code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
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import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...utils import CONFIG_NAME, FEATURE_EXTRACTOR_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,
)
a_ = logging.get_logger(__name__)
a_ = OrderedDict(
[
('''audio-spectrogram-transformer''', '''ASTFeatureExtractor'''),
('''beit''', '''BeitFeatureExtractor'''),
('''chinese_clip''', '''ChineseCLIPFeatureExtractor'''),
('''clap''', '''ClapFeatureExtractor'''),
('''clip''', '''CLIPFeatureExtractor'''),
('''clipseg''', '''ViTFeatureExtractor'''),
('''conditional_detr''', '''ConditionalDetrFeatureExtractor'''),
('''convnext''', '''ConvNextFeatureExtractor'''),
('''cvt''', '''ConvNextFeatureExtractor'''),
('''data2vec-audio''', '''Wav2Vec2FeatureExtractor'''),
('''data2vec-vision''', '''BeitFeatureExtractor'''),
('''deformable_detr''', '''DeformableDetrFeatureExtractor'''),
('''deit''', '''DeiTFeatureExtractor'''),
('''detr''', '''DetrFeatureExtractor'''),
('''dinat''', '''ViTFeatureExtractor'''),
('''donut-swin''', '''DonutFeatureExtractor'''),
('''dpt''', '''DPTFeatureExtractor'''),
('''encodec''', '''EncodecFeatureExtractor'''),
('''flava''', '''FlavaFeatureExtractor'''),
('''glpn''', '''GLPNFeatureExtractor'''),
('''groupvit''', '''CLIPFeatureExtractor'''),
('''hubert''', '''Wav2Vec2FeatureExtractor'''),
('''imagegpt''', '''ImageGPTFeatureExtractor'''),
('''layoutlmv2''', '''LayoutLMv2FeatureExtractor'''),
('''layoutlmv3''', '''LayoutLMv3FeatureExtractor'''),
('''levit''', '''LevitFeatureExtractor'''),
('''maskformer''', '''MaskFormerFeatureExtractor'''),
('''mctct''', '''MCTCTFeatureExtractor'''),
('''mobilenet_v1''', '''MobileNetV1FeatureExtractor'''),
('''mobilenet_v2''', '''MobileNetV2FeatureExtractor'''),
('''mobilevit''', '''MobileViTFeatureExtractor'''),
('''nat''', '''ViTFeatureExtractor'''),
('''owlvit''', '''OwlViTFeatureExtractor'''),
('''perceiver''', '''PerceiverFeatureExtractor'''),
('''poolformer''', '''PoolFormerFeatureExtractor'''),
('''regnet''', '''ConvNextFeatureExtractor'''),
('''resnet''', '''ConvNextFeatureExtractor'''),
('''segformer''', '''SegformerFeatureExtractor'''),
('''sew''', '''Wav2Vec2FeatureExtractor'''),
('''sew-d''', '''Wav2Vec2FeatureExtractor'''),
('''speech_to_text''', '''Speech2TextFeatureExtractor'''),
('''speecht5''', '''SpeechT5FeatureExtractor'''),
('''swiftformer''', '''ViTFeatureExtractor'''),
('''swin''', '''ViTFeatureExtractor'''),
('''swinv2''', '''ViTFeatureExtractor'''),
('''table-transformer''', '''DetrFeatureExtractor'''),
('''timesformer''', '''VideoMAEFeatureExtractor'''),
('''tvlt''', '''TvltFeatureExtractor'''),
('''unispeech''', '''Wav2Vec2FeatureExtractor'''),
('''unispeech-sat''', '''Wav2Vec2FeatureExtractor'''),
('''van''', '''ConvNextFeatureExtractor'''),
('''videomae''', '''VideoMAEFeatureExtractor'''),
('''vilt''', '''ViltFeatureExtractor'''),
('''vit''', '''ViTFeatureExtractor'''),
('''vit_mae''', '''ViTFeatureExtractor'''),
('''vit_msn''', '''ViTFeatureExtractor'''),
('''wav2vec2''', '''Wav2Vec2FeatureExtractor'''),
('''wav2vec2-conformer''', '''Wav2Vec2FeatureExtractor'''),
('''wavlm''', '''Wav2Vec2FeatureExtractor'''),
('''whisper''', '''WhisperFeatureExtractor'''),
('''xclip''', '''CLIPFeatureExtractor'''),
('''yolos''', '''YolosFeatureExtractor'''),
]
)
a_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)
def _a ( UpperCamelCase_ : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
if class_name in extractors:
lowerCAmelCase__ = model_type_to_module_name(_A )
lowerCAmelCase__ = importlib.import_module(F".{module_name}" , "transformers.models" )
try:
return getattr(_A , _A )
except AttributeError:
continue
for _, extractor in FEATURE_EXTRACTOR_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.
lowerCAmelCase__ = importlib.import_module("transformers" )
if hasattr(_A , _A ):
return getattr(_A , _A )
return None
def _a ( UpperCamelCase_ : str , UpperCamelCase_ : Optional[int] = None , UpperCamelCase_ : Tuple = False , UpperCamelCase_ : Any = False , UpperCamelCase_ : List[str] = None , UpperCamelCase_ : int = None , UpperCamelCase_ : Dict = None , UpperCamelCase_ : Optional[int] = False , **UpperCamelCase_ : Dict , ) -> List[str]:
"""simple docstring"""
lowerCAmelCase__ = 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 feature extractor configuration file, will try to use the model config instead." )
return {}
with open(_A , encoding="utf-8" ) as reader:
return json.load(_A )
class lowercase__ :
def __init__( self )-> Any:
'''simple docstring'''
raise EnvironmentError(
"AutoFeatureExtractor is designed to be instantiated "
"using the `AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path)` method." )
@classmethod
@replace_list_option_in_docstrings(__lowerCamelCase )
def UpperCAmelCase ( cls , __UpperCAmelCase , **__UpperCAmelCase )-> List[str]:
'''simple docstring'''
lowerCAmelCase__ = kwargs.pop("config" , __lowerCamelCase )
lowerCAmelCase__ = kwargs.pop("trust_remote_code" , __lowerCamelCase )
lowerCAmelCase__ = True
lowerCAmelCase__ , lowerCAmelCase__ = FeatureExtractionMixin.get_feature_extractor_dict(__lowerCamelCase , **__lowerCamelCase )
lowerCAmelCase__ = config_dict.get("feature_extractor_type" , __lowerCamelCase )
lowerCAmelCase__ = None
if "AutoFeatureExtractor" in config_dict.get("auto_map" , {} ):
lowerCAmelCase__ = config_dict["auto_map"]["AutoFeatureExtractor"]
# If we don't find the feature extractor class in the feature extractor config, let's try the model config.
if feature_extractor_class is None and feature_extractor_auto_map is None:
if not isinstance(__lowerCamelCase , __lowerCamelCase ):
lowerCAmelCase__ = AutoConfig.from_pretrained(__lowerCamelCase , **__lowerCamelCase )
# It could be in `config.feature_extractor_type``
lowerCAmelCase__ = getattr(__lowerCamelCase , "feature_extractor_type" , __lowerCamelCase )
if hasattr(__lowerCamelCase , "auto_map" ) and "AutoFeatureExtractor" in config.auto_map:
lowerCAmelCase__ = config.auto_map["AutoFeatureExtractor"]
if feature_extractor_class is not None:
lowerCAmelCase__ = feature_extractor_class_from_name(__lowerCamelCase )
lowerCAmelCase__ = feature_extractor_auto_map is not None
lowerCAmelCase__ = feature_extractor_class is not None or type(__lowerCamelCase ) in FEATURE_EXTRACTOR_MAPPING
lowerCAmelCase__ = resolve_trust_remote_code(
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
if has_remote_code and trust_remote_code:
lowerCAmelCase__ = get_class_from_dynamic_module(
__lowerCamelCase , __lowerCamelCase , **__lowerCamelCase )
lowerCAmelCase__ = kwargs.pop("code_revision" , __lowerCamelCase )
if os.path.isdir(__lowerCamelCase ):
feature_extractor_class.register_for_auto_class()
return feature_extractor_class.from_dict(__lowerCamelCase , **__lowerCamelCase )
elif feature_extractor_class is not None:
return feature_extractor_class.from_dict(__lowerCamelCase , **__lowerCamelCase )
# Last try: we use the FEATURE_EXTRACTOR_MAPPING.
elif type(__lowerCamelCase ) in FEATURE_EXTRACTOR_MAPPING:
lowerCAmelCase__ = FEATURE_EXTRACTOR_MAPPING[type(__lowerCamelCase )]
return feature_extractor_class.from_dict(__lowerCamelCase , **__lowerCamelCase )
raise ValueError(
F"Unrecognized feature extractor in {pretrained_model_name_or_path}. Should have a "
F"`feature_extractor_type` key in its {FEATURE_EXTRACTOR_NAME} of {CONFIG_NAME}, or one of the following "
F"`model_type` keys in its {CONFIG_NAME}: {', '.join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys() )}" )
@staticmethod
def UpperCAmelCase ( __UpperCAmelCase , __UpperCAmelCase )-> Union[str, Any]:
'''simple docstring'''
FEATURE_EXTRACTOR_MAPPING.register(__lowerCamelCase , __lowerCamelCase )
| 340 |
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging
sys.path.append(os.path.join(os.getcwd())) # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score # noqa: E402 # isort:skip
_SCREAMING_SNAKE_CASE : str = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)
transformers_logging.set_verbosity_info()
def UpperCAmelCase_ ( _A ):
'''simple docstring'''
if "token" in model_name_or_path:
return "rag_token"
if "sequence" in model_name_or_path:
return "rag_sequence"
if "bart" in model_name_or_path:
return "bart"
return None
def UpperCAmelCase_ ( _A , _A , _A ):
'''simple docstring'''
return max(metric_fn(_A , _A ) for gt in ground_truths )
def UpperCAmelCase_ ( _A , _A , _A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = [line.strip() for line in open(_A , '''r''' ).readlines()]
SCREAMING_SNAKE_CASE__ = []
if args.gold_data_mode == "qa":
SCREAMING_SNAKE_CASE__ = pd.read_csv(_A , sep='''\t''' , header=_A )
for answer_list in data[1]:
SCREAMING_SNAKE_CASE__ = ast.literal_eval(_A )
answers.append(_A )
else:
SCREAMING_SNAKE_CASE__ = [line.strip() for line in open(_A , '''r''' ).readlines()]
SCREAMING_SNAKE_CASE__ = [[reference] for reference in references]
SCREAMING_SNAKE_CASE__ = SCREAMING_SNAKE_CASE__ = SCREAMING_SNAKE_CASE__ = 0
for prediction, ground_truths in zip(_A , _A ):
total += 1
em += metric_max_over_ground_truths(_A , _A , _A )
fa += metric_max_over_ground_truths(_A , _A , _A )
SCREAMING_SNAKE_CASE__ = 1_0_0.0 * em / total
SCREAMING_SNAKE_CASE__ = 1_0_0.0 * fa / total
logger.info(F'''F1: {fa:.2f}''' )
logger.info(F'''EM: {em:.2f}''' )
def UpperCAmelCase_ ( _A , _A , _A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = args.k
SCREAMING_SNAKE_CASE__ = [line.strip() for line in open(_A , '''r''' ).readlines()]
SCREAMING_SNAKE_CASE__ = [line.strip() for line in open(_A , '''r''' ).readlines()]
SCREAMING_SNAKE_CASE__ = SCREAMING_SNAKE_CASE__ = 0
for hypo, reference in zip(_A , _A ):
SCREAMING_SNAKE_CASE__ = set(hypo.split('''\t''' )[:k] )
SCREAMING_SNAKE_CASE__ = set(reference.split('''\t''' ) )
total += 1
em += len(hypo_provenance & ref_provenance ) / k
SCREAMING_SNAKE_CASE__ = 1_0_0.0 * em / total
logger.info(F'''Precision@{k}: {em: .2f}''' )
def UpperCAmelCase_ ( _A , _A , _A ):
'''simple docstring'''
def strip_title(_A ):
if title.startswith('''"''' ):
SCREAMING_SNAKE_CASE__ = title[1:]
if title.endswith('''"''' ):
SCREAMING_SNAKE_CASE__ = title[:-1]
return title
SCREAMING_SNAKE_CASE__ = rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
_A , return_tensors='''pt''' , padding=_A , truncation=_A , )['''input_ids'''].to(args.device )
SCREAMING_SNAKE_CASE__ = rag_model.rag.question_encoder(_A )
SCREAMING_SNAKE_CASE__ = question_enc_outputs[0]
SCREAMING_SNAKE_CASE__ = rag_model.retriever(
_A , question_enc_pool_output.cpu().detach().to(torch.floataa ).numpy() , prefix=rag_model.rag.generator.config.prefix , n_docs=rag_model.config.n_docs , return_tensors='''pt''' , )
SCREAMING_SNAKE_CASE__ = rag_model.retriever.index.get_doc_dicts(result.doc_ids )
SCREAMING_SNAKE_CASE__ = []
for docs in all_docs:
SCREAMING_SNAKE_CASE__ = [strip_title(_A ) for title in docs['''title''']]
provenance_strings.append('''\t'''.join(_A ) )
return provenance_strings
def UpperCAmelCase_ ( _A , _A , _A ):
'''simple docstring'''
with torch.no_grad():
SCREAMING_SNAKE_CASE__ = rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
_A , return_tensors='''pt''' , padding=_A , truncation=_A )
SCREAMING_SNAKE_CASE__ = inputs_dict.input_ids.to(args.device )
SCREAMING_SNAKE_CASE__ = inputs_dict.attention_mask.to(args.device )
SCREAMING_SNAKE_CASE__ = rag_model.generate( # rag_model overwrites generate
_A , attention_mask=_A , num_beams=args.num_beams , min_length=args.min_length , max_length=args.max_length , early_stopping=_A , num_return_sequences=1 , bad_words_ids=[[0, 0]] , )
SCREAMING_SNAKE_CASE__ = rag_model.retriever.generator_tokenizer.batch_decode(_A , skip_special_tokens=_A )
if args.print_predictions:
for q, a in zip(_A , _A ):
logger.info('''Q: {} - A: {}'''.format(_A , _A ) )
return answers
def UpperCAmelCase_ ( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = argparse.ArgumentParser()
parser.add_argument(
'''--model_type''' , choices=['''rag_sequence''', '''rag_token''', '''bart'''] , type=_A , help=(
'''RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the'''
''' model_name_or_path'''
) , )
parser.add_argument(
'''--index_name''' , default=_A , choices=['''exact''', '''compressed''', '''legacy'''] , type=_A , help='''RAG model retriever type''' , )
parser.add_argument(
'''--index_path''' , default=_A , type=_A , help='''Path to the retrieval index''' , )
parser.add_argument('''--n_docs''' , default=5 , type=_A , help='''Number of retrieved docs''' )
parser.add_argument(
'''--model_name_or_path''' , default=_A , type=_A , required=_A , help='''Path to pretrained checkpoints or model identifier from huggingface.co/models''' , )
parser.add_argument(
'''--eval_mode''' , choices=['''e2e''', '''retrieval'''] , default='''e2e''' , type=_A , help=(
'''Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates'''
''' precision@k.'''
) , )
parser.add_argument('''--k''' , default=1 , type=_A , help='''k for the precision@k calculation''' )
parser.add_argument(
'''--evaluation_set''' , default=_A , type=_A , required=_A , help='''Path to a file containing evaluation samples''' , )
parser.add_argument(
'''--gold_data_path''' , default=_A , type=_A , required=_A , help='''Path to a tab-separated file with gold samples''' , )
parser.add_argument(
'''--gold_data_mode''' , default='''qa''' , type=_A , choices=['''qa''', '''ans'''] , help=(
'''Format of the gold data file'''
'''qa - a single line in the following format: question [tab] answer_list'''
'''ans - a single line of the gold file contains the expected answer string'''
) , )
parser.add_argument(
'''--predictions_path''' , type=_A , default='''predictions.txt''' , help='''Name of the predictions file, to be stored in the checkpoints directory''' , )
parser.add_argument(
'''--eval_all_checkpoints''' , action='''store_true''' , help='''Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number''' , )
parser.add_argument(
'''--eval_batch_size''' , default=8 , type=_A , help='''Batch size per GPU/CPU for evaluation.''' , )
parser.add_argument(
'''--recalculate''' , help='''Recalculate predictions even if the prediction file exists''' , action='''store_true''' , )
parser.add_argument(
'''--num_beams''' , default=4 , type=_A , help='''Number of beams to be used when generating answers''' , )
parser.add_argument('''--min_length''' , default=1 , type=_A , help='''Min length of the generated answers''' )
parser.add_argument('''--max_length''' , default=50 , type=_A , help='''Max length of the generated answers''' )
parser.add_argument(
'''--print_predictions''' , action='''store_true''' , help='''If True, prints predictions while evaluating.''' , )
parser.add_argument(
'''--print_docs''' , action='''store_true''' , help='''If True, prints docs retried while generating.''' , )
SCREAMING_SNAKE_CASE__ = parser.parse_args()
SCREAMING_SNAKE_CASE__ = torch.device('''cuda''' if torch.cuda.is_available() else '''cpu''' )
return args
def UpperCAmelCase_ ( _A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = {}
if args.model_type is None:
SCREAMING_SNAKE_CASE__ = infer_model_type(args.model_name_or_path )
assert args.model_type is not None
if args.model_type.startswith('''rag''' ):
SCREAMING_SNAKE_CASE__ = RagTokenForGeneration if args.model_type == '''rag_token''' else RagSequenceForGeneration
SCREAMING_SNAKE_CASE__ = args.n_docs
if args.index_name is not None:
SCREAMING_SNAKE_CASE__ = args.index_name
if args.index_path is not None:
SCREAMING_SNAKE_CASE__ = args.index_path
else:
SCREAMING_SNAKE_CASE__ = BartForConditionalGeneration
SCREAMING_SNAKE_CASE__ = (
[f.path for f in os.scandir(args.model_name_or_path ) if f.is_dir()]
if args.eval_all_checkpoints
else [args.model_name_or_path]
)
logger.info('''Evaluate the following checkpoints: %s''' , _A )
SCREAMING_SNAKE_CASE__ = get_scores if args.eval_mode == '''e2e''' else get_precision_at_k
SCREAMING_SNAKE_CASE__ = evaluate_batch_eae if args.eval_mode == '''e2e''' else evaluate_batch_retrieval
for checkpoint in checkpoints:
if os.path.exists(args.predictions_path ) and (not args.recalculate):
logger.info('''Calculating metrics based on an existing predictions file: {}'''.format(args.predictions_path ) )
score_fn(_A , args.predictions_path , args.gold_data_path )
continue
logger.info('''***** Running evaluation for {} *****'''.format(_A ) )
logger.info(''' Batch size = %d''' , args.eval_batch_size )
logger.info(''' Predictions will be stored under {}'''.format(args.predictions_path ) )
if args.model_type.startswith('''rag''' ):
SCREAMING_SNAKE_CASE__ = RagRetriever.from_pretrained(_A , **_A )
SCREAMING_SNAKE_CASE__ = model_class.from_pretrained(_A , retriever=_A , **_A )
model.retriever.init_retrieval()
else:
SCREAMING_SNAKE_CASE__ = model_class.from_pretrained(_A , **_A )
model.to(args.device )
with open(args.evaluation_set , '''r''' ) as eval_file, open(args.predictions_path , '''w''' ) as preds_file:
SCREAMING_SNAKE_CASE__ = []
for line in tqdm(_A ):
questions.append(line.strip() )
if len(_A ) == args.eval_batch_size:
SCREAMING_SNAKE_CASE__ = evaluate_batch_fn(_A , _A , _A )
preds_file.write('''\n'''.join(_A ) + '''\n''' )
preds_file.flush()
SCREAMING_SNAKE_CASE__ = []
if len(_A ) > 0:
SCREAMING_SNAKE_CASE__ = evaluate_batch_fn(_A , _A , _A )
preds_file.write('''\n'''.join(_A ) )
preds_file.flush()
score_fn(_A , args.predictions_path , args.gold_data_path )
if __name__ == "__main__":
_SCREAMING_SNAKE_CASE : int = get_args()
main(args)
| 314 | 0 |
"""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,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
_UpperCamelCase = logging.get_logger(__name__)
if is_vision_available():
import PIL
class lowerCamelCase__ ( _lowerCAmelCase ):
SCREAMING_SNAKE_CASE = ["pixel_values"]
def __init__( self ,A = True ,A = None ,A = PILImageResampling.BICUBIC ,A = True ,A = None ,A = True ,A = 1 / 255 ,A = True ,A = None ,A = None ,A = True ,**A ,):
super().__init__(**_lowercase )
UpperCAmelCase = size if size is not None else {"""shortest_edge""": 224}
UpperCAmelCase = get_size_dict(_lowercase ,default_to_square=_lowercase )
UpperCAmelCase = crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
UpperCAmelCase = get_size_dict(_lowercase ,default_to_square=_lowercase ,param_name="""crop_size""" )
UpperCAmelCase = do_resize
UpperCAmelCase = size
UpperCAmelCase = resample
UpperCAmelCase = do_center_crop
UpperCAmelCase = crop_size
UpperCAmelCase = do_rescale
UpperCAmelCase = rescale_factor
UpperCAmelCase = do_normalize
UpperCAmelCase = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
UpperCAmelCase = image_std if image_std is not None else OPENAI_CLIP_STD
UpperCAmelCase = do_convert_rgb
def _UpperCamelCase ( self ,A ,A ,A = PILImageResampling.BICUBIC ,A = None ,**A ,):
UpperCAmelCase = get_size_dict(_lowercase ,default_to_square=_lowercase )
if "shortest_edge" not in size:
raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}''' )
UpperCAmelCase = get_resize_output_image_size(_lowercase ,size=size["""shortest_edge"""] ,default_to_square=_lowercase )
return resize(_lowercase ,size=_lowercase ,resample=_lowercase ,data_format=_lowercase ,**_lowercase )
def _UpperCamelCase ( self ,A ,A ,A = None ,**A ,):
UpperCAmelCase = get_size_dict(_lowercase )
if "height" not in size or "width" not in size:
raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}''' )
return center_crop(_lowercase ,size=(size["""height"""], size["""width"""]) ,data_format=_lowercase ,**_lowercase )
def _UpperCamelCase ( self ,A ,A ,A = None ,**A ,):
return rescale(_lowercase ,scale=_lowercase ,data_format=_lowercase ,**_lowercase )
def _UpperCamelCase ( self ,A ,A ,A ,A = None ,**A ,):
return normalize(_lowercase ,mean=_lowercase ,std=_lowercase ,data_format=_lowercase ,**_lowercase )
def _UpperCamelCase ( self ,A ,A = None ,A = None ,A = None ,A = None ,A = None ,A = None ,A = None ,A = None ,A = None ,A = None ,A = None ,A = None ,A = ChannelDimension.FIRST ,**A ,):
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(_lowercase ,param_name="""size""" ,default_to_square=_lowercase )
UpperCAmelCase = resample if resample is not None else self.resample
UpperCAmelCase = do_center_crop if do_center_crop is not None else self.do_center_crop
UpperCAmelCase = crop_size if crop_size is not None else self.crop_size
UpperCAmelCase = get_size_dict(_lowercase ,param_name="""crop_size""" ,default_to_square=_lowercase )
UpperCAmelCase = do_rescale if do_rescale is not None else self.do_rescale
UpperCAmelCase = rescale_factor if rescale_factor is not None else self.rescale_factor
UpperCAmelCase = do_normalize if do_normalize is not None else self.do_normalize
UpperCAmelCase = image_mean if image_mean is not None else self.image_mean
UpperCAmelCase = image_std if image_std is not None else self.image_std
UpperCAmelCase = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
UpperCAmelCase = make_list_of_images(_lowercase )
if not valid_images(_lowercase ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
if do_resize and size is None:
raise ValueError("""Size must be specified if do_resize is True.""" )
if do_center_crop and crop_size is None:
raise ValueError("""Crop size must be specified if do_center_crop is True.""" )
if do_rescale and rescale_factor is None:
raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("""Image mean and std must be specified if do_normalize is True.""" )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
UpperCAmelCase = [convert_to_rgb(_lowercase ) for image in images]
# All transformations expect numpy arrays.
UpperCAmelCase = [to_numpy_array(_lowercase ) for image in images]
if do_resize:
UpperCAmelCase = [self.resize(image=_lowercase ,size=_lowercase ,resample=_lowercase ) for image in images]
if do_center_crop:
UpperCAmelCase = [self.center_crop(image=_lowercase ,size=_lowercase ) for image in images]
if do_rescale:
UpperCAmelCase = [self.rescale(image=_lowercase ,scale=_lowercase ) for image in images]
if do_normalize:
UpperCAmelCase = [self.normalize(image=_lowercase ,mean=_lowercase ,std=_lowercase ) for image in images]
UpperCAmelCase = [to_channel_dimension_format(_lowercase ,_lowercase ) for image in images]
UpperCAmelCase = {"""pixel_values""": images}
return BatchFeature(data=_lowercase ,tensor_type=_lowercase )
| 370 |
"""simple docstring"""
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures
# Importing the dataset
_UpperCamelCase = pd.read_csv(
"""https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/"""
"""position_salaries.csv"""
)
_UpperCamelCase = dataset.iloc[:, 1:2].values
_UpperCamelCase = dataset.iloc[:, 2].values
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase = train_test_split(X, y, test_size=0.2, random_state=0)
_UpperCamelCase = PolynomialFeatures(degree=4)
_UpperCamelCase = poly_reg.fit_transform(X)
_UpperCamelCase = LinearRegression()
pol_reg.fit(X_poly, y)
def _a ( ):
"""simple docstring"""
plt.scatter(_snake_case , _snake_case , color="""red""" )
plt.plot(_snake_case , pol_reg.predict(poly_reg.fit_transform(_snake_case ) ) , color="""blue""" )
plt.title("""Truth or Bluff (Linear Regression)""" )
plt.xlabel("""Position level""" )
plt.ylabel("""Salary""" )
plt.show()
if __name__ == "__main__":
viz_polymonial()
# Predicting a new result with Polymonial Regression
pol_reg.predict(poly_reg.fit_transform([[5.5]]))
# output should be 132148.43750003
| 234 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCAmelCase = {
'configuration_clipseg': [
'CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP',
'CLIPSegConfig',
'CLIPSegTextConfig',
'CLIPSegVisionConfig',
],
'processing_clipseg': ['CLIPSegProcessor'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase = [
'CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST',
'CLIPSegModel',
'CLIPSegPreTrainedModel',
'CLIPSegTextModel',
'CLIPSegVisionModel',
'CLIPSegForImageSegmentation',
]
if TYPE_CHECKING:
from .configuration_clipseg import (
CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
CLIPSegConfig,
CLIPSegTextConfig,
CLIPSegVisionConfig,
)
from .processing_clipseg import CLIPSegProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_clipseg import (
CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
CLIPSegForImageSegmentation,
CLIPSegModel,
CLIPSegPreTrainedModel,
CLIPSegTextModel,
CLIPSegVisionModel,
)
else:
import sys
__lowerCAmelCase = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 341 |
'''simple docstring'''
from __future__ import annotations
def __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE ):
if len(_SCREAMING_SNAKE_CASE ) == 0:
return []
_snake_case, _snake_case = min(_SCREAMING_SNAKE_CASE ), max(_SCREAMING_SNAKE_CASE )
_snake_case = int(max_value - min_value ) + 1
_snake_case = [[] for _ in range(_SCREAMING_SNAKE_CASE )]
for i in my_list:
buckets[int(i - min_value )].append(_SCREAMING_SNAKE_CASE )
return [v for bucket in buckets for v in sorted(_SCREAMING_SNAKE_CASE )]
if __name__ == "__main__":
from doctest import testmod
testmod()
assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
assert bucket_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15] | 341 | 1 |
import re
from filelock import FileLock
try:
import nltk
_lowerCamelCase = True
except (ImportError, ModuleNotFoundError):
_lowerCamelCase = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('punkt', quiet=True)
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : str ) -> str:
re.sub('''<n>''' , '''''' , __UpperCamelCase ) # remove pegasus newline char
assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
return "\n".join(nltk.sent_tokenize(__UpperCamelCase ) )
| 351 |
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : Dict=2_8123 ) -> str:
UpperCAmelCase_ = [1] * (limit + 1)
for i in range(2 , int(limit**0.5 ) + 1 ):
sum_divs[i * i] += i
for k in range(i + 1 , limit // i + 1 ):
sum_divs[k * i] += k + i
UpperCAmelCase_ = set()
UpperCAmelCase_ = 0
for n in range(1 , limit + 1 ):
if sum_divs[n] > n:
abundants.add(__UpperCamelCase )
if not any((n - a in abundants) for a in abundants ):
res += n
return res
if __name__ == "__main__":
print(solution())
| 177 | 0 |
def UpperCamelCase ( __magic_name__ : float , __magic_name__ : float ) -> float:
"""simple docstring"""
return price * (1 + tax_rate)
if __name__ == "__main__":
print(F'{price_plus_tax(1_0_0, 0.25) = }')
print(F'{price_plus_tax(125.50, 0.05) = }')
| 305 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import require_elasticsearch, require_faiss
UpperCAmelCase__ = pytest.mark.integration
@require_faiss
class lowercase_ ( lowercase ):
'''simple docstring'''
def __lowerCAmelCase ( self : Tuple ) ->Any:
"""simple docstring"""
a = Dataset.from_dict({'''filename''': ['''my_name-train''' + '''_''' + str(__UpperCAmelCase ) for x in np.arange(30 ).tolist()]} )
return dset
def __lowerCAmelCase ( self : Tuple ) ->Any:
"""simple docstring"""
import faiss
a = self._create_dummy_dataset()
a = dset.map(
lambda __UpperCAmelCase , __UpperCAmelCase : {"vecs": i * np.ones(5 , dtype=np.floataa )} , with_indices=__UpperCAmelCase , keep_in_memory=__UpperCAmelCase )
a = dset.add_faiss_index('''vecs''' , batch_size=100 , metric_type=faiss.METRIC_INNER_PRODUCT )
a , a = dset.get_nearest_examples('''vecs''' , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples['''filename'''][0] , '''my_name-train_29''' )
dset.drop_index('''vecs''' )
def __lowerCAmelCase ( self : Union[str, Any] ) ->Optional[Any]:
"""simple docstring"""
import faiss
a = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1 , 1 ) , index_name='''vecs''' , batch_size=100 , metric_type=faiss.METRIC_INNER_PRODUCT , )
a , a = dset.get_nearest_examples('''vecs''' , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples['''filename'''][0] , '''my_name-train_29''' )
def __lowerCAmelCase ( self : Any ) ->Union[str, Any]:
"""simple docstring"""
import faiss
a = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1 , 1 ) , index_name='''vecs''' , metric_type=faiss.METRIC_INNER_PRODUCT , )
# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
# see https://bugs.python.org/issue14243 and
# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
with tempfile.NamedTemporaryFile(delete=__UpperCAmelCase ) as tmp_file:
dset.save_faiss_index('''vecs''' , tmp_file.name )
dset.load_faiss_index('''vecs2''' , tmp_file.name )
os.unlink(tmp_file.name )
a , a = dset.get_nearest_examples('''vecs2''' , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples['''filename'''][0] , '''my_name-train_29''' )
def __lowerCAmelCase ( self : Optional[Any] ) ->Any:
"""simple docstring"""
a = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1 , 1 ) , index_name='''vecs''' )
dset.drop_index('''vecs''' )
self.assertRaises(__UpperCAmelCase , partial(dset.get_nearest_examples , '''vecs2''' , np.ones(5 , dtype=np.floataa ) ) )
def __lowerCAmelCase ( self : List[Any] ) ->List[str]:
"""simple docstring"""
from elasticsearch import Elasticsearch
a = self._create_dummy_dataset()
with patch('''elasticsearch.Elasticsearch.search''' ) as mocked_search, patch(
'''elasticsearch.client.IndicesClient.create''' ) as mocked_index_create, patch('''elasticsearch.helpers.streaming_bulk''' ) as mocked_bulk:
a = {'''acknowledged''': True}
mocked_bulk.return_value([(True, None)] * 30 )
a = {'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 29}]}}
a = Elasticsearch()
dset.add_elasticsearch_index('''filename''' , es_client=__UpperCAmelCase )
a , a = dset.get_nearest_examples('''filename''' , '''my_name-train_29''' )
self.assertEqual(examples['''filename'''][0] , '''my_name-train_29''' )
@require_faiss
class lowercase_ ( lowercase ):
'''simple docstring'''
def __lowerCAmelCase ( self : Any ) ->Any:
"""simple docstring"""
import faiss
a = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
# add vectors
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsNotNone(index.faiss_index )
self.assertEqual(index.faiss_index.ntotal , 5 )
index.add_vectors(np.zeros((5, 5) , dtype=np.floataa ) )
self.assertEqual(index.faiss_index.ntotal , 10 )
# single query
a = np.zeros(5 , dtype=np.floataa )
a = 1
a , a = index.search(__UpperCAmelCase )
self.assertRaises(__UpperCAmelCase , index.search , query.reshape(-1 , 1 ) )
self.assertGreater(scores[0] , 0 )
self.assertEqual(indices[0] , 1 )
# batched queries
a = np.eye(5 , dtype=np.floataa )[::-1]
a , a = index.search_batch(__UpperCAmelCase )
self.assertRaises(__UpperCAmelCase , index.search_batch , queries[0] )
a = [scores[0] for scores in total_scores]
a = [indices[0] for indices in total_indices]
self.assertGreater(np.min(__UpperCAmelCase ) , 0 )
self.assertListEqual([4, 3, 2, 1, 0] , __UpperCAmelCase )
def __lowerCAmelCase ( self : Any ) ->List[Any]:
"""simple docstring"""
import faiss
a = FaissIndex(string_factory='''Flat''' )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexFlat )
a = FaissIndex(string_factory='''LSH''' )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexLSH )
with self.assertRaises(__UpperCAmelCase ):
a = FaissIndex(string_factory='''Flat''' , custom_index=faiss.IndexFlat(5 ) )
def __lowerCAmelCase ( self : int ) ->Optional[Any]:
"""simple docstring"""
import faiss
a = faiss.IndexFlat(5 )
a = FaissIndex(custom_index=__UpperCAmelCase )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexFlat )
def __lowerCAmelCase ( self : int ) ->Dict:
"""simple docstring"""
import faiss
a = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
# see https://bugs.python.org/issue14243 and
# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
with tempfile.NamedTemporaryFile(delete=__UpperCAmelCase ) as tmp_file:
index.save(tmp_file.name )
a = FaissIndex.load(tmp_file.name )
os.unlink(tmp_file.name )
a = np.zeros(5 , dtype=np.floataa )
a = 1
a , a = index.search(__UpperCAmelCase )
self.assertGreater(scores[0] , 0 )
self.assertEqual(indices[0] , 1 )
@require_faiss
def _a ( a :Dict ) -> Any:
import faiss
a = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
a = '''index.faiss'''
a = F"""mock://{index_name}"""
index.save(a , storage_options=mockfs.storage_options )
a = FaissIndex.load(a , storage_options=mockfs.storage_options )
a = np.zeros(5 , dtype=np.floataa )
a = 1
a , a = index.search(a )
assert scores[0] > 0
assert indices[0] == 1
@require_elasticsearch
class lowercase_ ( lowercase ):
'''simple docstring'''
def __lowerCAmelCase ( self : int ) ->List[Any]:
"""simple docstring"""
from elasticsearch import Elasticsearch
with patch('''elasticsearch.Elasticsearch.search''' ) as mocked_search, patch(
'''elasticsearch.client.IndicesClient.create''' ) as mocked_index_create, patch('''elasticsearch.helpers.streaming_bulk''' ) as mocked_bulk:
a = Elasticsearch()
a = {'''acknowledged''': True}
a = ElasticSearchIndex(es_client=__UpperCAmelCase )
mocked_bulk.return_value([(True, None)] * 3 )
index.add_documents(['''foo''', '''bar''', '''foobar'''] )
# single query
a = '''foo'''
a = {'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 0}]}}
a , a = index.search(__UpperCAmelCase )
self.assertEqual(scores[0] , 1 )
self.assertEqual(indices[0] , 0 )
# single query with timeout
a = '''foo'''
a = {'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 0}]}}
a , a = index.search(__UpperCAmelCase , request_timeout=30 )
self.assertEqual(scores[0] , 1 )
self.assertEqual(indices[0] , 0 )
# batched queries
a = ['''foo''', '''bar''', '''foobar''']
a = {'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 1}]}}
a , a = index.search_batch(__UpperCAmelCase )
a = [scores[0] for scores in total_scores]
a = [indices[0] for indices in total_indices]
self.assertGreater(np.min(__UpperCAmelCase ) , 0 )
self.assertListEqual([1, 1, 1] , __UpperCAmelCase )
# batched queries with timeout
a = ['''foo''', '''bar''', '''foobar''']
a = {'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 1}]}}
a , a = index.search_batch(__UpperCAmelCase , request_timeout=30 )
a = [scores[0] for scores in total_scores]
a = [indices[0] for indices in total_indices]
self.assertGreater(np.min(__UpperCAmelCase ) , 0 )
self.assertListEqual([1, 1, 1] , __UpperCAmelCase )
| 0 | 0 |
def lowercase__ ( __snake_case : str , __snake_case : str ):
'''simple docstring'''
def get_matched_characters(__snake_case : str , __snake_case : str ) -> str:
UpperCAmelCase_ : List[Any] = []
UpperCAmelCase_ : Any = min(len(_stra ) , len(_stra ) ) // 2
for i, l in enumerate(_stra ):
UpperCAmelCase_ : str = int(max(0 , i - limit ) )
UpperCAmelCase_ : Optional[Any] = int(min(i + limit + 1 , len(_stra ) ) )
if l in _stra[left:right]:
matched.append(__snake_case )
UpperCAmelCase_ : List[str] = F"{_stra[0:_stra.index(__snake_case )]} {_stra[_stra.index(__snake_case ) + 1:]}"
return "".join(__snake_case )
# matching characters
UpperCAmelCase_ : List[Any] = get_matched_characters(__snake_case , __snake_case )
UpperCAmelCase_ : Union[str, Any] = get_matched_characters(__snake_case , __snake_case )
UpperCAmelCase_ : List[Any] = len(__snake_case )
# transposition
UpperCAmelCase_ : List[str] = (
len([(ca, ca) for ca, ca in zip(__snake_case , __snake_case ) if ca != ca] ) // 2
)
if not match_count:
UpperCAmelCase_ : Optional[int] = 0.0
else:
UpperCAmelCase_ : Tuple = (
1
/ 3
* (
match_count / len(__snake_case )
+ match_count / len(__snake_case )
+ (match_count - transpositions) / match_count
)
)
# common prefix up to 4 characters
UpperCAmelCase_ : Optional[Any] = 0
for ca, ca in zip(stra[:4] , stra[:4] ):
if ca == ca:
prefix_len += 1
else:
break
return jaro + 0.1 * prefix_len * (1 - jaro)
if __name__ == "__main__":
import doctest
doctest.testmod()
print(jaro_winkler('hello', 'world'))
| 357 |
def lowercase__ ( __snake_case : Tuple , __snake_case : List[Any] ):
'''simple docstring'''
UpperCAmelCase_ : Any = ''
for i in table:
res += inp[i - 1]
return res
def lowercase__ ( __snake_case : Optional[int] ):
'''simple docstring'''
return data[1:] + data[0]
def lowercase__ ( __snake_case : Union[str, Any] , __snake_case : Optional[int] ):
'''simple docstring'''
UpperCAmelCase_ : Any = ''
for i in range(len(__snake_case ) ):
if a[i] == b[i]:
res += "0"
else:
res += "1"
return res
def lowercase__ ( __snake_case : Dict , __snake_case : Optional[int] ):
'''simple docstring'''
UpperCAmelCase_ : Optional[Any] = int('0b' + data[0] + data[-1] , 2 )
UpperCAmelCase_ : Any = int('0b' + data[1:3] , 2 )
return bin(s[row][col] )[2:]
def lowercase__ ( __snake_case : Tuple , __snake_case : List[Any] , __snake_case : List[Any] , __snake_case : Optional[int] , __snake_case : str ):
'''simple docstring'''
UpperCAmelCase_ : Union[str, Any] = message[:4]
UpperCAmelCase_ : Optional[int] = message[4:]
UpperCAmelCase_ : Union[str, Any] = apply_table(__snake_case , __snake_case )
UpperCAmelCase_ : Union[str, Any] = xor(__snake_case , __snake_case )
UpperCAmelCase_ : Dict = apply_sbox(__snake_case , temp[:4] ) # noqa: E741
UpperCAmelCase_ : Any = apply_sbox(__snake_case , temp[4:] )
UpperCAmelCase_ : str = '0' * (2 - len(__snake_case )) + l # noqa: E741
UpperCAmelCase_ : int = '0' * (2 - len(__snake_case )) + r
UpperCAmelCase_ : int = apply_table(l + r , __snake_case )
UpperCAmelCase_ : int = xor(__snake_case , __snake_case )
return temp + right
if __name__ == "__main__":
__UpperCAmelCase = input('Enter 10 bit key: ')
__UpperCAmelCase = input('Enter 8 bit message: ')
__UpperCAmelCase = [6, 3, 7, 4, 8, 5, 10, 9]
__UpperCAmelCase = [3, 5, 2, 7, 4, 10, 1, 9, 8, 6]
__UpperCAmelCase = [2, 4, 3, 1]
__UpperCAmelCase = [2, 6, 3, 1, 4, 8, 5, 7]
__UpperCAmelCase = [4, 1, 3, 5, 7, 2, 8, 6]
__UpperCAmelCase = [4, 1, 2, 3, 2, 3, 4, 1]
__UpperCAmelCase = [[1, 0, 3, 2], [3, 2, 1, 0], [0, 2, 1, 3], [3, 1, 3, 2]]
__UpperCAmelCase = [[0, 1, 2, 3], [2, 0, 1, 3], [3, 0, 1, 0], [2, 1, 0, 3]]
# key generation
__UpperCAmelCase = apply_table(key, paa_table)
__UpperCAmelCase = temp[:5]
__UpperCAmelCase = temp[5:]
__UpperCAmelCase = left_shift(left)
__UpperCAmelCase = left_shift(right)
__UpperCAmelCase = apply_table(left + right, pa_table)
__UpperCAmelCase = left_shift(left)
__UpperCAmelCase = left_shift(right)
__UpperCAmelCase = left_shift(left)
__UpperCAmelCase = left_shift(right)
__UpperCAmelCase = apply_table(left + right, pa_table)
# encryption
__UpperCAmelCase = apply_table(message, IP)
__UpperCAmelCase = function(expansion, sa, sa, keya, temp)
__UpperCAmelCase = temp[4:] + temp[:4]
__UpperCAmelCase = function(expansion, sa, sa, keya, temp)
__UpperCAmelCase = apply_table(temp, IP_inv)
print('Cipher text is:', CT)
# decryption
__UpperCAmelCase = apply_table(CT, IP)
__UpperCAmelCase = function(expansion, sa, sa, keya, temp)
__UpperCAmelCase = temp[4:] + temp[:4]
__UpperCAmelCase = function(expansion, sa, sa, keya, temp)
__UpperCAmelCase = apply_table(temp, IP_inv)
print('Plain text after decypting is:', PT)
| 145 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'facebook/dpr-ctx_encoder-single-nq-base': (
'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json'
),
'facebook/dpr-question_encoder-single-nq-base': (
'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json'
),
'facebook/dpr-reader-single-nq-base': (
'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json'
),
'facebook/dpr-ctx_encoder-multiset-base': (
'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json'
),
'facebook/dpr-question_encoder-multiset-base': (
'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json'
),
'facebook/dpr-reader-multiset-base': (
'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json'
),
}
class _lowerCamelCase ( lowercase_ ):
UpperCAmelCase_ = "dpr"
def __init__(self , __a=3_05_22 , __a=7_68 , __a=12 , __a=12 , __a=30_72 , __a="gelu" , __a=0.1 , __a=0.1 , __a=5_12 , __a=2 , __a=0.02 , __a=1e-1_2 , __a=0 , __a="absolute" , __a = 0 , **__a , ) -> Union[str, Any]:
super().__init__(pad_token_id=__UpperCamelCase , **__UpperCamelCase )
UpperCamelCase = vocab_size
UpperCamelCase = hidden_size
UpperCamelCase = num_hidden_layers
UpperCamelCase = num_attention_heads
UpperCamelCase = hidden_act
UpperCamelCase = intermediate_size
UpperCamelCase = hidden_dropout_prob
UpperCamelCase = attention_probs_dropout_prob
UpperCamelCase = max_position_embeddings
UpperCamelCase = type_vocab_size
UpperCamelCase = initializer_range
UpperCamelCase = layer_norm_eps
UpperCamelCase = projection_dim
UpperCamelCase = position_embedding_type
| 153 |
"""simple docstring"""
class _UpperCAmelCase :
def __init__( self :List[str] , __UpperCamelCase :Union[str, Any] , __UpperCamelCase :Tuple ):
A = name
A = val
def __str__( self :str ):
return f"{self.__class__.__name__}({self.name}, {self.val})"
def __lt__( self :List[Any] , __UpperCamelCase :Union[str, Any] ):
return self.val < other.val
class _UpperCAmelCase :
def __init__( self :List[str] , __UpperCamelCase :Optional[Any] ):
A = {}
A = {}
A = self.build_heap(__UpperCamelCase )
def __getitem__( self :int , __UpperCamelCase :Optional[int] ):
return self.get_value(__UpperCamelCase )
def lowerCamelCase ( self :List[Any] , __UpperCamelCase :str ):
return (idx - 1) // 2
def lowerCamelCase ( self :int , __UpperCamelCase :Optional[Any] ):
return idx * 2 + 1
def lowerCamelCase ( self :Union[str, Any] , __UpperCamelCase :Optional[int] ):
return idx * 2 + 2
def lowerCamelCase ( self :Optional[Any] , __UpperCamelCase :str ):
return self.heap_dict[key]
def lowerCamelCase ( self :int , __UpperCamelCase :Optional[Any] ):
A = len(__UpperCamelCase ) - 1
A = self.get_parent_idx(__UpperCamelCase )
for idx, i in enumerate(__UpperCamelCase ):
A = idx
A = i.val
for i in range(__UpperCamelCase , -1 , -1 ):
self.sift_down(__UpperCamelCase , __UpperCamelCase )
return array
def lowerCamelCase ( self :str , __UpperCamelCase :Optional[Any] , __UpperCamelCase :Dict ):
while True:
A = self.get_left_child_idx(__UpperCamelCase ) # noqa: E741
A = self.get_right_child_idx(__UpperCamelCase )
A = idx
if l < len(__UpperCamelCase ) and array[l] < array[idx]:
A = l
if r < len(__UpperCamelCase ) and array[r] < array[smallest]:
A = r
if smallest != idx:
A, A = array[smallest], array[idx]
(
(
A
), (
A
),
) = (
self.idx_of_element[array[smallest]],
self.idx_of_element[array[idx]],
)
A = smallest
else:
break
def lowerCamelCase ( self :Optional[Any] , __UpperCamelCase :Optional[int] ):
A = self.get_parent_idx(__UpperCamelCase )
while p >= 0 and self.heap[p] > self.heap[idx]:
A, A = self.heap[idx], self.heap[p]
A, A = (
self.idx_of_element[self.heap[idx]],
self.idx_of_element[self.heap[p]],
)
A = p
A = self.get_parent_idx(__UpperCamelCase )
def lowerCamelCase ( self :Any ):
return self.heap[0]
def lowerCamelCase ( self :Tuple ):
A, A = self.heap[-1], self.heap[0]
A, A = (
self.idx_of_element[self.heap[-1]],
self.idx_of_element[self.heap[0]],
)
A = self.heap.pop()
del self.idx_of_element[x]
self.sift_down(0 , self.heap )
return x
def lowerCamelCase ( self :Optional[int] , __UpperCamelCase :Optional[int] ):
self.heap.append(__UpperCamelCase )
A = len(self.heap ) - 1
A = node.val
self.sift_up(len(self.heap ) - 1 )
def lowerCamelCase ( self :Tuple ):
return len(self.heap ) == 0
def lowerCamelCase ( self :Any , __UpperCamelCase :str , __UpperCamelCase :Dict ):
assert (
self.heap[self.idx_of_element[node]].val > new_value
), "newValue must be less that current value"
A = new_value
A = new_value
self.sift_up(self.idx_of_element[node] )
_snake_case : Optional[int] = Node('R', -1)
_snake_case : Tuple = Node('B', 6)
_snake_case : Tuple = Node('A', 3)
_snake_case : Optional[int] = Node('X', 1)
_snake_case : List[Any] = Node('E', 4)
# Use one of these two ways to generate Min-Heap
# Generating Min-Heap from array
_snake_case : Tuple = MinHeap([r, b, a, x, e])
# Generating Min-Heap by Insert method
# myMinHeap.insert(a)
# myMinHeap.insert(b)
# myMinHeap.insert(x)
# myMinHeap.insert(r)
# myMinHeap.insert(e)
# Before
print('Min Heap - before decrease key')
for i in my_min_heap.heap:
print(i)
print('Min Heap - After decrease key of node [B -> -17]')
my_min_heap.decrease_key(b, -17)
# After
for i in my_min_heap.heap:
print(i)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 292 | 0 |
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
def A ( lowercase ) -> Dict:
'''simple docstring'''
UpperCamelCase , UpperCamelCase = image.size
UpperCamelCase , UpperCamelCase = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32
UpperCamelCase = image.resize((w, h) , resample=PIL_INTERPOLATION['lanczos'] )
UpperCamelCase = np.array(lowercase ).astype(np.floataa ) / 2_5_5.0
UpperCamelCase = image[None].transpose(0 , 3 , 1 , 2 )
UpperCamelCase = torch.from_numpy(lowercase )
return 2.0 * image - 1.0
class lowercase ( _SCREAMING_SNAKE_CASE ):
def __init__( self , A_ , A_ , A_ , ) -> Dict:
"""simple docstring"""
super().__init__()
self.register_modules(vqvae=A_ , unet=A_ , scheduler=A_ )
@torch.no_grad()
def __call__( self , A_ = None , A_ = 1 , A_ = 100 , A_ = 0.0 , A_ = None , A_ = "pil" , A_ = True , ) -> Union[Tuple, ImagePipelineOutput]:
"""simple docstring"""
if isinstance(A_ , PIL.Image.Image ):
UpperCamelCase = 1
elif isinstance(A_ , torch.Tensor ):
UpperCamelCase = image.shape[0]
else:
raise ValueError(F'''`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(A_ )}''' )
if isinstance(A_ , PIL.Image.Image ):
UpperCamelCase = preprocess(A_ )
UpperCamelCase , UpperCamelCase = image.shape[-2:]
# in_channels should be 6: 3 for latents, 3 for low resolution image
UpperCamelCase = (batch_size, self.unet.config.in_channels // 2, height, width)
UpperCamelCase = next(self.unet.parameters() ).dtype
UpperCamelCase = randn_tensor(A_ , generator=A_ , device=self.device , dtype=A_ )
UpperCamelCase = image.to(device=self.device , dtype=A_ )
# set timesteps and move to the correct device
self.scheduler.set_timesteps(A_ , device=self.device )
UpperCamelCase = self.scheduler.timesteps
# scale the initial noise by the standard deviation required by the scheduler
UpperCamelCase = 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]
UpperCamelCase = 'eta' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
UpperCamelCase = {}
if accepts_eta:
UpperCamelCase = eta
for t in self.progress_bar(A_ ):
