point[0] - points_sorted_on_x[mid][0] ) < closest_pair_dis:
cross_strip.append(lowerCamelCase )
UpperCamelCase_: Dict = dis_between_closest_in_strip(
lowerCamelCase , len(lowerCamelCase ) , lowerCamelCase )
return min(lowerCamelCase , lowerCamelCase )
def A__ ( lowerCamelCase , lowerCamelCase ) -> List[str]:
UpperCamelCase_: str = column_based_sort(lowerCamelCase , column=0 )
UpperCamelCase_: Tuple = column_based_sort(lowerCamelCase , column=1 )
return (
closest_pair_of_points_sqr(
lowerCamelCase , lowerCamelCase , lowerCamelCase )
) ** 0.5
if __name__ == "__main__":
lowerCamelCase_ : int = [(2, 3), (12, 30), (40, 50), (5, 1), (12, 10), (3, 4)]
print("""Distance:""", closest_pair_of_points(points, len(points)))
| 670
|
import json
import logging
import os
import sys
from pathlib import Path
import finetune_rag
from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
require_ray,
require_torch_gpu,
require_torch_multi_gpu,
)
logging.basicConfig(level=logging.DEBUG)
lowerCamelCase_ : Union[str, Any] = logging.getLogger()
lowerCamelCase_ : List[str] = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class _UpperCamelCase ( _A ):
'''simple docstring'''
def lowerCAmelCase__ ( self : List[Any] , snake_case_ : Dict ):
os.makedirs(snake_case_ , exist_ok=snake_case_ )
UpperCamelCase_: int = {"""source""": """What is love ?""", """target""": """life"""}
UpperCamelCase_: Tuple = {"""train""": 12, """val""": 2, """test""": 2}
for split in ["train", "test", "val"]:
for field in ["source", "target"]:
UpperCamelCase_: Tuple = """\n""".join([contents[field]] * n_lines[split] )
with open(os.path.join(snake_case_ , f'''{split}.{field}''' ) , """w""" ) as f:
f.write(snake_case_ )
def lowerCAmelCase__ ( self : Dict , snake_case_ : int , snake_case_ : str = "pytorch" ):
UpperCamelCase_: Optional[Any] = self.get_auto_remove_tmp_dir()
UpperCamelCase_: Dict = os.path.join(snake_case_ , """output""" )
UpperCamelCase_: Any = os.path.join(snake_case_ , """data""" )
self._create_dummy_data(data_dir=snake_case_ )
UpperCamelCase_: Union[str, Any] = f'''
--data_dir {data_dir} \
--output_dir {output_dir} \
--model_name_or_path facebook/rag-sequence-base \
--model_type rag_sequence \
--do_train \
--do_predict \
--n_val -1 \
--val_check_interval 1.0 \
--train_batch_size 2 \
--eval_batch_size 1 \
--max_source_length 25 \
--max_target_length 25 \
--val_max_target_length 25 \
--test_max_target_length 25 \
--label_smoothing 0.1 \
--dropout 0.1 \
--attention_dropout 0.1 \
--weight_decay 0.001 \
--adam_epsilon 1e-08 \
--max_grad_norm 0.1 \
--lr_scheduler polynomial \
--learning_rate 3e-04 \
--num_train_epochs 1 \
--warmup_steps 4 \
--gradient_accumulation_steps 1 \
--distributed-port 8787 \
--use_dummy_dataset 1 \
--distributed_retriever {distributed_retriever} \
'''.split()
if gpus > 0:
testargs.append(f'''--gpus={gpus}''' )
if is_apex_available():
testargs.append("""--fp16""" )
else:
testargs.append("""--gpus=0""" )
testargs.append("""--distributed_backend=ddp_cpu""" )
testargs.append("""--num_processes=2""" )
UpperCamelCase_: Optional[Any] = [sys.executable, str(Path(finetune_rag.__file__ ).resolve() )] + testargs
execute_subprocess_async(snake_case_ , env=self.get_env() )
UpperCamelCase_: Optional[int] = os.path.join(snake_case_ , """metrics.json""" )
with open(snake_case_ ) as f:
UpperCamelCase_: Any = json.load(snake_case_ )
return result
@require_torch_gpu
def lowerCAmelCase__ ( self : Optional[Any] ):
UpperCamelCase_: List[str] = self._run_finetune(gpus=1 )
self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""] , 0.2 )
@require_torch_multi_gpu
def lowerCAmelCase__ ( self : Optional[Any] ):
UpperCamelCase_: Any = self._run_finetune(gpus=2 )
self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""] , 0.2 )
@require_torch_gpu
@require_ray
def lowerCAmelCase__ ( self : Dict ):
UpperCamelCase_: List[Any] = self._run_finetune(gpus=1 , distributed_retriever="""ray""" )
self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""] , 0.2 )
@require_torch_multi_gpu
@require_ray
def lowerCAmelCase__ ( self : Dict ):
UpperCamelCase_: List[Any] = self._run_finetune(gpus=1 , distributed_retriever="""ray""" )
self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""] , 0.2 )
| 670
| 1
|
"""simple docstring"""
from urllib.parse import quote
import pytest
from datasets.utils.hub import hf_hub_url
@pytest.mark.parametrize("repo_id" , ["canonical_dataset_name", "org-name/dataset-name"] )
@pytest.mark.parametrize("path" , ["filename.csv", "filename with blanks.csv"] )
@pytest.mark.parametrize("revision" , [None, "v2"] )
def lowerCamelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Optional[int]:
'''simple docstring'''
lowerCamelCase__ =hf_hub_url(repo_id=__lowerCAmelCase , path=__lowerCAmelCase , revision=__lowerCAmelCase )
assert url == F'''https://huggingface.co/datasets/{repo_id}/resolve/{revision or 'main'}/{quote(__lowerCAmelCase )}'''
| 530
|
"""simple docstring"""
def lowerCamelCase_ ( __lowerCAmelCase ) -> list:
'''simple docstring'''
if len(__lowerCAmelCase ) <= 1:
return [tuple(__lowerCAmelCase )]
lowerCamelCase__ =[]
def generate(__lowerCAmelCase , __lowerCAmelCase ):
lowerCamelCase__ =[0] * n
res.append(tuple(__lowerCAmelCase ) )
lowerCamelCase__ =0
while i < n:
if c[i] < i:
if i % 2 == 0:
lowerCamelCase__ , lowerCamelCase__ =arr[i], arr[0]
else:
lowerCamelCase__ , lowerCamelCase__ =arr[i], arr[c[i]]
res.append(tuple(__lowerCAmelCase ) )
c[i] += 1
lowerCamelCase__ =0
else:
lowerCamelCase__ =0
i += 1
generate(len(__lowerCAmelCase ) , __lowerCAmelCase )
return res
if __name__ == "__main__":
a =input('Enter numbers separated by a comma:\n').strip()
a =[int(item) for item in user_input.split(',')]
print(heaps(arr))
| 530
| 1
|
import argparse
import torch
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
if __name__ == "__main__":
snake_case_ = argparse.ArgumentParser()
parser.add_argument(
'--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
)
# !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
parser.add_argument(
'--original_config_file',
default=None,
type=str,
help='The YAML config file corresponding to the original architecture.',
)
parser.add_argument(
'--num_in_channels',
default=None,
type=int,
help='The number of input channels. If `None` number of input channels will be automatically inferred.',
)
parser.add_argument(
'--scheduler_type',
default='pndm',
type=str,
help='Type of scheduler to use. Should be one of [\'pndm\', \'lms\', \'ddim\', \'euler\', \'euler-ancestral\', \'dpm\']',
)
parser.add_argument(
'--pipeline_type',
default=None,
type=str,
help=(
'The pipeline type. One of \'FrozenOpenCLIPEmbedder\', \'FrozenCLIPEmbedder\', \'PaintByExample\''
'. If `None` pipeline will be automatically inferred.'
),
)
parser.add_argument(
'--image_size',
default=None,
type=int,
help=(
'The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'
' Base. Use 768 for Stable Diffusion v2.'
),
)
parser.add_argument(
'--prediction_type',
default=None,
type=str,
help=(
'The prediction type that the model was trained on. Use \'epsilon\' for Stable Diffusion v1.X and Stable'
' Diffusion v2 Base. Use \'v_prediction\' for Stable Diffusion v2.'
),
)
parser.add_argument(
'--extract_ema',
action='store_true',
help=(
'Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'
' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'
' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'
),
)
parser.add_argument(
'--upcast_attention',
action='store_true',
help=(
'Whether the attention computation should always be upcasted. This is necessary when running stable'
' diffusion 2.1.'
),
)
parser.add_argument(
'--from_safetensors',
action='store_true',
help='If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.',
)
parser.add_argument(
'--to_safetensors',
action='store_true',
help='Whether to store pipeline in safetensors format or not.',
)
parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
parser.add_argument('--device', type=str, help='Device to use (e.g. cpu, cuda:0, cuda:1, etc.)')
parser.add_argument(
'--stable_unclip',
type=str,
default=None,
required=False,
help='Set if this is a stable unCLIP model. One of \'txt2img\' or \'img2img\'.',
)
parser.add_argument(
'--stable_unclip_prior',
type=str,
default=None,
required=False,
help='Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.',
)
parser.add_argument(
'--clip_stats_path',
type=str,
help='Path to the clip stats file. Only required if the stable unclip model\'s config specifies `model.params.noise_aug_config.params.clip_stats_path`.',
required=False,
)
parser.add_argument(
'--controlnet', action='store_true', default=None, help='Set flag if this is a controlnet checkpoint.'
)
parser.add_argument('--half', action='store_true', help='Save weights in half precision.')
parser.add_argument(
'--vae_path',
type=str,
default=None,
required=False,
help='Set to a path, hub id to an already converted vae to not convert it again.',
)
snake_case_ = parser.parse_args()
snake_case_ = download_from_original_stable_diffusion_ckpt(
checkpoint_path=args.checkpoint_path,
original_config_file=args.original_config_file,
image_size=args.image_size,
prediction_type=args.prediction_type,
model_type=args.pipeline_type,
extract_ema=args.extract_ema,
scheduler_type=args.scheduler_type,
num_in_channels=args.num_in_channels,
upcast_attention=args.upcast_attention,
from_safetensors=args.from_safetensors,
device=args.device,
stable_unclip=args.stable_unclip,
stable_unclip_prior=args.stable_unclip_prior,
clip_stats_path=args.clip_stats_path,
controlnet=args.controlnet,
vae_path=args.vae_path,
)
if args.half:
pipe.to(torch_dtype=torch.floataa)
if args.controlnet:
# only save the controlnet model
pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
else:
pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
| 388
|
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 SCREAMING_SNAKE_CASE__ :
@staticmethod
def a (*a__ : Optional[Any] , **a__ : Optional[Any] ):
"""simple docstring"""
pass
@is_pipeline_test
@require_vision
@require_timm
@require_torch
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
A_ : Optional[Any] = MODEL_FOR_OBJECT_DETECTION_MAPPING
def a (self : str , a__ : List[Any] , a__ : Optional[Any] , a__ : List[str] ):
"""simple docstring"""
__snake_case = ObjectDetectionPipeline(model=a__ , image_processor=a__ )
return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]
def a (self : List[str] , a__ : Optional[Any] , a__ : Union[str, Any] ):
"""simple docstring"""
__snake_case = 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
__snake_case = datasets.load_dataset('''hf-internal-testing/fixtures_image_utils''' , '''image''' , split='''test''' )
__snake_case = [
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'''],
]
__snake_case = 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 a (self : Union[str, Any] ):
"""simple docstring"""
pass
@require_torch
def a (self : Any ):
"""simple docstring"""
__snake_case = '''hf-internal-testing/tiny-detr-mobilenetsv3'''
__snake_case = AutoModelForObjectDetection.from_pretrained(a__ )
__snake_case = AutoFeatureExtractor.from_pretrained(a__ )
__snake_case = ObjectDetectionPipeline(model=a__ , feature_extractor=a__ )
__snake_case = object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg''' , threshold=0.0 )
self.assertEqual(
nested_simplify(a__ , decimals=4 ) , [
{'''score''': 0.3_3_7_6, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
{'''score''': 0.3_3_7_6, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
] , )
__snake_case = 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''': 0.3_3_7_6, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
{'''score''': 0.3_3_7_6, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
],
[
{'''score''': 0.3_3_7_6, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
{'''score''': 0.3_3_7_6, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
],
] , )
@require_torch
@slow
def a (self : int ):
"""simple docstring"""
__snake_case = '''facebook/detr-resnet-50'''
__snake_case = AutoModelForObjectDetection.from_pretrained(a__ )
__snake_case = AutoFeatureExtractor.from_pretrained(a__ )
__snake_case = ObjectDetectionPipeline(model=a__ , feature_extractor=a__ )
__snake_case = object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg''' )
self.assertEqual(
nested_simplify(a__ , decimals=4 ) , [
{'''score''': 0.9_9_8_2, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
{'''score''': 0.9_9_6_0, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
{'''score''': 0.9_9_5_5, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
{'''score''': 0.9_9_8_8, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
{'''score''': 0.9_9_8_7, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
] , )
__snake_case = object_detector(
[
'''http://images.cocodataset.org/val2017/000000039769.jpg''',
'''http://images.cocodataset.org/val2017/000000039769.jpg''',
] )
self.assertEqual(
nested_simplify(a__ , decimals=4 ) , [
[
{'''score''': 0.9_9_8_2, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
{'''score''': 0.9_9_6_0, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
{'''score''': 0.9_9_5_5, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
{'''score''': 0.9_9_8_8, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
{'''score''': 0.9_9_8_7, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
],
[
{'''score''': 0.9_9_8_2, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
{'''score''': 0.9_9_6_0, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
{'''score''': 0.9_9_5_5, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
{'''score''': 0.9_9_8_8, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
{'''score''': 0.9_9_8_7, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
],
] , )
@require_torch
@slow
def a (self : List[Any] ):
"""simple docstring"""
__snake_case = '''facebook/detr-resnet-50'''
__snake_case = pipeline('''object-detection''' , model=a__ )
__snake_case = object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg''' )
self.assertEqual(
nested_simplify(a__ , decimals=4 ) , [
{'''score''': 0.9_9_8_2, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
{'''score''': 0.9_9_6_0, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
{'''score''': 0.9_9_5_5, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
{'''score''': 0.9_9_8_8, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
{'''score''': 0.9_9_8_7, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
] , )
__snake_case = object_detector(
[
'''http://images.cocodataset.org/val2017/000000039769.jpg''',
'''http://images.cocodataset.org/val2017/000000039769.jpg''',
] )
self.assertEqual(
nested_simplify(a__ , decimals=4 ) , [
[
{'''score''': 0.9_9_8_2, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
{'''score''': 0.9_9_6_0, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
{'''score''': 0.9_9_5_5, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
{'''score''': 0.9_9_8_8, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
{'''score''': 0.9_9_8_7, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
],
[
{'''score''': 0.9_9_8_2, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
{'''score''': 0.9_9_6_0, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
{'''score''': 0.9_9_5_5, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
{'''score''': 0.9_9_8_8, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
{'''score''': 0.9_9_8_7, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
],
] , )
@require_torch
@slow
def a (self : str ):
"""simple docstring"""
__snake_case = 0.9_9_8_5
__snake_case = '''facebook/detr-resnet-50'''
__snake_case = pipeline('''object-detection''' , model=a__ )
__snake_case = object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg''' , threshold=a__ )
self.assertEqual(
nested_simplify(a__ , decimals=4 ) , [
{'''score''': 0.9_9_8_8, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
{'''score''': 0.9_9_8_7, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
] , )
@require_torch
@require_pytesseract
@slow
def a (self : Dict ):
"""simple docstring"""
__snake_case = '''Narsil/layoutlmv3-finetuned-funsd'''
__snake_case = 0.9_9_9_3
__snake_case = pipeline('''object-detection''' , model=a__ , threshold=a__ )
__snake_case = object_detector(
'''https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png''' )
self.assertEqual(
nested_simplify(a__ , decimals=4 ) , [
{'''score''': 0.9_9_9_3, '''label''': '''I-ANSWER''', '''box''': {'''xmin''': 294, '''ymin''': 254, '''xmax''': 343, '''ymax''': 264}},
{'''score''': 0.9_9_9_3, '''label''': '''I-ANSWER''', '''box''': {'''xmin''': 294, '''ymin''': 254, '''xmax''': 343, '''ymax''': 264}},
] , )
| 388
| 1
|
import re
from ..utils import cached_file
# docstyle-ignore
_lowercase = '''
Human: <<task>>
Assistant: '''
_lowercase = '''huggingface-tools/default-prompts'''
_lowercase = {'''chat''': '''chat_prompt_template.txt''', '''run''': '''run_prompt_template.txt'''}
def _A (UpperCamelCase : int , UpperCamelCase : int , UpperCamelCase : Tuple="run" ) ->List[Any]:
'''simple docstring'''
if prompt_or_repo_id is None:
lowerCamelCase__ : Optional[Any] = DEFAULT_PROMPTS_REPO
# prompt is considered a repo ID when it does not contain any kind of space
if re.search("""\\s""" , lowerCAmelCase_ ) is not None:
return prompt_or_repo_id
lowerCamelCase__ : Union[str, Any] = cached_file(
lowerCAmelCase_ , PROMPT_FILES[mode] , repo_type="""dataset""" , user_agent={"""agent""": agent_name} )
with open(lowerCAmelCase_ , """r""" , encoding="""utf-8""" ) as f:
return f.read()
| 157
|
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.whisper import WhisperForConditionalGeneration, WhisperProcessor
from .base import PipelineTool
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
A__ : str = '''openai/whisper-base'''
A__ : List[Any] = (
'''This is a tool that transcribes an audio into text. It takes an input named `audio` and returns the '''
'''transcribed text.'''
)
A__ : str = '''transcriber'''
A__ : List[Any] = WhisperProcessor
A__ : Optional[int] = WhisperForConditionalGeneration
A__ : List[str] = ['''audio''']
A__ : Optional[int] = ['''text''']
def __UpperCAmelCase ( self : Tuple , __lowerCamelCase : List[Any] ):
"""simple docstring"""
return self.pre_processor(__lowerCamelCase , return_tensors='''pt''' ).input_features
def __UpperCAmelCase ( self : Optional[int] , __lowerCamelCase : List[str] ):
"""simple docstring"""
return self.model.generate(inputs=__lowerCamelCase )
def __UpperCAmelCase ( self : List[str] , __lowerCamelCase : int ):
"""simple docstring"""
return self.pre_processor.batch_decode(__lowerCamelCase , skip_special_tokens=__lowerCamelCase )[0]
| 103
| 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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
_snake_case = logging.get_logger(__name__)
def __lowerCamelCase ( _lowercase , _lowercase ) -> int:
UpperCamelCase = b.T
UpperCamelCase = np.sum(np.square(_lowercase ) , axis=1 )
UpperCamelCase = np.sum(np.square(_lowercase ) , axis=0 )
UpperCamelCase = np.matmul(_lowercase , _lowercase )
UpperCamelCase = aa[:, None] - 2 * ab + ba[None, :]
return d
def __lowerCamelCase ( _lowercase , _lowercase ) -> Dict:
UpperCamelCase = x.reshape(-1 , 3 )
UpperCamelCase = squared_euclidean_distance(_lowercase , _lowercase )
return np.argmin(_lowercase , axis=1 )
class _lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Union[str, Any] =["pixel_values"]
def __init__( self : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Optional[Union[List[List[int]], np.ndarray]] = None , SCREAMING_SNAKE_CASE__ : bool = True , SCREAMING_SNAKE_CASE__ : Dict[str, int] = None , SCREAMING_SNAKE_CASE__ : PILImageResampling = PILImageResampling.BILINEAR , SCREAMING_SNAKE_CASE__ : bool = True , SCREAMING_SNAKE_CASE__ : bool = True , **SCREAMING_SNAKE_CASE__ : Union[str, Any] , ):
"""simple docstring"""
super().__init__(**SCREAMING_SNAKE_CASE__ )
UpperCamelCase = size if size is not None else {'height': 2_56, 'width': 2_56}
UpperCamelCase = get_size_dict(SCREAMING_SNAKE_CASE__ )
UpperCamelCase = np.array(SCREAMING_SNAKE_CASE__ ) if clusters is not None else None
UpperCamelCase = do_resize
UpperCamelCase = size
UpperCamelCase = resample
UpperCamelCase = do_normalize
UpperCamelCase = do_color_quantize
def __lowerCAmelCase ( self : Tuple , SCREAMING_SNAKE_CASE__ : np.ndarray , SCREAMING_SNAKE_CASE__ : Dict[str, int] , SCREAMING_SNAKE_CASE__ : PILImageResampling = PILImageResampling.BILINEAR , SCREAMING_SNAKE_CASE__ : Optional[Union[str, ChannelDimension]] = None , **SCREAMING_SNAKE_CASE__ : List[str] , ):
"""simple docstring"""
UpperCamelCase = get_size_dict(SCREAMING_SNAKE_CASE__ )
if "height" not in size or "width" not in size:
raise ValueError(F'Size dictionary must contain both height and width keys. Got {size.keys()}' )
return resize(
SCREAMING_SNAKE_CASE__ , size=(size['height'], size['width']) , resample=SCREAMING_SNAKE_CASE__ , data_format=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
def __lowerCAmelCase ( self : Tuple , SCREAMING_SNAKE_CASE__ : np.ndarray , SCREAMING_SNAKE_CASE__ : Optional[Union[str, ChannelDimension]] = None , ):
"""simple docstring"""
UpperCamelCase = rescale(image=SCREAMING_SNAKE_CASE__ , scale=1 / 127.5 , data_format=SCREAMING_SNAKE_CASE__ )
UpperCamelCase = image - 1
return image
def __lowerCAmelCase ( self : Optional[int] , SCREAMING_SNAKE_CASE__ : ImageInput , SCREAMING_SNAKE_CASE__ : bool = None , SCREAMING_SNAKE_CASE__ : Dict[str, int] = None , SCREAMING_SNAKE_CASE__ : PILImageResampling = None , SCREAMING_SNAKE_CASE__ : bool = None , SCREAMING_SNAKE_CASE__ : Optional[bool] = None , SCREAMING_SNAKE_CASE__ : Optional[Union[List[List[int]], np.ndarray]] = None , SCREAMING_SNAKE_CASE__ : Optional[Union[str, TensorType]] = None , SCREAMING_SNAKE_CASE__ : Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST , **SCREAMING_SNAKE_CASE__ : Any , ):
"""simple docstring"""
UpperCamelCase = do_resize if do_resize is not None else self.do_resize
UpperCamelCase = size if size is not None else self.size
UpperCamelCase = get_size_dict(SCREAMING_SNAKE_CASE__ )
UpperCamelCase = resample if resample is not None else self.resample
UpperCamelCase = do_normalize if do_normalize is not None else self.do_normalize
UpperCamelCase = do_color_quantize if do_color_quantize is not None else self.do_color_quantize
UpperCamelCase = clusters if clusters is not None else self.clusters
UpperCamelCase = np.array(SCREAMING_SNAKE_CASE__ )
UpperCamelCase = make_list_of_images(SCREAMING_SNAKE_CASE__ )
if not valid_images(SCREAMING_SNAKE_CASE__ ):
raise ValueError(
'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
'torch.Tensor, tf.Tensor or jax.ndarray.' )
if do_resize and size is None or resample is None:
raise ValueError('Size and resample must be specified if do_resize is True.' )
if do_color_quantize and clusters is None:
raise ValueError('Clusters must be specified if do_color_quantize is True.' )
# All transformations expect numpy arrays.
UpperCamelCase = [to_numpy_array(SCREAMING_SNAKE_CASE__ ) for image in images]
if do_resize:
UpperCamelCase = [self.resize(image=SCREAMING_SNAKE_CASE__ , size=SCREAMING_SNAKE_CASE__ , resample=SCREAMING_SNAKE_CASE__ ) for image in images]
if do_normalize:
UpperCamelCase = [self.normalize(image=SCREAMING_SNAKE_CASE__ ) for image in images]
if do_color_quantize:
UpperCamelCase = [to_channel_dimension_format(SCREAMING_SNAKE_CASE__ , ChannelDimension.LAST ) for image in images]
# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
UpperCamelCase = np.array(SCREAMING_SNAKE_CASE__ )
UpperCamelCase = color_quantize(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ).reshape(images.shape[:-1] )
# flatten to (batch_size, height*width)
UpperCamelCase = images.shape[0]
UpperCamelCase = images.reshape(SCREAMING_SNAKE_CASE__ , -1 )
# We need to convert back to a list of images to keep consistent behaviour across processors.
UpperCamelCase = list(SCREAMING_SNAKE_CASE__ )
else:
UpperCamelCase = [to_channel_dimension_format(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) for image in images]
UpperCamelCase = {'input_ids': images}
return BatchFeature(data=SCREAMING_SNAKE_CASE__ , tensor_type=SCREAMING_SNAKE_CASE__ )
| 170
|
from typing import Dict, Optional
import numpy as np
import datasets
_snake_case = '''
IoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union
between the predicted segmentation and the ground truth. For binary (two classes) or multi-class segmentation,
the mean IoU of the image is calculated by taking the IoU of each class and averaging them.
'''
_snake_case = '''
Args:
predictions (`List[ndarray]`):
List of predicted segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.
references (`List[ndarray]`):
List of ground truth segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.
num_labels (`int`):
Number of classes (categories).
ignore_index (`int`):
Index that will be ignored during evaluation.
nan_to_num (`int`, *optional*):
If specified, NaN values will be replaced by the number defined by the user.
label_map (`dict`, *optional*):
If specified, dictionary mapping old label indices to new label indices.
reduce_labels (`bool`, *optional*, defaults to `False`):
Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background,
and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255.
Returns:
`Dict[str, float | ndarray]` comprising various elements:
- *mean_iou* (`float`):
Mean Intersection-over-Union (IoU averaged over all categories).
- *mean_accuracy* (`float`):
Mean accuracy (averaged over all categories).
- *overall_accuracy* (`float`):
Overall accuracy on all images.
- *per_category_accuracy* (`ndarray` of shape `(num_labels,)`):
Per category accuracy.
- *per_category_iou* (`ndarray` of shape `(num_labels,)`):
Per category IoU.
Examples:
>>> import numpy as np
>>> mean_iou = datasets.load_metric("mean_iou")
>>> # suppose one has 3 different segmentation maps predicted
>>> predicted_1 = np.array([[1, 2], [3, 4], [5, 255]])
>>> actual_1 = np.array([[0, 3], [5, 4], [6, 255]])
>>> predicted_2 = np.array([[2, 7], [9, 2], [3, 6]])
>>> actual_2 = np.array([[1, 7], [9, 2], [3, 6]])
>>> predicted_3 = np.array([[2, 2, 3], [8, 2, 4], [3, 255, 2]])
>>> actual_3 = np.array([[1, 2, 2], [8, 2, 1], [3, 255, 1]])
>>> predicted = [predicted_1, predicted_2, predicted_3]
>>> ground_truth = [actual_1, actual_2, actual_3]
>>> results = mean_iou.compute(predictions=predicted, references=ground_truth, num_labels=10, ignore_index=255, reduce_labels=False)
>>> print(results) # doctest: +NORMALIZE_WHITESPACE
{\'mean_iou\': 0.47750000000000004, \'mean_accuracy\': 0.5916666666666666, \'overall_accuracy\': 0.5263157894736842, \'per_category_iou\': array([0. , 0. , 0.375, 0.4 , 0.5 , 0. , 0.5 , 1. , 1. , 1. ]), \'per_category_accuracy\': array([0. , 0. , 0.75 , 0.66666667, 1. , 0. , 0.5 , 1. , 1. , 1. ])}
'''
_snake_case = '''\
@software{MMSegmentation_Contributors_OpenMMLab_Semantic_Segmentation_2020,
author = {{MMSegmentation Contributors}},
license = {Apache-2.0},
month = {7},
title = {{OpenMMLab Semantic Segmentation Toolbox and Benchmark}},
url = {https://github.com/open-mmlab/mmsegmentation},
year = {2020}
}'''
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase = None , _lowercase = False , ) -> List[str]:
if label_map is not None:
for old_id, new_id in label_map.items():
UpperCamelCase = new_id
# turn into Numpy arrays
UpperCamelCase = np.array(_lowercase )
UpperCamelCase = np.array(_lowercase )
if reduce_labels:
UpperCamelCase = 255
UpperCamelCase = label - 1
UpperCamelCase = 255
UpperCamelCase = label != ignore_index
UpperCamelCase = np.not_equal(_lowercase , _lowercase )
UpperCamelCase = pred_label[mask]
UpperCamelCase = np.array(_lowercase )[mask]
UpperCamelCase = pred_label[pred_label == label]
UpperCamelCase = np.histogram(_lowercase , bins=_lowercase , range=(0, num_labels - 1) )[0]
UpperCamelCase = np.histogram(_lowercase , bins=_lowercase , range=(0, num_labels - 1) )[0]
UpperCamelCase = np.histogram(_lowercase , bins=_lowercase , range=(0, num_labels - 1) )[0]
UpperCamelCase = area_pred_label + area_label - area_intersect
return area_intersect, area_union, area_pred_label, area_label
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase = None , _lowercase = False , ) -> Optional[Any]:
UpperCamelCase = np.zeros((num_labels,) , dtype=np.floataa )
UpperCamelCase = np.zeros((num_labels,) , dtype=np.floataa )
UpperCamelCase = np.zeros((num_labels,) , dtype=np.floataa )
UpperCamelCase = np.zeros((num_labels,) , dtype=np.floataa )
for result, gt_seg_map in zip(_lowercase , _lowercase ):
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase = intersect_and_union(
_lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase )
total_area_intersect += area_intersect
total_area_union += area_union
total_area_pred_label += area_pred_label
total_area_label += area_label
return total_area_intersect, total_area_union, total_area_pred_label, total_area_label
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase = None , _lowercase = None , _lowercase = False , ) -> List[Any]:
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase = total_intersect_and_union(
_lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase )
# compute metrics
UpperCamelCase = {}
UpperCamelCase = total_area_intersect.sum() / total_area_label.sum()
UpperCamelCase = total_area_intersect / total_area_union
UpperCamelCase = total_area_intersect / total_area_label
UpperCamelCase = np.nanmean(_lowercase )
UpperCamelCase = np.nanmean(_lowercase )
UpperCamelCase = all_acc
UpperCamelCase = iou
UpperCamelCase = acc
if nan_to_num is not None:
UpperCamelCase = {metric: np.nan_to_num(_lowercase , nan=_lowercase ) for metric, metric_value in metrics.items()}
return metrics
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _lowerCAmelCase ( datasets.Metric ):
"""simple docstring"""
def __lowerCAmelCase ( self : List[str] ):
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
# 1st Seq - height dim, 2nd - width dim
{
'predictions': datasets.Sequence(datasets.Sequence(datasets.Value('uint16' ) ) ),
'references': datasets.Sequence(datasets.Sequence(datasets.Value('uint16' ) ) ),
} ) , reference_urls=[
'https://github.com/open-mmlab/mmsegmentation/blob/71c201b1813267d78764f306a297ca717827c4bf/mmseg/core/evaluation/metrics.py'
] , )
def __lowerCAmelCase ( self : str , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : bool , SCREAMING_SNAKE_CASE__ : Optional[int] = None , SCREAMING_SNAKE_CASE__ : Optional[Dict[int, int]] = None , SCREAMING_SNAKE_CASE__ : bool = False , ):
"""simple docstring"""
UpperCamelCase = mean_iou(
results=SCREAMING_SNAKE_CASE__ , gt_seg_maps=SCREAMING_SNAKE_CASE__ , num_labels=SCREAMING_SNAKE_CASE__ , ignore_index=SCREAMING_SNAKE_CASE__ , nan_to_num=SCREAMING_SNAKE_CASE__ , label_map=SCREAMING_SNAKE_CASE__ , reduce_labels=SCREAMING_SNAKE_CASE__ , )
return iou_result
| 170
| 1
|
"""simple docstring"""
from math import sqrt
def SCREAMING_SNAKE_CASE ( __UpperCAmelCase ) -> int:
SCREAMING_SNAKE_CASE__ = 0
for i in range(1 , int(sqrt(__UpperCAmelCase ) + 1 ) ):
if n % i == 0 and i != sqrt(__UpperCAmelCase ):
total += i + n // i
elif i == sqrt(__UpperCAmelCase ):
total += i
return total - n
def SCREAMING_SNAKE_CASE ( __UpperCAmelCase = 10_000 ) -> int:
SCREAMING_SNAKE_CASE__ = sum(
i
for i in range(1 , __UpperCAmelCase )
if sum_of_divisors(sum_of_divisors(__UpperCAmelCase ) ) == i and sum_of_divisors(__UpperCAmelCase ) != i )
return total
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 159
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_A = {
'configuration_squeezebert': [
'SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'SqueezeBertConfig',
'SqueezeBertOnnxConfig',
],
'tokenization_squeezebert': ['SqueezeBertTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A = ['SqueezeBertTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A = [
'SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'SqueezeBertForMaskedLM',
'SqueezeBertForMultipleChoice',
'SqueezeBertForQuestionAnswering',
'SqueezeBertForSequenceClassification',
'SqueezeBertForTokenClassification',
'SqueezeBertModel',
'SqueezeBertModule',
'SqueezeBertPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_squeezebert import (
SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
SqueezeBertConfig,
SqueezeBertOnnxConfig,
)
from .tokenization_squeezebert import SqueezeBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_squeezebert import (
SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
SqueezeBertModel,
SqueezeBertModule,
SqueezeBertPreTrainedModel,
)
else:
import sys
_A = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 159
| 1
|
import copy
import re
class _snake_case :
_lowercase : Tuple = '''hp'''
_lowercase : Optional[int] = {}
_lowercase : Union[str, Any] = None
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls , a , a) -> Union[str, Any]:
SCREAMING_SNAKE_CASE = prefix
SCREAMING_SNAKE_CASE = defaults
cls.build_naming_info()
@staticmethod
def SCREAMING_SNAKE_CASE__ ( a , a) -> Union[str, Any]:
if len(a) == 0:
return ""
SCREAMING_SNAKE_CASE = None
if any(char.isdigit() for char in word):
raise Exception(f'''Parameters should not contain numbers: \'{word}\' contains a number''')
if word in info["short_word"]:
return info["short_word"][word]
for prefix_len in range(1 , len(a) + 1):
SCREAMING_SNAKE_CASE = word[:prefix_len]
if prefix in info["reverse_short_word"]:
continue
else:
SCREAMING_SNAKE_CASE = prefix
break
if short_word is None:
# Paranoid fallback
def int_to_alphabetic(a):
SCREAMING_SNAKE_CASE = ''
while integer != 0:
SCREAMING_SNAKE_CASE = chr(ord('A') + integer % 10) + s
integer //= 10
return s
SCREAMING_SNAKE_CASE = 0
while True:
SCREAMING_SNAKE_CASE = word + '#' + int_to_alphabetic(a)
if sword in info["reverse_short_word"]:
continue
else:
SCREAMING_SNAKE_CASE = sword
break
SCREAMING_SNAKE_CASE = short_word
SCREAMING_SNAKE_CASE = word
return short_word
@staticmethod
def SCREAMING_SNAKE_CASE__ ( a , a) -> List[Any]:
SCREAMING_SNAKE_CASE = param_name.split('_')
SCREAMING_SNAKE_CASE = [TrialShortNamer.shortname_for_word(a , a) for word in words]
# We try to create a separatorless short name, but if there is a collision we have to fallback
# to a separated short name
SCREAMING_SNAKE_CASE = ['', '_']
for separator in separators:
SCREAMING_SNAKE_CASE = separator.join(a)
if shortname not in info["reverse_short_param"]:
SCREAMING_SNAKE_CASE = shortname
SCREAMING_SNAKE_CASE = param_name
return shortname
return param_name
@staticmethod
def SCREAMING_SNAKE_CASE__ ( a , a) -> int:
SCREAMING_SNAKE_CASE = TrialShortNamer.shortname_for_key(a , a)
SCREAMING_SNAKE_CASE = short_name
SCREAMING_SNAKE_CASE = param_name
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls) -> List[str]:
if cls.NAMING_INFO is not None:
return
SCREAMING_SNAKE_CASE = {
'short_word': {},
'reverse_short_word': {},
'short_param': {},
'reverse_short_param': {},
}
SCREAMING_SNAKE_CASE = list(cls.DEFAULTS.keys())
for k in field_keys:
cls.add_new_param_name(a , a)
SCREAMING_SNAKE_CASE = info
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls , a) -> Optional[int]:
cls.build_naming_info()
assert cls.PREFIX is not None
SCREAMING_SNAKE_CASE = [copy.copy(cls.PREFIX)]
for k, v in params.items():
if k not in cls.DEFAULTS:
raise Exception(f'''You should provide a default value for the param name {k} with value {v}''')
if v == cls.DEFAULTS[k]:
# The default value is not added to the name
continue
SCREAMING_SNAKE_CASE = cls.NAMING_INFO['short_param'][k]
if isinstance(a , a):
SCREAMING_SNAKE_CASE = 1 if v else 0
SCREAMING_SNAKE_CASE = '' if isinstance(a , (int, float)) else '-'
SCREAMING_SNAKE_CASE = f'''{key}{sep}{v}'''
name.append(a)
return "_".join(a)
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls , a) -> Optional[int]:
SCREAMING_SNAKE_CASE = repr[len(cls.PREFIX) + 1 :]
if repr == "":
SCREAMING_SNAKE_CASE = []
else:
SCREAMING_SNAKE_CASE = repr.split('_')
SCREAMING_SNAKE_CASE = {}
for value in values:
if "-" in value:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = value.split('-')
else:
SCREAMING_SNAKE_CASE = re.sub('[0-9.]' , '' , a)
SCREAMING_SNAKE_CASE = float(re.sub('[^0-9.]' , '' , a))
SCREAMING_SNAKE_CASE = cls.NAMING_INFO['reverse_short_param'][p_k]
SCREAMING_SNAKE_CASE = p_v
for k in cls.DEFAULTS:
if k not in parameters:
SCREAMING_SNAKE_CASE = cls.DEFAULTS[k]
return parameters
| 444
|
from typing import Any, Dict, List, Union
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 ..image_utils import load_image
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
a_ : str = logging.get_logger(__name__)
a_ : List[Any] = Dict[str, Any]
a_ : Optional[int] = List[Prediction]
@add_end_docstrings(A__ )
class _snake_case ( A__ ):
def __init__( self , *a , **a) -> int:
super().__init__(*a , **a)
if self.framework == "tf":
raise ValueError(f'''The {self.__class__} is only available in PyTorch.''')
requires_backends(self , 'vision')
self.check_model_type(
dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items()))
def SCREAMING_SNAKE_CASE__ ( self , **a) -> int:
SCREAMING_SNAKE_CASE = {}
if "threshold" in kwargs:
SCREAMING_SNAKE_CASE = kwargs['threshold']
return {}, {}, postprocess_kwargs
def __call__( self , *a , **a) -> Union[Predictions, List[Prediction]]:
return super().__call__(*a , **a)
def SCREAMING_SNAKE_CASE__ ( self , a) -> List[str]:
SCREAMING_SNAKE_CASE = load_image(a)
SCREAMING_SNAKE_CASE = torch.IntTensor([[image.height, image.width]])
SCREAMING_SNAKE_CASE = self.image_processor(images=[image] , return_tensors='pt')
if self.tokenizer is not None:
SCREAMING_SNAKE_CASE = self.tokenizer(text=inputs['words'] , boxes=inputs['boxes'] , return_tensors='pt')
SCREAMING_SNAKE_CASE = target_size
return inputs
def SCREAMING_SNAKE_CASE__ ( self , a) -> int:
SCREAMING_SNAKE_CASE = model_inputs.pop('target_size')
SCREAMING_SNAKE_CASE = self.model(**a)
SCREAMING_SNAKE_CASE = outputs.__class__({'target_size': target_size, **outputs})
if self.tokenizer is not None:
SCREAMING_SNAKE_CASE = model_inputs['bbox']
return model_outputs
def SCREAMING_SNAKE_CASE__ ( self , a , a=0.9) -> Dict:
SCREAMING_SNAKE_CASE = model_outputs['target_size']
if self.tokenizer is not None:
# This is a LayoutLMForTokenClassification variant.
# The OCR got the boxes and the model classified the words.
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = target_size[0].tolist()
def unnormalize(a):
return self._get_bounding_box(
torch.Tensor(
[
(width * bbox[0] / 1000),
(height * bbox[1] / 1000),
(width * bbox[2] / 1000),
(height * bbox[3] / 1000),
]))
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = model_outputs['logits'].squeeze(0).softmax(dim=-1).max(dim=-1)
SCREAMING_SNAKE_CASE = [self.model.config.idalabel[prediction] for prediction in classes.tolist()]
SCREAMING_SNAKE_CASE = [unnormalize(a) for bbox in model_outputs['bbox'].squeeze(0)]
SCREAMING_SNAKE_CASE = ['score', 'label', 'box']
SCREAMING_SNAKE_CASE = [dict(zip(a , a)) for vals in zip(scores.tolist() , a , a) if vals[0] > threshold]
else:
# This is a regular ForObjectDetectionModel
SCREAMING_SNAKE_CASE = self.image_processor.post_process_object_detection(a , a , a)
SCREAMING_SNAKE_CASE = raw_annotations[0]
SCREAMING_SNAKE_CASE = raw_annotation['scores']
SCREAMING_SNAKE_CASE = raw_annotation['labels']
SCREAMING_SNAKE_CASE = raw_annotation['boxes']
SCREAMING_SNAKE_CASE = scores.tolist()
SCREAMING_SNAKE_CASE = [self.model.config.idalabel[label.item()] for label in labels]
SCREAMING_SNAKE_CASE = [self._get_bounding_box(a) for box in boxes]
# {"scores": [...], ...} --> [{"score":x, ...}, ...]
SCREAMING_SNAKE_CASE = ['score', 'label', 'box']
SCREAMING_SNAKE_CASE = [
dict(zip(a , a))
for vals in zip(raw_annotation['scores'] , raw_annotation['labels'] , raw_annotation['boxes'])
]
return annotation
def SCREAMING_SNAKE_CASE__ ( self , a) -> Dict[str, int]:
if self.framework != "pt":
raise ValueError('The ObjectDetectionPipeline is only available in PyTorch.')
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = box.int().tolist()
SCREAMING_SNAKE_CASE = {
'xmin': xmin,
'ymin': ymin,
'xmax': xmax,
'ymax': ymax,
}
return bbox
| 444
| 1
|
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import (
BackboneOutput,
BaseModelOutputWithNoAttention,
BaseModelOutputWithPoolingAndNoAttention,
ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
replace_return_docstrings,
)
from ...utils.backbone_utils import BackboneMixin
from .configuration_resnet import ResNetConfig
_lowerCamelCase : Tuple = logging.get_logger(__name__)
# General docstring
_lowerCamelCase : Union[str, Any] = '''ResNetConfig'''
# Base docstring
_lowerCamelCase : int = '''microsoft/resnet-50'''
_lowerCamelCase : Optional[Any] = [1, 2_048, 7, 7]
# Image classification docstring
_lowerCamelCase : int = '''microsoft/resnet-50'''
_lowerCamelCase : Optional[int] = '''tiger cat'''
_lowerCamelCase : str = [
'''microsoft/resnet-50''',
# See all resnet models at https://huggingface.co/models?filter=resnet
]
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
'''simple docstring'''
def __init__( self : Optional[int] , lowercase : int , lowercase : int , lowercase : int = 3 , lowercase : int = 1 , lowercase : str = "relu" ):
'''simple docstring'''
super().__init__()
_snake_case = nn.Convad(
lowercase , lowercase , kernel_size=lowercase , stride=lowercase , padding=kernel_size // 2 , bias=lowercase )
_snake_case = nn.BatchNormad(lowercase )
_snake_case = ACTaFN[activation] if activation is not None else nn.Identity()
def A ( self : Union[str, Any] , lowercase : Tensor ):
'''simple docstring'''
_snake_case = self.convolution(lowercase )
_snake_case = self.normalization(lowercase )
_snake_case = self.activation(lowercase )
return hidden_state
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
'''simple docstring'''
def __init__( self : Optional[Any] , lowercase : ResNetConfig ):
'''simple docstring'''
super().__init__()
_snake_case = ResNetConvLayer(
config.num_channels , config.embedding_size , kernel_size=7 , stride=2 , activation=config.hidden_act )
_snake_case = nn.MaxPoolad(kernel_size=3 , stride=2 , padding=1 )
_snake_case = config.num_channels
def A ( self : Tuple , lowercase : Tensor ):
'''simple docstring'''
_snake_case = pixel_values.shape[1]
if num_channels != self.num_channels:
raise ValueError(
'Make sure that the channel dimension of the pixel values match with the one set in the configuration.' )
_snake_case = self.embedder(lowercase )
_snake_case = self.pooler(lowercase )
return embedding
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
'''simple docstring'''
def __init__( self : Union[str, Any] , lowercase : int , lowercase : int , lowercase : int = 2 ):
'''simple docstring'''
super().__init__()
_snake_case = nn.Convad(lowercase , lowercase , kernel_size=1 , stride=lowercase , bias=lowercase )
_snake_case = nn.BatchNormad(lowercase )
def A ( self : List[str] , lowercase : Tensor ):
'''simple docstring'''
_snake_case = self.convolution(lowercase )
_snake_case = self.normalization(lowercase )
return hidden_state
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
'''simple docstring'''
def __init__( self : Optional[Any] , lowercase : int , lowercase : int , lowercase : int = 1 , lowercase : str = "relu" ):
'''simple docstring'''
super().__init__()
_snake_case = in_channels != out_channels or stride != 1
_snake_case = (
ResNetShortCut(lowercase , lowercase , stride=lowercase ) if should_apply_shortcut else nn.Identity()
)
_snake_case = nn.Sequential(
ResNetConvLayer(lowercase , lowercase , stride=lowercase ) , ResNetConvLayer(lowercase , lowercase , activation=lowercase ) , )
_snake_case = ACTaFN[activation]
def A ( self : List[str] , lowercase : List[str] ):
'''simple docstring'''
_snake_case = hidden_state
_snake_case = self.layer(lowercase )
_snake_case = self.shortcut(lowercase )
hidden_state += residual
_snake_case = self.activation(lowercase )
return hidden_state
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
'''simple docstring'''
def __init__( self : Optional[int] , lowercase : int , lowercase : int , lowercase : int = 1 , lowercase : str = "relu" , lowercase : int = 4 ):
'''simple docstring'''
super().__init__()
_snake_case = in_channels != out_channels or stride != 1
_snake_case = out_channels // reduction
_snake_case = (
ResNetShortCut(lowercase , lowercase , stride=lowercase ) if should_apply_shortcut else nn.Identity()
)
_snake_case = nn.Sequential(
ResNetConvLayer(lowercase , lowercase , kernel_size=1 ) , ResNetConvLayer(lowercase , lowercase , stride=lowercase ) , ResNetConvLayer(lowercase , lowercase , kernel_size=1 , activation=lowercase ) , )
_snake_case = ACTaFN[activation]
def A ( self : Dict , lowercase : Union[str, Any] ):
'''simple docstring'''
_snake_case = hidden_state
_snake_case = self.layer(lowercase )
_snake_case = self.shortcut(lowercase )
hidden_state += residual
_snake_case = self.activation(lowercase )
return hidden_state
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
'''simple docstring'''
def __init__( self : Dict , lowercase : ResNetConfig , lowercase : int , lowercase : int , lowercase : int = 2 , lowercase : int = 2 , ):
'''simple docstring'''
super().__init__()
_snake_case = ResNetBottleNeckLayer if config.layer_type == 'bottleneck' else ResNetBasicLayer
_snake_case = nn.Sequential(
# downsampling is done in the first layer with stride of 2
layer(lowercase , lowercase , stride=lowercase , activation=config.hidden_act ) , *[layer(lowercase , lowercase , activation=config.hidden_act ) for _ in range(depth - 1 )] , )
def A ( self : List[str] , lowercase : Tensor ):
'''simple docstring'''
_snake_case = input
for layer in self.layers:
_snake_case = layer(lowercase )
return hidden_state
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
'''simple docstring'''
def __init__( self : Optional[Any] , lowercase : ResNetConfig ):
'''simple docstring'''
super().__init__()
_snake_case = nn.ModuleList([] )
# based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
self.stages.append(
ResNetStage(
lowercase , config.embedding_size , config.hidden_sizes[0] , stride=2 if config.downsample_in_first_stage else 1 , depth=config.depths[0] , ) )
_snake_case = zip(config.hidden_sizes , config.hidden_sizes[1:] )
for (in_channels, out_channels), depth in zip(lowercase , config.depths[1:] ):
self.stages.append(ResNetStage(lowercase , lowercase , lowercase , depth=lowercase ) )
def A ( self : str , lowercase : Tensor , lowercase : bool = False , lowercase : bool = True ):
'''simple docstring'''
_snake_case = () if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
_snake_case = hidden_states + (hidden_state,)
_snake_case = stage_module(lowercase )
if output_hidden_states:
_snake_case = hidden_states + (hidden_state,)
if not return_dict:
return tuple(v for v in [hidden_state, hidden_states] if v is not None )
return BaseModelOutputWithNoAttention(
last_hidden_state=lowercase , hidden_states=lowercase , )
class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase ):
'''simple docstring'''
_UpperCAmelCase : Union[str, Any] = ResNetConfig
_UpperCAmelCase : Tuple = "resnet"
_UpperCAmelCase : Optional[Any] = "pixel_values"
_UpperCAmelCase : Dict = True
def A ( self : List[str] , lowercase : Dict ):
'''simple docstring'''
if isinstance(lowercase , nn.Convad ):
nn.init.kaiming_normal_(module.weight , mode='fan_out' , nonlinearity='relu' )
elif isinstance(lowercase , (nn.BatchNormad, nn.GroupNorm) ):
nn.init.constant_(module.weight , 1 )
nn.init.constant_(module.bias , 0 )
def A ( self : Tuple , lowercase : List[Any] , lowercase : Optional[Any]=False ):
'''simple docstring'''
if isinstance(lowercase , lowercase ):
_snake_case = value
_lowerCamelCase : str = r'''
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`ResNetConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
'''
_lowerCamelCase : int = r'''
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`ConvNextImageProcessor.__call__`] for details.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
'''
@add_start_docstrings(
"The bare ResNet model outputting raw features without any specific head on top." ,UpperCAmelCase ,)
class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase ):
'''simple docstring'''
def __init__( self : Optional[Any] , lowercase : Any ):
'''simple docstring'''
super().__init__(lowercase )
_snake_case = config
_snake_case = ResNetEmbeddings(lowercase )
_snake_case = ResNetEncoder(lowercase )
_snake_case = nn.AdaptiveAvgPoolad((1, 1) )
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(lowercase )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=lowercase , config_class=_CONFIG_FOR_DOC , modality='vision' , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def A ( self : Union[str, Any] , lowercase : Tensor , lowercase : Optional[bool] = None , lowercase : Optional[bool] = None ):
'''simple docstring'''
_snake_case = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
_snake_case = return_dict if return_dict is not None else self.config.use_return_dict
_snake_case = self.embedder(lowercase )
_snake_case = self.encoder(
lowercase , output_hidden_states=lowercase , return_dict=lowercase )
_snake_case = encoder_outputs[0]
_snake_case = self.pooler(lowercase )
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=lowercase , pooler_output=lowercase , hidden_states=encoder_outputs.hidden_states , )
@add_start_docstrings(
"\n ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n ImageNet.\n " ,UpperCAmelCase ,)
class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase ):
'''simple docstring'''
def __init__( self : List[Any] , lowercase : int ):
'''simple docstring'''
super().__init__(lowercase )
_snake_case = config.num_labels
_snake_case = ResNetModel(lowercase )
# classification head
_snake_case = nn.Sequential(
nn.Flatten() , nn.Linear(config.hidden_sizes[-1] , config.num_labels ) if config.num_labels > 0 else nn.Identity() , )
# initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(lowercase )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=lowercase , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def A ( self : Union[str, Any] , lowercase : Optional[torch.FloatTensor] = None , lowercase : Optional[torch.LongTensor] = None , lowercase : Optional[bool] = None , lowercase : Optional[bool] = None , ):
'''simple docstring'''
_snake_case = return_dict if return_dict is not None else self.config.use_return_dict
_snake_case = self.resnet(lowercase , output_hidden_states=lowercase , return_dict=lowercase )
_snake_case = outputs.pooler_output if return_dict else outputs[1]
_snake_case = self.classifier(lowercase )
_snake_case = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
_snake_case = 'regression'
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
_snake_case = 'single_label_classification'
else:
_snake_case = 'multi_label_classification'
if self.config.problem_type == "regression":
_snake_case = MSELoss()
if self.num_labels == 1:
_snake_case = loss_fct(logits.squeeze() , labels.squeeze() )
else:
_snake_case = loss_fct(lowercase , lowercase )
elif self.config.problem_type == "single_label_classification":
_snake_case = CrossEntropyLoss()
_snake_case = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
_snake_case = BCEWithLogitsLoss()
_snake_case = loss_fct(lowercase , lowercase )
if not return_dict:
_snake_case = (logits,) + outputs[2:]
return (loss,) + output if loss is not None else output
return ImageClassifierOutputWithNoAttention(loss=lowercase , logits=lowercase , hidden_states=outputs.hidden_states )
@add_start_docstrings(
"\n ResNet backbone, to be used with frameworks like DETR and MaskFormer.\n " ,UpperCAmelCase ,)
class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase ,UpperCAmelCase ):
'''simple docstring'''
def __init__( self : Tuple , lowercase : Union[str, Any] ):
'''simple docstring'''
super().__init__(lowercase )
super()._init_backbone(lowercase )
_snake_case = [config.embedding_size] + config.hidden_sizes
_snake_case = ResNetEmbeddings(lowercase )
_snake_case = ResNetEncoder(lowercase )
# initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(lowercase )
@replace_return_docstrings(output_type=lowercase , config_class=_CONFIG_FOR_DOC )
def A ( self : Dict , lowercase : Tensor , lowercase : Optional[bool] = None , lowercase : Optional[bool] = None ):
'''simple docstring'''
_snake_case = return_dict if return_dict is not None else self.config.use_return_dict
_snake_case = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
_snake_case = self.embedder(lowercase )
_snake_case = self.encoder(lowercase , output_hidden_states=lowercase , return_dict=lowercase )
_snake_case = outputs.hidden_states
_snake_case = ()
for idx, stage in enumerate(self.stage_names ):
if stage in self.out_features:
feature_maps += (hidden_states[idx],)
if not return_dict:
_snake_case = (feature_maps,)
if output_hidden_states:
output += (outputs.hidden_states,)
return output
return BackboneOutput(
feature_maps=lowercase , hidden_states=outputs.hidden_states if output_hidden_states else None , attentions=lowercase , )
| 686
|
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def a_ ( ) -> Optional[int]:
_snake_case , _snake_case = 9, 14 # noqa: F841
_snake_case = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2, 8, 2],
[8, 6, 6],
[2, 3, 7],
[2, 5, 4],
[6, 5, 2],
[3, 5, 14],
[3, 4, 9],
[5, 4, 10],
[1, 7, 11],
]
_snake_case = defaultdict(__lowercase )
for nodea, nodea, cost in edges:
adjancency[nodea].append([nodea, cost] )
adjancency[nodea].append([nodea, cost] )
_snake_case = mst(__lowercase )
_snake_case = [
[7, 6, 1],
[2, 8, 2],
[6, 5, 2],
[0, 1, 4],
[2, 5, 4],
[2, 3, 7],
[0, 7, 8],
[3, 4, 9],
]
for answer in expected:
_snake_case = tuple(answer[:2] )
_snake_case = tuple(edge[::-1] )
assert edge in result or reverse in result
| 686
| 1
|
"""simple docstring"""
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax import jit
from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
__lowerCAmelCase : Optional[Any] = "0.12" # assumed parallelism: 8
if is_torch_available():
import torch
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__=None ):
"""simple docstring"""
if rng is None:
lowerCAmelCase__ = random.Random()
lowerCAmelCase__ = 1
for dim in shape:
total_dims *= dim
lowerCAmelCase__ = []
for _ in range(lowerCamelCase__ ):
values.append(rng.randint(0 , vocab_size - 1 ) )
lowerCAmelCase__ = np.array(lowerCamelCase__ , dtype=jnp.intaa ).reshape(lowerCamelCase__ )
return output
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__=None ):
"""simple docstring"""
lowerCAmelCase__ = ids_tensor(lowerCamelCase__ , vocab_size=2 , rng=lowerCamelCase__ )
# make sure that at least one token is attended to for each batch
lowerCAmelCase__ = 1
return attn_mask
@require_flax
class a_ :
UpperCamelCase_ : List[str] = None
UpperCamelCase_ : Any = ()
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
lowerCAmelCase__ , lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
# cut to half length & take max batch_size 3
lowerCAmelCase__ = 2
lowerCAmelCase__ = inputs["""input_ids"""].shape[-1] // 2
lowerCAmelCase__ = inputs["""input_ids"""][:max_batch_size, :sequence_length]
lowerCAmelCase__ = jnp.ones_like(snake_case__ )
lowerCAmelCase__ = attention_mask[:max_batch_size, :sequence_length]
# generate max 5 tokens
lowerCAmelCase__ = input_ids.shape[-1] + 5
if config.eos_token_id is not None and config.pad_token_id is None:
# hack to allow generate for models such as GPT2 as is done in `generate()`
lowerCAmelCase__ = config.eos_token_id
return config, input_ids, attention_mask, max_length
@is_pt_flax_cross_test
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = self._get_input_ids_and_config()
lowerCAmelCase__ = False
lowerCAmelCase__ = max_length
lowerCAmelCase__ = 0
for model_class in self.all_generative_model_classes:
lowerCAmelCase__ = model_class(snake_case__ )
lowerCAmelCase__ = model_class.__name__[4:] # Skip the "Flax" at the beginning
lowerCAmelCase__ = getattr(snake_case__ , snake_case__ )
lowerCAmelCase__ = pt_model_class(snake_case__ ).eval()
lowerCAmelCase__ = load_flax_weights_in_pytorch_model(snake_case__ , flax_model.params )
lowerCAmelCase__ = flax_model.generate(snake_case__ ).sequences
lowerCAmelCase__ = pt_model.generate(torch.tensor(snake_case__ , dtype=torch.long ) )
if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
lowerCAmelCase__ = flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]
self.assertListEqual(pt_generation_outputs.numpy().tolist() , flax_generation_outputs.tolist() )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = self._get_input_ids_and_config()
lowerCAmelCase__ = False
lowerCAmelCase__ = max_length
for model_class in self.all_generative_model_classes:
lowerCAmelCase__ = model_class(snake_case__ )
lowerCAmelCase__ = model.generate(snake_case__ ).sequences
self.assertEqual(generation_outputs.shape[-1] , snake_case__ )
lowerCAmelCase__ = jit(model.generate )
lowerCAmelCase__ = jit_generate(snake_case__ ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def _SCREAMING_SNAKE_CASE ( self : int ):
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = self._get_input_ids_and_config()
lowerCAmelCase__ = True
lowerCAmelCase__ = max_length
for model_class in self.all_generative_model_classes:
lowerCAmelCase__ = model_class(snake_case__ )
lowerCAmelCase__ = model.generate(snake_case__ ).sequences
self.assertEqual(generation_outputs.shape[-1] , snake_case__ )
lowerCAmelCase__ = jit(model.generate )
lowerCAmelCase__ = jit_generate(snake_case__ ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def _SCREAMING_SNAKE_CASE ( self : str ):
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = self._get_input_ids_and_config()
lowerCAmelCase__ = False
lowerCAmelCase__ = max_length
lowerCAmelCase__ = 2
for model_class in self.all_generative_model_classes:
lowerCAmelCase__ = model_class(snake_case__ )
lowerCAmelCase__ = model.generate(snake_case__ ).sequences
self.assertEqual(generation_outputs.shape[-1] , snake_case__ )
lowerCAmelCase__ = jit(model.generate )
lowerCAmelCase__ = jit_generate(snake_case__ ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def _SCREAMING_SNAKE_CASE ( self : List[Any] ):
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = self._get_input_ids_and_config()
lowerCAmelCase__ = False
lowerCAmelCase__ = max_length
lowerCAmelCase__ = 2
lowerCAmelCase__ = 2
for model_class in self.all_generative_model_classes:
lowerCAmelCase__ = model_class(snake_case__ )
lowerCAmelCase__ = model.generate(snake_case__ ).sequences
self.assertEqual(generation_outputs.shape[0] , input_ids.shape[0] * config.num_return_sequences )
def _SCREAMING_SNAKE_CASE ( self : str ):
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = self._get_input_ids_and_config()
lowerCAmelCase__ = True
lowerCAmelCase__ = max_length
lowerCAmelCase__ = 0.8
lowerCAmelCase__ = 10
lowerCAmelCase__ = 0.3
lowerCAmelCase__ = 1
lowerCAmelCase__ = 8
lowerCAmelCase__ = 9
for model_class in self.all_generative_model_classes:
lowerCAmelCase__ = model_class(snake_case__ )
lowerCAmelCase__ = model.generate(snake_case__ ).sequences
self.assertEqual(generation_outputs.shape[-1] , snake_case__ )
lowerCAmelCase__ = jit(model.generate )
lowerCAmelCase__ = jit_generate(snake_case__ ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def _SCREAMING_SNAKE_CASE ( self : int ):
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = self._get_input_ids_and_config()
lowerCAmelCase__ = max_length
lowerCAmelCase__ = 1
lowerCAmelCase__ = 8
lowerCAmelCase__ = 9
for model_class in self.all_generative_model_classes:
lowerCAmelCase__ = model_class(snake_case__ )
lowerCAmelCase__ = model.generate(snake_case__ ).sequences
self.assertEqual(generation_outputs.shape[-1] , snake_case__ )
lowerCAmelCase__ = jit(model.generate )
lowerCAmelCase__ = jit_generate(snake_case__ ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def _SCREAMING_SNAKE_CASE ( self : List[str] ):
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = self._get_input_ids_and_config()
lowerCAmelCase__ = max_length
lowerCAmelCase__ = 2
lowerCAmelCase__ = 1
lowerCAmelCase__ = 8
lowerCAmelCase__ = 9
for model_class in self.all_generative_model_classes:
lowerCAmelCase__ = model_class(snake_case__ )
lowerCAmelCase__ = model.generate(snake_case__ ).sequences
self.assertEqual(generation_outputs.shape[-1] , snake_case__ )
lowerCAmelCase__ = jit(model.generate )
lowerCAmelCase__ = jit_generate(snake_case__ ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def _SCREAMING_SNAKE_CASE ( self : Tuple ):
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = self._get_input_ids_and_config()
# pad attention mask on the left
lowerCAmelCase__ = attention_mask.at[(0, 0)].set(0 )
lowerCAmelCase__ = False
lowerCAmelCase__ = max_length
for model_class in self.all_generative_model_classes:
lowerCAmelCase__ = model_class(snake_case__ )
lowerCAmelCase__ = model.generate(snake_case__ , attention_mask=snake_case__ ).sequences
self.assertEqual(generation_outputs.shape[-1] , snake_case__ )
lowerCAmelCase__ = jit(model.generate )
lowerCAmelCase__ = jit_generate(snake_case__ , attention_mask=snake_case__ ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = self._get_input_ids_and_config()
# pad attention mask on the left
lowerCAmelCase__ = attention_mask.at[(0, 0)].set(0 )
lowerCAmelCase__ = True
lowerCAmelCase__ = max_length
for model_class in self.all_generative_model_classes:
lowerCAmelCase__ = model_class(snake_case__ )
lowerCAmelCase__ = model.generate(snake_case__ , attention_mask=snake_case__ ).sequences
self.assertEqual(generation_outputs.shape[-1] , snake_case__ )
lowerCAmelCase__ = jit(model.generate )
lowerCAmelCase__ = jit_generate(snake_case__ , attention_mask=snake_case__ ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = self._get_input_ids_and_config()
# pad attention mask on the left
lowerCAmelCase__ = attention_mask.at[(0, 0)].set(0 )
lowerCAmelCase__ = 2
lowerCAmelCase__ = max_length
for model_class in self.all_generative_model_classes:
lowerCAmelCase__ = model_class(snake_case__ )
lowerCAmelCase__ = model.generate(snake_case__ , attention_mask=snake_case__ ).sequences
self.assertEqual(generation_outputs.shape[-1] , snake_case__ )
lowerCAmelCase__ = jit(model.generate )
lowerCAmelCase__ = jit_generate(snake_case__ , attention_mask=snake_case__ ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
@require_flax
class a_ ( unittest.TestCase ):
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ):
lowerCAmelCase__ = AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-bert""" )
lowerCAmelCase__ = FlaxAutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-bert-flax-only""" )
lowerCAmelCase__ = """Hello world"""
lowerCAmelCase__ = tokenizer(snake_case__ , return_tensors="""np""" ).input_ids
# typos are quickly detected (the correct argument is `do_sample`)
with self.assertRaisesRegex(snake_case__ , """do_samples""" ):
model.generate(snake_case__ , do_samples=snake_case__ )
# arbitrary arguments that will not be used anywhere are also not accepted
with self.assertRaisesRegex(snake_case__ , """foo""" ):
lowerCAmelCase__ = {"""foo""": """bar"""}
model.generate(snake_case__ , **snake_case__ )
| 674
|
"""simple docstring"""
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER
sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
class a_ ( unittest.TestCase ):
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ):
lowerCAmelCase__ = 0
def _SCREAMING_SNAKE_CASE ( self : List[Any] ):
lowerCAmelCase__ = AutoImageProcessor.from_pretrained("""openai/clip-vit-base-patch32""" )
self.assertIsInstance(snake_case__ , snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : Tuple ):
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCAmelCase__ = Path(snake_case__ ) / """preprocessor_config.json"""
lowerCAmelCase__ = Path(snake_case__ ) / """config.json"""
json.dump(
{"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""} , open(snake_case__ , """w""" ) , )
json.dump({"""model_type""": """clip"""} , open(snake_case__ , """w""" ) )
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(snake_case__ )
self.assertIsInstance(snake_case__ , snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
# Ensure we can load the image processor from the feature extractor config
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCAmelCase__ = Path(snake_case__ ) / """preprocessor_config.json"""
lowerCAmelCase__ = Path(snake_case__ ) / """config.json"""
json.dump(
{"""feature_extractor_type""": """CLIPFeatureExtractor""", """processor_class""": """CLIPProcessor"""} , open(snake_case__ , """w""" ) , )
json.dump({"""model_type""": """clip"""} , open(snake_case__ , """w""" ) )
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(snake_case__ )
self.assertIsInstance(snake_case__ , snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ):
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCAmelCase__ = CLIPConfig()
# Create a dummy config file with image_proceesor_type
lowerCAmelCase__ = Path(snake_case__ ) / """preprocessor_config.json"""
lowerCAmelCase__ = Path(snake_case__ ) / """config.json"""
json.dump(
{"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""} , open(snake_case__ , """w""" ) , )
json.dump({"""model_type""": """clip"""} , open(snake_case__ , """w""" ) )
# remove image_processor_type to make sure config.json alone is enough to load image processor locally
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(snake_case__ ).to_dict()
config_dict.pop("""image_processor_type""" )
lowerCAmelCase__ = CLIPImageProcessor(**snake_case__ )
# save in new folder
model_config.save_pretrained(snake_case__ )
config.save_pretrained(snake_case__ )
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(snake_case__ )
# make sure private variable is not incorrectly saved
lowerCAmelCase__ = json.loads(config.to_json_string() )
self.assertTrue("""_processor_class""" not in dict_as_saved )
self.assertIsInstance(snake_case__ , snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : Dict ):
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCAmelCase__ = Path(snake_case__ ) / """preprocessor_config.json"""
json.dump(
{"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""} , open(snake_case__ , """w""" ) , )
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(snake_case__ )
self.assertIsInstance(snake_case__ , snake_case__ )
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ):
with self.assertRaisesRegex(
snake_case__ , """clip-base is not a local folder and is not a valid model identifier""" ):
lowerCAmelCase__ = AutoImageProcessor.from_pretrained("""clip-base""" )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
with self.assertRaisesRegex(
snake_case__ , R"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)""" ):
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(snake_case__ , revision="""aaaaaa""" )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
with self.assertRaisesRegex(
snake_case__ , """hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.""" , ):
lowerCAmelCase__ = AutoImageProcessor.from_pretrained("""hf-internal-testing/config-no-model""" )
def _SCREAMING_SNAKE_CASE ( self : Any ):
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(snake_case__ ):
lowerCAmelCase__ = AutoImageProcessor.from_pretrained("""hf-internal-testing/test_dynamic_image_processor""" )
# If remote code is disabled, we can't load this config.
with self.assertRaises(snake_case__ ):
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(
"""hf-internal-testing/test_dynamic_image_processor""" , trust_remote_code=snake_case__ )
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(
"""hf-internal-testing/test_dynamic_image_processor""" , trust_remote_code=snake_case__ )
self.assertEqual(image_processor.__class__.__name__ , """NewImageProcessor""" )
# Test image processor can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(snake_case__ )
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(snake_case__ , trust_remote_code=snake_case__ )
self.assertEqual(reloaded_image_processor.__class__.__name__ , """NewImageProcessor""" )
def _SCREAMING_SNAKE_CASE ( self : Dict ):
try:
AutoConfig.register("""custom""" , snake_case__ )
AutoImageProcessor.register(snake_case__ , snake_case__ )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(snake_case__ ):
AutoImageProcessor.register(snake_case__ , snake_case__ )
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCAmelCase__ = Path(snake_case__ ) / """preprocessor_config.json"""
lowerCAmelCase__ = Path(snake_case__ ) / """config.json"""
json.dump(
{"""feature_extractor_type""": """CLIPFeatureExtractor""", """processor_class""": """CLIPProcessor"""} , open(snake_case__ , """w""" ) , )
json.dump({"""model_type""": """clip"""} , open(snake_case__ , """w""" ) )
lowerCAmelCase__ = CustomImageProcessor.from_pretrained(snake_case__ )
# Now that the config is registered, it can be used as any other config with the auto-API
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(snake_case__ )
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(snake_case__ )
self.assertIsInstance(snake_case__ , snake_case__ )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
def _SCREAMING_SNAKE_CASE ( self : List[str] ):
class a_ ( __UpperCamelCase ):
UpperCamelCase_ : Tuple = True
try:
AutoConfig.register("""custom""" , snake_case__ )
AutoImageProcessor.register(snake_case__ , snake_case__ )
# If remote code is not set, the default is to use local
lowerCAmelCase__ = AutoImageProcessor.from_pretrained("""hf-internal-testing/test_dynamic_image_processor""" )
self.assertEqual(image_processor.__class__.__name__ , """NewImageProcessor""" )
self.assertTrue(image_processor.is_local )
# If remote code is disabled, we load the local one.
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(
"""hf-internal-testing/test_dynamic_image_processor""" , trust_remote_code=snake_case__ )
self.assertEqual(image_processor.__class__.__name__ , """NewImageProcessor""" )
self.assertTrue(image_processor.is_local )
# If remote is enabled, we load from the Hub
lowerCAmelCase__ = AutoImageProcessor.from_pretrained(
"""hf-internal-testing/test_dynamic_image_processor""" , trust_remote_code=snake_case__ )
self.assertEqual(image_processor.__class__.__name__ , """NewImageProcessor""" )
self.assertTrue(not hasattr(snake_case__ , """is_local""" ) )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
| 674
| 1
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCamelCase = {
"""configuration_mega""": ["""MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MegaConfig""", """MegaOnnxConfig"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase = [
"""MEGA_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""MegaForCausalLM""",
"""MegaForMaskedLM""",
"""MegaForMultipleChoice""",
"""MegaForQuestionAnswering""",
"""MegaForSequenceClassification""",
"""MegaForTokenClassification""",
"""MegaModel""",
"""MegaPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mega import (
MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
MegaForCausalLM,
MegaForMaskedLM,
MegaForMultipleChoice,
MegaForQuestionAnswering,
MegaForSequenceClassification,
MegaForTokenClassification,
MegaModel,
MegaPreTrainedModel,
)
else:
import sys
UpperCamelCase = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 104
|
import os
import unittest
from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir
from ...test_tokenization_common import TokenizerTesterMixin
_A : Dict = get_tests_dir("""fixtures/test_sentencepiece_bpe.model""")
class __snake_case ( __SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ : Any = BartphoTokenizer
lowerCamelCase__ : Tuple = False
lowerCamelCase__ : Dict = True
def lowercase_ ( self ):
'''simple docstring'''
super().setUp()
SCREAMING_SNAKE_CASE__ = ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']
SCREAMING_SNAKE_CASE__ = dict(zip(A_ , range(len(A_ ) ) ) )
SCREAMING_SNAKE_CASE__ = {'''unk_token''': '''<unk>'''}
SCREAMING_SNAKE_CASE__ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''monolingual_vocab_file'''] )
with open(self.monolingual_vocab_file , '''w''' , encoding='''utf-8''' ) as fp:
for token in vocab_tokens:
fp.write(f'''{token} {vocab_tokens[token]}\n''' )
SCREAMING_SNAKE_CASE__ = BartphoTokenizer(A_ , self.monolingual_vocab_file , **self.special_tokens_map )
tokenizer.save_pretrained(self.tmpdirname )
def lowercase_ ( self , **A_ ):
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return BartphoTokenizer.from_pretrained(self.tmpdirname , **A_ )
def lowercase_ ( self , A_ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = '''This is a là test'''
SCREAMING_SNAKE_CASE__ = '''This is a<unk><unk> test'''
return input_text, output_text
def lowercase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = BartphoTokenizer(A_ , self.monolingual_vocab_file , **self.special_tokens_map )
SCREAMING_SNAKE_CASE__ = '''This is a là test'''
SCREAMING_SNAKE_CASE__ = '''▁This ▁is ▁a ▁l à ▁t est'''.split()
SCREAMING_SNAKE_CASE__ = tokenizer.tokenize(A_ )
self.assertListEqual(A_ , A_ )
SCREAMING_SNAKE_CASE__ = tokens + [tokenizer.unk_token]
SCREAMING_SNAKE_CASE__ = [4, 5, 6, 3, 3, 7, 8, 3]
self.assertListEqual(tokenizer.convert_tokens_to_ids(A_ ) , A_ )
| 100
| 0
|
'''simple docstring'''
from collections.abc import Generator
def _SCREAMING_SNAKE_CASE ( ):
"""simple docstring"""
lowerCAmelCase__ , lowerCAmelCase__ : List[str] = 0, 1
while True:
lowerCAmelCase__ , lowerCAmelCase__ : Optional[int] = b, a + b
yield b
def _SCREAMING_SNAKE_CASE ( UpperCamelCase = 1000 ):
"""simple docstring"""
lowerCAmelCase__ : Dict = 1
lowerCAmelCase__ : Optional[Any] = fibonacci_generator()
while len(str(next(UpperCamelCase ) ) ) < n:
answer += 1
return answer + 1
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 711
|
'''simple docstring'''
import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def _SCREAMING_SNAKE_CASE ( *UpperCamelCase ):
"""simple docstring"""
if not isinstance(UpperCamelCase , UpperCamelCase ):
lowerCAmelCase__ : List[Any] = list(UpperCamelCase )
for i in range(len(UpperCamelCase ) ):
lowerCAmelCase__ : Dict = None
gc.collect()
if is_xpu_available():
torch.xpu.empty_cache()
elif is_npu_available():
torch.npu.empty_cache()
else:
torch.cuda.empty_cache()
return objects
def _SCREAMING_SNAKE_CASE ( UpperCamelCase ):
"""simple docstring"""
lowerCAmelCase__ : Union[str, Any] = [
"""CUDA out of memory.""", # CUDA OOM
"""cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.""", # CUDNN SNAFU
"""DefaultCPUAllocator: can't allocate memory""", # CPU OOM
]
if isinstance(UpperCamelCase , UpperCamelCase ) and len(exception.args ) == 1:
return any(err in exception.args[0] for err in _statements )
return False
def _SCREAMING_SNAKE_CASE ( UpperCamelCase = None , UpperCamelCase = 128 ):
"""simple docstring"""
if function is None:
return functools.partial(UpperCamelCase , starting_batch_size=UpperCamelCase )
lowerCAmelCase__ : Any = starting_batch_size
def decorator(*UpperCamelCase , **UpperCamelCase ):
nonlocal batch_size
gc.collect()
if is_xpu_available():
torch.xpu.empty_cache()
elif is_npu_available():
torch.npu.empty_cache()
else:
torch.cuda.empty_cache()
lowerCAmelCase__ : Optional[Any] = list(inspect.signature(UpperCamelCase ).parameters.keys() )
# Guard against user error
if len(UpperCamelCase ) < (len(UpperCamelCase ) + 1):
lowerCAmelCase__ : Any = """, """.join([f"""{arg}={value}""" for arg, value in zip(params[1:] , args[1:] )] )
raise TypeError(
f"""Batch size was passed into `{function.__name__}` as the first argument when called."""
f"""Remove this as the decorator already does so: `{function.__name__}({arg_str})`""" )
while True:
if batch_size == 0:
raise RuntimeError("""No executable batch size found, reached zero.""" )
try:
return function(UpperCamelCase , *UpperCamelCase , **UpperCamelCase )
except Exception as e:
if should_reduce_batch_size(UpperCamelCase ):
gc.collect()
if is_xpu_available():
torch.xpu.empty_cache()
elif is_npu_available():
torch.npu.empty_cache()
else:
torch.cuda.empty_cache()
batch_size //= 2
else:
raise
return decorator
| 160
| 0
|
"""simple docstring"""
import itertools
import random
import unittest
import numpy as np
from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
lowerCAmelCase__ = random.Random()
if is_torch_available():
import torch
def snake_case_ ( A_ : Tuple, A_ : List[Any]=1.0, A_ : Optional[Any]=None, A_ : Optional[int]=None ):
'''simple docstring'''
if rng is None:
_lowerCamelCase : Dict = global_rng
_lowerCamelCase : Any = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
class __snake_case ( unittest.TestCase):
def __init__( self : int , __lowerCAmelCase : Tuple , __lowerCAmelCase : List[Any]=7 , __lowerCAmelCase : int=4_0_0 , __lowerCAmelCase : List[str]=2_0_0_0 , __lowerCAmelCase : str=1 , __lowerCAmelCase : Tuple=0.0 , __lowerCAmelCase : Dict=1_6_0_0_0 , __lowerCAmelCase : Dict=True , __lowerCAmelCase : Any=True , ):
"""simple docstring"""
_lowerCamelCase : int = parent
_lowerCamelCase : Optional[Any] = batch_size
_lowerCamelCase : int = min_seq_length
_lowerCamelCase : Any = max_seq_length
_lowerCamelCase : int = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
_lowerCamelCase : Dict = feature_size
_lowerCamelCase : int = padding_value
_lowerCamelCase : Tuple = sampling_rate
_lowerCamelCase : Optional[int] = return_attention_mask
_lowerCamelCase : List[str] = do_normalize
def SCREAMING_SNAKE_CASE ( self : Optional[int] ):
"""simple docstring"""
return {
"feature_size": self.feature_size,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"return_attention_mask": self.return_attention_mask,
"do_normalize": self.do_normalize,
}
def SCREAMING_SNAKE_CASE ( self : Dict , __lowerCAmelCase : Any=False , __lowerCAmelCase : int=False ):
"""simple docstring"""
def _flatten(__lowerCAmelCase : int ):
return list(itertools.chain(*__lowerCAmelCase ) )
if equal_length:
_lowerCamelCase : str = floats_list((self.batch_size, self.max_seq_length) )
else:
# make sure that inputs increase in size
_lowerCamelCase : Optional[Any] = [
_flatten(floats_list((x, self.feature_size) ) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
_lowerCamelCase : Dict = [np.asarray(__lowerCAmelCase ) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class __snake_case ( _lowercase , unittest.TestCase):
snake_case__ : List[str] = ASTFeatureExtractor
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
"""simple docstring"""
_lowerCamelCase : Union[str, Any] = ASTFeatureExtractionTester(self )
def SCREAMING_SNAKE_CASE ( self : Dict ):
"""simple docstring"""
_lowerCamelCase : Tuple = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
_lowerCamelCase : List[str] = [floats_list((1, x) )[0] for x in range(8_0_0 , 1_4_0_0 , 2_0_0 )]
_lowerCamelCase : Tuple = [np.asarray(__lowerCAmelCase ) for speech_input in speech_inputs]
# Test not batched input
_lowerCamelCase : Optional[Any] = feat_extract(speech_inputs[0] , return_tensors='''np''' ).input_values
_lowerCamelCase : Optional[int] = feat_extract(np_speech_inputs[0] , return_tensors='''np''' ).input_values
self.assertTrue(np.allclose(__lowerCAmelCase , __lowerCAmelCase , atol=1E-3 ) )
# Test batched
_lowerCamelCase : Optional[Any] = feat_extract(__lowerCAmelCase , padding=__lowerCAmelCase , return_tensors='''np''' ).input_values
_lowerCamelCase : Optional[Any] = feat_extract(__lowerCAmelCase , padding=__lowerCAmelCase , return_tensors='''np''' ).input_values
for enc_seq_a, enc_seq_a in zip(__lowerCAmelCase , __lowerCAmelCase ):
self.assertTrue(np.allclose(__lowerCAmelCase , __lowerCAmelCase , atol=1E-3 ) )
# Test 2-D numpy arrays are batched.
_lowerCamelCase : str = [floats_list((1, x) )[0] for x in (8_0_0, 8_0_0, 8_0_0)]
_lowerCamelCase : Any = np.asarray(__lowerCAmelCase )
_lowerCamelCase : Dict = feat_extract(__lowerCAmelCase , return_tensors='''np''' ).input_values
_lowerCamelCase : List[Any] = feat_extract(__lowerCAmelCase , return_tensors='''np''' ).input_values
for enc_seq_a, enc_seq_a in zip(__lowerCAmelCase , __lowerCAmelCase ):
self.assertTrue(np.allclose(__lowerCAmelCase , __lowerCAmelCase , atol=1E-3 ) )
@require_torch
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
"""simple docstring"""
import torch
_lowerCamelCase : Any = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
_lowerCamelCase : Any = np.random.rand(1_0_0 ).astype(np.floataa )
_lowerCamelCase : int = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
_lowerCamelCase : Union[str, Any] = feature_extractor.pad([{'''input_values''': inputs}] , return_tensors='''np''' )
self.assertTrue(np_processed.input_values.dtype == np.floataa )
_lowerCamelCase : Tuple = feature_extractor.pad([{'''input_values''': inputs}] , return_tensors='''pt''' )
self.assertTrue(pt_processed.input_values.dtype == torch.floataa )
def SCREAMING_SNAKE_CASE ( self : Optional[Any] , __lowerCAmelCase : List[str] ):
"""simple docstring"""
from datasets import load_dataset
_lowerCamelCase : str = load_dataset('''hf-internal-testing/librispeech_asr_dummy''' , '''clean''' , split='''validation''' )
# automatic decoding with librispeech
_lowerCamelCase : int = ds.sort('''id''' ).select(range(__lowerCAmelCase ) )[:num_samples]['''audio''']
return [x["array"] for x in speech_samples]
@require_torch
def SCREAMING_SNAKE_CASE ( self : Optional[int] ):
"""simple docstring"""
_lowerCamelCase : Optional[int] = torch.tensor(
[-0.98_94, -1.27_76, -0.90_66, -1.27_76, -0.93_49, -1.26_09, -1.03_86, -1.27_76,
-1.15_61, -1.27_76, -1.20_52, -1.27_23, -1.21_90, -1.21_32, -1.27_76, -1.11_33,
-1.19_53, -1.13_43, -1.15_84, -1.22_03, -1.17_70, -1.24_74, -1.23_81, -1.19_36,
-0.92_70, -0.83_17, -0.80_49, -0.77_06, -0.75_65, -0.78_69] )
# fmt: on
_lowerCamelCase : Optional[Any] = self._load_datasamples(1 )
_lowerCamelCase : int = ASTFeatureExtractor()
_lowerCamelCase : Tuple = feature_extractor(__lowerCAmelCase , return_tensors='''pt''' ).input_values
self.assertEquals(input_values.shape , (1, 1_0_2_4, 1_2_8) )
self.assertTrue(torch.allclose(input_values[0, 0, :3_0] , __lowerCAmelCase , atol=1E-4 ) )
| 83
|
"""simple docstring"""
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from transformers import BartTokenizer, RagTokenizer, TaTokenizer
def snake_case_ ( A_ : Tuple, A_ : List[str], A_ : Optional[Any], A_ : Dict, A_ : Dict=True, A_ : int="pt" ):
'''simple docstring'''
_lowerCamelCase : str = {'''add_prefix_space''': True} if isinstance(A_, A_ ) and not line.startswith(''' ''' ) else {}
_lowerCamelCase : Union[str, Any] = padding_side
return tokenizer(
[line], max_length=A_, padding='''max_length''' if pad_to_max_length else None, truncation=A_, return_tensors=A_, add_special_tokens=A_, **A_, )
def snake_case_ ( A_ : Any, A_ : Optional[int], A_ : List[Any]=None, ):
'''simple docstring'''
_lowerCamelCase : Optional[int] = input_ids.ne(A_ ).any(dim=0 )
if attention_mask is None:
return input_ids[:, keep_column_mask]
else:
return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
class __snake_case ( _lowercase):
def __init__( self : Tuple , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Dict , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Tuple="train" , __lowerCAmelCase : List[str]=None , __lowerCAmelCase : Optional[int]=None , __lowerCAmelCase : Any=None , __lowerCAmelCase : Union[str, Any]="" , ):
"""simple docstring"""
super().__init__()
_lowerCamelCase : Optional[int] = Path(__lowerCAmelCase ).joinpath(type_path + '''.source''' )
_lowerCamelCase : List[str] = Path(__lowerCAmelCase ).joinpath(type_path + '''.target''' )
_lowerCamelCase : List[Any] = self.get_char_lens(self.src_file )
_lowerCamelCase : Optional[int] = max_source_length
_lowerCamelCase : Optional[Any] = max_target_length
assert min(self.src_lens ) > 0, f'''found empty line in {self.src_file}'''
_lowerCamelCase : List[Any] = tokenizer
_lowerCamelCase : List[Any] = prefix
if n_obs is not None:
_lowerCamelCase : List[str] = self.src_lens[:n_obs]
_lowerCamelCase : int = src_lang
_lowerCamelCase : Union[str, Any] = tgt_lang
def __len__( self : int ):
"""simple docstring"""
return len(self.src_lens )
def __getitem__( self : Dict , __lowerCAmelCase : Optional[Any] ):
"""simple docstring"""
_lowerCamelCase : str = index + 1 # linecache starts at 1
_lowerCamelCase : Union[str, Any] = self.prefix + linecache.getline(str(self.src_file ) , __lowerCAmelCase ).rstrip('''\n''' )
_lowerCamelCase : Optional[Any] = linecache.getline(str(self.tgt_file ) , __lowerCAmelCase ).rstrip('''\n''' )
assert source_line, f'''empty source line for index {index}'''
assert tgt_line, f'''empty tgt line for index {index}'''
# Need to add eos token manually for T5
if isinstance(self.tokenizer , __lowerCAmelCase ):
source_line += self.tokenizer.eos_token
tgt_line += self.tokenizer.eos_token
# Pad source and target to the right
_lowerCamelCase : Optional[int] = (
self.tokenizer.question_encoder if isinstance(self.tokenizer , __lowerCAmelCase ) else self.tokenizer
)
_lowerCamelCase : Union[str, Any] = self.tokenizer.generator if isinstance(self.tokenizer , __lowerCAmelCase ) else self.tokenizer
_lowerCamelCase : List[str] = encode_line(__lowerCAmelCase , __lowerCAmelCase , self.max_source_length , '''right''' )
_lowerCamelCase : List[str] = encode_line(__lowerCAmelCase , __lowerCAmelCase , self.max_target_length , '''right''' )
_lowerCamelCase : Optional[Any] = source_inputs['''input_ids'''].squeeze()
_lowerCamelCase : Union[str, Any] = target_inputs['''input_ids'''].squeeze()
_lowerCamelCase : Any = source_inputs['''attention_mask'''].squeeze()
return {
"input_ids": source_ids,
"attention_mask": src_mask,
"decoder_input_ids": target_ids,
}
@staticmethod
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase : str ):
"""simple docstring"""
return [len(__lowerCAmelCase ) for x in Path(__lowerCAmelCase ).open().readlines()]
def SCREAMING_SNAKE_CASE ( self : List[Any] , __lowerCAmelCase : Any ):
"""simple docstring"""
_lowerCamelCase : List[Any] = torch.stack([x['''input_ids'''] for x in batch] )
_lowerCamelCase : Tuple = torch.stack([x['''attention_mask'''] for x in batch] )
_lowerCamelCase : Union[str, Any] = torch.stack([x['''decoder_input_ids'''] for x in batch] )
_lowerCamelCase : Tuple = (
self.tokenizer.generator.pad_token_id
if isinstance(self.tokenizer , __lowerCAmelCase )
else self.tokenizer.pad_token_id
)
_lowerCamelCase : Tuple = (
self.tokenizer.question_encoder.pad_token_id
if isinstance(self.tokenizer , __lowerCAmelCase )
else self.tokenizer.pad_token_id
)
_lowerCamelCase : Union[str, Any] = trim_batch(__lowerCAmelCase , __lowerCAmelCase )
_lowerCamelCase , _lowerCamelCase : List[str] = trim_batch(__lowerCAmelCase , __lowerCAmelCase , attention_mask=__lowerCAmelCase )
_lowerCamelCase : Optional[int] = {
'''input_ids''': source_ids,
'''attention_mask''': source_mask,
'''decoder_input_ids''': y,
}
return batch
lowerCAmelCase__ = getLogger(__name__)
def snake_case_ ( A_ : List[List] ):
'''simple docstring'''
return list(itertools.chain.from_iterable(A_ ) )
def snake_case_ ( A_ : str ):
'''simple docstring'''
_lowerCamelCase : Dict = get_git_info()
save_json(A_, os.path.join(A_, '''git_log.json''' ) )
def snake_case_ ( A_ : str, A_ : Union[str, Any], A_ : int=4, **A_ : Optional[int] ):
'''simple docstring'''
with open(A_, '''w''' ) as f:
json.dump(A_, A_, indent=A_, **A_ )
def snake_case_ ( A_ : Any ):
'''simple docstring'''
with open(A_ ) as f:
return json.load(A_ )
def snake_case_ ( ):
'''simple docstring'''
_lowerCamelCase : List[str] = git.Repo(search_parent_directories=A_ )
_lowerCamelCase : str = {
'''repo_id''': str(A_ ),
'''repo_sha''': str(repo.head.object.hexsha ),
'''repo_branch''': str(repo.active_branch ),
'''hostname''': str(socket.gethostname() ),
}
return repo_infos
def snake_case_ ( A_ : Callable, A_ : Iterable ):
'''simple docstring'''
return list(map(A_, A_ ) )
def snake_case_ ( A_ : str, A_ : Tuple ):
'''simple docstring'''
with open(A_, '''wb''' ) as f:
return pickle.dump(A_, A_ )
def snake_case_ ( A_ : List[str] ):
'''simple docstring'''
def remove_articles(A_ : str ):
return re.sub(R'''\b(a|an|the)\b''', ''' ''', A_ )
def white_space_fix(A_ : Any ):
return " ".join(text.split() )
def remove_punc(A_ : List[Any] ):
_lowerCamelCase : Any = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(A_ : Optional[int] ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(A_ ) ) ) )
def snake_case_ ( A_ : int, A_ : List[Any] ):
'''simple docstring'''
_lowerCamelCase : str = normalize_answer(A_ ).split()
_lowerCamelCase : int = normalize_answer(A_ ).split()
_lowerCamelCase : str = Counter(A_ ) & Counter(A_ )
_lowerCamelCase : Any = sum(common.values() )
if num_same == 0:
return 0
_lowerCamelCase : int = 1.0 * num_same / len(A_ )
_lowerCamelCase : str = 1.0 * num_same / len(A_ )
_lowerCamelCase : List[Any] = (2 * precision * recall) / (precision + recall)
return fa
def snake_case_ ( A_ : Dict, A_ : str ):
'''simple docstring'''
return normalize_answer(A_ ) == normalize_answer(A_ )
def snake_case_ ( A_ : List[str], A_ : List[str] ):
'''simple docstring'''
assert len(A_ ) == len(A_ )
_lowerCamelCase : Optional[Any] = 0
for hypo, pred in zip(A_, A_ ):
em += exact_match_score(A_, A_ )
if len(A_ ) > 0:
em /= len(A_ )
return {"em": em}
def snake_case_ ( A_ : Optional[int] ):
'''simple docstring'''
return model_prefix.startswith('''rag''' )
def snake_case_ ( A_ : Dict, A_ : int, A_ : List[Any] ):
'''simple docstring'''
_lowerCamelCase : Dict = {p: p for p in extra_params}
# T5 models don't have `dropout` param, they have `dropout_rate` instead
_lowerCamelCase : Tuple = '''dropout_rate'''
for p in extra_params:
if getattr(A_, A_, A_ ):
if not hasattr(A_, A_ ) and not hasattr(A_, equivalent_param[p] ):
logger.info('''config doesn\'t have a `{}` attribute'''.format(A_ ) )
delattr(A_, A_ )
continue
_lowerCamelCase : Union[str, Any] = p if hasattr(A_, A_ ) else equivalent_param[p]
setattr(A_, A_, getattr(A_, A_ ) )
delattr(A_, A_ )
return hparams, config
| 83
| 1
|
"""simple docstring"""
from math import factorial
lowerCamelCase__ : dict[str, int] = {str(digit): factorial(digit) for digit in range(10)}
def __A ( a_ : List[Any] )-> int:
'''simple docstring'''
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise TypeError('''Parameter number must be int''' )
if number < 0:
raise ValueError('''Parameter number must be greater than or equal to 0''' )
# Converts number in string to iterate on its digits and adds its factorial.
return sum(DIGIT_FACTORIAL[digit] for digit in str(__lowerCAmelCase ) )
def __A ( a_ : Tuple = 60 , a_ : List[str] = 1_00_00_00 )-> int:
'''simple docstring'''
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ) or not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise TypeError('''Parameters chain_length and number_limit must be int''' )
if chain_length <= 0 or number_limit <= 0:
raise ValueError(
'''Parameters chain_length and number_limit must be greater than 0''' )
# the counter for the chains with the exact desired length
SCREAMING_SNAKE_CASE : str = 0
# the cached sizes of the previous chains
SCREAMING_SNAKE_CASE : dict[int, int] = {}
for start_chain_element in range(1 , __lowerCAmelCase ):
# The temporary set will contain the elements of the chain
SCREAMING_SNAKE_CASE : Optional[Any] = set()
SCREAMING_SNAKE_CASE : List[str] = 0
# Stop computing the chain when you find a cached size, a repeating item or the
# length is greater then the desired one.
SCREAMING_SNAKE_CASE : str = start_chain_element
while (
chain_element not in chain_sets_lengths
and chain_element not in chain_set
and chain_set_length <= chain_length
):
chain_set.add(__lowerCAmelCase )
chain_set_length += 1
SCREAMING_SNAKE_CASE : Union[str, Any] = digit_factorial_sum(__lowerCAmelCase )
if chain_element in chain_sets_lengths:
chain_set_length += chain_sets_lengths[chain_element]
SCREAMING_SNAKE_CASE : str = chain_set_length
# If chain contains the exact amount of elements increase the counter
if chain_set_length == chain_length:
chains_counter += 1
return chains_counter
if __name__ == "__main__":
import doctest
doctest.testmod()
print(f'''{solution()}''')
| 712
|
"""simple docstring"""
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging
lowerCamelCase__ : Tuple = logging.get_logger(__name__)
lowerCamelCase__ : str = {
"facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json",
# See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}
class lowercase__( _UpperCAmelCase ):
'''simple docstring'''
UpperCamelCase = """blenderbot-small"""
UpperCamelCase = ["""past_key_values"""]
UpperCamelCase = {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
def __init__( self :Any , lowerCamelCase_ :Dict=5_02_65 , lowerCamelCase_ :str=5_12 , lowerCamelCase_ :Tuple=8 , lowerCamelCase_ :int=20_48 , lowerCamelCase_ :str=16 , lowerCamelCase_ :Optional[int]=8 , lowerCamelCase_ :str=20_48 , lowerCamelCase_ :Optional[Any]=16 , lowerCamelCase_ :Union[str, Any]=0.0 , lowerCamelCase_ :List[str]=0.0 , lowerCamelCase_ :Tuple=True , lowerCamelCase_ :Tuple=True , lowerCamelCase_ :int="gelu" , lowerCamelCase_ :Tuple=5_12 , lowerCamelCase_ :Optional[int]=0.1 , lowerCamelCase_ :int=0.0 , lowerCamelCase_ :Tuple=0.0 , lowerCamelCase_ :Optional[int]=0.0_2 , lowerCamelCase_ :Union[str, Any]=1 , lowerCamelCase_ :Dict=False , lowerCamelCase_ :Optional[int]=0 , lowerCamelCase_ :List[Any]=1 , lowerCamelCase_ :Any=2 , lowerCamelCase_ :Optional[Any]=2 , **lowerCamelCase_ :Dict , ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE : int = vocab_size
SCREAMING_SNAKE_CASE : List[str] = max_position_embeddings
SCREAMING_SNAKE_CASE : Optional[Any] = d_model
SCREAMING_SNAKE_CASE : Dict = encoder_ffn_dim
SCREAMING_SNAKE_CASE : Tuple = encoder_layers
SCREAMING_SNAKE_CASE : Dict = encoder_attention_heads
SCREAMING_SNAKE_CASE : Any = decoder_ffn_dim
SCREAMING_SNAKE_CASE : str = decoder_layers
SCREAMING_SNAKE_CASE : str = decoder_attention_heads
SCREAMING_SNAKE_CASE : List[Any] = dropout
SCREAMING_SNAKE_CASE : Optional[Any] = attention_dropout
SCREAMING_SNAKE_CASE : Any = activation_dropout
SCREAMING_SNAKE_CASE : List[str] = activation_function
SCREAMING_SNAKE_CASE : Optional[int] = init_std
SCREAMING_SNAKE_CASE : List[Any] = encoder_layerdrop
SCREAMING_SNAKE_CASE : Union[str, Any] = decoder_layerdrop
SCREAMING_SNAKE_CASE : List[Any] = use_cache
SCREAMING_SNAKE_CASE : Union[str, Any] = encoder_layers
SCREAMING_SNAKE_CASE : List[Any] = scale_embedding # scale factor will be sqrt(d_model) if True
super().__init__(
pad_token_id=lowerCamelCase_ , bos_token_id=lowerCamelCase_ , eos_token_id=lowerCamelCase_ , is_encoder_decoder=lowerCamelCase_ , decoder_start_token_id=lowerCamelCase_ , forced_eos_token_id=lowerCamelCase_ , **lowerCamelCase_ , )
class lowercase__( _UpperCAmelCase ):
'''simple docstring'''
@property
def __lowerCAmelCase ( self :Any ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
SCREAMING_SNAKE_CASE : Tuple = OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
] )
if self.use_past:
SCREAMING_SNAKE_CASE : Union[str, Any] = {0: '''batch'''}
SCREAMING_SNAKE_CASE : List[Any] = {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
else:
SCREAMING_SNAKE_CASE : Optional[Any] = {0: '''batch''', 1: '''decoder_sequence'''}
SCREAMING_SNAKE_CASE : Union[str, Any] = {0: '''batch''', 1: '''decoder_sequence'''}
if self.use_past:
self.fill_with_past_key_values_(lowerCamelCase_ , direction='''inputs''' )
elif self.task == "causal-lm":
# TODO: figure this case out.
SCREAMING_SNAKE_CASE : Tuple = OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
] )
if self.use_past:
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : List[Any] = self.num_layers
for i in range(lowerCamelCase_ ):
SCREAMING_SNAKE_CASE : int = {0: '''batch''', 2: '''past_sequence + sequence'''}
SCREAMING_SNAKE_CASE : List[str] = {0: '''batch''', 2: '''past_sequence + sequence'''}
else:
SCREAMING_SNAKE_CASE : Any = OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
] )
return common_inputs
@property
def __lowerCAmelCase ( self :List[str] ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
SCREAMING_SNAKE_CASE : Any = super().outputs
else:
SCREAMING_SNAKE_CASE : Tuple = super(lowerCamelCase_ , self ).outputs
if self.use_past:
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : Union[str, Any] = self.num_layers
for i in range(lowerCamelCase_ ):
SCREAMING_SNAKE_CASE : Union[str, Any] = {0: '''batch''', 2: '''past_sequence + sequence'''}
SCREAMING_SNAKE_CASE : str = {0: '''batch''', 2: '''past_sequence + sequence'''}
return common_outputs
def __lowerCAmelCase ( self :int , lowerCamelCase_ :PreTrainedTokenizer , lowerCamelCase_ :int = -1 , lowerCamelCase_ :int = -1 , lowerCamelCase_ :bool = False , lowerCamelCase_ :Optional[TensorType] = None , ) -> Mapping[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )
# Generate decoder inputs
SCREAMING_SNAKE_CASE : Optional[int] = seq_length if not self.use_past else 1
SCREAMING_SNAKE_CASE : Optional[int] = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[Any] = {f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
SCREAMING_SNAKE_CASE : str = dict(**lowerCamelCase_ , **lowerCamelCase_ )
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : Any = common_inputs['''input_ids'''].shape
SCREAMING_SNAKE_CASE : str = common_inputs['''decoder_input_ids'''].shape[1]
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : Any = self.num_attention_heads
SCREAMING_SNAKE_CASE : str = (
batch,
num_encoder_attention_heads,
encoder_seq_length,
self._config.hidden_size // num_encoder_attention_heads,
)
SCREAMING_SNAKE_CASE : Optional[Any] = decoder_seq_length + 3
SCREAMING_SNAKE_CASE : int = (
batch,
num_decoder_attention_heads,
decoder_past_length,
self._config.hidden_size // num_decoder_attention_heads,
)
SCREAMING_SNAKE_CASE : List[Any] = torch.cat(
[common_inputs['''decoder_attention_mask'''], torch.ones(lowerCamelCase_ , lowerCamelCase_ )] , dim=1 )
SCREAMING_SNAKE_CASE : Optional[int] = []
# If the number of encoder and decoder layers are present in the model configuration, both are considered
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : Optional[int] = self.num_layers
SCREAMING_SNAKE_CASE : int = min(lowerCamelCase_ , lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Optional[int] = max(lowerCamelCase_ , lowerCamelCase_ ) - min_num_layers
SCREAMING_SNAKE_CASE : Tuple = '''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''
for _ in range(lowerCamelCase_ ):
common_inputs["past_key_values"].append(
(
torch.zeros(lowerCamelCase_ ),
torch.zeros(lowerCamelCase_ ),
torch.zeros(lowerCamelCase_ ),
torch.zeros(lowerCamelCase_ ),
) )
# TODO: test this.
SCREAMING_SNAKE_CASE : int = encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
for _ in range(lowerCamelCase_ , lowerCamelCase_ ):
common_inputs["past_key_values"].append((torch.zeros(lowerCamelCase_ ), torch.zeros(lowerCamelCase_ )) )
return common_inputs
def __lowerCAmelCase ( self :Any , lowerCamelCase_ :PreTrainedTokenizer , lowerCamelCase_ :int = -1 , lowerCamelCase_ :int = -1 , lowerCamelCase_ :bool = False , lowerCamelCase_ :Optional[TensorType] = None , ) -> Mapping[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : Tuple = common_inputs['''input_ids'''].shape
# Not using the same length for past_key_values
SCREAMING_SNAKE_CASE : List[str] = seqlen + 2
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : Dict = self.num_layers
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : List[Any] = self.num_attention_heads
SCREAMING_SNAKE_CASE : Union[str, Any] = (
batch,
num_encoder_attention_heads,
past_key_values_length,
self._config.hidden_size // num_encoder_attention_heads,
)
SCREAMING_SNAKE_CASE : Tuple = common_inputs['''attention_mask'''].dtype
SCREAMING_SNAKE_CASE : Any = torch.cat(
[common_inputs['''attention_mask'''], torch.ones(lowerCamelCase_ , lowerCamelCase_ , dtype=lowerCamelCase_ )] , dim=1 )
SCREAMING_SNAKE_CASE : Optional[int] = [
(torch.zeros(lowerCamelCase_ ), torch.zeros(lowerCamelCase_ )) for _ in range(lowerCamelCase_ )
]
return common_inputs
def __lowerCAmelCase ( self :Union[str, Any] , lowerCamelCase_ :PreTrainedTokenizer , lowerCamelCase_ :int = -1 , lowerCamelCase_ :int = -1 , lowerCamelCase_ :bool = False , lowerCamelCase_ :Optional[TensorType] = None , ) -> Mapping[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = compute_effective_axis_dimension(
lowerCamelCase_ , fixed_dimension=OnnxConfig.default_fixed_batch , num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
SCREAMING_SNAKE_CASE : int = tokenizer.num_special_tokens_to_add(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : int = compute_effective_axis_dimension(
lowerCamelCase_ , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=lowerCamelCase_ )
# Generate dummy inputs according to compute batch and sequence
SCREAMING_SNAKE_CASE : Tuple = [''' '''.join([tokenizer.unk_token] ) * seq_length] * batch_size
SCREAMING_SNAKE_CASE : Any = dict(tokenizer(lowerCamelCase_ , return_tensors=lowerCamelCase_ ) )
return common_inputs
def __lowerCAmelCase ( self :List[Any] , lowerCamelCase_ :PreTrainedTokenizer , lowerCamelCase_ :int = -1 , lowerCamelCase_ :int = -1 , lowerCamelCase_ :bool = False , lowerCamelCase_ :Optional[TensorType] = None , ) -> Mapping[str, Any]:
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
SCREAMING_SNAKE_CASE : Dict = self._generate_dummy_inputs_for_default_and_seqaseq_lm(
lowerCamelCase_ , batch_size=lowerCamelCase_ , seq_length=lowerCamelCase_ , is_pair=lowerCamelCase_ , framework=lowerCamelCase_ )
elif self.task == "causal-lm":
SCREAMING_SNAKE_CASE : Union[str, Any] = self._generate_dummy_inputs_for_causal_lm(
lowerCamelCase_ , batch_size=lowerCamelCase_ , seq_length=lowerCamelCase_ , is_pair=lowerCamelCase_ , framework=lowerCamelCase_ )
else:
SCREAMING_SNAKE_CASE : List[Any] = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
lowerCamelCase_ , batch_size=lowerCamelCase_ , seq_length=lowerCamelCase_ , is_pair=lowerCamelCase_ , framework=lowerCamelCase_ )
return common_inputs
def __lowerCAmelCase ( self :Dict , lowerCamelCase_ :int , lowerCamelCase_ :List[str] , lowerCamelCase_ :List[str] , lowerCamelCase_ :Dict ) -> List[Any]:
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
SCREAMING_SNAKE_CASE : Optional[Any] = super()._flatten_past_key_values_(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )
else:
SCREAMING_SNAKE_CASE : Tuple = super(lowerCamelCase_ , self )._flatten_past_key_values_(
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )
| 18
| 0
|
import argparse
import logging
import os
import sys
import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers
import transformers
from transformers import BartForConditionalGeneration, BartTokenizer
logging.basicConfig(
format="""%(asctime)s | %(levelname)s | %(name)s | [%(filename)s:%(lineno)d] %(message)s""",
datefmt="""%Y-%m-%d %H:%M:%S""",
level=os.environ.get("""LOGLEVEL""", """INFO""").upper(),
stream=sys.stdout,
)
__lowerCamelCase = logging.getLogger(__name__)
__lowerCamelCase = {'facebook/bart-base': BartForConditionalGeneration}
__lowerCamelCase = {'facebook/bart-base': BartTokenizer}
def UpperCamelCase ( ):
snake_case : Any = argparse.ArgumentParser(description="Export Bart model + Beam Search to ONNX graph." )
parser.add_argument(
"--validation_file" , type=_UpperCAmelCase , default=_UpperCAmelCase , help="A csv or a json file containing the validation data." )
parser.add_argument(
"--max_length" , type=_UpperCAmelCase , default=5 , help="The maximum total input sequence length after tokenization." , )
parser.add_argument(
"--num_beams" , type=_UpperCAmelCase , default=_UpperCAmelCase , help=(
"Number of beams to use for evaluation. This argument will be "
"passed to ``model.generate``, which is used during ``evaluate`` and ``predict``."