# concat latents and low resolution image in the channel dimension.
UpperCamelCase = torch.cat([latents, image] , dim=1 )
UpperCamelCase = self.scheduler.scale_model_input(A_ , A_ )
# predict the noise residual
UpperCamelCase = self.unet(A_ , A_ ).sample
# compute the previous noisy sample x_t -> x_t-1
UpperCamelCase = self.scheduler.step(A_ , A_ , A_ , **A_ ).prev_sample
# decode the image latents with the VQVAE
UpperCamelCase = self.vqvae.decode(A_ ).sample
UpperCamelCase = torch.clamp(A_ , -1.0 , 1.0 )
UpperCamelCase = image / 2 + 0.5
UpperCamelCase = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
UpperCamelCase = self.numpy_to_pil(A_ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=A_ )
| 110 |
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer
class lowercase :
def __init__( self , A_ , A_=13 , A_=7 , A_=True , A_=True , A_=False , A_=True , A_=99 , A_=32 , A_=5 , A_=4 , A_=37 , A_="gelu" , A_=0.1 , A_=0.1 , A_=512 , A_=16 , A_=2 , A_=0.02 , A_=3 , A_=4 , A_=None , ) -> Tuple:
"""simple docstring"""
UpperCamelCase = parent
UpperCamelCase = batch_size
UpperCamelCase = seq_length
UpperCamelCase = is_training
UpperCamelCase = use_input_mask
UpperCamelCase = use_token_type_ids
UpperCamelCase = use_labels
UpperCamelCase = vocab_size
UpperCamelCase = hidden_size
UpperCamelCase = num_hidden_layers
UpperCamelCase = num_attention_heads
UpperCamelCase = intermediate_size
UpperCamelCase = hidden_act
UpperCamelCase = hidden_dropout_prob
UpperCamelCase = attention_probs_dropout_prob
UpperCamelCase = max_position_embeddings
UpperCamelCase = type_vocab_size
UpperCamelCase = type_sequence_label_size
UpperCamelCase = initializer_range
UpperCamelCase = num_labels
UpperCamelCase = num_choices
UpperCamelCase = scope
def __UpperCamelCase ( self ) -> Any:
"""simple docstring"""
UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase = None
if self.use_input_mask:
UpperCamelCase = random_attention_mask([self.batch_size, self.seq_length] )
UpperCamelCase = None
if self.use_token_type_ids:
UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
UpperCamelCase = None
UpperCamelCase = None
UpperCamelCase = None
if self.use_labels:
UpperCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCamelCase = ids_tensor([self.batch_size] , self.num_choices )
UpperCamelCase = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __UpperCamelCase ( self ) -> List[Any]:
"""simple docstring"""
return LlamaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=A_ , initializer_range=self.initializer_range , )
def __UpperCamelCase ( self , A_ , A_ , A_ , A_ , A_ , A_ , A_ ) -> int:
"""simple docstring"""
UpperCamelCase = LlamaModel(config=A_ )
model.to(A_ )
model.eval()
UpperCamelCase = model(A_ , attention_mask=A_ )
UpperCamelCase = model(A_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __UpperCamelCase ( self , A_ , A_ , A_ , A_ , A_ , A_ , A_ , A_ , A_ , ) -> List[Any]:
"""simple docstring"""
UpperCamelCase = True
UpperCamelCase = LlamaModel(A_ )
model.to(A_ )
model.eval()
UpperCamelCase = model(
A_ , attention_mask=A_ , encoder_hidden_states=A_ , encoder_attention_mask=A_ , )
UpperCamelCase = model(
A_ , attention_mask=A_ , encoder_hidden_states=A_ , )
UpperCamelCase = model(A_ , attention_mask=A_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __UpperCamelCase ( self , A_ , A_ , A_ , A_ , A_ , A_ , A_ , A_ , A_ , ) -> str:
"""simple docstring"""
UpperCamelCase = LlamaForCausalLM(config=A_ )
model.to(A_ )
model.eval()
UpperCamelCase = model(A_ , attention_mask=A_ , labels=A_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __UpperCamelCase ( self , A_ , A_ , A_ , A_ , A_ , A_ , A_ , A_ , A_ , ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase = True
UpperCamelCase = True
UpperCamelCase = LlamaForCausalLM(config=A_ )
model.to(A_ )
model.eval()
# first forward pass
UpperCamelCase = model(
A_ , attention_mask=A_ , encoder_hidden_states=A_ , encoder_attention_mask=A_ , use_cache=A_ , )
UpperCamelCase = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
UpperCamelCase = ids_tensor((self.batch_size, 3) , config.vocab_size )
UpperCamelCase = ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
UpperCamelCase = torch.cat([input_ids, next_tokens] , dim=-1 )
UpperCamelCase = torch.cat([input_mask, next_mask] , dim=-1 )
UpperCamelCase = model(
A_ , attention_mask=A_ , encoder_hidden_states=A_ , encoder_attention_mask=A_ , output_hidden_states=A_ , )['hidden_states'][0]
UpperCamelCase = model(
A_ , attention_mask=A_ , encoder_hidden_states=A_ , encoder_attention_mask=A_ , past_key_values=A_ , output_hidden_states=A_ , )['hidden_states'][0]
# select random slice
UpperCamelCase = ids_tensor((1,) , output_from_past.shape[-1] ).item()
UpperCamelCase = output_from_no_past[:, -3:, random_slice_idx].detach()
UpperCamelCase = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(A_ , A_ , atol=1e-3 ) )
def __UpperCamelCase ( self ) -> Tuple:
"""simple docstring"""
UpperCamelCase = self.prepare_config_and_inputs()
(
(
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) ,
) = config_and_inputs
UpperCamelCase = {'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , unittest.TestCase ):
__lowercase : str = (LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
__lowercase : str = (LlamaForCausalLM,) if is_torch_available() else ()
__lowercase : Any = (
{
"feature-extraction": LlamaModel,
"text-classification": LlamaForSequenceClassification,
"text-generation": LlamaForCausalLM,
"zero-shot": LlamaForSequenceClassification,
}
if is_torch_available()
else {}
)
__lowercase : int = False
__lowercase : Optional[int] = False
def __UpperCamelCase ( self ) -> int:
"""simple docstring"""
UpperCamelCase = LlamaModelTester(self )
UpperCamelCase = ConfigTester(self , config_class=A_ , hidden_size=37 )
def __UpperCamelCase ( self ) -> Union[str, Any]:
"""simple docstring"""
self.config_tester.run_common_tests()
def __UpperCamelCase ( self ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A_ )
def __UpperCamelCase ( self ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
UpperCamelCase = type
self.model_tester.create_and_check_model(*A_ )
def __UpperCamelCase ( self ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase , UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
UpperCamelCase = 3
UpperCamelCase = input_dict['input_ids']
UpperCamelCase = input_ids.ne(1 ).to(A_ )
UpperCamelCase = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
UpperCamelCase = LlamaForSequenceClassification(A_ )
model.to(A_ )
model.eval()
UpperCamelCase = model(A_ , attention_mask=A_ , labels=A_ )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def __UpperCamelCase ( self ) -> Dict:
"""simple docstring"""
UpperCamelCase , UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
UpperCamelCase = 3
UpperCamelCase = 'single_label_classification'
UpperCamelCase = input_dict['input_ids']
UpperCamelCase = input_ids.ne(1 ).to(A_ )
UpperCamelCase = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
UpperCamelCase = LlamaForSequenceClassification(A_ )
model.to(A_ )
model.eval()
UpperCamelCase = model(A_ , attention_mask=A_ , labels=A_ )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def __UpperCamelCase ( self ) -> List[str]:
"""simple docstring"""
UpperCamelCase , UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
UpperCamelCase = 3
UpperCamelCase = 'multi_label_classification'
UpperCamelCase = input_dict['input_ids']
UpperCamelCase = input_ids.ne(1 ).to(A_ )
UpperCamelCase = ids_tensor(
[self.model_tester.batch_size, config.num_labels] , self.model_tester.type_sequence_label_size ).to(torch.float )
UpperCamelCase = LlamaForSequenceClassification(A_ )
model.to(A_ )
model.eval()
UpperCamelCase = model(A_ , attention_mask=A_ , labels=A_ )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
@unittest.skip('LLaMA buffers include complex numbers, which breaks this test' )
def __UpperCamelCase ( self ) -> Union[str, Any]:
"""simple docstring"""
pass
@parameterized.expand([('linear',), ('dynamic',)] )
def __UpperCamelCase ( self , A_ ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase , UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
UpperCamelCase = ids_tensor([1, 10] , config.vocab_size )
UpperCamelCase = ids_tensor([1, int(config.max_position_embeddings * 1.5 )] , config.vocab_size )
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
UpperCamelCase = LlamaModel(A_ )
original_model.to(A_ )
original_model.eval()
UpperCamelCase = original_model(A_ ).last_hidden_state
UpperCamelCase = original_model(A_ ).last_hidden_state
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
UpperCamelCase = {'type': scaling_type, 'factor': 10.0}
UpperCamelCase = LlamaModel(A_ )
scaled_model.to(A_ )
scaled_model.eval()
UpperCamelCase = scaled_model(A_ ).last_hidden_state
UpperCamelCase = scaled_model(A_ ).last_hidden_state
# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
# maximum sequence length, so the outputs for the short input should match.
if scaling_type == "dynamic":
self.assertTrue(torch.allclose(A_ , A_ , atol=1e-5 ) )
else:
self.assertFalse(torch.allclose(A_ , A_ , atol=1e-5 ) )
# The output should be different for long inputs
self.assertFalse(torch.allclose(A_ , A_ , atol=1e-5 ) )
@require_torch
class lowercase ( unittest.TestCase ):
@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!' )
@slow
def __UpperCamelCase ( self ) -> List[Any]:
"""simple docstring"""
UpperCamelCase = [1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
UpperCamelCase = LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-7b-hf' , device_map='auto' )
UpperCamelCase = model(torch.tensor([input_ids] ) )
# Expected mean on dim = -1
UpperCamelCase = torch.tensor([[-6.6550, -4.1227, -4.9859, -3.2406, 0.8262, -3.0033, 1.2964, -3.3699]] )
torch.testing.assert_close(out.mean(-1 ) , A_ , atol=1e-2 , rtol=1e-2 )
# slicing logits[0, 0, 0:30]
# fmt: off
UpperCamelCase = torch.tensor([-12.8281, -7.4453, -0.4639, -8.0625, -7.2500, -8.0000, -6.4883, -7.7695, -7.8438, -7.0312, -6.2188, -7.1328, -1.8496, 1.9961, -8.6250, -6.7227, -12.8281, -6.9492, -7.0742, -7.7852, -7.5820, -7.9062, -6.9375, -7.9805, -8.3438, -8.1562, -8.0469, -7.6250, -7.7422, -7.3398,] )
# fmt: on
torch.testing.assert_close(out[0, 0, :30] , A_ , atol=1e-5 , rtol=1e-5 )
@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!' )
@slow
def __UpperCamelCase ( self ) -> List[Any]:
"""simple docstring"""
UpperCamelCase = [1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
UpperCamelCase = LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-hf' , device_map='auto' )
UpperCamelCase = model(torch.tensor(A_ ) )
# Expected mean on dim = -1
UpperCamelCase = torch.tensor([[-2.0622, -1.2794, -1.1638, -0.9788, -1.4603, -1.0238, -1.7893, -1.4411]] )
torch.testing.assert_close(out.mean(-1 ) , A_ , atol=1e-2 , rtol=1e-2 )
# slicing logits[0, 0, 0:30]
# fmt: off
UpperCamelCase = torch.tensor([-8.1406, -8.0547, 2.7461, -1.2344, -0.1448, -1.8262, -1.0020, -1.8154, -1.6895, -1.8516, -2.3574, -0.9277, 3.7598, 6.5742, -1.2998, -0.1177, -8.1406, -2.9688, -2.9199, -3.1699, -3.5254, -2.3555, -2.7988, -3.4141, -2.8262, -4.5195, -3.3379, -3.3164, -2.7832, -3.0273] )
# fmt: on
torch.testing.assert_close(out[0, 0, :30] , A_ , atol=1e-5 , rtol=1e-5 )
@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!' )
@slow
def __UpperCamelCase ( self ) -> List[Any]:
"""simple docstring"""
UpperCamelCase = [1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
UpperCamelCase = LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-chat-hf' , device_map='auto' )
UpperCamelCase = model(torch.tensor(A_ ) )
# Expected mean on dim = -1
UpperCamelCase = torch.tensor([[-0.8562, -1.8520, -0.7551, -0.4162, -1.5161, -1.2038, -2.4823, -2.3254]] )
torch.testing.assert_close(out.mean(-1 ) , A_ , atol=1e-2 , rtol=1e-2 )
# slicing logits[0, 0, 0:30]
# fmt: off
UpperCamelCase = torch.tensor([-2.2227, 4.8828, 0.9023, -0.4578, -0.7871, -0.1033, -0.6221, -0.5786, -0.7803, -1.0674, -1.2920, -0.1570, 0.8008, 2.0723, -0.9497, 0.2771, -2.2227, -0.7612, -1.4346, -1.2061, -1.6426, -0.3000, -0.7139, -1.1934, -1.8691, -1.6973, -1.5947, -1.2705, -0.3523, -0.5513] )
# fmt: on
torch.testing.assert_close(out.mean(-1 ) , A_ , atol=1e-2 , rtol=1e-2 )
@unittest.skip(
'Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test' )
@slow
def __UpperCamelCase ( self ) -> List[str]:
"""simple docstring"""
UpperCamelCase = [1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
UpperCamelCase = LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-70b-hf' , device_map='auto' )
UpperCamelCase = model(torch.tensor(A_ ) )
UpperCamelCase = torch.tensor(
[[-4.2327, -3.3360, -4.6665, -4.7631, -1.8180, -3.4170, -1.4211, -3.1810]] , dtype=torch.floataa )
torch.testing.assert_close(out.mean(-1 ) , A_ , atol=1e-2 , rtol=1e-2 )
# fmt: off
UpperCamelCase = torch.tensor([-9.4922, -3.9551, 1.7998, -5.6758, -5.1055, -5.8984, -4.8320, -6.8086, -6.5391, -5.6172, -5.5820, -5.5352, 1.7881, 3.6289, -6.5117, -3.4785, -9.5000, -6.0352, -6.8125, -6.0195, -6.6836, -5.4727, -6.2812, -6.0391, -7.3398, -7.4297, -7.4844, -6.5820, -5.8789, -5.5312] )
# fmt: on
torch.testing.assert_close(out[0, 0, :30] , A_ , atol=1e-5 , rtol=1e-5 )
@unittest.skip('Model is curently gated' )
@slow
def __UpperCamelCase ( self ) -> Tuple:
"""simple docstring"""
UpperCamelCase = 'Simply put, the theory of relativity states that 1) the laws of physics are the same everywhere in the universe and 2) the passage of time and the length of objects can vary depending on the observer\'s frame of reference.\n\nThe first part of the theory, that the laws of physics are the same everywhere, is known as the "princi'
UpperCamelCase = 'Simply put, the theory of relativity states that '
UpperCamelCase = LlamaTokenizer.from_pretrained('meta-llama/Llama-2-13b-chat-hf' )
UpperCamelCase = tokenizer.encode(A_ , return_tensors='pt' )
UpperCamelCase = LlamaForCausalLM.from_pretrained(
'meta-llama/Llama-2-13b-chat-hf' , device_map='sequential' , use_safetensors=A_ )
# greedy generation outputs
UpperCamelCase = model.generate(A_ , max_new_tokens=64 , top_p=A_ , temperature=1 , do_sample=A_ )
UpperCamelCase = tokenizer.decode(generated_ids[0] , skip_special_tokens=A_ )
self.assertEqual(A_ , A_ )
| 110 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
UpperCAmelCase_ = logging.get_logger(__name__)
class lowerCAmelCase_ ( lowerCamelCase_ , lowerCamelCase_ ):
'''simple docstring'''
lowerCAmelCase_ : Optional[Any] = """maskformer-swin"""
lowerCAmelCase_ : Optional[Any] = {
"""num_attention_heads""": """num_heads""",
"""num_hidden_layers""": """num_layers""",
}
def __init__( self : Dict , _UpperCAmelCase : Optional[Any]=2_24 , _UpperCAmelCase : List[str]=4 , _UpperCAmelCase : str=3 , _UpperCAmelCase : str=96 , _UpperCAmelCase : List[str]=[2, 2, 6, 2] , _UpperCAmelCase : str=[3, 6, 12, 24] , _UpperCAmelCase : str=7 , _UpperCAmelCase : int=4.0 , _UpperCAmelCase : Union[str, Any]=True , _UpperCAmelCase : List[Any]=0.0 , _UpperCAmelCase : str=0.0 , _UpperCAmelCase : int=0.1 , _UpperCAmelCase : Any="gelu" , _UpperCAmelCase : Optional[int]=False , _UpperCAmelCase : int=0.02 , _UpperCAmelCase : Dict=1E-5 , _UpperCAmelCase : List[Any]=None , _UpperCAmelCase : List[Any]=None , **_UpperCAmelCase : Dict , ):
"""simple docstring"""
super().__init__(**_UpperCAmelCase )
UpperCAmelCase__ = image_size
UpperCAmelCase__ = patch_size
UpperCAmelCase__ = num_channels
UpperCAmelCase__ = embed_dim
UpperCAmelCase__ = depths
UpperCAmelCase__ = len(_UpperCAmelCase )
UpperCAmelCase__ = num_heads
UpperCAmelCase__ = window_size
UpperCAmelCase__ = mlp_ratio
UpperCAmelCase__ = qkv_bias
UpperCAmelCase__ = hidden_dropout_prob
UpperCAmelCase__ = attention_probs_dropout_prob
UpperCAmelCase__ = drop_path_rate
UpperCAmelCase__ = hidden_act
UpperCAmelCase__ = use_absolute_embeddings
UpperCAmelCase__ = layer_norm_eps
UpperCAmelCase__ = initializer_range
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
UpperCAmelCase__ = int(embed_dim * 2 ** (len(_UpperCAmelCase ) - 1) )
UpperCAmelCase__ = ["""stem"""] + [f'''stage{idx}''' for idx in range(1 , len(_UpperCAmelCase ) + 1 )]
UpperCAmelCase__ , UpperCAmelCase__ = get_aligned_output_features_output_indices(
out_features=_UpperCAmelCase , out_indices=_UpperCAmelCase , stage_names=self.stage_names )
| 346 |
'''simple docstring'''
from math import factorial
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int = 20 ):
'''simple docstring'''
UpperCAmelCase__ = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
UpperCAmelCase__ = n // 2
return int(factorial(SCREAMING_SNAKE_CASE__ ) / (factorial(SCREAMING_SNAKE_CASE__ ) * factorial(n - k )) )
if __name__ == "__main__":
import sys
if len(sys.argv) == 1:
print(solution(2_0))
else:
try:
UpperCAmelCase_ = int(sys.argv[1])
print(solution(n))
except ValueError:
print('Invalid entry - please enter a number.')
| 346 | 1 |
'''simple docstring'''
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
SCREAMING_SNAKE_CASE_: Any =argparse.ArgumentParser()
parser.add_argument(
'--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
)
parser.add_argument(
'--original_config_file',
type=str,
required=True,
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(
'--image_size',
default=5_12,
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(
'--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.)')
def lowerCAmelCase_ ( snake_case_ : str ) -> Any:
'''simple docstring'''
if string == "True":
return True
elif string == "False":
return False
else:
raise ValueError(f"""could not parse string as bool {string}""" )
parser.add_argument(
'--use_linear_projection', help='Override for use linear projection', required=False, type=parse_bool
)
parser.add_argument('--cross_attention_dim', help='Override for cross attention_dim', required=False, type=int)
SCREAMING_SNAKE_CASE_: Tuple =parser.parse_args()
SCREAMING_SNAKE_CASE_: Any =download_controlnet_from_original_ckpt(
checkpoint_path=args.checkpoint_path,
original_config_file=args.original_config_file,
image_size=args.image_size,
extract_ema=args.extract_ema,
num_in_channels=args.num_in_channels,
upcast_attention=args.upcast_attention,
from_safetensors=args.from_safetensors,
device=args.device,
use_linear_projection=args.use_linear_projection,
cross_attention_dim=args.cross_attention_dim,
)
controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
| 106 | '''simple docstring'''
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import jax
import jaxlib
SCREAMING_SNAKE_CASE_: List[str] =get_logger()
SCREAMING_SNAKE_CASE_: Optional[dict] =None
class __A ( TensorFormatter[Mapping, """jax.Array""", Mapping] ):
def __init__(self : List[Any] , __a : Optional[int]=None , __a : Any=None , **__a : Dict ):
super().__init__(features=__a )
import jax
from jaxlib.xla_client import Device
if isinstance(__a , __a ):
raise ValueError(
f"""Expected {device} to be a `str` not {type(__a )}, as `jaxlib.xla_extension.Device` """
"is not serializable neither with `pickle` nor with `dill`. Instead you can surround "
"the device with `str()` to get its string identifier that will be internally mapped "
"to the actual `jaxlib.xla_extension.Device`." )
UpperCAmelCase_ = device if isinstance(__a , __a ) else str(jax.devices()[0] )
# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
# with `pickle` nor with `dill`, so we need to use a global variable instead
global DEVICE_MAPPING
if DEVICE_MAPPING is None:
UpperCAmelCase_ = self._map_devices_to_str()
if self.device not in list(DEVICE_MAPPING.keys() ):
logger.warning(
f"""Device with string identifier {self.device} not listed among the available """
f"""devices: {list(DEVICE_MAPPING.keys() )}, so falling back to the default """
f"""device: {str(jax.devices()[0] )}.""" )
UpperCAmelCase_ = str(jax.devices()[0] )
UpperCAmelCase_ = jnp_array_kwargs
@staticmethod
def _lowercase ():
import jax
return {str(__a ): device for device in jax.devices()}
def _lowercase (self : str , __a : Tuple ):
import jax
import jax.numpy as jnp
if isinstance(__a , __a ) and column:
if all(
isinstance(__a , jax.Array ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column ):
return jnp.stack(__a , axis=0 )
return column
def _lowercase (self : Any , __a : Optional[int] ):
import jax
import jax.numpy as jnp
if isinstance(__a , (str, bytes, type(__a )) ):
return value
elif isinstance(__a , (np.character, np.ndarray) ) and np.issubdtype(value.dtype , np.character ):
return value.tolist()
UpperCAmelCase_ = {}
if isinstance(__a , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.integer ):
# the default int precision depends on the jax config
# see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
if jax.config.jax_enable_xaa:
UpperCAmelCase_ = {"dtype": jnp.intaa}
else:
UpperCAmelCase_ = {"dtype": jnp.intaa}
elif isinstance(__a , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.floating ):
UpperCAmelCase_ = {"dtype": jnp.floataa}
elif config.PIL_AVAILABLE and "PIL" in sys.modules:
import PIL.Image
if isinstance(__a , PIL.Image.Image ):
UpperCAmelCase_ = np.asarray(__a )
# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
# with `pickle` nor with `dill`, so we need to use a global variable instead
global DEVICE_MAPPING
if DEVICE_MAPPING is None:
UpperCAmelCase_ = self._map_devices_to_str()
with jax.default_device(DEVICE_MAPPING[self.device] ):
# calling jnp.array on a np.ndarray does copy the data
# see https://github.com/google/jax/issues/4486
return jnp.array(__a , **{**default_dtype, **self.jnp_array_kwargs} )
def _lowercase (self : int , __a : Any ):
import jax
# support for torch, tf, jax etc.
if config.TORCH_AVAILABLE and "torch" in sys.modules:
import torch
if isinstance(__a , torch.Tensor ):
return self._tensorize(data_struct.detach().cpu().numpy()[()] )
if hasattr(__a , "__array__" ) and not isinstance(__a , jax.Array ):
UpperCAmelCase_ = data_struct.__array__()
# support for nested types like struct of list of struct
if isinstance(__a , np.ndarray ):
if data_struct.dtype == object: # jax arrays cannot be instantied from an array of objects
return self._consolidate([self.recursive_tensorize(__a ) for substruct in data_struct] )
elif isinstance(__a , (list, tuple) ):
return self._consolidate([self.recursive_tensorize(__a ) for substruct in data_struct] )
return self._tensorize(__a )
def _lowercase (self : Union[str, Any] , __a : dict ):
return map_nested(self._recursive_tensorize , __a , map_list=__a )
def _lowercase (self : str , __a : pa.Table ):
UpperCAmelCase_ = self.numpy_arrow_extractor().extract_row(__a )
UpperCAmelCase_ = self.python_features_decoder.decode_row(__a )
return self.recursive_tensorize(__a )
def _lowercase (self : Tuple , __a : pa.Table ):
UpperCAmelCase_ = self.numpy_arrow_extractor().extract_column(__a )
UpperCAmelCase_ = self.python_features_decoder.decode_column(__a , pa_table.column_names[0] )
UpperCAmelCase_ = self.recursive_tensorize(__a )
UpperCAmelCase_ = self._consolidate(__a )
return column
def _lowercase (self : str , __a : pa.Table ):
UpperCAmelCase_ = self.numpy_arrow_extractor().extract_batch(__a )
UpperCAmelCase_ = self.python_features_decoder.decode_batch(__a )
UpperCAmelCase_ = self.recursive_tensorize(__a )
for column_name in batch:
UpperCAmelCase_ = self._consolidate(batch[column_name] )
return batch
| 106 | 1 |
"""simple docstring"""
a : Tuple = '''
# Transformers 설치 방법
! pip install transformers datasets
# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
a : Optional[int] = [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
a : Union[str, Any] = {
'''{processor_class}''': '''FakeProcessorClass''',
'''{model_class}''': '''FakeModelClass''',
'''{object_class}''': '''FakeObjectClass''',
}
| 105 |
from torch import nn
class a ( nn.Module ):
def __init__( self :Tuple ,__lowercase :Optional[int] ,__lowercase :int ):
super().__init__()
snake_case__ : Optional[Any] = class_size
snake_case__ : Dict = embed_size
# self.mlp1 = nn.Linear(embed_size, embed_size)
# self.mlp2 = (nn.Linear(embed_size, class_size))
snake_case__ : Dict = nn.Linear(__lowercase ,__lowercase )
def __lowerCamelCase ( self :str ,__lowercase :int ):
# hidden_state = nn.functional.relu(self.mlp1(hidden_state))
# hidden_state = self.mlp2(hidden_state)
snake_case__ : Optional[Any] = self.mlp(__lowercase )
return logits
| 230 | 0 |
"""simple docstring"""
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
lowerCAmelCase__ : List[Any] =(720, 1280) # Height, Width
lowerCAmelCase__ : Union[str, Any] =(0.4, 0.6) # if height or width lower than this scale, drop it.
lowerCAmelCase__ : Union[str, Any] =1 / 100
lowerCAmelCase__ : int =''''''
lowerCAmelCase__ : List[Any] =''''''
lowerCAmelCase__ : List[str] =''''''
lowerCAmelCase__ : str =250
def __lowercase ( ) -> None:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = get_dataset(a__ , a__ )
for index in range(a__ ):
__SCREAMING_SNAKE_CASE = random.sample(range(len(a__ ) ) , 4 )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = update_image_and_anno(
a__ , a__ , a__ , a__ , a__ , filter_scale=a__ , )
# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
__SCREAMING_SNAKE_CASE = random_chars(32 )
__SCREAMING_SNAKE_CASE = path.split(os.sep )[-1].rsplit('.' , 1 )[0]
__SCREAMING_SNAKE_CASE = f"""{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"""
cva.imwrite(f"""{file_root}.jpg""" , a__ , [cva.IMWRITE_JPEG_QUALITY, 85] )
print(f"""Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}""" )
__SCREAMING_SNAKE_CASE = []
for anno in new_annos:
__SCREAMING_SNAKE_CASE = anno[3] - anno[1]
__SCREAMING_SNAKE_CASE = anno[4] - anno[2]
__SCREAMING_SNAKE_CASE = anno[1] + width / 2
__SCREAMING_SNAKE_CASE = anno[2] + height / 2
__SCREAMING_SNAKE_CASE = f"""{anno[0]} {x_center} {y_center} {width} {height}"""
annos_list.append(a__ )
with open(f"""{file_root}.txt""" , 'w' ) as outfile:
outfile.write('\n'.join(line for line in annos_list ) )
def __lowercase ( a__ , a__ ) -> tuple[list, list]:
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = []
for label_file in glob.glob(os.path.join(a__ , '*.txt' ) ):
__SCREAMING_SNAKE_CASE = label_file.split(os.sep )[-1].rsplit('.' , 1 )[0]
with open(a__ ) as in_file:
__SCREAMING_SNAKE_CASE = in_file.readlines()
__SCREAMING_SNAKE_CASE = os.path.join(a__ , f"""{label_name}.jpg""" )
__SCREAMING_SNAKE_CASE = []
for obj_list in obj_lists:
__SCREAMING_SNAKE_CASE = obj_list.rstrip('\n' ).split(' ' )
__SCREAMING_SNAKE_CASE = float(obj[1] ) - float(obj[3] ) / 2
__SCREAMING_SNAKE_CASE = float(obj[2] ) - float(obj[4] ) / 2
__SCREAMING_SNAKE_CASE = float(obj[1] ) + float(obj[3] ) / 2
__SCREAMING_SNAKE_CASE = float(obj[2] ) + float(obj[4] ) / 2
boxes.append([int(obj[0] ), xmin, ymin, xmax, ymax] )
if not boxes:
continue
img_paths.append(a__ )
labels.append(a__ )
return img_paths, labels
def __lowercase ( a__ , a__ , a__ , a__ , a__ , a__ = 0.0 , ) -> tuple[list, list, str]:
__SCREAMING_SNAKE_CASE = np.zeros([output_size[0], output_size[1], 3] , dtype=np.uinta )
__SCREAMING_SNAKE_CASE = scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
__SCREAMING_SNAKE_CASE = scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
__SCREAMING_SNAKE_CASE = int(scale_x * output_size[1] )
__SCREAMING_SNAKE_CASE = int(scale_y * output_size[0] )
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = []
for i, index in enumerate(a__ ):
__SCREAMING_SNAKE_CASE = all_img_list[index]
path_list.append(a__ )
__SCREAMING_SNAKE_CASE = all_annos[index]
__SCREAMING_SNAKE_CASE = cva.imread(a__ )
if i == 0: # top-left
__SCREAMING_SNAKE_CASE = cva.resize(a__ , (divid_point_x, divid_point_y) )
__SCREAMING_SNAKE_CASE = img
for bbox in img_annos:
__SCREAMING_SNAKE_CASE = bbox[1] * scale_x
__SCREAMING_SNAKE_CASE = bbox[2] * scale_y
__SCREAMING_SNAKE_CASE = bbox[3] * scale_x
__SCREAMING_SNAKE_CASE = bbox[4] * scale_y
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
elif i == 1: # top-right
__SCREAMING_SNAKE_CASE = cva.resize(a__ , (output_size[1] - divid_point_x, divid_point_y) )
__SCREAMING_SNAKE_CASE = img
for bbox in img_annos:
__SCREAMING_SNAKE_CASE = scale_x + bbox[1] * (1 - scale_x)
__SCREAMING_SNAKE_CASE = bbox[2] * scale_y
__SCREAMING_SNAKE_CASE = scale_x + bbox[3] * (1 - scale_x)
__SCREAMING_SNAKE_CASE = bbox[4] * scale_y
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
elif i == 2: # bottom-left
__SCREAMING_SNAKE_CASE = cva.resize(a__ , (divid_point_x, output_size[0] - divid_point_y) )
__SCREAMING_SNAKE_CASE = img
for bbox in img_annos:
__SCREAMING_SNAKE_CASE = bbox[1] * scale_x
__SCREAMING_SNAKE_CASE = scale_y + bbox[2] * (1 - scale_y)
__SCREAMING_SNAKE_CASE = bbox[3] * scale_x
__SCREAMING_SNAKE_CASE = scale_y + bbox[4] * (1 - scale_y)
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
else: # bottom-right
__SCREAMING_SNAKE_CASE = cva.resize(
a__ , (output_size[1] - divid_point_x, output_size[0] - divid_point_y) )
__SCREAMING_SNAKE_CASE = img
for bbox in img_annos:
__SCREAMING_SNAKE_CASE = scale_x + bbox[1] * (1 - scale_x)
__SCREAMING_SNAKE_CASE = scale_y + bbox[2] * (1 - scale_y)
__SCREAMING_SNAKE_CASE = scale_x + bbox[3] * (1 - scale_x)
__SCREAMING_SNAKE_CASE = scale_y + bbox[4] * (1 - scale_y)
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
# Remove bounding box small than scale of filter
if filter_scale > 0:
__SCREAMING_SNAKE_CASE = [
anno
for anno in new_anno
if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
]
return output_img, new_anno, path_list[0]
def __lowercase ( a__ ) -> str:
assert number_char > 1, "The number of character should greater than 1"
__SCREAMING_SNAKE_CASE = ascii_lowercase + digits
return "".join(random.choice(a__ ) for _ in range(a__ ) )
if __name__ == "__main__":
main()
print('''DONE ✅''')
| 359 |
from ..utils import DummyObject, requires_backends
class UpperCAmelCase_ ( metaclass=UpperCamelCase_ ):
'''simple docstring'''
UpperCamelCase__ : Optional[int] = ['''speech''']
def __init__( self , *_A , **_A ):
'''simple docstring'''
requires_backends(self , ['speech'] )
class UpperCAmelCase_ ( metaclass=UpperCamelCase_ ):
'''simple docstring'''
UpperCamelCase__ : Optional[int] = ['''speech''']
def __init__( self , *_A , **_A ):
'''simple docstring'''
requires_backends(self , ['speech'] )
| 118 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
A_ : Tuple = {
"""configuration_transfo_xl""": ["""TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TransfoXLConfig"""],
"""tokenization_transfo_xl""": ["""TransfoXLCorpus""", """TransfoXLTokenizer"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Optional[Any] = [
"""TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""AdaptiveEmbedding""",
"""TransfoXLForSequenceClassification""",
"""TransfoXLLMHeadModel""",
"""TransfoXLModel""",
"""TransfoXLPreTrainedModel""",
"""load_tf_weights_in_transfo_xl""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Optional[Any] = [
"""TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFAdaptiveEmbedding""",
"""TFTransfoXLForSequenceClassification""",
"""TFTransfoXLLMHeadModel""",
"""TFTransfoXLMainLayer""",
"""TFTransfoXLModel""",
"""TFTransfoXLPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_transfo_xl import (
TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
AdaptiveEmbedding,
TransfoXLForSequenceClassification,
TransfoXLLMHeadModel,
TransfoXLModel,
TransfoXLPreTrainedModel,
load_tf_weights_in_transfo_xl,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_transfo_xl import (
TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFAdaptiveEmbedding,
TFTransfoXLForSequenceClassification,
TFTransfoXLLMHeadModel,
TFTransfoXLMainLayer,
TFTransfoXLModel,
TFTransfoXLPreTrainedModel,
)
else:
import sys
A_ : List[Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 215 |
'''simple docstring'''
from numpy import exp, pi, sqrt
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ = 0.0 , lowerCAmelCase_ = 1.0 )-> int:
'''simple docstring'''
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 215 | 1 |
'''simple docstring'''
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def lowerCamelCase__ ( __lowerCamelCase : ndarray ):
return np.dot(__lowerCamelCase , __lowerCamelCase )
class __magic_name__ :
def __init__( self , *,
snake_case = np.inf , snake_case = "linear" , snake_case = 0.0 , ) -> Dict:
'''simple docstring'''
_UpperCAmelCase : Union[str, Any] =regularization
_UpperCAmelCase : Optional[int] =gamma
if kernel == "linear":
_UpperCAmelCase : Union[str, Any] =self.__linear
elif kernel == "rbf":
if self.gamma == 0:
raise ValueError('rbf kernel requires gamma')
if not isinstance(self.gamma , (float, int)):
raise ValueError('gamma must be float or int')
if not self.gamma > 0:
raise ValueError('gamma must be > 0')
_UpperCAmelCase : List[str] =self.__rbf
# in the future, there could be a default value like in sklearn
# sklear: def_gamma = 1/(n_features * X.var()) (wiki)
# previously it was 1/(n_features)
else:
_UpperCAmelCase : Any =f"Unknown kernel: {kernel}"
raise ValueError(__A)
def lowerCAmelCase ( self , snake_case , snake_case) -> Union[str, Any]:
'''simple docstring'''
return np.dot(__A , __A)
def lowerCAmelCase ( self , snake_case , snake_case) -> int:
'''simple docstring'''
return np.exp(-(self.gamma * norm_squared(vectora - vectora)))
def lowerCAmelCase ( self , snake_case , snake_case) -> Tuple:
'''simple docstring'''
_UpperCAmelCase : str =observations
_UpperCAmelCase : Optional[int] =classes
# using Wolfe's Dual to calculate w.
# Primal problem: minimize 1/2*norm_squared(w)
# constraint: yn(w . xn + b) >= 1
#
# With l a vector
# Dual problem: maximize sum_n(ln) -
# 1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm))
# constraint: self.C >= ln >= 0
# and sum_n(ln*yn) = 0
# Then we get w using w = sum_n(ln*yn*xn)
# At the end we can get b ~= mean(yn - w . xn)
#
# Since we use kernels, we only need l_star to calculate b
# and to classify observations
(_UpperCAmelCase ) : str =np.shape(__A)
def to_minimize(snake_case) -> float:
_UpperCAmelCase : Dict =0
(_UpperCAmelCase ) : List[Any] =np.shape(__A)
for i in range(__A):
for j in range(__A):
s += (
candidate[i]
* candidate[j]
* classes[i]
* classes[j]
* self.kernel(observations[i] , observations[j])
)
return 1 / 2 * s - sum(__A)
_UpperCAmelCase : List[str] =LinearConstraint(__A , 0 , 0)
_UpperCAmelCase : Any =Bounds(0 , self.regularization)
_UpperCAmelCase : str =minimize(
__A , np.ones(__A) , bounds=__A , constraints=[ly_contraint]).x
_UpperCAmelCase : List[str] =l_star
# calculating mean offset of separation plane to points
_UpperCAmelCase : Tuple =0
for i in range(__A):
for j in range(__A):
s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
observations[i] , observations[j])
_UpperCAmelCase : List[str] =s / n
def lowerCAmelCase ( self , snake_case) -> str:
'''simple docstring'''
_UpperCAmelCase : str =sum(
self.optimum[n]
* self.classes[n]
* self.kernel(self.observations[n] , __A)
for n in range(len(self.classes)))
return 1 if s + self.offset >= 0 else -1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 361 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowercase ={
'configuration_groupvit': [
'GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'GroupViTConfig',
'GroupViTOnnxConfig',
'GroupViTTextConfig',
'GroupViTVisionConfig',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase =[
'GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
'GroupViTModel',
'GroupViTPreTrainedModel',
'GroupViTTextModel',
'GroupViTVisionModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase =[
'TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFGroupViTModel',
'TFGroupViTPreTrainedModel',
'TFGroupViTTextModel',
'TFGroupViTVisionModel',
]
if TYPE_CHECKING:
from .configuration_groupvit import (
GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GroupViTConfig,
GroupViTOnnxConfig,
GroupViTTextConfig,
GroupViTVisionConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_groupvit import (
GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
GroupViTModel,
GroupViTPreTrainedModel,
GroupViTTextModel,
GroupViTVisionModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_groupvit import (
TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFGroupViTModel,
TFGroupViTPreTrainedModel,
TFGroupViTTextModel,
TFGroupViTVisionModel,
)
else:
import sys
lowercase =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 242 | 0 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
__lowerCAmelCase : str = logging.get_logger(__name__)
__lowerCAmelCase : Dict = '▁'
__lowerCAmelCase : Optional[Any] = {'vocab_file': 'sentencepiece.bpe.model'}
__lowerCAmelCase : Union[str, Any] = {
'vocab_file': {
'facebook/mbart-large-en-ro': (
'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'
),
'facebook/mbart-large-cc25': (
'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'
),
}
}
__lowerCAmelCase : Union[str, Any] = {
'facebook/mbart-large-en-ro': 1024,
'facebook/mbart-large-cc25': 1024,
}
# fmt: off
__lowerCAmelCase : Optional[Any] = ['ar_AR', 'cs_CZ', 'de_DE', 'en_XX', 'es_XX', 'et_EE', 'fi_FI', 'fr_XX', 'gu_IN', 'hi_IN', 'it_IT', 'ja_XX', 'kk_KZ', 'ko_KR', 'lt_LT', 'lv_LV', 'my_MM', 'ne_NP', 'nl_XX', 'ro_RO', 'ru_RU', 'si_LK', 'tr_TR', 'vi_VN', 'zh_CN']
class UpperCAmelCase_ ( _A ):
'''simple docstring'''
a__ = VOCAB_FILES_NAMES
a__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a__ = PRETRAINED_VOCAB_FILES_MAP
a__ = ["""input_ids""", """attention_mask"""]
a__ = []
a__ = []
def __init__( self : List[str] , UpperCamelCase__ : Tuple , UpperCamelCase__ : Tuple="<s>" , UpperCamelCase__ : int="</s>" , UpperCamelCase__ : Any="</s>" , UpperCamelCase__ : int="<s>" , UpperCamelCase__ : Dict="<unk>" , UpperCamelCase__ : Tuple="<pad>" , UpperCamelCase__ : str="<mask>" , UpperCamelCase__ : Dict=None , UpperCamelCase__ : str=None , UpperCamelCase__ : Any=None , UpperCamelCase__ : Optional[Dict[str, Any]] = None , UpperCamelCase__ : Optional[int]=None , **UpperCamelCase__ : int , ) -> List[Any]:
"""simple docstring"""
__magic_name__ = AddedToken(UpperCamelCase__ , lstrip=UpperCamelCase__ , rstrip=UpperCamelCase__ ) if isinstance(UpperCamelCase__ , UpperCamelCase__ ) else mask_token
__magic_name__ = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=UpperCamelCase__ , eos_token=UpperCamelCase__ , unk_token=UpperCamelCase__ , sep_token=UpperCamelCase__ , cls_token=UpperCamelCase__ , pad_token=UpperCamelCase__ , mask_token=UpperCamelCase__ , tokenizer_file=UpperCamelCase__ , src_lang=UpperCamelCase__ , tgt_lang=UpperCamelCase__ , additional_special_tokens=UpperCamelCase__ , sp_model_kwargs=self.sp_model_kwargs , **UpperCamelCase__ , )
__magic_name__ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(UpperCamelCase__ ) )
__magic_name__ = vocab_file
# Original fairseq vocab and spm vocab must be "aligned":
# Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
# fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-'
# spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a'
# Mimic fairseq token-to-id alignment for the first 4 token
__magic_name__ = {"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}
# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
__magic_name__ = 1
__magic_name__ = len(self.sp_model )
__magic_name__ = {
code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(UpperCamelCase__ )
}
__magic_name__ = {v: k for k, v in self.lang_code_to_id.items()}
__magic_name__ = len(self.sp_model ) + len(self.lang_code_to_id ) + self.fairseq_offset
self.fairseq_tokens_to_ids.update(self.lang_code_to_id )
__magic_name__ = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
__magic_name__ = list(self.lang_code_to_id.keys() )
if additional_special_tokens is not None:
# Only add those special tokens if they are not already there.
self._additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in self._additional_special_tokens] )
__magic_name__ = src_lang if src_lang is not None else """en_XX"""
__magic_name__ = self.lang_code_to_id[self._src_lang]
__magic_name__ = tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
def __getstate__( self : int ) -> int:
"""simple docstring"""
__magic_name__ = self.__dict__.copy()
__magic_name__ = None
__magic_name__ = self.sp_model.serialized_model_proto()
return state
def __setstate__( self : Tuple , UpperCamelCase__ : int ) -> Dict:
"""simple docstring"""
__magic_name__ = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
__magic_name__ = {}
__magic_name__ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.LoadFromSerializedProto(self.sp_model_proto )
@property
def _lowercase ( self : Dict ) -> str:
"""simple docstring"""
return len(self.sp_model ) + len(self.lang_code_to_id ) + self.fairseq_offset + 1 # Plus 1 for the mask token
@property
def _lowercase ( self : Union[str, Any] ) -> str:
"""simple docstring"""
return self._src_lang
@src_lang.setter
def _lowercase ( self : Optional[int] , UpperCamelCase__ : str ) -> None:
"""simple docstring"""
__magic_name__ = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def _lowercase ( self : Union[str, Any] , UpperCamelCase__ : List[int] , UpperCamelCase__ : Optional[List[int]] = None , UpperCamelCase__ : bool = False ) -> List[int]:
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=UpperCamelCase__ , token_ids_a=UpperCamelCase__ , already_has_special_tokens=UpperCamelCase__ )
__magic_name__ = [1] * len(self.prefix_tokens )
__magic_name__ = [1] * len(self.suffix_tokens )
if token_ids_a is None:
return prefix_ones + ([0] * len(UpperCamelCase__ )) + suffix_ones
return prefix_ones + ([0] * len(UpperCamelCase__ )) + ([0] * len(UpperCamelCase__ )) + suffix_ones
def _lowercase ( self : List[Any] , UpperCamelCase__ : List[int] , UpperCamelCase__ : Optional[List[int]] = None ) -> List[int]:
"""simple docstring"""
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# 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.suffix_tokens
def _lowercase ( self : Union[str, Any] , UpperCamelCase__ : List[int] , UpperCamelCase__ : Optional[List[int]] = None ) -> List[int]:
"""simple docstring"""
__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 + sep + token_ids_a + sep ) * [0]
def _lowercase ( self : Optional[Any] , UpperCamelCase__ : Tuple , UpperCamelCase__ : str , UpperCamelCase__ : Optional[str] , UpperCamelCase__ : Optional[str] , **UpperCamelCase__ : int ) -> List[Any]:
"""simple docstring"""
if src_lang is None or tgt_lang is None:
raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model""" )
__magic_name__ = src_lang
__magic_name__ = self(UpperCamelCase__ , add_special_tokens=UpperCamelCase__ , return_tensors=UpperCamelCase__ , **UpperCamelCase__ )
__magic_name__ = self.convert_tokens_to_ids(UpperCamelCase__ )
__magic_name__ = tgt_lang_id
return inputs
def _lowercase ( self : Optional[int] ) -> Any:
"""simple docstring"""
__magic_name__ = {self.convert_ids_to_tokens(UpperCamelCase__ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def _lowercase ( self : Optional[Any] , UpperCamelCase__ : str ) -> List[str]:
"""simple docstring"""
return self.sp_model.encode(UpperCamelCase__ , out_type=UpperCamelCase__ )
def _lowercase ( self : Dict , UpperCamelCase__ : Optional[int] ) -> List[str]:
"""simple docstring"""
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
__magic_name__ = self.sp_model.PieceToId(UpperCamelCase__ )
# Need to return unknown token if the SP model returned 0
return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
def _lowercase ( self : Any , UpperCamelCase__ : List[Any] ) -> Any:
"""simple docstring"""
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset )
def _lowercase ( self : Any , UpperCamelCase__ : Tuple ) -> str:
"""simple docstring"""
__magic_name__ = """""".join(UpperCamelCase__ ).replace(UpperCamelCase__ , """ """ ).strip()
return out_string
def _lowercase ( self : Dict , UpperCamelCase__ : str , UpperCamelCase__ : Optional[str] = None ) -> Tuple[str]:
"""simple docstring"""
if not os.path.isdir(UpperCamelCase__ ):
logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
return
__magic_name__ = os.path.join(
UpperCamelCase__ , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(UpperCamelCase__ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , UpperCamelCase__ )
elif not os.path.isfile(self.vocab_file ):
with open(UpperCamelCase__ , """wb""" ) as fi:
__magic_name__ = self.sp_model.serialized_model_proto()
fi.write(UpperCamelCase__ )
return (out_vocab_file,)
def _lowercase ( self : List[str] , UpperCamelCase__ : List[str] , UpperCamelCase__ : str = "en_XX" , UpperCamelCase__ : Optional[List[str]] = None , UpperCamelCase__ : str = "ro_RO" , **UpperCamelCase__ : int , ) -> BatchEncoding:
"""simple docstring"""
__magic_name__ = src_lang
__magic_name__ = tgt_lang
return super().prepare_seqaseq_batch(UpperCamelCase__ , UpperCamelCase__ , **UpperCamelCase__ )
def _lowercase ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
return self.set_src_lang_special_tokens(self.src_lang )
def _lowercase ( self : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def _lowercase ( self : Union[str, Any] , UpperCamelCase__ : str ) -> None:
"""simple docstring"""
__magic_name__ = self.lang_code_to_id[src_lang]
__magic_name__ = []
__magic_name__ = [self.eos_token_id, self.cur_lang_code]
def _lowercase ( self : Tuple , UpperCamelCase__ : str ) -> None:
"""simple docstring"""
__magic_name__ = self.lang_code_to_id[lang]
__magic_name__ = []
__magic_name__ = [self.eos_token_id, self.cur_lang_code]
| 88 |
import re
import string
import numpy as np
import datasets
__lowerCAmelCase : Optional[int] = '\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n'
__lowerCAmelCase : Optional[int] = '\nArgs:\n predictions: List of predicted texts.\n references: List of reference texts.\n regexes_to_ignore: List, defaults to None. Regex expressions of characters to\n ignore when calculating the exact matches. Note: these regexes are removed\n from the input data before the changes based on the options below (e.g. ignore_case,\n ignore_punctuation, ignore_numbers) are applied.\n ignore_case: Boolean, defaults to False. If true, turns everything\n to lowercase so that capitalization differences are ignored.\n ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before\n comparing predictions and references.\n ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before\n comparing predictions and references.\nReturns:\n exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.\nExamples:\n >>> exact_match = datasets.load_metric("exact_match")\n >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n >>> preds = ["cat?", "theater", "yelling", "agent"]\n >>> results = exact_match.compute(references=refs, predictions=preds)\n >>> print(round(results["exact_match"], 1))\n 25.0\n\n >>> exact_match = datasets.load_metric("exact_match")\n >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n >>> preds = ["cat?", "theater", "yelling", "agent"]\n >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell"], ignore_case=True, ignore_punctuation=True)\n >>> print(round(results["exact_match"], 1))\n 50.0\n\n\n >>> exact_match = datasets.load_metric("exact_match")\n >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n >>> preds = ["cat?", "theater", "yelling", "agent"]\n >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True)\n >>> print(round(results["exact_match"], 1))\n 75.0\n\n >>> exact_match = datasets.load_metric("exact_match")\n >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n >>> preds = ["cat?", "theater", "yelling", "agent"]\n >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)\n >>> print(round(results["exact_match"], 1))\n 100.0\n\n >>> exact_match = datasets.load_metric("exact_match")\n >>> refs = ["The cat sat on the mat.", "Theaters are great.", "It\'s like comparing oranges and apples."]\n >>> preds = ["The cat sat on the mat?", "Theaters are great.", "It\'s like comparing apples and oranges."]\n >>> results = exact_match.compute(references=refs, predictions=preds)\n >>> print(round(results["exact_match"], 1))\n 33.3\n\n'
__lowerCAmelCase : Optional[int] = '\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class UpperCAmelCase_ ( datasets.Metric ):
'''simple docstring'''
def _lowercase ( self : str ) -> Optional[int]:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Value("""string""" , id="""sequence""" ),
"""references""": datasets.Value("""string""" , id="""sequence""" ),
} ) , reference_urls=[] , )
def _lowercase ( self : Optional[int] , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : Tuple , UpperCamelCase__ : Optional[Any]=None , UpperCamelCase__ : Optional[Any]=False , UpperCamelCase__ : int=False , UpperCamelCase__ : Tuple=False , ) -> Dict:
"""simple docstring"""
if regexes_to_ignore is not None:
for s in regexes_to_ignore:
__magic_name__ = np.array([re.sub(UpperCamelCase__ , """""" , UpperCamelCase__ ) for x in predictions] )
__magic_name__ = np.array([re.sub(UpperCamelCase__ , """""" , UpperCamelCase__ ) for x in references] )
else:
__magic_name__ = np.asarray(UpperCamelCase__ )
__magic_name__ = np.asarray(UpperCamelCase__ )
if ignore_case:
__magic_name__ = np.char.lower(UpperCamelCase__ )
__magic_name__ = np.char.lower(UpperCamelCase__ )
if ignore_punctuation:
__magic_name__ = string.punctuation.maketrans("""""" , """""" , string.punctuation )
__magic_name__ = np.char.translate(UpperCamelCase__ , table=UpperCamelCase__ )
__magic_name__ = np.char.translate(UpperCamelCase__ , table=UpperCamelCase__ )
if ignore_numbers:
__magic_name__ = string.digits.maketrans("""""" , """""" , string.digits )
__magic_name__ = np.char.translate(UpperCamelCase__ , table=UpperCamelCase__ )
__magic_name__ = np.char.translate(UpperCamelCase__ , table=UpperCamelCase__ )
__magic_name__ = predictions == references
return {"exact_match": np.mean(UpperCamelCase__ ) * 100}
| 88 | 1 |
class _a :
def __init__(self ) -> str:
UpperCAmelCase_: Dict = 0
UpperCAmelCase_: Any = 0
UpperCAmelCase_: List[Any] = {}
def __snake_case (self, SCREAMING_SNAKE_CASE_ ) -> str:
if vertex not in self.adjacency:
UpperCAmelCase_: Dict = {}
self.num_vertices += 1
def __snake_case (self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ) -> int:
self.add_vertex(SCREAMING_SNAKE_CASE_ )
self.add_vertex(SCREAMING_SNAKE_CASE_ )
if head == tail:
return
UpperCAmelCase_: Optional[int] = weight
UpperCAmelCase_: List[str] = weight
def __snake_case (self ) -> Tuple:
UpperCAmelCase_: str = self.get_edges()
for edge in edges:
UpperCAmelCase_: Optional[Any] = edge
edges.remove((tail, head, weight) )
for i in range(len(SCREAMING_SNAKE_CASE_ ) ):
UpperCAmelCase_: Union[str, Any] = list(edges[i] )
edges.sort(key=lambda SCREAMING_SNAKE_CASE_ : e[2] )
for i in range(len(SCREAMING_SNAKE_CASE_ ) - 1 ):
if edges[i][2] >= edges[i + 1][2]:
UpperCAmelCase_: str = edges[i][2] + 1
for edge in edges:
UpperCAmelCase_: List[Any] = edge
UpperCAmelCase_: Dict = weight
UpperCAmelCase_: Any = weight
def __str__(self ) -> str:
UpperCAmelCase_: int = """"""
for tail in self.adjacency:
for head in self.adjacency[tail]:
UpperCAmelCase_: str = self.adjacency[head][tail]
string += f'{head} -> {tail} == {weight}\n'
return string.rstrip("""\n""" )
def __snake_case (self ) -> Optional[int]:
UpperCAmelCase_: int = []
for tail in self.adjacency:
for head in self.adjacency[tail]:
output.append((tail, head, self.adjacency[head][tail]) )
return output
def __snake_case (self ) -> Optional[int]:
return self.adjacency.keys()
@staticmethod
def __snake_case (SCREAMING_SNAKE_CASE_=None, SCREAMING_SNAKE_CASE_=None ) -> Optional[int]:
UpperCAmelCase_: Union[str, Any] = Graph()
if vertices is None:
UpperCAmelCase_: str = []
if edges is None:
UpperCAmelCase_: Any = []
for vertex in vertices:
g.add_vertex(SCREAMING_SNAKE_CASE_ )
for edge in edges:
g.add_edge(*SCREAMING_SNAKE_CASE_ )
return g
class _a :
def __init__(self ) -> Any:
UpperCAmelCase_: Optional[int] = {}
UpperCAmelCase_: Union[str, Any] = {}
def __len__(self ) -> List[Any]:
return len(self.parent )
def __snake_case (self, SCREAMING_SNAKE_CASE_ ) -> List[Any]:
if item in self.parent:
return self.find(SCREAMING_SNAKE_CASE_ )
UpperCAmelCase_: Optional[Any] = item
UpperCAmelCase_: Optional[int] = 0
return item
def __snake_case (self, SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
if item not in self.parent:
return self.make_set(SCREAMING_SNAKE_CASE_ )
if item != self.parent[item]:
UpperCAmelCase_: Union[str, Any] = self.find(self.parent[item] )
return self.parent[item]
def __snake_case (self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
UpperCAmelCase_: Union[str, Any] = self.find(SCREAMING_SNAKE_CASE_ )
UpperCAmelCase_: List[Any] = self.find(SCREAMING_SNAKE_CASE_ )
if roota == roota:
return roota
if self.rank[roota] > self.rank[roota]:
UpperCAmelCase_: List[Any] = roota
return roota
if self.rank[roota] < self.rank[roota]:
UpperCAmelCase_: Tuple = roota
return roota
if self.rank[roota] == self.rank[roota]:
self.rank[roota] += 1
UpperCAmelCase_: List[Any] = roota
return roota
return None
@staticmethod
def __snake_case (SCREAMING_SNAKE_CASE_ ) -> Tuple:
UpperCAmelCase_: Tuple = graph.num_vertices
UpperCAmelCase_: List[str] = Graph.UnionFind()
UpperCAmelCase_: Any = []
while num_components > 1:
UpperCAmelCase_: List[str] = {}
for vertex in graph.get_vertices():
UpperCAmelCase_: Optional[Any] = -1
UpperCAmelCase_: str = graph.get_edges()
for edge in edges:
UpperCAmelCase_: Any = edge
edges.remove((tail, head, weight) )
for edge in edges:
UpperCAmelCase_: List[Any] = edge
UpperCAmelCase_: Union[str, Any] = union_find.find(SCREAMING_SNAKE_CASE_ )
UpperCAmelCase_: Union[str, Any] = union_find.find(SCREAMING_SNAKE_CASE_ )
if seta != seta:
if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
UpperCAmelCase_: Optional[Any] = [head, tail, weight]
if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
UpperCAmelCase_: Union[str, Any] = [head, tail, weight]
for vertex in cheap_edge:
if cheap_edge[vertex] != -1:
UpperCAmelCase_: Any = cheap_edge[vertex]
if union_find.find(SCREAMING_SNAKE_CASE_ ) != union_find.find(SCREAMING_SNAKE_CASE_ ):
union_find.union(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ )
mst_edges.append(cheap_edge[vertex] )
UpperCAmelCase_: Optional[int] = num_components - 1
UpperCAmelCase_: Optional[Any] = Graph.build(edges=SCREAMING_SNAKE_CASE_ )
return mst
| 360 |
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : Tuple = logging.get_logger(__name__)
a : Optional[Any] = {
'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 ( _lowerCAmelCase ):
A = '''encodec'''
def __init__(self, SCREAMING_SNAKE_CASE_=[1.5, 3.0, 6.0, 1_2.0, 2_4.0], SCREAMING_SNAKE_CASE_=24000, SCREAMING_SNAKE_CASE_=1, SCREAMING_SNAKE_CASE_=False, SCREAMING_SNAKE_CASE_=None, SCREAMING_SNAKE_CASE_=None, SCREAMING_SNAKE_CASE_=128, SCREAMING_SNAKE_CASE_=32, SCREAMING_SNAKE_CASE_=1, SCREAMING_SNAKE_CASE_=[8, 5, 4, 2], SCREAMING_SNAKE_CASE_="weight_norm", SCREAMING_SNAKE_CASE_=7, SCREAMING_SNAKE_CASE_=7, SCREAMING_SNAKE_CASE_=3, SCREAMING_SNAKE_CASE_=2, SCREAMING_SNAKE_CASE_=True, SCREAMING_SNAKE_CASE_="reflect", SCREAMING_SNAKE_CASE_=2, SCREAMING_SNAKE_CASE_=2, SCREAMING_SNAKE_CASE_=1.0, SCREAMING_SNAKE_CASE_=1024, SCREAMING_SNAKE_CASE_=None, SCREAMING_SNAKE_CASE_=True, **SCREAMING_SNAKE_CASE_, ) -> Optional[int]:
UpperCAmelCase_: List[Any] = target_bandwidths
UpperCAmelCase_: str = sampling_rate
UpperCAmelCase_: Any = audio_channels
UpperCAmelCase_: List[str] = normalize
UpperCAmelCase_: List[Any] = chunk_length_s
UpperCAmelCase_: List[Any] = overlap
UpperCAmelCase_: Any = hidden_size
UpperCAmelCase_: str = num_filters
UpperCAmelCase_: Any = num_residual_layers
UpperCAmelCase_: int = upsampling_ratios
UpperCAmelCase_: Tuple = norm_type
UpperCAmelCase_: Union[str, Any] = kernel_size
UpperCAmelCase_: str = last_kernel_size
UpperCAmelCase_: Union[str, Any] = residual_kernel_size
UpperCAmelCase_: str = dilation_growth_rate
UpperCAmelCase_: int = use_causal_conv
UpperCAmelCase_: int = pad_mode
UpperCAmelCase_: List[Any] = compress
UpperCAmelCase_: Dict = num_lstm_layers
UpperCAmelCase_: List[Any] = trim_right_ratio
UpperCAmelCase_: List[Any] = codebook_size
UpperCAmelCase_: List[Any] = codebook_dim if codebook_dim is not None else hidden_size
UpperCAmelCase_: Optional[Any] = 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__(**SCREAMING_SNAKE_CASE_ )
@property
def __snake_case (self ) -> Optional[int]:
if self.chunk_length_s is None:
return None
else:
return int(self.chunk_length_s * self.sampling_rate )
@property
def __snake_case (self ) -> Optional[int]:
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 __snake_case (self ) -> int:
UpperCAmelCase_: Optional[int] = np.prod(self.upsampling_ratios )
return math.ceil(self.sampling_rate / hop_length )
@property
def __snake_case (self ) -> int:
return int(1000 * self.target_bandwidths[-1] // (self.frame_rate * 10) )
| 82 | 0 |
'''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,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
a__ : List[str] = logging.get_logger(__name__)
if is_vision_available():
import PIL
class lowercase_ ( a__ ):
__UpperCAmelCase = ['pixel_values']
def __init__( self , a = True , a = None , a = PILImageResampling.BICUBIC , a = True , a = None , a = True , a = 1 / 2_55 , a = True , a = None , a = None , a = True , **a , ):
super().__init__(**a )
UpperCamelCase__ = size if size is not None else {"shortest_edge": 2_24}
UpperCamelCase__ = get_size_dict(a , default_to_square=a )
UpperCamelCase__ = crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
UpperCamelCase__ = get_size_dict(a , default_to_square=a , param_name="crop_size" )
UpperCamelCase__ = do_resize
UpperCamelCase__ = size
UpperCamelCase__ = resample
UpperCamelCase__ = do_center_crop
UpperCamelCase__ = crop_size
UpperCamelCase__ = do_rescale
UpperCamelCase__ = rescale_factor
UpperCamelCase__ = do_normalize
UpperCamelCase__ = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
UpperCamelCase__ = image_std if image_std is not None else OPENAI_CLIP_STD
UpperCamelCase__ = do_convert_rgb
def __a ( self , a , a , a = PILImageResampling.BICUBIC , a = None , **a , ):
UpperCamelCase__ = get_size_dict(a , default_to_square=a )
if "shortest_edge" not in size:
raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}''' )
UpperCamelCase__ = get_resize_output_image_size(a , size=size["shortest_edge"] , default_to_square=a )
return resize(a , size=a , resample=a , data_format=a , **a )
def __a ( self , a , a , a = None , **a , ):
UpperCamelCase__ = get_size_dict(a )
if "height" not in size or "width" not in size:
raise ValueError(f'''The `size` parameter must contain the keys (height, width). Got {size.keys()}''' )
return center_crop(a , size=(size["height"], size["width"]) , data_format=a , **a )
def __a ( self , a , a , a = None , **a , ):
return rescale(a , scale=a , data_format=a , **a )
def __a ( self , a , a , a , a = None , **a , ):
return normalize(a , mean=a , std=a , data_format=a , **a )
def __a ( self , a , a = None , a = None , a = None , a = None , a = None , a = None , a = None , a = None , a = None , a = None , a = None , a = None , a = ChannelDimension.FIRST , **a , ):
UpperCamelCase__ = do_resize if do_resize is not None else self.do_resize
UpperCamelCase__ = size if size is not None else self.size
UpperCamelCase__ = get_size_dict(a , param_name="size" , default_to_square=a )
UpperCamelCase__ = resample if resample is not None else self.resample
UpperCamelCase__ = do_center_crop if do_center_crop is not None else self.do_center_crop
UpperCamelCase__ = crop_size if crop_size is not None else self.crop_size
UpperCamelCase__ = get_size_dict(a , param_name="crop_size" , default_to_square=a )
UpperCamelCase__ = do_rescale if do_rescale is not None else self.do_rescale
UpperCamelCase__ = rescale_factor if rescale_factor is not None else self.rescale_factor
UpperCamelCase__ = do_normalize if do_normalize is not None else self.do_normalize
UpperCamelCase__ = image_mean if image_mean is not None else self.image_mean
UpperCamelCase__ = image_std if image_std is not None else self.image_std
UpperCamelCase__ = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
UpperCamelCase__ = make_list_of_images(a )
if not valid_images(a ):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray." )
if do_resize and size is None:
raise ValueError("Size must be specified if do_resize is True." )
if do_center_crop and crop_size is None:
raise ValueError("Crop size must be specified if do_center_crop is True." )
if do_rescale and rescale_factor is None:
raise ValueError("Rescale factor must be specified if do_rescale is True." )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("Image mean and std must be specified if do_normalize is True." )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
UpperCamelCase__ = [convert_to_rgb(a ) for image in images]
# All transformations expect numpy arrays.
UpperCamelCase__ = [to_numpy_array(a ) for image in images]
if do_resize:
UpperCamelCase__ = [self.resize(image=a , size=a , resample=a ) for image in images]
if do_center_crop:
UpperCamelCase__ = [self.center_crop(image=a , size=a ) for image in images]
if do_rescale:
UpperCamelCase__ = [self.rescale(image=a , scale=a ) for image in images]
if do_normalize:
UpperCamelCase__ = [self.normalize(image=a , mean=a , std=a ) for image in images]
UpperCamelCase__ = [to_channel_dimension_format(a , a ) for image in images]
UpperCamelCase__ = {"pixel_values": images}
return BatchFeature(data=a , tensor_type=a )
| 80 |
'''simple docstring'''
from __future__ import annotations
lowerCamelCase__ = 'Muhammad Umer Farooq'
lowerCamelCase__ = 'MIT'
lowerCamelCase__ = '1.0.0'
lowerCamelCase__ = 'Muhammad Umer Farooq'
lowerCamelCase__ = 'contact@muhammadumerfarooq.me'
lowerCamelCase__ = 'Alpha'
import re
from html.parser import HTMLParser
from urllib import parse
import requests
class lowerCAmelCase__ ( UpperCAmelCase__ ):
def __init__( self : Any , lowerCamelCase__ : str ) ->None:
'''simple docstring'''
super().__init__()
_UpperCAmelCase : list[str] = []
_UpperCAmelCase : List[str] = domain
def lowerCAmelCase__ ( self : Optional[Any] , lowerCamelCase__ : str , lowerCamelCase__ : list[tuple[str, str | None]] ) ->None:
'''simple docstring'''
if tag == "a":
# Check the list of defined attributes.
for name, value in attrs:
# If href is defined, and not empty nor # print it.
if name == "href" and value != "#" and value != "":
# If not already in urls.
if value not in self.urls:
_UpperCAmelCase : Dict = parse.urljoin(self.domain , lowerCamelCase__ )
self.urls.append(lowerCamelCase__ )
def __lowerCAmelCase (__lowerCAmelCase ):
return ".".join(get_sub_domain_name(__lowerCAmelCase ).split("." )[-2:] )
def __lowerCAmelCase (__lowerCAmelCase ):
return parse.urlparse(__lowerCAmelCase ).netloc
def __lowerCAmelCase (__lowerCAmelCase = "https://github.com" ):
_UpperCAmelCase : List[str] = get_domain_name(__lowerCAmelCase )
# Initialize the parser
_UpperCAmelCase : int = Parser(__lowerCAmelCase )
try:
# Open URL
_UpperCAmelCase : Any = requests.get(__lowerCAmelCase )
# pass the raw HTML to the parser to get links
parser.feed(r.text )
# Get links and loop through
_UpperCAmelCase : Any = set()
for link in parser.urls:
# open URL.
# read = requests.get(link)
try:
_UpperCAmelCase : List[str] = requests.get(__lowerCAmelCase )
# Get the valid email.
_UpperCAmelCase : Optional[int] = re.findall("[a-zA-Z0-9]+@" + domain , read.text )
# If not in list then append it.
for email in emails:
valid_emails.add(__lowerCAmelCase )
except ValueError:
pass
except ValueError:
raise SystemExit(1 )
# Finally return a sorted list of email addresses with no duplicates.
return sorted(__lowerCAmelCase )
if __name__ == "__main__":
lowerCamelCase__ = emails_from_url('https://github.com')
print(F'''{len(emails)} emails found:''')
print('\n'.join(sorted(emails)))
| 234 | 0 |
from typing import List, Optional, Union
import numpy as np
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
__A : Optional[Any] = logging.get_logger(__name__)
class _SCREAMING_SNAKE_CASE ( lowerCAmelCase__):
_UpperCamelCase:Optional[Any] = ["input_values", "padding_mask"]
def __init__( self , _SCREAMING_SNAKE_CASE = 1 , _SCREAMING_SNAKE_CASE = 2_4000 , _SCREAMING_SNAKE_CASE = 0.0 , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , **_SCREAMING_SNAKE_CASE , )-> int:
super().__init__(feature_size=_SCREAMING_SNAKE_CASE , sampling_rate=_SCREAMING_SNAKE_CASE , padding_value=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
lowerCamelCase_ =chunk_length_s
lowerCamelCase_ =overlap
@property
def _snake_case ( self )-> Optional[int]:
if self.chunk_length_s is None:
return None
else:
return int(self.chunk_length_s * self.sampling_rate )
@property
def _snake_case ( self )-> Optional[int]:
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 ) )
def __call__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = False , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , )-> BatchFeature:
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
f'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
f' {self.sampling_rate}. Please make sure that the provided audio input was sampled with'
f' {self.sampling_rate} and not {sampling_rate}.' )
else:
logger.warning(
"""It is strongly recommended to pass the `sampling_rate` argument to this function. """
"""Failing to do so can result in silent errors that might be hard to debug.""" )
if padding and truncation:
raise ValueError("""Both padding and truncation were set. Make sure you only set one.""" )
elif padding is None:
# by default let's pad the inputs
lowerCamelCase_ =True
lowerCamelCase_ =bool(
isinstance(_SCREAMING_SNAKE_CASE , (list, tuple) ) and (isinstance(raw_audio[0] , (np.ndarray, tuple, list) )) )
if is_batched:
lowerCamelCase_ =[np.asarray(_SCREAMING_SNAKE_CASE , dtype=np.floataa ).T for audio in raw_audio]
elif not is_batched and not isinstance(_SCREAMING_SNAKE_CASE , np.ndarray ):
lowerCamelCase_ =np.asarray(_SCREAMING_SNAKE_CASE , dtype=np.floataa )
elif isinstance(_SCREAMING_SNAKE_CASE , np.ndarray ) and raw_audio.dtype is np.dtype(np.floataa ):
lowerCamelCase_ =raw_audio.astype(np.floataa )
# always return batch
if not is_batched:
lowerCamelCase_ =[np.asarray(_SCREAMING_SNAKE_CASE ).T]
# verify inputs are valid
for idx, example in enumerate(_SCREAMING_SNAKE_CASE ):
if example.ndim > 2:
raise ValueError(f'Expected input shape (channels, length) but got shape {example.shape}' )
if self.feature_size == 1 and example.ndim != 1:
raise ValueError(f'Expected mono audio but example has {example.shape[-1]} channels' )
if self.feature_size == 2 and example.shape[-1] != 2:
raise ValueError(f'Expected stereo audio but example has {example.shape[-1]} channels' )
lowerCamelCase_ =None
lowerCamelCase_ =BatchFeature({"""input_values""": raw_audio} )
if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
if truncation:
lowerCamelCase_ =min(array.shape[0] for array in raw_audio )
lowerCamelCase_ =int(np.floor(max_length / self.chunk_stride ) )
lowerCamelCase_ =(nb_step - 1) * self.chunk_stride + self.chunk_length
elif padding:
lowerCamelCase_ =max(array.shape[0] for array in raw_audio )
lowerCamelCase_ =int(np.ceil(max_length / self.chunk_stride ) )
lowerCamelCase_ =(nb_step - 1) * self.chunk_stride + self.chunk_length
lowerCamelCase_ ="""max_length"""
else:
lowerCamelCase_ =input_values
# normal padding on batch
if padded_inputs is None:
lowerCamelCase_ =self.pad(
_SCREAMING_SNAKE_CASE , max_length=_SCREAMING_SNAKE_CASE , truncation=_SCREAMING_SNAKE_CASE , padding=_SCREAMING_SNAKE_CASE , return_attention_mask=_SCREAMING_SNAKE_CASE , )
if padding:
lowerCamelCase_ =padded_inputs.pop("""attention_mask""" )
lowerCamelCase_ =[]
for example in padded_inputs.pop("""input_values""" ):
if self.feature_size == 1:
lowerCamelCase_ =example[..., None]
input_values.append(example.T )
lowerCamelCase_ =input_values
if return_tensors is not None:
lowerCamelCase_ =padded_inputs.convert_to_tensors(_SCREAMING_SNAKE_CASE )
return padded_inputs
| 49 |
import faiss # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import requests # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn # noqa: F401 # Here to have a nice missing dependency error message early on
import tqdm # noqa: F401 # Here to have a nice missing dependency error message early on
from mauve import compute_mauve # From: mauve-text
import datasets
__A : int = '\\n@inproceedings{pillutla-etal:mauve:neurips2021,\n title={MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers},\n author={Pillutla, Krishna and Swayamdipta, Swabha and Zellers, Rowan and Thickstun, John and Welleck, Sean and Choi, Yejin and Harchaoui, Zaid},\n booktitle = {NeurIPS},\n year = {2021}\n}\n\n'
__A : Any = '\\nMAUVE is a library built on PyTorch and HuggingFace Transformers to measure the gap between neural text and human text with the eponymous MAUVE measure.\n\nMAUVE summarizes both Type I and Type II errors measured softly using Kullback–Leibler (KL) divergences.\n\nFor details, see the MAUVE paper: https://arxiv.org/abs/2102.01454 (Neurips, 2021).\n\nThis metrics is a wrapper around the official implementation of MAUVE:\nhttps://github.com/krishnap25/mauve\n'
__A : Union[str, Any] = '\nCalculates MAUVE scores between two lists of generated text and reference text.\nArgs:\n predictions: list of generated text to score. Each predictions\n should be a string with tokens separated by spaces.\n references: list of reference for each prediction. Each\n reference should be a string with tokens separated by spaces.\nOptional Args:\n num_buckets: the size of the histogram to quantize P and Q. Options: \'auto\' (default) or an integer\n pca_max_data: the number data points to use for PCA dimensionality reduction prior to clustering. If -1, use all the data. Default -1\n kmeans_explained_var: amount of variance of the data to keep in dimensionality reduction by PCA. Default 0.9\n kmeans_num_redo: number of times to redo k-means clustering (the best objective is kept). Default 5\n kmeans_max_iter: maximum number of k-means iterations. Default 500\n featurize_model_name: name of the model from which features are obtained. Default \'gpt2-large\' Use one of [\'gpt2\', \'gpt2-medium\', \'gpt2-large\', \'gpt2-xl\'].\n device_id: Device for featurization. Supply a GPU id (e.g. 0 or 3) to use GPU. If no GPU with this id is found, use CPU\n max_text_length: maximum number of tokens to consider. Default 1024\n divergence_curve_discretization_size: Number of points to consider on the divergence curve. Default 25\n mauve_scaling_factor: "c" from the paper. Default 5.\n verbose: If True (default), print running time updates\n seed: random seed to initialize k-means cluster assignments.\nReturns:\n mauve: MAUVE score, a number between 0 and 1. Larger values indicate that P and Q are closer,\n frontier_integral: Frontier Integral, a number between 0 and 1. Smaller values indicate that P and Q are closer,\n divergence_curve: a numpy.ndarray of shape (m, 2); plot it with matplotlib to view the divergence curve,\n p_hist: a discrete distribution, which is a quantized version of the text distribution p_text,\n q_hist: same as above, but with q_text.\nExamples:\n\n >>> # faiss segfaults in doctest for some reason, so the .compute call is not tested with doctest\n >>> import datasets\n >>> mauve = datasets.load_metric(\'mauve\')\n >>> predictions = ["hello there", "general kenobi"]\n >>> references = ["hello there", "general kenobi"]\n >>> out = mauve.compute(predictions=predictions, references=references) # doctest: +SKIP\n >>> print(out.mauve) # doctest: +SKIP\n 1.0\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION)
class _SCREAMING_SNAKE_CASE ( datasets.Metric):
def _snake_case ( self )-> Any:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , homepage="""https://github.com/krishnap25/mauve""" , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Value("""string""" , id="""sequence""" ),
"""references""": datasets.Value("""string""" , id="""sequence""" ),
} ) , codebase_urls=["""https://github.com/krishnap25/mauve"""] , reference_urls=[
"""https://arxiv.org/abs/2102.01454""",
"""https://github.com/krishnap25/mauve""",
] , )
def _snake_case ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE="auto" , _SCREAMING_SNAKE_CASE=-1 , _SCREAMING_SNAKE_CASE=0.9 , _SCREAMING_SNAKE_CASE=5 , _SCREAMING_SNAKE_CASE=500 , _SCREAMING_SNAKE_CASE="gpt2-large" , _SCREAMING_SNAKE_CASE=-1 , _SCREAMING_SNAKE_CASE=1024 , _SCREAMING_SNAKE_CASE=25 , _SCREAMING_SNAKE_CASE=5 , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=25 , )-> List[str]:
lowerCamelCase_ =compute_mauve(
p_text=_SCREAMING_SNAKE_CASE , q_text=_SCREAMING_SNAKE_CASE , p_features=_SCREAMING_SNAKE_CASE , q_features=_SCREAMING_SNAKE_CASE , p_tokens=_SCREAMING_SNAKE_CASE , q_tokens=_SCREAMING_SNAKE_CASE , num_buckets=_SCREAMING_SNAKE_CASE , pca_max_data=_SCREAMING_SNAKE_CASE , kmeans_explained_var=_SCREAMING_SNAKE_CASE , kmeans_num_redo=_SCREAMING_SNAKE_CASE , kmeans_max_iter=_SCREAMING_SNAKE_CASE , featurize_model_name=_SCREAMING_SNAKE_CASE , device_id=_SCREAMING_SNAKE_CASE , max_text_length=_SCREAMING_SNAKE_CASE , divergence_curve_discretization_size=_SCREAMING_SNAKE_CASE , mauve_scaling_factor=_SCREAMING_SNAKE_CASE , verbose=_SCREAMING_SNAKE_CASE , seed=_SCREAMING_SNAKE_CASE , )
return out
| 49 | 1 |
from typing import List, Union
import numpy as np
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING
_A = logging.get_logger(__name__)
@add_end_docstrings(_UpperCAmelCase )
class _lowerCAmelCase ( _UpperCAmelCase ):
def __init__( self , *_UpperCamelCase , **_UpperCamelCase ) -> Union[str, Any]:
super().__init__(*_UpperCAmelCase , **_UpperCAmelCase )
requires_backends(self , "vision" )
self.check_model_type(_UpperCAmelCase )
def __call__( self , _UpperCamelCase , **_UpperCamelCase ) -> Any:
return super().__call__(_UpperCAmelCase , **_UpperCAmelCase )
def __a ( self , **_UpperCamelCase ) -> Tuple:
return {}, {}, {}
def __a ( self , _UpperCamelCase ) -> Dict:
lowerCAmelCase_ = load_image(_UpperCAmelCase )
lowerCAmelCase_ = image.size
lowerCAmelCase_ = self.image_processor(images=_UpperCAmelCase , return_tensors=self.framework )
return model_inputs
def __a ( self , _UpperCamelCase ) -> Any:
lowerCAmelCase_ = self.model(**_UpperCAmelCase )
return model_outputs
def __a ( self , _UpperCamelCase ) -> int:
lowerCAmelCase_ = model_outputs.predicted_depth
lowerCAmelCase_ = torch.nn.functional.interpolate(
predicted_depth.unsqueeze(1 ) , size=self.image_size[::-1] , mode="bicubic" , align_corners=_UpperCAmelCase )
lowerCAmelCase_ = prediction.squeeze().cpu().numpy()
lowerCAmelCase_ = (output * 255 / np.max(_UpperCAmelCase )).astype("uint8" )
lowerCAmelCase_ = Image.fromarray(_UpperCAmelCase )
lowerCAmelCase_ = {}
lowerCAmelCase_ = predicted_depth
lowerCAmelCase_ = depth
return output_dict
| 231 | """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_retribert import RetriBertTokenizer
__A = logging.get_logger(__name__)
__A = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
__A = {
"vocab_file": {
"yjernite/retribert-base-uncased": (
"https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/vocab.txt"
),
},
"tokenizer_file": {
"yjernite/retribert-base-uncased": (
"https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/tokenizer.json"
),
},
}
__A = {
"yjernite/retribert-base-uncased": 5_1_2,
}
__A = {
"yjernite/retribert-base-uncased": {"do_lower_case": True},
}
class UpperCAmelCase (_UpperCAmelCase ):
"""simple docstring"""
_UpperCAmelCase :Dict = VOCAB_FILES_NAMES
_UpperCAmelCase :str = PRETRAINED_VOCAB_FILES_MAP
_UpperCAmelCase :Optional[int] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_UpperCAmelCase :Optional[int] = PRETRAINED_INIT_CONFIGURATION
_UpperCAmelCase :str = RetriBertTokenizer
_UpperCAmelCase :List[str] = ["input_ids", "attention_mask"]
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 , )
lowercase__: Optional[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
):
lowercase__: str = getattr(_UpperCAmelCase , normalizer_state.pop('''type''' ) )
lowercase__: Optional[Any] = do_lower_case
lowercase__: Tuple = strip_accents
lowercase__: str = tokenize_chinese_chars
lowercase__: Union[str, Any] = normalizer_class(**_UpperCAmelCase )
lowercase__: List[str] = do_lower_case
def _snake_case ( self , _UpperCAmelCase , _UpperCAmelCase=None ):
lowercase__: Union[str, 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 _snake_case ( self , _UpperCAmelCase , _UpperCAmelCase = None ):
lowercase__: Optional[int] = [self.sep_token_id]
lowercase__: Union[str, Any] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def _snake_case ( self , _UpperCAmelCase , _UpperCAmelCase = None ):
lowercase__: List[str] = self._tokenizer.model.save(_UpperCAmelCase , name=_UpperCAmelCase )
return tuple(_UpperCAmelCase )
| 177 | 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 :
'''simple docstring'''
def __init__( self , lowercase , lowercase=1_4 , lowercase=7 , lowercase=True , lowercase=True , lowercase=False , lowercase=True , lowercase=9_9 , lowercase=3_2 , lowercase=4 , lowercase=4 , lowercase=4 , lowercase=3_7 , lowercase="gelu" , lowercase=0.1 , lowercase=0.1 , lowercase=5_1_2 , lowercase=0.02 , ):
"""simple docstring"""
A_ : Dict = parent
A_ : Union[str, Any] = batch_size
A_ : Tuple = seq_length
A_ : Tuple = is_training
A_ : Optional[Any] = use_input_mask
A_ : Any = use_token_type_ids
A_ : List[str] = use_labels
A_ : List[str] = vocab_size
A_ : int = hidden_size
A_ : Any = rotary_dim
A_ : List[Any] = num_hidden_layers
A_ : Dict = num_attention_heads
A_ : List[Any] = intermediate_size
A_ : List[str] = hidden_act
A_ : Dict = hidden_dropout_prob
A_ : int = attention_probs_dropout_prob
A_ : str = max_position_embeddings
A_ : Optional[int] = initializer_range
A_ : Tuple = None
A_ : Optional[Any] = vocab_size - 1
A_ : Any = vocab_size - 1
A_ : Union[str, Any] = vocab_size - 1
def lowerCAmelCase_ ( self ):
"""simple docstring"""
A_ : Dict = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
A_ : int = None
if self.use_input_mask:
A_ : Optional[Any] = random_attention_mask([self.batch_size, self.seq_length] )
A_ : List[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=lowercase , 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 lowerCAmelCase_ ( self ):
"""simple docstring"""
A_ : List[Any] = self.prepare_config_and_inputs()
A_ : List[Any] = config_and_inputs
A_ : List[str] = {"""input_ids""": input_ids, """attention_mask""": attention_mask}
return config, inputs_dict
def lowerCAmelCase_ ( self , lowercase , lowercase , lowercase , lowercase ):
"""simple docstring"""
A_ : Tuple = 2_0
A_ : int = model_class_name(lowercase )
A_ : List[Any] = model.init_cache(input_ids.shape[0] , lowercase )
A_ : Tuple = jnp.ones((input_ids.shape[0], max_decoder_length) , dtype='i4' )
A_ : List[Any] = jnp.broadcast_to(
jnp.arange(input_ids.shape[-1] - 1 )[None, :] , (input_ids.shape[0], input_ids.shape[-1] - 1) )
A_ : Tuple = model(
input_ids[:, :-1] , attention_mask=lowercase , past_key_values=lowercase , position_ids=lowercase , )
A_ : Any = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] , dtype='i4' )
A_ : Tuple = model(
input_ids[:, -1:] , attention_mask=lowercase , past_key_values=outputs_cache.past_key_values , position_ids=lowercase , )
A_ : Tuple = model(lowercase )
A_ : Tuple = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1E-3 , msg=F'''Max diff is {diff}''' )
def lowerCAmelCase_ ( self , lowercase , lowercase , lowercase , lowercase ):
"""simple docstring"""
A_ : List[Any] = 2_0
A_ : List[str] = model_class_name(lowercase )
A_ : int = jnp.concatenate(
[attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]) )] , axis=-1 , )
A_ : Any = model.init_cache(input_ids.shape[0] , lowercase )
A_ : List[str] = jnp.broadcast_to(
jnp.arange(input_ids.shape[-1] - 1 )[None, :] , (input_ids.shape[0], input_ids.shape[-1] - 1) )
A_ : int = model(
input_ids[:, :-1] , attention_mask=lowercase , past_key_values=lowercase , position_ids=lowercase , )
A_ : str = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] , dtype='i4' )
A_ : Optional[Any] = model(
input_ids[:, -1:] , past_key_values=outputs_cache.past_key_values , attention_mask=lowercase , position_ids=lowercase , )
A_ : str = model(lowercase , attention_mask=lowercase )
A_ : 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 ( __lowercase , __lowercase , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase_ = (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
lowerCamelCase_ = (FlaxGPTJForCausalLM,) if is_flax_available() else ()
def lowerCAmelCase_ ( self ):
"""simple docstring"""
A_ : Optional[int] = FlaxGPTJModelTester(self )
def lowerCAmelCase_ ( self ):
"""simple docstring"""
for model_class_name in self.all_model_classes:
A_ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_use_cache_forward(lowercase , lowercase , lowercase , lowercase )
def lowerCAmelCase_ ( self ):
"""simple docstring"""
for model_class_name in self.all_model_classes:
A_ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_use_cache_forward_with_attn_mask(
lowercase , lowercase , lowercase , lowercase )
@tooslow
def lowerCAmelCase_ ( self ):
"""simple docstring"""
A_ : List[str] = GPTaTokenizer.from_pretrained('gpt2' , pad_token='<|endoftext|>' , padding_side='left' )
A_ : Dict = tokenizer(['Hello this is a long string', 'Hey'] , return_tensors='np' , padding=lowercase , truncation=lowercase )
A_ : Union[str, Any] = FlaxGPTJForCausalLM.from_pretrained('EleutherAI/gpt-j-6B' )
A_ : str = False
A_ : Optional[Any] = model.config.eos_token_id
A_ : Optional[Any] = jax.jit(model.generate )
A_ : Any = jit_generate(
inputs['input_ids'] , attention_mask=inputs['attention_mask'] , pad_token_id=tokenizer.pad_token_id ).sequences
A_ : Optional[int] = tokenizer.batch_decode(lowercase , skip_special_tokens=lowercase )
A_ : int = [
"""Hello this is a long string of text.\n\nI'm trying to get the text of the""",
"""Hey, I'm a little late to the party. I'm going to""",
]
self.assertListEqual(lowercase , lowercase )
@is_pt_flax_cross_test
def lowerCAmelCase_ ( self ):
"""simple docstring"""
A_ : Optional[Any] = 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
A_ : Union[str, Any] = self._prepare_for_class(lowercase , lowercase )
A_ : List[Any] = {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}
# load corresponding PyTorch class
A_ : Optional[Any] = model_class.__name__[4:] # Skip the "Flax" at the beginning
A_ : int = getattr(lowercase , lowercase )
A_ : Dict = pt_inputs["""input_ids"""].shape
A_ : str = np.random.randint(0 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(lowercase ):
A_ : Tuple = 0
A_ : Union[str, Any] = 1
A_ : Any = 0
A_ : int = 1
A_ : Optional[Any] = pt_model_class(lowercase ).eval()
A_ : List[str] = model_class(lowercase , dtype=jnp.floataa )
A_ : Optional[int] = convert_pytorch_state_dict_to_flax(pt_model.state_dict() , lowercase )
A_ : Tuple = fx_state
with torch.no_grad():
A_ : Tuple = pt_model(**lowercase ).to_tuple()
A_ : Dict = fx_model(**lowercase ).to_tuple()
self.assertEqual(len(lowercase ) , len(lowercase ) , 'Output lengths differ between Flax and PyTorch' )
for fx_output, pt_output in zip(lowercase , lowercase ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4E-2 )
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(lowercase )
A_ : int = model_class.from_pretrained(lowercase , from_pt=lowercase )
A_ : Tuple = fx_model_loaded(**lowercase ).to_tuple()
self.assertEqual(
len(lowercase ) , len(lowercase ) , 'Output lengths differ between Flax and PyTorch' )
for fx_output_loaded, pt_output in zip(lowercase , lowercase ):
self.assert_almost_equals(fx_output_loaded[:, -1] , pt_output[:, -1].numpy() , 4E-2 )
@is_pt_flax_cross_test
def lowerCAmelCase_ ( self ):
"""simple docstring"""
A_ : Any = 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
A_ : Any = self._prepare_for_class(lowercase , lowercase )
A_ : Tuple = {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}
# load corresponding PyTorch class
A_ : Optional[Any] = model_class.__name__[4:] # Skip the "Flax" at the beginning
A_ : int = getattr(lowercase , lowercase )
A_ : Tuple = pt_model_class(lowercase ).eval()
A_ : Union[str, Any] = model_class(lowercase , dtype=jnp.floataa )
A_ : Union[str, Any] = load_flax_weights_in_pytorch_model(lowercase , fx_model.params )
A_ : Any = pt_inputs["""input_ids"""].shape
A_ : str = np.random.randint(0 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(lowercase ):
A_ : Dict = 0
A_ : str = 1
A_ : Tuple = 0
A_ : Union[str, Any] = 1
# make sure weights are tied in PyTorch
pt_model.tie_weights()
with torch.no_grad():
A_ : str = pt_model(**lowercase ).to_tuple()
A_ : List[Any] = fx_model(**lowercase ).to_tuple()
self.assertEqual(len(lowercase ) , len(lowercase ) , 'Output lengths differ between Flax and PyTorch' )
for fx_output, pt_output in zip(lowercase , lowercase ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4E-2 )
with tempfile.TemporaryDirectory() as tmpdirname:
fx_model.save_pretrained(lowercase )
A_ : Optional[int] = pt_model_class.from_pretrained(lowercase , from_flax=lowercase )
with torch.no_grad():
A_ : str = pt_model_loaded(**lowercase ).to_tuple()
self.assertEqual(
len(lowercase ) , len(lowercase ) , 'Output lengths differ between Flax and PyTorch' )
for fx_output, pt_output in zip(lowercase , lowercase ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4E-2 )
@tooslow
def lowerCAmelCase_ ( self ):
"""simple docstring"""
for model_class_name in self.all_model_classes:
A_ : Any = model_class_name.from_pretrained('EleutherAI/gpt-j-6B' )
A_ : List[Any] = model(np.ones((1, 1) ) )
self.assertIsNotNone(lowercase )
| 363 | from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
BaseOutput,
is_accelerate_available,
logging,
randn_tensor,
replace_example_docstring,
)
from .renderer import ShapERenderer
_UpperCAmelCase = logging.get_logger(__name__) # pylint: disable=invalid-name
_UpperCAmelCase = """
Examples:
```py
>>> from PIL import Image
>>> import torch
>>> from diffusers import DiffusionPipeline
>>> from diffusers.utils import export_to_gif, load_image
>>> device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")
>>> repo = \"openai/shap-e-img2img\"
>>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)
>>> pipe = pipe.to(device)
>>> guidance_scale = 3.0
>>> image_url = \"https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png\"
>>> image = load_image(image_url).convert(\"RGB\")
>>> images = pipe(
... image,
... guidance_scale=guidance_scale,
... num_inference_steps=64,
... frame_size=256,
... ).images
>>> gif_path = export_to_gif(images[0], \"corgi_3d.gif\")
```
"""
@dataclass
class UpperCAmelCase ( __A ):
'''simple docstring'''
lowerCamelCase_ = 42
class UpperCAmelCase ( __A ):
'''simple docstring'''
def __init__( self , lowercase , lowercase , lowercase , lowercase , lowercase , ):
"""simple docstring"""
super().__init__()
self.register_modules(
prior=lowercase , image_encoder=lowercase , image_processor=lowercase , scheduler=lowercase , renderer=lowercase , )
def lowerCAmelCase_ ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase ):
"""simple docstring"""
if latents is None:
A_ : Optional[Any] = randn_tensor(lowercase , generator=lowercase , device=lowercase , dtype=lowercase )
else:
if latents.shape != shape:
raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}''' )
A_ : Optional[int] = latents.to(lowercase )
A_ : List[Any] = latents * scheduler.init_noise_sigma
return latents
def lowerCAmelCase_ ( self , lowercase=0 ):
"""simple docstring"""
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError('Please install accelerate via `pip install accelerate`' )
A_ : Tuple = torch.device(F'''cuda:{gpu_id}''' )
A_ : Dict = [self.image_encoder, self.prior]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(lowercase , lowercase )
@property
def lowerCAmelCase_ ( self ):
"""simple docstring"""
if self.device != torch.device('meta' ) or not hasattr(self.image_encoder , '_hf_hook' ):
return self.device
for module in self.image_encoder.modules():
if (
hasattr(lowercase , '_hf_hook' )
and hasattr(module._hf_hook , 'execution_device' )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
def lowerCAmelCase_ ( self , lowercase , lowercase , lowercase , lowercase , ):
"""simple docstring"""
if isinstance(lowercase , lowercase ) and isinstance(image[0] , torch.Tensor ):
A_ : Tuple = torch.cat(lowercase , axis=0 ) if image[0].ndim == 4 else torch.stack(lowercase , axis=0 )
if not isinstance(lowercase , torch.Tensor ):
A_ : Dict = self.image_processor(lowercase , return_tensors='pt' ).pixel_values[0].unsqueeze(0 )
A_ : List[str] = image.to(dtype=self.image_encoder.dtype , device=lowercase )
A_ : Tuple = self.image_encoder(lowercase )['last_hidden_state']
A_ : Dict = image_embeds[:, 1:, :].contiguous() # batch_size, dim, 256
A_ : List[str] = image_embeds.repeat_interleave(lowercase , dim=0 )
if do_classifier_free_guidance:
A_ : str = torch.zeros_like(lowercase )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
A_ : Optional[Any] = torch.cat([negative_image_embeds, image_embeds] )
return image_embeds
@torch.no_grad()
@replace_example_docstring(lowercase )
def __call__( self , lowercase , lowercase = 1 , lowercase = 2_5 , lowercase = None , lowercase = None , lowercase = 4.0 , lowercase = 6_4 , lowercase = "pil" , lowercase = True , ):
"""simple docstring"""
if isinstance(lowercase , PIL.Image.Image ):
A_ : int = 1
elif isinstance(lowercase , torch.Tensor ):
A_ : int = image.shape[0]
elif isinstance(lowercase , lowercase ) and isinstance(image[0] , (torch.Tensor, PIL.Image.Image) ):
A_ : List[str] = len(lowercase )
else:
raise ValueError(
F'''`image` has to be of type `PIL.Image.Image`, `torch.Tensor`, `List[PIL.Image.Image]` or `List[torch.Tensor]` but is {type(lowercase )}''' )
A_ : Any = self._execution_device
A_ : List[Any] = batch_size * num_images_per_prompt
A_ : int = guidance_scale > 1.0
A_ : Optional[int] = self._encode_image(lowercase , lowercase , lowercase , lowercase )
# prior
self.scheduler.set_timesteps(lowercase , device=lowercase )
A_ : Dict = self.scheduler.timesteps
A_ : int = self.prior.config.num_embeddings
A_ : int = self.prior.config.embedding_dim
A_ : Dict = self.prepare_latents(
(batch_size, num_embeddings * embedding_dim) , image_embeds.dtype , lowercase , lowercase , lowercase , self.scheduler , )
# YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
A_ : Union[str, Any] = latents.reshape(latents.shape[0] , lowercase , lowercase )
for i, t in enumerate(self.progress_bar(lowercase ) ):
# expand the latents if we are doing classifier free guidance
A_ : List[str] = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
A_ : List[Any] = self.scheduler.scale_model_input(lowercase , lowercase )
A_ : Any = self.prior(
lowercase , timestep=lowercase , proj_embedding=lowercase , ).predicted_image_embedding
# remove the variance
A_ , A_ : int = noise_pred.split(
scaled_model_input.shape[2] , dim=2 ) # batch_size, num_embeddings, embedding_dim
if do_classifier_free_guidance is not None:
A_ , A_ : List[Any] = noise_pred.chunk(2 )
A_ : str = noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)
A_ : Optional[int] = self.scheduler.step(
lowercase , timestep=lowercase , sample=lowercase , ).prev_sample
if output_type == "latent":
return ShapEPipelineOutput(images=lowercase )
A_ : str = []
for i, latent in enumerate(lowercase ):
print()
A_ : Optional[Any] = self.renderer.decode(
latent[None, :] , lowercase , size=lowercase , ray_batch_size=4_0_9_6 , n_coarse_samples=6_4 , n_fine_samples=1_2_8 , )
images.append(lowercase )
A_ : Dict = torch.stack(lowercase )
if output_type not in ["np", "pil"]:
raise ValueError(F'''Only the output types `pil` and `np` are supported not output_type={output_type}''' )
A_ : Dict = images.cpu().numpy()
if output_type == "pil":
A_ : str = [self.numpy_to_pil(lowercase ) for image in images]
# Offload last model to CPU
if hasattr(self , 'final_offload_hook' ) and self.final_offload_hook is not None:
self.final_offload_hook.offload()
if not return_dict:
return (images,)
return ShapEPipelineOutput(images=lowercase )
| 192 | 0 |
import os
import pytest
from transformers.dynamic_module_utils import get_imports
SCREAMING_SNAKE_CASE__ : str = '\nimport os\n'
SCREAMING_SNAKE_CASE__ : str = '\ndef foo():\n import os\n return False\n'
SCREAMING_SNAKE_CASE__ : Any = '\ndef foo():\n def bar():\n if True:\n import os\n return False\n return bar()\n'
SCREAMING_SNAKE_CASE__ : Optional[int] = '\nimport os\n\ntry:\n import bar\nexcept ImportError:\n raise ValueError()\n'
SCREAMING_SNAKE_CASE__ : Union[str, Any] = '\nimport os\n\ndef foo():\n try:\n import bar\n except ImportError:\n raise ValueError()\n'
SCREAMING_SNAKE_CASE__ : List[str] = '\nimport os\n\ntry:\n import bar\nexcept (ImportError, AttributeError):\n raise ValueError()\n'
SCREAMING_SNAKE_CASE__ : Any = '\nimport os\n\ntry:\n import bar\nexcept ImportError as e:\n raise ValueError()\n'
SCREAMING_SNAKE_CASE__ : str = '\nimport os\n\ntry:\n import bar\nexcept:\n raise ValueError()\n'
SCREAMING_SNAKE_CASE__ : Optional[Any] = '\nimport os\n\ntry:\n import bar\n import baz\nexcept ImportError:\n raise ValueError()\n'
SCREAMING_SNAKE_CASE__ : Tuple = '\nimport os\n\ntry:\n import bar\n import baz\nexcept ImportError:\n x = 1\n raise ValueError()\n'
SCREAMING_SNAKE_CASE__ : Optional[Any] = [
TOP_LEVEL_IMPORT,
IMPORT_IN_FUNCTION,
DEEPLY_NESTED_IMPORT,
TOP_LEVEL_TRY_IMPORT,
GENERIC_EXCEPT_IMPORT,
MULTILINE_TRY_IMPORT,
MULTILINE_BOTH_IMPORT,
MULTIPLE_EXCEPTS_IMPORT,
EXCEPT_AS_IMPORT,
TRY_IMPORT_IN_FUNCTION,
]
@pytest.mark.parametrize("case" ,_SCREAMING_SNAKE_CASE )
def A ( _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) -> Optional[int]:
lowerCamelCase : Optional[int] = os.path.join(_SCREAMING_SNAKE_CASE ,"test_file.py" )
with open(_SCREAMING_SNAKE_CASE ,"w" ) as _tmp_file:
_tmp_file.write(_SCREAMING_SNAKE_CASE )
lowerCamelCase : Any = get_imports(_SCREAMING_SNAKE_CASE )
assert parsed_imports == ["os"]
| 48 | '''simple docstring'''
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def __UpperCAmelCase ( a_: str = "isbn/0140328726" ):
_UpperCAmelCase : Dict = olid.strip().strip("/" ) # Remove leading/trailing whitespace & slashes
if new_olid.count("/" ) != 1:
_UpperCAmelCase : Optional[Any] = f"""{olid} is not a valid Open Library olid"""
raise ValueError(a_ )
return requests.get(f"""https://openlibrary.org/{new_olid}.json""" ).json()
def __UpperCAmelCase ( a_: dict ):
_UpperCAmelCase : Dict = {
"title": "Title",
"publish_date": "Publish date",
"authors": "Authors",
"number_of_pages": "Number of pages:",
"first_sentence": "First sentence",
"isbn_10": "ISBN (10)",
"isbn_13": "ISBN (13)",
}
_UpperCAmelCase : Any = {better_key: ol_book_data[key] for key, better_key in desired_keys.items()}
_UpperCAmelCase : Tuple = [
get_openlibrary_data(author["key"] )["name"] for author in data["Authors"]
]
_UpperCAmelCase : Optional[int] = data["First sentence"]["value"]
for key, value in data.items():
if isinstance(a_, a_ ):
_UpperCAmelCase : List[Any] = ", ".join(a_ )
return data
if __name__ == "__main__":
import doctest
doctest.testmod()
while True:
__a = input('\nEnter the ISBN code to search (or \'quit\' to stop): ').strip()
if isbn.lower() in ("", "q", "quit", "exit", "stop"):
break
if len(isbn) not in (10, 13) or not isbn.isdigit():
print(f'Sorry, {isbn} is not a valid ISBN. Please, input a valid ISBN.')
continue
print(f'\nSearching Open Library for ISBN: {isbn}...\n')
try:
__a = summarize_book(get_openlibrary_data(f'isbn/{isbn}'))
print('\n'.join(f'{key}: {value}' for key, value in book_summary.items()))
except JSONDecodeError: # Workaround for requests.exceptions.RequestException:
print(f'Sorry, there are no results for ISBN: {isbn}.') | 145 | 0 |
import math
def lowerCamelCase_ ( UpperCamelCase__ : Any , UpperCamelCase__ : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
if 0 not in (x, y):
# We use the relation x^y = y*log10(x), where 10 is the base.
return y * math.logaa(UpperCamelCase__ )
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.