) , )
parser.add_argument(
"--model_name_or_path" , type=_UpperCAmelCase , help="Path to pretrained model or model identifier from huggingface.co/models." , required=_UpperCAmelCase , )
parser.add_argument(
"--config_name" , type=_UpperCAmelCase , default=_UpperCAmelCase , help="Pretrained config name or path if not the same as model_name" , )
parser.add_argument(
"--device" , type=_UpperCAmelCase , default="cpu" , help="Device where the model will be run" , )
parser.add_argument("--output_file_path" , type=_UpperCAmelCase , default=_UpperCAmelCase , help="Where to store the final ONNX file." )
snake_case : Optional[int] = parser.parse_args()
return args
def UpperCamelCase ( __lowerCamelCase : Any , __lowerCamelCase : Optional[int]="cpu" ):
snake_case : Optional[int] = model_dict[model_name].from_pretrained(_UpperCAmelCase ).to(_UpperCAmelCase )
snake_case : Optional[int] = tokenizer_dict[model_name].from_pretrained(_UpperCAmelCase )
if model_name in ["facebook/bart-base"]:
snake_case : Any = 0
snake_case : int = None
snake_case : Union[str, Any] = 0
return huggingface_model, tokenizer
def UpperCamelCase ( __lowerCamelCase : List[Any] , __lowerCamelCase : str , __lowerCamelCase : List[Any] , __lowerCamelCase : List[str] , __lowerCamelCase : int ):
model.eval()
snake_case : Tuple = None
snake_case : List[Any] = torch.jit.script(BARTBeamSearchGenerator(_UpperCAmelCase ) )
with torch.no_grad():
snake_case : Optional[int] = "My friends are cool but they eat too many carbs."
snake_case : Optional[Any] = tokenizer([ARTICLE_TO_SUMMARIZE] , max_length=1024 , return_tensors="pt" ).to(model.device )
snake_case : Any = model.generate(
inputs["input_ids"] , attention_mask=inputs["attention_mask"] , num_beams=_UpperCAmelCase , max_length=_UpperCAmelCase , early_stopping=_UpperCAmelCase , decoder_start_token_id=model.config.decoder_start_token_id , )
torch.onnx.export(
_UpperCAmelCase , (
inputs["input_ids"],
inputs["attention_mask"],
num_beams,
max_length,
model.config.decoder_start_token_id,
) , _UpperCAmelCase , opset_version=14 , input_names=["input_ids", "attention_mask", "num_beams", "max_length", "decoder_start_token_id"] , output_names=["output_ids"] , dynamic_axes={
"input_ids": {0: "batch", 1: "seq"},
"output_ids": {0: "batch", 1: "seq_out"},
} , example_outputs=_UpperCAmelCase , )
logger.info("Model exported to {}".format(_UpperCAmelCase ) )
snake_case : List[str] = remove_dup_initializers(os.path.abspath(_UpperCAmelCase ) )
logger.info("Deduplicated and optimized model written to {}".format(_UpperCAmelCase ) )
snake_case : Optional[int] = onnxruntime.InferenceSession(_UpperCAmelCase )
snake_case : List[str] = ort_sess.run(
_UpperCAmelCase , {
"input_ids": inputs["input_ids"].cpu().numpy(),
"attention_mask": inputs["attention_mask"].cpu().numpy(),
"num_beams": np.array(_UpperCAmelCase ),
"max_length": np.array(_UpperCAmelCase ),
"decoder_start_token_id": np.array(model.config.decoder_start_token_id ),
} , )
np.testing.assert_allclose(summary_ids.cpu().numpy() , ort_out[0] , rtol=1E-3 , atol=1E-3 )
logger.info("Model outputs from torch and ONNX Runtime are similar." )
logger.info("Success." )
def UpperCamelCase ( ):
snake_case : Tuple = parse_args()
snake_case : Dict = 5
snake_case : List[Any] = 4
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" , datefmt="%m/%d/%Y %H:%M:%S" , level=logging.INFO , )
logger.setLevel(logging.INFO )
transformers.utils.logging.set_verbosity_error()
snake_case : List[str] = torch.device(args.device )
snake_case , snake_case : Any = load_model_tokenizer(args.model_name_or_path , _UpperCAmelCase )
if model.config.decoder_start_token_id is None:
raise ValueError("Make sure that `config.decoder_start_token_id` is correctly defined" )
model.to(_UpperCAmelCase )
if args.max_length:
snake_case : int = args.max_length
if args.num_beams:
snake_case : List[Any] = args.num_beams
if args.output_file_path:
snake_case : Dict = args.output_file_path
else:
snake_case : Union[str, Any] = "BART.onnx"
logger.info("Exporting model to ONNX" )
export_and_validate_model(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
if __name__ == "__main__":
main()
| 204
|
import heapq as hq
import math
from collections.abc import Iterator
class _snake_case :
def __init__( self , a) -> Optional[Any]:
SCREAMING_SNAKE_CASE = str(id_)
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = {} # {vertex:distance}
def __lt__( self , a) -> Dict:
return self.key < other.key
def __repr__( self) -> Optional[Any]:
return self.id
def SCREAMING_SNAKE_CASE__ ( self , a) -> Optional[Any]:
self.neighbors.append(a)
def SCREAMING_SNAKE_CASE__ ( self , a , a) -> Tuple:
SCREAMING_SNAKE_CASE = weight
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase):
# add the neighbors:
graph[a - 1].add_neighbor(graph[b - 1])
graph[b - 1].add_neighbor(graph[a - 1])
# add the edges:
graph[a - 1].add_edge(graph[b - 1] , _UpperCAmelCase)
graph[b - 1].add_edge(graph[a - 1] , _UpperCAmelCase)
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase):
SCREAMING_SNAKE_CASE = []
for u in graph:
SCREAMING_SNAKE_CASE = math.inf
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = 0
SCREAMING_SNAKE_CASE = graph[:]
while q:
SCREAMING_SNAKE_CASE = min(_UpperCAmelCase)
q.remove(_UpperCAmelCase)
for v in u.neighbors:
if (v in q) and (u.edges[v.id] < v.key):
SCREAMING_SNAKE_CASE = u
SCREAMING_SNAKE_CASE = u.edges[v.id]
for i in range(1 , len(_UpperCAmelCase)):
a.append((int(graph[i].id) + 1, int(graph[i].pi.id) + 1))
return a
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase):
for u in graph:
SCREAMING_SNAKE_CASE = math.inf
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = 0
SCREAMING_SNAKE_CASE = list(_UpperCAmelCase)
hq.heapify(_UpperCAmelCase)
while h:
SCREAMING_SNAKE_CASE = hq.heappop(_UpperCAmelCase)
for v in u.neighbors:
if (v in h) and (u.edges[v.id] < v.key):
SCREAMING_SNAKE_CASE = u
SCREAMING_SNAKE_CASE = u.edges[v.id]
hq.heapify(_UpperCAmelCase)
for i in range(1 , len(_UpperCAmelCase)):
yield (int(graph[i].id) + 1, int(graph[i].pi.id) + 1)
def lowerCamelCase__ ():
pass
if __name__ == "__main__":
import doctest
doctest.testmod()
| 73
| 0
|
'''simple docstring'''
def __a ( A__ , A__ , A__ ) -> list:
lowerCAmelCase = len(A__ )
lowerCAmelCase = [[0] * n for i in range(A__ )]
for i in range(A__ ):
lowerCAmelCase = y_points[i]
for i in range(2 , A__ ):
for j in range(A__ , A__ ):
lowerCAmelCase = (
(xa - x_points[j - i + 1]) * q[j][i - 1]
- (xa - x_points[j]) * q[j - 1][i - 1]
) / (x_points[j] - x_points[j - i + 1])
return [q[n - 1][n - 1], q]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 159
|
'''simple docstring'''
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_torch_available():
import torch
lowercase : Optional[int] = random.Random()
def __a ( A__ , A__=1.0 , A__=None , A__=None ) -> Any:
if rng is None:
lowerCAmelCase = global_rng
lowerCAmelCase = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
@require_torch
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def __init__( self : Union[str, Any] , SCREAMING_SNAKE_CASE : List[str] , SCREAMING_SNAKE_CASE : int=7 , SCREAMING_SNAKE_CASE : Optional[Any]=4_0_0 , SCREAMING_SNAKE_CASE : Optional[Any]=2_0_0_0 , SCREAMING_SNAKE_CASE : Union[str, Any]=1 , SCREAMING_SNAKE_CASE : int=0.0 , SCREAMING_SNAKE_CASE : Optional[int]=1_6_0_0_0 , SCREAMING_SNAKE_CASE : Any=True , SCREAMING_SNAKE_CASE : Optional[Any]=8_0 , SCREAMING_SNAKE_CASE : int=1_6 , SCREAMING_SNAKE_CASE : Any=6_4 , SCREAMING_SNAKE_CASE : List[Any]="hann_window" , SCREAMING_SNAKE_CASE : Dict=8_0 , SCREAMING_SNAKE_CASE : Any=7_6_0_0 , SCREAMING_SNAKE_CASE : Optional[Any]=1E-10 , SCREAMING_SNAKE_CASE : Union[str, Any]=True , ) -> Any:
"""simple docstring"""
lowerCAmelCase = parent
lowerCAmelCase = batch_size
lowerCAmelCase = min_seq_length
lowerCAmelCase = max_seq_length
lowerCAmelCase = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
lowerCAmelCase = feature_size
lowerCAmelCase = padding_value
lowerCAmelCase = sampling_rate
lowerCAmelCase = do_normalize
lowerCAmelCase = num_mel_bins
lowerCAmelCase = hop_length
lowerCAmelCase = win_length
lowerCAmelCase = win_function
lowerCAmelCase = fmin
lowerCAmelCase = fmax
lowerCAmelCase = mel_floor
lowerCAmelCase = return_attention_mask
def __A ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
return {
"feature_size": self.feature_size,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"do_normalize": self.do_normalize,
"num_mel_bins": self.num_mel_bins,
"hop_length": self.hop_length,
"win_length": self.win_length,
"win_function": self.win_function,
"fmin": self.fmin,
"fmax": self.fmax,
"mel_floor": self.mel_floor,
"return_attention_mask": self.return_attention_mask,
}
def __A ( self : List[str] , SCREAMING_SNAKE_CASE : int=False , SCREAMING_SNAKE_CASE : Optional[Any]=False ) -> str:
"""simple docstring"""
def _flatten(SCREAMING_SNAKE_CASE : List[Any] ):
return list(itertools.chain(*SCREAMING_SNAKE_CASE ) )
if equal_length:
lowerCAmelCase = floats_list((self.batch_size, self.max_seq_length) )
else:
# make sure that inputs increase in size
lowerCAmelCase = [
_flatten(floats_list((x, self.feature_size) ) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
lowerCAmelCase = [np.asarray(SCREAMING_SNAKE_CASE ) for x in speech_inputs]
return speech_inputs
def __A ( self : List[str] , SCREAMING_SNAKE_CASE : Tuple=False , SCREAMING_SNAKE_CASE : Optional[int]=False ) -> str:
"""simple docstring"""
if equal_length:
lowerCAmelCase = [floats_list((self.max_seq_length, self.num_mel_bins) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
lowerCAmelCase = [
floats_list((x, self.num_mel_bins) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
lowerCAmelCase = [np.asarray(SCREAMING_SNAKE_CASE ) for x in speech_inputs]
return speech_inputs
@require_torch
class _lowerCAmelCase ( UpperCamelCase_ , unittest.TestCase ):
"""simple docstring"""
lowerCAmelCase = SpeechTaFeatureExtractor
def __A ( self : Optional[int] ) -> Dict:
"""simple docstring"""
lowerCAmelCase = SpeechTaFeatureExtractionTester(self )
def __A ( self : Union[str, Any] , SCREAMING_SNAKE_CASE : Union[str, Any] ) -> List[str]:
"""simple docstring"""
self.assertTrue(np.all(np.mean(SCREAMING_SNAKE_CASE , axis=0 ) < 1E-3 ) )
self.assertTrue(np.all(np.abs(np.var(SCREAMING_SNAKE_CASE , axis=0 ) - 1 ) < 1E-3 ) )
def __A ( self : int ) -> Union[str, Any]:
"""simple docstring"""
lowerCAmelCase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
lowerCAmelCase = [floats_list((1, x) )[0] for x in range(8_0_0 , 1_4_0_0 , 2_0_0 )]
lowerCAmelCase = [np.asarray(SCREAMING_SNAKE_CASE ) for speech_input in speech_inputs]
# Test not batched input
lowerCAmelCase = feat_extract(speech_inputs[0] , return_tensors="np" ).input_values
lowerCAmelCase = feat_extract(np_speech_inputs[0] , return_tensors="np" ).input_values
self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , atol=1E-3 ) )
# Test batched
lowerCAmelCase = feat_extract(SCREAMING_SNAKE_CASE , return_tensors="np" ).input_values
lowerCAmelCase = feat_extract(SCREAMING_SNAKE_CASE , return_tensors="np" ).input_values
for enc_seq_a, enc_seq_a in zip(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , atol=1E-3 ) )
def __A ( self : Any ) -> Union[str, Any]:
"""simple docstring"""
lowerCAmelCase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
lowerCAmelCase = [floats_list((1, x) )[0] for x in range(8_0_0 , 1_4_0_0 , 2_0_0 )]
lowerCAmelCase = ["longest", "max_length", "do_not_pad"]
lowerCAmelCase = [None, 1_6_0_0, None]
for max_length, padding in zip(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
lowerCAmelCase = feat_extract(SCREAMING_SNAKE_CASE , padding=SCREAMING_SNAKE_CASE , max_length=SCREAMING_SNAKE_CASE , return_tensors="np" )
lowerCAmelCase = processed.input_values
self._check_zero_mean_unit_variance(input_values[0][:8_0_0] )
self.assertTrue(input_values[0][8_0_0:].sum() < 1E-6 )
self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0] )
self.assertTrue(input_values[0][1_0_0_0:].sum() < 1E-6 )
self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0] )
def __A ( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
lowerCAmelCase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
lowerCAmelCase = range(8_0_0 , 1_4_0_0 , 2_0_0 )
lowerCAmelCase = [floats_list((1, x) )[0] for x in lengths]
lowerCAmelCase = ["longest", "max_length", "do_not_pad"]
lowerCAmelCase = [None, 1_6_0_0, None]
for max_length, padding in zip(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
lowerCAmelCase = feat_extract(SCREAMING_SNAKE_CASE , max_length=SCREAMING_SNAKE_CASE , padding=SCREAMING_SNAKE_CASE )
lowerCAmelCase = processed.input_values
self._check_zero_mean_unit_variance(input_values[0][:8_0_0] )
self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0] )
self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0] )
def __A ( self : str ) -> Any:
"""simple docstring"""
lowerCAmelCase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
lowerCAmelCase = [floats_list((1, x) )[0] for x in range(8_0_0 , 1_4_0_0 , 2_0_0 )]
lowerCAmelCase = feat_extract(
SCREAMING_SNAKE_CASE , truncation=SCREAMING_SNAKE_CASE , max_length=1_0_0_0 , padding="max_length" , return_tensors="np" )
lowerCAmelCase = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :8_0_0] )
self._check_zero_mean_unit_variance(input_values[1] )
self._check_zero_mean_unit_variance(input_values[2] )
def __A ( self : int ) -> Optional[int]:
"""simple docstring"""
lowerCAmelCase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
lowerCAmelCase = [floats_list((1, x) )[0] for x in range(8_0_0 , 1_4_0_0 , 2_0_0 )]
lowerCAmelCase = feat_extract(
SCREAMING_SNAKE_CASE , truncation=SCREAMING_SNAKE_CASE , max_length=1_0_0_0 , padding="longest" , return_tensors="np" )
lowerCAmelCase = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :8_0_0] )
self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0] )
self._check_zero_mean_unit_variance(input_values[2] )
# make sure that if max_length < longest -> then pad to max_length
self.assertTrue(input_values.shape == (3, 1_0_0_0) )
lowerCAmelCase = [floats_list((1, x) )[0] for x in range(8_0_0 , 1_4_0_0 , 2_0_0 )]
lowerCAmelCase = feat_extract(
SCREAMING_SNAKE_CASE , truncation=SCREAMING_SNAKE_CASE , max_length=2_0_0_0 , padding="longest" , return_tensors="np" )
lowerCAmelCase = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :8_0_0] )
self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0] )
self._check_zero_mean_unit_variance(input_values[2] )
# make sure that if max_length > longest -> then pad to longest
self.assertTrue(input_values.shape == (3, 1_2_0_0) )
def __A ( self : Optional[int] ) -> Any:
"""simple docstring"""
lowerCAmelCase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
lowerCAmelCase = np.random.rand(1_0_0 ).astype(np.floataa )
lowerCAmelCase = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
lowerCAmelCase = feature_extractor.pad([{"input_values": inputs}] , return_tensors="np" )
self.assertTrue(np_processed.input_values.dtype == np.floataa )
lowerCAmelCase = feature_extractor.pad([{"input_values": inputs}] , return_tensors="pt" )
self.assertTrue(pt_processed.input_values.dtype == torch.floataa )
def __A ( self : int ) -> Optional[int]:
"""simple docstring"""
lowerCAmelCase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
lowerCAmelCase = [floats_list((1, x) )[0] for x in range(8_0_0 , 1_4_0_0 , 2_0_0 )]
lowerCAmelCase = [np.asarray(SCREAMING_SNAKE_CASE ) for speech_input in speech_inputs]
# Test feature size
lowerCAmelCase = feature_extractor(audio_target=SCREAMING_SNAKE_CASE , padding=SCREAMING_SNAKE_CASE , return_tensors="np" ).input_values
self.assertTrue(input_values.ndim == 3 )
self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins )
# Test not batched input
lowerCAmelCase = feature_extractor(speech_inputs[0] , return_tensors="np" ).input_values
lowerCAmelCase = feature_extractor(np_speech_inputs[0] , return_tensors="np" ).input_values
self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , atol=1E-3 ) )
# Test batched
lowerCAmelCase = feature_extractor(SCREAMING_SNAKE_CASE , return_tensors="np" ).input_values
lowerCAmelCase = feature_extractor(SCREAMING_SNAKE_CASE , return_tensors="np" ).input_values
for enc_seq_a, enc_seq_a in zip(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , atol=1E-3 ) )
# Test 2-D numpy arrays are batched.
lowerCAmelCase = [floats_list((1, x) )[0] for x in (8_0_0, 8_0_0, 8_0_0)]
lowerCAmelCase = np.asarray(SCREAMING_SNAKE_CASE )
lowerCAmelCase = feature_extractor(SCREAMING_SNAKE_CASE , return_tensors="np" ).input_values
lowerCAmelCase = feature_extractor(SCREAMING_SNAKE_CASE , return_tensors="np" ).input_values
for enc_seq_a, enc_seq_a in zip(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , atol=1E-3 ) )
def __A ( self : Dict ) -> Optional[Any]:
"""simple docstring"""
lowerCAmelCase = self.feat_extract_tester.prepare_inputs_for_target()
lowerCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
lowerCAmelCase = feat_extract.model_input_names[0]
lowerCAmelCase = BatchFeature({input_name: speech_inputs} )
self.assertTrue(all(len(SCREAMING_SNAKE_CASE ) == len(SCREAMING_SNAKE_CASE ) for x, y in zip(SCREAMING_SNAKE_CASE , processed_features[input_name] ) ) )
lowerCAmelCase = self.feat_extract_tester.prepare_inputs_for_target(equal_length=SCREAMING_SNAKE_CASE )
lowerCAmelCase = BatchFeature({input_name: speech_inputs} , tensor_type="np" )
lowerCAmelCase = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
lowerCAmelCase = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.num_mel_bins) )
@require_torch
def __A ( self : List[str] ) -> Optional[int]:
"""simple docstring"""
lowerCAmelCase = self.feat_extract_tester.prepare_inputs_for_target(equal_length=SCREAMING_SNAKE_CASE )
lowerCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
lowerCAmelCase = feat_extract.model_input_names[0]
lowerCAmelCase = BatchFeature({input_name: speech_inputs} , tensor_type="pt" )
lowerCAmelCase = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
lowerCAmelCase = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.num_mel_bins) )
@require_torch
def __A ( self : Optional[int] ) -> Dict:
"""simple docstring"""
lowerCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
lowerCAmelCase = self.feat_extract_tester.prepare_inputs_for_target()
lowerCAmelCase = feat_extract.model_input_names[0]
lowerCAmelCase = BatchFeature({input_name: speech_inputs} )
lowerCAmelCase = feat_extract.num_mel_bins # hack!
lowerCAmelCase = feat_extract.pad(SCREAMING_SNAKE_CASE , padding="longest" , return_tensors="np" )[input_name]
lowerCAmelCase = feat_extract.pad(SCREAMING_SNAKE_CASE , padding="longest" , return_tensors="pt" )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_pt.numpy().astype(np.floataa ).sum() ) < 1E-2 )
def __A ( self : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
lowerCAmelCase = self.feat_extract_dict
lowerCAmelCase = True
lowerCAmelCase = self.feature_extraction_class(**SCREAMING_SNAKE_CASE )
lowerCAmelCase = self.feat_extract_tester.prepare_inputs_for_target()
lowerCAmelCase = [len(SCREAMING_SNAKE_CASE ) for x in speech_inputs]
lowerCAmelCase = feat_extract.model_input_names[0]
lowerCAmelCase = BatchFeature({input_name: speech_inputs} )
lowerCAmelCase = feat_extract.num_mel_bins # hack!
lowerCAmelCase = feat_extract.pad(SCREAMING_SNAKE_CASE , padding="longest" , return_tensors="np" )
self.assertIn("attention_mask" , SCREAMING_SNAKE_CASE )
self.assertListEqual(list(processed.attention_mask.shape ) , list(processed[input_name].shape[:2] ) )
self.assertListEqual(processed.attention_mask.sum(-1 ).tolist() , SCREAMING_SNAKE_CASE )
def __A ( self : Tuple ) -> List[Any]:
"""simple docstring"""
lowerCAmelCase = self.feat_extract_dict
lowerCAmelCase = True
lowerCAmelCase = self.feature_extraction_class(**SCREAMING_SNAKE_CASE )
lowerCAmelCase = self.feat_extract_tester.prepare_inputs_for_target()
lowerCAmelCase = [len(SCREAMING_SNAKE_CASE ) for x in speech_inputs]
lowerCAmelCase = feat_extract.model_input_names[0]
lowerCAmelCase = BatchFeature({input_name: speech_inputs} )
lowerCAmelCase = min(SCREAMING_SNAKE_CASE )
lowerCAmelCase = feat_extract.num_mel_bins # hack!
lowerCAmelCase = feat_extract.pad(
SCREAMING_SNAKE_CASE , padding="max_length" , max_length=SCREAMING_SNAKE_CASE , truncation=SCREAMING_SNAKE_CASE , return_tensors="np" )
self.assertIn("attention_mask" , SCREAMING_SNAKE_CASE )
self.assertListEqual(
list(processed_pad.attention_mask.shape ) , [processed_pad[input_name].shape[0], max_length] )
self.assertListEqual(
processed_pad.attention_mask[:, :max_length].sum(-1 ).tolist() , [max_length for x in speech_inputs] )
def __A ( self : Tuple , SCREAMING_SNAKE_CASE : Optional[Any] ) -> List[Any]:
"""simple docstring"""
from datasets import load_dataset
lowerCAmelCase = load_dataset("hf-internal-testing/librispeech_asr_dummy" , "clean" , split="validation" )
# automatic decoding with librispeech
lowerCAmelCase = ds.sort("id" ).select(range(SCREAMING_SNAKE_CASE ) )[:num_samples]["audio"]
return [x["array"] for x in speech_samples]
def __A ( self : List[Any] ) -> int:
"""simple docstring"""
lowerCAmelCase = torch.tensor(
[2.38_04E-03, 2.07_52E-03, 1.98_36E-03, 2.10_57E-03, 1.61_74E-03,
3.05_18E-04, 9.15_53E-05, 3.35_69E-04, 9.76_56E-04, 1.83_11E-03,
2.01_42E-03, 2.10_57E-03, 1.73_95E-03, 4.57_76E-04, -3.96_73E-04,
4.57_76E-04, 1.00_71E-03, 9.15_53E-05, 4.88_28E-04, 1.15_97E-03,
7.32_42E-04, 9.46_04E-04, 1.80_05E-03, 1.83_11E-03, 8.85_01E-04,
4.27_25E-04, 4.88_28E-04, 7.32_42E-04, 1.09_86E-03, 2.10_57E-03] )
# fmt: on
lowerCAmelCase = self._load_datasamples(1 )
lowerCAmelCase = SpeechTaFeatureExtractor()
lowerCAmelCase = feature_extractor(SCREAMING_SNAKE_CASE , return_tensors="pt" ).input_values
self.assertEquals(input_values.shape , (1, 9_3_6_8_0) )
self.assertTrue(torch.allclose(input_values[0, :3_0] , SCREAMING_SNAKE_CASE , atol=1E-6 ) )
def __A ( self : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
lowerCAmelCase = torch.tensor(
[-2.6_8_7_0, -3.0_1_0_4, -3.1_3_5_6, -3.5_3_5_2, -3.0_0_4_4, -3.0_3_5_3, -3.4_7_1_9, -3.6_7_7_7,
-3.1_5_2_0, -2.9_4_3_5, -2.6_5_5_3, -2.8_7_9_5, -2.9_9_4_4, -2.5_9_2_1, -3.0_2_7_9, -3.0_3_8_6,
-3.0_8_6_4, -3.1_2_9_1, -3.2_3_5_3, -2.7_4_4_4, -2.6_8_3_1, -2.7_2_8_7, -3.1_7_6_1, -3.1_5_7_1,
-3.2_7_2_6, -3.0_5_8_2, -3.1_0_0_7, -3.4_5_3_3, -3.4_6_9_5, -3.0_9_9_8] )
# fmt: on
lowerCAmelCase = self._load_datasamples(1 )
lowerCAmelCase = SpeechTaFeatureExtractor()
lowerCAmelCase = feature_extractor(audio_target=SCREAMING_SNAKE_CASE , return_tensors="pt" ).input_values
self.assertEquals(input_values.shape , (1, 3_6_6, 8_0) )
self.assertTrue(torch.allclose(input_values[0, 0, :3_0] , SCREAMING_SNAKE_CASE , atol=1E-4 ) )
| 159
| 1
|
'''simple docstring'''
import math
def A__ ( UpperCAmelCase_ ):
_UpperCamelCase : Any = []
_UpperCamelCase : List[str] = 2
_UpperCamelCase : Dict = int(math.sqrt(UpperCAmelCase_ ) ) # Size of every segment
_UpperCamelCase : Tuple = [True] * (end + 1)
_UpperCamelCase : Dict = []
while start <= end:
if temp[start] is True:
in_prime.append(UpperCAmelCase_ )
for i in range(start * start , end + 1 , UpperCAmelCase_ ):
_UpperCamelCase : Optional[int] = False
start += 1
prime += in_prime
_UpperCamelCase : str = end + 1
_UpperCamelCase : Optional[Any] = min(2 * end , UpperCAmelCase_ )
while low <= n:
_UpperCamelCase : Dict = [True] * (high - low + 1)
for each in in_prime:
_UpperCamelCase : int = math.floor(low / each ) * each
if t < low:
t += each
for j in range(UpperCAmelCase_ , high + 1 , UpperCAmelCase_ ):
_UpperCamelCase : Optional[int] = False
for j in range(len(UpperCAmelCase_ ) ):
if temp[j] is True:
prime.append(j + low )
_UpperCamelCase : List[str] = high + 1
_UpperCamelCase : List[str] = min(high + end , UpperCAmelCase_ )
return prime
print(sieve(10**6))
| 195
|
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import MobileNetVaImageProcessor
class __UpperCamelCase ( unittest.TestCase ):
"""simple docstring"""
def __init__( self : Any , _A : int , _A : Any=7 , _A : List[str]=3 , _A : Optional[Any]=18 , _A : List[str]=30 , _A : Optional[Any]=400 , _A : Any=True , _A : List[str]=None , _A : Union[str, Any]=True , _A : Optional[int]=None , ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : List[Any] = size if size is not None else {'''shortest_edge''': 20}
__SCREAMING_SNAKE_CASE : List[str] = crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
__SCREAMING_SNAKE_CASE : int = parent
__SCREAMING_SNAKE_CASE : Optional[int] = batch_size
__SCREAMING_SNAKE_CASE : Optional[Any] = num_channels
__SCREAMING_SNAKE_CASE : List[str] = image_size
__SCREAMING_SNAKE_CASE : int = min_resolution
__SCREAMING_SNAKE_CASE : Optional[int] = max_resolution
__SCREAMING_SNAKE_CASE : List[Any] = do_resize
__SCREAMING_SNAKE_CASE : Union[str, Any] = size
__SCREAMING_SNAKE_CASE : str = do_center_crop
__SCREAMING_SNAKE_CASE : Any = crop_size
def UpperCAmelCase__ ( self : Dict ):
"""simple docstring"""
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
}
@require_torch
@require_vision
class __UpperCamelCase ( lowerCAmelCase__ , unittest.TestCase ):
"""simple docstring"""
lowerCAmelCase_ = MobileNetVaImageProcessor if is_vision_available() else None
def UpperCAmelCase__ ( self : Optional[Any] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : str = MobileNetVaImageProcessingTester(self )
@property
def UpperCAmelCase__ ( self : Union[str, Any] ):
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def UpperCAmelCase__ ( self : Union[str, Any] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Dict = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_A , '''do_resize''' ) )
self.assertTrue(hasattr(_A , '''size''' ) )
self.assertTrue(hasattr(_A , '''do_center_crop''' ) )
self.assertTrue(hasattr(_A , '''crop_size''' ) )
def UpperCAmelCase__ ( self : Optional[Any] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : List[Any] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''shortest_edge''': 20} )
self.assertEqual(image_processor.crop_size , {'''height''': 18, '''width''': 18} )
__SCREAMING_SNAKE_CASE : Tuple = self.image_processing_class.from_dict(self.image_processor_dict , size=42 , crop_size=84 )
self.assertEqual(image_processor.size , {'''shortest_edge''': 42} )
self.assertEqual(image_processor.crop_size , {'''height''': 84, '''width''': 84} )
def UpperCAmelCase__ ( self : int ):
"""simple docstring"""
pass
def UpperCAmelCase__ ( self : Dict ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : str = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__SCREAMING_SNAKE_CASE : Dict = prepare_image_inputs(self.image_processor_tester , equal_resolution=_A )
for image in image_inputs:
self.assertIsInstance(_A , Image.Image )
# Test not batched input
__SCREAMING_SNAKE_CASE : Dict = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
__SCREAMING_SNAKE_CASE : List[Any] = image_processing(_A , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def UpperCAmelCase__ ( self : Optional[Any] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : int = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
__SCREAMING_SNAKE_CASE : Any = prepare_image_inputs(self.image_processor_tester , equal_resolution=_A , numpify=_A )
for image in image_inputs:
self.assertIsInstance(_A , np.ndarray )
# Test not batched input
__SCREAMING_SNAKE_CASE : List[str] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
__SCREAMING_SNAKE_CASE : Any = image_processing(_A , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def UpperCAmelCase__ ( self : Optional[Any] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Any = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
__SCREAMING_SNAKE_CASE : Optional[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=_A , torchify=_A )
for image in image_inputs:
self.assertIsInstance(_A , torch.Tensor )
# Test not batched input
__SCREAMING_SNAKE_CASE : int = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
__SCREAMING_SNAKE_CASE : Dict = image_processing(_A , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
| 74
| 0
|
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
_lowercase: List[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}
}
'''
_lowercase: List[Any] = '''\
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.
'''
_lowercase: List[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 lowerCamelCase__ ( datasets.Metric ):
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ):
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 SCREAMING_SNAKE_CASE__ ( self : List[Any] , lowercase__ : List[List[List[str]]] , lowercase__ : List[List[str]] , lowercase__ : int = 1 , lowercase__ : int = 4 , ):
return {
"google_bleu": gleu_score.corpus_gleu(
list_of_references=lowercase__ , hypotheses=lowercase__ , min_len=lowercase__ , max_len=lowercase__ )
}
| 702
|
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class lowerCamelCase__ ( UpperCAmelCase ):
@staticmethod
@abstractmethod
def SCREAMING_SNAKE_CASE__ ( lowercase__ : ArgumentParser ):
raise NotImplementedError()
@abstractmethod
def SCREAMING_SNAKE_CASE__ ( self : Dict ):
raise NotImplementedError()
| 225
| 0
|
from __future__ import annotations
__A : List[str] = 1.6021e-19 # units = C
def __a ( A__ : float , A__ : float , A__ : float , ):
if (conductivity, electron_conc, mobility).count(0 ) != 1:
raise ValueError("You cannot supply more or less than 2 values" )
elif conductivity < 0:
raise ValueError("Conductivity cannot be negative" )
elif electron_conc < 0:
raise ValueError("Electron concentration cannot be negative" )
elif mobility < 0:
raise ValueError("mobility cannot be negative" )
elif conductivity == 0:
return (
"conductivity",
mobility * electron_conc * ELECTRON_CHARGE,
)
elif electron_conc == 0:
return (
"electron_conc",
conductivity / (mobility * ELECTRON_CHARGE),
)
else:
return (
"mobility",
conductivity / (electron_conc * ELECTRON_CHARGE),
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 16
|
"""simple docstring"""
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class SCREAMING_SNAKE_CASE__ :
lowercase__ = 42
lowercase__ = None
lowercase__ = None
UpperCAmelCase : Dict = namedtuple("CoinsDistribResult", "moves excess")
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> int:
'''simple docstring'''
if root is None:
return 0
# Validation
def count_nodes(__lowerCAmelCase ) -> int:
if node is None:
return 0
return count_nodes(node.left ) + count_nodes(node.right ) + 1
def count_coins(__lowerCAmelCase ) -> int:
if node is None:
return 0
return count_coins(node.left ) + count_coins(node.right ) + node.data
if count_nodes(__lowerCAmelCase ) != count_coins(__lowerCAmelCase ):
raise ValueError("""The nodes number should be same as the number of coins""" )
# Main calculation
def get_distrib(__lowerCAmelCase ) -> CoinsDistribResult:
if node is None:
return CoinsDistribResult(0 , 1 )
lowercase_ , lowercase_ = get_distrib(node.left )
lowercase_ , lowercase_ = get_distrib(node.right )
lowercase_ = 1 - left_distrib_excess
lowercase_ = 1 - right_distrib_excess
lowercase_ = (
left_distrib_moves
+ right_distrib_moves
+ abs(__lowerCAmelCase )
+ abs(__lowerCAmelCase )
)
lowercase_ = node.data - coins_to_left - coins_to_right
return CoinsDistribResult(__lowerCAmelCase , __lowerCAmelCase )
return get_distrib(__lowerCAmelCase )[0]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 567
| 0
|
import sys
import turtle
def __lowerCamelCase ( snake_case__ ,snake_case__ ) -> tuple[float, float]:
"""simple docstring"""
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def __lowerCamelCase ( snake_case__ ,snake_case__ ,snake_case__ ,snake_case__ ,) -> None:
"""simple docstring"""
my_pen.up()
my_pen.goto(vertexa[0] ,vertexa[1] )
my_pen.down()
my_pen.goto(vertexa[0] ,vertexa[1] )
my_pen.goto(vertexa[0] ,vertexa[1] )
my_pen.goto(vertexa[0] ,vertexa[1] )
if depth == 0:
return
triangle(_lowerCAmelCase ,get_mid(_lowerCAmelCase ,_lowerCAmelCase ) ,get_mid(_lowerCAmelCase ,_lowerCAmelCase ) ,depth - 1 )
triangle(_lowerCAmelCase ,get_mid(_lowerCAmelCase ,_lowerCAmelCase ) ,get_mid(_lowerCAmelCase ,_lowerCAmelCase ) ,depth - 1 )
triangle(_lowerCAmelCase ,get_mid(_lowerCAmelCase ,_lowerCAmelCase ) ,get_mid(_lowerCAmelCase ,_lowerCAmelCase ) ,depth - 1 )
if __name__ == "__main__":
if len(sys.argv) != 2:
raise ValueError(
'''Correct format for using this script: '''
'''python fractals.py <int:depth_for_fractal>'''
)
UpperCamelCase = turtle.Turtle()
my_pen.ht()
my_pen.speed(5)
my_pen.pencolor('''red''')
UpperCamelCase = [(-175, -125), (0, 175), (175, -125)] # vertices of triangle
triangle(vertices[0], vertices[1], vertices[2], int(sys.argv[1]))
| 716
|
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
class __UpperCAmelCase (TensorFormatter[Mapping, "torch.Tensor", Mapping] ):
def __init__( self: str , UpperCAmelCase_: Union[str, Any]=None , **UpperCAmelCase_: Dict ):
'''simple docstring'''
super().__init__(features=UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = torch_tensor_kwargs
import torch # noqa import torch at initialization
def UpperCamelCase ( self: Optional[Any] , UpperCAmelCase_: Optional[int] ):
'''simple docstring'''
import torch
if isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) and column:
if all(
isinstance(UpperCAmelCase_ , torch.Tensor ) and x.shape == column[0].shape and x.dtype == column[0].dtype
for x in column ):
return torch.stack(UpperCAmelCase_ )
return column
def UpperCamelCase ( self: int , UpperCAmelCase_: Optional[int] ):
'''simple docstring'''
import torch
if isinstance(UpperCAmelCase_ , (str, bytes, type(UpperCAmelCase_ )) ):
return value
elif isinstance(UpperCAmelCase_ , (np.character, np.ndarray) ) and np.issubdtype(value.dtype , np.character ):
return value.tolist()
_SCREAMING_SNAKE_CASE = {}
if isinstance(UpperCAmelCase_ , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.integer ):
_SCREAMING_SNAKE_CASE = {"""dtype""": torch.intaa}
elif isinstance(UpperCAmelCase_ , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.floating ):
_SCREAMING_SNAKE_CASE = {"""dtype""": torch.floataa}
elif config.PIL_AVAILABLE and "PIL" in sys.modules:
import PIL.Image
if isinstance(UpperCAmelCase_ , PIL.Image.Image ):
_SCREAMING_SNAKE_CASE = np.asarray(UpperCAmelCase_ )
return torch.tensor(UpperCAmelCase_ , **{**default_dtype, **self.torch_tensor_kwargs} )
def UpperCamelCase ( self: Tuple , UpperCAmelCase_: str ):
'''simple docstring'''