__A = "Enter the base and the power separated by a comma: "
__A , __A = map(int, input(prompt).split(","))
__A , __A = map(int, input(prompt).split(","))
# We find the log of each number, using the function res(), which takes two
# arguments.
__A = res(xa, ya)
__A = 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")
| 348 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel
if is_vision_available():
from transformers import MaskaFormerImageProcessor
if is_vision_available():
from PIL import Image
class __lowerCAmelCase :
"""simple docstring"""
def __init__( self , lowerCamelCase__ , lowerCamelCase__=2 , lowerCamelCase__=True , lowerCamelCase__=False , lowerCamelCase__=10 , lowerCamelCase__=3 , lowerCamelCase__=32 * 8 , lowerCamelCase__=32 * 8 , lowerCamelCase__=4 , lowerCamelCase__=64 , ) -> Optional[Any]:
'''simple docstring'''
__lowerCamelCase = parent
__lowerCamelCase = batch_size
__lowerCamelCase = is_training
__lowerCamelCase = use_auxiliary_loss
__lowerCamelCase = num_queries
__lowerCamelCase = num_channels
__lowerCamelCase = min_size
__lowerCamelCase = max_size
__lowerCamelCase = num_labels
__lowerCamelCase = hidden_dim
__lowerCamelCase = hidden_dim
def lowercase_ ( self ) -> Optional[int]:
'''simple docstring'''
__lowerCamelCase = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
lowerCamelCase__ )
__lowerCamelCase = torch.ones([self.batch_size, self.min_size, self.max_size] , device=lowerCamelCase__ )
__lowerCamelCase = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=lowerCamelCase__ ) > 0.5
).float()
__lowerCamelCase = (torch.rand((self.batch_size, self.num_labels) , device=lowerCamelCase__ ) > 0.5).long()
__lowerCamelCase = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = MaskaFormerConfig(
hidden_size=self.hidden_dim , )
__lowerCamelCase = self.num_queries
__lowerCamelCase = self.num_labels
__lowerCamelCase = [1, 1, 1, 1]
__lowerCamelCase = self.num_channels
__lowerCamelCase = 64
__lowerCamelCase = 128
__lowerCamelCase = self.hidden_dim
__lowerCamelCase = self.hidden_dim
__lowerCamelCase = self.hidden_dim
return config
def lowercase_ ( self ) -> str:
'''simple docstring'''
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase = self.prepare_config_and_inputs()
__lowerCamelCase = {'pixel_values': pixel_values, 'pixel_mask': pixel_mask}
return config, inputs_dict
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ ) -> Any:
'''simple docstring'''
__lowerCamelCase = output.encoder_hidden_states
__lowerCamelCase = output.pixel_decoder_hidden_states
__lowerCamelCase = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(lowerCamelCase__ ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(lowerCamelCase__ ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(lowerCamelCase__ ) , config.decoder_layers )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__=False ) -> Tuple:
'''simple docstring'''
with torch.no_grad():
__lowerCamelCase = MaskaFormerModel(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
__lowerCamelCase = model(pixel_values=lowerCamelCase__ , pixel_mask=lowerCamelCase__ )
__lowerCamelCase = model(lowerCamelCase__ , output_hidden_states=lowerCamelCase__ )
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.hidden_dim) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(lowerCamelCase__ , lowerCamelCase__ )
def lowercase_ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> Tuple:
'''simple docstring'''
__lowerCamelCase = MaskaFormerForUniversalSegmentation(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
def comm_check_on_output(lowerCamelCase__ ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
__lowerCamelCase = model(pixel_values=lowerCamelCase__ , pixel_mask=lowerCamelCase__ )
__lowerCamelCase = model(lowerCamelCase__ )
comm_check_on_output(lowerCamelCase__ )
__lowerCamelCase = model(
pixel_values=lowerCamelCase__ , pixel_mask=lowerCamelCase__ , mask_labels=lowerCamelCase__ , class_labels=lowerCamelCase__ )
comm_check_on_output(lowerCamelCase__ )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class __lowerCAmelCase ( __magic_name__ , __magic_name__ , unittest.TestCase ):
"""simple docstring"""
snake_case_ = (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
snake_case_ = {'''feature-extraction''': MaskaFormerModel} if is_torch_available() else {}
snake_case_ = False
snake_case_ = False
snake_case_ = False
snake_case_ = False
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
__lowerCamelCase = MaskaFormerModelTester(self )
__lowerCamelCase = ConfigTester(self , config_class=lowerCamelCase__ , has_text_modality=lowerCamelCase__ )
def lowercase_ ( self ) -> Tuple:
'''simple docstring'''
self.config_tester.run_common_tests()
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
__lowerCamelCase , __lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskaformer_model(lowerCamelCase__ , **lowerCamelCase__ , output_hidden_states=lowerCamelCase__ )
def lowercase_ ( self ) -> Optional[Any]:
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*lowerCamelCase__ )
@unittest.skip(reason='Mask2Former does not use inputs_embeds' )
def lowercase_ ( self ) -> Any:
'''simple docstring'''
pass
@unittest.skip(reason='Mask2Former does not have a get_input_embeddings method' )
def lowercase_ ( self ) -> Tuple:
'''simple docstring'''
pass
@unittest.skip(reason='Mask2Former is not a generative model' )
def lowercase_ ( self ) -> Optional[Any]:
'''simple docstring'''
pass
@unittest.skip(reason='Mask2Former does not use token embeddings' )
def lowercase_ ( self ) -> Optional[int]:
'''simple docstring'''
pass
@require_torch_multi_gpu
@unittest.skip(
reason='Mask2Former has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`' )
def lowercase_ ( self ) -> Dict:
'''simple docstring'''
pass
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
pass
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
__lowerCamelCase , __lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowerCamelCase = model_class(lowerCamelCase__ )
__lowerCamelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__lowerCamelCase = [*signature.parameters.keys()]
__lowerCamelCase = ['pixel_values']
self.assertListEqual(arg_names[:1] , lowerCamelCase__ )
@slow
def lowercase_ ( self ) -> int:
'''simple docstring'''
for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
__lowerCamelCase = MaskaFormerModel.from_pretrained(lowerCamelCase__ )
self.assertIsNotNone(lowerCamelCase__ )
def lowercase_ ( self ) -> Optional[Any]:
'''simple docstring'''
__lowerCamelCase = (self.model_tester.min_size,) * 2
__lowerCamelCase = {
'pixel_values': torch.randn((2, 3, *size) , device=lowerCamelCase__ ),
'mask_labels': torch.randn((2, 10, *size) , device=lowerCamelCase__ ),
'class_labels': torch.zeros(2 , 10 , device=lowerCamelCase__ ).long(),
}
__lowerCamelCase = self.model_tester.get_config()
__lowerCamelCase = MaskaFormerForUniversalSegmentation(lowerCamelCase__ ).to(lowerCamelCase__ )
__lowerCamelCase = model(**lowerCamelCase__ )
self.assertTrue(outputs.loss is not None )
def lowercase_ ( self ) -> Dict:
'''simple docstring'''
__lowerCamelCase , __lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskaformer_model(lowerCamelCase__ , **lowerCamelCase__ , output_hidden_states=lowerCamelCase__ )
def lowercase_ ( self ) -> Optional[Any]:
'''simple docstring'''
__lowerCamelCase , __lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowerCamelCase = model_class(lowerCamelCase__ ).to(lowerCamelCase__ )
__lowerCamelCase = model(**lowerCamelCase__ , output_attentions=lowerCamelCase__ )
self.assertTrue(outputs.attentions is not None )
def lowercase_ ( self ) -> Optional[Any]:
'''simple docstring'''
if not self.model_tester.is_training:
return
__lowerCamelCase = self.all_model_classes[1]
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase = self.model_tester.prepare_config_and_inputs()
__lowerCamelCase = model_class(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.train()
__lowerCamelCase = model(lowerCamelCase__ , mask_labels=lowerCamelCase__ , class_labels=lowerCamelCase__ ).loss
loss.backward()
def lowercase_ ( self ) -> Dict:
'''simple docstring'''
__lowerCamelCase = self.all_model_classes[1]
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase = self.model_tester.prepare_config_and_inputs()
__lowerCamelCase = True
__lowerCamelCase = True
__lowerCamelCase = model_class(lowerCamelCase__ ).to(lowerCamelCase__ )
model.train()
__lowerCamelCase = model(lowerCamelCase__ , mask_labels=lowerCamelCase__ , class_labels=lowerCamelCase__ )
__lowerCamelCase = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
__lowerCamelCase = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
__lowerCamelCase = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
__lowerCamelCase = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=lowerCamelCase__ )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
__A = 1e-4
def lowerCamelCase_ ( ) -> List[Any]:
"""simple docstring"""
__lowerCamelCase = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_vision
@slow
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def lowercase_ ( self ) -> List[str]:
'''simple docstring'''
return "facebook/mask2former-swin-small-coco-instance"
@cached_property
def lowercase_ ( self ) -> Dict:
'''simple docstring'''
return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints ) if is_vision_available() else None
def lowercase_ ( self ) -> Any:
'''simple docstring'''
__lowerCamelCase = MaskaFormerModel.from_pretrained(self.model_checkpoints ).to(lowerCamelCase__ )
__lowerCamelCase = self.default_image_processor
__lowerCamelCase = prepare_img()
__lowerCamelCase = image_processor(lowerCamelCase__ , return_tensors='pt' ).to(lowerCamelCase__ )
__lowerCamelCase = inputs['pixel_values'].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(lowerCamelCase__ , (1, 3, 384, 384) )
with torch.no_grad():
__lowerCamelCase = model(**lowerCamelCase__ )
__lowerCamelCase = torch.tensor(
[[-0.27_90, -1.07_17, -1.16_68], [-0.51_28, -0.31_28, -0.49_87], [-0.58_32, 0.19_71, -0.01_97]] ).to(lowerCamelCase__ )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , lowerCamelCase__ , atol=lowerCamelCase__ ) )
__lowerCamelCase = torch.tensor(
[[0.89_73, 1.18_47, 1.17_76], [1.19_34, 1.50_40, 1.51_28], [1.11_53, 1.44_86, 1.49_51]] ).to(lowerCamelCase__ )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , lowerCamelCase__ , atol=lowerCamelCase__ ) )
__lowerCamelCase = torch.tensor(
[[2.11_52, 1.70_00, -0.86_03], [1.58_08, 1.80_04, -0.93_53], [1.60_43, 1.74_95, -0.59_99]] ).to(lowerCamelCase__ )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , lowerCamelCase__ , atol=lowerCamelCase__ ) )
def lowercase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(lowerCamelCase__ ).eval()
__lowerCamelCase = self.default_image_processor
__lowerCamelCase = prepare_img()
__lowerCamelCase = image_processor(lowerCamelCase__ , return_tensors='pt' ).to(lowerCamelCase__ )
__lowerCamelCase = inputs['pixel_values'].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(lowerCamelCase__ , (1, 3, 384, 384) )
with torch.no_grad():
__lowerCamelCase = model(**lowerCamelCase__ )
# masks_queries_logits
__lowerCamelCase = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) )
__lowerCamelCase = [
[-8.78_39, -9.00_56, -8.81_21],
[-7.41_04, -7.03_13, -6.54_01],
[-6.61_05, -6.34_27, -6.46_75],
]
__lowerCamelCase = torch.tensor(lowerCamelCase__ ).to(lowerCamelCase__ )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , lowerCamelCase__ , atol=lowerCamelCase__ ) )
# class_queries_logits
__lowerCamelCase = outputs.class_queries_logits
self.assertEqual(class_queries_logits.shape , (1, model.config.num_queries, model.config.num_labels + 1) )
__lowerCamelCase = torch.tensor(
[
[1.83_24, -8.08_35, -4.19_22],
[0.84_50, -9.00_50, -3.60_53],
[0.30_45, -7.72_93, -3.02_75],
] ).to(lowerCamelCase__ )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , lowerCamelCase__ , atol=lowerCamelCase__ ) )
def lowercase_ ( self ) -> str:
'''simple docstring'''
__lowerCamelCase = MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(lowerCamelCase__ ).eval()
__lowerCamelCase = self.default_image_processor
__lowerCamelCase = image_processor(
[np.zeros((3, 800, 1_333) ), np.zeros((3, 800, 1_333) )] , segmentation_maps=[np.zeros((384, 384) ).astype(np.floataa ), np.zeros((384, 384) ).astype(np.floataa )] , return_tensors='pt' , )
__lowerCamelCase = inputs['pixel_values'].to(lowerCamelCase__ )
__lowerCamelCase = [el.to(lowerCamelCase__ ) for el in inputs['mask_labels']]
__lowerCamelCase = [el.to(lowerCamelCase__ ) for el in inputs['class_labels']]
with torch.no_grad():
__lowerCamelCase = model(**lowerCamelCase__ )
self.assertTrue(outputs.loss is not None )
| 348 | 1 |
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class _a ( UpperCamelCase__ , unittest.TestCase ):
_lowercase : Tuple = CTRLTokenizer
_lowercase : Optional[int] = False
_lowercase : Dict = False
def lowerCamelCase_ ( self: List[Any] ) -> int:
"""simple docstring"""
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
lowercase__ = ['''adapt''', '''re@@''', '''a@@''', '''apt''', '''c@@''', '''t''', '''<unk>''']
lowercase__ = dict(zip(UpperCamelCase_ , range(len(UpperCamelCase_ ) ) ) )
lowercase__ = ['''#version: 0.2''', '''a p''', '''ap t</w>''', '''r e''', '''a d''', '''ad apt</w>''', '''''']
lowercase__ = {'''unk_token''': '''<unk>'''}
lowercase__ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
lowercase__ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''merges_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(UpperCamelCase_ ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(UpperCamelCase_ ) )
def lowerCamelCase_ ( self: List[Any] , **UpperCamelCase_: List[str] ) -> Any:
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return CTRLTokenizer.from_pretrained(self.tmpdirname , **UpperCamelCase_ )
def lowerCamelCase_ ( self: Tuple , UpperCamelCase_: Optional[int] ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = '''adapt react readapt apt'''
lowercase__ = '''adapt react readapt apt'''
return input_text, output_text
def lowerCamelCase_ ( self: Dict ) -> int:
"""simple docstring"""
lowercase__ = CTRLTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
lowercase__ = '''adapt react readapt apt'''
lowercase__ = '''adapt re@@ a@@ c@@ t re@@ adapt apt'''.split()
lowercase__ = tokenizer.tokenize(UpperCamelCase_ )
self.assertListEqual(UpperCamelCase_ , UpperCamelCase_ )
lowercase__ = tokens + [tokenizer.unk_token]
lowercase__ = [0, 1, 2, 4, 5, 1, 0, 3, 6]
self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase_ ) , UpperCamelCase_ )
| 110 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class _a ( UpperCamelCase__ , unittest.TestCase ):
_lowercase : Union[str, Any] = KandinskyVaaImgaImgPipeline
_lowercase : Tuple = ['''image_embeds''', '''negative_image_embeds''', '''image''']
_lowercase : Any = [
'''image_embeds''',
'''negative_image_embeds''',
'''image''',
]
_lowercase : Union[str, Any] = [
'''generator''',
'''height''',
'''width''',
'''strength''',
'''guidance_scale''',
'''num_inference_steps''',
'''return_dict''',
'''guidance_scale''',
'''num_images_per_prompt''',
'''output_type''',
'''return_dict''',
]
_lowercase : Optional[Any] = False
@property
def lowerCamelCase_ ( self: Union[str, Any] ) -> Dict:
"""simple docstring"""
return 32
@property
def lowerCamelCase_ ( self: Optional[int] ) -> Optional[Any]:
"""simple docstring"""
return 32
@property
def lowerCamelCase_ ( self: Any ) -> Any:
"""simple docstring"""
return self.time_input_dim
@property
def lowerCamelCase_ ( self: Tuple ) -> Any:
"""simple docstring"""
return self.time_input_dim * 4
@property
def lowerCamelCase_ ( self: List[Any] ) -> Optional[Any]:
"""simple docstring"""
return 100
@property
def lowerCamelCase_ ( self: int ) -> int:
"""simple docstring"""
torch.manual_seed(0 )
lowercase__ = {
'''in_channels''': 4,
# Out channels is double in channels because predicts mean and variance
'''out_channels''': 8,
'''addition_embed_type''': '''image''',
'''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
'''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
'''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
'''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
'''layers_per_block''': 1,
'''encoder_hid_dim''': self.text_embedder_hidden_size,
'''encoder_hid_dim_type''': '''image_proj''',
'''cross_attention_dim''': self.cross_attention_dim,
'''attention_head_dim''': 4,
'''resnet_time_scale_shift''': '''scale_shift''',
'''class_embed_type''': None,
}
lowercase__ = UNetaDConditionModel(**UpperCamelCase_ )
return model
@property
def lowerCamelCase_ ( self: Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def lowerCamelCase_ ( self: Optional[Any] ) -> int:
"""simple docstring"""
torch.manual_seed(0 )
lowercase__ = VQModel(**self.dummy_movq_kwargs )
return model
def lowerCamelCase_ ( self: Optional[int] ) -> Optional[int]:
"""simple docstring"""
lowercase__ = self.dummy_unet
lowercase__ = self.dummy_movq
lowercase__ = {
'''num_train_timesteps''': 1_000,
'''beta_schedule''': '''linear''',
'''beta_start''': 0.00085,
'''beta_end''': 0.012,
'''clip_sample''': False,
'''set_alpha_to_one''': False,
'''steps_offset''': 0,
'''prediction_type''': '''epsilon''',
'''thresholding''': False,
}
lowercase__ = DDIMScheduler(**UpperCamelCase_ )
lowercase__ = {
'''unet''': unet,
'''scheduler''': scheduler,
'''movq''': movq,
}
return components
def lowerCamelCase_ ( self: Optional[int] , UpperCamelCase_: Optional[Any] , UpperCamelCase_: Optional[int]=0 ) -> Optional[int]:
"""simple docstring"""
lowercase__ = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(UpperCamelCase_ ) ).to(UpperCamelCase_ )
lowercase__ = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
UpperCamelCase_ )
# create init_image
lowercase__ = floats_tensor((1, 3, 64, 64) , rng=random.Random(UpperCamelCase_ ) ).to(UpperCamelCase_ )
lowercase__ = image.cpu().permute(0 , 2 , 3 , 1 )[0]
lowercase__ = Image.fromarray(np.uinta(UpperCamelCase_ ) ).convert('''RGB''' ).resize((256, 256) )
if str(UpperCamelCase_ ).startswith('''mps''' ):
lowercase__ = torch.manual_seed(UpperCamelCase_ )
else:
lowercase__ = torch.Generator(device=UpperCamelCase_ ).manual_seed(UpperCamelCase_ )
lowercase__ = {
'''image''': init_image,
'''image_embeds''': image_embeds,
'''negative_image_embeds''': negative_image_embeds,
'''generator''': generator,
'''height''': 64,
'''width''': 64,
'''num_inference_steps''': 10,
'''guidance_scale''': 7.0,
'''strength''': 0.2,
'''output_type''': '''np''',
}
return inputs
def lowerCamelCase_ ( self: Optional[int] ) -> Dict:
"""simple docstring"""
lowercase__ = '''cpu'''
lowercase__ = self.get_dummy_components()
lowercase__ = self.pipeline_class(**UpperCamelCase_ )
lowercase__ = pipe.to(UpperCamelCase_ )
pipe.set_progress_bar_config(disable=UpperCamelCase_ )
lowercase__ = pipe(**self.get_dummy_inputs(UpperCamelCase_ ) )
lowercase__ = output.images
lowercase__ = pipe(
**self.get_dummy_inputs(UpperCamelCase_ ) , return_dict=UpperCamelCase_ , )[0]
lowercase__ = image[0, -3:, -3:, -1]
lowercase__ = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowercase__ = np.array(
[0.6199778, 0.63984406, 0.46145785, 0.62944984, 0.5622215, 0.47306132, 0.47441456, 0.4607606, 0.48719263] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
), f' expected_slice {expected_slice}, but got {image_slice.flatten()}'
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
), f' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'
@slow
@require_torch_gpu
class _a ( unittest.TestCase ):
def lowerCamelCase_ ( self: str ) -> List[Any]:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase_ ( self: List[str] ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/kandinskyv22/kandinskyv22_img2img_frog.npy''' )
lowercase__ = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png''' )
lowercase__ = '''A red cartoon frog, 4k'''
lowercase__ = KandinskyVaaPriorPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-prior''' , torch_dtype=torch.floataa )
pipe_prior.to(UpperCamelCase_ )
lowercase__ = KandinskyVaaImgaImgPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-decoder''' , torch_dtype=torch.floataa )
lowercase__ = pipeline.to(UpperCamelCase_ )
pipeline.set_progress_bar_config(disable=UpperCamelCase_ )
lowercase__ = torch.Generator(device='''cpu''' ).manual_seed(0 )
lowercase__ , lowercase__ = pipe_prior(
UpperCamelCase_ , generator=UpperCamelCase_ , num_inference_steps=5 , negative_prompt='''''' , ).to_tuple()
lowercase__ = pipeline(
image=UpperCamelCase_ , image_embeds=UpperCamelCase_ , negative_image_embeds=UpperCamelCase_ , generator=UpperCamelCase_ , num_inference_steps=100 , height=768 , width=768 , strength=0.2 , output_type='''np''' , )
lowercase__ = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(UpperCamelCase_ , UpperCamelCase_ )
| 110 | 1 |
"""simple docstring"""
import os
_a = {"""I""": 1, """V""": 5, """X""": 10, """L""": 50, """C""": 1_00, """D""": 5_00, """M""": 10_00}
def _A ( UpperCamelCase_ : Optional[int]) -> Optional[Any]:
'''simple docstring'''
__lowercase = 0
__lowercase = 0
while index < len(_A) - 1:
__lowercase = SYMBOLS[numerals[index]]
__lowercase = 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 ( UpperCamelCase_ : str) -> Dict:
'''simple docstring'''
__lowercase = ''
__lowercase = num // 1000
numerals += m_count * "M"
num %= 1000
__lowercase = 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
__lowercase = 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 ( UpperCamelCase_ : List[str] = "/p089_roman.txt") -> Dict:
'''simple docstring'''
__lowercase = 0
with open(os.path.dirname(_A) + roman_numerals_filename) as filea:
__lowercase = filea.readlines()
for line in lines:
__lowercase = line.strip()
__lowercase = parse_roman_numerals(_A)
__lowercase = generate_roman_numerals(_A)
savings += len(_A) - len(_A)
return savings
if __name__ == "__main__":
print(F"{solution() = }")
| 353 |
"""simple docstring"""
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .notes_encoder import SpectrogramNotesEncoder
from .continous_encoder import SpectrogramContEncoder
from .pipeline_spectrogram_diffusion import (
SpectrogramContEncoder,
SpectrogramDiffusionPipeline,
TaFilmDecoder,
)
try:
if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_transformers_and_torch_and_note_seq_objects import * # noqa F403
else:
from .midi_utils import MidiProcessor
| 144 | 0 |
"""simple docstring"""
import darl # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline
__UpperCamelCase : Optional[int] = {
'''n_samples''': 6_4,
'''horizon''': 3_2,
'''num_inference_steps''': 2_0,
'''n_guide_steps''': 2, # can set to 0 for faster sampling, does not use value network
'''scale_grad_by_std''': True,
'''scale''': 0.1,
'''eta''': 0.0,
'''t_grad_cutoff''': 2,
'''device''': '''cpu''',
}
if __name__ == "__main__":
__UpperCamelCase : Optional[int] = '''hopper-medium-v2'''
__UpperCamelCase : Optional[int] = gym.make(env_name)
__UpperCamelCase : List[Any] = ValueGuidedRLPipeline.from_pretrained(
'''bglick13/hopper-medium-v2-value-function-hor32''',
env=env,
)
env.seed(0)
__UpperCamelCase : List[Any] = env.reset()
__UpperCamelCase : Optional[Any] = 0
__UpperCamelCase : Any = 0
__UpperCamelCase : Any = 1_0_0_0
__UpperCamelCase : Dict = [obs.copy()]
try:
for t in tqdm.tqdm(range(T)):
# call the policy
__UpperCamelCase : Optional[int] = pipeline(obs, planning_horizon=3_2)
# execute action in environment
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase : Optional[Any] = env.step(denorm_actions)
__UpperCamelCase : Optional[Any] = env.get_normalized_score(total_reward)
# update return
total_reward += reward
total_score += score
print(
F'''Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:'''
F''' {total_score}'''
)
# save observations for rendering
rollout.append(next_observation.copy())
__UpperCamelCase : str = next_observation
except KeyboardInterrupt:
pass
print(F'''Total reward: {total_reward}''')
| 106 |
"""simple docstring"""
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union
import flax
import jax.numpy as jnp
from ..utils import BaseOutput
__UpperCamelCase : Optional[Any] = '''scheduler_config.json'''
class SCREAMING_SNAKE_CASE ( a_ ):
"""simple docstring"""
lowercase__ = 1
lowercase__ = 2
lowercase__ = 3
lowercase__ = 4
lowercase__ = 5
@dataclass
class SCREAMING_SNAKE_CASE ( a_ ):
"""simple docstring"""
lowercase__ = 42
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
lowercase__ = SCHEDULER_CONFIG_NAME
lowercase__ = ["dtype"]
lowercase__ = []
lowercase__ = True
@classmethod
def __lowerCAmelCase ( cls : List[Any] ,lowercase_ : Dict[str, Any] = None ,lowercase_ : Optional[str] = None ,lowercase_ : Optional[int]=False ,**lowercase_ : Any ,):
lowerCAmelCase__ ,lowerCAmelCase__ : Dict = cls.load_config(
pretrained_model_name_or_path=lowercase_ ,subfolder=lowercase_ ,return_unused_kwargs=lowercase_ ,**lowercase_ ,)
lowerCAmelCase__ ,lowerCAmelCase__ : Union[str, Any] = cls.from_config(lowercase_ ,return_unused_kwargs=lowercase_ ,**lowercase_ )
if hasattr(lowercase_ ,'''create_state''' ) and getattr(lowercase_ ,'''has_state''' ,lowercase_ ):
lowerCAmelCase__ : List[Any] = scheduler.create_state()
if return_unused_kwargs:
return scheduler, state, unused_kwargs
return scheduler, state
def __lowerCAmelCase ( self : Tuple ,lowercase_ : Union[str, os.PathLike] ,lowercase_ : bool = False ,**lowercase_ : str ):
self.save_config(save_directory=lowercase_ ,push_to_hub=lowercase_ ,**lowercase_ )
@property
def __lowerCAmelCase ( self : List[str] ):
return self._get_compatibles()
@classmethod
def __lowerCAmelCase ( cls : List[Any] ):
lowerCAmelCase__ : Tuple = list(set([cls.__name__] + cls._compatibles ) )
lowerCAmelCase__ : Tuple = importlib.import_module(__name__.split('''.''' )[0] )
lowerCAmelCase__ : Union[str, Any] = [
getattr(lowercase_ ,lowercase_ ) for c in compatible_classes_str if hasattr(lowercase_ ,lowercase_ )
]
return compatible_classes
def __SCREAMING_SNAKE_CASE ( A_ , A_ ):
assert len(A_ ) >= x.ndim
return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(A_ ) - x.ndim) ) , A_ )
def __SCREAMING_SNAKE_CASE ( A_ , A_=0.999 , A_=jnp.floataa ):
def alpha_bar(A_ ):
return math.cos((time_step + 0.008) / 1.008 * math.pi / 2 ) ** 2
lowerCAmelCase__ : Optional[Any] = []
for i in range(A_ ):
lowerCAmelCase__ : str = i / num_diffusion_timesteps
lowerCAmelCase__ : Union[str, Any] = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar(A_ ) / alpha_bar(A_ ) , A_ ) )
return jnp.array(A_ , dtype=A_ )
@flax.struct.dataclass
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
lowercase__ = 42
lowercase__ = 42
lowercase__ = 42
@classmethod
def __lowerCAmelCase ( cls : Union[str, Any] ,lowercase_ : List[Any] ):
lowerCAmelCase__ : Optional[int] = scheduler.config
if config.trained_betas is not None:
lowerCAmelCase__ : Any = jnp.asarray(config.trained_betas ,dtype=scheduler.dtype )
elif config.beta_schedule == "linear":
lowerCAmelCase__ : Union[str, Any] = jnp.linspace(config.beta_start ,config.beta_end ,config.num_train_timesteps ,dtype=scheduler.dtype )
elif config.beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
lowerCAmelCase__ : int = (
jnp.linspace(
config.beta_start**0.5 ,config.beta_end**0.5 ,config.num_train_timesteps ,dtype=scheduler.dtype )
** 2
)
elif config.beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
lowerCAmelCase__ : List[Any] = betas_for_alpha_bar(config.num_train_timesteps ,dtype=scheduler.dtype )
else:
raise NotImplementedError(
F'beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}' )
lowerCAmelCase__ : str = 1.0 - betas
lowerCAmelCase__ : Union[str, Any] = jnp.cumprod(lowercase_ ,axis=0 )
return cls(
alphas=lowercase_ ,betas=lowercase_ ,alphas_cumprod=lowercase_ ,)
def __SCREAMING_SNAKE_CASE ( A_ , A_ , A_ , A_ ):
lowerCAmelCase__ : Any = state.alphas_cumprod
lowerCAmelCase__ : Optional[Any] = alphas_cumprod[timesteps] ** 0.5
lowerCAmelCase__ : Tuple = sqrt_alpha_prod.flatten()
lowerCAmelCase__ : str = broadcast_to_shape_from_left(A_ , original_samples.shape )
lowerCAmelCase__ : Optional[Any] = (1 - alphas_cumprod[timesteps]) ** 0.5
lowerCAmelCase__ : Optional[Any] = sqrt_one_minus_alpha_prod.flatten()
lowerCAmelCase__ : Optional[int] = broadcast_to_shape_from_left(A_ , original_samples.shape )
return sqrt_alpha_prod, sqrt_one_minus_alpha_prod
def __SCREAMING_SNAKE_CASE ( A_ , A_ , A_ , A_ ):
lowerCAmelCase__ ,lowerCAmelCase__ : List[Any] = get_sqrt_alpha_prod(A_ , A_ , A_ , A_ )
lowerCAmelCase__ : str = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
return noisy_samples
def __SCREAMING_SNAKE_CASE ( A_ , A_ , A_ , A_ ):
lowerCAmelCase__ ,lowerCAmelCase__ : List[Any] = get_sqrt_alpha_prod(A_ , A_ , A_ , A_ )
lowerCAmelCase__ : Union[str, Any] = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
return velocity
| 106 | 1 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Tuple
import numpy as np
import torch
@dataclass
class __SCREAMING_SNAKE_CASE :
'''simple docstring'''
lowerCamelCase_ :torch.Tensor # [batch_size x 3]
lowerCamelCase_ :torch.Tensor # [batch_size x 3]
lowerCamelCase_ :torch.Tensor # [batch_size x 3]
lowerCamelCase_ :torch.Tensor # [batch_size x 3]
lowerCamelCase_ :int
lowerCamelCase_ :int
lowerCamelCase_ :float
lowerCamelCase_ :float
lowerCamelCase_ :Tuple[int]
def _UpperCamelCase ( self ):
'''simple docstring'''
assert self.x.shape[0] == self.y.shape[0] == self.z.shape[0] == self.origin.shape[0]
assert self.x.shape[1] == self.y.shape[1] == self.z.shape[1] == self.origin.shape[1] == 3
assert len(self.x.shape ) == len(self.y.shape ) == len(self.z.shape ) == len(self.origin.shape ) == 2
def _UpperCamelCase ( self ):
'''simple docstring'''
return torch.from_numpy(np.array([self.width, self.height] , dtype=np.floataa ) )
def _UpperCamelCase ( self ):
'''simple docstring'''
return torch.from_numpy(np.array([self.x_fov, self.y_fov] , dtype=np.floataa ) )
def _UpperCamelCase ( self ):
'''simple docstring'''
UpperCAmelCase_ : List[Any] = torch.arange(self.height * self.width )
UpperCAmelCase_ : Optional[int] = torch.stack(
[
pixel_indices % self.width,
torch.div(UpperCamelCase_ , self.width , rounding_mode='trunc' ),
] , axis=1 , )
return coords
@property
def _UpperCamelCase ( self ):
'''simple docstring'''
UpperCAmelCase_ , *UpperCAmelCase_ : int = self.shape
UpperCAmelCase_ : int = int(np.prod(UpperCamelCase_ ) )
UpperCAmelCase_ : List[str] = self.get_image_coords()
UpperCAmelCase_ : Union[str, Any] = torch.broadcast_to(coords.unsqueeze(0 ) , [batch_size * inner_batch_size, *coords.shape] )
UpperCAmelCase_ : Tuple = self.get_camera_rays(UpperCamelCase_ )
UpperCAmelCase_ : Union[str, Any] = rays.view(UpperCamelCase_ , inner_batch_size * self.height * self.width , 2 , 3 )
return rays
def _UpperCamelCase ( self , snake_case_ ):
'''simple docstring'''
UpperCAmelCase_ , *UpperCAmelCase_ , UpperCAmelCase_ : Union[str, Any] = coords.shape
assert n_coords == 2
assert batch_size == self.origin.shape[0]
UpperCAmelCase_ : Tuple = coords.view(UpperCamelCase_ , -1 , 2 )
UpperCAmelCase_ : Tuple = self.resolution()
UpperCAmelCase_ : str = self.fov()
UpperCAmelCase_ : List[Any] = (flat.float() / (res - 1)) * 2 - 1
UpperCAmelCase_ : Any = fracs * torch.tan(fov / 2 )
UpperCAmelCase_ : str = fracs.view(UpperCamelCase_ , -1 , 2 )
UpperCAmelCase_ : Dict = (
self.z.view(UpperCamelCase_ , 1 , 3 )
+ self.x.view(UpperCamelCase_ , 1 , 3 ) * fracs[:, :, :1]
+ self.y.view(UpperCamelCase_ , 1 , 3 ) * fracs[:, :, 1:]
)
UpperCAmelCase_ : Tuple = directions / directions.norm(dim=-1 , keepdim=UpperCamelCase_ )
UpperCAmelCase_ : Dict = torch.stack(
[
torch.broadcast_to(self.origin.view(UpperCamelCase_ , 1 , 3 ) , [batch_size, directions.shape[1], 3] ),
directions,
] , dim=2 , )
return rays.view(UpperCamelCase_ , *UpperCamelCase_ , 2 , 3 )
def _UpperCamelCase ( self , snake_case_ , snake_case_ ):
'''simple docstring'''
assert width * self.height == height * self.width, "The aspect ratio should not change."