import torch
# support for torch, tf, jax etc.
if hasattr(UpperCAmelCase_ , """__array__""" ) and not isinstance(UpperCAmelCase_ , torch.Tensor ):
_SCREAMING_SNAKE_CASE = data_struct.__array__()
# support for nested types like struct of list of struct
if isinstance(UpperCAmelCase_ , np.ndarray ):
if data_struct.dtype == object: # torch tensors cannot be instantied from an array of objects
return self._consolidate([self.recursive_tensorize(UpperCAmelCase_ ) for substruct in data_struct] )
elif isinstance(UpperCAmelCase_ , (list, tuple) ):
return self._consolidate([self.recursive_tensorize(UpperCAmelCase_ ) for substruct in data_struct] )
return self._tensorize(UpperCAmelCase_ )
def UpperCamelCase ( self: Dict , UpperCAmelCase_: dict ):
'''simple docstring'''
return map_nested(self._recursive_tensorize , UpperCAmelCase_ , map_list=UpperCAmelCase_ )
def UpperCamelCase ( self: str , UpperCAmelCase_: pa.Table ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = self.numpy_arrow_extractor().extract_row(UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = self.python_features_decoder.decode_row(UpperCAmelCase_ )
return self.recursive_tensorize(UpperCAmelCase_ )
def UpperCamelCase ( self: Any , UpperCAmelCase_: pa.Table ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = self.numpy_arrow_extractor().extract_column(UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = self.python_features_decoder.decode_column(UpperCAmelCase_ , pa_table.column_names[0] )
_SCREAMING_SNAKE_CASE = self.recursive_tensorize(UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = self._consolidate(UpperCAmelCase_ )
return column
def UpperCamelCase ( self: str , UpperCAmelCase_: pa.Table ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = self.numpy_arrow_extractor().extract_batch(UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = self.python_features_decoder.decode_batch(UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = self.recursive_tensorize(UpperCAmelCase_ )
for column_name in batch:
_SCREAMING_SNAKE_CASE = self._consolidate(batch[column_name] )
return batch
| 569
| 0
|
"""simple docstring"""
def snake_case ( A__ ,A__ ):
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
UpperCAmelCase_ : str = str(bin(A__ ) )[2:] # remove the leading "0b"
UpperCAmelCase_ : Optional[int] = str(bin(A__ ) )[2:] # remove the leading "0b"
UpperCAmelCase_ : str = max(len(A__ ) ,len(A__ ) )
return "0b" + "".join(
str(int(char_a == "1" and char_b == "1" ) )
for char_a, char_b in zip(a_binary.zfill(A__ ) ,b_binary.zfill(A__ ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 95
|
"""simple docstring"""
def A ( __snake_case: int = 1_0_0_0_0_0_0 ) -> int:
"""simple docstring"""
__magic_name__ = limit + 1
__magic_name__ = [0] * limit
for first_term in range(1 , __snake_case ):
for n in range(__snake_case , __snake_case , __snake_case ):
__magic_name__ = first_term + n / first_term
if common_difference % 4: # d must be divisble by 4
continue
else:
common_difference /= 4
if (
first_term > common_difference
and first_term < 4 * common_difference
): # since x,y,z are positive integers
frequency[n] += 1 # so z>0 and a>d ,also 4d<a
__magic_name__ = sum(1 for x in frequency[1:limit] if x == 1_0 )
return count
if __name__ == "__main__":
print(f"""{solution() = }""")
| 545
| 0
|
"""simple docstring"""
from math import factorial
def A__ ( UpperCamelCase = 100 ):
return sum(int(UpperCamelCase ) for x in str(factorial(UpperCamelCase ) ) )
if __name__ == "__main__":
print(solution(int(input('Enter the Number: ').strip())))
| 700
|
"""simple docstring"""
import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable
import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import from_bytes, to_bytes
from flax.training import train_state
from flax.training.common_utils import shard
from tqdm.auto import tqdm
from transformers import BigBirdConfig, FlaxBigBirdForQuestionAnswering
from transformers.models.big_bird.modeling_flax_big_bird import FlaxBigBirdForQuestionAnsweringModule
class _UpperCAmelCase ( lowercase_ ):
UpperCamelCase = 42
UpperCamelCase = jnp.floataa
UpperCamelCase = True
def lowerCamelCase ( self :Optional[int] ):
super().setup()
A = nn.Dense(5 , dtype=self.dtype )
def __call__( self :Tuple , *__UpperCamelCase :str , **__UpperCamelCase :List[Any] ):
A = super().__call__(*__UpperCamelCase , **__UpperCamelCase )
A = self.cls(outputs[2] )
return outputs[:2] + (cls_out,)
class _UpperCAmelCase ( lowercase_ ):
UpperCamelCase = FlaxBigBirdForNaturalQuestionsModule
def A__ ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ):
def cross_entropy(UpperCamelCase , UpperCamelCase , UpperCamelCase=None ):
A = logits.shape[-1]
A = (labels[..., None] == jnp.arange(UpperCamelCase )[None]).astype("f4" )
A = jax.nn.log_softmax(UpperCamelCase , axis=-1 )
A = -jnp.sum(labels * logits , axis=-1 )
if reduction is not None:
A = reduction(UpperCamelCase )
return loss
A = partial(UpperCamelCase , reduction=jnp.mean )
A = cross_entropy(UpperCamelCase , UpperCamelCase )
A = cross_entropy(UpperCamelCase , UpperCamelCase )
A = cross_entropy(UpperCamelCase , UpperCamelCase )
return (start_loss + end_loss + pooled_loss) / 3
@dataclass
class _UpperCAmelCase :
UpperCamelCase = "google/bigbird-roberta-base"
UpperCamelCase = 3_0_0_0
UpperCamelCase = 1_0_5_0_0
UpperCamelCase = 1_2_8
UpperCamelCase = 3
UpperCamelCase = 1
UpperCamelCase = 5
# tx_args
UpperCamelCase = 3e-5
UpperCamelCase = 0.0
UpperCamelCase = 2_0_0_0_0
UpperCamelCase = 0.0095
UpperCamelCase = "bigbird-roberta-natural-questions"
UpperCamelCase = "training-expt"
UpperCamelCase = "data/nq-training.jsonl"
UpperCamelCase = "data/nq-validation.jsonl"
def lowerCamelCase ( self :Optional[Any] ):
os.makedirs(self.base_dir , exist_ok=__UpperCamelCase )
A = os.path.join(self.base_dir , self.save_dir )
A = self.batch_size_per_device * jax.device_count()
@dataclass
class _UpperCAmelCase :
UpperCamelCase = 42
UpperCamelCase = 4_0_9_6 # no dynamic padding on TPUs
def __call__( self :List[Any] , __UpperCamelCase :Union[str, Any] ):
A = self.collate_fn(__UpperCamelCase )
A = jax.tree_util.tree_map(__UpperCamelCase , __UpperCamelCase )
return batch
def lowerCamelCase ( self :Tuple , __UpperCamelCase :Tuple ):
A, A = self.fetch_inputs(features["input_ids"] )
A = {
"input_ids": jnp.array(__UpperCamelCase , dtype=jnp.intaa ),
"attention_mask": jnp.array(__UpperCamelCase , dtype=jnp.intaa ),
"start_labels": jnp.array(features["start_token"] , dtype=jnp.intaa ),
"end_labels": jnp.array(features["end_token"] , dtype=jnp.intaa ),
"pooled_labels": jnp.array(features["category"] , dtype=jnp.intaa ),
}
return batch
def lowerCamelCase ( self :int , __UpperCamelCase :list ):
A = [self._fetch_inputs(__UpperCamelCase ) for ids in input_ids]
return zip(*__UpperCamelCase )
def lowerCamelCase ( self :Union[str, Any] , __UpperCamelCase :list ):
A = [1 for _ in range(len(__UpperCamelCase ) )]
while len(__UpperCamelCase ) < self.max_length:
input_ids.append(self.pad_id )
attention_mask.append(0 )
return input_ids, attention_mask
def A__ ( UpperCamelCase , UpperCamelCase , UpperCamelCase=None ):
if seed is not None:
A = dataset.shuffle(seed=UpperCamelCase )
for i in range(len(UpperCamelCase ) // batch_size ):
A = dataset[i * batch_size : (i + 1) * batch_size]
yield dict(UpperCamelCase )
@partial(jax.pmap , axis_name="batch" )
def A__ ( UpperCamelCase , UpperCamelCase , **UpperCamelCase ):
def loss_fn(UpperCamelCase ):
A = model_inputs.pop("start_labels" )
A = model_inputs.pop("end_labels" )
A = model_inputs.pop("pooled_labels" )
A = state.apply_fn(**UpperCamelCase , params=UpperCamelCase , dropout_rng=UpperCamelCase , train=UpperCamelCase )
A, A, A = outputs
return state.loss_fn(
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , )
A, A = jax.random.split(UpperCamelCase )
A = jax.value_and_grad(UpperCamelCase )
A, A = grad_fn(state.params )
A = jax.lax.pmean({"loss": loss} , axis_name="batch" )
A = jax.lax.pmean(UpperCamelCase , "batch" )
A = state.apply_gradients(grads=UpperCamelCase )
return state, metrics, new_drp_rng
@partial(jax.pmap , axis_name="batch" )
def A__ ( UpperCamelCase , **UpperCamelCase ):
A = model_inputs.pop("start_labels" )
A = model_inputs.pop("end_labels" )
A = model_inputs.pop("pooled_labels" )
A = state.apply_fn(**UpperCamelCase , params=state.params , train=UpperCamelCase )
A, A, A = outputs
A = state.loss_fn(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase )
A = jax.lax.pmean({"loss": loss} , axis_name="batch" )
return metrics
class _UpperCAmelCase ( train_state.TrainState ):
UpperCamelCase = struct.field(pytree_node=lowercase_ )
@dataclass
class _UpperCAmelCase :
UpperCamelCase = 42
UpperCamelCase = 42
UpperCamelCase = 42
UpperCamelCase = 42
UpperCamelCase = 42
UpperCamelCase = 42
UpperCamelCase = None
def lowerCamelCase ( self :List[Any] , __UpperCamelCase :Optional[int] , __UpperCamelCase :List[str] , __UpperCamelCase :Any , __UpperCamelCase :int=None ):
A = model.params
A = TrainState.create(
apply_fn=model.__call__ , params=__UpperCamelCase , tx=__UpperCamelCase , loss_fn=__UpperCamelCase , )
if ckpt_dir is not None:
A, A, A, A, A = restore_checkpoint(__UpperCamelCase , __UpperCamelCase )
A = {
"lr": args.lr,
"init_lr": args.init_lr,
"warmup_steps": args.warmup_steps,
"num_train_steps": num_train_steps,
"weight_decay": args.weight_decay,
}
A, A = build_tx(**__UpperCamelCase )
A = train_state.TrainState(
step=__UpperCamelCase , apply_fn=model.__call__ , params=__UpperCamelCase , tx=__UpperCamelCase , opt_state=__UpperCamelCase , )
A = args
A = data_collator
A = lr
A = params
A = jax_utils.replicate(__UpperCamelCase )
return state
def lowerCamelCase ( self :List[str] , __UpperCamelCase :List[Any] , __UpperCamelCase :List[Any] , __UpperCamelCase :Optional[int] ):
A = self.args
A = len(__UpperCamelCase ) // args.batch_size
A = jax.random.PRNGKey(0 )
A = jax.random.split(__UpperCamelCase , jax.device_count() )
for epoch in range(args.max_epochs ):
A = jnp.array(0 , dtype=jnp.floataa )
A = get_batched_dataset(__UpperCamelCase , args.batch_size , seed=__UpperCamelCase )
A = 0
for batch in tqdm(__UpperCamelCase , total=__UpperCamelCase , desc=f"Running EPOCH-{epoch}" ):
A = self.data_collator(__UpperCamelCase )
A, A, A = self.train_step_fn(__UpperCamelCase , __UpperCamelCase , **__UpperCamelCase )
running_loss += jax_utils.unreplicate(metrics["loss"] )
i += 1
if i % args.logging_steps == 0:
A = jax_utils.unreplicate(state.step )
A = running_loss.item() / i
A = self.scheduler_fn(state_step - 1 )
A = self.evaluate(__UpperCamelCase , __UpperCamelCase )
A = {
"step": state_step.item(),
"eval_loss": eval_loss.item(),
"tr_loss": tr_loss,
"lr": lr.item(),
}
tqdm.write(str(__UpperCamelCase ) )
self.logger.log(__UpperCamelCase , commit=__UpperCamelCase )
if i % args.save_steps == 0:
self.save_checkpoint(args.save_dir + f"-e{epoch}-s{i}" , state=__UpperCamelCase )
def lowerCamelCase ( self :int , __UpperCamelCase :Optional[Any] , __UpperCamelCase :List[Any] ):
A = get_batched_dataset(__UpperCamelCase , self.args.batch_size )
A = len(__UpperCamelCase ) // self.args.batch_size
A = jnp.array(0 , dtype=jnp.floataa )
A = 0
for batch in tqdm(__UpperCamelCase , total=__UpperCamelCase , desc="Evaluating ... " ):
A = self.data_collator(__UpperCamelCase )
A = self.val_step_fn(__UpperCamelCase , **__UpperCamelCase )
running_loss += jax_utils.unreplicate(metrics["loss"] )
i += 1
return running_loss / i
def lowerCamelCase ( self :List[str] , __UpperCamelCase :List[str] , __UpperCamelCase :Optional[int] ):
A = jax_utils.unreplicate(__UpperCamelCase )
print(f"SAVING CHECKPOINT IN {save_dir}" , end=" ... " )
self.model_save_fn(__UpperCamelCase , params=state.params )
with open(os.path.join(__UpperCamelCase , "opt_state.msgpack" ) , "wb" ) as f:
f.write(to_bytes(state.opt_state ) )
joblib.dump(self.args , os.path.join(__UpperCamelCase , "args.joblib" ) )
joblib.dump(self.data_collator , os.path.join(__UpperCamelCase , "data_collator.joblib" ) )
with open(os.path.join(__UpperCamelCase , "training_state.json" ) , "w" ) as f:
json.dump({"step": state.step.item()} , __UpperCamelCase )
print("DONE" )
def A__ ( UpperCamelCase , UpperCamelCase ):
print(F"RESTORING CHECKPOINT FROM {save_dir}" , end=" ... " )
with open(os.path.join(UpperCamelCase , "flax_model.msgpack" ) , "rb" ) as f:
A = from_bytes(state.params , f.read() )
with open(os.path.join(UpperCamelCase , "opt_state.msgpack" ) , "rb" ) as f:
A = from_bytes(state.opt_state , f.read() )
A = joblib.load(os.path.join(UpperCamelCase , "args.joblib" ) )
A = joblib.load(os.path.join(UpperCamelCase , "data_collator.joblib" ) )
with open(os.path.join(UpperCamelCase , "training_state.json" ) , "r" ) as f:
A = json.load(UpperCamelCase )
A = training_state["step"]
print("DONE" )
return params, opt_state, step, args, data_collator
def A__ ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ):
A = num_train_steps - warmup_steps
A = optax.linear_schedule(init_value=UpperCamelCase , end_value=UpperCamelCase , transition_steps=UpperCamelCase )
A = optax.linear_schedule(init_value=UpperCamelCase , end_value=1E-7 , transition_steps=UpperCamelCase )
A = optax.join_schedules(schedules=[warmup_fn, decay_fn] , boundaries=[warmup_steps] )
return lr
def A__ ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ):
def weight_decay_mask(UpperCamelCase ):
A = traverse_util.flatten_dict(UpperCamelCase )
A = {k: (v[-1] != "bias" and v[-2:] != ("LayerNorm", "scale")) for k, v in params.items()}
return traverse_util.unflatten_dict(UpperCamelCase )
A = scheduler_fn(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase )
A = optax.adamw(learning_rate=UpperCamelCase , weight_decay=UpperCamelCase , mask=UpperCamelCase )
return tx, lr
| 524
| 0
|
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor
@require_vision
class UpperCAmelCase_ ( unittest.TestCase ):
def _lowerCamelCase ( self ) -> str:
__lowercase : Dict = tempfile.mkdtemp()
__lowercase : Optional[Any] = [
'''[UNK]''',
'''[CLS]''',
'''[SEP]''',
'''[PAD]''',
'''[MASK]''',
'''的''',
'''价''',
'''格''',
'''是''',
'''15''',
'''便''',
'''alex''',
'''##andra''',
'''，''',
'''。''',
'''-''',
'''t''',
'''shirt''',
]
__lowercase : str = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
__lowercase : str = {
'''do_resize''': True,
'''size''': {'''height''': 2_24, '''width''': 2_24},
'''do_center_crop''': True,
'''crop_size''': {'''height''': 18, '''width''': 18},
'''do_normalize''': True,
'''image_mean''': [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
'''image_std''': [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
'''do_convert_rgb''': True,
}
__lowercase : Union[str, Any] = os.path.join(self.tmpdirname , UpperCamelCase_ )
with open(self.image_processor_file , '''w''' , encoding='''utf-8''' ) as fp:
json.dump(UpperCamelCase_ , UpperCamelCase_ )
def _lowerCamelCase ( self , **UpperCamelCase_ ) -> List[str]:
return BertTokenizer.from_pretrained(self.tmpdirname , **UpperCamelCase_ )
def _lowerCamelCase ( self , **UpperCamelCase_ ) -> List[Any]:
return BertTokenizerFast.from_pretrained(self.tmpdirname , **UpperCamelCase_ )
def _lowerCamelCase ( self , **UpperCamelCase_ ) -> str:
return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname , **UpperCamelCase_ )
def _lowerCamelCase ( self ) -> int:
shutil.rmtree(self.tmpdirname )
def _lowerCamelCase ( self ) -> Optional[Any]:
__lowercase : Tuple = [np.random.randint(2_55 , size=(3, 30, 4_00) , dtype=np.uinta )]
__lowercase : Dict = [Image.fromarray(np.moveaxis(UpperCamelCase_ , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def _lowerCamelCase ( self ) -> str:
__lowercase : Tuple = self.get_tokenizer()
__lowercase : int = self.get_rust_tokenizer()
__lowercase : Union[str, Any] = self.get_image_processor()
__lowercase : List[Any] = ChineseCLIPProcessor(tokenizer=UpperCamelCase_ , image_processor=UpperCamelCase_ )
processor_slow.save_pretrained(self.tmpdirname )
__lowercase : int = ChineseCLIPProcessor.from_pretrained(self.tmpdirname , use_fast=UpperCamelCase_ )
__lowercase : Tuple = ChineseCLIPProcessor(tokenizer=UpperCamelCase_ , image_processor=UpperCamelCase_ )
processor_fast.save_pretrained(self.tmpdirname )
__lowercase : List[Any] = ChineseCLIPProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer , UpperCamelCase_ )
self.assertIsInstance(processor_fast.tokenizer , UpperCamelCase_ )
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor , UpperCamelCase_ )
self.assertIsInstance(processor_fast.image_processor , UpperCamelCase_ )
def _lowerCamelCase ( self ) -> Optional[Any]:
__lowercase : Dict = ChineseCLIPProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
__lowercase : Tuple = self.get_tokenizer(cls_token='''(CLS)''' , sep_token='''(SEP)''' )
__lowercase : str = self.get_image_processor(do_normalize=UpperCamelCase_ )
__lowercase : Union[str, Any] = ChineseCLIPProcessor.from_pretrained(
self.tmpdirname , cls_token='''(CLS)''' , sep_token='''(SEP)''' , do_normalize=UpperCamelCase_ )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , UpperCamelCase_ )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , UpperCamelCase_ )
def _lowerCamelCase ( self ) -> Dict:
__lowercase : Dict = self.get_image_processor()
__lowercase : str = self.get_tokenizer()
__lowercase : Optional[int] = ChineseCLIPProcessor(tokenizer=UpperCamelCase_ , image_processor=UpperCamelCase_ )
__lowercase : Optional[Any] = self.prepare_image_inputs()
__lowercase : Union[str, Any] = image_processor(UpperCamelCase_ , return_tensors='''np''' )
__lowercase : Dict = processor(images=UpperCamelCase_ , return_tensors='''np''' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2 )
def _lowerCamelCase ( self ) -> int:
__lowercase : Dict = self.get_image_processor()
__lowercase : Optional[int] = self.get_tokenizer()
__lowercase : List[str] = ChineseCLIPProcessor(tokenizer=UpperCamelCase_ , image_processor=UpperCamelCase_ )
__lowercase : Any = '''Alexandra，T-shirt的价格是15便士。'''
__lowercase : Optional[int] = processor(text=UpperCamelCase_ )
__lowercase : Optional[int] = tokenizer(UpperCamelCase_ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def _lowerCamelCase ( self ) -> List[Any]:
__lowercase : str = self.get_image_processor()
__lowercase : Dict = self.get_tokenizer()
__lowercase : Any = ChineseCLIPProcessor(tokenizer=UpperCamelCase_ , image_processor=UpperCamelCase_ )
__lowercase : str = '''Alexandra，T-shirt的价格是15便士。'''
__lowercase : Optional[Any] = self.prepare_image_inputs()
__lowercase : str = processor(text=UpperCamelCase_ , images=UpperCamelCase_ )
self.assertListEqual(list(inputs.keys() ) , ['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values'''] )
# test if it raises when no input is passed
with pytest.raises(UpperCamelCase_ ):
processor()
def _lowerCamelCase ( self ) -> Any:
__lowercase : int = self.get_image_processor()
__lowercase : Optional[Any] = self.get_tokenizer()
__lowercase : str = ChineseCLIPProcessor(tokenizer=UpperCamelCase_ , image_processor=UpperCamelCase_ )
__lowercase : str = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
__lowercase : List[str] = processor.batch_decode(UpperCamelCase_ )
__lowercase : Optional[int] = tokenizer.batch_decode(UpperCamelCase_ )
self.assertListEqual(UpperCamelCase_ , UpperCamelCase_ )
def _lowerCamelCase ( self ) -> int:
__lowercase : int = self.get_image_processor()
__lowercase : Dict = self.get_tokenizer()
__lowercase : Any = ChineseCLIPProcessor(tokenizer=UpperCamelCase_ , image_processor=UpperCamelCase_ )
__lowercase : List[str] = '''Alexandra，T-shirt的价格是15便士。'''
__lowercase : Any = self.prepare_image_inputs()
__lowercase : int = processor(text=UpperCamelCase_ , images=UpperCamelCase_ )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 76
|
'''simple docstring'''
def lowerCAmelCase_ ( a : int , a : int ):
return 1 if input_a == input_a else 0
def lowerCAmelCase_ ( ):
assert xnor_gate(0 , 0 ) == 1
assert xnor_gate(0 , 1 ) == 0
assert xnor_gate(1 , 0 ) == 0
assert xnor_gate(1 , 1 ) == 1
if __name__ == "__main__":
print(xnor_gate(0, 0))
print(xnor_gate(0, 1))
print(xnor_gate(1, 0))
print(xnor_gate(1, 1))
| 394
| 0
|
'''simple docstring'''
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
'facebook/encodec_24khz': 'https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json',
'facebook/encodec_48khz': 'https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json',
}
class __a ( _snake_case ):
__SCREAMING_SNAKE_CASE : str = 'encodec'
def __init__( self : Any , lowercase__ : Union[str, Any]=[1.5, 3.0, 6.0, 12.0, 24.0] , lowercase__ : Optional[int]=2_40_00 , lowercase__ : Any=1 , lowercase__ : List[Any]=False , lowercase__ : Optional[Any]=None , lowercase__ : Dict=None , lowercase__ : Any=1_28 , lowercase__ : Tuple=32 , lowercase__ : Tuple=1 , lowercase__ : List[Any]=[8, 5, 4, 2] , lowercase__ : List[str]="weight_norm" , lowercase__ : Union[str, Any]=7 , lowercase__ : Tuple=7 , lowercase__ : Union[str, Any]=3 , lowercase__ : Optional[int]=2 , lowercase__ : int=True , lowercase__ : int="reflect" , lowercase__ : str=2 , lowercase__ : int=2 , lowercase__ : Any=1.0 , lowercase__ : List[Any]=10_24 , lowercase__ : Optional[Any]=None , lowercase__ : str=True , **lowercase__ : List[str] , ) ->int:
"""simple docstring"""
_lowercase = target_bandwidths
_lowercase = sampling_rate
_lowercase = audio_channels
_lowercase = normalize
_lowercase = chunk_length_s
_lowercase = overlap
_lowercase = hidden_size
_lowercase = num_filters
_lowercase = num_residual_layers
_lowercase = upsampling_ratios
_lowercase = norm_type
_lowercase = kernel_size
_lowercase = last_kernel_size
_lowercase = residual_kernel_size
_lowercase = dilation_growth_rate
_lowercase = use_causal_conv
_lowercase = pad_mode
_lowercase = compress
_lowercase = num_lstm_layers
_lowercase = trim_right_ratio
_lowercase = codebook_size
_lowercase = codebook_dim if codebook_dim is not None else hidden_size
_lowercase = use_conv_shortcut
if self.norm_type not in ["weight_norm", "time_group_norm"]:
raise ValueError(
f"""self.norm_type must be one of `\"weight_norm\"`, `\"time_group_norm\"`), got {self.norm_type}""")
super().__init__(**lowercase__)
@property
def _UpperCAmelCase ( self : Optional[Any]) ->Optional[int]:
"""simple docstring"""
if self.chunk_length_s is None:
return None
else:
return int(self.chunk_length_s * self.sampling_rate)
@property
def _UpperCAmelCase ( self : int) ->Optional[int]:
"""simple docstring"""
if self.chunk_length_s is None or self.overlap is None:
return None
else:
return max(1 , int((1.0 - self.overlap) * self.chunk_length))
@property
def _UpperCAmelCase ( self : Dict) ->int:
"""simple docstring"""
_lowercase = np.prod(self.upsampling_ratios)
return math.ceil(self.sampling_rate / hop_length)
@property
def _UpperCAmelCase ( self : List[Any]) ->int:
"""simple docstring"""
return int(10_00 * self.target_bandwidths[-1] // (self.frame_rate * 10))
| 718
|
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( snake_case_ ):
_lowercase = int(snake_case_ )
if n_element < 1:
_lowercase = ValueError("""a should be a positive number""" )
raise my_error
_lowercase = [1]
_lowercase , _lowercase , _lowercase = (0, 0, 0)
_lowercase = 1
while index < n_element:
while hamming_list[i] * 2 <= hamming_list[-1]:
i += 1
while hamming_list[j] * 3 <= hamming_list[-1]:
j += 1
while hamming_list[k] * 5 <= hamming_list[-1]:
k += 1
hamming_list.append(
min(hamming_list[i] * 2 , hamming_list[j] * 3 , hamming_list[k] * 5 ) )
index += 1
return hamming_list
if __name__ == "__main__":
_lowerCamelCase = input('Enter the last number (nth term) of the Hamming Number Series: ')
print('Formula of Hamming Number Series => 2^i * 3^j * 5^k')
_lowerCamelCase = hamming(int(n))
print('-----------------------------------------------------')
print(F"""The list with nth numbers is: {hamming_numbers}""")
print('-----------------------------------------------------')
| 572
| 0
|
"""simple docstring"""
from __future__ import annotations
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> Optional[int]:
'''simple docstring'''
lowercase_ = 0.00
lowercase_ = 0
for resistor in resistors:
if resistor <= 0:
lowercase_ = F'''Resistor at index {index} has a negative or zero value!'''
raise ValueError(snake_case_ )
first_sum += 1 / float(snake_case_ )
index += 1
return 1 / first_sum
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> int:
'''simple docstring'''
lowercase_ = 0.00
lowercase_ = 0
for resistor in resistors:
sum_r += resistor
if resistor < 0:
lowercase_ = F'''Resistor at index {index} has a negative value!'''
raise ValueError(snake_case_ )
index += 1
return sum_r
if __name__ == "__main__":
import doctest
doctest.testmod()
| 567
|
import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class SCREAMING_SNAKE_CASE__ ( UpperCamelCase_ ):
"""simple docstring"""
a_ = ""
a_ = (
None # protocol passed in prefix to the url. ex: "gzip", for gzip://file.txt::http://foo.bar/file.txt.gz
)
a_ = None # compression type in fsspec. ex: "gzip"
a_ = None # extension of the filename to strip. ex: "".gz" to get file.txt from file.txt.gz
def __init__( self : Tuple , __A : str = "" , __A : Optional[str] = None , __A : Optional[dict] = None , **__A : Optional[int] ):
super().__init__(self , **__A )
# always open as "rb" since fsspec can then use the TextIOWrapper to make it work for "r" mode
snake_case__ : Any = fsspec.open(
__A , mode="rb" , protocol=__A , compression=self.compression , client_kwargs={
"requote_redirect_url": False, # see https://github.com/huggingface/datasets/pull/5459
"trust_env": True, # Enable reading proxy env variables.
**(target_options or {}).pop("client_kwargs" , {} ), # To avoid issues if it was already passed.
} , **(target_options or {}) , )
snake_case__ : Dict = os.path.basename(self.file.path.split("::" )[0] )
snake_case__ : Tuple = (
self.compressed_name[: self.compressed_name.rindex("." )]
if "." in self.compressed_name
else self.compressed_name
)
snake_case__ : Union[str, Any] = None
@classmethod
def _lowercase ( cls : Union[str, Any] , __A : Optional[int] ):
# compressed file paths are always relative to the archive root
return super()._strip_protocol(__A ).lstrip("/" )
def _lowercase ( self : Dict ):
if self.dir_cache is None:
snake_case__ : int = {**self.file.fs.info(self.file.path ), "name": self.uncompressed_name}
snake_case__ : Optional[Any] = {f["name"]: f}
def _lowercase ( self : Union[str, Any] , __A : str ):
return self.file.open().read()
def _lowercase ( self : str , __A : str , __A : str = "rb" , __A : str=None , __A : Tuple=True , __A : Optional[Any]=None , **__A : Optional[int] , ):
snake_case__ : Optional[Any] = self._strip_protocol(__A )
if mode != "rb":
raise ValueError(f'''Tried to read with mode {mode} on file {self.file.path} opened with mode \'rb\'''' )
return self.file.open()
class SCREAMING_SNAKE_CASE__ ( UpperCamelCase_ ):
"""simple docstring"""
a_ = "bz2"
a_ = "bz2"
a_ = ".bz2"
class SCREAMING_SNAKE_CASE__ ( UpperCamelCase_ ):
"""simple docstring"""
a_ = "gzip"
a_ = "gzip"
a_ = ".gz"
class SCREAMING_SNAKE_CASE__ ( UpperCamelCase_ ):
"""simple docstring"""
a_ = "lz4"
a_ = "lz4"
a_ = ".lz4"
class SCREAMING_SNAKE_CASE__ ( UpperCamelCase_ ):
"""simple docstring"""
a_ = "xz"
a_ = "xz"
a_ = ".xz"
class SCREAMING_SNAKE_CASE__ ( UpperCamelCase_ ):
"""simple docstring"""
a_ = "zstd"
a_ = "zstd"
a_ = ".zst"
def __init__( self : Union[str, Any] , __A : str , __A : str = "rb" , __A : Optional[str] = None , __A : Optional[dict] = None , __A : int = DEFAULT_BLOCK_SIZE , **__A : Tuple , ):
super().__init__(
fo=__A , mode=__A , target_protocol=__A , target_options=__A , block_size=__A , **__A , )
# We need to wrap the zstd decompressor to avoid this error in fsspec==2021.7.0 and zstandard==0.15.2:
#
# File "/Users/user/.virtualenvs/hf-datasets/lib/python3.7/site-packages/fsspec/core.py", line 145, in open
# out.close = close
# AttributeError: 'zstd.ZstdDecompressionReader' object attribute 'close' is read-only
#
# see https://github.com/intake/filesystem_spec/issues/725
snake_case__ : Union[str, Any] = self.file.__enter__
class SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
def __init__( self : Optional[int] , __A : Dict ):
snake_case__ : Tuple = file_
def __enter__( self : str ):
self._file.__enter__()
return self
def __exit__( self : List[Any] , *__A : List[Any] , **__A : Union[str, Any] ):
self._file.__exit__(*__A , **__A )
def __iter__( self : List[Any] ):
return iter(self._file )
def _lowercase ( self : Optional[Any] ):
return next(self._file )
def __getattr__( self : Union[str, Any] , __A : Optional[int] ):
return getattr(self._file , __A )
def fixed_enter(*__A : Tuple , **__A : Dict ):
return WrappedFile(_enter(*__A , **__A ) )
snake_case__ : List[Any] = fixed_enter
| 297
| 0
|
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def lowerCAmelCase( __lowerCamelCase ):
__a = int(number**0.5 )
return number == sq * sq
def lowerCAmelCase( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ):
__a = x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
__a = x_den * y_den * z_den
__a = gcd(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
top //= hcf
bottom //= hcf
return top, bottom
def lowerCAmelCase( __lowerCamelCase = 35 ):
__a = set()
__a = 42
__a = Fraction(0 )
__a = 42
for x_num in range(1 , order + 1 ):
for x_den in range(x_num + 1 , order + 1 ):
for y_num in range(1 , order + 1 ):
for y_den in range(y_num + 1 , order + 1 ):
# n=1
__a = x_num * y_den + x_den * y_num
__a = x_den * y_den
__a = gcd(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
__a = add_three(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
unique_s.add(SCREAMING_SNAKE_CASE_ )
# n=2
__a = (
x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
)
__a = x_den * x_den * y_den * y_den
if is_sq(SCREAMING_SNAKE_CASE_ ) and is_sq(SCREAMING_SNAKE_CASE_ ):
__a = int(sqrt(SCREAMING_SNAKE_CASE_ ) )
__a = int(sqrt(SCREAMING_SNAKE_CASE_ ) )
__a = gcd(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
__a = add_three(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
unique_s.add(SCREAMING_SNAKE_CASE_ )
# n=-1
__a = x_num * y_num
__a = x_den * y_num + x_num * y_den
__a = gcd(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
__a = add_three(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
unique_s.add(SCREAMING_SNAKE_CASE_ )
# n=2
__a = x_num * x_num * y_num * y_num
__a = (
x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
)
if is_sq(SCREAMING_SNAKE_CASE_ ) and is_sq(SCREAMING_SNAKE_CASE_ ):
__a = int(sqrt(SCREAMING_SNAKE_CASE_ ) )
__a = int(sqrt(SCREAMING_SNAKE_CASE_ ) )
__a = gcd(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
__a = add_three(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
unique_s.add(SCREAMING_SNAKE_CASE_ )
for num, den in unique_s:
total += Fraction(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
return total.denominator + total.numerator
if __name__ == "__main__":
print(F'''{solution() = }''')
| 700
|
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common import TokenizerTesterMixin
lowerCamelCase_ : Optional[int] = get_tests_dir("""fixtures/test_sentencepiece.model""")
if is_torch_available():
from transformers.models.mbart.modeling_mbart import shift_tokens_right
lowerCamelCase_ : Union[str, Any] = 250_004
lowerCamelCase_ : List[str] = 250_020
@require_sentencepiece
@require_tokenizers
class a__ ( __snake_case , unittest.TestCase ):
A__ : Optional[Any] = MBartTokenizer
A__ : Optional[Any] = MBartTokenizerFast
A__ : Dict = True
A__ : Dict = True
def __SCREAMING_SNAKE_CASE ( self ) -> List[Any]:
super().setUp()
# We have a SentencePiece fixture for testing
__a = MBartTokenizer(UpperCAmelCase , keep_accents=UpperCAmelCase )
tokenizer.save_pretrained(self.tmpdirname )
def __SCREAMING_SNAKE_CASE ( self ) -> str:
__a = MBartTokenizer(UpperCAmelCase , keep_accents=UpperCAmelCase )
__a = tokenizer.tokenize('This is a test' )
self.assertListEqual(UpperCAmelCase , ['▁This', '▁is', '▁a', '▁t', 'est'] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(UpperCAmelCase ) , [value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]] , )
__a = tokenizer.tokenize('I was born in 92000, and this is falsé.' )
self.assertListEqual(
UpperCAmelCase , [
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',
'.',
] , )
__a = tokenizer.convert_tokens_to_ids(UpperCAmelCase )
self.assertListEqual(
UpperCAmelCase , [
value + tokenizer.fairseq_offset
for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
] , )
__a = tokenizer.convert_ids_to_tokens(UpperCAmelCase )
self.assertListEqual(
UpperCAmelCase , [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'<unk>',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'<unk>',
'.',
] , )
def __SCREAMING_SNAKE_CASE ( self ) -> Optional[int]:
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
__a = (self.rust_tokenizer_class, 'hf-internal-testing/tiny-random-mbart', {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
__a = self.rust_tokenizer_class.from_pretrained(UpperCAmelCase , **UpperCAmelCase )
__a = self.tokenizer_class.from_pretrained(UpperCAmelCase , **UpperCAmelCase )
__a = tempfile.mkdtemp()
__a = tokenizer_r.save_pretrained(UpperCAmelCase )
__a = tokenizer_p.save_pretrained(UpperCAmelCase )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files ) )
__a = tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f )
self.assertSequenceEqual(UpperCAmelCase , UpperCAmelCase )
# Checks everything loads correctly in the same way
__a = tokenizer_r.from_pretrained(UpperCAmelCase )
__a = tokenizer_p.from_pretrained(UpperCAmelCase )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(UpperCAmelCase , UpperCAmelCase ) )
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(UpperCAmelCase )
# Save tokenizer rust, legacy_format=True
__a = tempfile.mkdtemp()
__a = tokenizer_r.save_pretrained(UpperCAmelCase , legacy_format=UpperCAmelCase )
__a = tokenizer_p.save_pretrained(UpperCAmelCase )
# Checks it save with the same files
self.assertSequenceEqual(UpperCAmelCase , UpperCAmelCase )
# Checks everything loads correctly in the same way
__a = tokenizer_r.from_pretrained(UpperCAmelCase )
__a = tokenizer_p.from_pretrained(UpperCAmelCase )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(UpperCAmelCase , UpperCAmelCase ) )
shutil.rmtree(UpperCAmelCase )
# Save tokenizer rust, legacy_format=False
__a = tempfile.mkdtemp()
__a = tokenizer_r.save_pretrained(UpperCAmelCase , legacy_format=UpperCAmelCase )
__a = tokenizer_p.save_pretrained(UpperCAmelCase )
# Checks it saved the tokenizer.json file
self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
__a = tokenizer_r.from_pretrained(UpperCAmelCase )
__a = tokenizer_p.from_pretrained(UpperCAmelCase )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(UpperCAmelCase , UpperCAmelCase ) )
shutil.rmtree(UpperCAmelCase )
@require_torch
@require_sentencepiece
@require_tokenizers
class a__ ( unittest.TestCase ):
A__ : Optional[Any] = 'facebook/mbart-large-en-ro'
A__ : Union[str, Any] = [
' UN Chief Says There Is No Military Solution in Syria',
' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.',
]
A__ : Tuple = [
'Şeful ONU declară că nu există o soluţie militară în Siria',
'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'
' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'
' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
]
A__ : Dict = [82_74, 12_78_73, 2_59_16, 7, 86_22, 20_71, 4_38, 6_74_85, 53, 18_78_95, 23, 5_17_12, 2, EN_CODE]
@classmethod
def __SCREAMING_SNAKE_CASE ( cls ) -> Optional[Any]:
__a = MBartTokenizer.from_pretrained(
cls.checkpoint_name , src_lang='en_XX' , tgt_lang='ro_RO' )
__a = 1
return cls
def __SCREAMING_SNAKE_CASE ( self ) -> str:
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ar_AR'] , 2_5_0_0_0_1 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['en_EN'] , 2_5_0_0_0_4 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ro_RO'] , 2_5_0_0_2_0 )
def __SCREAMING_SNAKE_CASE ( self ) -> Union[str, Any]:
__a = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , UpperCAmelCase )
def __SCREAMING_SNAKE_CASE ( self ) -> Tuple:
self.assertIn(UpperCAmelCase , self.tokenizer.all_special_ids )
__a = [RO_CODE, 8_8_4, 9_0_1_9, 9_6, 9, 9_1_6, 8_6_7_9_2, 3_6, 1_8_7_4_3, 1_5_5_9_6, 5, 2]
__a = self.tokenizer.decode(UpperCAmelCase , skip_special_tokens=UpperCAmelCase )
__a = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=UpperCAmelCase )
self.assertEqual(UpperCAmelCase , UpperCAmelCase )
self.assertNotIn(self.tokenizer.eos_token , UpperCAmelCase )
def __SCREAMING_SNAKE_CASE ( self ) -> List[str]:
__a = ['this is gunna be a long sentence ' * 2_0]
assert isinstance(src_text[0] , UpperCAmelCase )
__a = 1_0
__a = self.tokenizer(UpperCAmelCase , max_length=UpperCAmelCase , truncation=UpperCAmelCase ).input_ids[0]
self.assertEqual(ids[-2] , 2 )
self.assertEqual(ids[-1] , UpperCAmelCase )
self.assertEqual(len(UpperCAmelCase ) , UpperCAmelCase )
def __SCREAMING_SNAKE_CASE ( self ) -> Union[str, Any]:
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR'] ) , [2_5_0_0_2_6, 2_5_0_0_0_1] )
def __SCREAMING_SNAKE_CASE ( self ) -> str:
__a = tempfile.mkdtemp()
__a = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(UpperCAmelCase )
__a = MBartTokenizer.from_pretrained(UpperCAmelCase )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , UpperCAmelCase )
@require_torch
def __SCREAMING_SNAKE_CASE ( self ) -> Optional[Any]:
__a = self.tokenizer(self.src_text , text_target=self.tgt_text , padding=UpperCAmelCase , return_tensors='pt' )
__a = shift_tokens_right(batch['labels'] , self.tokenizer.pad_token_id )
# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
assert batch.decoder_input_ids[1][-1] == 2
assert batch.labels[1][-2:].tolist() == [2, RO_CODE]
@require_torch
def __SCREAMING_SNAKE_CASE ( self ) -> Union[str, Any]:
__a = self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=UpperCAmelCase , truncation=UpperCAmelCase , max_length=len(self.expected_src_tokens ) , return_tensors='pt' , )
__a = shift_tokens_right(batch['labels'] , self.tokenizer.pad_token_id )
self.assertIsInstance(UpperCAmelCase , UpperCAmelCase )
self.assertEqual((2, 1_4) , batch.input_ids.shape )
self.assertEqual((2, 1_4) , batch.attention_mask.shape )
__a = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , UpperCAmelCase )
self.assertEqual(2 , batch.decoder_input_ids[0, -1] ) # EOS
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens , [] )
self.assertEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id, EN_CODE] )
def __SCREAMING_SNAKE_CASE ( self ) -> Optional[int]:
__a = self.tokenizer(self.src_text , padding=UpperCAmelCase , truncation=UpperCAmelCase , max_length=3 , return_tensors='pt' )
__a = self.tokenizer(
text_target=self.tgt_text , padding=UpperCAmelCase , truncation=UpperCAmelCase , max_length=1_0 , return_tensors='pt' )
__a = targets['input_ids']
__a = shift_tokens_right(UpperCAmelCase , self.tokenizer.pad_token_id )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.decoder_input_ids.shape[1] , 1_0 )
@require_torch
def __SCREAMING_SNAKE_CASE ( self ) -> Optional[int]:
__a = self.tokenizer._build_translation_inputs(
'A test' , return_tensors='pt' , src_lang='en_XX' , tgt_lang='ar_AR' )
self.assertEqual(
nested_simplify(UpperCAmelCase ) , {
# A, test, EOS, en_XX
'input_ids': [[6_2, 3_0_3_4, 2, 2_5_0_0_0_4]],
'attention_mask': [[1, 1, 1, 1]],
# ar_AR
'forced_bos_token_id': 2_5_0_0_0_1,
} , )
| 246
| 0
|
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCamelCase : Union[str, Any] = logging.get_logger(__name__)
_lowerCamelCase : Tuple = {
'''YituTech/conv-bert-base''': '''https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json''',
'''YituTech/conv-bert-medium-small''': (
'''https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json'''
),
'''YituTech/conv-bert-small''': '''https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json''',
# See all ConvBERT models at https://huggingface.co/models?filter=convbert
}
class lowercase ( a ):
lowercase__ : Any = """convbert"""
def __init__( self : Union[str, Any] , _UpperCamelCase : Union[str, Any]=30_522 , _UpperCamelCase : str=768 , _UpperCamelCase : Optional[int]=12 , _UpperCamelCase : Tuple=12 , _UpperCamelCase : Union[str, Any]=3_072 , _UpperCamelCase : Union[str, Any]="gelu" , _UpperCamelCase : List[str]=0.1 , _UpperCamelCase : Tuple=0.1 , _UpperCamelCase : Optional[Any]=512 , _UpperCamelCase : Tuple=2 , _UpperCamelCase : Dict=0.0_2 , _UpperCamelCase : Any=1e-12 , _UpperCamelCase : Tuple=1 , _UpperCamelCase : Optional[int]=0 , _UpperCamelCase : List[str]=2 , _UpperCamelCase : Union[str, Any]=768 , _UpperCamelCase : Optional[int]=2 , _UpperCamelCase : Tuple=9 , _UpperCamelCase : Optional[int]=1 , _UpperCamelCase : List[str]=None , **_UpperCamelCase : Tuple , ) -> List[Any]:
'''simple docstring'''
super().__init__(
pad_token_id=_UpperCamelCase , bos_token_id=_UpperCamelCase , eos_token_id=_UpperCamelCase , **_UpperCamelCase , )
SCREAMING_SNAKE_CASE = vocab_size
SCREAMING_SNAKE_CASE = hidden_size
SCREAMING_SNAKE_CASE = num_hidden_layers
SCREAMING_SNAKE_CASE = num_attention_heads
SCREAMING_SNAKE_CASE = intermediate_size
SCREAMING_SNAKE_CASE = hidden_act
SCREAMING_SNAKE_CASE = hidden_dropout_prob
SCREAMING_SNAKE_CASE = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE = max_position_embeddings
SCREAMING_SNAKE_CASE = type_vocab_size
SCREAMING_SNAKE_CASE = initializer_range
SCREAMING_SNAKE_CASE = layer_norm_eps
SCREAMING_SNAKE_CASE = embedding_size
SCREAMING_SNAKE_CASE = head_ratio
SCREAMING_SNAKE_CASE = conv_kernel_size
SCREAMING_SNAKE_CASE = num_groups
SCREAMING_SNAKE_CASE = classifier_dropout
class lowercase ( a ):
@property
def __snake_case( self : Any ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task == "multiple-choice":
SCREAMING_SNAKE_CASE = {0: "batch", 1: "choice", 2: "sequence"}
else:
SCREAMING_SNAKE_CASE = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
("token_type_ids", dynamic_axis),
] )
| 403
|
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / '''utils'''))
from test_module.custom_feature_extraction import CustomFeatureExtractor # noqa E402
_lowerCamelCase : Union[str, Any] = get_tests_dir('''fixtures''')
class lowercase ( unittest.TestCase ):
def __snake_case( self : str ) -> List[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = mock.Mock()
SCREAMING_SNAKE_CASE = 500
SCREAMING_SNAKE_CASE = {}
SCREAMING_SNAKE_CASE = HTTPError
SCREAMING_SNAKE_CASE = {}
# Download this model to make sure it's in the cache.
SCREAMING_SNAKE_CASE = WavaVecaFeatureExtractor.from_pretrained("hf-internal-testing/tiny-random-wav2vec2" )
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch("requests.Session.request" , return_value=_UpperCamelCase ) as mock_head:
SCREAMING_SNAKE_CASE = WavaVecaFeatureExtractor.from_pretrained("hf-internal-testing/tiny-random-wav2vec2" )
# This check we did call the fake head request
mock_head.assert_called()
def __snake_case( self : List[str] ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = WavaVecaFeatureExtractor.from_pretrained(
"https://huggingface.co/hf-internal-testing/tiny-random-wav2vec2/resolve/main/preprocessor_config.json" )
@is_staging_test
class lowercase ( unittest.TestCase ):
@classmethod
def __snake_case( cls : Optional[Any] ) -> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = TOKEN
HfFolder.save_token(_UpperCamelCase )
@classmethod
def __snake_case( cls : Dict ) -> List[str]:
'''simple docstring'''
try:
delete_repo(token=cls._token , repo_id="test-feature-extractor" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="valid_org/test-feature-extractor-org" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="test-dynamic-feature-extractor" )
except HTTPError:
pass
def __snake_case( self : Any ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = WavaVecaFeatureExtractor.from_pretrained(_UpperCamelCase )
feature_extractor.push_to_hub("test-feature-extractor" , use_auth_token=self._token )
SCREAMING_SNAKE_CASE = WavaVecaFeatureExtractor.from_pretrained(F"{USER}/test-feature-extractor" )
for k, v in feature_extractor.__dict__.items():
self.assertEqual(_UpperCamelCase , getattr(_UpperCamelCase , _UpperCamelCase ) )
# Reset repo
delete_repo(token=self._token , repo_id="test-feature-extractor" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
feature_extractor.save_pretrained(
_UpperCamelCase , repo_id="test-feature-extractor" , push_to_hub=_UpperCamelCase , use_auth_token=self._token )
SCREAMING_SNAKE_CASE = WavaVecaFeatureExtractor.from_pretrained(F"{USER}/test-feature-extractor" )
for k, v in feature_extractor.__dict__.items():
self.assertEqual(_UpperCamelCase , getattr(_UpperCamelCase , _UpperCamelCase ) )
def __snake_case( self : Optional[int] ) -> Tuple:
'''simple docstring'''
SCREAMING_SNAKE_CASE = WavaVecaFeatureExtractor.from_pretrained(_UpperCamelCase )
feature_extractor.push_to_hub("valid_org/test-feature-extractor" , use_auth_token=self._token )
SCREAMING_SNAKE_CASE = WavaVecaFeatureExtractor.from_pretrained("valid_org/test-feature-extractor" )
for k, v in feature_extractor.__dict__.items():
self.assertEqual(_UpperCamelCase , getattr(_UpperCamelCase , _UpperCamelCase ) )
# Reset repo
delete_repo(token=self._token , repo_id="valid_org/test-feature-extractor" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
feature_extractor.save_pretrained(
_UpperCamelCase , repo_id="valid_org/test-feature-extractor-org" , push_to_hub=_UpperCamelCase , use_auth_token=self._token )
SCREAMING_SNAKE_CASE = WavaVecaFeatureExtractor.from_pretrained("valid_org/test-feature-extractor-org" )
for k, v in feature_extractor.__dict__.items():
self.assertEqual(_UpperCamelCase , getattr(_UpperCamelCase , _UpperCamelCase ) )
def __snake_case( self : List[str] ) -> Tuple:
'''simple docstring'''
CustomFeatureExtractor.register_for_auto_class()
SCREAMING_SNAKE_CASE = CustomFeatureExtractor.from_pretrained(_UpperCamelCase )
feature_extractor.push_to_hub("test-dynamic-feature-extractor" , use_auth_token=self._token )
# This has added the proper auto_map field to the config
self.assertDictEqual(
feature_extractor.auto_map , {"AutoFeatureExtractor": "custom_feature_extraction.CustomFeatureExtractor"} , )
SCREAMING_SNAKE_CASE = AutoFeatureExtractor.from_pretrained(
F"{USER}/test-dynamic-feature-extractor" , trust_remote_code=_UpperCamelCase )
# Can't make an isinstance check because the new_feature_extractor is from the CustomFeatureExtractor class of a dynamic module
self.assertEqual(new_feature_extractor.__class__.__name__ , "CustomFeatureExtractor" )
| 403
| 1
|
'''simple docstring'''
import unittest
from datasets import load_dataset
from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
@is_pipeline_test
@require_torch
class _a ( unittest.TestCase ):
"""simple docstring"""
@require_torch
def lowerCamelCase__ ( self : Tuple ):
'''simple docstring'''
lowercase_ = pipeline(
task="""zero-shot-audio-classification""" , model="""hf-internal-testing/tiny-clap-htsat-unfused""" )
lowercase_ = load_dataset("""ashraq/esc50""" )
lowercase_ = dataset["""train"""]["""audio"""][-1]["""array"""]
lowercase_ = audio_classifier(lowercase_ , candidate_labels=["""Sound of a dog""", """Sound of vaccum cleaner"""] )
self.assertEqual(
nested_simplify(lowercase_ ) , [{"""score""": 0.5_0_1, """label""": """Sound of a dog"""}, {"""score""": 0.4_9_9, """label""": """Sound of vaccum cleaner"""}] , )
@unittest.skip("""No models are available in TF""" )
def lowerCamelCase__ ( self : Dict ):
'''simple docstring'''
pass
@slow
@require_torch
def lowerCamelCase__ ( self : List[str] ):
'''simple docstring'''
lowercase_ = pipeline(
task="""zero-shot-audio-classification""" , model="""laion/clap-htsat-unfused""" , )
# This is an audio of a dog
lowercase_ = load_dataset("""ashraq/esc50""" )
lowercase_ = dataset["""train"""]["""audio"""][-1]["""array"""]
lowercase_ = audio_classifier(lowercase_ , candidate_labels=["""Sound of a dog""", """Sound of vaccum cleaner"""] )
self.assertEqual(
nested_simplify(lowercase_ ) , [
{"""score""": 0.9_9_9, """label""": """Sound of a dog"""},
{"""score""": 0.0_0_1, """label""": """Sound of vaccum cleaner"""},
] , )
lowercase_ = audio_classifier([audio] * 5 , candidate_labels=["""Sound of a dog""", """Sound of vaccum cleaner"""] )
self.assertEqual(
nested_simplify(lowercase_ ) , [
[
{"""score""": 0.9_9_9, """label""": """Sound of a dog"""},
{"""score""": 0.0_0_1, """label""": """Sound of vaccum cleaner"""},
],
]
* 5 , )
lowercase_ = audio_classifier(
[audio] * 5 , candidate_labels=["""Sound of a dog""", """Sound of vaccum cleaner"""] , batch_size=5 )
self.assertEqual(
nested_simplify(lowercase_ ) , [
[
{"""score""": 0.9_9_9, """label""": """Sound of a dog"""},
{"""score""": 0.0_0_1, """label""": """Sound of vaccum cleaner"""},
],
]
* 5 , )
@unittest.skip("""No models are available in TF""" )
def lowerCamelCase__ ( self : Tuple ):
'''simple docstring'''
pass
| 603
|
'''simple docstring'''
import os
def A_ ( ) ->Any:
with open(os.path.dirname(SCREAMING_SNAKE_CASE_ ) + """/p022_names.txt""" ) as file:
lowercase_ = str(file.readlines()[0] )
lowercase_ = names.replace("""\"""" , """""" ).split(""",""" )
names.sort()
lowercase_ = 0
lowercase_ = 0
for i, name in enumerate(SCREAMING_SNAKE_CASE_ ):
for letter in name:
name_score += ord(SCREAMING_SNAKE_CASE_ ) - 64
total_score += (i + 1) * name_score
lowercase_ = 0
return total_score
if __name__ == "__main__":
print(solution())
| 603
| 1
|
'''simple docstring'''
import argparse
import json
import re
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileNetVaConfig,
MobileNetVaForImageClassification,
MobileNetVaImageProcessor,
load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging
logging.set_verbosity_info()
_UpperCamelCase : Any = logging.get_logger(__name__)
def __snake_case ( lowerCAmelCase : Optional[Any] ):
__UpperCAmelCase = MobileNetVaConfig(layer_norm_eps=0.0_01 )
if "_quant" in model_name:
raise ValueError('Quantized models are not supported.' )
__UpperCAmelCase = re.match(r'^mobilenet_v1_([^_]*)_([^_]*)$' , __UpperCamelCase )
if matches:
__UpperCAmelCase = float(matches[1] )
__UpperCAmelCase = int(matches[2] )
# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
# the usual 1000. The first class (index 0) is "background".
__UpperCAmelCase = 1001
__UpperCAmelCase = '''imagenet-1k-id2label.json'''
__UpperCAmelCase = '''huggingface/label-files'''
__UpperCAmelCase = json.load(open(hf_hub_download(__UpperCamelCase , __UpperCamelCase , repo_type='dataset' ) , 'r' ) )
__UpperCAmelCase = {int(__UpperCamelCase ) + 1: v for k, v in idalabel.items()}
__UpperCAmelCase = '''background'''
__UpperCAmelCase = idalabel
__UpperCAmelCase = {v: k for k, v in idalabel.items()}
return config
def __snake_case ( ):
__UpperCAmelCase = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
__UpperCAmelCase = Image.open(requests.get(__UpperCamelCase , stream=__UpperCamelCase ).raw )
return im
@torch.no_grad()
def __snake_case ( lowerCAmelCase : Dict , lowerCAmelCase : Optional[int] , lowerCAmelCase : Tuple , lowerCAmelCase : str=False ):
__UpperCAmelCase = get_mobilenet_va_config(__UpperCamelCase )
# Load 🤗 model
__UpperCAmelCase = MobileNetVaForImageClassification(__UpperCamelCase ).eval()
# Load weights from TensorFlow checkpoint
load_tf_weights_in_mobilenet_va(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
# Check outputs on an image, prepared by MobileNetV1ImageProcessor
__UpperCAmelCase = MobileNetVaImageProcessor(
crop_size={'width': config.image_size, 'height': config.image_size} , size={'shortest_edge': config.image_size + 32} , )
__UpperCAmelCase = image_processor(images=prepare_img() , return_tensors='pt' )
__UpperCAmelCase = model(**__UpperCamelCase )
__UpperCAmelCase = outputs.logits
assert logits.shape == (1, 1001)
if model_name == "mobilenet_v1_1.0_224":
__UpperCAmelCase = torch.tensor([-4.17_39, -1.12_33, 3.12_05] )
elif model_name == "mobilenet_v1_0.75_192":
__UpperCAmelCase = torch.tensor([-3.94_40, -2.31_41, -0.33_33] )
else:
__UpperCAmelCase = None
if expected_logits is not None:
assert torch.allclose(logits[0, :3] , __UpperCamelCase , atol=1E-4 )
Path(__UpperCamelCase ).mkdir(exist_ok=__UpperCamelCase )
print(F"""Saving model {model_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(__UpperCamelCase )
print(F"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(__UpperCamelCase )
if push_to_hub:
print('Pushing to the hub...' )
__UpperCAmelCase = '''google/''' + model_name
image_processor.push_to_hub(__UpperCamelCase )
model.push_to_hub(__UpperCamelCase )
if __name__ == "__main__":
_UpperCamelCase : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='mobilenet_v1_1.0_224',
type=str,
help='Name of the MobileNetV1 model you\'d like to convert. Should in the form \'mobilenet_v1_<depth>_<size>\'.',
)
parser.add_argument(
'--checkpoint_path', required=True, type=str, help='Path to the original TensorFlow checkpoint (.ckpt file).'
)
parser.add_argument(
'--pytorch_dump_folder_path', required=True, type=str, help='Path to the output PyTorch model directory.'
)
parser.add_argument(
'--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
)
_UpperCamelCase : Tuple = parser.parse_args()
convert_movilevit_checkpoint(
args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
)
| 396
|
"""simple docstring"""
a_ = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'
def __UpperCAmelCase ( __UpperCamelCase ):
# Make sure the supplied data is a bytes-like object
if not isinstance(__UpperCamelCase , __UpperCamelCase ):
__lowercase : str = f"""a bytes-like object is required, not '{data.__class__.__name__}'"""
raise TypeError(__UpperCamelCase )
__lowercase : Any = ''''''.join(bin(__UpperCamelCase )[2:].zfill(8 ) for byte in data )
__lowercase : List[str] = len(__UpperCamelCase ) % 6 != 0
if padding_needed:
# The padding that will be added later
__lowercase : int = B'''=''' * ((6 - len(__UpperCamelCase ) % 6) // 2)
# Append binary_stream with arbitrary binary digits (0's by default) to make its
# length a multiple of 6.
binary_stream += "0" * (6 - len(__UpperCamelCase ) % 6)
else:
__lowercase : Any = B''''''
# Encode every 6 binary digits to their corresponding Base64 character
return (
"".join(
B64_CHARSET[int(binary_stream[index : index + 6] , 2 )]
for index in range(0 , len(__UpperCamelCase ) , 6 ) ).encode()
+ padding
)
def __UpperCAmelCase ( __UpperCamelCase ):
# Make sure encoded_data is either a string or a bytes-like object
if not isinstance(__UpperCamelCase , __UpperCamelCase ) and not isinstance(__UpperCamelCase , __UpperCamelCase ):
__lowercase : List[str] = (
'''argument should be a bytes-like object or ASCII string, '''
f"""not '{encoded_data.__class__.__name__}'"""
)
raise TypeError(__UpperCamelCase )
# In case encoded_data is a bytes-like object, make sure it contains only
# ASCII characters so we convert it to a string object
if isinstance(__UpperCamelCase , __UpperCamelCase ):
try:
__lowercase : List[str] = encoded_data.decode('''utf-8''' )
except UnicodeDecodeError:
raise ValueError('''base64 encoded data should only contain ASCII characters''' )
__lowercase : Dict = encoded_data.count('''=''' )
# Check if the encoded string contains non base64 characters
if padding:
assert all(
char in B64_CHARSET for char in encoded_data[:-padding] ), "Invalid base64 character(s) found."
else:
assert all(
char in B64_CHARSET for char in encoded_data ), "Invalid base64 character(s) found."
# Check the padding
assert len(__UpperCamelCase ) % 4 == 0 and padding < 3, "Incorrect padding"
if padding:
# Remove padding if there is one
__lowercase : Tuple = encoded_data[:-padding]
__lowercase : str = ''''''.join(
bin(B64_CHARSET.index(__UpperCamelCase ) )[2:].zfill(6 ) for char in encoded_data )[: -padding * 2]
else:
__lowercase : Any = ''''''.join(
bin(B64_CHARSET.index(__UpperCamelCase ) )[2:].zfill(6 ) for char in encoded_data )
__lowercase : int = [
int(binary_stream[index : index + 8] , 2 )
for index in range(0 , len(__UpperCamelCase ) , 8 )
]
return bytes(__UpperCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 76
| 0
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
__UpperCAmelCase = {
'''configuration_speecht5''': [
'''SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP''',
'''SpeechT5Config''',
'''SpeechT5HifiGanConfig''',
],
'''feature_extraction_speecht5''': ['''SpeechT5FeatureExtractor'''],
'''processing_speecht5''': ['''SpeechT5Processor'''],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = ['''SpeechT5Tokenizer''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
'''SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''SpeechT5ForSpeechToText''',
'''SpeechT5ForSpeechToSpeech''',
'''SpeechT5ForTextToSpeech''',
'''SpeechT5Model''',
'''SpeechT5PreTrainedModel''',
'''SpeechT5HifiGan''',
]
if TYPE_CHECKING:
from .configuration_speechta import (
SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
SpeechTaConfig,
SpeechTaHifiGanConfig,
)
from .feature_extraction_speechta import SpeechTaFeatureExtractor
from .processing_speechta import SpeechTaProcessor
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_speechta import SpeechTaTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speechta import (
SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaHifiGan,
SpeechTaModel,
SpeechTaPreTrainedModel,
)
else:
import sys
__UpperCAmelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 712
|
"""simple docstring"""
import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/config.json''',
# See all BART models at https://huggingface.co/models?filter=bart
}
class __UpperCAmelCase ( _UpperCamelCase ):
__lowerCamelCase : str = "bart"
__lowerCamelCase : Dict = ["past_key_values"]
__lowerCamelCase : List[str] = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
def __init__( self : Union[str, Any] , a_ : Union[str, Any]=5_02_65 , a_ : List[str]=10_24 , a_ : str=12 , a_ : Union[str, Any]=40_96 , a_ : Tuple=16 , a_ : List[str]=12 , a_ : int=40_96 , a_ : Tuple=16 , a_ : int=0.0 , a_ : Optional[int]=0.0 , a_ : Dict="gelu" , a_ : Optional[int]=10_24 , a_ : Tuple=0.1 , a_ : str=0.0 , a_ : str=0.0 , a_ : Optional[Any]=0.02 , a_ : Any=0.0 , a_ : int=False , a_ : Dict=True , a_ : List[Any]=3 , a_ : Tuple=1 , a_ : Optional[Any]=0 , a_ : Any=2 , a_ : List[Any]=True , a_ : Dict=2 , a_ : List[str]=2 , **a_ : Dict , ) -> Dict:
'''simple docstring'''
a__ : List[Any] = vocab_size
a__ : Dict = max_position_embeddings
a__ : Optional[Any] = d_model
a__ : Optional[Any] = encoder_ffn_dim
a__ : Union[str, Any] = encoder_layers
a__ : Union[str, Any] = encoder_attention_heads
a__ : Tuple = decoder_ffn_dim
a__ : Union[str, Any] = decoder_layers
a__ : Union[str, Any] = decoder_attention_heads
a__ : Optional[Any] = dropout
a__ : str = attention_dropout
a__ : Dict = activation_dropout
a__ : List[Any] = activation_function
a__ : Dict = init_std
a__ : Dict = encoder_layerdrop
a__ : List[Any] = decoder_layerdrop
a__ : List[Any] = classifier_dropout
a__ : Union[str, Any] = use_cache
a__ : Any = encoder_layers
a__ : str = scale_embedding # scale factor will be sqrt(d_model) if True
super().__init__(
num_labels=a_ , pad_token_id=a_ , bos_token_id=a_ , eos_token_id=a_ , is_encoder_decoder=a_ , decoder_start_token_id=a_ , forced_eos_token_id=a_ , **a_ , )
# ensure backward compatibility for BART CNN models
if self.forced_bos_token_id is None and kwargs.get("force_bos_token_to_be_generated" , a_ ):
a__ : List[Any] = self.bos_token_id
warnings.warn(
F"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. "
"The config can simply be saved and uploaded again to be fixed." )
class __UpperCAmelCase ( _UpperCamelCase ):
@property
def UpperCAmelCase ( self : Optional[int] ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
a__ : Optional[Any] = OrderedDict(
[
("input_ids", {0: "batch", 1: "encoder_sequence"}),
("attention_mask", {0: "batch", 1: "encoder_sequence"}),
] )
if self.use_past:
a__ : Optional[int] = {0: "batch"}
a__ : Dict = {0: "batch", 1: "past_decoder_sequence + sequence"}
else:
a__ : Dict = {0: "batch", 1: "decoder_sequence"}
a__ : Union[str, Any] = {0: "batch", 1: "decoder_sequence"}
if self.use_past:
self.fill_with_past_key_values_(a_ , direction="inputs" )
elif self.task == "causal-lm":
# TODO: figure this case out.
a__ : int = OrderedDict(
[
("input_ids", {0: "batch", 1: "encoder_sequence"}),
("attention_mask", {0: "batch", 1: "encoder_sequence"}),
] )
if self.use_past:
a__ , a__ : Any = self.num_layers
for i in range(a_ ):
a__ : Tuple = {0: "batch", 2: "past_sequence + sequence"}
a__ : Optional[int] = {0: "batch", 2: "past_sequence + sequence"}
else:
a__ : List[Any] = OrderedDict(
[
("input_ids", {0: "batch", 1: "encoder_sequence"}),
("attention_mask", {0: "batch", 1: "encoder_sequence"}),
("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
] )
return common_inputs
@property
def UpperCAmelCase ( self : List[Any] ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
a__ : int = super().outputs
else:
a__ : Tuple = super(a_ , self ).outputs
if self.use_past:
a__ , a__ : List[str] = self.num_layers
for i in range(a_ ):
a__ : List[Any] = {0: "batch", 2: "past_sequence + sequence"}
a__ : List[str] = {0: "batch", 2: "past_sequence + sequence"}
return common_outputs
def UpperCAmelCase ( self : Dict , a_ : PreTrainedTokenizer , a_ : int = -1 , a_ : int = -1 , a_ : bool = False , a_ : Optional[TensorType] = None , ) -> Mapping[str, Any]:
'''simple docstring'''
a__ : Optional[Any] = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
a_ , a_ , a_ , a_ , a_ )
# Generate decoder inputs
a__ : Optional[Any] = seq_length if not self.use_past else 1
a__ : Tuple = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
a_ , a_ , a_ , a_ , a_ )
a__ : Union[str, Any] = {F"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
a__ : str = dict(**a_ , **a_ )
if self.use_past:
if not is_torch_available():
raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed." )
else:
import torch
a__ , a__ : Union[str, Any] = common_inputs["input_ids"].shape
a__ : List[Any] = common_inputs["decoder_input_ids"].shape[1]
a__ , a__ : Tuple = self.num_attention_heads
a__ : List[Any] = (
batch,
num_encoder_attention_heads,
encoder_seq_length,
self._config.hidden_size // num_encoder_attention_heads,
)
a__ : List[str] = decoder_seq_length + 3
a__ : Tuple = (
batch,
num_decoder_attention_heads,
decoder_past_length,
self._config.hidden_size // num_decoder_attention_heads,
)
a__ : List[Any] = torch.cat(
[common_inputs["decoder_attention_mask"], torch.ones(a_ , a_ )] , dim=1 )
a__ : Tuple = []
# If the number of encoder and decoder layers are present in the model configuration, both are considered
a__ , a__ : Any = self.num_layers
a__ : Dict = min(a_ , a_ )
a__ : Optional[int] = max(a_ , a_ ) - min_num_layers
a__ : str = "encoder" if num_encoder_layers > num_decoder_layers else "decoder"
for _ in range(a_ ):
common_inputs["past_key_values"].append(
(
torch.zeros(a_ ),
torch.zeros(a_ ),
torch.zeros(a_ ),
torch.zeros(a_ ),
) )
# TODO: test this.
a__ : List[Any] = encoder_shape if remaining_side_name == "encoder" else decoder_shape
for _ in range(a_ , a_ ):
common_inputs["past_key_values"].append((torch.zeros(a_ ), torch.zeros(a_ )) )
return common_inputs
def UpperCAmelCase ( self : Optional[Any] , a_ : PreTrainedTokenizer , a_ : int = -1 , a_ : int = -1 , a_ : bool = False , a_ : Optional[TensorType] = None , ) -> Mapping[str, Any]:
'''simple docstring'''
a__ : List[str] = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
a_ , a_ , a_ , a_ , a_ )
if self.use_past:
if not is_torch_available():
raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed." )
else:
import torch
a__ , a__ : List[str] = common_inputs["input_ids"].shape
# Not using the same length for past_key_values
a__ : Any = seqlen + 2
a__ , a__ : Any = self.num_layers
a__ , a__ : List[Any] = self.num_attention_heads
a__ : Optional[int] = (
batch,
num_encoder_attention_heads,
past_key_values_length,
self._config.hidden_size // num_encoder_attention_heads,
)
a__ : str = common_inputs["attention_mask"].dtype
a__ : List[str] = torch.cat(
[common_inputs["attention_mask"], torch.ones(a_ , a_ , dtype=a_ )] , dim=1 )
a__ : Optional[Any] = [
(torch.zeros(a_ ), torch.zeros(a_ )) for _ in range(a_ )
]
return common_inputs
def UpperCAmelCase ( self : List[Any] , a_ : PreTrainedTokenizer , a_ : int = -1 , a_ : int = -1 , a_ : bool = False , a_ : Optional[TensorType] = None , ) -> Mapping[str, Any]:
'''simple docstring'''
a__ : Any = compute_effective_axis_dimension(
a_ , fixed_dimension=OnnxConfig.default_fixed_batch , num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
a__ : Any = tokenizer.num_special_tokens_to_add(a_ )
a__ : Dict = compute_effective_axis_dimension(
a_ , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=a_ )
# Generate dummy inputs according to compute batch and sequence
a__ : int = [" ".join([tokenizer.unk_token] ) * seq_length] * batch_size
a__ : Any = dict(tokenizer(a_ , return_tensors=a_ ) )
return common_inputs
def UpperCAmelCase ( self : List[Any] , a_ : PreTrainedTokenizer , a_ : int = -1 , a_ : int = -1 , a_ : bool = False , a_ : Optional[TensorType] = None , ) -> Mapping[str, Any]:
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
a__ : List[Any] = self._generate_dummy_inputs_for_default_and_seqaseq_lm(
a_ , batch_size=a_ , seq_length=a_ , is_pair=a_ , framework=a_ )
elif self.task == "causal-lm":
a__ : Optional[int] = self._generate_dummy_inputs_for_causal_lm(
a_ , batch_size=a_ , seq_length=a_ , is_pair=a_ , framework=a_ )
else:
a__ : int = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
a_ , batch_size=a_ , seq_length=a_ , is_pair=a_ , framework=a_ )
return common_inputs
def UpperCAmelCase ( self : Optional[Any] , a_ : int , a_ : Optional[Any] , a_ : Union[str, Any] , a_ : List[Any] ) -> str:
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
a__ : int = super()._flatten_past_key_values_(a_ , a_ , a_ , a_ )
else:
a__ : Union[str, Any] = super(a_ , self )._flatten_past_key_values_(
a_ , a_ , a_ , a_ )
| 251
| 0
|
def __UpperCamelCase ( lowerCAmelCase__ : int , lowerCAmelCase__ : int ):
while a != 0:
__a , __a : Union[str, Any] = b % a, a
return b
def __UpperCamelCase ( lowerCAmelCase__ : int , lowerCAmelCase__ : int ):
if gcd(lowerCAmelCase__ , lowerCAmelCase__ ) != 1:
__a : List[str] = f"mod inverse of {a!r} and {m!r} does not exist"
raise ValueError(lowerCAmelCase__ )
__a , __a , __a : Any = 1, 0, a
__a , __a , __a : str = 0, 1, m
while va != 0:
__a : List[str] = ua // va
__a , __a , __a , __a , __a , __a : List[Any] = (ua - q * va), (ua - q * va), (ua - q * va), va, va, va
return ua % m
| 521
|
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__ :
@staticmethod
def lowerCAmelCase (*snake_case_ : int , **snake_case_ : List[str] ):
pass
@is_pipeline_test
@require_vision
@require_timm
@require_torch
class UpperCamelCase__ ( unittest.TestCase ):
_SCREAMING_SNAKE_CASE : Any = MODEL_FOR_OBJECT_DETECTION_MAPPING
def lowerCAmelCase (self : Tuple , snake_case_ : Union[str, Any] , snake_case_ : List[Any] , snake_case_ : Dict ):
__a : Union[str, Any] = ObjectDetectionPipeline(model=snake_case_ , image_processor=snake_case_ )
return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]
def lowerCAmelCase (self : Tuple , snake_case_ : List[str] , snake_case_ : Any ):
__a : Any = object_detector('''./tests/fixtures/tests_samples/COCO/000000039769.png''' , threshold=0.0 )
self.assertGreater(len(snake_case_ ) , 0 )
for detected_object in outputs:
self.assertEqual(
snake_case_ , {
'''score''': ANY(snake_case_ ),
'''label''': ANY(snake_case_ ),
'''box''': {'''xmin''': ANY(snake_case_ ), '''ymin''': ANY(snake_case_ ), '''xmax''': ANY(snake_case_ ), '''ymax''': ANY(snake_case_ )},
} , )
import datasets
__a : List[Any] = datasets.load_dataset('''hf-internal-testing/fixtures_image_utils''' , '''image''' , split='''test''' )
__a : List[str] = [
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'''],
]
__a : Optional[int] = object_detector(snake_case_ , threshold=0.0 )
self.assertEqual(len(snake_case_ ) , len(snake_case_ ) )
for outputs in batch_outputs:
self.assertGreater(len(snake_case_ ) , 0 )
for detected_object in outputs:
self.assertEqual(
snake_case_ , {
'''score''': ANY(snake_case_ ),
'''label''': ANY(snake_case_ ),
'''box''': {'''xmin''': ANY(snake_case_ ), '''ymin''': ANY(snake_case_ ), '''xmax''': ANY(snake_case_ ), '''ymax''': ANY(snake_case_ )},
} , )
@require_tf
@unittest.skip('''Object detection not implemented in TF''' )
def lowerCAmelCase (self : int ):
pass
@require_torch
def lowerCAmelCase (self : Tuple ):
__a : str = '''hf-internal-testing/tiny-detr-mobilenetsv3'''
__a : int = AutoModelForObjectDetection.from_pretrained(snake_case_ )
__a : int = AutoFeatureExtractor.from_pretrained(snake_case_ )
__a : Union[str, Any] = ObjectDetectionPipeline(model=snake_case_ , feature_extractor=snake_case_ )
__a : List[str] = object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg''' , threshold=0.0 )
self.assertEqual(
nested_simplify(snake_case_ , decimals=4 ) , [
{'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 1_5_9, '''ymin''': 1_2_0, '''xmax''': 4_8_0, '''ymax''': 3_5_9}},
{'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 1_5_9, '''ymin''': 1_2_0, '''xmax''': 4_8_0, '''ymax''': 3_5_9}},
] , )
__a : Dict = object_detector(
[
'''http://images.cocodataset.org/val2017/000000039769.jpg''',
'''http://images.cocodataset.org/val2017/000000039769.jpg''',
] , threshold=0.0 , )
self.assertEqual(
nested_simplify(snake_case_ , decimals=4 ) , [
[
{'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 1_5_9, '''ymin''': 1_2_0, '''xmax''': 4_8_0, '''ymax''': 3_5_9}},
{'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 1_5_9, '''ymin''': 1_2_0, '''xmax''': 4_8_0, '''ymax''': 3_5_9}},
],
[
{'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 1_5_9, '''ymin''': 1_2_0, '''xmax''': 4_8_0, '''ymax''': 3_5_9}},
{'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 1_5_9, '''ymin''': 1_2_0, '''xmax''': 4_8_0, '''ymax''': 3_5_9}},
],
] , )
@require_torch
@slow
def lowerCAmelCase (self : List[Any] ):
__a : Optional[Any] = '''facebook/detr-resnet-50'''
__a : int = AutoModelForObjectDetection.from_pretrained(snake_case_ )
__a : str = AutoFeatureExtractor.from_pretrained(snake_case_ )
__a : Tuple = ObjectDetectionPipeline(model=snake_case_ , feature_extractor=snake_case_ )
__a : Union[str, Any] = object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg''' )
self.assertEqual(
nested_simplify(snake_case_ , decimals=4 ) , [
{'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_0, '''xmax''': 1_7_5, '''ymax''': 1_1_7}},
{'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 3_3_3, '''ymin''': 7_2, '''xmax''': 3_6_8, '''ymax''': 1_8_7}},
{'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 6_3_9, '''ymax''': 4_7_3}},
{'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 1_3, '''ymin''': 5_2, '''xmax''': 3_1_4, '''ymax''': 4_7_0}},
{'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 3_4_5, '''ymin''': 2_3, '''xmax''': 6_4_0, '''ymax''': 3_6_8}},
] , )
__a : Optional[Any] = object_detector(
[
'''http://images.cocodataset.org/val2017/000000039769.jpg''',
'''http://images.cocodataset.org/val2017/000000039769.jpg''',
] )
self.assertEqual(
nested_simplify(snake_case_ , decimals=4 ) , [
[
{'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_0, '''xmax''': 1_7_5, '''ymax''': 1_1_7}},
{'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 3_3_3, '''ymin''': 7_2, '''xmax''': 3_6_8, '''ymax''': 1_8_7}},
{'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 6_3_9, '''ymax''': 4_7_3}},
{'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 1_3, '''ymin''': 5_2, '''xmax''': 3_1_4, '''ymax''': 4_7_0}},
{'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 3_4_5, '''ymin''': 2_3, '''xmax''': 6_4_0, '''ymax''': 3_6_8}},
],
[
{'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_0, '''xmax''': 1_7_5, '''ymax''': 1_1_7}},
{'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 3_3_3, '''ymin''': 7_2, '''xmax''': 3_6_8, '''ymax''': 1_8_7}},
{'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 6_3_9, '''ymax''': 4_7_3}},
{'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 1_3, '''ymin''': 5_2, '''xmax''': 3_1_4, '''ymax''': 4_7_0}},
{'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 3_4_5, '''ymin''': 2_3, '''xmax''': 6_4_0, '''ymax''': 3_6_8}},
],
] , )
@require_torch
@slow
def lowerCAmelCase (self : Optional[int] ):
__a : Any = '''facebook/detr-resnet-50'''
__a : Optional[Any] = pipeline('''object-detection''' , model=snake_case_ )
__a : Optional[int] = object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg''' )
self.assertEqual(
nested_simplify(snake_case_ , decimals=4 ) , [
{'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_0, '''xmax''': 1_7_5, '''ymax''': 1_1_7}},
{'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 3_3_3, '''ymin''': 7_2, '''xmax''': 3_6_8, '''ymax''': 1_8_7}},
{'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 6_3_9, '''ymax''': 4_7_3}},
{'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 1_3, '''ymin''': 5_2, '''xmax''': 3_1_4, '''ymax''': 4_7_0}},
{'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 3_4_5, '''ymin''': 2_3, '''xmax''': 6_4_0, '''ymax''': 3_6_8}},
] , )
__a : List[str] = object_detector(
[
'''http://images.cocodataset.org/val2017/000000039769.jpg''',
'''http://images.cocodataset.org/val2017/000000039769.jpg''',
] )
self.assertEqual(
nested_simplify(snake_case_ , decimals=4 ) , [
[
{'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_0, '''xmax''': 1_7_5, '''ymax''': 1_1_7}},
{'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 3_3_3, '''ymin''': 7_2, '''xmax''': 3_6_8, '''ymax''': 1_8_7}},
{'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 6_3_9, '''ymax''': 4_7_3}},
{'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 1_3, '''ymin''': 5_2, '''xmax''': 3_1_4, '''ymax''': 4_7_0}},
{'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 3_4_5, '''ymin''': 2_3, '''xmax''': 6_4_0, '''ymax''': 3_6_8}},
],
[
{'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_0, '''xmax''': 1_7_5, '''ymax''': 1_1_7}},
{'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 3_3_3, '''ymin''': 7_2, '''xmax''': 3_6_8, '''ymax''': 1_8_7}},
{'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 6_3_9, '''ymax''': 4_7_3}},
{'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 1_3, '''ymin''': 5_2, '''xmax''': 3_1_4, '''ymax''': 4_7_0}},
{'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 3_4_5, '''ymin''': 2_3, '''xmax''': 6_4_0, '''ymax''': 3_6_8}},
],
] , )
@require_torch
@slow
def lowerCAmelCase (self : Union[str, Any] ):
__a : Tuple = 0.9985
__a : Tuple = '''facebook/detr-resnet-50'''
__a : int = pipeline('''object-detection''' , model=snake_case_ )
__a : Optional[Any] = object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg''' , threshold=snake_case_ )
self.assertEqual(
nested_simplify(snake_case_ , decimals=4 ) , [
{'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 1_3, '''ymin''': 5_2, '''xmax''': 3_1_4, '''ymax''': 4_7_0}},
{'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 3_4_5, '''ymin''': 2_3, '''xmax''': 6_4_0, '''ymax''': 3_6_8}},
] , )
@require_torch
@require_pytesseract
@slow
def lowerCAmelCase (self : List[str] ):
__a : Optional[int] = '''Narsil/layoutlmv3-finetuned-funsd'''
__a : Any = 0.9993
__a : Tuple = pipeline('''object-detection''' , model=snake_case_ , threshold=snake_case_ )
__a : Optional[Any] = object_detector(
'''https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png''' )
self.assertEqual(
nested_simplify(snake_case_ , decimals=4 ) , [
{'''score''': 0.9993, '''label''': '''I-ANSWER''', '''box''': {'''xmin''': 2_9_4, '''ymin''': 2_5_4, '''xmax''': 3_4_3, '''ymax''': 2_6_4}},
{'''score''': 0.9993, '''label''': '''I-ANSWER''', '''box''': {'''xmin''': 2_9_4, '''ymin''': 2_5_4, '''xmax''': 3_4_3, '''ymax''': 2_6_4}},
] , )
| 521
| 1
|
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor
from accelerate import Accelerator
def UpperCamelCase__ ( SCREAMING_SNAKE_CASE__ ):
__lowerCamelCase : Optional[Any] = fname.split(os.path.sep )[-1]
return re.search(r'^(.*)_\d+\.jpg$' , SCREAMING_SNAKE_CASE__ ).groups()[0]
class A_ ( __UpperCamelCase ):
'''simple docstring'''
def __init__( self: str , a: int , a: Union[str, Any]=None , a: Optional[Any]=None ):
__lowerCamelCase : List[str] = file_names
__lowerCamelCase : List[Any] = image_transform
__lowerCamelCase : Tuple = label_to_id
def __len__( self: Optional[int] ):
return len(self.file_names )
def __getitem__( self: Dict , a: Union[str, Any] ):
__lowerCamelCase : Union[str, Any] = self.file_names[idx]
__lowerCamelCase : Optional[Any] = PIL.Image.open(a )
__lowerCamelCase : Tuple = raw_image.convert('RGB' )
if self.image_transform is not None:
__lowerCamelCase : Dict = self.image_transform(a )
__lowerCamelCase : Optional[int] = extract_label(a )
if self.label_to_id is not None:
__lowerCamelCase : Any = self.label_to_id[label]
return {"image": image, "label": label}
def UpperCamelCase__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
# Initialize accelerator
if args.with_tracking:
__lowerCamelCase : Optional[int] = Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , log_with='all' , project_dir=args.project_dir )
else:
__lowerCamelCase : str = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
__lowerCamelCase : List[str] = config['lr']
__lowerCamelCase : List[Any] = int(config['num_epochs'] )
__lowerCamelCase : Tuple = int(config['seed'] )
__lowerCamelCase : List[Any] = int(config['batch_size'] )
__lowerCamelCase : Union[str, Any] = config['image_size']
if not isinstance(SCREAMING_SNAKE_CASE__ , (list, tuple) ):
__lowerCamelCase : str = (image_size, image_size)
# Parse out whether we are saving every epoch or after a certain number of batches
if hasattr(args.checkpointing_steps , 'isdigit' ):
if args.checkpointing_steps == "epoch":
__lowerCamelCase : Dict = args.checkpointing_steps
elif args.checkpointing_steps.isdigit():
__lowerCamelCase : int = int(args.checkpointing_steps )
else:
raise ValueError(
f'Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.' )
else:
__lowerCamelCase : Any = None
# We need to initialize the trackers we use, and also store our configuration
if args.with_tracking:
__lowerCamelCase : int = os.path.split(SCREAMING_SNAKE_CASE__ )[-1].split('.' )[0]
accelerator.init_trackers(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
# Grab all the image filenames
__lowerCamelCase : Optional[int] = [os.path.join(args.data_dir , SCREAMING_SNAKE_CASE__ ) for fname in os.listdir(args.data_dir ) if fname.endswith('.jpg' )]
# Build the label correspondences
__lowerCamelCase : List[str] = [extract_label(SCREAMING_SNAKE_CASE__ ) for fname in file_names]
__lowerCamelCase : Tuple = list(set(SCREAMING_SNAKE_CASE__ ) )
id_to_label.sort()
__lowerCamelCase : Dict = {lbl: i for i, lbl in enumerate(SCREAMING_SNAKE_CASE__ )}
# Set the seed before splitting the data.
np.random.seed(SCREAMING_SNAKE_CASE__ )
torch.manual_seed(SCREAMING_SNAKE_CASE__ )
torch.cuda.manual_seed_all(SCREAMING_SNAKE_CASE__ )
# Split our filenames between train and validation
__lowerCamelCase : Union[str, Any] = np.random.permutation(len(SCREAMING_SNAKE_CASE__ ) )
__lowerCamelCase : List[str] = int(0.8 * len(SCREAMING_SNAKE_CASE__ ) )
__lowerCamelCase : Union[str, Any] = random_perm[:cut]
__lowerCamelCase : Dict = random_perm[cut:]
# For training we use a simple RandomResizedCrop
__lowerCamelCase : List[Any] = Compose([RandomResizedCrop(SCREAMING_SNAKE_CASE__ , scale=(0.5, 1.0) ), ToTensor()] )
__lowerCamelCase : List[str] = PetsDataset(
[file_names[i] for i in train_split] , image_transform=SCREAMING_SNAKE_CASE__ , label_to_id=SCREAMING_SNAKE_CASE__ )
# For evaluation, we use a deterministic Resize
__lowerCamelCase : Optional[int] = Compose([Resize(SCREAMING_SNAKE_CASE__ ), ToTensor()] )
__lowerCamelCase : List[str] = PetsDataset([file_names[i] for i in eval_split] , image_transform=SCREAMING_SNAKE_CASE__ , label_to_id=SCREAMING_SNAKE_CASE__ )
# Instantiate dataloaders.
__lowerCamelCase : Optional[Any] = DataLoader(SCREAMING_SNAKE_CASE__ , shuffle=SCREAMING_SNAKE_CASE__ , batch_size=SCREAMING_SNAKE_CASE__ , num_workers=4 )
__lowerCamelCase : str = DataLoader(SCREAMING_SNAKE_CASE__ , shuffle=SCREAMING_SNAKE_CASE__ , batch_size=SCREAMING_SNAKE_CASE__ , num_workers=4 )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
__lowerCamelCase : List[str] = create_model('resnet50d' , pretrained=SCREAMING_SNAKE_CASE__ , num_classes=len(SCREAMING_SNAKE_CASE__ ) )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
__lowerCamelCase : Dict = model.to(accelerator.device )
# Freezing the base model
for param in model.parameters():
__lowerCamelCase : Union[str, Any] = False
for param in model.get_classifier().parameters():
__lowerCamelCase : Optional[Any] = True
# We normalize the batches of images to be a bit faster.
__lowerCamelCase : List[str] = torch.tensor(model.default_cfg['mean'] )[None, :, None, None].to(accelerator.device )
__lowerCamelCase : List[str] = torch.tensor(model.default_cfg['std'] )[None, :, None, None].to(accelerator.device )
# Instantiate optimizer
__lowerCamelCase : Any = torch.optim.Adam(params=model.parameters() , lr=lr / 25 )
# Instantiate learning rate scheduler
__lowerCamelCase : Dict = OneCycleLR(optimizer=SCREAMING_SNAKE_CASE__ , max_lr=SCREAMING_SNAKE_CASE__ , epochs=SCREAMING_SNAKE_CASE__ , steps_per_epoch=len(SCREAMING_SNAKE_CASE__ ) )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
__lowerCamelCase : str = accelerator.prepare(
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
# We need to keep track of how many total steps we have iterated over
__lowerCamelCase : int = 0
# We also need to keep track of the starting epoch so files are named properly
__lowerCamelCase : Union[str, Any] = 0
# Potentially load in the weights and states from a previous save
if args.resume_from_checkpoint:
if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
accelerator.print(f'Resumed from checkpoint: {args.resume_from_checkpoint}' )
accelerator.load_state(args.resume_from_checkpoint )
__lowerCamelCase : Optional[int] = os.path.basename(args.resume_from_checkpoint )
else:
# Get the most recent checkpoint
__lowerCamelCase : Any = [f.name for f in os.scandir(os.getcwd() ) if f.is_dir()]
dirs.sort(key=os.path.getctime )
__lowerCamelCase : Dict = dirs[-1] # Sorts folders by date modified, most recent checkpoint is the last
# Extract `epoch_{i}` or `step_{i}`
__lowerCamelCase : Any = os.path.splitext(SCREAMING_SNAKE_CASE__ )[0]
if "epoch" in training_difference:
__lowerCamelCase : int = int(training_difference.replace('epoch_' , '' ) ) + 1
__lowerCamelCase : int = None
else:
__lowerCamelCase : Any = int(training_difference.replace('step_' , '' ) )
__lowerCamelCase : List[Any] = resume_step // len(SCREAMING_SNAKE_CASE__ )
resume_step -= starting_epoch * len(SCREAMING_SNAKE_CASE__ )
# Now we train the model
for epoch in range(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
model.train()
if args.with_tracking:
__lowerCamelCase : Optional[Any] = 0
if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
# We need to skip steps until we reach the resumed step
__lowerCamelCase : str = accelerator.skip_first_batches(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
overall_step += resume_step
else:
# After the first iteration though, we need to go back to the original dataloader
__lowerCamelCase : Optional[int] = train_dataloader
for batch in active_dataloader:
# We could avoid this line since we set the accelerator with `device_placement=True`.
__lowerCamelCase : str = {k: v.to(accelerator.device ) for k, v in batch.items()}
__lowerCamelCase : Optional[int] = (batch['image'] - mean) / std
__lowerCamelCase : Union[str, Any] = model(SCREAMING_SNAKE_CASE__ )
__lowerCamelCase : str = torch.nn.functional.cross_entropy(SCREAMING_SNAKE_CASE__ , batch['label'] )
# We keep track of the loss at each epoch
if args.with_tracking:
total_loss += loss.detach().float()
accelerator.backward(SCREAMING_SNAKE_CASE__ )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
__lowerCamelCase : Any = f'step_{overall_step}'
if overall_step % checkpointing_steps == 0:
if args.output_dir is not None:
__lowerCamelCase : Union[str, Any] = os.path.join(args.output_dir , SCREAMING_SNAKE_CASE__ )
accelerator.save_state(SCREAMING_SNAKE_CASE__ )
model.eval()
__lowerCamelCase : List[str] = 0
__lowerCamelCase : List[str] = 0
for step, batch in enumerate(SCREAMING_SNAKE_CASE__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
__lowerCamelCase : Optional[int] = {k: v.to(accelerator.device ) for k, v in batch.items()}
__lowerCamelCase : Optional[Any] = (batch['image'] - mean) / std
with torch.no_grad():
__lowerCamelCase : str = model(SCREAMING_SNAKE_CASE__ )
__lowerCamelCase : Optional[int] = outputs.argmax(dim=-1 )
__lowerCamelCase : Union[str, Any] = accelerator.gather_for_metrics((predictions, batch['label']) )
__lowerCamelCase : Optional[int] = predictions == references
num_elems += accurate_preds.shape[0]
accurate += accurate_preds.long().sum()
__lowerCamelCase : Optional[int] = accurate.item() / num_elems
# Use accelerator.print to print only on the main process.
accelerator.print(f'epoch {epoch}: {100 * eval_metric:.2f}' )
if args.with_tracking:
accelerator.log(
{
'accuracy': 100 * eval_metric,
'train_loss': total_loss.item() / len(SCREAMING_SNAKE_CASE__ ),
'epoch': epoch,
} , step=SCREAMING_SNAKE_CASE__ , )
if checkpointing_steps == "epoch":
__lowerCamelCase : Optional[int] = f'epoch_{epoch}'
if args.output_dir is not None:
__lowerCamelCase : Optional[Any] = os.path.join(args.output_dir , SCREAMING_SNAKE_CASE__ )
accelerator.save_state(SCREAMING_SNAKE_CASE__ )
if args.with_tracking:
accelerator.end_training()
def UpperCamelCase__ ( ):
__lowerCamelCase : Dict = argparse.ArgumentParser(description='Simple example of training script.' )
parser.add_argument('--data_dir' , required=SCREAMING_SNAKE_CASE__ , help='The data folder on disk.' )
parser.add_argument('--fp16' , action='store_true' , help='If passed, will use FP16 training.' )
parser.add_argument(
'--mixed_precision' , type=SCREAMING_SNAKE_CASE__ , default=SCREAMING_SNAKE_CASE__ , choices=['no', 'fp16', 'bf16', 'fp8'] , help='Whether to use mixed precision. Choose'
'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
'and an Nvidia Ampere GPU.' , )
parser.add_argument('--cpu' , action='store_true' , help='If passed, will train on the CPU.' )
parser.add_argument(
'--checkpointing_steps' , type=SCREAMING_SNAKE_CASE__ , default=SCREAMING_SNAKE_CASE__ , help='Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.' , )
parser.add_argument(
'--output_dir' , type=SCREAMING_SNAKE_CASE__ , default='.' , help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.' , )
parser.add_argument(
'--resume_from_checkpoint' , type=SCREAMING_SNAKE_CASE__ , default=SCREAMING_SNAKE_CASE__ , help='If the training should continue from a checkpoint folder.' , )
parser.add_argument(
'--with_tracking' , action='store_true' , help='Whether to load in all available experiment trackers from the environment and use them for logging.' , )
parser.add_argument(
'--project_dir' , type=SCREAMING_SNAKE_CASE__ , default='logs' , help='Location on where to store experiment tracking logs` and relevent project information' , )
__lowerCamelCase : Optional[Any] = parser.parse_args()
__lowerCamelCase : List[Any] = {'lr': 3e-2, 'num_epochs': 3, 'seed': 42, 'batch_size': 64, 'image_size': 224}
training_function(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
if __name__ == "__main__":
main()
| 710
|
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def UpperCamelCase__ ( SCREAMING_SNAKE_CASE__ ):
return getitem, k
def UpperCamelCase__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
return setitem, k, v
def UpperCamelCase__ ( SCREAMING_SNAKE_CASE__ ):
return delitem, k
def UpperCamelCase__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , *SCREAMING_SNAKE_CASE__ ):
try:
return fun(SCREAMING_SNAKE_CASE__ , *SCREAMING_SNAKE_CASE__ ), None
except Exception as e:
return None, e
lowercase_ = (
_set('key_a', 'val_a'),
_set('key_b', 'val_b'),
)
lowercase_ = [
_set('key_a', 'val_a'),
_set('key_a', 'val_b'),
]
lowercase_ = [
_set('key_a', 'val_a'),
_set('key_b', 'val_b'),
_del('key_a'),
_del('key_b'),
_set('key_a', 'val_a'),
_del('key_a'),
]
lowercase_ = [
_get('key_a'),
_del('key_a'),
_set('key_a', 'val_a'),
_del('key_a'),
_del('key_a'),
_get('key_a'),
]
lowercase_ = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
]
lowercase_ = [
*[_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 UpperCamelCase__ ( SCREAMING_SNAKE_CASE__ ):
__lowerCamelCase : int = HashMap(initial_block_size=4 )
__lowerCamelCase : Dict = {}
for _, (fun, *args) in enumerate(SCREAMING_SNAKE_CASE__ ):
__lowerCamelCase , __lowerCamelCase : Union[str, Any] = _run_operation(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , *SCREAMING_SNAKE_CASE__ )
__lowerCamelCase , __lowerCamelCase : str = _run_operation(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , *SCREAMING_SNAKE_CASE__ )
assert my_res == py_res
assert str(SCREAMING_SNAKE_CASE__ ) == str(SCREAMING_SNAKE_CASE__ )
assert set(SCREAMING_SNAKE_CASE__ ) == set(SCREAMING_SNAKE_CASE__ )
assert len(SCREAMING_SNAKE_CASE__ ) == len(SCREAMING_SNAKE_CASE__ )
assert set(my.items() ) == set(py.items() )
def UpperCamelCase__ ( ):
def is_public(SCREAMING_SNAKE_CASE__ ) -> bool:
return not name.startswith('_' )
__lowerCamelCase : Optional[Any] = {name for name in dir({} ) if is_public(SCREAMING_SNAKE_CASE__ )}
__lowerCamelCase : Dict = {name for name in dir(HashMap() ) if is_public(SCREAMING_SNAKE_CASE__ )}
assert dict_public_names > hash_public_names
| 230
| 0
|
"""simple docstring"""
import os
from pathlib import Path
def snake_case ( ) -> Tuple:
from torch.utils.cpp_extension import load
_snake_case = Path(lowerCAmelCase_ ).resolve().parent.parent.parent / '''kernels''' / '''deformable_detr'''
_snake_case = [
root / filename
for filename in [
'''vision.cpp''',
os.path.join('''cpu''' , '''ms_deform_attn_cpu.cpp''' ),
os.path.join('''cuda''' , '''ms_deform_attn_cuda.cu''' ),
]
]
load(
'''MultiScaleDeformableAttention''' , lowerCAmelCase_ , with_cuda=lowerCAmelCase_ , extra_include_paths=[str(lowerCAmelCase_ )] , extra_cflags=['''-DWITH_CUDA=1'''] , extra_cuda_cflags=[
'''-DCUDA_HAS_FP16=1''',
'''-D__CUDA_NO_HALF_OPERATORS__''',
'''-D__CUDA_NO_HALF_CONVERSIONS__''',
'''-D__CUDA_NO_HALF2_OPERATORS__''',
] , )
import MultiScaleDeformableAttention as MSDA
return MSDA
| 103
|
from math import sqrt
def lowercase_ ( __snake_case : int ) -> bool:
'''simple docstring'''
assert isinstance(__snake_case , __snake_case ) and (
number >= 0
), "'number' must been an int and positive"
snake_case__ :List[str] = True
# 0 and 1 are none primes.
if number <= 1:
snake_case__ :List[str] = False
for divisor in range(2 , int(round(sqrt(__snake_case ) ) ) + 1 ):
# if 'number' divisible by 'divisor' then sets 'status'
# of false and break up the loop.
if number % divisor == 0:
snake_case__ :Optional[int] = False
break
# precondition
assert isinstance(__snake_case , __snake_case ), "'status' must been from type bool"
return status
def lowercase_ ( __snake_case : Optional[Any] ) -> Dict:
'''simple docstring'''
assert isinstance(__snake_case , __snake_case ) and (n > 2), "'N' must been an int and > 2"
# beginList: contains all natural numbers from 2 up to N
snake_case__ :Union[str, Any] = list(range(2 , n + 1 ) )
snake_case__ :Dict = [] # this list will be returns.
# actual sieve of erathostenes
for i in range(len(__snake_case ) ):
for j in range(i + 1 , len(__snake_case ) ):
if (begin_list[i] != 0) and (begin_list[j] % begin_list[i] == 0):
snake_case__ :List[Any] = 0
# filters actual prime numbers.
snake_case__ :Tuple = [x for x in begin_list if x != 0]
# precondition
assert isinstance(__snake_case , __snake_case ), "'ans' must been from type list"
return ans
def lowercase_ ( __snake_case : str ) -> int:
'''simple docstring'''
assert isinstance(__snake_case , __snake_case ) and (n > 2), "'N' must been an int and > 2"
snake_case__ :List[str] = []
# iterates over all numbers between 2 up to N+1
# if a number is prime then appends to list 'ans'
for number in range(2 , n + 1 ):
if is_prime(__snake_case ):
ans.append(__snake_case )
# precondition
assert isinstance(__snake_case , __snake_case ), "'ans' must been from type list"
return ans
def lowercase_ ( __snake_case : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
assert isinstance(__snake_case , __snake_case ) and number >= 0, "'number' must been an int and >= 0"
snake_case__ :int = [] # this list will be returns of the function.
# potential prime number factors.
snake_case__ :Tuple = 2
snake_case__ :Union[str, Any] = number
if number == 0 or number == 1:
ans.append(__snake_case )
# if 'number' not prime then builds the prime factorization of 'number'
elif not is_prime(__snake_case ):
while quotient != 1:
if is_prime(__snake_case ) and (quotient % factor == 0):
ans.append(__snake_case )
quotient /= factor
else:
factor += 1
else:
ans.append(__snake_case )
# precondition
assert isinstance(__snake_case , __snake_case ), "'ans' must been from type list"
return ans
def lowercase_ ( __snake_case : Any ) -> List[str]:
'''simple docstring'''
assert isinstance(__snake_case , __snake_case ) and (
number >= 0
), "'number' bust been an int and >= 0"
snake_case__ :Union[str, Any] = 0
# prime factorization of 'number'
snake_case__ :List[Any] = prime_factorization(__snake_case )
snake_case__ :Any = max(__snake_case )
# precondition
assert isinstance(__snake_case , __snake_case ), "'ans' must been from type int"
return ans
def lowercase_ ( __snake_case : Optional[int] ) -> int:
'''simple docstring'''
assert isinstance(__snake_case , __snake_case ) and (
number >= 0
), "'number' bust been an int and >= 0"
snake_case__ :Union[str, Any] = 0
# prime factorization of 'number'
snake_case__ :Tuple = prime_factorization(__snake_case )
snake_case__ :Dict = min(__snake_case )
# precondition
assert isinstance(__snake_case , __snake_case ), "'ans' must been from type int"
return ans
def lowercase_ ( __snake_case : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
assert isinstance(__snake_case , __snake_case ), "'number' must been an int"
assert isinstance(number % 2 == 0 , __snake_case ), "compare bust been from type bool"
return number % 2 == 0
def lowercase_ ( __snake_case : Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
assert isinstance(__snake_case , __snake_case ), "'number' must been an int"
assert isinstance(number % 2 != 0 , __snake_case ), "compare bust been from type bool"
return number % 2 != 0
def lowercase_ ( __snake_case : Optional[Any] ) -> str:
'''simple docstring'''
assert (
isinstance(__snake_case , __snake_case ) and (number > 2) and is_even(__snake_case )
), "'number' must been an int, even and > 2"
snake_case__ :Dict = [] # this list will returned
# creates a list of prime numbers between 2 up to 'number'
snake_case__ :int = get_prime_numbers(__snake_case )
snake_case__ :Tuple = len(__snake_case )
# run variable for while-loops.
snake_case__ :List[Any] = 0
snake_case__ :Tuple = None
# exit variable. for break up the loops
snake_case__ :Any = True
while i < len_pn and loop:
snake_case__ :List[str] = i + 1
while j < len_pn and loop:
if prime_numbers[i] + prime_numbers[j] == number:
snake_case__ :Optional[int] = False
ans.append(prime_numbers[i] )
ans.append(prime_numbers[j] )
j += 1
i += 1
# precondition
assert (
isinstance(__snake_case , __snake_case )
and (len(__snake_case ) == 2)
and (ans[0] + ans[1] == number)
and is_prime(ans[0] )
and is_prime(ans[1] )
), "'ans' must contains two primes. And sum of elements must been eq 'number'"
return ans
def lowercase_ ( __snake_case : Optional[int] , __snake_case : Tuple ) -> int:
'''simple docstring'''
assert (
isinstance(__snake_case , __snake_case )
and isinstance(__snake_case , __snake_case )
and (numbera >= 0)
and (numbera >= 0)
), "'number1' and 'number2' must been positive integer."
snake_case__ :Optional[Any] = 0
while numbera != 0:
snake_case__ :int = numbera % numbera
snake_case__ :Any = numbera
snake_case__ :List[Any] = rest
# precondition
assert isinstance(__snake_case , __snake_case ) and (
numbera >= 0
), "'number' must been from type int and positive"
return numbera
def lowercase_ ( __snake_case : Union[str, Any] , __snake_case : List[Any] ) -> List[Any]:
'''simple docstring'''
assert (
isinstance(__snake_case , __snake_case )
and isinstance(__snake_case , __snake_case )
and (numbera >= 1)
and (numbera >= 1)
), "'number1' and 'number2' must been positive integer."
snake_case__ :List[str] = 1 # actual answer that will be return.
# for kgV (x,1)
if numbera > 1 and numbera > 1:
# builds the prime factorization of 'number1' and 'number2'
snake_case__ :Tuple = prime_factorization(__snake_case )
snake_case__ :Union[str, Any] = prime_factorization(__snake_case )
elif numbera == 1 or numbera == 1:
snake_case__ :Tuple = []
snake_case__ :Dict = []
snake_case__ :str = max(__snake_case , __snake_case )
snake_case__ :Union[str, Any] = 0
snake_case__ :List[Any] = 0
snake_case__ :str = [] # captured numbers int both 'primeFac1' and 'primeFac2'
# iterates through primeFac1
for n in prime_fac_a:
if n not in done:
if n in prime_fac_a:
snake_case__ :Union[str, Any] = prime_fac_a.count(__snake_case )
snake_case__ :Tuple = prime_fac_a.count(__snake_case )
for _ in range(max(__snake_case , __snake_case ) ):
ans *= n
else:
snake_case__ :str = prime_fac_a.count(__snake_case )
for _ in range(__snake_case ):
ans *= n
done.append(__snake_case )
# iterates through primeFac2
for n in prime_fac_a:
if n not in done:
snake_case__ :Any = prime_fac_a.count(__snake_case )
for _ in range(__snake_case ):
ans *= n
done.append(__snake_case )
# precondition
assert isinstance(__snake_case , __snake_case ) and (
ans >= 0
), "'ans' must been from type int and positive"
return ans
def lowercase_ ( __snake_case : Optional[Any] ) -> int:
'''simple docstring'''
assert isinstance(__snake_case , __snake_case ) and (n >= 0), "'number' must been a positive int"
snake_case__ :Tuple = 0
snake_case__ :List[Any] = 2 # this variable holds the answer
while index < n:
index += 1
ans += 1 # counts to the next number
# if ans not prime then
# runs to the next prime number.
while not is_prime(__snake_case ):
ans += 1
# precondition
assert isinstance(__snake_case , __snake_case ) and is_prime(
__snake_case ), "'ans' must been a prime number and from type int"
return ans
def lowercase_ ( __snake_case : Tuple , __snake_case : Optional[Any] ) -> List[Any]:
'''simple docstring'''
assert (
is_prime(__snake_case ) and is_prime(__snake_case ) and (p_number_a < p_number_a)
), "The arguments must been prime numbers and 'pNumber1' < 'pNumber2'"
snake_case__ :Any = p_number_a + 1 # jump to the next number
snake_case__ :List[Any] = [] # this list will be returns.
# if number is not prime then
# fetch the next prime number.
while not is_prime(__snake_case ):
number += 1
while number < p_number_a:
ans.append(__snake_case )
number += 1
# fetch the next prime number.
while not is_prime(__snake_case ):
number += 1
# precondition
assert (
isinstance(__snake_case , __snake_case )
and ans[0] != p_number_a
and ans[len(__snake_case ) - 1] != p_number_a
), "'ans' must been a list without the arguments"
# 'ans' contains not 'pNumber1' and 'pNumber2' !
return ans
def lowercase_ ( __snake_case : List[Any] ) -> Optional[Any]:
'''simple docstring'''
assert isinstance(__snake_case , __snake_case ) and (n >= 1), "'n' must been int and >= 1"
snake_case__ :Optional[int] = [] # will be returned.
for divisor in range(1 , n + 1 ):
if n % divisor == 0:
ans.append(__snake_case )
# precondition
assert ans[0] == 1 and ans[len(__snake_case ) - 1] == n, "Error in function getDivisiors(...)"
return ans
def lowercase_ ( __snake_case : Tuple ) -> Tuple:
'''simple docstring'''
assert isinstance(__snake_case , __snake_case ) and (
number > 1
), "'number' must been an int and >= 1"
snake_case__ :List[str] = get_divisors(__snake_case )
# precondition
assert (
isinstance(__snake_case , __snake_case )
and (divisors[0] == 1)
and (divisors[len(__snake_case ) - 1] == number)
), "Error in help-function getDivisiors(...)"