return DifferentiableProjectiveCamera(
origin=self.origin , x=self.x , y=self.y , z=self.z , width=UpperCamelCase_ , height=UpperCamelCase_ , x_fov=self.x_fov , y_fov=self.y_fov , )
def _lowerCamelCase ( lowerCamelCase_ : List[Any] ):
"""simple docstring"""
UpperCAmelCase_ : Optional[Any] = []
UpperCAmelCase_ : List[str] = []
UpperCAmelCase_ : str = []
UpperCAmelCase_ : Any = []
for theta in np.linspace(0 , 2 * np.pi , num=20 ):
UpperCAmelCase_ : Dict = np.array([np.sin(lowerCamelCase_ ), np.cos(lowerCamelCase_ ), -0.5] )
z /= np.sqrt(np.sum(z**2 ) )
UpperCAmelCase_ : int = -z * 4
UpperCAmelCase_ : Tuple = np.array([np.cos(lowerCamelCase_ ), -np.sin(lowerCamelCase_ ), 0.0] )
UpperCAmelCase_ : List[Any] = np.cross(lowerCamelCase_ , lowerCamelCase_ )
origins.append(lowerCamelCase_ )
xs.append(lowerCamelCase_ )
ys.append(lowerCamelCase_ )
zs.append(lowerCamelCase_ )
return DifferentiableProjectiveCamera(
origin=torch.from_numpy(np.stack(lowerCamelCase_ , axis=0 ) ).float() , x=torch.from_numpy(np.stack(lowerCamelCase_ , axis=0 ) ).float() , y=torch.from_numpy(np.stack(lowerCamelCase_ , axis=0 ) ).float() , z=torch.from_numpy(np.stack(lowerCamelCase_ , axis=0 ) ).float() , width=lowerCamelCase_ , height=lowerCamelCase_ , x_fov=0.7 , y_fov=0.7 , shape=(1, len(lowerCamelCase_ )) , )
| 355 | '''simple docstring'''
from manim import *
class __SCREAMING_SNAKE_CASE ( lowerCamelCase_ ):
'''simple docstring'''
def _UpperCamelCase ( self ):
'''simple docstring'''
UpperCAmelCase_ : Dict = Rectangle(height=0.5 , width=0.5 )
UpperCAmelCase_ : Any = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 )
UpperCAmelCase_ : List[str] = Rectangle(height=0.25 , width=0.25 )
UpperCAmelCase_ : Any = [mem.copy() for i in range(6 )]
UpperCAmelCase_ : Union[str, Any] = [mem.copy() for i in range(6 )]
UpperCAmelCase_ : Optional[int] = VGroup(*snake_case_ ).arrange(snake_case_ , buff=0 )
UpperCAmelCase_ : Any = VGroup(*snake_case_ ).arrange(snake_case_ , buff=0 )
UpperCAmelCase_ : str = VGroup(snake_case_ , snake_case_ ).arrange(snake_case_ , buff=0 )
UpperCAmelCase_ : Any = Text('CPU' , font_size=2_4 )
UpperCAmelCase_ : Tuple = Group(snake_case_ , snake_case_ ).arrange(snake_case_ , buff=0.5 , aligned_edge=snake_case_ )
cpu.move_to([-2.5, -0.5, 0] )
self.add(snake_case_ )
UpperCAmelCase_ : str = [mem.copy() for i in range(4 )]
UpperCAmelCase_ : Dict = VGroup(*snake_case_ ).arrange(snake_case_ , buff=0 )
UpperCAmelCase_ : List[str] = Text('GPU' , font_size=2_4 )
UpperCAmelCase_ : Optional[int] = Group(snake_case_ , snake_case_ ).arrange(snake_case_ , buff=0.5 , aligned_edge=snake_case_ )
gpu.move_to([-1, -1, 0] )
self.add(snake_case_ )
UpperCAmelCase_ : str = [mem.copy() for i in range(6 )]
UpperCAmelCase_ : Tuple = VGroup(*snake_case_ ).arrange(snake_case_ , buff=0 )
UpperCAmelCase_ : str = Text('Model' , font_size=2_4 )
UpperCAmelCase_ : Optional[int] = Group(snake_case_ , snake_case_ ).arrange(snake_case_ , buff=0.5 , aligned_edge=snake_case_ )
model.move_to([3, -1.0, 0] )
self.add(snake_case_ )
UpperCAmelCase_ : str = []
UpperCAmelCase_ : Optional[Any] = []
for i, rect in enumerate(snake_case_ ):
UpperCAmelCase_ : str = fill.copy().set_fill(snake_case_ , opacity=0.8 )
target.move_to(snake_case_ )
model_arr.append(snake_case_ )
UpperCAmelCase_ : int = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0.0 ).set_fill(snake_case_ , opacity=0.8 )
cpu_target.move_to(cpu_left_col_base[i] )
model_cpu_arr.append(snake_case_ )
self.add(*snake_case_ , *snake_case_ )
UpperCAmelCase_ : List[Any] = [meta_mem.copy() for i in range(6 )]
UpperCAmelCase_ : List[str] = [meta_mem.copy() for i in range(6 )]
UpperCAmelCase_ : Tuple = VGroup(*snake_case_ ).arrange(snake_case_ , buff=0 )
UpperCAmelCase_ : Dict = VGroup(*snake_case_ ).arrange(snake_case_ , buff=0 )
UpperCAmelCase_ : Optional[Any] = VGroup(snake_case_ , snake_case_ ).arrange(snake_case_ , buff=0 )
UpperCAmelCase_ : Tuple = Text('Disk' , font_size=2_4 )
UpperCAmelCase_ : Union[str, Any] = Group(snake_case_ , snake_case_ ).arrange(snake_case_ , buff=0.5 , aligned_edge=snake_case_ )
disk.move_to([-4, -1.25, 0] )
self.add(snake_case_ , snake_case_ )
UpperCAmelCase_ : List[Any] = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
UpperCAmelCase_ : Any = MarkupText(
F'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model''' , font_size=1_8 , )
key_text.move_to([-5, 2.4, 0] )
self.add(snake_case_ , snake_case_ )
UpperCAmelCase_ : Dict = MarkupText(
F'''<span fgcolor=\'{BLUE}\'>●</span> Checkpoint''' , font_size=1_8 , )
blue_text.next_to(snake_case_ , DOWN * 2.4 , aligned_edge=key_text.get_left() )
self.add(snake_case_ )
UpperCAmelCase_ : Optional[Any] = MarkupText(
F'''Now watch as an input is passed through the model\nand how the memory is utilized and handled.''' , font_size=2_4 , )
step_a.move_to([2, 2, 0] )
self.play(Write(snake_case_ ) )
UpperCAmelCase_ : Tuple = Square(0.3 )
input.set_fill(snake_case_ , opacity=1.0 )
input.set_stroke(width=0.0 )
input.next_to(model_base[0] , snake_case_ , buff=0.5 )
self.play(Write(snake_case_ ) )
input.generate_target()
input.target.next_to(model_arr[0] , direction=snake_case_ , buff=0.02 )
self.play(MoveToTarget(snake_case_ ) )
self.play(FadeOut(snake_case_ ) )
UpperCAmelCase_ : Any = Arrow(start=snake_case_ , end=snake_case_ , color=snake_case_ , buff=0.5 )
a.next_to(model_arr[0].get_left() , snake_case_ , buff=0.2 )
model_cpu_arr[0].generate_target()
model_cpu_arr[0].target.move_to(gpu_rect[0] )
UpperCAmelCase_ : List[str] = MarkupText(
F'''As the input reaches a layer, the hook triggers\nand weights are moved from the CPU\nto the GPU and back.''' , font_size=2_4 , )
step_a.move_to([2, 2, 0] )
self.play(Write(snake_case_ , run_time=3 ) )
UpperCAmelCase_ : List[Any] = {'run_time': 1, 'fade_in': True, 'fade_out': True, 'buff': 0.02}
self.play(
Write(snake_case_ ) , Circumscribe(model_arr[0] , color=snake_case_ , **snake_case_ ) , Circumscribe(model_cpu_arr[0] , color=snake_case_ , **snake_case_ ) , Circumscribe(gpu_rect[0] , color=snake_case_ , **snake_case_ ) , )
self.play(MoveToTarget(model_cpu_arr[0] ) )
UpperCAmelCase_ : Union[str, Any] = a.copy()
for i in range(6 ):
a_c.next_to(model_arr[i].get_right() + 0.02 , snake_case_ , buff=0.2 )
input.generate_target()
input.target.move_to(model_arr[i].get_right() + 0.02 )
UpperCAmelCase_ : Tuple = AnimationGroup(
FadeOut(snake_case_ , run_time=0.5 ) , MoveToTarget(snake_case_ , run_time=0.5 ) , FadeIn(snake_case_ , run_time=0.5 ) , lag_ratio=0.2 )
self.play(snake_case_ )
model_cpu_arr[i].generate_target()
model_cpu_arr[i].target.move_to(cpu_left_col_base[i] )
if i < 5:
model_cpu_arr[i + 1].generate_target()
model_cpu_arr[i + 1].target.move_to(gpu_rect[0] )
if i >= 1:
UpperCAmelCase_ : Any = 0.7
self.play(
Circumscribe(model_arr[i] , **snake_case_ ) , Circumscribe(cpu_left_col_base[i] , **snake_case_ ) , Circumscribe(cpu_left_col_base[i + 1] , color=snake_case_ , **snake_case_ ) , Circumscribe(gpu_rect[0] , color=snake_case_ , **snake_case_ ) , Circumscribe(model_arr[i + 1] , color=snake_case_ , **snake_case_ ) , )
if i < 1:
self.play(
MoveToTarget(model_cpu_arr[i] ) , MoveToTarget(model_cpu_arr[i + 1] ) , )
else:
self.play(
MoveToTarget(model_cpu_arr[i] , run_time=0.7 ) , MoveToTarget(model_cpu_arr[i + 1] , run_time=0.7 ) , )
else:
model_cpu_arr[i].generate_target()
model_cpu_arr[i].target.move_to(cpu_left_col_base[-1] )
input.generate_target()
input.target.next_to(model_arr[-1].get_right() , RIGHT + 0.02 , buff=0.2 )
self.play(
Circumscribe(model_arr[-1] , color=snake_case_ , **snake_case_ ) , Circumscribe(cpu_left_col_base[-1] , color=snake_case_ , **snake_case_ ) , Circumscribe(gpu_rect[0] , color=snake_case_ , **snake_case_ ) , )
self.play(MoveToTarget(model_cpu_arr[i] ) )
UpperCAmelCase_ : Any = a_c
UpperCAmelCase_ : int = a_c.copy()
input.generate_target()
input.target.next_to(model_base[-1] , RIGHT + 0.02 , buff=0.5 )
self.play(
FadeOut(snake_case_ ) , FadeOut(snake_case_ , run_time=0.5 ) , )
UpperCAmelCase_ : Optional[Any] = MarkupText(F'''Inference on a model too large for GPU memory\nis successfully completed.''' , font_size=2_4 )
step_a.move_to([2, 2, 0] )
self.play(Write(snake_case_ , run_time=3 ) , MoveToTarget(snake_case_ ) )
self.wait()
| 274 | 0 |
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_torch_available():
import torch
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
lowerCamelCase : Any = logging.get_logger(__name__)
@dataclass
class __lowercase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_snake_case = [
"""no_inference""",
"""no_cuda""",
"""no_tpu""",
"""no_speed""",
"""no_memory""",
"""no_env_print""",
"""no_multi_process""",
]
def __init__( self , **A ) -> List[Any]:
for deprecated_arg in self.deprecated_args:
if deprecated_arg in kwargs:
snake_case : int = deprecated_arg[3:]
setattr(self , A , not kwargs.pop(A ) )
logger.warning(
f"""{deprecated_arg} is depreciated. Please use --no_{positive_arg} or"""
f""" {positive_arg}={kwargs[positive_arg]}""" )
snake_case : Tuple = kwargs.pop("""torchscript""" , self.torchscript )
snake_case : Tuple = kwargs.pop("""torch_xla_tpu_print_metrics""" , self.torch_xla_tpu_print_metrics )
snake_case : List[str] = kwargs.pop("""fp16_opt_level""" , self.fpaa_opt_level )
super().__init__(**A )
_snake_case = field(default=SCREAMING_SNAKE_CASE__ , metadata={"""help""": """Trace the models using torchscript"""} )
_snake_case = field(default=SCREAMING_SNAKE_CASE__ , metadata={"""help""": """Print Xla/PyTorch tpu metrics"""} )
_snake_case = field(
default="""O1""" , metadata={
"""help""": (
"""For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\']. """
"""See details at https://nvidia.github.io/apex/amp.html"""
)
} , )
@cached_property
def UpperCAmelCase ( self ) -> Tuple["torch.device", int]:
requires_backends(self , ["""torch"""] )
logger.info("""PyTorch: setting up devices""" )
if not self.cuda:
snake_case : int = torch.device("""cpu""" )
snake_case : str = 0
elif is_torch_tpu_available():
snake_case : Optional[Any] = xm.xla_device()
snake_case : Optional[int] = 0
else:
snake_case : Optional[int] = torch.device("""cuda""" if torch.cuda.is_available() else """cpu""" )
snake_case : Optional[int] = torch.cuda.device_count()
return device, n_gpu
@property
def UpperCAmelCase ( self ) -> int:
return is_torch_tpu_available() and self.tpu
@property
def UpperCAmelCase ( self ) -> int:
requires_backends(self , ["""torch"""] )
# TODO(PVP): currently only single GPU is supported
return torch.cuda.current_device()
@property
def UpperCAmelCase ( self ) -> "torch.device":
requires_backends(self , ["""torch"""] )
return self._setup_devices[0]
@property
def UpperCAmelCase ( self ) -> Dict:
requires_backends(self , ["""torch"""] )
return self._setup_devices[1]
@property
def UpperCAmelCase ( self ) -> List[Any]:
return self.n_gpu > 0
| 124 | import os
import unittest
from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow
from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class lowerCamelCase (SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
"""simple docstring"""
lowerCamelCase__ = CpmAntTokenizer
lowerCamelCase__ = False
def __A ( self : List[str] ) -> str:
super().setUp()
SCREAMING_SNAKE_CASE_ = [
"<d>",
"</d>",
"<s>",
"</s>",
"</_>",
"<unk>",
"<pad>",
"</n>",
"我",
"是",
"C",
"P",
"M",
"A",
"n",
"t",
]
SCREAMING_SNAKE_CASE_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
with open(self.vocab_file , "w" , encoding="utf-8" ) as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in vocab_tokens] ) )
@tooslow
def __A ( self : int ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE_ = CpmAntTokenizer.from_pretrained("openbmb/cpm-ant-10b" )
SCREAMING_SNAKE_CASE_ = "今天天气真好！"
SCREAMING_SNAKE_CASE_ = ["今天", "天气", "真", "好", "！"]
SCREAMING_SNAKE_CASE_ = tokenizer.tokenize(__magic_name__ )
self.assertListEqual(__magic_name__ , __magic_name__ )
SCREAMING_SNAKE_CASE_ = "今天天气真好！"
SCREAMING_SNAKE_CASE_ = [tokenizer.bos_token] + tokens
SCREAMING_SNAKE_CASE_ = [6, 9_802, 14_962, 2_082, 831, 244]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__magic_name__ ) , __magic_name__ )
SCREAMING_SNAKE_CASE_ = tokenizer.decode(__magic_name__ )
self.assertEqual(__magic_name__ , __magic_name__ )
| 118 | 0 |
'''simple docstring'''
import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def __lowerCAmelCase (*__lowerCAmelCase ):
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
_UpperCAmelCase : Any = list(__lowerCAmelCase )
for i in range(len(__lowerCAmelCase ) ):
_UpperCAmelCase : str = 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 __lowerCAmelCase (__lowerCAmelCase ):
_UpperCAmelCase : Any = [
"CUDA out of memory.", # CUDA OOM
"cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.", # CUDNN SNAFU
"DefaultCPUAllocator: can't allocate memory", # CPU OOM
]
if isinstance(__lowerCAmelCase , __lowerCAmelCase ) and len(exception.args ) == 1:
return any(err in exception.args[0] for err in _statements )
return False
def __lowerCAmelCase (__lowerCAmelCase = None , __lowerCAmelCase = 128 ):
if function is None:
return functools.partial(__lowerCAmelCase , starting_batch_size=__lowerCAmelCase )
_UpperCAmelCase : Any = starting_batch_size
def decorator(*__lowerCAmelCase , **__lowerCAmelCase ):
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()
_UpperCAmelCase : str = list(inspect.signature(__lowerCAmelCase ).parameters.keys() )
# Guard against user error
if len(__lowerCAmelCase ) < (len(__lowerCAmelCase ) + 1):
_UpperCAmelCase : Dict = ", ".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(__lowerCAmelCase , *__lowerCAmelCase , **__lowerCAmelCase )
except Exception as e:
if should_reduce_batch_size(__lowerCAmelCase ):
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
| 322 |
'''simple docstring'''
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def __lowerCAmelCase (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=1_024 , __lowerCAmelCase=1_024 , __lowerCAmelCase=False , **__lowerCAmelCase ):
_UpperCAmelCase : Any = AutoTokenizer.from_pretrained(__lowerCAmelCase )
_UpperCAmelCase : List[str] = SeqaSeqDataset(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , type_path="train" , **__lowerCAmelCase )
_UpperCAmelCase : Dict = tok.pad_token_id
def get_lens(__lowerCAmelCase ):
_UpperCAmelCase : Union[str, Any] = tqdm(
DataLoader(__lowerCAmelCase , batch_size=512 , num_workers=8 , shuffle=__lowerCAmelCase , collate_fn=ds.collate_fn ) , desc=str(ds.len_file ) , )
_UpperCAmelCase : List[str] = []
for batch in dl:
_UpperCAmelCase : Any = batch["input_ids"].ne(__lowerCAmelCase ).sum(1 ).tolist()
_UpperCAmelCase : Tuple = batch["labels"].ne(__lowerCAmelCase ).sum(1 ).tolist()
if consider_target:
for src, tgt in zip(__lowerCAmelCase , __lowerCAmelCase ):
max_lens.append(max(__lowerCAmelCase , __lowerCAmelCase ) )
else:
max_lens.extend(__lowerCAmelCase )
return max_lens
_UpperCAmelCase : Dict = get_lens(__lowerCAmelCase )
_UpperCAmelCase : Optional[Any] = SeqaSeqDataset(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , type_path="val" , **__lowerCAmelCase )
_UpperCAmelCase : Union[str, Any] = get_lens(__lowerCAmelCase )
pickle_save(__lowerCAmelCase , train_ds.len_file )
pickle_save(__lowerCAmelCase , val_ds.len_file )
if __name__ == "__main__":
fire.Fire(save_len_file)
| 322 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase : List[Any] = {
"configuration_nllb_moe": [
"NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP",
"NllbMoeConfig",
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase : Optional[Any] = [
"NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST",
"NllbMoeForConditionalGeneration",
"NllbMoeModel",
"NllbMoePreTrainedModel",
"NllbMoeTop2Router",
"NllbMoeSparseMLP",
]
if TYPE_CHECKING:
from .configuration_nllb_moe import (
NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
NllbMoeConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_nllb_moe import (
NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
NllbMoeForConditionalGeneration,
NllbMoeModel,
NllbMoePreTrainedModel,
NllbMoeSparseMLP,
NllbMoeTopaRouter,
)
else:
import sys
_lowercase : Optional[int] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 93 |
"""simple docstring"""
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transformers
from transformers import AutoModel, AutoModelForSeqaSeqLM, AutoTokenizer
_A = """bart"""
_A = True
@st.cache(allow_output_mutation=__UpperCAmelCase )
def lowercase_ ( ) -> Optional[Any]:
if LOAD_DENSE_INDEX:
lowerCAmelCase__ : Union[str, Any] = AutoTokenizer.from_pretrained("""yjernite/retribert-base-uncased""" )
lowerCAmelCase__ : Tuple = AutoModel.from_pretrained("""yjernite/retribert-base-uncased""" ).to("""cuda:0""" )
lowerCAmelCase__ : List[str] = qar_model.eval()
else:
lowerCAmelCase__ , lowerCAmelCase__ : Union[str, Any] = (None, None)
if MODEL_TYPE == "bart":
lowerCAmelCase__ : Union[str, Any] = AutoTokenizer.from_pretrained("""yjernite/bart_eli5""" )
lowerCAmelCase__ : List[Any] = AutoModelForSeqaSeqLM.from_pretrained("""yjernite/bart_eli5""" ).to("""cuda:0""" )
lowerCAmelCase__ : List[str] = torch.load("""seq2seq_models/eli5_bart_model_blm_2.pth""" )
sas_model.load_state_dict(save_dict["""model"""] )
lowerCAmelCase__ : Dict = sas_model.eval()
else:
lowerCAmelCase__ , lowerCAmelCase__ : Union[str, Any] = make_qa_sas_model(
model_name="""t5-small""" , from_file="""seq2seq_models/eli5_t5_model_1024_4.pth""" , device="""cuda:0""" )
return (qar_tokenizer, qar_model, sas_tokenizer, sas_model)
@st.cache(allow_output_mutation=__UpperCAmelCase )
def lowercase_ ( ) -> Union[str, Any]:
if LOAD_DENSE_INDEX:
lowerCAmelCase__ : Union[str, Any] = faiss.StandardGpuResources()
lowerCAmelCase__ : List[Any] = datasets.load_dataset(path="""wiki_snippets""" , name="""wiki40b_en_100_0""" )["""train"""]
lowerCAmelCase__ : Dict = np.memmap(
"""wiki40b_passages_reps_32_l-8_h-768_b-512-512.dat""" , dtype="""float32""" , mode="""r""" , shape=(wikiaab_passages.num_rows, 128) , )
lowerCAmelCase__ : str = faiss.IndexFlatIP(128 )
lowerCAmelCase__ : Optional[int] = faiss.index_cpu_to_gpu(__UpperCAmelCase , 1 , __UpperCAmelCase )
wikiaab_gpu_index_flat.add(__UpperCAmelCase ) # TODO fix for larger GPU
else:
lowerCAmelCase__ , lowerCAmelCase__ : List[Any] = (None, None)
lowerCAmelCase__ : Optional[int] = Elasticsearch([{"""host""": """localhost""", """port""": """9200"""}] )
return (wikiaab_passages, wikiaab_gpu_index_flat, es_client)
@st.cache(allow_output_mutation=__UpperCAmelCase )
def lowercase_ ( ) -> List[str]:
lowerCAmelCase__ : List[Any] = datasets.load_dataset("""eli5""" , name="""LFQA_reddit""" )
lowerCAmelCase__ : Tuple = elia["""train_eli5"""]
lowerCAmelCase__ : Dict = np.memmap(
"""eli5_questions_reps.dat""" , dtype="""float32""" , mode="""r""" , shape=(elia_train.num_rows, 128) )
lowerCAmelCase__ : Dict = faiss.IndexFlatIP(128 )
eli5_train_q_index.add(__UpperCAmelCase )
return (elia_train, eli5_train_q_index)
_A , _A , _A = load_indexes()
_A , _A , _A , _A = load_models()
_A , _A = load_train_data()
def lowercase_ ( __UpperCAmelCase , __UpperCAmelCase=10 ) -> Optional[Any]:
lowerCAmelCase__ : str = embed_questions_for_retrieval([question] , __UpperCAmelCase , __UpperCAmelCase )
lowerCAmelCase__ , lowerCAmelCase__ : Optional[int] = eli5_train_q_index.search(__UpperCAmelCase , __UpperCAmelCase )
lowerCAmelCase__ : List[Any] = [elia_train[int(__UpperCAmelCase )] for i in I[0]]
return nn_examples
def lowercase_ ( __UpperCAmelCase , __UpperCAmelCase="wiki40b" , __UpperCAmelCase="dense" , __UpperCAmelCase=10 ) -> List[str]:
if source == "none":
lowerCAmelCase__ , lowerCAmelCase__ : List[Any] = (""" <P> """.join(["""""" for _ in range(11 )] ).strip(), [])
else:
if method == "dense":
lowerCAmelCase__ , lowerCAmelCase__ : Union[str, Any] = query_qa_dense_index(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
else:
lowerCAmelCase__ , lowerCAmelCase__ : int = query_es_index(
__UpperCAmelCase , __UpperCAmelCase , index_name="""english_wiki40b_snippets_100w""" , n_results=__UpperCAmelCase , )
lowerCAmelCase__ : Optional[int] = [
(res["""article_title"""], res["""section_title"""].strip(), res["""score"""], res["""passage_text"""]) for res in hit_lst
]
lowerCAmelCase__ : Optional[Any] = """question: {} context: {}""".format(__UpperCAmelCase , __UpperCAmelCase )
return question_doc, support_list
@st.cache(
hash_funcs={
torch.Tensor: (lambda __UpperCAmelCase : None),
transformers.models.bart.tokenization_bart.BartTokenizer: (lambda __UpperCAmelCase : None),
} )
def lowercase_ ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=64 , __UpperCAmelCase=256 , __UpperCAmelCase=False , __UpperCAmelCase=2 , __UpperCAmelCase=0.95 , __UpperCAmelCase=0.8 ) -> Optional[int]:
with torch.no_grad():
lowerCAmelCase__ : List[Any] = qa_sas_generate(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , num_answers=1 , num_beams=__UpperCAmelCase , min_len=__UpperCAmelCase , max_len=__UpperCAmelCase , do_sample=__UpperCAmelCase , temp=__UpperCAmelCase , top_p=__UpperCAmelCase , top_k=__UpperCAmelCase , max_input_length=1024 , device="""cuda:0""" , )[0]
return (answer, support_list)
st.title("""Long Form Question Answering with ELI5""")
# Start sidebar
_A = """<img src='https://huggingface.co/front/assets/huggingface_logo.svg'>"""
_A = """
<html>
<head>
<style>
.img-container {
padding-left: 90px;
padding-right: 90px;
padding-top: 50px;
padding-bottom: 50px;
background-color: #f0f3f9;
}
</style>
</head>
<body>
<span class=\"img-container\"> <!-- Inline parent element -->
%s
</span>
</body>
</html>
""" % (
header_html,
)
st.sidebar.markdown(
header_full,
unsafe_allow_html=True,
)
# Long Form QA with ELI5 and Wikipedia
_A = """
This demo presents a model trained to [provide long-form answers to open-domain questions](https://yjernite.github.io/lfqa.html).
First, a document retriever fetches a set of relevant Wikipedia passages given the question from the [Wiki40b](https://research.google/pubs/pub49029/) dataset,
a pre-processed fixed snapshot of Wikipedia.
"""
st.sidebar.markdown(description, unsafe_allow_html=True)
_A = [
"""Answer the question""",
"""View the retrieved document only""",
"""View the most similar ELI5 question and answer""",
"""Show me everything, please!""",
]
_A = st.sidebar.checkbox("""Demo options""")
if demo_options:
_A = st.sidebar.selectbox(
"""""",
action_list,
index=3,
)
_A = action_list.index(action_st)
_A = st.sidebar.selectbox(
"""""",
["""Show full text of passages""", """Show passage section titles"""],
index=0,
)
_A = show_type == """Show full text of passages"""
else:
_A = 3
_A = True
_A = st.sidebar.checkbox("""Retrieval options""")
if retrieval_options:
_A = """
### Information retriever options
The **sparse** retriever uses ElasticSearch, while the **dense** retriever uses max-inner-product search between a question and passage embedding
trained using the [ELI5](https://arxiv.org/abs/1907.09190) questions-answer pairs.
The answer is then generated by sequence to sequence model which takes the question and retrieved document as input.
"""
st.sidebar.markdown(retriever_info)
_A = st.sidebar.selectbox("""Which Wikipedia format should the model use?""", ["""wiki40b""", """none"""])
_A = st.sidebar.selectbox("""Which Wikipedia indexer should the model use?""", ["""dense""", """sparse""", """mixed"""])
else:
_A = """wiki40b"""
_A = """dense"""
_A = """beam"""
_A = 2
_A = 6_4
_A = 2_5_6
_A = None
_A = None
_A = st.sidebar.checkbox("""Generation options""")
if generate_options:
_A = """
### Answer generation options
The sequence-to-sequence model was initialized with [BART](https://huggingface.co/facebook/bart-large)
weights and fine-tuned on the ELI5 QA pairs and retrieved documents. You can use the model for greedy decoding with
**beam** search, or **sample** from the decoder's output probabilities.
"""
st.sidebar.markdown(generate_info)
_A = st.sidebar.selectbox("""Would you like to use beam search or sample an answer?""", ["""beam""", """sampled"""])
_A = st.sidebar.slider(
"""Minimum generation length""", min_value=8, max_value=2_5_6, value=6_4, step=8, format=None, key=None
)
_A = st.sidebar.slider(
"""Maximum generation length""", min_value=6_4, max_value=5_1_2, value=2_5_6, step=1_6, format=None, key=None
)
if sampled == "beam":
_A = st.sidebar.slider("""Beam size""", min_value=1, max_value=8, value=2, step=None, format=None, key=None)
else:
_A = st.sidebar.slider(
"""Nucleus sampling p""", min_value=0.1, max_value=1.0, value=0.95, step=0.01, format=None, key=None
)
_A = st.sidebar.slider(
"""Temperature""", min_value=0.1, max_value=1.0, value=0.7, step=0.01, format=None, key=None
)
_A = None
# start main text
_A = [
"""<MY QUESTION>""",
"""How do people make chocolate?""",
"""Why do we get a fever when we are sick?""",
"""How can different animals perceive different colors?""",
"""What is natural language processing?""",
"""What's the best way to treat a sunburn?""",
"""What exactly are vitamins ?""",
"""How does nuclear energy provide electricity?""",
"""What's the difference between viruses and bacteria?""",
"""Why are flutes classified as woodwinds when most of them are made out of metal ?""",
"""Why do people like drinking coffee even though it tastes so bad?""",
"""What happens when wine ages? How does it make the wine taste better?""",
"""If an animal is an herbivore, where does it get the protein that it needs to survive if it only eats grass?""",
"""How can we set a date to the beginning or end of an artistic period? Doesn't the change happen gradually?""",
"""How does New Zealand have so many large bird predators?""",
]
_A = st.selectbox(
"""What would you like to ask? ---- select <MY QUESTION> to enter a new query""",
questions_list,
index=1,
)
if question_s == "<MY QUESTION>":
_A = st.text_input("""Enter your question here:""", """""")
else:
_A = question_s
if st.button("""Show me!"""):
if action in [0, 1, 3]:
if index_type == "mixed":
_A , _A = make_support(question, source=wiki_source, method="""dense""", n_results=1_0)
_A , _A = make_support(question, source=wiki_source, method="""sparse""", n_results=1_0)
_A = []
for res_d, res_s in zip(support_list_dense, support_list_sparse):
if tuple(res_d) not in support_list:
support_list += [tuple(res_d)]
if tuple(res_s) not in support_list:
support_list += [tuple(res_s)]
_A = support_list[:1_0]
_A = """<P> """ + """ <P> """.join([res[-1] for res in support_list])
else:
_A , _A = make_support(question, source=wiki_source, method=index_type, n_results=1_0)
if action in [0, 3]:
_A , _A = answer_question(
question_doc,
sas_model,
sas_tokenizer,
min_len=min_len,
max_len=int(max_len),
sampling=(sampled == """sampled"""),
n_beams=n_beams,
top_p=top_p,
temp=temp,
)
st.markdown("""### The model generated answer is:""")
st.write(answer)
if action in [0, 1, 3] and wiki_source != "none":
st.markdown("""--- \n ### The model is drawing information from the following Wikipedia passages:""")
for i, res in enumerate(support_list):
_A = """https://en.wikipedia.org/wiki/{}""".format(res[0].replace(""" """, """_"""))
_A = res[1].strip()
if sec_titles == "":
_A = """[{}]({})""".format(res[0], wiki_url)
else:
_A = sec_titles.split(""" & """)
_A = """ & """.join(
["""[{}]({}#{})""".format(sec.strip(), wiki_url, sec.strip().replace(""" """, """_""")) for sec in sec_list]
)
st.markdown(
"""{0:02d} - **Article**: {1:<18} <br> _Section_: {2}""".format(i + 1, res[0], sections),
unsafe_allow_html=True,
)
if show_passages:
st.write(
"""> <span style=\"font-family:arial; font-size:10pt;\">""" + res[-1] + """</span>""", unsafe_allow_html=True
)
if action in [2, 3]:
_A = find_nearest_training(question)
_A = nn_train_list[0]
st.markdown(
"""--- \n ### The most similar question in the ELI5 training set was: \n\n {}""".format(train_exple["""title"""])
)
_A = [
"""{}. {}""".format(i + 1, """ \n""".join([line.strip() for line in ans.split("""\n""") if line.strip() != """"""]))
for i, (ans, sc) in enumerate(zip(train_exple["""answers"""]["""text"""], train_exple["""answers"""]["""score"""]))
if i == 0 or sc > 2
]
st.markdown("""##### Its answers were: \n\n {}""".format("""\n""".join(answers_st)))
_A = """
---
**Disclaimer**
*The intent of this app is to provide some (hopefully entertaining) insights into the behavior of a current LFQA system.
Evaluating biases of such a model and ensuring factual generations are still very much open research problems.
Therefore, until some significant progress is achieved, we caution against using the generated answers for practical purposes.*
"""
st.sidebar.markdown(disclaimer, unsafe_allow_html=True)
| 242 | 0 |
from __future__ import annotations
from typing import Any
class SCREAMING_SNAKE_CASE (UpperCAmelCase ):
pass
class SCREAMING_SNAKE_CASE :
def __init__( self : List[str] , a : Any )-> None:
"""simple docstring"""
lowercase__ = data
lowercase__ = None
def __iter__( self : str )-> Optional[Any]:
"""simple docstring"""
lowercase__ = self
lowercase__ = []
while node:
if node in visited:
raise ContainsLoopError
visited.append(a )
yield node.data
lowercase__ = node.next_node
@property
def SCREAMING_SNAKE_CASE_ ( self : Tuple )-> bool:
"""simple docstring"""
try:
list(self )
return False
except ContainsLoopError:
return True
if __name__ == "__main__":
lowercase_ = Node(1)
lowercase_ = Node(2)
lowercase_ = Node(3)
lowercase_ = Node(4)
print(root_node.has_loop) # False
lowercase_ = root_node.next_node
print(root_node.has_loop) # True
lowercase_ = Node(5)
lowercase_ = Node(6)
lowercase_ = Node(5)
lowercase_ = Node(6)
print(root_node.has_loop) # False
lowercase_ = Node(1)
print(root_node.has_loop) # False
| 269 |
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class SCREAMING_SNAKE_CASE (UpperCAmelCase ):
def __init__( self : List[str] , a : Callable , a : Optional[Features] = None , a : str = None , a : bool = False , a : bool = False , a : Optional[dict] = None , a : Optional[int] = None , **a : str , )-> Tuple:
"""simple docstring"""
super().__init__(
features=a , cache_dir=a , keep_in_memory=a , streaming=a , num_proc=a , **a , )
lowercase__ = Generator(
cache_dir=a , features=a , generator=a , gen_kwargs=a , **a , )
def SCREAMING_SNAKE_CASE_ ( self : Any )-> Dict:
"""simple docstring"""
if self.streaming:
lowercase__ = self.builder.as_streaming_dataset(split='train' )
# Build regular (map-style) dataset
else:
lowercase__ = None
lowercase__ = None
lowercase__ = None
lowercase__ = None
self.builder.download_and_prepare(
download_config=a , download_mode=a , verification_mode=a , base_path=a , num_proc=self.num_proc , )
lowercase__ = self.builder.as_dataset(
split='train' , verification_mode=a , in_memory=self.keep_in_memory )
return dataset
| 269 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionInstructPixaPixPipeline,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
"""simple docstring"""
a_ =StableDiffusionInstructPixaPixPipeline
a_ =TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width""", """cross_attention_kwargs"""}
a_ =TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
a_ =IMAGE_TO_IMAGE_IMAGE_PARAMS
a_ =IMAGE_TO_IMAGE_IMAGE_PARAMS
def _lowercase ( self : Tuple ) -> Tuple:
torch.manual_seed(0 )
__lowerCamelCase : Optional[int] = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=8 , out_channels=4 , down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') , up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D') , cross_attention_dim=32 , )
__lowerCamelCase : Tuple = PNDMScheduler(skip_prk_steps=_snake_case )
torch.manual_seed(0 )
__lowerCamelCase : Tuple = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] , up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] , latent_channels=4 , )
torch.manual_seed(0 )
__lowerCamelCase : Dict = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
__lowerCamelCase : Optional[int] = CLIPTextModel(_snake_case )
__lowerCamelCase : Optional[int] = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
__lowerCamelCase : List[str] = {
'unet': unet,
'scheduler': scheduler,
'vae': vae,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'safety_checker': None,
'feature_extractor': None,
}
return components
def _lowercase ( self : str , _a : List[Any] , _a : Any=0 ) -> Any:
__lowerCamelCase : Optional[int] = floats_tensor((1, 3, 32, 32) , rng=random.Random(_snake_case ) ).to(_snake_case )
__lowerCamelCase : Optional[int] = image.cpu().permute(0 , 2 , 3 , 1 )[0]
__lowerCamelCase : List[Any] = Image.fromarray(np.uinta(_snake_case ) ).convert('RGB' )
if str(_snake_case ).startswith('mps' ):
__lowerCamelCase : Tuple = torch.manual_seed(_snake_case )
else:
__lowerCamelCase : List[str] = torch.Generator(device=_snake_case ).manual_seed(_snake_case )
__lowerCamelCase : Optional[Any] = {
'prompt': 'A painting of a squirrel eating a burger',
'image': image,
'generator': generator,
'num_inference_steps': 2,
'guidance_scale': 6.0,
'image_guidance_scale': 1,
'output_type': 'numpy',
}
return inputs
def _lowercase ( self : Any ) -> Any:
__lowerCamelCase : Optional[int] = 'cpu' # ensure determinism for the device-dependent torch.Generator
__lowerCamelCase : Optional[Any] = self.get_dummy_components()
__lowerCamelCase : Optional[Any] = StableDiffusionInstructPixaPixPipeline(**_snake_case )
__lowerCamelCase : int = sd_pipe.to(_snake_case )
sd_pipe.set_progress_bar_config(disable=_snake_case )
__lowerCamelCase : Dict = self.get_dummy_inputs(_snake_case )
__lowerCamelCase : Any = sd_pipe(**_snake_case ).images
__lowerCamelCase : Optional[int] = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
__lowerCamelCase : Union[str, Any] = np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def _lowercase ( self : str ) -> str:
__lowerCamelCase : int = 'cpu' # ensure determinism for the device-dependent torch.Generator
__lowerCamelCase : Any = self.get_dummy_components()
__lowerCamelCase : List[Any] = StableDiffusionInstructPixaPixPipeline(**_snake_case )
__lowerCamelCase : str = sd_pipe.to(_snake_case )
sd_pipe.set_progress_bar_config(disable=_snake_case )
__lowerCamelCase : str = self.get_dummy_inputs(_snake_case )
__lowerCamelCase : Tuple = 'french fries'
__lowerCamelCase : Any = sd_pipe(**_snake_case , negative_prompt=_snake_case )
__lowerCamelCase : Optional[Any] = output.images
__lowerCamelCase : Tuple = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
__lowerCamelCase : str = np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def _lowercase ( self : Tuple ) -> int:
__lowerCamelCase : Union[str, Any] = 'cpu' # ensure determinism for the device-dependent torch.Generator
__lowerCamelCase : int = self.get_dummy_components()
__lowerCamelCase : Optional[Any] = StableDiffusionInstructPixaPixPipeline(**_snake_case )
__lowerCamelCase : Optional[Any] = sd_pipe.to(_snake_case )
sd_pipe.set_progress_bar_config(disable=_snake_case )
__lowerCamelCase : str = self.get_dummy_inputs(_snake_case )
__lowerCamelCase : List[Any] = [inputs['prompt']] * 2
__lowerCamelCase : Dict = np.array(inputs['image'] ).astype(np.floataa ) / 255.0
__lowerCamelCase : Dict = torch.from_numpy(_snake_case ).unsqueeze(0 ).to(_snake_case )
__lowerCamelCase : Union[str, Any] = image / 2 + 0.5
__lowerCamelCase : str = image.permute(0 , 3 , 1 , 2 )
__lowerCamelCase : int = image.repeat(2 , 1 , 1 , 1 )
__lowerCamelCase : Tuple = sd_pipe(**_snake_case ).images
__lowerCamelCase : str = image[-1, -3:, -3:, -1]
assert image.shape == (2, 32, 32, 3)
__lowerCamelCase : Union[str, Any] = np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def _lowercase ( self : str ) -> Union[str, Any]:
__lowerCamelCase : Any = 'cpu' # ensure determinism for the device-dependent torch.Generator
__lowerCamelCase : Optional[Any] = self.get_dummy_components()
__lowerCamelCase : Any = EulerAncestralDiscreteScheduler(
beta_start=0.00085 , beta_end=0.012 , beta_schedule='scaled_linear' )
__lowerCamelCase : List[str] = StableDiffusionInstructPixaPixPipeline(**_snake_case )
__lowerCamelCase : int = sd_pipe.to(_snake_case )
sd_pipe.set_progress_bar_config(disable=_snake_case )
__lowerCamelCase : Any = self.get_dummy_inputs(_snake_case )
__lowerCamelCase : Dict = sd_pipe(**_snake_case ).images
__lowerCamelCase : List[Any] = image[0, -3:, -3:, -1]
__lowerCamelCase : int = [round(_snake_case , 4 ) for x in image_slice.flatten().tolist()]
print(','.join([str(_snake_case ) for x in slice] ) )
assert image.shape == (1, 32, 32, 3)
__lowerCamelCase : Optional[int] = np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def _lowercase ( self : Optional[Any] ) -> Union[str, Any]:
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
def _lowercase ( self : Any ) -> Optional[int]:
__lowerCamelCase : int = self.get_dummy_components()
__lowerCamelCase : Tuple = StableDiffusionInstructPixaPixPipeline(**_snake_case )
__lowerCamelCase : List[Any] = VaeImageProcessor(do_resize=_snake_case , do_normalize=_snake_case )
__lowerCamelCase : Any = pipe.to(_snake_case )
pipe.set_progress_bar_config(disable=_snake_case )
__lowerCamelCase : Optional[Any] = pipe(**self.get_dummy_inputs_by_type(_snake_case , input_image_type='pt' ) )[0]
__lowerCamelCase : List[str] = components['vae']
__lowerCamelCase : Tuple = self.get_dummy_inputs_by_type(_snake_case , input_image_type='pt' )
for image_param in self.image_latents_params:
if image_param in inputs.keys():
__lowerCamelCase : List[Any] = vae.encode(inputs[image_param] ).latent_dist.mode()
__lowerCamelCase : Dict = pipe(**_snake_case )[0]
__lowerCamelCase : Optional[int] = np.abs(out - out_latents_inputs ).max()
self.assertLess(_snake_case , 1e-4 , 'passing latents as image input generate different result from passing image' )
@slow
@require_torch_gpu
class lowerCamelCase_ ( unittest.TestCase ):
"""simple docstring"""
def _lowercase ( self : Union[str, Any] ) -> Optional[int]:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _lowercase ( self : Optional[Any] , _a : Optional[int]=0 ) -> Dict:
__lowerCamelCase : List[Any] = torch.manual_seed(_snake_case )
__lowerCamelCase : Optional[Any] = load_image(
'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg' )
__lowerCamelCase : List[str] = {
'prompt': 'turn him into a cyborg',
'image': image,
'generator': generator,
'num_inference_steps': 3,
'guidance_scale': 7.5,
'image_guidance_scale': 1.0,
'output_type': 'numpy',
}
return inputs
def _lowercase ( self : Tuple ) -> Dict:
__lowerCamelCase : int = StableDiffusionInstructPixaPixPipeline.from_pretrained(
'timbrooks/instruct-pix2pix' , safety_checker=_snake_case )
pipe.to(_snake_case )
pipe.set_progress_bar_config(disable=_snake_case )
pipe.enable_attention_slicing()
__lowerCamelCase : Any = self.get_inputs()
__lowerCamelCase : Optional[Any] = pipe(**_snake_case ).images
__lowerCamelCase : Tuple = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
__lowerCamelCase : Union[str, Any] = np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def _lowercase ( self : str ) -> List[str]:
__lowerCamelCase : Tuple = StableDiffusionInstructPixaPixPipeline.from_pretrained(
'timbrooks/instruct-pix2pix' , safety_checker=_snake_case )
__lowerCamelCase : List[str] = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.to(_snake_case )
pipe.set_progress_bar_config(disable=_snake_case )
pipe.enable_attention_slicing()
__lowerCamelCase : Dict = self.get_inputs()
__lowerCamelCase : Union[str, Any] = pipe(**_snake_case ).images
__lowerCamelCase : Optional[Any] = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
__lowerCamelCase : List[str] = np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def _lowercase ( self : Tuple ) -> int:
__lowerCamelCase : List[str] = StableDiffusionInstructPixaPixPipeline.from_pretrained(
'timbrooks/instruct-pix2pix' , safety_checker=_snake_case )
__lowerCamelCase : int = DDIMScheduler.from_config(pipe.scheduler.config )
pipe.to(_snake_case )
pipe.set_progress_bar_config(disable=_snake_case )
pipe.enable_attention_slicing()
__lowerCamelCase : Optional[int] = self.get_inputs()
__lowerCamelCase : Dict = pipe(**_snake_case ).images
__lowerCamelCase : Optional[Any] = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
__lowerCamelCase : Optional[int] = np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def _lowercase ( self : List[Any] ) -> Optional[Any]:
__lowerCamelCase : Optional[int] = 0
def callback_fn(_a : int , _a : str , _a : Optional[int] ) -> None:
__lowerCamelCase : Any = True
nonlocal number_of_steps
number_of_steps += 1
if step == 1:
__lowerCamelCase : Optional[int] = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 64)
__lowerCamelCase : int = latents[0, -3:, -3:, -1]
__lowerCamelCase : Dict = np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2
elif step == 2:
__lowerCamelCase : Tuple = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 64)
__lowerCamelCase : Dict = latents[0, -3:, -3:, -1]
__lowerCamelCase : Optional[int] = np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2
__lowerCamelCase : List[str] = False
__lowerCamelCase : Tuple = StableDiffusionInstructPixaPixPipeline.from_pretrained(
'timbrooks/instruct-pix2pix' , safety_checker=_snake_case , torch_dtype=torch.floataa )
__lowerCamelCase : int = pipe.to(_snake_case )
pipe.set_progress_bar_config(disable=_snake_case )
pipe.enable_attention_slicing()
__lowerCamelCase : Union[str, Any] = self.get_inputs()
pipe(**_snake_case , callback=_snake_case , callback_steps=1 )
assert callback_fn.has_been_called
assert number_of_steps == 3
def _lowercase ( self : List[Any] ) -> List[str]:
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
__lowerCamelCase : Dict = StableDiffusionInstructPixaPixPipeline.from_pretrained(
'timbrooks/instruct-pix2pix' , safety_checker=_snake_case , torch_dtype=torch.floataa )
__lowerCamelCase : int = pipe.to(_snake_case )
pipe.set_progress_bar_config(disable=_snake_case )
pipe.enable_attention_slicing(1 )
pipe.enable_sequential_cpu_offload()
__lowerCamelCase : Tuple = self.get_inputs()
__lowerCamelCase : List[str] = pipe(**_snake_case )
__lowerCamelCase : List[Any] = torch.cuda.max_memory_allocated()
# make sure that less than 2.2 GB is allocated
assert mem_bytes < 2.2 * 10**9
def _lowercase ( self : Union[str, Any] ) -> Tuple:
__lowerCamelCase : Tuple = self.get_inputs()
# resize to resolution that is divisible by 8 but not 16 or 32
__lowerCamelCase : List[Any] = inputs['image'].resize((504, 504) )
__lowerCamelCase : Dict = 'timbrooks/instruct-pix2pix'
__lowerCamelCase : str = StableDiffusionInstructPixaPixPipeline.from_pretrained(
_snake_case , safety_checker=_snake_case , )
pipe.to(_snake_case )
pipe.set_progress_bar_config(disable=_snake_case )
pipe.enable_attention_slicing()
__lowerCamelCase : Tuple = pipe(**_snake_case )
__lowerCamelCase : List[Any] = output.images[0]
__lowerCamelCase : str = image[255:258, 383:386, -1]
assert image.shape == (504, 504, 3)
__lowerCamelCase : Any = np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-3
| 208 |
from __future__ import annotations
import math
def _UpperCAmelCase ( 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
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = str(snake_case )
_lowerCAmelCase = [n]
for i in range(1 , len(snake_case ) ):
list_nums.append(int(str_num[i:] ) )
list_nums.append(int(str_num[:-i] ) )
return list_nums
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
if len(str(snake_case ) ) > 3:
if not is_prime(int(str(snake_case )[-3:] ) ) or not is_prime(int(str(snake_case )[:3] ) ):
return False
return True
def _UpperCAmelCase ( snake_case = 11 ):
"""simple docstring"""
_lowerCAmelCase = []
_lowerCAmelCase = 13
while len(snake_case ) != count:
if validate(snake_case ):
_lowerCAmelCase = list_truncated_nums(snake_case )
if all(is_prime(snake_case ) for i in list_nums ):
list_truncated_primes.append(snake_case )
num += 2
return list_truncated_primes
def _UpperCAmelCase ( ):
"""simple docstring"""
return sum(compute_truncated_primes(11 ) )
if __name__ == "__main__":
print(f"{sum(compute_truncated_primes(11)) = }")
| 82 | 0 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class __UpperCAmelCase ( _lowerCamelCase ):
@staticmethod
@abstractmethod
def lowerCamelCase ( lowerCAmelCase_ ):
"""simple docstring"""
raise NotImplementedError()
@abstractmethod
def lowerCamelCase ( self ):
"""simple docstring"""
raise NotImplementedError()
| 353 |
'''simple docstring'''
from __future__ import annotations
from collections import namedtuple
def SCREAMING_SNAKE_CASE__ ( __A , __A , __A ) -> tuple:
_snake_case = namedtuple('result' , 'name value' )
if (voltage, current, power).count(0 ) != 1:
raise ValueError('Only one argument must be 0' )
elif power < 0:
raise ValueError(
'Power cannot be negative in any electrical/electronics system' )
elif voltage == 0:
return result('voltage' , power / current )
elif current == 0:
return result('current' , power / voltage )
elif power == 0:
return result('power' , float(round(abs(voltage * current ) , 2 ) ) )
else:
raise ValueError('Exactly one argument must be 0' )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 160 | 0 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import GPTaTokenizer, TFOPTForCausalLM, TFOPTModel
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase=None , _UpperCAmelCase=None ):
if attention_mask is None:
__a = tf.cast(tf.math.not_equal(_UpperCAmelCase , config.pad_token_id ) , tf.inta )
return {"input_ids": input_ids, "attention_mask": attention_mask}
@require_tf
class _A :
UpperCamelCase__ : List[str] = OPTConfig
UpperCamelCase__ : Any = {}
UpperCamelCase__ : Optional[int] = '''gelu'''
def __init__( self : Any , __SCREAMING_SNAKE_CASE : List[str] , __SCREAMING_SNAKE_CASE : List[Any]=13 , __SCREAMING_SNAKE_CASE : Union[str, Any]=7 , __SCREAMING_SNAKE_CASE : Optional[Any]=True , __SCREAMING_SNAKE_CASE : int=False , __SCREAMING_SNAKE_CASE : List[str]=99 , __SCREAMING_SNAKE_CASE : str=16 , __SCREAMING_SNAKE_CASE : Optional[Any]=2 , __SCREAMING_SNAKE_CASE : List[str]=4 , __SCREAMING_SNAKE_CASE : str=4 , __SCREAMING_SNAKE_CASE : List[Any]="gelu" , __SCREAMING_SNAKE_CASE : Dict=0.1 , __SCREAMING_SNAKE_CASE : str=0.1 , __SCREAMING_SNAKE_CASE : int=20 , __SCREAMING_SNAKE_CASE : List[Any]=2 , __SCREAMING_SNAKE_CASE : Optional[int]=1 , __SCREAMING_SNAKE_CASE : Optional[int]=0 , __SCREAMING_SNAKE_CASE : Optional[Any]=16 , __SCREAMING_SNAKE_CASE : str=16 , ):
'''simple docstring'''
__a = parent
__a = batch_size
__a = seq_length
__a = is_training
__a = use_labels
__a = vocab_size
__a = hidden_size
__a = num_hidden_layers
__a = num_attention_heads
__a = intermediate_size
__a = hidden_act
__a = hidden_dropout_prob
__a = attention_probs_dropout_prob
__a = max_position_embeddings
__a = eos_token_id
__a = pad_token_id
__a = bos_token_id
__a = embed_dim
__a = word_embed_proj_dim
__a = False
def _lowerCamelCase ( self : str):
'''simple docstring'''
__a = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size)
__a = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size) , 1)
__a = tf.concat([input_ids, eos_tensor] , axis=1)
__a = self.config_cls(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_id=self.eos_token_id , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , embed_dim=self.embed_dim , word_embed_proj_dim=self.word_embed_proj_dim , is_encoder_decoder=__SCREAMING_SNAKE_CASE , **self.config_updates , )
__a = prepare_opt_inputs_dict(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE)
return config, inputs_dict
def _lowerCamelCase ( self : Dict , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : Any):
'''simple docstring'''
__a = TFOPTModel(config=__SCREAMING_SNAKE_CASE)
__a = inputs_dict['''input_ids''']
__a = input_ids[:1, :]
__a = inputs_dict['''attention_mask'''][:1, :]
__a = 1
# first forward pass
__a = model(__SCREAMING_SNAKE_CASE , attention_mask=__SCREAMING_SNAKE_CASE , use_cache=__SCREAMING_SNAKE_CASE)
__a , __a = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
__a = ids_tensor((self.batch_size, 3) , config.vocab_size)
__a = tf.cast(ids_tensor((self.batch_size, 3) , 2) , tf.inta)
# append to next input_ids and
__a = tf.concat([input_ids, next_tokens] , axis=-1)
__a = tf.concat([attention_mask, next_attn_mask] , axis=-1)
__a = model(__SCREAMING_SNAKE_CASE , attention_mask=__SCREAMING_SNAKE_CASE)[0]
__a = model(__SCREAMING_SNAKE_CASE , attention_mask=__SCREAMING_SNAKE_CASE , past_key_values=__SCREAMING_SNAKE_CASE)[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1])
# select random slice
__a = int(ids_tensor((1,) , output_from_past.shape[-1]))
__a = output_from_no_past[:, -3:, random_slice_idx]
__a = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , rtol=1E-3)
@require_tf
class _A ( __UpperCAmelCase ,__UpperCAmelCase ,unittest.TestCase ):
UpperCamelCase__ : Optional[Any] = (TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
UpperCamelCase__ : int = (TFOPTForCausalLM,) if is_tf_available() else ()
UpperCamelCase__ : str = (
{'''feature-extraction''': TFOPTModel, '''text-generation''': TFOPTForCausalLM} if is_tf_available() else {}
)
UpperCamelCase__ : Optional[Any] = False
UpperCamelCase__ : Optional[int] = False
UpperCamelCase__ : Union[str, Any] = False
UpperCamelCase__ : int = 10
def _lowerCamelCase ( self : Union[str, Any]):
'''simple docstring'''
__a = TFOPTModelTester(self)
__a = ConfigTester(self , config_class=__SCREAMING_SNAKE_CASE)
def _lowerCamelCase ( self : List[Any]):
'''simple docstring'''
self.config_tester.run_common_tests()
def _lowerCamelCase ( self : Optional[int]):
'''simple docstring'''
__a = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*__SCREAMING_SNAKE_CASE)
def _lowerCamelCase ( self : Optional[Any]):
'''simple docstring'''
__a , __a = self.model_tester.prepare_config_and_inputs_for_common()
def _get_word_embedding_weight(__SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : int):
if hasattr(__SCREAMING_SNAKE_CASE , '''weight'''):
return embedding_layer.weight
else:
# Here we build the word embeddings weights if not exists.
# And then we retry to get the attribute once built.
model.build()
if hasattr(__SCREAMING_SNAKE_CASE , '''weight'''):
return embedding_layer.weight
else:
return None
for model_class in self.all_model_classes:
for size in [config.vocab_size - 10, config.vocab_size + 10]:
# build the embeddings
__a = model_class(config=__SCREAMING_SNAKE_CASE)
__a = _get_word_embedding_weight(__SCREAMING_SNAKE_CASE , model.get_input_embeddings())
__a = _get_word_embedding_weight(__SCREAMING_SNAKE_CASE , model.get_output_embeddings())
# reshape the embeddings
model.resize_token_embeddings(__SCREAMING_SNAKE_CASE)
__a = _get_word_embedding_weight(__SCREAMING_SNAKE_CASE , model.get_input_embeddings())
__a = _get_word_embedding_weight(__SCREAMING_SNAKE_CASE , model.get_output_embeddings())
# check that the resized embeddings size matches the desired size.
__a = size if size is not None else config.vocab_size
self.assertEqual(new_input_embeddings.shape[0] , __SCREAMING_SNAKE_CASE)
# check that weights remain the same after resizing
__a = True
for pa, pa in zip(old_input_embeddings.value() , new_input_embeddings.value()):
if tf.math.reduce_sum(tf.math.abs(pa - pa)) > 0:
__a = False
self.assertTrue(__SCREAMING_SNAKE_CASE)
if old_output_embeddings is not None and new_output_embeddings is not None:
self.assertEqual(new_output_embeddings.shape[0] , __SCREAMING_SNAKE_CASE)
__a = True
for pa, pa in zip(old_output_embeddings.value() , new_output_embeddings.value()):
if tf.math.reduce_sum(tf.math.abs(pa - pa)) > 0:
__a = False
self.assertTrue(__SCREAMING_SNAKE_CASE)
def __snake_case ( _UpperCAmelCase ):
return tf.constant(_UpperCAmelCase , dtype=tf.intaa )
@require_tf
class _A ( unittest.TestCase ):
UpperCamelCase__ : Optional[int] = 99
def _lowerCamelCase ( self : List[str]):
'''simple docstring'''
__a = tf.ones((4, 1) , dtype=tf.intaa) * 2
__a = tf.concat([ids_tensor((4, 6) , self.vocab_size - 3) + 3, eos_column_vector] , axis=1)
__a = input_ids.shape[0]
__a = OPTConfig(
vocab_size=self.vocab_size , hidden_size=24 , num_hidden_layers=2 , num_attention_heads=2 , ffn_dim=32 , max_position_embeddings=48 , eos_token_id=2 , pad_token_id=1 , bos_token_id=0 , )
return config, input_ids, batch_size
@require_sentencepiece
@require_tf
class _A ( unittest.TestCase ):
@slow
def _lowerCamelCase ( self : Optional[int]):
'''simple docstring'''
__a = TFOPTModel.from_pretrained('''facebook/opt-350m''')
__a = _long_tensor([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]])
__a = tf.not_equal(__SCREAMING_SNAKE_CASE , model.config.pad_token_id)
with tf.GradientTape():
__a = model(input_ids=__SCREAMING_SNAKE_CASE , attention_mask=__SCREAMING_SNAKE_CASE).last_hidden_state
__a = (1, 11, 512)
self.assertEqual(output.shape , __SCREAMING_SNAKE_CASE)
__a = tf.constant(
[[-0.28_73, -1.92_18, -0.30_33], [-1.27_10, -0.13_38, -0.19_02], [0.40_95, 0.12_14, -1.31_21]])
self.assertTrue(np.allclose(output[:, :3, :3] , __SCREAMING_SNAKE_CASE , atol=4E-3))
__a = tf.function(__SCREAMING_SNAKE_CASE , jit_compile=__SCREAMING_SNAKE_CASE)
__a = xla_generate(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE)[0]
self.assertTrue(np.allclose(output[:, :3, :3] , __SCREAMING_SNAKE_CASE , atol=4E-2))
@require_tf
@slow
class _A ( unittest.TestCase ):
def _lowerCamelCase ( self : int):
'''simple docstring'''
super().setUp()
__a = '''facebook/opt-350m'''
def _lowerCamelCase ( self : Tuple):
'''simple docstring'''
__a = TFOPTForCausalLM.from_pretrained(self.path_model)
__a = GPTaTokenizer.from_pretrained(self.path_model)
__a = [
'''Today is a beautiful day and I want to''',
'''In the city of''',
'''Paris is the capital of France and''',
'''Computers and mobile phones have taken''',
]
# verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
__a = tokenizer(__SCREAMING_SNAKE_CASE , return_tensors='''tf''' , padding=__SCREAMING_SNAKE_CASE , add_special_tokens=__SCREAMING_SNAKE_CASE)
__a = tf.math.reduce_mean(model(inputs.input_ids , attention_mask=inputs.attention_mask)[0] , axis=-1)
__a = tf.constant(
[
[1.38_51, -13.89_23, -10.52_29, -10.75_33, -0.23_09, -10.23_84, -0.53_65, -9.09_47, -5.16_70],
[-4.70_73, -10.62_76, -3.94_15, -21.52_42, -0.28_22, -0.28_22, -0.28_22, -0.28_22, -0.28_22],
[0.62_47, -3.42_29, -8.91_79, -1.42_97, -14.16_50, 1.41_46, -9.02_18, -0.27_03, -0.27_03],
[6.47_83, -1.99_13, -10.79_26, -2.33_36, 1.50_92, -0.99_74, -6.82_13, 1.34_77, 1.34_77],
])
self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , atol=1E-4))
__a = tf.function(__SCREAMING_SNAKE_CASE , jit_compile=__SCREAMING_SNAKE_CASE)
__a = tf.math.reduce_mean(xla_generate(inputs.input_ids , attention_mask=inputs.attention_mask)[0] , axis=-1)
self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , atol=1E-4))
@require_tf
@slow
class _A ( unittest.TestCase ):
@property
def _lowerCamelCase ( self : List[str]):
'''simple docstring'''
return [
"Today is a beautiful day and I want",
"In the city of",
"Paris is the capital of France and",
"Computers and mobile phones have taken",
]
def _lowerCamelCase ( self : List[str]):
'''simple docstring'''
__a = '''facebook/opt-125m'''
__a = [
'''Today is a beautiful day and I want to''',
'''In the city of New York, the city''',
'''Paris is the capital of France and the capital''',
'''Computers and mobile phones have taken over the''',
]
__a = []
__a = GPTaTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE)
__a = TFOPTForCausalLM.from_pretrained(__SCREAMING_SNAKE_CASE)
for prompt in self.prompts:
__a = tokenizer(__SCREAMING_SNAKE_CASE , return_tensors='''tf''').input_ids
__a = model.generate(__SCREAMING_SNAKE_CASE , max_length=10)
__a = tokenizer.batch_decode(__SCREAMING_SNAKE_CASE , skip_special_tokens=__SCREAMING_SNAKE_CASE)
predicted_outputs += generated_string
self.assertListEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE)
def _lowerCamelCase ( self : str):
'''simple docstring'''
__a = '''facebook/opt-350m'''
__a = GPTaTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE)
__a = TFOPTForCausalLM.from_pretrained(__SCREAMING_SNAKE_CASE)
__a = '''left'''
# use different length sentences to test batching
__a = [
'''Hello, my dog is a little''',
'''Today, I''',
]
__a = tokenizer(__SCREAMING_SNAKE_CASE , return_tensors='''tf''' , padding=__SCREAMING_SNAKE_CASE)
__a = inputs['''input_ids''']
__a = model.generate(input_ids=__SCREAMING_SNAKE_CASE , attention_mask=inputs['''attention_mask'''])
__a = tokenizer(sentences[0] , return_tensors='''tf''').input_ids
__a = model.generate(input_ids=__SCREAMING_SNAKE_CASE)
__a = inputs_non_padded.shape[-1] - tf.math.reduce_sum(
tf.cast(inputs['''attention_mask'''][-1] , tf.intaa))
__a = tokenizer(sentences[1] , return_tensors='''tf''').input_ids
__a = model.generate(input_ids=__SCREAMING_SNAKE_CASE , max_length=model.config.max_length - num_paddings)
__a = tokenizer.batch_decode(__SCREAMING_SNAKE_CASE , skip_special_tokens=__SCREAMING_SNAKE_CASE)
__a = tokenizer.decode(output_non_padded[0] , skip_special_tokens=__SCREAMING_SNAKE_CASE)
__a = tokenizer.decode(output_padded[0] , skip_special_tokens=__SCREAMING_SNAKE_CASE)
__a = [
'''Hello, my dog is a little bit of a dork.\nI\'m a little bit''',
'''Today, I was in the middle of a conversation with a friend about the''',
]
self.assertListEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE)
self.assertListEqual(__SCREAMING_SNAKE_CASE , [non_padded_sentence, padded_sentence])
def _lowerCamelCase ( self : int):
'''simple docstring'''
__a = '''facebook/opt-350m'''
__a = [
'''Today is a beautiful day and I want to''',
'''In the city of San Francisco, the city''',
'''Paris is the capital of France and the capital''',
'''Computers and mobile phones have taken over the''',
]
__a = []
__a = GPTaTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE)
__a = TFOPTForCausalLM.from_pretrained(__SCREAMING_SNAKE_CASE)
for prompt in self.prompts:
__a = tokenizer(__SCREAMING_SNAKE_CASE , return_tensors='''tf''').input_ids
__a = model.generate(__SCREAMING_SNAKE_CASE , max_length=10)
__a = tokenizer.batch_decode(__SCREAMING_SNAKE_CASE , skip_special_tokens=__SCREAMING_SNAKE_CASE)
predicted_outputs += generated_string
self.assertListEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE)
| 49 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case :Union[str, Any] = logging.get_logger(__name__)
__snake_case :Any = {
'''google/switch-base-8''': '''https://huggingface.co/google/switch-base-8/blob/main/config.json''',
}
class _A ( __UpperCAmelCase ):
UpperCamelCase__ : Optional[int] = '''switch_transformers'''
UpperCamelCase__ : Optional[Any] = ['''past_key_values''']
UpperCamelCase__ : Optional[Any] = {'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}
def __init__( self : Optional[Any] , __SCREAMING_SNAKE_CASE : str=32_128 , __SCREAMING_SNAKE_CASE : int=768 , __SCREAMING_SNAKE_CASE : Any=64 , __SCREAMING_SNAKE_CASE : Optional[int]=2_048 , __SCREAMING_SNAKE_CASE : List[str]=64 , __SCREAMING_SNAKE_CASE : int=12 , __SCREAMING_SNAKE_CASE : Any=3 , __SCREAMING_SNAKE_CASE : Optional[Any]=12 , __SCREAMING_SNAKE_CASE : Optional[int]=3 , __SCREAMING_SNAKE_CASE : Any=12 , __SCREAMING_SNAKE_CASE : Tuple=8 , __SCREAMING_SNAKE_CASE : Optional[Any]=False , __SCREAMING_SNAKE_CASE : Union[str, Any]=0.01 , __SCREAMING_SNAKE_CASE : Dict="float32" , __SCREAMING_SNAKE_CASE : Optional[Any]=False , __SCREAMING_SNAKE_CASE : Optional[Any]=32 , __SCREAMING_SNAKE_CASE : int=128 , __SCREAMING_SNAKE_CASE : Any=0.1 , __SCREAMING_SNAKE_CASE : int=1E-6 , __SCREAMING_SNAKE_CASE : Dict=0.0_01 , __SCREAMING_SNAKE_CASE : List[str]=0.0_01 , __SCREAMING_SNAKE_CASE : List[Any]=1.0 , __SCREAMING_SNAKE_CASE : Optional[int]="relu" , __SCREAMING_SNAKE_CASE : Optional[int]=True , __SCREAMING_SNAKE_CASE : Optional[int]=False , __SCREAMING_SNAKE_CASE : str=True , __SCREAMING_SNAKE_CASE : int=0 , __SCREAMING_SNAKE_CASE : List[Any]=1 , **__SCREAMING_SNAKE_CASE : Dict , ):
'''simple docstring'''
__a = vocab_size
__a = d_model
__a = d_kv
__a = d_ff
__a = num_sparse_encoder_layers
__a = num_layers
__a = (
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
__a = num_sparse_decoder_layers
# This tells us, each how many encoder layer we'll have to set a sparse layer.
if self.num_sparse_encoder_layers > 0:
__a = self.num_layers // self.num_sparse_encoder_layers
else:
__a = self.num_layers # HACK: this will create 0 sparse layers
# This tells us, each how many encoder layer we'll have to set a sparse layer.
if self.num_sparse_decoder_layers > 0:
__a = self.num_decoder_layers // self.num_sparse_decoder_layers
else:
__a = self.num_decoder_layers # HACK: this will create 0 sparse layers
__a = num_heads
__a = num_experts
__a = expert_capacity
__a = router_bias
__a = router_jitter_noise
if router_dtype not in ["float32", "float16", "bfloat16"]:
raise ValueError(F'`router_dtype` must be one of \'float32\', \'float16\' or \'bfloat16\', got {router_dtype}')
__a = router_dtype
__a = router_ignore_padding_tokens
__a = relative_attention_num_buckets
__a = relative_attention_max_distance
__a = dropout_rate
__a = layer_norm_epsilon
__a = initializer_factor
__a = feed_forward_proj
__a = use_cache
__a = add_router_probs
__a = router_z_loss_coef
__a = router_aux_loss_coef
__a = self.feed_forward_proj.split('''-''')
__a = act_info[-1]
__a = act_info[0] == '''gated'''
if len(__SCREAMING_SNAKE_CASE) > 1 and act_info[0] != "gated" or len(__SCREAMING_SNAKE_CASE) > 2:
raise ValueError(
F'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
'''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
'''\'gated-gelu\' or \'relu\'''')
# for backwards compatibility
if feed_forward_proj == "gated-gelu":
__a = '''gelu_new'''
super().__init__(
pad_token_id=__SCREAMING_SNAKE_CASE , eos_token_id=__SCREAMING_SNAKE_CASE , is_encoder_decoder=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE , )
| 49 | 1 |
"""simple docstring"""
import argparse
import logging
import sys
from unittest.mock import patch
import run_glue_deebert
from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
logging.basicConfig(level=logging.DEBUG)
UpperCAmelCase = logging.getLogger()
def lowerCamelCase () -> int:
lowercase :str = argparse.ArgumentParser()
parser.add_argument('''-f''')
lowercase :Optional[Any] = parser.parse_args()
return args.f
class __magic_name__ ( __UpperCAmelCase ):
def __snake_case ( self : List[Any] ):
'''simple docstring'''
lowercase :Dict = logging.StreamHandler(sys.stdout )
logger.addHandler(snake_case__ )
def __snake_case ( self : int , snake_case__ : List[str] ):
'''simple docstring'''
lowercase :str = get_gpu_count()
if n_gpu > 1:
pass
# XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
# script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
# distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
# cmd = [sys.executable] + distributed_args + args
# execute_subprocess_async(cmd, env=self.get_env())
# XXX: test the results - need to save them first into .json file
else:
args.insert(0 , '''run_glue_deebert.py''' )
with patch.object(snake_case__ , '''argv''' , snake_case__ ):
lowercase :Optional[Any] = run_glue_deebert.main()
for value in result.values():
self.assertGreaterEqual(snake_case__ , 0.6_66 )
@slow
@require_torch_non_multi_gpu
def __snake_case ( self : Tuple ):
'''simple docstring'''
lowercase :Optional[Any] = '''
--model_type roberta
--model_name_or_path roberta-base
--task_name MRPC
--do_train
--do_eval
--do_lower_case
--data_dir ./tests/fixtures/tests_samples/MRPC/
--max_seq_length 128
--per_gpu_eval_batch_size=1
--per_gpu_train_batch_size=8
--learning_rate 2e-4
--num_train_epochs 3
--overwrite_output_dir
--seed 42
--output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
--plot_data_dir ./examples/deebert/results/
--save_steps 0
--overwrite_cache
--eval_after_first_stage
'''.split()
self.run_and_check(snake_case__ )
lowercase :Any = '''
--model_type roberta
--model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
--task_name MRPC
--do_eval
--do_lower_case
--data_dir ./tests/fixtures/tests_samples/MRPC/
--output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
--plot_data_dir ./examples/deebert/results/
--max_seq_length 128
--eval_each_highway
--eval_highway
--overwrite_cache
--per_gpu_eval_batch_size=1
'''.split()
self.run_and_check(snake_case__ )
lowercase :Union[str, Any] = '''
--model_type roberta
--model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
--task_name MRPC
--do_eval
--do_lower_case
--data_dir ./tests/fixtures/tests_samples/MRPC/
--output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
--plot_data_dir ./examples/deebert/results/
--max_seq_length 128
--early_exit_entropy 0.1
--eval_highway
--overwrite_cache
--per_gpu_eval_batch_size=1
'''.split()
self.run_and_check(snake_case__ )
| 366 |
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCAmelCase = logging.get_logger(__name__)
# TODO: upload to AWS
UpperCAmelCase = {
'''yjernite/retribert-base-uncased''': (
'''https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/config.json'''
),
}
class __magic_name__ ( __UpperCAmelCase ):
__A : List[Any] = "retribert"
def __init__( self : Dict , snake_case__ : Union[str, Any]=3_0_5_2_2 , snake_case__ : Union[str, Any]=7_6_8 , snake_case__ : Optional[Any]=8 , snake_case__ : int=1_2 , snake_case__ : Optional[int]=3_0_7_2 , snake_case__ : Any="gelu" , snake_case__ : str=0.1 , snake_case__ : Optional[Any]=0.1 , snake_case__ : List[str]=5_1_2 , snake_case__ : Union[str, Any]=2 , snake_case__ : Dict=0.02 , snake_case__ : Tuple=1e-1_2 , snake_case__ : Any=True , snake_case__ : Tuple=1_2_8 , snake_case__ : Optional[int]=0 , **snake_case__ : List[str] , ):
'''simple docstring'''
super().__init__(pad_token_id=snake_case__ , **snake_case__ )
lowercase :Any = vocab_size
lowercase :Optional[Any] = hidden_size
lowercase :str = num_hidden_layers
lowercase :List[str] = num_attention_heads
lowercase :Union[str, Any] = hidden_act
lowercase :Any = intermediate_size
lowercase :str = hidden_dropout_prob
lowercase :str = attention_probs_dropout_prob
lowercase :Optional[Any] = max_position_embeddings
lowercase :Union[str, Any] = type_vocab_size
lowercase :Any = initializer_range
lowercase :int = layer_norm_eps
lowercase :List[str] = share_encoders
lowercase :Union[str, Any] = projection_dim
| 172 | 0 |
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class _lowercase ( A__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : str = ['''image_processor''', '''tokenizer''']
SCREAMING_SNAKE_CASE__ : Dict = '''LayoutLMv2ImageProcessor'''
SCREAMING_SNAKE_CASE__ : Dict = ('''LayoutXLMTokenizer''', '''LayoutXLMTokenizerFast''')
def __init__( self :Union[str, Any] , lowerCAmelCase__ :int=None , lowerCAmelCase__ :List[str]=None , **lowerCAmelCase__ :Optional[int] ) -> Union[str, Any]:
if "feature_extractor" in kwargs:
warnings.warn(
'''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
''' instead.''' , lowerCAmelCase__ , )
__SCREAMING_SNAKE_CASE : Union[str, Any] = kwargs.pop('''feature_extractor''' )
__SCREAMING_SNAKE_CASE : Optional[int] = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('''You need to specify an `image_processor`.''' )
if tokenizer is None:
raise ValueError('''You need to specify a `tokenizer`.''' )
super().__init__(lowerCAmelCase__ , lowerCAmelCase__ )
def __call__( self :Optional[int] , lowerCAmelCase__ :List[Any] , lowerCAmelCase__ :Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None , lowerCAmelCase__ :Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None , lowerCAmelCase__ :Union[List[List[int]], List[List[List[int]]]] = None , lowerCAmelCase__ :Optional[Union[List[int], List[List[int]]]] = None , lowerCAmelCase__ :bool = True , lowerCAmelCase__ :Union[bool, str, PaddingStrategy] = False , lowerCAmelCase__ :Union[bool, str, TruncationStrategy] = None , lowerCAmelCase__ :Optional[int] = None , lowerCAmelCase__ :int = 0 , lowerCAmelCase__ :Optional[int] = None , lowerCAmelCase__ :Optional[bool] = None , lowerCAmelCase__ :Optional[bool] = None , lowerCAmelCase__ :bool = False , lowerCAmelCase__ :bool = False , lowerCAmelCase__ :bool = False , lowerCAmelCase__ :bool = False , lowerCAmelCase__ :bool = True , lowerCAmelCase__ :Optional[Union[str, TensorType]] = None , **lowerCAmelCase__ :List[Any] , ) -> BatchEncoding:
# verify input
if self.image_processor.apply_ocr and (boxes is not None):
raise ValueError(
'''You cannot provide bounding boxes '''
'''if you initialized the image processor with apply_ocr set to True.''' )
if self.image_processor.apply_ocr and (word_labels is not None):
raise ValueError(
'''You cannot provide word labels if you initialized the image processor with apply_ocr set to True.''' )
if return_overflowing_tokens is True and return_offsets_mapping is False:
raise ValueError('''You cannot return overflowing tokens without returning the offsets mapping.''' )
# first, apply the image processor
__SCREAMING_SNAKE_CASE : str = self.image_processor(images=lowerCAmelCase__ , return_tensors=lowerCAmelCase__ )
# second, apply the tokenizer
if text is not None and self.image_processor.apply_ocr and text_pair is None:
if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
__SCREAMING_SNAKE_CASE : Any = [text] # add batch dimension (as the image processor always adds a batch dimension)
__SCREAMING_SNAKE_CASE : List[str] = features['''words''']
__SCREAMING_SNAKE_CASE : str = self.tokenizer(
text=text if text is not None else features['''words'''] , text_pair=text_pair if text_pair is not None else None , boxes=boxes if boxes is not None else features['''boxes'''] , word_labels=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ , padding=lowerCAmelCase__ , truncation=lowerCAmelCase__ , max_length=lowerCAmelCase__ , stride=lowerCAmelCase__ , pad_to_multiple_of=lowerCAmelCase__ , return_token_type_ids=lowerCAmelCase__ , return_attention_mask=lowerCAmelCase__ , return_overflowing_tokens=lowerCAmelCase__ , return_special_tokens_mask=lowerCAmelCase__ , return_offsets_mapping=lowerCAmelCase__ , return_length=lowerCAmelCase__ , verbose=lowerCAmelCase__ , return_tensors=lowerCAmelCase__ , **lowerCAmelCase__ , )
# add pixel values
__SCREAMING_SNAKE_CASE : Optional[Any] = features.pop('''pixel_values''' )
if return_overflowing_tokens is True:
__SCREAMING_SNAKE_CASE : List[str] = self.get_overflowing_images(lowerCAmelCase__ , encoded_inputs['''overflow_to_sample_mapping'''] )
__SCREAMING_SNAKE_CASE : List[str] = images
return encoded_inputs
def __magic_name__( self :int , lowerCAmelCase__ :List[str] , lowerCAmelCase__ :Optional[int] ) -> Optional[int]:
# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
__SCREAMING_SNAKE_CASE : Dict = []
for sample_idx in overflow_to_sample_mapping:
images_with_overflow.append(images[sample_idx] )
if len(lowerCAmelCase__ ) != len(lowerCAmelCase__ ):
raise ValueError(
'''Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'''
f''' {len(lowerCAmelCase__ )} and {len(lowerCAmelCase__ )}''' )
return images_with_overflow
def __magic_name__( self :Any , *lowerCAmelCase__ :Optional[int] , **lowerCAmelCase__ :Optional[Any] ) -> Union[str, Any]:
return self.tokenizer.batch_decode(*lowerCAmelCase__ , **lowerCAmelCase__ )
def __magic_name__( self :Union[str, Any] , *lowerCAmelCase__ :Tuple , **lowerCAmelCase__ :Union[str, Any] ) -> str:
return self.tokenizer.decode(*lowerCAmelCase__ , **lowerCAmelCase__ )
@property
def __magic_name__( self :Union[str, Any] ) -> List[Any]:
return ["input_ids", "bbox", "attention_mask", "image"]
@property
def __magic_name__( self :str ) -> Dict:
warnings.warn(
'''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' , lowerCAmelCase__ , )
return self.image_processor_class
@property
def __magic_name__( self :Tuple ) -> List[Any]:
warnings.warn(
'''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' , lowerCAmelCase__ , )
return self.image_processor
| 9 |
import darl # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline
A_ : List[str] = {
'n_samples': 64,
'horizon': 32,
'num_inference_steps': 20,
'n_guide_steps': 2, # can set to 0 for faster sampling, does not use value network
'scale_grad_by_std': True,
'scale': 0.1,
'eta': 0.0,
't_grad_cutoff': 2,
'device': 'cpu',
}
if __name__ == "__main__":
A_ : Optional[int] = 'hopper-medium-v2'
A_ : List[Any] = gym.make(env_name)
A_ : str = ValueGuidedRLPipeline.from_pretrained(
'bglick13/hopper-medium-v2-value-function-hor32',
env=env,
)
env.seed(0)
A_ : List[Any] = env.reset()
A_ : Optional[int] = 0
A_ : str = 0
A_ : Optional[Any] = 1000
A_ : Union[str, Any] = [obs.copy()]
try:
for t in tqdm.tqdm(range(T)):
# call the policy
A_ : Tuple = pipeline(obs, planning_horizon=32)
# execute action in environment
A_ , A_ , A_ , A_ : Dict = env.step(denorm_actions)
A_ : List[str] = env.get_normalized_score(total_reward)
# update return
total_reward += reward
total_score += score
print(
f'''Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:'''
f''' {total_score}'''
)
# save observations for rendering
rollout.append(next_observation.copy())
A_ : int = next_observation
except KeyboardInterrupt:
pass
print(f'''Total reward: {total_reward}''')
| 192 | 0 |
def _a ( SCREAMING_SNAKE_CASE__ : int ) -> bool:
'''simple docstring'''
if not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
SCREAMING_SNAKE_CASE__ : Union[str, Any] = f'''Input value of [number={number}] must be an integer'''
raise TypeError(SCREAMING_SNAKE_CASE__ )
if number < 0:
return False
SCREAMING_SNAKE_CASE__ : Optional[int] = number * number
while number > 0:
if number % 10 != number_square % 10:
return False
number //= 10
number_square //= 10
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
| 191 |
import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
_lowerCamelCase : Union[str, Any] = '''%20'''.join(argv[1:]) if len(argv) > 1 else quote(str(input('''Search: ''')))
print('''Googling.....''')
_lowerCamelCase : Any = f"https://www.google.com/search?q={query}&num=100"
_lowerCamelCase : Optional[Any] = requests.get(
url,
headers={'''User-Agent''': str(UserAgent().random)},
)
try:
_lowerCamelCase : Union[str, Any] = (
BeautifulSoup(res.text, '''html.parser''')
.find('''div''', attrs={'''class''': '''yuRUbf'''})
.find('''a''')
.get('''href''')
)
except AttributeError:
_lowerCamelCase : Optional[Any] = parse_qs(
BeautifulSoup(res.text, '''html.parser''')
.find('''div''', attrs={'''class''': '''kCrYT'''})
.find('''a''')
.get('''href''')
)['''url'''][0]
webbrowser.open(link)
| 191 | 1 |
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ , lowercase_ ) -> Optional[Any]:
"""simple docstring"""
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
A__ = mf_knapsack(i - 1 , lowercase_ , lowercase_ , lowercase_ )
else:
A__ = max(
mf_knapsack(i - 1 , lowercase_ , lowercase_ , lowercase_ ) , mf_knapsack(i - 1 , lowercase_ , lowercase_ , j - wt[i - 1] ) + val[i - 1] , )
A__ = val
return f[i][j]
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ , lowercase_ ) -> str:
"""simple docstring"""
A__ = [[0] * (w + 1) for _ in range(n + 1 )]
for i in range(1 , n + 1 ):
for w_ in range(1 , w + 1 ):
if wt[i - 1] <= w_:
A__ = max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]] , dp[i - 1][w_] )
else:
A__ = dp[i - 1][w_]
return dp[n][w_], dp
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ ) -> Dict:
"""simple docstring"""
if not (isinstance(lowercase_ , (list, tuple) ) and isinstance(lowercase_ , (list, tuple) )):
raise ValueError(
'''Both the weights and values vectors must be either lists or tuples''' )
A__ = len(lowercase_ )
if num_items != len(lowercase_ ):
A__ = (
'''The number of weights must be the same as the number of values.\n'''
f"""But got {num_items} weights and {len(lowercase_ )} values"""
)
raise ValueError(lowercase_ )
for i in range(lowercase_ ):
if not isinstance(wt[i] , lowercase_ ):
A__ = (
'''All weights must be integers but got weight of '''
f"""type {type(wt[i] )} at index {i}"""
)
raise TypeError(lowercase_ )
A__ , A__ = knapsack(lowercase_ , lowercase_ , lowercase_ , lowercase_ )
A__ = set()
_construct_solution(lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ )
return optimal_val, example_optional_set
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ ) -> Tuple:
"""simple docstring"""
if i > 0 and j > 0:
if dp[i - 1][j] == dp[i][j]:
_construct_solution(lowercase_ , lowercase_ , i - 1 , lowercase_ , lowercase_ )
else:
optimal_set.add(lowercase_ )
_construct_solution(lowercase_ , lowercase_ , i - 1 , j - wt[i - 1] , lowercase_ )
if __name__ == "__main__":
_lowerCamelCase : str = [3, 2, 4, 4]
_lowerCamelCase : Tuple = [4, 3, 2, 3]
_lowerCamelCase : int = 4
_lowerCamelCase : Any = 6
_lowerCamelCase : Optional[int] = [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
_lowerCamelCase , _lowerCamelCase : str = knapsack(w, wt, val, n)
print(optimal_solution)
print(mf_knapsack(n, wt, val, w)) # switched the n and w
# testing the dynamic programming problem with example
# the optimal subset for the above example are items 3 and 4
_lowerCamelCase , _lowerCamelCase : str = knapsack_with_example_solution(w, wt, val)
assert optimal_solution == 8
assert optimal_subset == {3, 4}
print("""optimal_value = """, optimal_solution)
print("""An optimal subset corresponding to the optimal value""", optimal_subset)
| 14 |
'''simple docstring'''
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class __lowercase ( tf.keras.layers.Layer ):
def __init__(self , A , A , A = None , A = None ):
super().__init__()
lowerCamelCase_ : List[Any] = pad_token_id
lowerCamelCase_ : Union[str, Any] = max_length
lowerCamelCase_ : List[Any] = vocab
lowerCamelCase_ : Optional[int] = merges
lowerCamelCase_ : List[str] = BytePairTokenizer(A , A , sequence_length=A )
@classmethod
def UpperCAmelCase__ (cls , A , *A , **A ):
lowerCamelCase_ : int = [''' '''.join(A ) for m in tokenizer.bpe_ranks.keys()]
lowerCamelCase_ : Dict = tokenizer.get_vocab()
return cls(A , A , *A , **A )
@classmethod
def UpperCAmelCase__ (cls , A , *A , **A ):
lowerCamelCase_ : Optional[int] = GPTaTokenizer.from_pretrained(A , *A , **A )
return cls.from_tokenizer(A , *A , **A )
@classmethod
def UpperCAmelCase__ (cls , A ):
return cls(**A )
def UpperCAmelCase__ (self ):
return {
"vocab": self.vocab,
"merges": self.merges,
"max_length": self.max_length,
"pad_token_id": self.pad_token_id,
}
def UpperCAmelCase__ (self , A , A = None ):
lowerCamelCase_ : str = self.tf_tokenizer(A )
lowerCamelCase_ : Any = tf.ones_like(A )
if self.pad_token_id is not None:
# pad the tokens up to max length
lowerCamelCase_ : Tuple = max_length if max_length is not None else self.max_length
if max_length is not None:
lowerCamelCase_, lowerCamelCase_ : Tuple = pad_model_inputs(
A , max_seq_length=A , pad_value=self.pad_token_id )
return {"attention_mask": attention_mask, "input_ids": input_ids}
| 318 | 0 |
"""simple docstring"""
import itertools
import random
import unittest
import numpy as np
from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_available():
from transformers import SpeechaTextFeatureExtractor
__UpperCAmelCase : Optional[Any] = random.Random()
def A__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=1.0 , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=None) -> Any:
if rng is None:
__snake_case: str = global_rng
__snake_case: Dict = []
for batch_idx in range(shape[0]):
values.append([])
for _ in range(shape[1]):
values[-1].append(rng.random() * scale)
return values
@require_torch
@require_torchaudio
class __snake_case ( unittest.TestCase ):
'''simple docstring'''
def __init__( self : Optional[int] , A : Any , A : Dict=7 , A : Optional[Any]=400 , A : Optional[Any]=2_000 , A : Dict=24 , A : Dict=24 , A : int=0.0 , A : List[str]=16_000 , A : Optional[Any]=True , A : Union[str, Any]=True , ):
__snake_case: List[Any] = parent
__snake_case: int = batch_size
__snake_case: Dict = min_seq_length
__snake_case: int = max_seq_length
__snake_case: int = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
__snake_case: Optional[int] = feature_size
__snake_case: Optional[Any] = num_mel_bins
__snake_case: Dict = padding_value
__snake_case: Union[str, Any] = sampling_rate
__snake_case: List[str] = return_attention_mask
__snake_case: List[str] = do_normalize
def UpperCAmelCase__ ( self : List[Any] ):
return {
"feature_size": self.feature_size,
"num_mel_bins": self.num_mel_bins,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"return_attention_mask": self.return_attention_mask,
"do_normalize": self.do_normalize,
}
def UpperCAmelCase__ ( self : Optional[int] , A : Union[str, Any]=False , A : List[Any]=False ):
def _flatten(A : int ):
return list(itertools.chain(*_A ) )
if equal_length:
__snake_case: Any = [floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
__snake_case: Tuple = [
floats_list((x, self.feature_size) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
__snake_case: Union[str, Any] = [np.asarray(_A ) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class __snake_case ( snake_case__ , unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase__ = SpeechaTextFeatureExtractor if is_speech_available() else None
def UpperCAmelCase__ ( self : Tuple ):
__snake_case: Optional[Any] = SpeechaTextFeatureExtractionTester(self )
def UpperCAmelCase__ ( self : Dict , A : Union[str, Any] ):
self.assertTrue(np.all(np.mean(_A , axis=0 ) < 1E-3 ) )
self.assertTrue(np.all(np.abs(np.var(_A , axis=0 ) - 1 ) < 1E-3 ) )
def UpperCAmelCase__ ( self : List[str] ):
__snake_case: Tuple = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
__snake_case: Any = [floats_list((1, x) )[0] for x in range(800 , 1_400 , 200 )]
__snake_case: Union[str, Any] = [np.asarray(_A ) for speech_input in speech_inputs]
# Test feature size
__snake_case: List[str] = feature_extractor(_A , padding=_A , return_tensors="""np""" ).input_features
self.assertTrue(input_features.ndim == 3 )
self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size )
# Test not batched input
__snake_case: Optional[Any] = feature_extractor(speech_inputs[0] , return_tensors="""np""" ).input_features
__snake_case: Any = feature_extractor(np_speech_inputs[0] , return_tensors="""np""" ).input_features
self.assertTrue(np.allclose(_A , _A , atol=1E-3 ) )
# Test batched
__snake_case: List[Any] = feature_extractor(_A , return_tensors="""np""" ).input_features
__snake_case: int = feature_extractor(_A , return_tensors="""np""" ).input_features
for enc_seq_a, enc_seq_a in zip(_A , _A ):
self.assertTrue(np.allclose(_A , _A , atol=1E-3 ) )