# summed all divisors up to 'number' (exclusive), hence [:-1]
return sum(divisors[:-1] ) == number
def lowercase_ ( __snake_case : str , __snake_case : Any ) -> str:
'''simple docstring'''
assert (
isinstance(__snake_case , __snake_case )
and isinstance(__snake_case , __snake_case )
and (denominator != 0)
), "The arguments must been from type int and 'denominator' != 0"
# build the greatest common divisor of numerator and denominator.
snake_case__ :Union[str, Any] = gcd(abs(__snake_case ) , abs(__snake_case ) )
# precondition
assert (
isinstance(__snake_case , __snake_case )
and (numerator % gcd_of_fraction == 0)
and (denominator % gcd_of_fraction == 0)
), "Error in function gcd(...,...)"
return (numerator // gcd_of_fraction, denominator // gcd_of_fraction)
def lowercase_ ( __snake_case : Tuple ) -> int:
'''simple docstring'''
assert isinstance(__snake_case , __snake_case ) and (n >= 0), "'n' must been a int and >= 0"
snake_case__ :int = 1 # this will be return.
for factor in range(1 , n + 1 ):
ans *= factor
return ans
def lowercase_ ( __snake_case : List[str] ) -> int:
'''simple docstring'''
assert isinstance(__snake_case , __snake_case ) and (n >= 0), "'n' must been an int and >= 0"
snake_case__ :Union[str, Any] = 0
snake_case__ :int = 1
snake_case__ :int = 1 # this will be return
for _ in range(n - 1 ):
snake_case__ :List[Any] = ans
ans += fiba
snake_case__ :Optional[Any] = tmp
return ans
| 241
| 0
|
'''simple docstring'''
from __future__ import annotations
def __a(SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : list[str] | None = None ):
_lowerCAmelCase = word_bank or []
# create a table
_lowerCAmelCase = len(SCREAMING_SNAKE_CASE_ ) + 1
_lowerCAmelCase = []
for _ in range(SCREAMING_SNAKE_CASE_ ):
table.append([] )
# seed value
_lowerCAmelCase = [[]] # because empty string has empty combination
# iterate through the indices
for i in range(SCREAMING_SNAKE_CASE_ ):
# condition
if table[i] != []:
for word in word_bank:
# slice condition
if target[i : i + len(SCREAMING_SNAKE_CASE_ )] == word:
_lowerCAmelCase = [
[word, *way] for way in table[i]
]
# adds the word to every combination the current position holds
# now,push that combination to the table[i+len(word)]
table[i + len(SCREAMING_SNAKE_CASE_ )] += new_combinations
# combinations are in reverse order so reverse for better output
for combination in table[len(SCREAMING_SNAKE_CASE_ )]:
combination.reverse()
return table[len(SCREAMING_SNAKE_CASE_ )]
if __name__ == "__main__":
print(all_construct("jwajalapa", ["jwa", "j", "w", "a", "la", "lapa"]))
print(all_construct("rajamati", ["s", "raj", "amat", "raja", "ma", "i", "t"]))
print(
all_construct(
"hexagonosaurus",
["h", "ex", "hex", "ag", "ago", "ru", "auru", "rus", "go", "no", "o", "s"],
)
)
| 700
|
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
class lowerCAmelCase_ ( __magic_name__ ):
__lowerCamelCase : List[str] = "bert-generation"
def __init__( self , _lowerCAmelCase=50358 , _lowerCAmelCase=1024 , _lowerCAmelCase=24 , _lowerCAmelCase=16 , _lowerCAmelCase=4096 , _lowerCAmelCase="gelu" , _lowerCAmelCase=0.1 , _lowerCAmelCase=0.1 , _lowerCAmelCase=512 , _lowerCAmelCase=0.02 , _lowerCAmelCase=1E-12 , _lowerCAmelCase=0 , _lowerCAmelCase=2 , _lowerCAmelCase=1 , _lowerCAmelCase="absolute" , _lowerCAmelCase=True , **_lowerCAmelCase , ) -> Tuple:
super().__init__(pad_token_id=_lowerCAmelCase , bos_token_id=_lowerCAmelCase , eos_token_id=_lowerCAmelCase , **_lowerCAmelCase )
_lowerCAmelCase = vocab_size
_lowerCAmelCase = hidden_size
_lowerCAmelCase = num_hidden_layers
_lowerCAmelCase = num_attention_heads
_lowerCAmelCase = hidden_act
_lowerCAmelCase = intermediate_size
_lowerCAmelCase = hidden_dropout_prob
_lowerCAmelCase = attention_probs_dropout_prob
_lowerCAmelCase = max_position_embeddings
_lowerCAmelCase = initializer_range
_lowerCAmelCase = layer_norm_eps
_lowerCAmelCase = position_embedding_type
_lowerCAmelCase = use_cache
| 489
| 0
|
'''simple docstring'''
__lowercase = {
'''A''': ['''B''', '''C''', '''E'''],
'''B''': ['''A''', '''D''', '''E'''],
'''C''': ['''A''', '''F''', '''G'''],
'''D''': ['''B'''],
'''E''': ['''A''', '''B''', '''D'''],
'''F''': ['''C'''],
'''G''': ['''C'''],
}
def snake_case__ ( _A: dict , _A: Optional[Any] , _A: Dict ) -> list[str]:
'''simple docstring'''
lowerCAmelCase = set()
# keep track of all the paths to be checked
lowerCAmelCase = [[start]]
# return path if start is goal
if start == goal:
return [start]
# keeps looping until all possible paths have been checked
while queue:
# pop the first path from the queue
lowerCAmelCase = queue.pop(0 )
# get the last node from the path
lowerCAmelCase = path[-1]
if node not in explored:
lowerCAmelCase = graph[node]
# go through all neighbour nodes, construct a new path and
# push it into the queue
for neighbour in neighbours:
lowerCAmelCase = list(_A )
new_path.append(_A )
queue.append(_A )
# return path if neighbour is goal
if neighbour == goal:
return new_path
# mark node as explored
explored.add(_A )
# in case there's no path between the 2 nodes
return []
def snake_case__ ( _A: dict , _A: List[str] , _A: List[str] ) -> int:
'''simple docstring'''
if not graph or start not in graph or target not in graph:
return -1
if start == target:
return 0
lowerCAmelCase = [start]
lowerCAmelCase = set(_A )
# Keep tab on distances from `start` node.
lowerCAmelCase = {start: 0, target: -1}
while queue:
lowerCAmelCase = queue.pop(0 )
if node == target:
lowerCAmelCase = (
dist[node] if dist[target] == -1 else min(dist[target] , dist[node] )
)
for adjacent in graph[node]:
if adjacent not in visited:
visited.add(_A )
queue.append(_A )
lowerCAmelCase = dist[node] + 1
return dist[target]
if __name__ == "__main__":
print(bfs_shortest_path(demo_graph, '''G''', '''D''')) # returns ['G', 'C', 'A', 'B', 'D']
print(bfs_shortest_path_distance(demo_graph, '''G''', '''D''')) # returns 4
| 370
|
'''simple docstring'''
import json
import os
import re
import sys
import urllib.request
import requests
from bsa import BeautifulSoup
__lowercase = {
'''User-Agent''': '''Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'''
''' (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582'''
}
def snake_case__ ( _A: str = "dhaka" , _A: int = 5 ) -> int:
'''simple docstring'''
lowerCAmelCase = min(_A , 50 ) # Prevent abuse!
lowerCAmelCase = {
"""q""": query,
"""tbm""": """isch""",
"""hl""": """en""",
"""ijn""": """0""",
}
lowerCAmelCase = requests.get("""https://www.google.com/search""" , params=_A , headers=_A )
lowerCAmelCase = BeautifulSoup(html.text , """html.parser""" )
lowerCAmelCase = """""".join(
re.findall(r"""AF_initDataCallback\(([^<]+)\);""" , str(soup.select("""script""" ) ) ) )
lowerCAmelCase = json.dumps(_A )
lowerCAmelCase = json.loads(_A )
lowerCAmelCase = re.findall(
r"""\[\"GRID_STATE0\",null,\[\[1,\[0,\".*?\",(.*),\"All\",""" , _A , )
if not matched_google_image_data:
return 0
lowerCAmelCase = re.sub(
r"""\[\"(https\:\/\/encrypted-tbn0\.gstatic\.com\/images\?.*?)\",\d+,\d+\]""" , """""" , str(_A ) , )
lowerCAmelCase = re.findall(
r"""(?:'|,),\[\"(https:|http.*?)\",\d+,\d+\]""" , _A , )
for index, fixed_full_res_image in enumerate(_A ):
if index >= max_images:
return index
lowerCAmelCase = bytes(_A , """ascii""" ).decode(
"""unicode-escape""" )
lowerCAmelCase = bytes(_A , """ascii""" ).decode(
"""unicode-escape""" )
lowerCAmelCase = urllib.request.build_opener()
lowerCAmelCase = [
(
"""User-Agent""",
"""Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"""
""" (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582""",
)
]
urllib.request.install_opener(_A )
lowerCAmelCase = f"query_{query.replace(' ' , '_' )}"
if not os.path.exists(_A ):
os.makedirs(_A )
urllib.request.urlretrieve( # noqa: S310
_A , f"{path_name}/original_size_img_{index}.jpg" )
return index
if __name__ == "__main__":
try:
__lowercase = download_images_from_google_query(sys.argv[1])
print(f'{image_count} images were downloaded to disk.')
except IndexError:
print('''Please provide a search term.''')
raise
| 370
| 1
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase_ : List[Any] = {
'''configuration_roberta''': ['''ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RobertaConfig''', '''RobertaOnnxConfig'''],
'''tokenization_roberta''': ['''RobertaTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ : Optional[int] = ['''RobertaTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ : Union[str, Any] = [
'''ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''RobertaForCausalLM''',
'''RobertaForMaskedLM''',
'''RobertaForMultipleChoice''',
'''RobertaForQuestionAnswering''',
'''RobertaForSequenceClassification''',
'''RobertaForTokenClassification''',
'''RobertaModel''',
'''RobertaPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ : Any = [
'''TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFRobertaForCausalLM''',
'''TFRobertaForMaskedLM''',
'''TFRobertaForMultipleChoice''',
'''TFRobertaForQuestionAnswering''',
'''TFRobertaForSequenceClassification''',
'''TFRobertaForTokenClassification''',
'''TFRobertaMainLayer''',
'''TFRobertaModel''',
'''TFRobertaPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ : Union[str, Any] = [
'''FlaxRobertaForCausalLM''',
'''FlaxRobertaForMaskedLM''',
'''FlaxRobertaForMultipleChoice''',
'''FlaxRobertaForQuestionAnswering''',
'''FlaxRobertaForSequenceClassification''',
'''FlaxRobertaForTokenClassification''',
'''FlaxRobertaModel''',
'''FlaxRobertaPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
from .tokenization_roberta import RobertaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_roberta_fast import RobertaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roberta import (
ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
RobertaForCausalLM,
RobertaForMaskedLM,
RobertaForMultipleChoice,
RobertaForQuestionAnswering,
RobertaForSequenceClassification,
RobertaForTokenClassification,
RobertaModel,
RobertaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roberta import (
TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRobertaForCausalLM,
TFRobertaForMaskedLM,
TFRobertaForMultipleChoice,
TFRobertaForQuestionAnswering,
TFRobertaForSequenceClassification,
TFRobertaForTokenClassification,
TFRobertaMainLayer,
TFRobertaModel,
TFRobertaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roberta import (
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaModel,
FlaxRobertaPreTrainedModel,
)
else:
import sys
lowerCAmelCase_ : Any = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 378
|
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class UpperCamelCase_ ( a_ ):
_A : Optional[int] = ['image_processor', 'tokenizer']
_A : Optional[Any] = 'ViTImageProcessor'
_A : Tuple = ('CLIPTokenizer', 'CLIPTokenizerFast')
def __init__( self , snake_case__=None , snake_case__=None , **snake_case__ ) -> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase = None
if "feature_extractor" in kwargs:
warnings.warn(
"""The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
""" instead.""" , snake_case__ , )
UpperCAmelCase = kwargs.pop("""feature_extractor""" )
UpperCAmelCase = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("""You need to specify an `image_processor`.""" )
if tokenizer is None:
raise ValueError("""You need to specify a `tokenizer`.""" )
super().__init__(snake_case__ , snake_case__ )
def __call__( self , snake_case__=None , snake_case__=None , snake_case__=None , snake_case__=None , **snake_case__ ) -> List[str]:
"""simple docstring"""
if text is None and visual_prompt is None and images is None:
raise ValueError("""You have to specify either text, visual prompt or images.""" )
if text is not None and visual_prompt is not None:
raise ValueError("""You have to specify exactly one type of prompt. Either text or visual prompt.""" )
if text is not None:
UpperCAmelCase = self.tokenizer(snake_case__ , return_tensors=snake_case__ , **snake_case__ )
if visual_prompt is not None:
UpperCAmelCase = self.image_processor(snake_case__ , return_tensors=snake_case__ , **snake_case__ )
if images is not None:
UpperCAmelCase = self.image_processor(snake_case__ , return_tensors=snake_case__ , **snake_case__ )
if visual_prompt is not None and images is not None:
UpperCAmelCase = {
"""pixel_values""": image_features.pixel_values,
"""conditional_pixel_values""": prompt_features.pixel_values,
}
return encoding
elif text is not None and images is not None:
UpperCAmelCase = image_features.pixel_values
return encoding
elif text is not None:
return encoding
elif visual_prompt is not None:
UpperCAmelCase = {
"""conditional_pixel_values""": prompt_features.pixel_values,
}
return encoding
else:
return BatchEncoding(data=dict(**snake_case__ ) , tensor_type=snake_case__ )
def UpperCamelCase_ ( self , *snake_case__ , **snake_case__ ) -> Tuple:
"""simple docstring"""
return self.tokenizer.batch_decode(*snake_case__ , **snake_case__ )
def UpperCamelCase_ ( self , *snake_case__ , **snake_case__ ) -> Optional[int]:
"""simple docstring"""
return self.tokenizer.decode(*snake_case__ , **snake_case__ )
@property
def UpperCamelCase_ ( self ) -> Tuple:
"""simple docstring"""
warnings.warn(
"""`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.""" , snake_case__ , )
return self.image_processor_class
@property
def UpperCamelCase_ ( self ) -> Any:
"""simple docstring"""
warnings.warn(
"""`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.""" , snake_case__ , )
return self.image_processor
| 378
| 1
|
'''simple docstring'''
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE : Optional[int] = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE : Optional[Any] = {
"post_extract_proj": "feature_projection.projection",
"encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
"self_attn.k_proj": "encoder.layers.*.attention.k_proj",
"self_attn.v_proj": "encoder.layers.*.attention.v_proj",
"self_attn.q_proj": "encoder.layers.*.attention.q_proj",
"self_attn.out_proj": "encoder.layers.*.attention.out_proj",
"self_attn_layer_norm": "encoder.layers.*.layer_norm",
"fc1": "encoder.layers.*.feed_forward.intermediate_dense",
"fc2": "encoder.layers.*.feed_forward.output_dense",
"final_layer_norm": "encoder.layers.*.final_layer_norm",
"encoder.layer_norm": "encoder.layer_norm",
"encoder.layer_norm_for_extract": "layer_norm_for_extract",
"w2v_model.layer_norm": "feature_projection.layer_norm",
"quantizer.weight_proj": "quantizer.weight_proj",
"quantizer.vars": "quantizer.codevectors",
"project_q": "project_q",
"final_proj": "project_hid",
"w2v_encoder.proj": "lm_head",
"label_embs_concat": "label_embeddings_concat",
"mask_emb": "masked_spec_embed",
"spk_proj": "speaker_proj",
}
_SCREAMING_SNAKE_CASE : List[Any] = [
"lm_head",
"quantizer.weight_proj",
"quantizer.codevectors",
"project_q",
"project_hid",
"label_embeddings_concat",
"speaker_proj",
"layer_norm_for_extract",
]
def UpperCamelCase_( snake_case : Tuple , snake_case : List[Any] , snake_case : Optional[int] , snake_case : Union[str, Any] , snake_case : Tuple ):
'''simple docstring'''
for attribute in key.split("." ):
snake_case_ = getattr(snake_case , snake_case )
if weight_type is not None:
snake_case_ = getattr(snake_case , snake_case ).shape
else:
snake_case_ = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
f'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
f' {value.shape} for {full_name}' )
if weight_type == "weight":
snake_case_ = value
elif weight_type == "weight_g":
snake_case_ = value
elif weight_type == "weight_v":
snake_case_ = value
elif weight_type == "bias":
snake_case_ = value
else:
snake_case_ = value
logger.info(f'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.' )
def UpperCamelCase_( snake_case : List[Any] , snake_case : Optional[int] ):
'''simple docstring'''
snake_case_ = []
snake_case_ = fairseq_model.state_dict()
snake_case_ = hf_model.unispeech_sat.feature_extractor
for name, value in fairseq_dict.items():
snake_case_ = False
if "conv_layers" in name:
load_conv_layer(
snake_case , snake_case , snake_case , snake_case , hf_model.config.feat_extract_norm == "group" , )
snake_case_ = True
else:
for key, mapped_key in MAPPING.items():
snake_case_ = "unispeech_sat." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split("w2v_model." )[-1] == name.split("." )[0]:
if "layer_norm_for_extract" in name and (".".join(name.split("." )[:-1] ) != key):
# special case since naming is very similar
continue
snake_case_ = True
if "*" in mapped_key:
snake_case_ = name.split(snake_case )[0].split("." )[-2]
snake_case_ = mapped_key.replace("*" , snake_case )
if "weight_g" in name:
snake_case_ = "weight_g"
elif "weight_v" in name:
snake_case_ = "weight_v"
elif "bias" in name:
snake_case_ = "bias"
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
snake_case_ = "weight"
else:
snake_case_ = None
set_recursively(snake_case , snake_case , snake_case , snake_case , snake_case )
continue
if not is_used:
unused_weights.append(snake_case )
logger.warning(f'Unused weights: {unused_weights}' )
def UpperCamelCase_( snake_case : str , snake_case : Tuple , snake_case : Optional[int] , snake_case : Any , snake_case : List[Any] ):
'''simple docstring'''
snake_case_ = full_name.split("conv_layers." )[-1]
snake_case_ = name.split("." )
snake_case_ = int(items[0] )
snake_case_ = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.' )
snake_case_ = value
logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.' )
snake_case_ = value
logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.' )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor[layer_id].layer_norm.bias.data.shape} was found.' )
snake_case_ = value
logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.' )
snake_case_ = value
logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
else:
unused_weights.append(snake_case )
@torch.no_grad()
def UpperCamelCase_( snake_case : str , snake_case : Tuple , snake_case : List[str]=None , snake_case : str=None , snake_case : List[str]=True ):
'''simple docstring'''
if config_path is not None:
snake_case_ = UniSpeechSatConfig.from_pretrained(snake_case )
else:
snake_case_ = UniSpeechSatConfig()
snake_case_ = ""
if is_finetuned:
snake_case_ = UniSpeechSatForCTC(snake_case )
else:
snake_case_ = UniSpeechSatForPreTraining(snake_case )
snake_case_ , snake_case_ , snake_case_ = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={"data": "/".join(dict_path.split("/" )[:-1] )} )
snake_case_ = model[0].eval()
recursively_load_weights(snake_case , snake_case )
hf_wavavec.save_pretrained(snake_case )
if __name__ == "__main__":
_SCREAMING_SNAKE_CASE : Optional[Any] = argparse.ArgumentParser()
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
parser.add_argument(
"--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
)
_SCREAMING_SNAKE_CASE : List[Any] = parser.parse_args()
convert_unispeech_sat_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 400
|
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block
@dataclass
class _snake_case ( lowercase_ ):
lowerCAmelCase_ : torch.FloatTensor
class _snake_case ( nn.Module ):
def __init__( self , a__=3 , a__=3 , a__=("DownEncoderBlock2D",) , a__=(64,) , a__=2 , a__=32 , a__="silu" , a__=True , ) -> Union[str, Any]:
'''simple docstring'''
super().__init__()
snake_case_ = layers_per_block
snake_case_ = torch.nn.Convad(
a__ , block_out_channels[0] , kernel_size=3 , stride=1 , padding=1 , )
snake_case_ = None
snake_case_ = nn.ModuleList([] )
# down
snake_case_ = block_out_channels[0]
for i, down_block_type in enumerate(a__ ):
snake_case_ = output_channel
snake_case_ = block_out_channels[i]
snake_case_ = i == len(a__ ) - 1
snake_case_ = get_down_block(
a__ , num_layers=self.layers_per_block , in_channels=a__ , out_channels=a__ , add_downsample=not is_final_block , resnet_eps=1e-6 , downsample_padding=0 , resnet_act_fn=a__ , resnet_groups=a__ , attention_head_dim=a__ , temb_channels=a__ , )
self.down_blocks.append(a__ )
# mid
snake_case_ = UNetMidBlockaD(
in_channels=block_out_channels[-1] , resnet_eps=1e-6 , resnet_act_fn=a__ , output_scale_factor=1 , resnet_time_scale_shift="default" , attention_head_dim=block_out_channels[-1] , resnet_groups=a__ , temb_channels=a__ , )
# out
snake_case_ = nn.GroupNorm(num_channels=block_out_channels[-1] , num_groups=a__ , eps=1e-6 )
snake_case_ = nn.SiLU()
snake_case_ = 2 * out_channels if double_z else out_channels
snake_case_ = nn.Convad(block_out_channels[-1] , a__ , 3 , padding=1 )
snake_case_ = False
def lowerCAmelCase__ ( self , a__ ) -> Tuple:
'''simple docstring'''
snake_case_ = x
snake_case_ = self.conv_in(a__ )
if self.training and self.gradient_checkpointing:
def create_custom_forward(a__ ):
def custom_forward(*a__ ):
return module(*a__ )
return custom_forward
# down
if is_torch_version(">=" , "1.11.0" ):
for down_block in self.down_blocks:
snake_case_ = torch.utils.checkpoint.checkpoint(
create_custom_forward(a__ ) , a__ , use_reentrant=a__ )
# middle
snake_case_ = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block ) , a__ , use_reentrant=a__ )
else:
for down_block in self.down_blocks:
snake_case_ = torch.utils.checkpoint.checkpoint(create_custom_forward(a__ ) , a__ )
# middle
snake_case_ = torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block ) , a__ )
else:
# down
for down_block in self.down_blocks:
snake_case_ = down_block(a__ )
# middle
snake_case_ = self.mid_block(a__ )
# post-process
snake_case_ = self.conv_norm_out(a__ )
snake_case_ = self.conv_act(a__ )
snake_case_ = self.conv_out(a__ )
return sample
class _snake_case ( nn.Module ):
def __init__( self , a__=3 , a__=3 , a__=("UpDecoderBlock2D",) , a__=(64,) , a__=2 , a__=32 , a__="silu" , a__="group" , ) -> int:
'''simple docstring'''
super().__init__()
snake_case_ = layers_per_block
snake_case_ = nn.Convad(
a__ , block_out_channels[-1] , kernel_size=3 , stride=1 , padding=1 , )
snake_case_ = None
snake_case_ = nn.ModuleList([] )
snake_case_ = in_channels if norm_type == "spatial" else None
# mid
snake_case_ = UNetMidBlockaD(
in_channels=block_out_channels[-1] , resnet_eps=1e-6 , resnet_act_fn=a__ , output_scale_factor=1 , resnet_time_scale_shift="default" if norm_type == "group" else norm_type , attention_head_dim=block_out_channels[-1] , resnet_groups=a__ , temb_channels=a__ , )
# up
snake_case_ = list(reversed(a__ ) )
snake_case_ = reversed_block_out_channels[0]
for i, up_block_type in enumerate(a__ ):
snake_case_ = output_channel
snake_case_ = reversed_block_out_channels[i]
snake_case_ = i == len(a__ ) - 1
snake_case_ = get_up_block(
a__ , num_layers=self.layers_per_block + 1 , in_channels=a__ , out_channels=a__ , prev_output_channel=a__ , add_upsample=not is_final_block , resnet_eps=1e-6 , resnet_act_fn=a__ , resnet_groups=a__ , attention_head_dim=a__ , temb_channels=a__ , resnet_time_scale_shift=a__ , )
self.up_blocks.append(a__ )
snake_case_ = output_channel
# out
if norm_type == "spatial":
snake_case_ = SpatialNorm(block_out_channels[0] , a__ )
else:
snake_case_ = nn.GroupNorm(num_channels=block_out_channels[0] , num_groups=a__ , eps=1e-6 )
snake_case_ = nn.SiLU()
snake_case_ = nn.Convad(block_out_channels[0] , a__ , 3 , padding=1 )
snake_case_ = False
def lowerCAmelCase__ ( self , a__ , a__=None ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ = z
snake_case_ = self.conv_in(a__ )
snake_case_ = next(iter(self.up_blocks.parameters() ) ).dtype
if self.training and self.gradient_checkpointing:
def create_custom_forward(a__ ):
def custom_forward(*a__ ):
return module(*a__ )
return custom_forward
if is_torch_version(">=" , "1.11.0" ):
# middle
snake_case_ = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block ) , a__ , a__ , use_reentrant=a__ )
snake_case_ = sample.to(a__ )
# up
for up_block in self.up_blocks:
snake_case_ = torch.utils.checkpoint.checkpoint(
create_custom_forward(a__ ) , a__ , a__ , use_reentrant=a__ )
else:
# middle
snake_case_ = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block ) , a__ , a__ )
snake_case_ = sample.to(a__ )
# up
for up_block in self.up_blocks:
snake_case_ = torch.utils.checkpoint.checkpoint(create_custom_forward(a__ ) , a__ , a__ )
else:
# middle
snake_case_ = self.mid_block(a__ , a__ )
snake_case_ = sample.to(a__ )
# up
for up_block in self.up_blocks:
snake_case_ = up_block(a__ , a__ )
# post-process
if latent_embeds is None:
snake_case_ = self.conv_norm_out(a__ )
else:
snake_case_ = self.conv_norm_out(a__ , a__ )
snake_case_ = self.conv_act(a__ )
snake_case_ = self.conv_out(a__ )
return sample
class _snake_case ( nn.Module ):
def __init__( self , a__ , a__ , a__ , a__=None , a__="random" , a__=False , a__=True ) -> Optional[Any]:
'''simple docstring'''
super().__init__()
snake_case_ = n_e
snake_case_ = vq_embed_dim
snake_case_ = beta
snake_case_ = legacy
snake_case_ = nn.Embedding(self.n_e , self.vq_embed_dim )
self.embedding.weight.data.uniform_(-1.0 / self.n_e , 1.0 / self.n_e )
snake_case_ = remap
if self.remap is not None:
self.register_buffer("used" , torch.tensor(np.load(self.remap ) ) )
snake_case_ = self.used.shape[0]
snake_case_ = unknown_index # "random" or "extra" or integer
if self.unknown_index == "extra":
snake_case_ = self.re_embed
snake_case_ = self.re_embed + 1
print(
F'Remapping {self.n_e} indices to {self.re_embed} indices. '
F'Using {self.unknown_index} for unknown indices.' )
else:
snake_case_ = n_e
snake_case_ = sane_index_shape
def lowerCAmelCase__ ( self , a__ ) -> Optional[int]:
'''simple docstring'''
snake_case_ = inds.shape
assert len(a__ ) > 1
snake_case_ = inds.reshape(ishape[0] , -1 )
snake_case_ = self.used.to(a__ )
snake_case_ = (inds[:, :, None] == used[None, None, ...]).long()
snake_case_ = match.argmax(-1 )
snake_case_ = match.sum(2 ) < 1
if self.unknown_index == "random":
snake_case_ = torch.randint(0 , self.re_embed , size=new[unknown].shape ).to(device=new.device )
else:
snake_case_ = self.unknown_index
return new.reshape(a__ )
def lowerCAmelCase__ ( self , a__ ) -> str:
'''simple docstring'''
snake_case_ = inds.shape
assert len(a__ ) > 1
snake_case_ = inds.reshape(ishape[0] , -1 )
snake_case_ = self.used.to(a__ )
if self.re_embed > self.used.shape[0]: # extra token
snake_case_ = 0 # simply set to zero
snake_case_ = torch.gather(used[None, :][inds.shape[0] * [0], :] , 1 , a__ )
return back.reshape(a__ )
def lowerCAmelCase__ ( self , a__ ) -> str:
'''simple docstring'''
snake_case_ = z.permute(0 , 2 , 3 , 1 ).contiguous()
snake_case_ = z.view(-1 , self.vq_embed_dim )
# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
snake_case_ = torch.argmin(torch.cdist(a__ , self.embedding.weight ) , dim=1 )
snake_case_ = self.embedding(a__ ).view(z.shape )
snake_case_ = None
snake_case_ = None
# compute loss for embedding
if not self.legacy:
snake_case_ = self.beta * torch.mean((z_q.detach() - z) ** 2 ) + torch.mean((z_q - z.detach()) ** 2 )
else:
snake_case_ = torch.mean((z_q.detach() - z) ** 2 ) + self.beta * torch.mean((z_q - z.detach()) ** 2 )
# preserve gradients
snake_case_ = z + (z_q - z).detach()
# reshape back to match original input shape
snake_case_ = z_q.permute(0 , 3 , 1 , 2 ).contiguous()
if self.remap is not None:
snake_case_ = min_encoding_indices.reshape(z.shape[0] , -1 ) # add batch axis
snake_case_ = self.remap_to_used(a__ )
snake_case_ = min_encoding_indices.reshape(-1 , 1 ) # flatten
if self.sane_index_shape:
snake_case_ = min_encoding_indices.reshape(z_q.shape[0] , z_q.shape[2] , z_q.shape[3] )
return z_q, loss, (perplexity, min_encodings, min_encoding_indices)
def lowerCAmelCase__ ( self , a__ , a__ ) -> List[str]:
'''simple docstring'''
if self.remap is not None:
snake_case_ = indices.reshape(shape[0] , -1 ) # add batch axis
snake_case_ = self.unmap_to_all(a__ )
snake_case_ = indices.reshape(-1 ) # flatten again
# get quantized latent vectors
snake_case_ = self.embedding(a__ )
if shape is not None:
snake_case_ = z_q.view(a__ )
# reshape back to match original input shape
snake_case_ = z_q.permute(0 , 3 , 1 , 2 ).contiguous()
return z_q
class _snake_case ( lowercase_ ):
def __init__( self , a__ , a__=False ) -> Optional[int]:
'''simple docstring'''
snake_case_ = parameters
snake_case_ , snake_case_ = torch.chunk(a__ , 2 , dim=1 )
snake_case_ = torch.clamp(self.logvar , -3_0.0 , 2_0.0 )
snake_case_ = deterministic
snake_case_ = torch.exp(0.5 * self.logvar )
snake_case_ = torch.exp(self.logvar )
if self.deterministic:
snake_case_ = snake_case_ = torch.zeros_like(
self.mean , device=self.parameters.device , dtype=self.parameters.dtype )
def lowerCAmelCase__ ( self , a__ = None ) -> torch.FloatTensor:
'''simple docstring'''
snake_case_ = randn_tensor(
self.mean.shape , generator=a__ , device=self.parameters.device , dtype=self.parameters.dtype )
snake_case_ = self.mean + self.std * sample
return x
def lowerCAmelCase__ ( self , a__=None ) -> List[str]:
'''simple docstring'''
if self.deterministic:
return torch.Tensor([0.0] )
else:
if other is None:
return 0.5 * torch.sum(torch.pow(self.mean , 2 ) + self.var - 1.0 - self.logvar , dim=[1, 2, 3] )
else:
return 0.5 * torch.sum(
torch.pow(self.mean - other.mean , 2 ) / other.var
+ self.var / other.var
- 1.0
- self.logvar
+ other.logvar , dim=[1, 2, 3] , )
def lowerCAmelCase__ ( self , a__ , a__=[1, 2, 3] ) -> Optional[int]:
'''simple docstring'''
if self.deterministic:
return torch.Tensor([0.0] )
snake_case_ = np.log(2.0 * np.pi )
return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean , 2 ) / self.var , dim=a__ )
def lowerCAmelCase__ ( self ) -> Any:
'''simple docstring'''
return self.mean
| 400
| 1
|
"""simple docstring"""
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
DPMSolverMultistepScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
PIL_INTERPOLATION,
randn_tensor,
)
def lowercase_ ( _lowerCamelCase: Optional[int] , _lowerCamelCase: List[str] , _lowerCamelCase: Dict ) -> Any:
'''simple docstring'''
if isinstance(_lowerCamelCase , torch.Tensor ):
return image
elif isinstance(_lowerCamelCase , PIL.Image.Image ):
__lowerCamelCase : Tuple = [image]
if isinstance(image[0] , PIL.Image.Image ):
__lowerCamelCase : Dict = [np.array(i.resize((w, h) , resample=PIL_INTERPOLATION["lanczos"] ) )[None, :] for i in image]
__lowerCamelCase : Dict = np.concatenate(_lowerCamelCase , axis=0 )
__lowerCamelCase : List[Any] = np.array(_lowerCamelCase ).astype(np.floataa ) / 255.0
__lowerCamelCase : int = image.transpose(0 , 3 , 1 , 2 )
__lowerCamelCase : Any = 2.0 * image - 1.0
__lowerCamelCase : Optional[Any] = torch.from_numpy(_lowerCamelCase )
elif isinstance(image[0] , torch.Tensor ):
__lowerCamelCase : Optional[int] = torch.cat(_lowerCamelCase , dim=0 )
return image
def lowercase_ ( _lowerCamelCase: Any , _lowerCamelCase: Tuple , _lowerCamelCase: Dict , _lowerCamelCase: List[str]=0.9995 ) -> List[Any]:
'''simple docstring'''
if not isinstance(_lowerCamelCase , np.ndarray ):
__lowerCamelCase : Optional[Any] = True
__lowerCamelCase : Optional[Any] = va.device
__lowerCamelCase : str = va.cpu().numpy()
__lowerCamelCase : Dict = va.cpu().numpy()
__lowerCamelCase : Optional[int] = np.sum(va * va / (np.linalg.norm(_lowerCamelCase ) * np.linalg.norm(_lowerCamelCase )) )
if np.abs(_lowerCamelCase ) > DOT_THRESHOLD:
__lowerCamelCase : Union[str, Any] = (1 - t) * va + t * va
else:
__lowerCamelCase : List[str] = np.arccos(_lowerCamelCase )
__lowerCamelCase : Optional[Any] = np.sin(_lowerCamelCase )
__lowerCamelCase : List[Any] = theta_a * t
__lowerCamelCase : Dict = np.sin(_lowerCamelCase )
__lowerCamelCase : Union[str, Any] = np.sin(theta_a - theta_t ) / sin_theta_a
__lowerCamelCase : List[str] = sin_theta_t / sin_theta_a
__lowerCamelCase : int = sa * va + sa * va
if inputs_are_torch:
__lowerCamelCase : List[Any] = torch.from_numpy(_lowerCamelCase ).to(_lowerCamelCase )
return va
def lowercase_ ( _lowerCamelCase: Tuple , _lowerCamelCase: List[Any] ) -> int:
'''simple docstring'''
__lowerCamelCase : Any = F.normalize(_lowerCamelCase , dim=-1 )
__lowerCamelCase : str = F.normalize(_lowerCamelCase , dim=-1 )
return (x - y).norm(dim=-1 ).div(2 ).arcsin().pow(2 ).mul(2 )
def lowercase_ ( _lowerCamelCase: Any , _lowerCamelCase: List[str] ) -> Optional[int]:
'''simple docstring'''
for param in model.parameters():
__lowerCamelCase : List[Any] = value
class _snake_case ( a__ ):
def __init__( self : Optional[Any] , UpperCAmelCase : AutoencoderKL , UpperCAmelCase : CLIPTextModel , UpperCAmelCase : CLIPModel , UpperCAmelCase : CLIPTokenizer , UpperCAmelCase : UNetaDConditionModel , UpperCAmelCase : Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler] , UpperCAmelCase : CLIPFeatureExtractor , UpperCAmelCase : Optional[Any]=None , UpperCAmelCase : List[str]=None , UpperCAmelCase : int=None , ):
super().__init__()
self.register_modules(
vae=UpperCAmelCase , text_encoder=UpperCAmelCase , clip_model=UpperCAmelCase , tokenizer=UpperCAmelCase , unet=UpperCAmelCase , scheduler=UpperCAmelCase , feature_extractor=UpperCAmelCase , coca_model=UpperCAmelCase , coca_tokenizer=UpperCAmelCase , coca_transform=UpperCAmelCase , )
__lowerCamelCase : List[str] = (
feature_extractor.size
if isinstance(feature_extractor.size , UpperCAmelCase )
else feature_extractor.size["shortest_edge"]
)
__lowerCamelCase : Any = transforms.Normalize(mean=feature_extractor.image_mean , std=feature_extractor.image_std )
set_requires_grad(self.text_encoder , UpperCAmelCase )
set_requires_grad(self.clip_model , UpperCAmelCase )
def lowerCamelCase__ ( self : List[Any] , UpperCAmelCase : Optional[Union[str, int]] = "auto" ):
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
__lowerCamelCase : List[Any] = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(UpperCAmelCase )
def lowerCamelCase__ ( self : List[Any] ):
self.enable_attention_slicing(UpperCAmelCase )
def lowerCamelCase__ ( self : str ):
set_requires_grad(self.vae , UpperCAmelCase )
def lowerCamelCase__ ( self : str ):
set_requires_grad(self.vae , UpperCAmelCase )
def lowerCamelCase__ ( self : List[Any] ):
set_requires_grad(self.unet , UpperCAmelCase )
def lowerCamelCase__ ( self : int ):
set_requires_grad(self.unet , UpperCAmelCase )
def lowerCamelCase__ ( self : List[str] , UpperCAmelCase : Dict , UpperCAmelCase : str , UpperCAmelCase : Any ):
# get the original timestep using init_timestep
__lowerCamelCase : str = min(int(num_inference_steps * strength ) , UpperCAmelCase )
__lowerCamelCase : Optional[int] = max(num_inference_steps - init_timestep , 0 )
__lowerCamelCase : List[str] = self.scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
def lowerCamelCase__ ( self : Union[str, Any] , UpperCAmelCase : Tuple , UpperCAmelCase : Optional[Any] , UpperCAmelCase : List[Any] , UpperCAmelCase : List[str] , UpperCAmelCase : Dict , UpperCAmelCase : Any=None ):
if not isinstance(UpperCAmelCase , torch.Tensor ):
raise ValueError(F"""`image` has to be of type `torch.Tensor` but is {type(UpperCAmelCase )}""" )
__lowerCamelCase : Tuple = image.to(device=UpperCAmelCase , dtype=UpperCAmelCase )
if isinstance(UpperCAmelCase , UpperCAmelCase ):
__lowerCamelCase : List[Any] = [
self.vae.encode(image[i : i + 1] ).latent_dist.sample(generator[i] ) for i in range(UpperCAmelCase )
]
__lowerCamelCase : int = torch.cat(UpperCAmelCase , dim=0 )
else:
__lowerCamelCase : int = self.vae.encode(UpperCAmelCase ).latent_dist.sample(UpperCAmelCase )
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
__lowerCamelCase : Any = 0.1_8_2_1_5 * init_latents
__lowerCamelCase : List[Any] = init_latents.repeat_interleave(UpperCAmelCase , dim=0 )
__lowerCamelCase : Union[str, Any] = randn_tensor(init_latents.shape , generator=UpperCAmelCase , device=UpperCAmelCase , dtype=UpperCAmelCase )
# get latents
__lowerCamelCase : List[str] = self.scheduler.add_noise(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
__lowerCamelCase : Any = init_latents
return latents
def lowerCamelCase__ ( self : str , UpperCAmelCase : Tuple ):
__lowerCamelCase : List[Any] = self.coca_transform(UpperCAmelCase ).unsqueeze(0 )
with torch.no_grad(), torch.cuda.amp.autocast():
__lowerCamelCase : str = self.coca_model.generate(transformed_image.to(device=self.device , dtype=self.coca_model.dtype ) )
__lowerCamelCase : Optional[int] = self.coca_tokenizer.decode(generated[0].cpu().numpy() )
return generated.split("<end_of_text>" )[0].replace("<start_of_text>" , "" ).rstrip(" .," )
def lowerCamelCase__ ( self : List[Any] , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Union[str, Any] ):
__lowerCamelCase : str = self.feature_extractor.preprocess(UpperCAmelCase )
__lowerCamelCase : int = torch.from_numpy(clip_image_input["pixel_values"][0] ).unsqueeze(0 ).to(self.device ).half()
__lowerCamelCase : Optional[int] = self.clip_model.get_image_features(UpperCAmelCase )
__lowerCamelCase : Any = image_embeddings_clip / image_embeddings_clip.norm(p=2 , dim=-1 , keepdim=UpperCAmelCase )
__lowerCamelCase : Optional[Any] = image_embeddings_clip.repeat_interleave(UpperCAmelCase , dim=0 )
return image_embeddings_clip
@torch.enable_grad()
def lowerCamelCase__ ( self : Optional[Any] , UpperCAmelCase : List[str] , UpperCAmelCase : Optional[int] , UpperCAmelCase : Optional[int] , UpperCAmelCase : Optional[Any] , UpperCAmelCase : List[Any] , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Any , ):
__lowerCamelCase : List[Any] = latents.detach().requires_grad_()
__lowerCamelCase : int = self.scheduler.scale_model_input(UpperCAmelCase , UpperCAmelCase )
# predict the noise residual
__lowerCamelCase : Any = self.unet(UpperCAmelCase , UpperCAmelCase , encoder_hidden_states=UpperCAmelCase ).sample
if isinstance(self.scheduler , (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler) ):
__lowerCamelCase : int = self.scheduler.alphas_cumprod[timestep]
__lowerCamelCase : Optional[Any] = 1 - alpha_prod_t
# compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
__lowerCamelCase : Optional[Any] = (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5
__lowerCamelCase : Union[str, Any] = torch.sqrt(UpperCAmelCase )
__lowerCamelCase : Tuple = pred_original_sample * (fac) + latents * (1 - fac)
elif isinstance(self.scheduler , UpperCAmelCase ):
__lowerCamelCase : List[Any] = self.scheduler.sigmas[index]
__lowerCamelCase : Optional[Any] = latents - sigma * noise_pred
else:
raise ValueError(F"""scheduler type {type(self.scheduler )} not supported""" )
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
__lowerCamelCase : Union[str, Any] = 1 / 0.1_8_2_1_5 * sample
__lowerCamelCase : List[Any] = self.vae.decode(UpperCAmelCase ).sample
__lowerCamelCase : Dict = (image / 2 + 0.5).clamp(0 , 1 )
__lowerCamelCase : Dict = transforms.Resize(self.feature_extractor_size )(UpperCAmelCase )
__lowerCamelCase : str = self.normalize(UpperCAmelCase ).to(latents.dtype )
__lowerCamelCase : str = self.clip_model.get_image_features(UpperCAmelCase )
__lowerCamelCase : str = image_embeddings_clip / image_embeddings_clip.norm(p=2 , dim=-1 , keepdim=UpperCAmelCase )
__lowerCamelCase : Dict = spherical_dist_loss(UpperCAmelCase , UpperCAmelCase ).mean() * clip_guidance_scale
__lowerCamelCase : Union[str, Any] = -torch.autograd.grad(UpperCAmelCase , UpperCAmelCase )[0]
if isinstance(self.scheduler , UpperCAmelCase ):
__lowerCamelCase : Any = latents.detach() + grads * (sigma**2)
__lowerCamelCase : List[str] = noise_pred_original
else:
__lowerCamelCase : Optional[Any] = noise_pred_original - torch.sqrt(UpperCAmelCase ) * grads
return noise_pred, latents
@torch.no_grad()
def __call__( self : Union[str, Any] , UpperCAmelCase : Union[torch.FloatTensor, PIL.Image.Image] , UpperCAmelCase : Union[torch.FloatTensor, PIL.Image.Image] , UpperCAmelCase : Optional[str] = None , UpperCAmelCase : Optional[str] = None , UpperCAmelCase : Optional[int] = 512 , UpperCAmelCase : Optional[int] = 512 , UpperCAmelCase : float = 0.6 , UpperCAmelCase : Optional[int] = 50 , UpperCAmelCase : Optional[float] = 7.5 , UpperCAmelCase : Optional[int] = 1 , UpperCAmelCase : float = 0.0 , UpperCAmelCase : Optional[float] = 100 , UpperCAmelCase : Optional[torch.Generator] = None , UpperCAmelCase : Optional[str] = "pil" , UpperCAmelCase : bool = True , UpperCAmelCase : float = 0.8 , UpperCAmelCase : float = 0.1 , UpperCAmelCase : float = 0.1 , ):
if isinstance(UpperCAmelCase , UpperCAmelCase ) and len(UpperCAmelCase ) != batch_size:
raise ValueError(F"""You have passed {batch_size} batch_size, but only {len(UpperCAmelCase )} generators.""" )
if height % 8 != 0 or width % 8 != 0:
raise ValueError(F"""`height` and `width` have to be divisible by 8 but are {height} and {width}.""" )
if isinstance(UpperCAmelCase , torch.Generator ) and batch_size > 1:
__lowerCamelCase : List[Any] = [generator] + [None] * (batch_size - 1)
__lowerCamelCase : Optional[int] = [
("model", self.coca_model is None),
("tokenizer", self.coca_tokenizer is None),
("transform", self.coca_transform is None),
]
__lowerCamelCase : Optional[Any] = [x[0] for x in coca_is_none if x[1]]
__lowerCamelCase : Tuple = ", ".join(UpperCAmelCase )
# generate prompts with coca model if prompt is None
if content_prompt is None:
if len(UpperCAmelCase ):
raise ValueError(
F"""Content prompt is None and CoCa [{coca_is_none_str}] is None."""
F"""Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.""" )
__lowerCamelCase : str = self.get_image_description(UpperCAmelCase )
if style_prompt is None:
if len(UpperCAmelCase ):
raise ValueError(
F"""Style prompt is None and CoCa [{coca_is_none_str}] is None."""
F""" Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.""" )
__lowerCamelCase : Optional[int] = self.get_image_description(UpperCAmelCase )
# get prompt text embeddings for content and style
__lowerCamelCase : str = self.tokenizer(
UpperCAmelCase , padding="max_length" , max_length=self.tokenizer.model_max_length , truncation=UpperCAmelCase , return_tensors="pt" , )
__lowerCamelCase : Dict = self.text_encoder(content_text_input.input_ids.to(self.device ) )[0]
__lowerCamelCase : Optional[Any] = self.tokenizer(
UpperCAmelCase , padding="max_length" , max_length=self.tokenizer.model_max_length , truncation=UpperCAmelCase , return_tensors="pt" , )
__lowerCamelCase : List[Any] = self.text_encoder(style_text_input.input_ids.to(self.device ) )[0]
__lowerCamelCase : List[str] = slerp(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
# duplicate text embeddings for each generation per prompt
__lowerCamelCase : Optional[int] = text_embeddings.repeat_interleave(UpperCAmelCase , dim=0 )
# set timesteps
__lowerCamelCase : Tuple = "offset" in set(inspect.signature(self.scheduler.set_timesteps ).parameters.keys() )
__lowerCamelCase : Optional[int] = {}
if accepts_offset:
__lowerCamelCase : Any = 1
self.scheduler.set_timesteps(UpperCAmelCase , **UpperCAmelCase )
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
self.scheduler.timesteps.to(self.device )
__lowerCamelCase , __lowerCamelCase : Union[str, Any] = self.get_timesteps(UpperCAmelCase , UpperCAmelCase , self.device )
__lowerCamelCase : Optional[int] = timesteps[:1].repeat(UpperCAmelCase )
# Preprocess image
__lowerCamelCase : int = preprocess(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
__lowerCamelCase : Optional[Any] = self.prepare_latents(
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , text_embeddings.dtype , self.device , UpperCAmelCase )
__lowerCamelCase : Optional[int] = preprocess(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
__lowerCamelCase : Optional[Any] = self.prepare_latents(
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , text_embeddings.dtype , self.device , UpperCAmelCase )
__lowerCamelCase : Dict = slerp(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
if clip_guidance_scale > 0:
__lowerCamelCase : str = self.get_clip_image_embeddings(UpperCAmelCase , UpperCAmelCase )
__lowerCamelCase : Any = self.get_clip_image_embeddings(UpperCAmelCase , UpperCAmelCase )
__lowerCamelCase : str = slerp(
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
__lowerCamelCase : Optional[int] = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
__lowerCamelCase : Dict = content_text_input.input_ids.shape[-1]
__lowerCamelCase : List[Any] = self.tokenizer([""] , padding="max_length" , max_length=UpperCAmelCase , return_tensors="pt" )
__lowerCamelCase : Optional[int] = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# duplicate unconditional embeddings for each generation per prompt
__lowerCamelCase : str = uncond_embeddings.repeat_interleave(UpperCAmelCase , dim=0 )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
__lowerCamelCase : str = torch.cat([uncond_embeddings, text_embeddings] )
# get the initial random noise unless the user supplied it
# Unlike in other pipelines, latents need to be generated in the target device
# for 1-to-1 results reproducibility with the CompVis implementation.
# However this currently doesn't work in `mps`.
__lowerCamelCase : Any = (batch_size, self.unet.config.in_channels, height // 8, width // 8)
__lowerCamelCase : Optional[int] = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not work reproducibly on mps
__lowerCamelCase : str = torch.randn(UpperCAmelCase , generator=UpperCAmelCase , device="cpu" , dtype=UpperCAmelCase ).to(
self.device )
else:
__lowerCamelCase : Optional[Any] = torch.randn(UpperCAmelCase , generator=UpperCAmelCase , device=self.device , dtype=UpperCAmelCase )
else:
if latents.shape != latents_shape:
raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}""" )
__lowerCamelCase : Dict = latents.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
__lowerCamelCase : Optional[Any] = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
__lowerCamelCase : Any = "eta" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
__lowerCamelCase : int = {}
if accepts_eta:
__lowerCamelCase : Union[str, Any] = eta
# check if the scheduler accepts generator
__lowerCamelCase : Optional[int] = "generator" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
if accepts_generator:
__lowerCamelCase : str = generator
with self.progress_bar(total=UpperCAmelCase ):
for i, t in enumerate(UpperCAmelCase ):
# expand the latents if we are doing classifier free guidance
__lowerCamelCase : Any = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
__lowerCamelCase : Union[str, Any] = self.scheduler.scale_model_input(UpperCAmelCase , UpperCAmelCase )
# predict the noise residual
__lowerCamelCase : Any = self.unet(UpperCAmelCase , UpperCAmelCase , encoder_hidden_states=UpperCAmelCase ).sample
# perform classifier free guidance
if do_classifier_free_guidance:
__lowerCamelCase , __lowerCamelCase : Tuple = noise_pred.chunk(2 )
__lowerCamelCase : Tuple = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# perform clip guidance
if clip_guidance_scale > 0:
__lowerCamelCase : Dict = (
text_embeddings.chunk(2 )[1] if do_classifier_free_guidance else text_embeddings
)
__lowerCamelCase , __lowerCamelCase : List[Any] = self.cond_fn(
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , )
# compute the previous noisy sample x_t -> x_t-1
__lowerCamelCase : Any = self.scheduler.step(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , **UpperCAmelCase ).prev_sample
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
__lowerCamelCase : int = 1 / 0.1_8_2_1_5 * latents
__lowerCamelCase : List[Any] = self.vae.decode(UpperCAmelCase ).sample
__lowerCamelCase : Union[str, Any] = (image / 2 + 0.5).clamp(0 , 1 )
__lowerCamelCase : str = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
__lowerCamelCase : int = self.numpy_to_pil(UpperCAmelCase )
if not return_dict:
return (image, None)
return StableDiffusionPipelineOutput(images=UpperCAmelCase , nsfw_content_detected=UpperCAmelCase )
| 366
|
"""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_funnel import FunnelTokenizer
__A = logging.get_logger(__name__)
__A = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}
__A = [
'''small''',
'''small-base''',
'''medium''',
'''medium-base''',
'''intermediate''',
'''intermediate-base''',
'''large''',
'''large-base''',
'''xlarge''',
'''xlarge-base''',
]
__A = {
'''vocab_file''': {
'''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/vocab.txt''',
'''funnel-transformer/small-base''': '''https://huggingface.co/funnel-transformer/small-base/resolve/main/vocab.txt''',
'''funnel-transformer/medium''': '''https://huggingface.co/funnel-transformer/medium/resolve/main/vocab.txt''',
'''funnel-transformer/medium-base''': (
'''https://huggingface.co/funnel-transformer/medium-base/resolve/main/vocab.txt'''
),
'''funnel-transformer/intermediate''': (
'''https://huggingface.co/funnel-transformer/intermediate/resolve/main/vocab.txt'''
),
'''funnel-transformer/intermediate-base''': (
'''https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/vocab.txt'''
),
'''funnel-transformer/large''': '''https://huggingface.co/funnel-transformer/large/resolve/main/vocab.txt''',
'''funnel-transformer/large-base''': '''https://huggingface.co/funnel-transformer/large-base/resolve/main/vocab.txt''',
'''funnel-transformer/xlarge''': '''https://huggingface.co/funnel-transformer/xlarge/resolve/main/vocab.txt''',
'''funnel-transformer/xlarge-base''': (
'''https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/vocab.txt'''
),
},
'''tokenizer_file''': {
'''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/tokenizer.json''',
'''funnel-transformer/small-base''': (
'''https://huggingface.co/funnel-transformer/small-base/resolve/main/tokenizer.json'''
),
'''funnel-transformer/medium''': '''https://huggingface.co/funnel-transformer/medium/resolve/main/tokenizer.json''',
'''funnel-transformer/medium-base''': (
'''https://huggingface.co/funnel-transformer/medium-base/resolve/main/tokenizer.json'''
),
'''funnel-transformer/intermediate''': (
'''https://huggingface.co/funnel-transformer/intermediate/resolve/main/tokenizer.json'''
),
'''funnel-transformer/intermediate-base''': (
'''https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/tokenizer.json'''
),
'''funnel-transformer/large''': '''https://huggingface.co/funnel-transformer/large/resolve/main/tokenizer.json''',
'''funnel-transformer/large-base''': (
'''https://huggingface.co/funnel-transformer/large-base/resolve/main/tokenizer.json'''
),
'''funnel-transformer/xlarge''': '''https://huggingface.co/funnel-transformer/xlarge/resolve/main/tokenizer.json''',
'''funnel-transformer/xlarge-base''': (
'''https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/tokenizer.json'''
),
},
}
__A = {F"""funnel-transformer/{name}""": 512 for name in _model_names}
__A = {F"""funnel-transformer/{name}""": {'''do_lower_case''': True} for name in _model_names}
class _snake_case ( a__ ):
snake_case__ = VOCAB_FILES_NAMES
snake_case__ = PRETRAINED_VOCAB_FILES_MAP
snake_case__ = PRETRAINED_INIT_CONFIGURATION
snake_case__ = FunnelTokenizer
snake_case__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
snake_case__ = 2
def __init__( self : List[Any] , UpperCAmelCase : List[str]=None , UpperCAmelCase : Optional[int]=None , UpperCAmelCase : List[Any]=True , UpperCAmelCase : int="<unk>" , UpperCAmelCase : List[Any]="<sep>" , UpperCAmelCase : List[str]="<pad>" , UpperCAmelCase : Union[str, Any]="<cls>" , UpperCAmelCase : int="<mask>" , UpperCAmelCase : List[str]="<s>" , UpperCAmelCase : Union[str, Any]="</s>" , UpperCAmelCase : List[Any]=True , UpperCAmelCase : Tuple=True , UpperCAmelCase : int=None , UpperCAmelCase : int="##" , **UpperCAmelCase : Dict , ):
super().__init__(
UpperCAmelCase , tokenizer_file=UpperCAmelCase , do_lower_case=UpperCAmelCase , unk_token=UpperCAmelCase , sep_token=UpperCAmelCase , pad_token=UpperCAmelCase , cls_token=UpperCAmelCase , mask_token=UpperCAmelCase , bos_token=UpperCAmelCase , eos_token=UpperCAmelCase , clean_text=UpperCAmelCase , tokenize_chinese_chars=UpperCAmelCase , strip_accents=UpperCAmelCase , wordpieces_prefix=UpperCAmelCase , **UpperCAmelCase , )
__lowerCamelCase : List[Any] = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("lowercase" , UpperCAmelCase ) != do_lower_case
or normalizer_state.get("strip_accents" , UpperCAmelCase ) != strip_accents
or normalizer_state.get("handle_chinese_chars" , UpperCAmelCase ) != tokenize_chinese_chars
):
__lowerCamelCase : Tuple = getattr(UpperCAmelCase , normalizer_state.pop("type" ) )
__lowerCamelCase : Optional[int] = do_lower_case
__lowerCamelCase : Union[str, Any] = strip_accents
__lowerCamelCase : Optional[Any] = tokenize_chinese_chars
__lowerCamelCase : Optional[Any] = normalizer_class(**UpperCAmelCase )
__lowerCamelCase : List[Any] = do_lower_case
def lowerCamelCase__ ( self : Union[str, Any] , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Tuple=None ):
__lowerCamelCase : List[Any] = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def lowerCamelCase__ ( self : Dict , UpperCAmelCase : List[int] , UpperCAmelCase : Optional[List[int]] = None ):
__lowerCamelCase : Tuple = [self.sep_token_id]
__lowerCamelCase : int = [self.cls_token_id]
if token_ids_a is None:
return len(cls ) * [self.cls_token_type_id] + len(token_ids_a + sep ) * [0]
return len(cls ) * [self.cls_token_type_id] + len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def lowerCamelCase__ ( self : Tuple , UpperCAmelCase : str , UpperCAmelCase : Optional[str] = None ):
__lowerCamelCase : List[Any] = self._tokenizer.model.save(UpperCAmelCase , name=UpperCAmelCase )
return tuple(UpperCAmelCase )
| 366
| 1
|
from __future__ import annotations
import typing
from collections import Counter
def lowerCamelCase_ ( __UpperCamelCase ):
A_ = Counter()
for base in range(1 , max_perimeter + 1 ):
for perpendicular in range(__UpperCamelCase , max_perimeter + 1 ):
A_ = (base * base + perpendicular * perpendicular) ** 0.5
if hypotenuse == int(__UpperCamelCase ):
A_ = int(base + perpendicular + hypotenuse )
if perimeter > max_perimeter:
continue
triplets[perimeter] += 1
return triplets
def lowerCamelCase_ ( __UpperCamelCase = 10_00 ):
A_ = pythagorean_triple(__UpperCamelCase )
return triplets.most_common(1 )[0][0]
if __name__ == "__main__":
print(f'''Perimeter {solution()} has maximum solutions''')
| 141
|
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
SCREAMING_SNAKE_CASE : Dict = abspath(join(dirname(__file__), "src"))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action="ignore", category=FutureWarning)
def lowerCamelCase_ ( __UpperCamelCase ):
config.addinivalue_line(
'''markers''' , '''is_pt_tf_cross_test: mark test to run only when PT and TF interactions are tested''' )
config.addinivalue_line(
'''markers''' , '''is_pt_flax_cross_test: mark test to run only when PT and FLAX interactions are tested''' )
config.addinivalue_line('''markers''' , '''is_pipeline_test: mark test to run only when pipelines are tested''' )
config.addinivalue_line('''markers''' , '''is_staging_test: mark test to run only in the staging environment''' )
config.addinivalue_line('''markers''' , '''accelerate_tests: mark test that require accelerate''' )
config.addinivalue_line('''markers''' , '''tool_tests: mark the tool tests that are run on their specific schedule''' )
def lowerCamelCase_ ( __UpperCamelCase ):
from transformers.testing_utils import pytest_addoption_shared
pytest_addoption_shared(__UpperCamelCase )
def lowerCamelCase_ ( __UpperCamelCase ):
from transformers.testing_utils import pytest_terminal_summary_main
A_ = terminalreporter.config.getoption('''--make-reports''' )
if make_reports:
pytest_terminal_summary_main(__UpperCamelCase , id=__UpperCamelCase )
def lowerCamelCase_ ( __UpperCamelCase , __UpperCamelCase ):
# If no tests are collected, pytest exists with code 5, which makes the CI fail.
if exitstatus == 5:
A_ = 0
# Doctest custom flag to ignore output.
SCREAMING_SNAKE_CASE : Dict = doctest.register_optionflag("IGNORE_RESULT")
SCREAMING_SNAKE_CASE : Optional[int] = doctest.OutputChecker
class __lowercase ( A ):
def lowerCAmelCase_ ( self , a__ , a__ , a__ ) -> List[str]:
'''simple docstring'''
if IGNORE_RESULT & optionflags:
return True
return OutputChecker.check_output(self , a__ , a__ , a__ )
SCREAMING_SNAKE_CASE : List[str] = CustomOutputChecker
SCREAMING_SNAKE_CASE : Optional[int] = HfDoctestModule
SCREAMING_SNAKE_CASE : Any = HfDocTestParser
| 141
| 1
|
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
snake_case_ : List[Any] = logging.get_logger(__name__)
class A__ ( _UpperCamelCase ):
def __init__( self : Optional[int] , *_a : Any , **_a : int ) -> Optional[int]:
"""simple docstring"""
warnings.warn(
'''The class MobileViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
''' Please use MobileViTImageProcessor instead.''' , _a , )
super().__init__(*_a , **_a )
| 715
|
import string
import numpy
def lowerCamelCase( a__ ,a__):
return b if a == 0 else greatest_common_divisor(b % a ,a__)
class A__ :
UpperCAmelCase = string.ascii_uppercase + string.digits
# This cipher takes alphanumerics into account
# i.e. a total of 36 characters
# take x and return x % len(key_string)
UpperCAmelCase = numpy.vectorize(lambda UpperCamelCase__ : x % 36 )
UpperCAmelCase = numpy.vectorize(UpperCamelCase__ )
def __init__( self : int , _a : numpy.ndarray ) -> None:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =self.modulus(_a ) # mod36 calc's on the encrypt key
self.check_determinant() # validate the determinant of the encryption key
_SCREAMING_SNAKE_CASE =encrypt_key.shape[0]
def __UpperCamelCase ( self : Dict , _a : str ) -> int:
"""simple docstring"""
return self.key_string.index(_a )
def __UpperCamelCase ( self : List[str] , _a : int ) -> str:
"""simple docstring"""
return self.key_string[round(_a )]
def __UpperCamelCase ( self : int ) -> None:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =round(numpy.linalg.det(self.encrypt_key ) )
if det < 0:
_SCREAMING_SNAKE_CASE =det % len(self.key_string )
_SCREAMING_SNAKE_CASE =len(self.key_string )
if greatest_common_divisor(_a , len(self.key_string ) ) != 1:
_SCREAMING_SNAKE_CASE =(
f"determinant modular {req_l} of encryption key({det}) "
f"is not co prime w.r.t {req_l}.\nTry another key."
)
raise ValueError(_a )
def __UpperCamelCase ( self : List[str] , _a : str ) -> str:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =[char for char in text.upper() if char in self.key_string]
_SCREAMING_SNAKE_CASE =chars[-1]
while len(_a ) % self.break_key != 0:
chars.append(_a )
return "".join(_a )
def __UpperCamelCase ( self : List[str] , _a : str ) -> str:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =self.process_text(text.upper() )
_SCREAMING_SNAKE_CASE =''''''
for i in range(0 , len(_a ) - self.break_key + 1 , self.break_key ):
_SCREAMING_SNAKE_CASE =text[i : i + self.break_key]
_SCREAMING_SNAKE_CASE =[self.replace_letters(_a ) for char in batch]
_SCREAMING_SNAKE_CASE =numpy.array([vec] ).T
_SCREAMING_SNAKE_CASE =self.modulus(self.encrypt_key.dot(_a ) ).T.tolist()[
0
]
_SCREAMING_SNAKE_CASE =''''''.join(
self.replace_digits(_a ) for num in batch_encrypted )
encrypted += encrypted_batch
return encrypted
def __UpperCamelCase ( self : Union[str, Any] ) -> numpy.ndarray:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =round(numpy.linalg.det(self.encrypt_key ) )
if det < 0:
_SCREAMING_SNAKE_CASE =det % len(self.key_string )
_SCREAMING_SNAKE_CASE =None
for i in range(len(self.key_string ) ):
if (det * i) % len(self.key_string ) == 1:
_SCREAMING_SNAKE_CASE =i
break
_SCREAMING_SNAKE_CASE =(
det_inv
* numpy.linalg.det(self.encrypt_key )
* numpy.linalg.inv(self.encrypt_key )
)
return self.to_int(self.modulus(_a ) )
def __UpperCamelCase ( self : str , _a : str ) -> str:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =self.make_decrypt_key()
_SCREAMING_SNAKE_CASE =self.process_text(text.upper() )
_SCREAMING_SNAKE_CASE =''''''
for i in range(0 , len(_a ) - self.break_key + 1 , self.break_key ):
_SCREAMING_SNAKE_CASE =text[i : i + self.break_key]
_SCREAMING_SNAKE_CASE =[self.replace_letters(_a ) for char in batch]
_SCREAMING_SNAKE_CASE =numpy.array([vec] ).T
_SCREAMING_SNAKE_CASE =self.modulus(decrypt_key.dot(_a ) ).T.tolist()[0]
_SCREAMING_SNAKE_CASE =''''''.join(
self.replace_digits(_a ) for num in batch_decrypted )
decrypted += decrypted_batch
return decrypted
def lowerCamelCase( ):
_SCREAMING_SNAKE_CASE =int(input('''Enter the order of the encryption key: '''))
_SCREAMING_SNAKE_CASE =[]
print('''Enter each row of the encryption key with space separated integers''')
for _ in range(a__):
_SCREAMING_SNAKE_CASE =[int(a__) for x in input().split()]
hill_matrix.append(a__)
_SCREAMING_SNAKE_CASE =HillCipher(numpy.array(a__))
print('''Would you like to encrypt or decrypt some text? (1 or 2)''')
_SCREAMING_SNAKE_CASE =input('''\n1. Encrypt\n2. Decrypt\n''')
if option == "1":
_SCREAMING_SNAKE_CASE =input('''What text would you like to encrypt?: ''')
print('''Your encrypted text is:''')
print(hc.encrypt(a__))
elif option == "2":
_SCREAMING_SNAKE_CASE =input('''What text would you like to decrypt?: ''')
print('''Your decrypted text is:''')
print(hc.decrypt(a__))
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 191
| 0
|
'''simple docstring'''
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datasets.features import ArrayaD, ClassLabel, Features, Image, Value
from datasets.features.features import ArrayaDExtensionType, cast_to_python_objects
from datasets.keyhash import DuplicatedKeysError, InvalidKeyError
from .utils import require_pil
class A ( _a ):
def __lowerCAmelCase ( self : Any ) -> Optional[int]:
"""simple docstring"""
_a = pa.array(TypedSequence([1, 2, 3] ) )
self.assertEqual(arr.type , pa.intaa() )
def __lowerCAmelCase ( self : Tuple ) -> List[str]:
"""simple docstring"""
with self.assertRaises(__a ):
_a = pa.array(TypedSequence([1, 2, 3] ) , type=pa.intaa() )
def __lowerCAmelCase ( self : List[str] ) -> Any:
"""simple docstring"""
with self.assertRaises(__a ):
_a = pa.array(TypedSequence([1, 2, 3] , try_type=Value('''bool''' ) , type=Value('''int64''' ) ) )
def __lowerCAmelCase ( self : List[str] ) -> Tuple:
"""simple docstring"""
_a = pa.array(TypedSequence([1, 2, 3] , type=Value('''int32''' ) ) )
self.assertEqual(arr.type , pa.intaa() )
def __lowerCAmelCase ( self : List[str] ) -> int:
"""simple docstring"""
with self.assertRaises((TypeError, pa.lib.ArrowInvalid) ):
_a = pa.array(TypedSequence(['''foo''', '''bar'''] , type=Value('''int64''' ) ) )
def __lowerCAmelCase ( self : str ) -> int:
"""simple docstring"""
_a = pa.array(TypedSequence([1, 2, 3] , try_type=Value('''int32''' ) ) )
self.assertEqual(arr.type , pa.intaa() )
def __lowerCAmelCase ( self : Tuple ) -> Tuple:
"""simple docstring"""
_a = pa.array(TypedSequence(['''foo''', '''bar'''] , try_type=Value('''int64''' ) ) )
self.assertEqual(arr.type , pa.string() )
def __lowerCAmelCase ( self : Any ) -> Any:
"""simple docstring"""
_a = pa.array(TypedSequence([[[1, 2, 3]]] , type=ArrayaD((1, 3) , '''int64''' ) ) )
self.assertEqual(arr.type , ArrayaDExtensionType((1, 3) , '''int64''' ) )
def __lowerCAmelCase ( self : str ) -> Optional[int]:
"""simple docstring"""
with self.assertRaises((TypeError, pa.lib.ArrowInvalid) ):
_a = pa.array(TypedSequence(['''foo''', '''bar'''] , type=ArrayaD((1, 3) , '''int64''' ) ) )
def __lowerCAmelCase ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
_a = pa.array(TypedSequence([[[1, 2, 3]]] , try_type=ArrayaD((1, 3) , '''int64''' ) ) )
self.assertEqual(arr.type , ArrayaDExtensionType((1, 3) , '''int64''' ) )
def __lowerCAmelCase ( self : Dict ) -> Tuple:
"""simple docstring"""
_a = pa.array(TypedSequence(['''foo''', '''bar'''] , try_type=ArrayaD((1, 3) , '''int64''' ) ) )
self.assertEqual(arr.type , pa.string() )
@require_pil
def __lowerCAmelCase ( self : Optional[int] ) -> List[Any]:
"""simple docstring"""
import PIL.Image
_a = PIL.Image.fromarray(np.arange(10 , dtype=np.uinta ).reshape(2 , 5 ) )
with patch(
'''datasets.arrow_writer.cast_to_python_objects''' , side_effect=__a ) as mock_cast_to_python_objects:
_a = pa.array(TypedSequence([{'''path''': None, '''bytes''': B'''image_bytes'''}, pil_image] , type=Image() ) )
_a , _a = mock_cast_to_python_objects.call_args_list[-1]
self.assertIn('''optimize_list_casting''' , __a )
self.assertFalse(kwargs['''optimize_list_casting'''] )
def snake_case_ (UpperCamelCase : Optional[Any] , UpperCamelCase : Any ):
'''simple docstring'''
_a = pa.BufferReader(__snake_case ) if isinstance(__snake_case , pa.Buffer ) else pa.memory_map(__snake_case )
_a = pa.ipc.open_stream(__snake_case )
_a = f.read_all()
assert len(pa_table.to_batches() ) == expected_num_chunks
assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
del pa_table
@pytest.mark.parametrize('''writer_batch_size''' , [None, 1, 10] )
@pytest.mark.parametrize(
'''fields''' , [None, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}] )
def snake_case_ (UpperCamelCase : Dict , UpperCamelCase : Any ):
'''simple docstring'''
_a = pa.BufferOutputStream()
_a = pa.schema(__snake_case ) if fields else None
with ArrowWriter(stream=__snake_case , schema=__snake_case , writer_batch_size=__snake_case ) as writer:
writer.write({'''col_1''': '''foo''', '''col_2''': 1} )
writer.write({'''col_1''': '''bar''', '''col_2''': 2} )
_a , _a = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
if not fields:
_a = {'''col_1''': pa.string(), '''col_2''': pa.intaa()}
assert writer._schema == pa.schema(__snake_case , metadata=writer._schema.metadata )
_check_output(output.getvalue() , expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
def snake_case_ ():
'''simple docstring'''
_a = pa.BufferOutputStream()
_a = Features({'''labels''': ClassLabel(names=['''neg''', '''pos'''] )} )
with ArrowWriter(stream=__snake_case , features=__snake_case ) as writer:
writer.write({'''labels''': 0} )
writer.write({'''labels''': 1} )
_a , _a = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
assert writer._schema == features.arrow_schema
assert writer._schema.metadata == features.arrow_schema.metadata
_a = pa.BufferReader(output.getvalue() )
_a = pa.ipc.open_stream(__snake_case )
_a = f.read_all()
_a = pa_table.schema
assert pa_table.num_rows == 2
assert schema == features.arrow_schema
assert schema.metadata == features.arrow_schema.metadata
assert features == Features.from_arrow_schema(__snake_case )
@pytest.mark.parametrize('''writer_batch_size''' , [None, 1, 10] )
def snake_case_ (UpperCamelCase : Dict ):
'''simple docstring'''
_a = pa.BufferOutputStream()
with ArrowWriter(
stream=__snake_case , writer_batch_size=__snake_case , hash_salt='''split_name''' , check_duplicates=__snake_case , ) as writer:
with pytest.raises(__snake_case ):
writer.write({'''col_1''': '''foo''', '''col_2''': 1} , key=[1, 2] )
_a , _a = writer.finalize()
@pytest.mark.parametrize('''writer_batch_size''' , [None, 2, 10] )
def snake_case_ (UpperCamelCase : int ):
'''simple docstring'''
_a = pa.BufferOutputStream()
with ArrowWriter(
stream=__snake_case , writer_batch_size=__snake_case , hash_salt='''split_name''' , check_duplicates=__snake_case , ) as writer:
with pytest.raises(__snake_case ):
writer.write({'''col_1''': '''foo''', '''col_2''': 1} , key=10 )
writer.write({'''col_1''': '''bar''', '''col_2''': 2} , key=10 )
_a , _a = writer.finalize()
@pytest.mark.parametrize('''writer_batch_size''' , [None, 2, 10] )
def snake_case_ (UpperCamelCase : List[Any] ):
'''simple docstring'''
_a = pa.BufferOutputStream()
with ArrowWriter(
stream=__snake_case , writer_batch_size=__snake_case , hash_salt='''split_name''' , check_duplicates=__snake_case , ) as writer:
writer.write({'''col_1''': '''foo''', '''col_2''': 1} , key=1 )
writer.write({'''col_1''': '''bar''', '''col_2''': 2} , key=2 )
_a , _a = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
_check_output(output.getvalue() , expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
@pytest.mark.parametrize('''writer_batch_size''' , [None, 1, 10] )
@pytest.mark.parametrize(
'''fields''' , [None, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}] )
def snake_case_ (UpperCamelCase : int , UpperCamelCase : Dict ):
'''simple docstring'''
_a = pa.BufferOutputStream()
_a = pa.schema(__snake_case ) if fields else None
with ArrowWriter(stream=__snake_case , schema=__snake_case , writer_batch_size=__snake_case ) as writer:
writer.write_batch({'''col_1''': ['''foo''', '''bar'''], '''col_2''': [1, 2]} )
writer.write_batch({'''col_1''': [], '''col_2''': []} )
_a , _a = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
if not fields:
_a = {'''col_1''': pa.string(), '''col_2''': pa.intaa()}
assert writer._schema == pa.schema(__snake_case , metadata=writer._schema.metadata )
_check_output(output.getvalue() , expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
@pytest.mark.parametrize('''writer_batch_size''' , [None, 1, 10] )
@pytest.mark.parametrize(
'''fields''' , [None, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}] )
def snake_case_ (UpperCamelCase : Tuple , UpperCamelCase : str ):
'''simple docstring'''
_a = pa.BufferOutputStream()
_a = pa.schema(__snake_case ) if fields else None
with ArrowWriter(stream=__snake_case , schema=__snake_case , writer_batch_size=__snake_case ) as writer:
writer.write_table(pa.Table.from_pydict({'''col_1''': ['''foo''', '''bar'''], '''col_2''': [1, 2]} ) )
_a , _a = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
if not fields:
_a = {'''col_1''': pa.string(), '''col_2''': pa.intaa()}
assert writer._schema == pa.schema(__snake_case , metadata=writer._schema.metadata )
_check_output(output.getvalue() , expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
@pytest.mark.parametrize('''writer_batch_size''' , [None, 1, 10] )
@pytest.mark.parametrize(
'''fields''' , [None, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}] )
def snake_case_ (UpperCamelCase : Optional[Any] , UpperCamelCase : int ):
'''simple docstring'''
_a = pa.BufferOutputStream()
_a = pa.schema(__snake_case ) if fields else None
with ArrowWriter(stream=__snake_case , schema=__snake_case , writer_batch_size=__snake_case ) as writer:
writer.write_row(pa.Table.from_pydict({'''col_1''': ['''foo'''], '''col_2''': [1]} ) )
writer.write_row(pa.Table.from_pydict({'''col_1''': ['''bar'''], '''col_2''': [2]} ) )
_a , _a = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
if not fields:
_a = {'''col_1''': pa.string(), '''col_2''': pa.intaa()}
assert writer._schema == pa.schema(__snake_case , metadata=writer._schema.metadata )
_check_output(output.getvalue() , expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
def snake_case_ ():
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmp_dir:
_a = {'''col_1''': pa.string(), '''col_2''': pa.intaa()}
_a = os.path.join(__snake_case , '''test.arrow''' )
with ArrowWriter(path=__snake_case , schema=pa.schema(__snake_case ) ) as writer:
writer.write_batch({'''col_1''': ['''foo''', '''bar'''], '''col_2''': [1, 2]} )
_a , _a = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
assert writer._schema == pa.schema(__snake_case , metadata=writer._schema.metadata )
_check_output(__snake_case , 1 )
def snake_case_ (UpperCamelCase : List[str] ):
'''simple docstring'''
if pa.types.is_list(__snake_case ):
return get_base_dtype(arr_type.value_type )
else:
return arr_type
def snake_case_ (UpperCamelCase : List[str] , UpperCamelCase : Tuple ):
'''simple docstring'''
if isinstance(lst[0] , __snake_case ):
change_first_primitive_element_in_list(lst[0] , __snake_case )
else:
_a = value
@pytest.mark.parametrize('''optimized_int_type, expected_dtype''' , [(None, pa.intaa()), (Value('''int32''' ), pa.intaa())] )
@pytest.mark.parametrize('''sequence''' , [[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]] )
def snake_case_ (UpperCamelCase : Optional[Any] , UpperCamelCase : Union[str, Any] , UpperCamelCase : List[Any] ):
'''simple docstring'''
_a = pa.array(TypedSequence(__snake_case , optimized_int_type=__snake_case ) )
assert get_base_dtype(arr.type ) == expected_dtype
@pytest.mark.parametrize(
'''col, expected_dtype''' , [
('''attention_mask''', pa.inta()),
('''special_tokens_mask''', pa.inta()),
('''token_type_ids''', pa.inta()),
('''input_ids''', pa.intaa()),
('''other''', pa.intaa()),
] , )
@pytest.mark.parametrize('''sequence''' , [[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]] )
def snake_case_ (UpperCamelCase : int , UpperCamelCase : str , UpperCamelCase : Tuple ):
'''simple docstring'''
_a = pa.array(OptimizedTypedSequence(__snake_case , col=__snake_case ) )
assert get_base_dtype(arr.type ) == expected_dtype
# not in range
if col != "other":
# avoids errors due to in-place modifications
_a = copy.deepcopy(__snake_case )
_a = np.iinfo(expected_dtype.to_pandas_dtype() ).max + 1
change_first_primitive_element_in_list(__snake_case , __snake_case )
_a = pa.array(OptimizedTypedSequence(__snake_case , col=__snake_case ) )
assert get_base_dtype(arr.type ) == pa.intaa()
@pytest.mark.parametrize('''raise_exception''' , [False, True] )
def snake_case_ (UpperCamelCase : Dict , UpperCamelCase : Optional[int] ):
'''simple docstring'''
_a = str(tmp_path / '''dataset-train.arrow''' )
try:
with ArrowWriter(path=__snake_case ) as writer:
if raise_exception:
raise pa.lib.ArrowInvalid()
else:
writer.stream.close()
except pa.lib.ArrowInvalid:
pass
finally:
assert writer.stream.closed
def snake_case_ (UpperCamelCase : Union[str, Any] ):
'''simple docstring'''
_a = '''mock://dataset-train.arrow'''
with ArrowWriter(path=__snake_case , storage_options=mockfs.storage_options ) as writer:
assert isinstance(writer._fs , type(__snake_case ) )
assert writer._fs.storage_options == mockfs.storage_options
writer.write({'''col_1''': '''foo''', '''col_2''': 1} )
writer.write({'''col_1''': '''bar''', '''col_2''': 2} )
_a , _a = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
assert mockfs.exists(__snake_case )
def snake_case_ ():
'''simple docstring'''
_a = pa.BufferOutputStream()
with ParquetWriter(stream=__snake_case ) as writer:
writer.write({'''col_1''': '''foo''', '''col_2''': 1} )
writer.write({'''col_1''': '''bar''', '''col_2''': 2} )
_a , _a = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
_a = pa.BufferReader(output.getvalue() )
_a = pq.read_table(__snake_case )
assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
@require_pil
@pytest.mark.parametrize('''embed_local_files''' , [False, True] )
def snake_case_ (UpperCamelCase : List[Any] , UpperCamelCase : str ):
'''simple docstring'''
import PIL.Image
_a = str(tmp_path / '''test_image_rgb.jpg''' )
PIL.Image.fromarray(np.zeros((5, 5) , dtype=np.uinta ) ).save(__snake_case , format='''png''' )
_a = pa.BufferOutputStream()
with ParquetWriter(
stream=__snake_case , features=Features({'''image''': Image()} ) , embed_local_files=__snake_case ) as writer:
writer.write({'''image''': image_path} )
writer.finalize()
_a = pa.BufferReader(output.getvalue() )
_a = pq.read_table(__snake_case )
_a = pa_table.to_pydict()
if embed_local_files:
assert isinstance(out['''image'''][0]['''path'''] , __snake_case )
with open(__snake_case , '''rb''' ) as f:
assert out["image"][0]["bytes"] == f.read()
else:
assert out["image"][0]["path"] == image_path
assert out["image"][0]["bytes"] is None
def snake_case_ ():
'''simple docstring'''
_a = pa.schema([pa.field('''col_1''' , pa.string() , nullable=__snake_case )] )
_a = pa.BufferOutputStream()
with ArrowWriter(stream=__snake_case ) as writer:
writer._build_writer(inferred_schema=__snake_case )
assert writer._schema == pa.schema([pa.field('''col_1''' , pa.string() )] )
| 22
|
"""simple docstring"""
import fire
from utils import calculate_rouge, save_json
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case=None, **__snake_case ) -> Optional[int]:
"""simple docstring"""
_UpperCamelCase = [x.strip() for x in open(__snake_case ).readlines()]
_UpperCamelCase = [x.strip() for x in open(__snake_case ).readlines()][: len(__snake_case )]
_UpperCamelCase = calculate_rouge(__snake_case, __snake_case, **__snake_case )
if save_path is not None:
save_json(__snake_case, __snake_case, indent=__snake_case )
return metrics # these print nicely
if __name__ == "__main__":
fire.Fire(calculate_rouge_path)
| 19
| 0
|
def _SCREAMING_SNAKE_CASE ( lowercase : str , lowercase : bool = False ):
'''simple docstring'''
if not isinstance(lowercase , lowercase ):
lowerCamelCase_ = f"""Expected string as input, found {type(lowercase )}"""
raise ValueError(lowercase )
if not isinstance(lowercase , lowercase ):
lowerCamelCase_ = f"""Expected boolean as use_pascal parameter, found {type(lowercase )}"""
raise ValueError(lowercase )
lowerCamelCase_ = input_str.split('_' )
lowerCamelCase_ = 0 if use_pascal else 1
lowerCamelCase_ = words[start_index:]
lowerCamelCase_ = [word[0].upper() + word[1:] for word in words_to_capitalize]
lowerCamelCase_ = '' if use_pascal else words[0]
return "".join([initial_word, *capitalized_words] )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 651
|
from typing import Dict, Iterable, 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_DEFAULT_MEAN,
IMAGENET_DEFAULT_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
lowerCamelCase : List[Any] = logging.get_logger(__name__)
class A( UpperCamelCase ):
'''simple docstring'''
UpperCamelCase = ['''pixel_values''']
def __init__( self : List[Any] , A_ : bool = True , A_ : Dict[str, int] = None , A_ : PILImageResampling = PILImageResampling.BICUBIC , A_ : bool = True , A_ : Dict[str, int] = None , A_ : bool = True , A_ : Union[int, float] = 1 / 255 , A_ : bool = True , A_ : Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN , A_ : Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD , **A_ : Tuple , ) -> None:
"""simple docstring"""
super().__init__(**A_ )
lowerCamelCase_ = size if size is not None else {'shortest_edge': 224}
lowerCamelCase_ = get_size_dict(A_ , default_to_square=A_ )
lowerCamelCase_ = crop_size if crop_size is not None else {'height': 224, 'width': 224}
lowerCamelCase_ = get_size_dict(A_ , param_name='crop_size' )
lowerCamelCase_ = do_resize
lowerCamelCase_ = size
lowerCamelCase_ = resample
lowerCamelCase_ = do_center_crop
lowerCamelCase_ = crop_size
lowerCamelCase_ = do_rescale
lowerCamelCase_ = rescale_factor
lowerCamelCase_ = do_normalize
lowerCamelCase_ = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
lowerCamelCase_ = image_std if image_std is not None else IMAGENET_DEFAULT_STD
def a__ ( self : Optional[Any] , A_ : np.ndarray , A_ : Dict[str, int] , A_ : PILImageResampling = PILImageResampling.BICUBIC , A_ : Optional[Union[str, ChannelDimension]] = None , **A_ : Tuple , ) -> np.ndarray:
"""simple docstring"""
lowerCamelCase_ = get_size_dict(A_ , default_to_square=A_ )
# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
if "shortest_edge" in size:
lowerCamelCase_ = int((256 / 224) * size['shortest_edge'] )
lowerCamelCase_ = get_resize_output_image_size(A_ , size=A_ , default_to_square=A_ )
lowerCamelCase_ = {'height': output_size[0], 'width': output_size[1]}
if "height" not in size_dict or "width" not in size_dict:
raise ValueError(
f"""Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}""" )
return resize(
A_ , size=(size_dict['height'], size_dict['width']) , resample=A_ , data_format=A_ , **A_ )
def a__ ( self : Any , A_ : np.ndarray , A_ : Dict[str, int] , A_ : Optional[Union[str, ChannelDimension]] = None , **A_ : Any , ) -> np.ndarray:
"""simple docstring"""
lowerCamelCase_ = get_size_dict(A_ )
if "height" not in size or "width" not in size:
raise ValueError(f"""Size dict must have keys 'height' and 'width'. Got {size.keys()}""" )
return center_crop(A_ , size=(size['height'], size['width']) , data_format=A_ , **A_ )
def a__ ( self : Optional[Any] , A_ : np.ndarray , A_ : Union[int, float] , A_ : Optional[Union[str, ChannelDimension]] = None , **A_ : Optional[int] , ) -> np.ndarray:
"""simple docstring"""
return rescale(A_ , scale=A_ , data_format=A_ , **A_ )
def a__ ( self : List[str] , A_ : np.ndarray , A_ : Union[float, List[float]] , A_ : Union[float, List[float]] , A_ : Optional[Union[str, ChannelDimension]] = None , **A_ : str , ) -> np.ndarray:
"""simple docstring"""
return normalize(A_ , mean=A_ , std=A_ , data_format=A_ , **A_ )
def a__ ( self : Optional[int] , A_ : ImageInput , A_ : Optional[bool] = None , A_ : Optional[Dict[str, int]] = None , A_ : PILImageResampling = None , A_ : Optional[bool] = None , A_ : Optional[Dict[str, int]] = None , A_ : Optional[bool] = None , A_ : Optional[float] = None , A_ : Optional[bool] = None , A_ : Optional[Union[float, Iterable[float]]] = None , A_ : Optional[Union[float, Iterable[float]]] = None , A_ : Optional[TensorType] = None , A_ : ChannelDimension = ChannelDimension.FIRST , **A_ : List[Any] , ) -> BatchFeature:
"""simple docstring"""
lowerCamelCase_ = do_resize if do_resize is not None else self.do_resize
lowerCamelCase_ = resample if resample is not None else self.resample
lowerCamelCase_ = do_center_crop if do_center_crop is not None else self.do_center_crop
lowerCamelCase_ = do_rescale if do_rescale is not None else self.do_rescale
lowerCamelCase_ = rescale_factor if rescale_factor is not None else self.rescale_factor
lowerCamelCase_ = do_normalize if do_normalize is not None else self.do_normalize
lowerCamelCase_ = image_mean if image_mean is not None else self.image_mean
lowerCamelCase_ = image_std if image_std is not None else self.image_std
lowerCamelCase_ = size if size is not None else self.size
lowerCamelCase_ = get_size_dict(A_ , default_to_square=A_ )
lowerCamelCase_ = crop_size if crop_size is not None else self.crop_size
lowerCamelCase_ = get_size_dict(A_ , param_name='crop_size' )
lowerCamelCase_ = 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.
lowerCamelCase_ = [to_numpy_array(A_ ) for image in images]
if do_resize:
lowerCamelCase_ = [self.resize(A_ , A_ , A_ ) for image in images]
if do_center_crop:
lowerCamelCase_ = [self.center_crop(A_ , A_ ) for image in images]
if do_rescale:
lowerCamelCase_ = [self.rescale(A_ , A_ ) for image in images]
if do_normalize:
lowerCamelCase_ = [self.normalize(A_ , A_ , A_ ) for image in images]
lowerCamelCase_ = [to_channel_dimension_format(A_ , A_ ) for image in images]
lowerCamelCase_ = {'pixel_values': images}
return BatchFeature(data=A_ , tensor_type=A_ )
| 651
| 1
|
'''simple docstring'''
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
UpperCAmelCase_ = WebClient(token=os.environ['CI_SLACK_BOT_TOKEN'])
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ = test_results.split(""" """ )
UpperCAmelCase__ = 0
UpperCAmelCase__ = 0
# When the output is short enough, the output is surrounded by = signs: "== OUTPUT =="
# When it is too long, those signs are not present.
UpperCAmelCase__ = expressions[-2] if """=""" in expressions[-1] else expressions[-1]
for i, expression in enumerate(SCREAMING_SNAKE_CASE__ ):
if "failed" in expression:
failed += int(expressions[i - 1] )
if "passed" in expression:
success += int(expressions[i - 1] )
return failed, success, time_spent
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int ):
'''simple docstring'''
UpperCAmelCase__ = {}
UpperCAmelCase__ = None
UpperCAmelCase__ = False
for line in failures_short_lines.split("""\n""" ):
if re.search(r"""_ \[doctest\]""" , SCREAMING_SNAKE_CASE__ ):
UpperCAmelCase__ = True
UpperCAmelCase__ = line.split(""" """ )[2]
elif in_error and not line.split(""" """ )[0].isdigit():
UpperCAmelCase__ = line
UpperCAmelCase__ = False
return failures
class lowerCAmelCase_ :
'''simple docstring'''
def __init__( self : Optional[int] , _UpperCAmelCase : str , _UpperCAmelCase : Dict ):
"""simple docstring"""
UpperCAmelCase__ = title
UpperCAmelCase__ = doc_test_results["""time_spent"""].split(""",""" )[0]
UpperCAmelCase__ = doc_test_results["""success"""]
UpperCAmelCase__ = doc_test_results["""failures"""]
UpperCAmelCase__ = self.n_success + self.n_failures
# Failures and success of the modeling tests
UpperCAmelCase__ = doc_test_results
@property
def SCREAMING_SNAKE_CASE__ ( self : Tuple ):
"""simple docstring"""
UpperCAmelCase__ = [self._time_spent]
UpperCAmelCase__ = 0
for time in time_spent:
UpperCAmelCase__ = time.split(""":""" )
# Time can be formatted as xx:xx:xx, as .xx, or as x.xx if the time spent was less than a minute.
if len(_UpperCAmelCase ) == 1:
UpperCAmelCase__ = [0, 0, time_parts[0]]
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = int(time_parts[0] ), int(time_parts[1] ), float(time_parts[2] )
total_secs += hours * 36_00 + minutes * 60 + seconds
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = total_secs // 36_00, (total_secs % 36_00) // 60, total_secs % 60
return f'''{int(_UpperCAmelCase )}h{int(_UpperCAmelCase )}m{int(_UpperCAmelCase )}s'''
@property
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ):
"""simple docstring"""
return {"type": "header", "text": {"type": "plain_text", "text": self.title}}
@property
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ):
"""simple docstring"""
return {
"type": "section",
"text": {
"type": "plain_text",
"text": f'''🌞 There were no failures: all {self.n_tests} tests passed. The suite ran in {self.time}.''',
"emoji": True,
},
"accessory": {
"type": "button",
"text": {"type": "plain_text", "text": "Check Action results", "emoji": True},
"url": f'''https://github.com/huggingface/transformers/actions/runs/{os.environ['GITHUB_RUN_ID']}''',
},
}
@property
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ):
"""simple docstring"""
return {
"type": "section",
"text": {
"type": "plain_text",
"text": (
f'''There were {self.n_failures} failures, out of {self.n_tests} tests.\nThe suite ran in'''
f''' {self.time}.'''
),
"emoji": True,
},
"accessory": {
"type": "button",
"text": {"type": "plain_text", "text": "Check Action results", "emoji": True},
"url": f'''https://github.com/huggingface/transformers/actions/runs/{os.environ['GITHUB_RUN_ID']}''',
},
}
@property
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ):
"""simple docstring"""
UpperCAmelCase__ = 40
UpperCAmelCase__ = {k: v["""failed"""] for k, v in doc_test_results.items() if isinstance(_UpperCAmelCase , _UpperCAmelCase )}
UpperCAmelCase__ = """"""
for category, failures in category_failures.items():
if len(_UpperCAmelCase ) == 0:
continue
if report != "":
report += "\n\n"
report += f'''*{category} failures*:'''.ljust(line_length // 2 ).rjust(line_length // 2 ) + "\n"
report += "`"
report += "`\n`".join(_UpperCAmelCase )
report += "`"
return {
"type": "section",
"text": {
"type": "mrkdwn",
"text": f'''The following examples had failures:\n\n\n{report}\n''',
},
}
@property
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ):
"""simple docstring"""
UpperCAmelCase__ = [self.header]
if self.n_failures > 0:
blocks.append(self.failures )
if self.n_failures > 0:
blocks.extend([self.category_failures] )
if self.n_failures == 0:
blocks.append(self.no_failures )
return json.dumps(_UpperCAmelCase )
@staticmethod
def SCREAMING_SNAKE_CASE__ ( ):
"""simple docstring"""
UpperCAmelCase__ = [
{
"""type""": """section""",
"""text""": {
"""type""": """plain_text""",
"""text""": """There was an issue running the tests.""",
},
"""accessory""": {
"""type""": """button""",
"""text""": {"""type""": """plain_text""", """text""": """Check Action results""", """emoji""": True},
"""url""": f'''https://github.com/huggingface/transformers/actions/runs/{os.environ['GITHUB_RUN_ID']}''',
},
}
]
print("""Sending the following payload""" )
print(json.dumps({"""blocks""": json.loads(_UpperCAmelCase )} ) )
client.chat_postMessage(
channel=os.environ["""CI_SLACK_CHANNEL_ID_DAILY"""] , text="""There was an issue running the tests.""" , blocks=_UpperCAmelCase , )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ):
"""simple docstring"""
print("""Sending the following payload""" )
print(json.dumps({"""blocks""": json.loads(self.payload )} ) )
UpperCAmelCase__ = f'''{self.n_failures} failures out of {self.n_tests} tests,''' if self.n_failures else """All tests passed."""
UpperCAmelCase__ = client.chat_postMessage(
channel=os.environ["""CI_SLACK_CHANNEL_ID_DAILY"""] , blocks=self.payload , text=_UpperCAmelCase , )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Any , _UpperCAmelCase : List[str] ):
"""simple docstring"""
UpperCAmelCase__ = """"""
for key, value in failures.items():
UpperCAmelCase__ = value[:2_00] + """ [Truncated]""" if len(_UpperCAmelCase ) > 2_50 else value
failures_text += f'''*{key}*\n_{value}_\n\n'''
UpperCAmelCase__ = job_name
UpperCAmelCase__ = {"""type""": """section""", """text""": {"""type""": """mrkdwn""", """text""": text}}
if job_link is not None:
UpperCAmelCase__ = {
"""type""": """button""",
"""text""": {"""type""": """plain_text""", """text""": """GitHub Action job""", """emoji""": True},
"""url""": job_link,
}
return [
{"type": "header", "text": {"type": "plain_text", "text": title.upper(), "emoji": True}},
content,
{"type": "section", "text": {"type": "mrkdwn", "text": failures_text}},
]
def SCREAMING_SNAKE_CASE__ ( self : int ):
"""simple docstring"""
if self.thread_ts is None:
raise ValueError("""Can only post reply if a post has been made.""" )
UpperCAmelCase__ = self.doc_test_results.pop("""job_link""" )
self.doc_test_results.pop("""failures""" )
self.doc_test_results.pop("""success""" )
self.doc_test_results.pop("""time_spent""" )
UpperCAmelCase__ = sorted(self.doc_test_results.items() , key=lambda _UpperCAmelCase : t[0] )
for job, job_result in sorted_dict:
if len(job_result["""failures"""] ):
UpperCAmelCase__ = f'''*Num failures* :{len(job_result['failed'] )} \n'''
UpperCAmelCase__ = job_result["""failures"""]
UpperCAmelCase__ = self.get_reply_blocks(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , text=_UpperCAmelCase )
print("""Sending the following reply""" )
print(json.dumps({"""blocks""": blocks} ) )
client.chat_postMessage(
channel=os.environ["""CI_SLACK_CHANNEL_ID_DAILY"""] , text=f'''Results for {job}''' , blocks=_UpperCAmelCase , thread_ts=self.thread_ts["""ts"""] , )
time.sleep(1 )
def _UpperCamelCase ( ):
'''simple docstring'''
UpperCAmelCase__ = os.environ["""GITHUB_RUN_ID"""]
UpperCAmelCase__ = F'''https://api.github.com/repos/huggingface/transformers/actions/runs/{run_id}/jobs?per_page=100'''
UpperCAmelCase__ = requests.get(SCREAMING_SNAKE_CASE__ ).json()
UpperCAmelCase__ = {}
try:
jobs.update({job["""name"""]: job["""html_url"""] for job in result["""jobs"""]} )
UpperCAmelCase__ = math.ceil((result["""total_count"""] - 100) / 100 )
for i in range(SCREAMING_SNAKE_CASE__ ):
UpperCAmelCase__ = requests.get(url + F'''&page={i + 2}''' ).json()
jobs.update({job["""name"""]: job["""html_url"""] for job in result["""jobs"""]} )
return jobs
except Exception as e:
print("""Unknown error, could not fetch links.""" , SCREAMING_SNAKE_CASE__ )
return {}
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : str ):
'''simple docstring'''
UpperCAmelCase__ = {}
if os.path.exists(SCREAMING_SNAKE_CASE__ ):
UpperCAmelCase__ = os.listdir(SCREAMING_SNAKE_CASE__ )
for file in files:
try:
with open(os.path.join(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) , encoding="""utf-8""" ) as f:
UpperCAmelCase__ = f.read()
except UnicodeDecodeError as e:
raise ValueError(F'''Could not open {os.path.join(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )}.''' ) from e
return _artifact
def _UpperCamelCase ( ):
'''simple docstring'''
class lowerCAmelCase_ :
'''simple docstring'''
def __init__( self : Union[str, Any] , _UpperCAmelCase : str ):
"""simple docstring"""
UpperCAmelCase__ = name
UpperCAmelCase__ = []
def __str__( self : str ):
"""simple docstring"""
return self.name
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , _UpperCAmelCase : str ):
"""simple docstring"""
self.paths.append({"""name""": self.name, """path""": path} )
UpperCAmelCase__ = {}
UpperCAmelCase__ = filter(os.path.isdir , os.listdir() )
for directory in directories:
UpperCAmelCase__ = directory
if artifact_name not in _available_artifacts:
UpperCAmelCase__ = Artifact(SCREAMING_SNAKE_CASE__ )
_available_artifacts[artifact_name].add_path(SCREAMING_SNAKE_CASE__ )
return _available_artifacts
if __name__ == "__main__":
UpperCAmelCase_ = get_job_links()
UpperCAmelCase_ = retrieve_available_artifacts()
UpperCAmelCase_ = collections.OrderedDict(
[
('*.py', 'API Examples'),
('*.md', 'MD Examples'),
]
)
# This dict will contain all the information relative to each doc test category:
# - failed: list of failed tests
# - failures: dict in the format 'test': 'error_message'
UpperCAmelCase_ = {
v: {
'failed': [],
'failures': {},
}
for v in docs.values()
}
# Link to the GitHub Action job
UpperCAmelCase_ = github_actions_job_links.get('run_doctests')
UpperCAmelCase_ = available_artifacts['doc_tests_gpu_test_reports'].paths[0]
UpperCAmelCase_ = retrieve_artifact(artifact_path['name'])
if "stats" in artifact:
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = handle_test_results(artifact['stats'])
UpperCAmelCase_ = failed
UpperCAmelCase_ = success
UpperCAmelCase_ = time_spent[1:-1] + ', '
UpperCAmelCase_ = extract_first_line_failure(artifact['failures_short'])
for line in artifact["summary_short"].split('\n'):
if re.search('FAILED', line):
UpperCAmelCase_ = line.replace('FAILED ', '')
UpperCAmelCase_ = line.split()[0].replace('\n', '')
if "::" in line:
UpperCAmelCase_ , UpperCAmelCase_ = line.split('::')
else:
UpperCAmelCase_ , UpperCAmelCase_ = line, line
for file_regex in docs.keys():
if fnmatch(file_path, file_regex):
UpperCAmelCase_ = docs[file_regex]
doc_test_results[category]["failed"].append(test)
UpperCAmelCase_ = all_failures[test] if test in all_failures else 'N/A'
UpperCAmelCase_ = failure
break
UpperCAmelCase_ = Message('🤗 Results of the doc tests.', doc_test_results)
message.post()
message.post_reply()
| 603
|
'''simple docstring'''
import torch
from transformers import AutoModel
class lowerCAmelCase_ ( torch.nn.Module ):
'''simple docstring'''
def __init__( self : Tuple , _UpperCAmelCase : List[str]="sayef/fsner-bert-base-uncased" ):
"""simple docstring"""
super(_UpperCAmelCase , self ).__init__()
UpperCAmelCase__ = AutoModel.from_pretrained(_UpperCAmelCase , return_dict=_UpperCAmelCase )
UpperCAmelCase__ = torch.nn.CosineSimilarity(3 , 1E-08 )
UpperCAmelCase__ = torch.nn.Softmax(dim=1 )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , **_UpperCAmelCase : List[str] ):
"""simple docstring"""
return self.bert(**_UpperCAmelCase ).last_hidden_state
def SCREAMING_SNAKE_CASE__ ( self : Tuple , _UpperCAmelCase : List[str] ):
"""simple docstring"""
return token_embeddings.sum(2 , keepdim=_UpperCAmelCase )
def SCREAMING_SNAKE_CASE__ ( self : str , _UpperCAmelCase : Any , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : List[Any]=1 ):
"""simple docstring"""
return self.softmax(T * self.cos(_UpperCAmelCase , _UpperCAmelCase ) )
def SCREAMING_SNAKE_CASE__ ( self : Dict , _UpperCAmelCase : Any , _UpperCAmelCase : List[str] ):
"""simple docstring"""
UpperCAmelCase__ = W_supports["""sizes"""].tolist()
UpperCAmelCase__ = W_supports["""start_token_id"""].item()
UpperCAmelCase__ = W_supports["""end_token_id"""].item()
del W_supports["sizes"]
del W_supports["start_token_id"]
del W_supports["end_token_id"]
UpperCAmelCase__ = self.BERT(**_UpperCAmelCase )
UpperCAmelCase__ = self.BERT(**_UpperCAmelCase )
UpperCAmelCase__ = None
UpperCAmelCase__ = None
UpperCAmelCase__ = W_supports["""input_ids"""] == start_token_id
UpperCAmelCase__ = W_supports["""input_ids"""] == end_token_id
for i, size in enumerate(_UpperCAmelCase ):
if i == 0:
UpperCAmelCase__ = 0
else:
UpperCAmelCase__ = support_sizes[i - 1]
UpperCAmelCase__ = S[s : s + size][start_token_masks[s : s + size]]
UpperCAmelCase__ = S[s : s + size][end_token_masks[s : s + size]]
UpperCAmelCase__ = torch.matmul(q[i] , s_start.T ).sum(1 ).softmax(0 )
UpperCAmelCase__ = torch.matmul(q[i] , s_end.T ).sum(1 ).softmax(0 )
if p_starts is not None:
UpperCAmelCase__ = torch.vstack((p_starts, p_start) )
UpperCAmelCase__ = torch.vstack((p_ends, p_end) )
else:
UpperCAmelCase__ = p_start
UpperCAmelCase__ = p_end
return p_starts, p_ends
| 603
| 1
|
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
lowercase__ : Optional[int] = "\nHugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.\n\nIn March 2021, Hugging Face raised $40 million in a Series B funding round.[3]\n\nOn April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]\n"
class UpperCAmelCase ( unittest.TestCase , UpperCAmelCase__ ):
'''simple docstring'''
def snake_case__ ( self : Any ):
"""simple docstring"""
snake_case_ = load_tool("text-question-answering" )
self.tool.setup()
snake_case_ = load_tool("text-question-answering" , remote=__lowercase )
def snake_case__ ( self : str ):
"""simple docstring"""
snake_case_ = self.tool(__lowercase , "What did Hugging Face do in April 2021?" )
self.assertEqual(__lowercase , "launched the BigScience Research Workshop" )
def snake_case__ ( self : Optional[int] ):
"""simple docstring"""
snake_case_ = self.remote_tool(__lowercase , "What did Hugging Face do in April 2021?" )
self.assertEqual(__lowercase , "launched the BigScience Research Workshop" )
def snake_case__ ( self : int ):
"""simple docstring"""
snake_case_ = self.tool(text=__lowercase , question="What did Hugging Face do in April 2021?" )
self.assertEqual(__lowercase , "launched the BigScience Research Workshop" )
def snake_case__ ( self : List[Any] ):
"""simple docstring"""
snake_case_ = self.remote_tool(text=__lowercase , question="What did Hugging Face do in April 2021?" )
self.assertEqual(__lowercase , "launched the BigScience Research Workshop" )
| 139
|
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings("ignore", category=UserWarning, module="torch.optim.lr_scheduler")
class UpperCAmelCase :
'''simple docstring'''
def __init__( self : Optional[Any] , __lowercase : List[str] , __lowercase : Tuple , __lowercase : bool = True , __lowercase : bool = False ):
"""simple docstring"""
snake_case_ = scheduler
snake_case_ = optimizers if isinstance(__lowercase , (list, tuple) ) else [optimizers]
snake_case_ = split_batches
snake_case_ = step_with_optimizer
snake_case_ = GradientState()
def snake_case__ ( self : Dict , *__lowercase : List[str] , **__lowercase : Any ):
"""simple docstring"""
if not self.step_with_optimizer:
# No link between scheduler and optimizer -> just step
self.scheduler.step(*__lowercase , **__lowercase )
return
# Otherwise, first make sure the optimizer was stepped.
if not self.gradient_state.sync_gradients:
if self.gradient_state.adjust_scheduler:
self.scheduler._step_count += 1
return
for opt in self.optimizers:
if opt.step_was_skipped:
return
if self.split_batches:
# Split batches -> the training dataloader batch size is not changed so one step per training step
self.scheduler.step(*__lowercase , **__lowercase )
else:
# Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
# num_processes steps per training step
snake_case_ = AcceleratorState().num_processes
for _ in range(__lowercase ):
# Special case when using OneCycle and `drop_last` was not used
if hasattr(self.scheduler , "total_steps" ):
if self.scheduler._step_count <= self.scheduler.total_steps:
self.scheduler.step(*__lowercase , **__lowercase )
else:
self.scheduler.step(*__lowercase , **__lowercase )
def snake_case__ ( self : Optional[Any] ):
"""simple docstring"""
return self.scheduler.get_last_lr()
def snake_case__ ( self : Optional[int] ):
"""simple docstring"""
return self.scheduler.state_dict()
def snake_case__ ( self : Union[str, Any] , __lowercase : Union[str, Any] ):
"""simple docstring"""
self.scheduler.load_state_dict(__lowercase )
def snake_case__ ( self : int ):
"""simple docstring"""
return self.scheduler.get_lr()
def snake_case__ ( self : str , *__lowercase : int , **__lowercase : Optional[Any] ):
"""simple docstring"""
return self.scheduler.print_lr(*__lowercase , **__lowercase )
| 139
| 1
|
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate
@dataclass(frozen=_a )
class __lowerCamelCase (_a ):
_lowercase = field(default="""audio-classification""" , metadata={"""include_in_asdict_even_if_is_default""": True} )
_lowercase = Features({"""audio""": Audio()} )
_lowercase = Features({"""labels""": ClassLabel} )
_lowercase = "audio"
_lowercase = "labels"
def snake_case_ ( self: str,A_: int ):
'''simple docstring'''
if self.label_column not in features:
raise ValueError(F'''Column {self.label_column} is not present in features.''' )
if not isinstance(features[self.label_column],A_ ):
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 snake_case_ ( self: Optional[int] ):
'''simple docstring'''
return {
self.audio_column: "audio",
self.label_column: "labels",
}
| 1
|
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
__snake_case = 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.''',
)
__snake_case = parser.parse_args()
__snake_case = UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)
__snake_case = CLIPImageProcessor()
__snake_case = CLIPVisionModelWithProjection.from_pretrained('''openai/clip-vit-large-patch14''')
__snake_case = 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)
| 1
| 1
|
"""simple docstring"""
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
lowercase = logging.get_logger(__name__)
lowercase = {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}
lowercase = {
"""vocab_file""": {
"""allegro/herbert-base-cased""": """https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json"""
},
"""merges_file""": {
"""allegro/herbert-base-cased""": """https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt"""
},
}
lowercase = {"""allegro/herbert-base-cased""": 514}
lowercase = {}
class SCREAMING_SNAKE_CASE_ ( _lowercase):
'''simple docstring'''
__magic_name__ : Optional[int] = VOCAB_FILES_NAMES
__magic_name__ : int = PRETRAINED_VOCAB_FILES_MAP
__magic_name__ : Optional[int] = PRETRAINED_INIT_CONFIGURATION
__magic_name__ : str = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__magic_name__ : Union[str, Any] = HerbertTokenizer
def __init__( self , lowerCamelCase__=None , lowerCamelCase__=None , lowerCamelCase__=None , lowerCamelCase__="<s>" , lowerCamelCase__="<unk>" , lowerCamelCase__="<pad>" , lowerCamelCase__="<mask>" , lowerCamelCase__="</s>" , **lowerCamelCase__ , ) -> int:
'''simple docstring'''
super().__init__(
lowerCamelCase__ , lowerCamelCase__ , tokenizer_file=lowerCamelCase__ , cls_token=lowerCamelCase__ , unk_token=lowerCamelCase__ , pad_token=lowerCamelCase__ , mask_token=lowerCamelCase__ , sep_token=lowerCamelCase__ , **lowerCamelCase__ , )
def UpperCAmelCase ( self , lowerCamelCase__ , lowerCamelCase__ = None) -> List[int]:
'''simple docstring'''
snake_case__ : str = [self.cls_token_id]
snake_case__ : Optional[Any] = [self.sep_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def UpperCAmelCase ( self , lowerCamelCase__ , lowerCamelCase__ = None , lowerCamelCase__ = False) -> List[int]:
'''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 None:
return [1] + ([0] * len(lowerCamelCase__)) + [1]
return [1] + ([0] * len(lowerCamelCase__)) + [1] + ([0] * len(lowerCamelCase__)) + [1]
def UpperCAmelCase ( self , lowerCamelCase__ , lowerCamelCase__ = None) -> List[int]:
'''simple docstring'''
snake_case__ : Union[str, Any] = [self.sep_token_id]
snake_case__ : Dict = [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 , lowerCamelCase__ , lowerCamelCase__ = None) -> Tuple[str]:
'''simple docstring'''
snake_case__ : List[Any] = self._tokenizer.model.save(lowerCamelCase__ , name=lowerCamelCase__)
return tuple(lowerCamelCase__)
| 150
|
"""simple docstring"""
import argparse
import re
import requests
import torch
# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode
from transformers import (
BertTokenizer,
BlipConfig,
BlipForConditionalGeneration,
BlipForImageTextRetrieval,
BlipForQuestionAnswering,
)
def A__ ( _UpperCAmelCase : int , _UpperCAmelCase : List[Any] ) -> Optional[Any]:
'''simple docstring'''
snake_case__ : Optional[int] = "https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg"
snake_case__ : List[Any] = Image.open(requests.get(_UpperCAmelCase , stream=_UpperCAmelCase ).raw ).convert("RGB" )
snake_case__ : List[str] = transforms.Compose(
[
transforms.Resize((image_size, image_size) , interpolation=InterpolationMode.BICUBIC ),
transforms.ToTensor(),
transforms.Normalize((0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3) , (0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1) ),
] )
snake_case__ : Any = transform(_UpperCAmelCase ).unsqueeze(0 ).to(_UpperCAmelCase )
return image
def A__ ( _UpperCAmelCase : Tuple ) -> Union[str, Any]:
'''simple docstring'''
if "visual_encoder" in key:
snake_case__ : Any = re.sub("visual_encoder*" , "vision_model.encoder" , _UpperCAmelCase )
if "blocks" in key:
snake_case__ : List[Any] = re.sub(r"blocks" , "layers" , _UpperCAmelCase )
if "attn" in key:
snake_case__ : Optional[Any] = re.sub(r"attn" , "self_attn" , _UpperCAmelCase )
if "norm1" in key:
snake_case__ : List[str] = re.sub(r"norm1" , "layer_norm1" , _UpperCAmelCase )
if "norm2" in key:
snake_case__ : Union[str, Any] = re.sub(r"norm2" , "layer_norm2" , _UpperCAmelCase )
if "encoder.norm" in key:
snake_case__ : List[Any] = re.sub(r"encoder.norm" , "post_layernorm" , _UpperCAmelCase )
if "encoder.patch_embed.proj" in key:
snake_case__ : List[str] = re.sub(r"encoder.patch_embed.proj" , "embeddings.patch_embedding" , _UpperCAmelCase )
if "encoder.pos_embed" in key:
snake_case__ : List[Any] = re.sub(r"encoder.pos_embed" , "embeddings.position_embedding" , _UpperCAmelCase )
if "encoder.cls_token" in key:
snake_case__ : Optional[Any] = re.sub(r"encoder.cls_token" , "embeddings.class_embedding" , _UpperCAmelCase )
if "self_attn" in key:
snake_case__ : Optional[Any] = re.sub(r"self_attn.proj" , "self_attn.projection" , _UpperCAmelCase )
return key
@torch.no_grad()
def A__ ( _UpperCAmelCase : Tuple , _UpperCAmelCase : Dict=None ) -> int:
'''simple docstring'''
if config_path is not None:
snake_case__ : List[Any] = BlipConfig.from_pretrained(_UpperCAmelCase )
else:
snake_case__ : Optional[int] = BlipConfig(projection_dim=5_12 , text_config={} , vision_config={} )
snake_case__ : Tuple = BlipForConditionalGeneration(_UpperCAmelCase ).eval()
snake_case__ : Optional[int] = "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth"
snake_case__ : Optional[Any] = blip_decoder(pretrained=_UpperCAmelCase , image_size=3_84 , vit="base" )
snake_case__ : str = pt_model.eval()
snake_case__ : Any = pt_model.state_dict()
for key in modified_state_dict.copy():
snake_case__ : Optional[int] = modified_state_dict.pop(_UpperCAmelCase )
snake_case__ : List[Any] = rename_key(_UpperCAmelCase )
snake_case__ : List[str] = value
hf_model.load_state_dict(_UpperCAmelCase )
snake_case__ : str = 3_84
snake_case__ : Dict = load_demo_image(image_size=_UpperCAmelCase , device="cpu" )
snake_case__ : int = BertTokenizer.from_pretrained("bert-base-uncased" )
snake_case__ : int = tokenizer(["a picture of"] ).input_ids
snake_case__ : List[str] = hf_model.generate(_UpperCAmelCase , _UpperCAmelCase )
assert out[0].tolist() == [3_05_22, 10_37, 38_61, 19_97, 10_37, 24_50, 35_64, 20_06, 19_96, 35_09, 20_07, 20_14, 38_99, 1_02]
snake_case__ : Tuple = hf_model.generate(_UpperCAmelCase )
assert out[0].tolist() == [3_05_22, 10_37, 24_50, 35_64, 20_06, 19_96, 35_09, 20_07, 20_14, 38_99, 1_02]
if pytorch_dump_folder_path is not None:
hf_model.save_pretrained(_UpperCAmelCase )
# model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_vqa.pth'
snake_case__ : Optional[int] = (
"https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth"
)
snake_case__ : str = blip_vqa(pretrained=_UpperCAmelCase , image_size=_UpperCAmelCase , vit="base" )
vqa_model.eval()
snake_case__ : Union[str, Any] = vqa_model.state_dict()
for key in modified_state_dict.copy():
snake_case__ : str = modified_state_dict.pop(_UpperCAmelCase )
snake_case__ : Any = rename_key(_UpperCAmelCase )
snake_case__ : Optional[Any] = value
snake_case__ : Union[str, Any] = BlipForQuestionAnswering(_UpperCAmelCase )
hf_vqa_model.load_state_dict(_UpperCAmelCase )
snake_case__ : List[Any] = ["How many dogs are in this image?"]
snake_case__ : Optional[Any] = tokenizer(_UpperCAmelCase , return_tensors="pt" ).input_ids
snake_case__ : List[Any] = hf_vqa_model.generate(_UpperCAmelCase , _UpperCAmelCase )
print(tokenizer.decode(answer[0] ) )
assert tokenizer.decode(answer[0] ) == "[UNK] 1 [SEP]"
if pytorch_dump_folder_path is not None:
hf_vqa_model.save_pretrained(pytorch_dump_folder_path + "_vqa" )
snake_case__ : List[str] = "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth"
snake_case__ : Optional[int] = blip_itm(pretrained=_UpperCAmelCase , image_size=_UpperCAmelCase , vit="base" )
itm_model.eval()
snake_case__ : str = itm_model.state_dict()
for key in modified_state_dict.copy():
snake_case__ : Tuple = modified_state_dict.pop(_UpperCAmelCase )
snake_case__ : Optional[Any] = rename_key(_UpperCAmelCase )
snake_case__ : List[str] = value
snake_case__ : Any = BlipForImageTextRetrieval(_UpperCAmelCase )
snake_case__ : Union[str, Any] = ["A picture of a woman with a dog sitting in a beach"]
snake_case__ : Optional[Any] = tokenizer(
_UpperCAmelCase , return_tensors="pt" , padding="max_length" , truncation=_UpperCAmelCase , max_length=35 , ).input_ids
hf_itm_model.load_state_dict(_UpperCAmelCase )
hf_itm_model.eval()
snake_case__ : List[str] = hf_itm_model(_UpperCAmelCase , _UpperCAmelCase , use_itm_head=_UpperCAmelCase )
snake_case__ : List[Any] = hf_itm_model(_UpperCAmelCase , _UpperCAmelCase , use_itm_head=_UpperCAmelCase )
assert out[0].item() == 0.2_1_1_0_6_8_7_4_9_4_2_7_7_9_5_4
assert torch.nn.functional.softmax(out_itm[0] , dim=1 )[:, 1].item() == 0.4_5_6_9_8_8_4_5_3_8_6_5_0_5_1_2_7
if pytorch_dump_folder_path is not None:
hf_itm_model.save_pretrained(pytorch_dump_folder_path + "_itm" )
if __name__ == "__main__":
lowercase = argparse.ArgumentParser()
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
lowercase = parser.parse_args()
convert_blip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
| 150
| 1
|
"""simple docstring"""
import colorsys
from PIL import Image # type: ignore
def SCREAMING_SNAKE_CASE ( snake_case, snake_case, snake_case):
__snake_case = x
__snake_case = y
for step in range(snake_case): # noqa: B007
__snake_case = a * a - b * b + x
__snake_case = 2 * a * b + y
__snake_case = a_new
# divergence happens for all complex number with an absolute value
# greater than 4
if a * a + b * b > 4:
break
return step / (max_step - 1)
def SCREAMING_SNAKE_CASE ( snake_case):
if distance == 1:
return (0, 0, 0)
else:
return (2_55, 2_55, 2_55)
def SCREAMING_SNAKE_CASE ( snake_case):
if distance == 1:
return (0, 0, 0)
else:
return tuple(round(i * 2_55) for i in colorsys.hsv_to_rgb(snake_case, 1, 1))
def SCREAMING_SNAKE_CASE ( snake_case = 8_00, snake_case = 6_00, snake_case = -0.6, snake_case = 0, snake_case = 3.2, snake_case = 50, snake_case = True, ):
__snake_case = Image.new('''RGB''', (image_width, image_height))
__snake_case = img.load()
# loop through the image-coordinates
for image_x in range(snake_case):
for image_y in range(snake_case):
# determine the figure-coordinates based on the image-coordinates
__snake_case = figure_width / image_width * image_height
__snake_case = figure_center_x + (image_x / image_width - 0.5) * figure_width
__snake_case = figure_center_y + (image_y / image_height - 0.5) * figure_height
__snake_case = get_distance(snake_case, snake_case, snake_case)
# color the corresponding pixel based on the selected coloring-function
if use_distance_color_coding:
__snake_case = get_color_coded_rgb(snake_case)
else:
__snake_case = get_black_and_white_rgb(snake_case)
return img
if __name__ == "__main__":
import doctest
doctest.testmod()
# colored version, full figure
__lowercase : Optional[Any] = get_image()
# uncomment for colored version, different section, zoomed in
# img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
# figure_width = 0.8)
# uncomment for black and white version, full figure
# img = get_image(use_distance_color_coding = False)
# uncomment to save the image
# img.save("mandelbrot.png")
img.show()
| 564
|
"""simple docstring"""
import warnings
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowercase : Union[str, Any] = logging.get_logger(__name__)
__lowercase : List[str] = {
"nvidia/segformer-b0-finetuned-ade-512-512": (
"https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json"
),
# See all SegFormer models at https://huggingface.co/models?filter=segformer
}
class _A ( _UpperCAmelCase ):
"""simple docstring"""
UpperCamelCase_ : Optional[Any] = '''segformer'''
def __init__( self : Optional[Any] , A_ : Tuple=3 , A_ : int=4 , A_ : int=[2, 2, 2, 2] , A_ : Any=[8, 4, 2, 1] , A_ : str=[32, 64, 160, 256] , A_ : str=[7, 3, 3, 3] , A_ : Dict=[4, 2, 2, 2] , A_ : List[str]=[1, 2, 5, 8] , A_ : Union[str, Any]=[4, 4, 4, 4] , A_ : Union[str, Any]="gelu" , A_ : int=0.0 , A_ : Tuple=0.0 , A_ : List[str]=0.1 , A_ : Tuple=0.02 , A_ : Optional[Any]=0.1 , A_ : int=1E-6 , A_ : Optional[int]=256 , A_ : Tuple=255 , **A_ : str , ) -> str:
super().__init__(**A_ )
if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
warnings.warn(
'''Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be'''
''' removed, as the behaviour will default to that of reshape_last_stage = True.''' , A_ , )
__snake_case = num_channels
__snake_case = num_encoder_blocks
__snake_case = depths
__snake_case = sr_ratios
__snake_case = hidden_sizes
__snake_case = patch_sizes
__snake_case = strides
__snake_case = mlp_ratios
__snake_case = num_attention_heads
__snake_case = hidden_act
__snake_case = hidden_dropout_prob
__snake_case = attention_probs_dropout_prob
__snake_case = classifier_dropout_prob
__snake_case = initializer_range
__snake_case = drop_path_rate
__snake_case = layer_norm_eps
__snake_case = decoder_hidden_size
__snake_case = kwargs.get('''reshape_last_stage''' , A_ )
__snake_case = semantic_loss_ignore_index
class _A ( _UpperCAmelCase ):
"""simple docstring"""
UpperCamelCase_ : Optional[Any] = version.parse('''1.11''' )
@property
def lowercase ( self : str ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def lowercase ( self : List[Any] ) -> float:
return 1E-4
@property
def lowercase ( self : Any ) -> int:
return 12
| 564
| 1
|
'''simple docstring'''
def _UpperCAmelCase ( _UpperCamelCase : str, _UpperCamelCase : str ) -> int:
if len(_UpperCamelCase ) != len(_UpperCamelCase ):
raise ValueError('''String lengths must match!''' )
A_ = 0
for chara, chara in zip(_UpperCamelCase, _UpperCamelCase ):
if chara != chara:
count += 1
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 174
|
'''simple docstring'''
import math
def _UpperCAmelCase ( _UpperCamelCase : int ) -> bool:
return math.sqrt(_UpperCamelCase ) * math.sqrt(_UpperCamelCase ) == num
def _UpperCAmelCase ( _UpperCamelCase : int ) -> bool:
A_ = 0
A_ = n
while left <= right:
A_ = (left + right) // 2
if mid**2 == n:
return True
elif mid**2 > n:
A_ = mid - 1
else:
A_ = mid + 1
return False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 174
| 1
|
'''simple docstring'''
from __future__ import annotations
import math
def lowerCAmelCase_ ( snake_case__ ):
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(snake_case__ ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
lowercase : List[str] = [num for num in range(3, 10_00_01, 2) if not is_prime(num)]
def lowerCAmelCase_ ( snake_case__ ):
'''simple docstring'''
if not isinstance(snake_case__ , snake_case__ ):
raise ValueError('''n must be an integer''' )
if n <= 0:
raise ValueError('''n must be >= 0''' )
A : int = []
for num in range(len(snake_case__ ) ):
A : Union[str, Any] = 0
while 2 * i * i <= odd_composites[num]:
A : List[Any] = odd_composites[num] - 2 * i * i
if is_prime(snake_case__ ):
break
i += 1
else:
list_nums.append(odd_composites[num] )
if len(snake_case__ ) == n:
return list_nums
return []
def lowerCAmelCase_ ( ):
'''simple docstring'''
return compute_nums(1 )[0]
if __name__ == "__main__":
print(f'''{solution() = }''')
| 634
|
'''simple docstring'''
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import ViTForImageClassification, ViTForMaskedImageModeling, ViTModel
from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class A :
def __init__( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=13 , SCREAMING_SNAKE_CASE=30 , SCREAMING_SNAKE_CASE=2 , SCREAMING_SNAKE_CASE=3 , SCREAMING_SNAKE_CASE=True , SCREAMING_SNAKE_CASE=True , SCREAMING_SNAKE_CASE=32 , SCREAMING_SNAKE_CASE=5 , SCREAMING_SNAKE_CASE=4 , SCREAMING_SNAKE_CASE=37 , SCREAMING_SNAKE_CASE="gelu" , SCREAMING_SNAKE_CASE=0.1 , SCREAMING_SNAKE_CASE=0.1 , SCREAMING_SNAKE_CASE=10 , SCREAMING_SNAKE_CASE=0.02 , SCREAMING_SNAKE_CASE=None , SCREAMING_SNAKE_CASE=2 , ) -> Any:
"""simple docstring"""
A : Union[str, Any] = parent
A : Optional[Any] = batch_size
A : Tuple = image_size
A : str = patch_size
A : Dict = num_channels
A : Tuple = is_training
A : List[Any] = use_labels
A : Optional[Any] = hidden_size
A : Dict = num_hidden_layers
A : Union[str, Any] = num_attention_heads
A : List[Any] = intermediate_size
A : Any = hidden_act
A : Dict = hidden_dropout_prob
A : Any = attention_probs_dropout_prob
A : Dict = type_sequence_label_size
A : List[Any] = initializer_range
A : Dict = scope
A : Any = encoder_stride
# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
A : int = (image_size // patch_size) ** 2
A : Optional[Any] = num_patches + 1
def __lowerCAmelCase ( self ) -> Optional[int]:
"""simple docstring"""
A : List[str] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
A : str = None
if self.use_labels:
A : str = ids_tensor([self.batch_size] , self.type_sequence_label_size )
A : Dict = self.get_config()
return config, pixel_values, labels
def __lowerCAmelCase ( self ) -> Any:
"""simple docstring"""
return ViTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=SCREAMING_SNAKE_CASE , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , )
def __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
A : str = ViTModel(config=SCREAMING_SNAKE_CASE )
model.to(SCREAMING_SNAKE_CASE )
model.eval()
A : Optional[Any] = model(SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> Any:
"""simple docstring"""
A : Any = ViTForMaskedImageModeling(config=SCREAMING_SNAKE_CASE )
model.to(SCREAMING_SNAKE_CASE )
model.eval()
A : Any = model(SCREAMING_SNAKE_CASE )
self.parent.assertEqual(
result.reconstruction.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
A : Optional[Any] = 1
A : str = ViTForMaskedImageModeling(SCREAMING_SNAKE_CASE )
model.to(SCREAMING_SNAKE_CASE )
model.eval()
A : List[str] = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
A : Union[str, Any] = model(SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.reconstruction.shape , (self.batch_size, 1, self.image_size, self.image_size) )
def __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
A : str = self.type_sequence_label_size
A : Optional[int] = ViTForImageClassification(SCREAMING_SNAKE_CASE )
model.to(SCREAMING_SNAKE_CASE )
model.eval()
A : List[Any] = model(SCREAMING_SNAKE_CASE , labels=SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
A : int = 1
A : Any = ViTForImageClassification(SCREAMING_SNAKE_CASE )
model.to(SCREAMING_SNAKE_CASE )
model.eval()
A : Dict = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
A : str = model(SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def __lowerCAmelCase ( self ) -> Optional[int]:
"""simple docstring"""
A : int = self.prepare_config_and_inputs()
(
(
A
), (
A
), (
A
),
) : int = config_and_inputs
A : Dict = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class A ( __snake_case , __snake_case , unittest.TestCase ):
__magic_name__ = (
(
ViTModel,
ViTForImageClassification,
ViTForMaskedImageModeling,
)
if is_torch_available()
else ()
)
__magic_name__ = (
{'''feature-extraction''': ViTModel, '''image-classification''': ViTForImageClassification}
if is_torch_available()
else {}
)
__magic_name__ = True
__magic_name__ = False
__magic_name__ = False
__magic_name__ = False
def __lowerCAmelCase ( self ) -> Tuple:
"""simple docstring"""
A : List[str] = ViTModelTester(self )
A : List[str] = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE , has_text_modality=SCREAMING_SNAKE_CASE , hidden_size=37 )
def __lowerCAmelCase ( self ) -> Optional[Any]:
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason='''ViT does not use inputs_embeds''' )
def __lowerCAmelCase ( self ) -> Dict:
"""simple docstring"""
pass
def __lowerCAmelCase ( self ) -> int:
"""simple docstring"""
A, A : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A : Any = model_class(SCREAMING_SNAKE_CASE )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
A : Optional[int] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE , nn.Linear ) )
def __lowerCAmelCase ( self ) -> Optional[int]:
"""simple docstring"""
A, A : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A : Tuple = model_class(SCREAMING_SNAKE_CASE )
A : str = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
A : List[str] = [*signature.parameters.keys()]
A : Optional[int] = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , SCREAMING_SNAKE_CASE )
def __lowerCAmelCase ( self ) -> Optional[int]:
"""simple docstring"""
A : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE )
def __lowerCAmelCase ( self ) -> Optional[int]:
"""simple docstring"""
A : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*SCREAMING_SNAKE_CASE )
def __lowerCAmelCase ( self ) -> Any:
"""simple docstring"""
A : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE )
@slow
def __lowerCAmelCase ( self ) -> List[str]:
"""simple docstring"""
for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A : Optional[Any] = ViTModel.from_pretrained(SCREAMING_SNAKE_CASE )
self.assertIsNotNone(SCREAMING_SNAKE_CASE )
def lowerCAmelCase_ ( ):
'''simple docstring'''
A : Optional[int] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class A ( unittest.TestCase ):
@cached_property
def __lowerCAmelCase ( self ) -> Union[str, Any]:
"""simple docstring"""
return ViTImageProcessor.from_pretrained('''google/vit-base-patch16-224''' ) if is_vision_available() else None
@slow
def __lowerCAmelCase ( self ) -> Any:
"""simple docstring"""
A : List[Any] = ViTForImageClassification.from_pretrained('''google/vit-base-patch16-224''' ).to(SCREAMING_SNAKE_CASE )
A : Optional[int] = self.default_image_processor
A : int = prepare_img()
A : Tuple = image_processor(images=SCREAMING_SNAKE_CASE , return_tensors='''pt''' ).to(SCREAMING_SNAKE_CASE )
# forward pass
with torch.no_grad():
A : Any = model(**SCREAMING_SNAKE_CASE )
# verify the logits
A : List[Any] = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , SCREAMING_SNAKE_CASE )
A : List[str] = torch.tensor([-0.2_744, 0.8_215, -0.0_836] ).to(SCREAMING_SNAKE_CASE )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , SCREAMING_SNAKE_CASE , atol=1e-4 ) )
@slow
def __lowerCAmelCase ( self ) -> Union[str, Any]:
"""simple docstring"""
A : Dict = ViTModel.from_pretrained('''facebook/dino-vits8''' ).to(SCREAMING_SNAKE_CASE )
A : int = ViTImageProcessor.from_pretrained('''facebook/dino-vits8''' , size=480 )
A : Tuple = prepare_img()
A : List[Any] = image_processor(images=SCREAMING_SNAKE_CASE , return_tensors='''pt''' )
A : Tuple = inputs.pixel_values.to(SCREAMING_SNAKE_CASE )
# forward pass
with torch.no_grad():
A : Any = model(SCREAMING_SNAKE_CASE , interpolate_pos_encoding=SCREAMING_SNAKE_CASE )
# verify the logits
A : int = torch.Size((1, 3601, 384) )
self.assertEqual(outputs.last_hidden_state.shape , SCREAMING_SNAKE_CASE )
A : Union[str, Any] = torch.tensor(
[[4.2_340, 4.3_906, -6.6_692], [4.5_463, 1.8_928, -6.7_257], [4.4_429, 0.8_496, -5.8_585]] ).to(SCREAMING_SNAKE_CASE )
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3] , SCREAMING_SNAKE_CASE , atol=1e-4 ) )
@slow
@require_accelerate
@require_torch_gpu
def __lowerCAmelCase ( self ) -> Union[str, Any]:
"""simple docstring"""
A : List[Any] = ViTModel.from_pretrained('''facebook/dino-vits8''' , torch_dtype=torch.floataa , device_map='''auto''' )
A : int = self.default_image_processor
A : List[str] = prepare_img()
A : Union[str, Any] = image_processor(images=SCREAMING_SNAKE_CASE , return_tensors='''pt''' )
A : List[Any] = inputs.pixel_values.to(SCREAMING_SNAKE_CASE )
# forward pass to make sure inference works in fp16
with torch.no_grad():
A : str = model(SCREAMING_SNAKE_CASE )
| 634
| 1
|
import argparse
import collections
import os
import re
import tempfile
import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/update_metadata.py
__A : Dict = 'src/transformers'
# This is to make sure the transformers module imported is the one in the repo.
__A : int = direct_transformers_import(TRANSFORMERS_PATH)
# Regexes that match TF/Flax/PT model names.
__A : List[Any] = re.compile(r'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
__A : Dict = re.compile(r'Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
__A : Dict = re.compile(r'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
__A : int = [
('pretraining', 'MODEL_FOR_PRETRAINING_MAPPING_NAMES', 'AutoModelForPreTraining'),
('feature-extraction', 'MODEL_MAPPING_NAMES', 'AutoModel'),
('audio-classification', 'MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForAudioClassification'),
('text-generation', 'MODEL_FOR_CAUSAL_LM_MAPPING_NAMES', 'AutoModelForCausalLM'),
('automatic-speech-recognition', 'MODEL_FOR_CTC_MAPPING_NAMES', 'AutoModelForCTC'),
('image-classification', 'MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForImageClassification'),
('image-segmentation', 'MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES', 'AutoModelForImageSegmentation'),
('fill-mask', 'MODEL_FOR_MASKED_LM_MAPPING_NAMES', 'AutoModelForMaskedLM'),
('object-detection', 'MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES', 'AutoModelForObjectDetection'),
(
'zero-shot-object-detection',
'MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES',
'AutoModelForZeroShotObjectDetection',
),
('question-answering', 'MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES', 'AutoModelForQuestionAnswering'),
('text2text-generation', 'MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES', 'AutoModelForSeq2SeqLM'),
('text-classification', 'MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForSequenceClassification'),
('automatic-speech-recognition', 'MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES', 'AutoModelForSpeechSeq2Seq'),
(
'table-question-answering',
'MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES',
'AutoModelForTableQuestionAnswering',
),
('token-classification', 'MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForTokenClassification'),
('multiple-choice', 'MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES', 'AutoModelForMultipleChoice'),
(
'next-sentence-prediction',
'MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES',
'AutoModelForNextSentencePrediction',
),
(
'audio-frame-classification',
'MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES',
'AutoModelForAudioFrameClassification',
),
('audio-xvector', 'MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES', 'AutoModelForAudioXVector'),
(
'document-question-answering',
'MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES',
'AutoModelForDocumentQuestionAnswering',
),
(
'visual-question-answering',
'MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES',
'AutoModelForVisualQuestionAnswering',
),
('image-to-text', 'MODEL_FOR_FOR_VISION_2_SEQ_MAPPING_NAMES', 'AutoModelForVision2Seq'),
(
'zero-shot-image-classification',
'MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES',
'AutoModelForZeroShotImageClassification',
),
('depth-estimation', 'MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES', 'AutoModelForDepthEstimation'),
('video-classification', 'MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForVideoClassification'),
('mask-generation', 'MODEL_FOR_MASK_GENERATION_MAPPING_NAMES', 'AutoModelForMaskGeneration'),
]
def __a ( A__ : Any ):
SCREAMING_SNAKE_CASE = re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)" , __lowercase )
return [m.group(0 ) for m in matches]
def __a ( ):
SCREAMING_SNAKE_CASE = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
SCREAMING_SNAKE_CASE = {
config.replace("Config" , "" ): model_type for model_type, config in config_maping_names.items()
}
# Dictionaries flagging if each model prefix has a backend in PT/TF/Flax.
SCREAMING_SNAKE_CASE = collections.defaultdict(__lowercase )
SCREAMING_SNAKE_CASE = collections.defaultdict(__lowercase )
SCREAMING_SNAKE_CASE = collections.defaultdict(__lowercase )
# Let's lookup through all transformers object (once) and find if models are supported by a given backend.
for attr_name in dir(__lowercase ):
SCREAMING_SNAKE_CASE = None
if _re_tf_models.match(__lowercase ) is not None:
SCREAMING_SNAKE_CASE = tf_models
SCREAMING_SNAKE_CASE = _re_tf_models.match(__lowercase ).groups()[0]
elif _re_flax_models.match(__lowercase ) is not None:
SCREAMING_SNAKE_CASE = flax_models
SCREAMING_SNAKE_CASE = _re_flax_models.match(__lowercase ).groups()[0]
elif _re_pt_models.match(__lowercase ) is not None:
SCREAMING_SNAKE_CASE = pt_models
SCREAMING_SNAKE_CASE = _re_pt_models.match(__lowercase ).groups()[0]
if lookup_dict is not None:
while len(__lowercase ) > 0:
if attr_name in model_prefix_to_model_type:
SCREAMING_SNAKE_CASE = True
break
# Try again after removing the last word in the name
SCREAMING_SNAKE_CASE = ''.join(camel_case_split(__lowercase )[:-1] )
SCREAMING_SNAKE_CASE = set(list(pt_models.keys() ) + list(tf_models.keys() ) + list(flax_models.keys() ) )
SCREAMING_SNAKE_CASE = list(__lowercase )
all_models.sort()
SCREAMING_SNAKE_CASE = {'model_type': all_models}
SCREAMING_SNAKE_CASE = [pt_models[t] for t in all_models]
SCREAMING_SNAKE_CASE = [tf_models[t] for t in all_models]
SCREAMING_SNAKE_CASE = [flax_models[t] for t in all_models]
# Now let's use the auto-mapping names to make sure
SCREAMING_SNAKE_CASE = {}
for t in all_models:
if t in transformers_module.models.auto.processing_auto.PROCESSOR_MAPPING_NAMES:
SCREAMING_SNAKE_CASE = 'AutoProcessor'
elif t in transformers_module.models.auto.tokenization_auto.TOKENIZER_MAPPING_NAMES:
SCREAMING_SNAKE_CASE = 'AutoTokenizer'
elif t in transformers_module.models.auto.feature_extraction_auto.FEATURE_EXTRACTOR_MAPPING_NAMES:
SCREAMING_SNAKE_CASE = 'AutoFeatureExtractor'
else:
# Default to AutoTokenizer if a model has nothing, for backward compatibility.
SCREAMING_SNAKE_CASE = 'AutoTokenizer'
SCREAMING_SNAKE_CASE = [processors[t] for t in all_models]
return pd.DataFrame(__lowercase )
def __a ( A__ : str ):
SCREAMING_SNAKE_CASE = [
transformers_module.models.auto.modeling_auto,
transformers_module.models.auto.modeling_tf_auto,
transformers_module.models.auto.modeling_flax_auto,
]
for pipeline_tag, model_mapping, auto_class in PIPELINE_TAGS_AND_AUTO_MODELS:
SCREAMING_SNAKE_CASE = [model_mapping, F"TF_{model_mapping}", F"FLAX_{model_mapping}"]
SCREAMING_SNAKE_CASE = [auto_class, F"TF_{auto_class}", F"Flax_{auto_class}"]
# Loop through all three frameworks
for module, cls, mapping in zip(__lowercase , __lowercase , __lowercase ):
# The type of pipeline may not exist in this framework
if not hasattr(__lowercase , __lowercase ):
continue
# First extract all model_names
SCREAMING_SNAKE_CASE = []
for name in getattr(__lowercase , __lowercase ).values():
if isinstance(__lowercase , __lowercase ):
model_names.append(__lowercase )
else:
model_names.extend(list(__lowercase ) )
# Add pipeline tag and auto model class for those models
table.update({model_name: (pipeline_tag, cls) for model_name in model_names} )
return table
def __a ( A__ : Optional[int] , A__ : Union[str, Any] ):
SCREAMING_SNAKE_CASE = get_frameworks_table()
SCREAMING_SNAKE_CASE = Dataset.from_pandas(__lowercase )
SCREAMING_SNAKE_CASE = hf_hub_download(
"huggingface/transformers-metadata" , "pipeline_tags.json" , repo_type="dataset" , token=__lowercase )
SCREAMING_SNAKE_CASE = Dataset.from_json(__lowercase )
SCREAMING_SNAKE_CASE = {
tags_dataset[i]['model_class']: (tags_dataset[i]['pipeline_tag'], tags_dataset[i]['auto_class'])
for i in range(len(__lowercase ) )
}
SCREAMING_SNAKE_CASE = update_pipeline_and_auto_class_table(__lowercase )
# Sort the model classes to avoid some nondeterministic updates to create false update commits.
SCREAMING_SNAKE_CASE = sorted(table.keys() )
SCREAMING_SNAKE_CASE = pd.DataFrame(
{
"model_class": model_classes,
"pipeline_tag": [table[m][0] for m in model_classes],
"auto_class": [table[m][1] for m in model_classes],
} )
SCREAMING_SNAKE_CASE = Dataset.from_pandas(__lowercase )
with tempfile.TemporaryDirectory() as tmp_dir:
frameworks_dataset.to_json(os.path.join(__lowercase , "frameworks.json" ) )
tags_dataset.to_json(os.path.join(__lowercase , "pipeline_tags.json" ) )
if commit_sha is not None:
SCREAMING_SNAKE_CASE = (
F"Update with commit {commit_sha}\n\nSee: "
F"https://github.com/huggingface/transformers/commit/{commit_sha}"
)
else:
SCREAMING_SNAKE_CASE = 'Update'
upload_folder(
repo_id="huggingface/transformers-metadata" , folder_path=__lowercase , repo_type="dataset" , token=__lowercase , commit_message=__lowercase , )
def __a ( ):
SCREAMING_SNAKE_CASE = {tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
SCREAMING_SNAKE_CASE = transformers_module.pipelines.SUPPORTED_TASKS
SCREAMING_SNAKE_CASE = []
for key in pipeline_tasks:
if key not in in_table:
SCREAMING_SNAKE_CASE = pipeline_tasks[key]['pt']
if isinstance(__lowercase , (list, tuple) ):
SCREAMING_SNAKE_CASE = model[0]
SCREAMING_SNAKE_CASE = model.__name__
if model not in in_table.values():
missing.append(__lowercase )
if len(__lowercase ) > 0:
SCREAMING_SNAKE_CASE = ', '.join(__lowercase )
raise ValueError(
"The following pipeline tags are not present in the `PIPELINE_TAGS_AND_AUTO_MODELS` constant inside "
F"`utils/update_metadata.py`: {msg}. Please add them!" )
if __name__ == "__main__":
__A : int = argparse.ArgumentParser()
parser.add_argument('--token', type=str, help='The token to use to push to the transformers-metadata dataset.')
parser.add_argument('--commit_sha', type=str, help='The sha of the commit going with this update.')
parser.add_argument('--check-only', action='store_true', help='Activate to just check all pipelines are present.')
__A : Optional[Any] = parser.parse_args()
if args.check_only:
check_pipeline_tags()
else:
update_metadata(args.token, args.commit_sha)
| 704
|
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
__A : Optional[Any] = datasets.load_iris()
__A : Optional[Any] = np.array(data['data'])
__A : Optional[int] = np.array(data['target'])
__A : Union[str, Any] = data['target_names']
__A , __A , __A , __A : Optional[int] = train_test_split(X, y)
def __a ( A__ : Optional[int] , A__ : Dict ):
return np.linalg.norm(np.array(A__ ) - np.array(A__ ) )
def __a ( A__ : Optional[Any] , A__ : int , A__ : Dict , A__ : Optional[Any] , A__ : Dict=5 ):
SCREAMING_SNAKE_CASE = zip(A__ , A__ )
# List of distances of all points from the point to be classified
SCREAMING_SNAKE_CASE = []
for data_point in data:
SCREAMING_SNAKE_CASE = euclidean_distance(data_point[0] , A__ )
distances.append((distance, data_point[1]) )
# Choosing 'k' points with the least distances.
SCREAMING_SNAKE_CASE = [i[1] for i in sorted(A__ )[:k]]
# Most commonly occurring class among them
# is the class into which the point is classified
SCREAMING_SNAKE_CASE = Counter(A__ ).most_common(1 )[0][0]
return classes[result]
if __name__ == "__main__":
print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))
| 698
| 0
|
import unittest
import torch
from torch import nn
from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory
def _UpperCAmelCase ( ):
raise RuntimeError("""CUDA out of memory.""" )
class _lowerCAmelCase ( nn.Module ):
"""simple docstring"""
def __init__( self : Optional[int]):
'''simple docstring'''
super().__init__()
snake_case__ = nn.Linear(3 , 4)
snake_case__ = nn.BatchNormad(4)
snake_case__ = nn.Linear(4 , 5)
def __magic_name__ ( self : Tuple , UpperCamelCase__ : int):
'''simple docstring'''
return self.lineara(self.batchnorm(self.lineara(UpperCamelCase__)))
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def __magic_name__ ( self : Tuple):
'''simple docstring'''
snake_case__ = []
@find_executable_batch_size(starting_batch_size=1_2_8)
def mock_training_loop_function(UpperCamelCase__ : List[Any]):
nonlocal batch_sizes
batch_sizes.append(UpperCamelCase__)
if batch_size != 8:
raise_fake_out_of_memory()
mock_training_loop_function()
self.assertListEqual(UpperCamelCase__ , [1_2_8, 6_4, 3_2, 1_6, 8])
def __magic_name__ ( self : int):
'''simple docstring'''
snake_case__ = []
@find_executable_batch_size(starting_batch_size=1_2_8)
def mock_training_loop_function(UpperCamelCase__ : int , UpperCamelCase__ : Optional[int]):
nonlocal batch_sizes
batch_sizes.append(UpperCamelCase__)
if batch_size != 8:
raise_fake_out_of_memory()
return batch_size, arga
snake_case__ , snake_case__ = mock_training_loop_function("""hello""")
self.assertListEqual(UpperCamelCase__ , [1_2_8, 6_4, 3_2, 1_6, 8])
self.assertListEqual([bs, arga] , [8, """hello"""])
def __magic_name__ ( self : Optional[int]):
'''simple docstring'''
@find_executable_batch_size(starting_batch_size=0)
def mock_training_loop_function(UpperCamelCase__ : Union[str, Any]):
pass
with self.assertRaises(UpperCamelCase__) as cm:
mock_training_loop_function()
self.assertIn("""No executable batch size found, reached zero.""" , cm.exception.args[0])
def __magic_name__ ( self : Optional[Any]):
'''simple docstring'''
@find_executable_batch_size(starting_batch_size=1_6)
def mock_training_loop_function(UpperCamelCase__ : List[str]):
if batch_size > 0:
raise_fake_out_of_memory()
pass
with self.assertRaises(UpperCamelCase__) as cm:
mock_training_loop_function()
self.assertIn("""No executable batch size found, reached zero.""" , cm.exception.args[0])
def __magic_name__ ( self : Optional[int]):
'''simple docstring'''
@find_executable_batch_size(starting_batch_size=1_2_8)
def mock_training_loop_function(UpperCamelCase__ : List[str] , UpperCamelCase__ : Tuple , UpperCamelCase__ : Union[str, Any]):
if batch_size != 8:
raise raise_fake_out_of_memory()
with self.assertRaises(UpperCamelCase__) as cm:
mock_training_loop_function(1_2_8 , """hello""" , """world""")
self.assertIn("""Batch size was passed into `f`""" , cm.exception.args[0])
self.assertIn("""`f(arg1='hello', arg2='world')""" , cm.exception.args[0])
def __magic_name__ ( self : Optional[Any]):
'''simple docstring'''
@find_executable_batch_size(starting_batch_size=1_6)
def mock_training_loop_function(UpperCamelCase__ : List[str]):
raise ValueError("""Oops, we had an error!""")
with self.assertRaises(UpperCamelCase__) as cm:
mock_training_loop_function()
self.assertIn("""Oops, we had an error!""" , cm.exception.args[0])
@require_cuda
def __magic_name__ ( self : List[Any]):
'''simple docstring'''
snake_case__ = torch.cuda.memory_allocated()
snake_case__ = ModelForTest()
model.cuda()
self.assertGreater(torch.cuda.memory_allocated() , UpperCamelCase__)
snake_case__ = release_memory(UpperCamelCase__)
self.assertEqual(torch.cuda.memory_allocated() , UpperCamelCase__)
| 654
|
def _UpperCAmelCase ( a : int ):
if number < 0:
raise ValueError("""number must not be negative""" )
return number & (number - 1) == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 654
| 1
|
'''simple docstring'''
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
lowerCAmelCase__ = logging.get_logger(__name__)
class _A ( UpperCamelCase ):
'''simple docstring'''
_lowercase = 'AutoTokenizer'
_lowercase = ['tokenizer']
_lowercase = {
'semantic_prompt': 1,
'coarse_prompt': 2,
'fine_prompt': 2,
}
def __init__( self : Dict , lowerCamelCase : str , lowerCamelCase : List[Any]=None )-> Dict:
super().__init__(lowerCamelCase )
snake_case__ : Optional[int] = speaker_embeddings
@classmethod
def __lowerCAmelCase ( cls : Optional[int] , lowerCamelCase : Any , lowerCamelCase : Any="speaker_embeddings_path.json" , **lowerCamelCase : List[Any] )-> List[str]:
if speaker_embeddings_dict_path is not None:
snake_case__ : List[Any] = get_file_from_repo(
lowerCamelCase , lowerCamelCase , subfolder=kwargs.pop("""subfolder""" , lowerCamelCase ) , cache_dir=kwargs.pop("""cache_dir""" , lowerCamelCase ) , force_download=kwargs.pop("""force_download""" , lowerCamelCase ) , proxies=kwargs.pop("""proxies""" , lowerCamelCase ) , resume_download=kwargs.pop("""resume_download""" , lowerCamelCase ) , local_files_only=kwargs.pop("""local_files_only""" , lowerCamelCase ) , use_auth_token=kwargs.pop("""use_auth_token""" , lowerCamelCase ) , revision=kwargs.pop("""revision""" , lowerCamelCase ) , )
if speaker_embeddings_path is None:
logger.warning(
F"""`{os.path.join(lowerCamelCase , lowerCamelCase )}` does not exists
, no preloaded speaker embeddings will be used - Make sure to provide a correct path to the json
dictionnary if wanted, otherwise set `speaker_embeddings_dict_path=None`.""" )
snake_case__ : Dict = None
else:
with open(lowerCamelCase ) as speaker_embeddings_json:
snake_case__ : Optional[Any] = json.load(lowerCamelCase )
else:
snake_case__ : List[str] = None
snake_case__ : Any = AutoTokenizer.from_pretrained(lowerCamelCase , **lowerCamelCase )
return cls(tokenizer=lowerCamelCase , speaker_embeddings=lowerCamelCase )
def __lowerCAmelCase ( self : Tuple , lowerCamelCase : Optional[Any] , lowerCamelCase : List[Any]="speaker_embeddings_path.json" , lowerCamelCase : int="speaker_embeddings" , lowerCamelCase : bool = False , **lowerCamelCase : Tuple , )-> List[Any]:
if self.speaker_embeddings is not None:
os.makedirs(os.path.join(lowerCamelCase , lowerCamelCase , """v2""" ) , exist_ok=lowerCamelCase )
snake_case__ : Tuple = {}
snake_case__ : List[Any] = save_directory
for prompt_key in self.speaker_embeddings:
if prompt_key != "repo_or_path":
snake_case__ : List[Any] = self._load_voice_preset(lowerCamelCase )
snake_case__ : int = {}
for key in self.speaker_embeddings[prompt_key]:
np.save(
os.path.join(
embeddings_dict["""repo_or_path"""] , lowerCamelCase , F"""{prompt_key}_{key}""" ) , voice_preset[key] , allow_pickle=lowerCamelCase , )
snake_case__ : Union[str, Any] = os.path.join(lowerCamelCase , F"""{prompt_key}_{key}.npy""" )
snake_case__ : Any = tmp_dict
with open(os.path.join(lowerCamelCase , lowerCamelCase ) , """w""" ) as fp:
json.dump(lowerCamelCase , lowerCamelCase )
super().save_pretrained(lowerCamelCase , lowerCamelCase , **lowerCamelCase )
def __lowerCAmelCase ( self : str , lowerCamelCase : str = None , **lowerCamelCase : Tuple )-> Dict:
snake_case__ : int = self.speaker_embeddings[voice_preset]
snake_case__ : List[Any] = {}
for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
if key not in voice_preset_paths:
raise ValueError(
F"""Voice preset unrecognized, missing {key} as a key in self.speaker_embeddings[{voice_preset}].""" )
snake_case__ : List[Any] = get_file_from_repo(
self.speaker_embeddings.get("""repo_or_path""" , """/""" ) , voice_preset_paths[key] , subfolder=kwargs.pop("""subfolder""" , lowerCamelCase ) , cache_dir=kwargs.pop("""cache_dir""" , lowerCamelCase ) , force_download=kwargs.pop("""force_download""" , lowerCamelCase ) , proxies=kwargs.pop("""proxies""" , lowerCamelCase ) , resume_download=kwargs.pop("""resume_download""" , lowerCamelCase ) , local_files_only=kwargs.pop("""local_files_only""" , lowerCamelCase ) , use_auth_token=kwargs.pop("""use_auth_token""" , lowerCamelCase ) , revision=kwargs.pop("""revision""" , lowerCamelCase ) , )
if path is None:
raise ValueError(
F"""`{os.path.join(self.speaker_embeddings.get("repo_or_path" , "/" ) , voice_preset_paths[key] )}` does not exists
, no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}
embeddings.""" )
snake_case__ : Union[str, Any] = np.load(lowerCamelCase )
return voice_preset_dict
def __lowerCAmelCase ( self : Tuple , lowerCamelCase : Optional[dict] = None )-> Optional[int]:
for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
if key not in voice_preset:
raise ValueError(F"""Voice preset unrecognized, missing {key} as a key.""" )
if not isinstance(voice_preset[key] , np.ndarray ):
raise ValueError(F"""{key} voice preset must be a {str(self.preset_shape[key] )}D ndarray.""" )
if len(voice_preset[key].shape ) != self.preset_shape[key]:
raise ValueError(F"""{key} voice preset must be a {str(self.preset_shape[key] )}D ndarray.""" )
def __call__( self : Dict , lowerCamelCase : List[str]=None , lowerCamelCase : Tuple=None , lowerCamelCase : List[str]="pt" , lowerCamelCase : int=256 , lowerCamelCase : Optional[Any]=False , lowerCamelCase : int=True , lowerCamelCase : Any=False , **lowerCamelCase : Optional[Any] , )-> Tuple:
if voice_preset is not None and not isinstance(lowerCamelCase , lowerCamelCase ):
if (
isinstance(lowerCamelCase , lowerCamelCase )
and self.speaker_embeddings is not None
and voice_preset in self.speaker_embeddings
):
snake_case__ : Tuple = self._load_voice_preset(lowerCamelCase )
else:
if isinstance(lowerCamelCase , lowerCamelCase ) and not voice_preset.endswith(""".npz""" ):
snake_case__ : List[str] = voice_preset + """.npz"""
snake_case__ : Union[str, Any] = np.load(lowerCamelCase )
if voice_preset is not None:
self._validate_voice_preset_dict(lowerCamelCase , **lowerCamelCase )
snake_case__ : str = BatchFeature(data=lowerCamelCase , tensor_type=lowerCamelCase )
snake_case__ : str = self.tokenizer(
lowerCamelCase , return_tensors=lowerCamelCase , padding="""max_length""" , max_length=lowerCamelCase , return_attention_mask=lowerCamelCase , return_token_type_ids=lowerCamelCase , add_special_tokens=lowerCamelCase , **lowerCamelCase , )
if voice_preset is not None:
snake_case__ : Union[str, Any] = voice_preset
return encoded_text
| 172
|
'''simple docstring'''
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def lowerCAmelCase__ ( UpperCAmelCase , UpperCAmelCase ):
"""simple docstring"""
snake_case__ : Any = torch.load(UpperCAmelCase , map_location="""cpu""" )
snake_case__ : List[Any] = chkpt["""model"""]
# We have the base model one level deeper than the original XLM repository
snake_case__ : List[Any] = {}
for k, v in state_dict.items():
if "pred_layer" in k:
snake_case__ : Union[str, Any] = v
else:
snake_case__ : str = v
snake_case__ : Optional[int] = chkpt["""params"""]
snake_case__ : List[Any] = {n: v for n, v in config.items() if not isinstance(UpperCAmelCase , (torch.FloatTensor, numpy.ndarray) )}
snake_case__ : Any = chkpt["""dico_word2id"""]
snake_case__ : Optional[int] = {s + """</w>""" if s.find("""@@""" ) == -1 and i > 13 else s.replace("""@@""" , """""" ): i for s, i in vocab.items()}
# Save pytorch-model
snake_case__ : str = pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
snake_case__ : Optional[int] = pytorch_dump_folder_path + """/""" + CONFIG_NAME
snake_case__ : str = pytorch_dump_folder_path + """/""" + VOCAB_FILES_NAMES["""vocab_file"""]
print(f"""Save PyTorch model to {pytorch_weights_dump_path}""" )
torch.save(UpperCAmelCase , UpperCAmelCase )
print(f"""Save configuration file to {pytorch_config_dump_path}""" )
with open(UpperCAmelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(UpperCAmelCase , indent=2 ) + """\n""" )
print(f"""Save vocab file to {pytorch_config_dump_path}""" )
with open(UpperCAmelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(UpperCAmelCase , indent=2 ) + """\n""" )
if __name__ == "__main__":
lowerCAmelCase__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--xlm_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.'
)
lowerCAmelCase__ = parser.parse_args()
convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)
| 172
| 1
|
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