# Test 2-D numpy arrays are batched.
__snake_case: List[Any] = [floats_list((1, x) )[0] for x in (800, 800, 800)]
__snake_case: List[str] = np.asarray(_A )
__snake_case: List[Any] = feature_extractor(_A , return_tensors="""np""" ).input_features
__snake_case: Union[str, Any] = feature_extractor(_A , return_tensors="""np""" ).input_features
for enc_seq_a, enc_seq_a in zip(_A , _A ):
self.assertTrue(np.allclose(_A , _A , atol=1E-3 ) )
def UpperCAmelCase__ ( self : Any ):
__snake_case: int = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
__snake_case: Optional[int] = [floats_list((1, x) )[0] for x in range(800 , 1_400 , 200 )]
__snake_case: List[Any] = ["""longest""", """max_length""", """do_not_pad"""]
__snake_case: str = [None, 16, None]
for max_length, padding in zip(_A , _A ):
__snake_case: Optional[Any] = feature_extractor(
_A , padding=_A , max_length=_A , return_attention_mask=_A )
__snake_case: Dict = inputs.input_features
__snake_case: Tuple = inputs.attention_mask
__snake_case: List[str] = [np.sum(_A ) for x in attention_mask]
self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]] )
self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]] )
self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]] )
def UpperCAmelCase__ ( self : Optional[Any] ):
__snake_case: int = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
__snake_case: int = [floats_list((1, x) )[0] for x in range(800 , 1_400 , 200 )]
__snake_case: int = ["""longest""", """max_length""", """do_not_pad"""]
__snake_case: Optional[Any] = [None, 16, None]
for max_length, padding in zip(_A , _A ):
__snake_case: str = feature_extractor(
_A , max_length=_A , padding=_A , return_tensors="""np""" , return_attention_mask=_A )
__snake_case: Optional[Any] = inputs.input_features
__snake_case: Optional[Any] = inputs.attention_mask
__snake_case: Union[str, Any] = [np.sum(_A ) for x in attention_mask]
self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]] )
self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1E-6 )
self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]] )
self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1E-6 )
self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]] )
def UpperCAmelCase__ ( self : List[str] ):
__snake_case: Any = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
__snake_case: Tuple = [floats_list((1, x) )[0] for x in range(800 , 1_400 , 200 )]
__snake_case: int = feature_extractor(
_A , padding="""max_length""" , max_length=4 , truncation=_A , return_tensors="""np""" , return_attention_mask=_A , )
__snake_case: Dict = inputs.input_features
__snake_case: List[Any] = inputs.attention_mask
__snake_case: Union[str, Any] = np.sum(attention_mask == 1 , axis=1 )
self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]] )
self._check_zero_mean_unit_variance(input_features[1] )
self._check_zero_mean_unit_variance(input_features[2] )
def UpperCAmelCase__ ( self : List[str] ):
__snake_case: Any = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
__snake_case: List[str] = [floats_list((1, x) )[0] for x in range(800 , 1_400 , 200 )]
__snake_case: Dict = feature_extractor(
_A , padding="""longest""" , max_length=4 , truncation=_A , return_tensors="""np""" , return_attention_mask=_A , )
__snake_case: Optional[int] = inputs.input_features
__snake_case: int = inputs.attention_mask
__snake_case: str = np.sum(attention_mask == 1 , axis=1 )
self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]] )
self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]] )
self._check_zero_mean_unit_variance(input_features[2] )
# make sure that if max_length < longest -> then pad to max_length
self.assertEqual(input_features.shape , (3, 4, 24) )
__snake_case: Optional[Any] = [floats_list((1, x) )[0] for x in range(800 , 1_400 , 200 )]
__snake_case: List[Any] = feature_extractor(
_A , padding="""longest""" , max_length=16 , truncation=_A , return_tensors="""np""" , return_attention_mask=_A , )
__snake_case: Any = inputs.input_features
__snake_case: List[Any] = inputs.attention_mask
__snake_case: Any = np.sum(attention_mask == 1 , axis=1 )
self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]] )
self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]] )
self._check_zero_mean_unit_variance(input_features[2] )
# make sure that if max_length < longest -> then pad to max_length
self.assertEqual(input_features.shape , (3, 6, 24) )
def UpperCAmelCase__ ( self : Dict ):
import torch
__snake_case: Optional[Any] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
__snake_case: Optional[int] = np.random.rand(100 , 32 ).astype(np.floataa )
__snake_case: str = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
__snake_case: str = feature_extractor.pad([{"""input_features""": inputs}] , return_tensors="""np""" )
self.assertTrue(np_processed.input_features.dtype == np.floataa )
__snake_case: Any = feature_extractor.pad([{"""input_features""": inputs}] , return_tensors="""pt""" )
self.assertTrue(pt_processed.input_features.dtype == torch.floataa )
def UpperCAmelCase__ ( self : Tuple , A : Tuple ):
from datasets import load_dataset
__snake_case: List[Any] = load_dataset("""hf-internal-testing/librispeech_asr_dummy""" , """clean""" , split="""validation""" )
# automatic decoding with librispeech
__snake_case: List[str] = ds.sort("""id""" ).select(range(_A ) )[:num_samples]["""audio"""]
return [x["array"] for x in speech_samples]
def UpperCAmelCase__ ( self : List[str] ):
__snake_case: str = np.array([
-1.5745, -1.7713, -1.7020, -1.6069, -1.2250, -1.1105, -0.9072, -0.8241,
-1.2310, -0.8098, -0.3320, -0.4101, -0.7985, -0.4996, -0.8213, -0.9128,
-1.0420, -1.1286, -1.0440, -0.7999, -0.8405, -1.2275, -1.5443, -1.4625,
] )
# fmt: on
__snake_case: Dict = self._load_datasamples(1 )
__snake_case: Union[str, Any] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
__snake_case: List[Any] = feature_extractor(_A , return_tensors="""pt""" ).input_features
self.assertEquals(input_features.shape , (1, 584, 24) )
self.assertTrue(np.allclose(input_features[0, 0, :30] , _A , atol=1E-4 ) )
| 364 |
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging
__UpperCAmelCase : Dict = logging.get_logger(__name__) # pylint: disable=invalid-name
class __snake_case ( __lowerCamelCase ):
'''simple docstring'''
def __init__( self : List[Any] , A : AutoencoderKL , A : CLIPTextModel , A : CLIPTokenizer , A : UNetaDConditionModel , A : Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler] , A : StableDiffusionSafetyChecker , A : CLIPImageProcessor , ):
super().__init__()
self.register_modules(
vae=A , text_encoder=A , tokenizer=A , unet=A , scheduler=A , safety_checker=A , feature_extractor=A , )
def UpperCAmelCase__ ( self : Optional[Any] , A : Optional[Union[str, int]] = "auto" ):
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
__snake_case: Tuple = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(A )
def UpperCAmelCase__ ( self : str ):
self.enable_attention_slicing(A )
@torch.no_grad()
def __call__( self : List[str] , A : Union[str, List[str]] , A : int = 512 , A : int = 512 , A : int = 50 , A : float = 7.5 , A : Optional[Union[str, List[str]]] = None , A : Optional[int] = 1 , A : float = 0.0 , A : Optional[torch.Generator] = None , A : Optional[torch.FloatTensor] = None , A : Optional[str] = "pil" , A : bool = True , A : Optional[Callable[[int, int, torch.FloatTensor], None]] = None , A : int = 1 , A : Optional[torch.FloatTensor] = None , **A : Optional[Any] , ):
if isinstance(A , A ):
__snake_case: int = 1
elif isinstance(A , A ):
__snake_case: Optional[Any] = len(A )
else:
raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(A )}''' )
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 (callback_steps is None) or (
callback_steps is not None and (not isinstance(A , A ) or callback_steps <= 0)
):
raise ValueError(
f'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
f''' {type(A )}.''' )
# get prompt text embeddings
__snake_case: Tuple = self.tokenizer(
A , padding="""max_length""" , max_length=self.tokenizer.model_max_length , return_tensors="""pt""" , )
__snake_case: Any = text_inputs.input_ids
if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
__snake_case: List[str] = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :] )
logger.warning(
"""The following part of your input was truncated because CLIP can only handle sequences up to"""
f''' {self.tokenizer.model_max_length} tokens: {removed_text}''' )
__snake_case: Dict = text_input_ids[:, : self.tokenizer.model_max_length]
if text_embeddings is None:
__snake_case: Union[str, Any] = self.text_encoder(text_input_ids.to(self.device ) )[0]
# duplicate text embeddings for each generation per prompt, using mps friendly method
__snake_case , __snake_case , __snake_case: List[Any] = text_embeddings.shape
__snake_case: Tuple = text_embeddings.repeat(1 , A , 1 )
__snake_case: Dict = text_embeddings.view(bs_embed * num_images_per_prompt , A , -1 )
# 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.
__snake_case: List[str] = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
__snake_case: List[str]
if negative_prompt is None:
__snake_case: Any = [""""""]
elif type(A ) is not type(A ):
raise TypeError(
f'''`negative_prompt` should be the same type to `prompt`, but got {type(A )} !='''
f''' {type(A )}.''' )
elif isinstance(A , A ):
__snake_case: List[str] = [negative_prompt]
elif batch_size != len(A ):
raise ValueError(
f'''`negative_prompt`: {negative_prompt} has batch size {len(A )}, but `prompt`:'''
f''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
""" the batch size of `prompt`.""" )
else:
__snake_case: str = negative_prompt
__snake_case: Any = text_input_ids.shape[-1]
__snake_case: Dict = self.tokenizer(
A , padding="""max_length""" , max_length=A , truncation=A , return_tensors="""pt""" , )
__snake_case: Tuple = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
__snake_case: Optional[Any] = uncond_embeddings.shape[1]
__snake_case: str = uncond_embeddings.repeat(A , A , 1 )
__snake_case: List[Any] = uncond_embeddings.view(batch_size * num_images_per_prompt , A , -1 )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
__snake_case: Any = 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`.
__snake_case: Tuple = (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
__snake_case: List[Any] = (batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
__snake_case: Optional[Any] = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not exist on mps
__snake_case: Any = torch.randn(
A , generator=A , device="""cpu""" , dtype=A ).to(self.device )
__snake_case: Tuple = torch.randn(A , generator=A , device="""cpu""" , dtype=A ).to(
self.device )
else:
__snake_case: Dict = torch.randn(
A , generator=A , device=self.device , dtype=A )
__snake_case: Optional[int] = torch.randn(A , generator=A , device=self.device , dtype=A )
else:
if latents_reference.shape != latents_shape:
raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}''' )
__snake_case: Optional[int] = latents_reference.to(self.device )
__snake_case: List[str] = latents.to(self.device )
# This is the key part of the pipeline where we
# try to ensure that the generated images w/ the same seed
# but different sizes actually result in similar images
__snake_case: int = (latents_shape[3] - latents_shape_reference[3]) // 2
__snake_case: Optional[int] = (latents_shape[2] - latents_shape_reference[2]) // 2
__snake_case: int = latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
__snake_case: Dict = latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
__snake_case: List[Any] = 0 if dx < 0 else dx
__snake_case: Dict = 0 if dy < 0 else dy
__snake_case: List[str] = max(-dx , 0 )
__snake_case: int = max(-dy , 0 )
# import pdb
# pdb.set_trace()
__snake_case: List[Any] = latents_reference[:, :, dy : dy + h, dx : dx + w]
# set timesteps
self.scheduler.set_timesteps(A )
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
__snake_case: str = self.scheduler.timesteps.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
__snake_case: 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]
__snake_case: Optional[int] = """eta""" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
__snake_case: int = {}
if accepts_eta:
__snake_case: Optional[Any] = eta
for i, t in enumerate(self.progress_bar(A ) ):
# expand the latents if we are doing classifier free guidance
__snake_case: str = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
__snake_case: Dict = self.scheduler.scale_model_input(A , A )
# predict the noise residual
__snake_case: List[Any] = self.unet(A , A , encoder_hidden_states=A ).sample
# perform guidance
if do_classifier_free_guidance:
__snake_case , __snake_case: Any = noise_pred.chunk(2 )
__snake_case: Optional[int] = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
__snake_case: str = self.scheduler.step(A , A , A , **A ).prev_sample
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(A , A , A )
__snake_case: Optional[int] = 1 / 0.1_8215 * latents
__snake_case: List[Any] = self.vae.decode(A ).sample
__snake_case: str = (image / 2 + 0.5).clamp(0 , 1 )
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
__snake_case: Any = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if self.safety_checker is not None:
__snake_case: List[Any] = self.feature_extractor(self.numpy_to_pil(A ) , return_tensors="""pt""" ).to(
self.device )
__snake_case , __snake_case: List[str] = self.safety_checker(
images=A , clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype ) )
else:
__snake_case: Optional[int] = None
if output_type == "pil":
__snake_case: Tuple = self.numpy_to_pil(A )
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=A , nsfw_content_detected=A )
| 293 | 0 |
'''simple docstring'''
import json
import os
from datetime import date
from pathlib import Path
from tabulate import DataRow, TableFormat, tabulate
__SCREAMING_SNAKE_CASE :Optional[int] = TableFormat(
lineabove=None,
linebelowheader=None,
linebetweenrows=None,
linebelow=None,
headerrow=DataRow('''''', '''|''', '''|'''),
datarow=DataRow('''''', '''|''', '''|'''),
padding=1,
with_header_hide=None,
)
__SCREAMING_SNAKE_CASE :Any = []
__SCREAMING_SNAKE_CASE :int = []
__SCREAMING_SNAKE_CASE :str = {'''type''': '''section''', '''text''': {'''type''': '''plain_text''', '''text''': '''No failed tests! 🤗''', '''emoji''': True}}
__SCREAMING_SNAKE_CASE :Any = [
{
'''type''': '''header''',
'''text''': {
'''type''': '''plain_text''',
'''text''': F"🤗 Accelerate nightly {os.environ.get('TEST_TYPE', '')} test results",
'''emoji''': True,
},
}
]
__SCREAMING_SNAKE_CASE :List[str] = 0
for log in Path().glob('''*.log'''):
__SCREAMING_SNAKE_CASE :Optional[int] = 0
with open(log, '''r''') as f:
for line in f:
__SCREAMING_SNAKE_CASE :Tuple = json.loads(line)
if line.get('''nodeid''', '''''') != "":
__SCREAMING_SNAKE_CASE :Optional[int] = line['''nodeid''']
if line.get('''duration''', None) is not None:
__SCREAMING_SNAKE_CASE :List[str] = F"{line['duration']:.4f}"
if line.get('''outcome''', '''''') == "failed":
section_num_failed += 1
failed.append([test, duration, log.name.split('''_''')[0]])
total_num_failed += 1
group_info.append([str(log), section_num_failed, failed])
__SCREAMING_SNAKE_CASE :int = []
log.unlink()
__SCREAMING_SNAKE_CASE :Any = ''''''
__SCREAMING_SNAKE_CASE :Union[str, Any] = []
if total_num_failed > 0:
for name, num_failed, failed_tests in group_info:
if num_failed > 0:
if num_failed == 1:
message += F"*{name[1:]}: {num_failed} failed test*\n"
else:
message += F"*{name[1:]}: {num_failed} failed tests*\n"
__SCREAMING_SNAKE_CASE :int = []
__SCREAMING_SNAKE_CASE :Any = {}
for test in failed_tests:
__SCREAMING_SNAKE_CASE :Any = test[0].split('''::''')
__SCREAMING_SNAKE_CASE :Optional[Any] = data[0].split('''/''')[-1]
if data[0] not in filesafailed:
__SCREAMING_SNAKE_CASE :List[Any] = [data[1:]]
else:
filesafailed[data[0]] += [data[1:]]
failed_table.append(data)
__SCREAMING_SNAKE_CASE :Dict = [test[0] for test in failed_table]
__SCREAMING_SNAKE_CASE :int = list(set(files))
# Count number of instances in failed_tests
__SCREAMING_SNAKE_CASE :List[Any] = []
for file in individual_files:
table.append([file, len(filesafailed[file])])
__SCREAMING_SNAKE_CASE :int = tabulate(
table,
headers=['''Test Location''', '''Num Failed'''],
tablefmt=hf_table_format,
stralign='''right''',
)
message += F"\n```\n{failed_table}\n```"
all_filesafailed.append(filesafailed)
if len(message) > 3000:
__SCREAMING_SNAKE_CASE :Union[str, Any] = '''Too many failed tests, please see the full report in the Action results.'''
__SCREAMING_SNAKE_CASE :List[Any] = len(err) + 10
__SCREAMING_SNAKE_CASE :List[str] = message[: 3000 - offset] + F"\n...\n```\n{err}"
print(F"### {message}")
else:
__SCREAMING_SNAKE_CASE :Optional[Any] = '''No failed tests! 🤗'''
print(F"## {message}")
payload.append(no_error_payload)
if os.environ.get('''TEST_TYPE''', '''''') != "":
from slack_sdk import WebClient
__SCREAMING_SNAKE_CASE :List[Any] = WebClient(token=os.environ['''SLACK_API_TOKEN'''])
if message != "No failed tests! 🤗":
__SCREAMING_SNAKE_CASE :Optional[Any] = {
'''type''': '''section''',
'''text''': {
'''type''': '''mrkdwn''',
'''text''': message,
},
}
payload.append(md_report)
__SCREAMING_SNAKE_CASE :str = {
'''type''': '''section''',
'''text''': {
'''type''': '''mrkdwn''',
'''text''': '''*For more details:*''',
},
'''accessory''': {
'''type''': '''button''',
'''text''': {
'''type''': '''plain_text''',
'''text''': '''Check Action results''',
'''emoji''': True,
},
'''url''': F"https://github.com/{os.environ['GITHUB_REPOSITORY']}/actions/runs/{os.environ['GITHUB_RUN_ID']}",
},
}
payload.append(action_button)
__SCREAMING_SNAKE_CASE :List[Any] = {
'''type''': '''context''',
'''elements''': [
{
'''type''': '''plain_text''',
'''text''': F"Nightly {os.environ.get('TEST_TYPE')} test results for {date.today()}",
}
],
}
payload.append(date_report)
__SCREAMING_SNAKE_CASE :Dict = client.chat_postMessage(channel='''#accelerate-ci-daily''', text=message, blocks=payload)
__SCREAMING_SNAKE_CASE :List[Any] = response.data['''ts''']
for failed_file in all_filesafailed:
for test_location, test_failures in failed_file.items():
# Keep only the first instance of the test name
__SCREAMING_SNAKE_CASE :Tuple = ''''''
for i, row in enumerate(test_failures):
if row[0] != test_class:
__SCREAMING_SNAKE_CASE :Any = row[0]
else:
__SCREAMING_SNAKE_CASE :Union[str, Any] = ''''''
__SCREAMING_SNAKE_CASE :Dict = {
'''type''': '''section''',
'''text''': {
'''type''': '''mrkdwn''',
'''text''': F"Test location: {test_location}\n```\n{tabulate(test_failures, headers=['Class', 'Test'], tablefmt=hf_table_format, stralign='right')}\n```",
},
}
client.chat_postMessage(
channel='''#accelerate-ci-daily''',
thread_ts=ts,
blocks=[payload],
)
| 22 |
"""simple docstring"""
import re
from filelock import FileLock
try:
import nltk
A__ : Any = True
except (ImportError, ModuleNotFoundError):
A__ : str = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('punkt', quiet=True)
def _snake_case ( lowerCamelCase__ : str ) -> str:
re.sub("<n>" , "" , lowerCamelCase__ ) # remove pegasus newline char
assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
return "\n".join(nltk.sent_tokenize(lowerCamelCase__ ) )
| 144 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import ViTMSNConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import ViTMSNForImageClassification, ViTMSNModel
from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class lowerCamelCase_ :
def __init__( self : Union[str, Any] , _A : Optional[int] , _A : List[str]=13 , _A : Union[str, Any]=30 , _A : int=2 , _A : List[str]=3 , _A : int=True , _A : Optional[Any]=True , _A : Optional[Any]=32 , _A : Optional[Any]=5 , _A : List[Any]=4 , _A : List[str]=37 , _A : Optional[Any]="gelu" , _A : Optional[int]=0.1 , _A : Optional[Any]=0.1 , _A : Union[str, Any]=10 , _A : Dict=0.0_2 , _A : Dict=None , ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = parent
UpperCAmelCase__ : str = batch_size
UpperCAmelCase__ : Tuple = image_size
UpperCAmelCase__ : List[Any] = patch_size
UpperCAmelCase__ : Tuple = num_channels
UpperCAmelCase__ : str = is_training
UpperCAmelCase__ : Optional[int] = use_labels
UpperCAmelCase__ : Optional[Any] = hidden_size
UpperCAmelCase__ : List[Any] = num_hidden_layers
UpperCAmelCase__ : Tuple = num_attention_heads
UpperCAmelCase__ : List[Any] = intermediate_size
UpperCAmelCase__ : Optional[Any] = hidden_act
UpperCAmelCase__ : Union[str, Any] = hidden_dropout_prob
UpperCAmelCase__ : Dict = attention_probs_dropout_prob
UpperCAmelCase__ : List[str] = type_sequence_label_size
UpperCAmelCase__ : Optional[Any] = initializer_range
UpperCAmelCase__ : List[str] = scope
# in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
UpperCAmelCase__ : int = (image_size // patch_size) ** 2
UpperCAmelCase__ : Union[str, Any] = num_patches + 1
def lowercase_ ( self : str ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCAmelCase__ : Optional[int] = None
if self.use_labels:
UpperCAmelCase__ : str = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCAmelCase__ : Tuple = self.get_config()
return config, pixel_values, labels
def lowercase_ ( self : Optional[int] ):
'''simple docstring'''
return ViTMSNConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , initializer_range=self.initializer_range , )
def lowercase_ ( self : Tuple , _A : List[Any] , _A : Dict , _A : Any ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = ViTMSNModel(config=_A )
model.to(_A )
model.eval()
UpperCAmelCase__ : Tuple = model(_A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowercase_ ( self : Optional[Any] , _A : Tuple , _A : Optional[Any] , _A : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ : str = self.type_sequence_label_size
UpperCAmelCase__ : Union[str, Any] = ViTMSNForImageClassification(_A )
model.to(_A )
model.eval()
UpperCAmelCase__ : List[str] = model(_A , labels=_A )
print('''Pixel and labels shape: {pixel_values.shape}, {labels.shape}''' )
print('''Labels: {labels}''' )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
UpperCAmelCase__ : Any = 1
UpperCAmelCase__ : Union[str, Any] = ViTMSNForImageClassification(_A )
model.to(_A )
model.eval()
UpperCAmelCase__ : str = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
UpperCAmelCase__ : Union[str, Any] = model(_A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def lowercase_ ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase__ : int = self.prepare_config_and_inputs()
UpperCAmelCase__ : List[Any] = config_and_inputs
UpperCAmelCase__ : List[Any] = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class lowerCamelCase_ ( snake_case_ , snake_case_ , unittest.TestCase ):
lowerCAmelCase__ = (ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
lowerCAmelCase__ = (
{'feature-extraction': ViTMSNModel, 'image-classification': ViTMSNForImageClassification}
if is_torch_available()
else {}
)
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = False
def lowercase_ ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = ViTMSNModelTester(self )
UpperCAmelCase__ : Tuple = ConfigTester(self , config_class=_A , has_text_modality=_A , hidden_size=37 )
def lowercase_ ( self : List[Any] ):
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason='''ViTMSN does not use inputs_embeds''' )
def lowercase_ ( self : Dict ):
'''simple docstring'''
pass
def lowercase_ ( self : str ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase__ : int = model_class(_A )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
UpperCAmelCase__ : List[Any] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_A , nn.Linear ) )
def lowercase_ ( self : Dict ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase__ : Optional[int] = model_class(_A )
UpperCAmelCase__ : Any = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase__ : Union[str, Any] = [*signature.parameters.keys()]
UpperCAmelCase__ : int = ['pixel_values']
self.assertListEqual(arg_names[:1] , _A )
def lowercase_ ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_A )
def lowercase_ ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_A )
@slow
def lowercase_ ( self : Union[str, Any] ):
'''simple docstring'''
for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase__ : str = ViTMSNModel.from_pretrained(_A )
self.assertIsNotNone(_A )
def a__ ( ) -> Optional[int]:
UpperCAmelCase__ : Union[str, Any] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class lowerCamelCase_ ( unittest.TestCase ):
@cached_property
def lowercase_ ( self : Tuple ):
'''simple docstring'''
return ViTImageProcessor.from_pretrained('''facebook/vit-msn-small''' ) if is_vision_available() else None
@slow
def lowercase_ ( self : int ):
'''simple docstring'''
torch.manual_seed(2 )
UpperCAmelCase__ : Optional[int] = ViTMSNForImageClassification.from_pretrained('''facebook/vit-msn-small''' ).to(_A )
UpperCAmelCase__ : Dict = self.default_image_processor
UpperCAmelCase__ : Union[str, Any] = prepare_img()
UpperCAmelCase__ : int = image_processor(images=_A , return_tensors='''pt''' ).to(_A )
# forward pass
with torch.no_grad():
UpperCAmelCase__ : Dict = model(**_A )
# verify the logits
UpperCAmelCase__ : Optional[Any] = torch.Size((1, 1_000) )
self.assertEqual(outputs.logits.shape , _A )
UpperCAmelCase__ : Optional[int] = torch.tensor([-0.0_8_0_3, -0.4_4_5_4, -0.2_3_7_5] ).to(_A )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _A , atol=1e-4 ) )
| 356 |
'''simple docstring'''
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
'''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''',
}
UpperCamelCase__ = [
'''lm_head''',
'''quantizer.weight_proj''',
'''quantizer.codevectors''',
'''project_q''',
'''project_hid''',
'''label_embeddings_concat''',
'''speaker_proj''',
'''layer_norm_for_extract''',
]
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> Optional[Any]:
for attribute in key.split('''.''' ):
UpperCAmelCase__ : Optional[int] = getattr(lowerCAmelCase__ , lowerCAmelCase__ )
if weight_type is not None:
UpperCAmelCase__ : Any = getattr(lowerCAmelCase__ , lowerCAmelCase__ ).shape
else:
UpperCAmelCase__ : Union[str, Any] = 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":
UpperCAmelCase__ : int = value
elif weight_type == "weight_g":
UpperCAmelCase__ : Dict = value
elif weight_type == "weight_v":
UpperCAmelCase__ : List[str] = value
elif weight_type == "bias":
UpperCAmelCase__ : Tuple = value
else:
UpperCAmelCase__ : Tuple = value
logger.info(F"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.""" )
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ ) -> Dict:
UpperCAmelCase__ : Optional[int] = []
UpperCAmelCase__ : Dict = fairseq_model.state_dict()
UpperCAmelCase__ : Union[str, Any] = hf_model.unispeech_sat.feature_extractor
for name, value in fairseq_dict.items():
UpperCAmelCase__ : Any = False
if "conv_layers" in name:
load_conv_layer(
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , hf_model.config.feat_extract_norm == '''group''' , )
UpperCAmelCase__ : str = True
else:
for key, mapped_key in MAPPING.items():
UpperCAmelCase__ : List[str] = '''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
UpperCAmelCase__ : Optional[int] = True
if "*" in mapped_key:
UpperCAmelCase__ : str = name.split(lowerCAmelCase__ )[0].split('''.''' )[-2]
UpperCAmelCase__ : Optional[int] = mapped_key.replace('''*''' , lowerCAmelCase__ )
if "weight_g" in name:
UpperCAmelCase__ : List[str] = '''weight_g'''
elif "weight_v" in name:
UpperCAmelCase__ : Dict = '''weight_v'''
elif "bias" in name:
UpperCAmelCase__ : Optional[int] = '''bias'''
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
UpperCAmelCase__ : Tuple = '''weight'''
else:
UpperCAmelCase__ : Optional[Any] = None
set_recursively(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
continue
if not is_used:
unused_weights.append(lowerCAmelCase__ )
logger.warning(F"""Unused weights: {unused_weights}""" )
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> List[str]:
UpperCAmelCase__ : Tuple = full_name.split('''conv_layers.''' )[-1]
UpperCAmelCase__ : Optional[Any] = name.split('''.''' )
UpperCAmelCase__ : Union[str, Any] = int(items[0] )
UpperCAmelCase__ : Tuple = 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.""" )
UpperCAmelCase__ : str = 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.""" )
UpperCAmelCase__ : Optional[int] = 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.""" )
UpperCAmelCase__ : List[str] = 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.""" )
UpperCAmelCase__ : Optional[Any] = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(lowerCAmelCase__ )
@torch.no_grad()
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__=None , lowerCAmelCase__=None , lowerCAmelCase__=True ) -> Any:
if config_path is not None:
UpperCAmelCase__ : Any = UniSpeechSatConfig.from_pretrained(lowerCAmelCase__ )
else:
UpperCAmelCase__ : int = UniSpeechSatConfig()
UpperCAmelCase__ : Tuple = ''''''
if is_finetuned:
UpperCAmelCase__ : Optional[int] = UniSpeechSatForCTC(lowerCAmelCase__ )
else:
UpperCAmelCase__ : List[Any] = UniSpeechSatForPreTraining(lowerCAmelCase__ )
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ : str = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] )} )
UpperCAmelCase__ : Union[str, Any] = model[0].eval()
recursively_load_weights(lowerCAmelCase__ , lowerCAmelCase__ )
hf_wavavec.save_pretrained(lowerCAmelCase__ )
if __name__ == "__main__":
UpperCamelCase__ = argparse.ArgumentParser()
parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to 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'''
)
UpperCamelCase__ = 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
)
| 299 | 0 |
"""simple docstring"""
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class __UpperCamelCase ( a__ ):
lowerCamelCase : List[str] =ComputeEnvironment.AMAZON_SAGEMAKER
lowerCamelCase : str =True
lowerCamelCase : Union[str, Any] ="""ml.p3.2xlarge"""
lowerCamelCase : str ="""accelerate_sagemaker_execution_role"""
lowerCamelCase : int ="""hf-sm"""
lowerCamelCase : int ="""us-east-1"""
lowerCamelCase : Tuple =1
lowerCamelCase : Any ="""accelerate-sagemaker-1"""
lowerCamelCase : str ="""1.6"""
lowerCamelCase : Tuple ="""4.4"""
lowerCamelCase : Optional[int] ="""train.py"""
lowerCamelCase : Optional[Any] =[
"""--model_name_or_path""",
"""bert""",
"""--do_train""",
"""False""",
"""--epochs""",
"""3""",
"""--learning_rate""",
"""5e-5""",
"""--max_steps""",
"""50.5""",
]
lowerCamelCase : Union[str, Any] =[
"""--model_name_or_path""",
"""bert""",
"""--do_train""",
"""--do_test""",
"""False""",
"""--do_predict""",
"""--epochs""",
"""3""",
"""--learning_rate""",
"""5e-5""",
"""--max_steps""",
"""50.5""",
]
class __UpperCamelCase ( unittest.TestCase ):
def __a ( self ) -> List[str]:
# If no defaults are changed, `to_kwargs` returns an empty dict.
a : str = _convert_nargs_to_dict(MockLaunchConfig.success_training_script_args )
assert isinstance(converted_args["model_name_or_path"] , lowerCAmelCase__ )
assert isinstance(converted_args["do_train"] , lowerCAmelCase__ )
assert isinstance(converted_args["epochs"] , lowerCAmelCase__ )
assert isinstance(converted_args["learning_rate"] , lowerCAmelCase__ )
assert isinstance(converted_args["max_steps"] , lowerCAmelCase__ )
with pytest.raises(lowerCAmelCase__ ):
_convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args )
| 105 |
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
A : Dict = logging.get_logger(__name__)
def __lowerCamelCase ( __a :int=None , __a :Optional[Any]=None ) -> int:
"""simple docstring"""
return field(default_factory=lambda: default , metadata=__a )
@dataclass
class A :
'''simple docstring'''
__lowerCamelCase : List[str] = list_field(
default=[] , metadata={
'''help''': (
'''Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version'''
''' of all available models'''
)
} , )
__lowerCamelCase : List[int] = list_field(
default=[8] , metadata={'''help''': '''List of batch sizes for which memory and time performance will be evaluated'''} )
__lowerCamelCase : List[int] = list_field(
default=[8, 32, 128, 512] , metadata={'''help''': '''List of sequence lengths for which memory and time performance will be evaluated'''} , )
__lowerCamelCase : bool = field(
default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''Whether to benchmark inference of model. Inference can be disabled via --no-inference.'''} , )
__lowerCamelCase : bool = field(
default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''Whether to run on available cuda devices. Cuda can be disabled via --no-cuda.'''} , )
__lowerCamelCase : bool = field(
default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''Whether to run on available tpu devices. TPU can be disabled via --no-tpu.'''} )
__lowerCamelCase : bool = field(default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''Use FP16 to accelerate inference.'''} )
__lowerCamelCase : bool = field(default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''Benchmark training of model'''} )
__lowerCamelCase : bool = field(default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''Verbose memory tracing'''} )
__lowerCamelCase : bool = field(
default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''Whether to perform speed measurements. Speed measurements can be disabled via --no-speed.'''} , )
__lowerCamelCase : bool = field(
default=SCREAMING_SNAKE_CASE , metadata={
'''help''': '''Whether to perform memory measurements. Memory measurements can be disabled via --no-memory'''
} , )
__lowerCamelCase : bool = field(default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''Trace memory line by line'''} )
__lowerCamelCase : bool = field(default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''Save result to a CSV file'''} )
__lowerCamelCase : bool = field(default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''Save all print statements in a log file'''} )
__lowerCamelCase : bool = field(default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''Whether to print environment information'''} )
__lowerCamelCase : bool = field(
default=SCREAMING_SNAKE_CASE , metadata={
'''help''': (
'''Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use'''
''' multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled'''
''' for debugging / testing and on TPU.'''
)
} , )
__lowerCamelCase : str = field(
default=F'''inference_time_{round(time() )}.csv''' , metadata={'''help''': '''CSV filename used if saving time results to csv.'''} , )
__lowerCamelCase : str = field(
default=F'''inference_memory_{round(time() )}.csv''' , metadata={'''help''': '''CSV filename used if saving memory results to csv.'''} , )
__lowerCamelCase : str = field(
default=F'''train_time_{round(time() )}.csv''' , metadata={'''help''': '''CSV filename used if saving time results to csv for training.'''} , )
__lowerCamelCase : str = field(
default=F'''train_memory_{round(time() )}.csv''' , metadata={'''help''': '''CSV filename used if saving memory results to csv for training.'''} , )
__lowerCamelCase : str = field(
default=F'''env_info_{round(time() )}.csv''' , metadata={'''help''': '''CSV filename used if saving environment information.'''} , )
__lowerCamelCase : str = field(
default=F'''log_{round(time() )}.csv''' , metadata={'''help''': '''Log filename used if print statements are saved in log.'''} , )
__lowerCamelCase : int = field(default=3 , metadata={'''help''': '''Times an experiment will be run.'''} )
__lowerCamelCase : bool = field(
default=SCREAMING_SNAKE_CASE , metadata={
'''help''': (
'''Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain'''
''' model weights.'''
)
} , )
def a_ ( self : Dict ) -> Union[str, Any]:
"""simple docstring"""
warnings.warn(
f'The class {self.__class__} is deprecated. Hugging Face Benchmarking utils'
""" are deprecated in general and it is advised to use external Benchmarking libraries """
""" to benchmark Transformer models.""" , __lowerCAmelCase , )
def a_ ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
return json.dumps(dataclasses.asdict(self ) , indent=2 )
@property
def a_ ( self : Tuple ) -> List[str]:
"""simple docstring"""
if len(self.models ) <= 0:
raise ValueError(
"""Please make sure you provide at least one model name / model identifier, *e.g.* `--models"""
""" bert-base-cased` or `args.models = ['bert-base-cased'].""" )
return self.models
@property
def a_ ( self : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
if not self.multi_process:
return False
elif self.is_tpu:
logger.info("""Multiprocessing is currently not possible on TPU.""" )
return False
else:
return True
| 274 | 0 |
"""simple docstring"""
import baseaa
def snake_case__ ( __lowerCamelCase : str ):
"""simple docstring"""
return baseaa.baaencode(string.encode('''utf-8''' ) )
def snake_case__ ( __lowerCamelCase : bytes ):
"""simple docstring"""
return baseaa.baadecode(__lowerCamelCase ).decode('''utf-8''' )
if __name__ == "__main__":
_lowercase : str = "Hello World!"
_lowercase : Any = baseaa_encode(test)
print(encoded)
_lowercase : Dict = baseaa_decode(encoded)
print(decoded)
| 355 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
_lowercase : Tuple = False
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
pass
@slow
@require_torch_gpu
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def snake_case ( self : List[Any] )-> Dict:
lowerCamelCase__ : str =VersatileDiffusionImageVariationPipeline.from_pretrained('''shi-labs/versatile-diffusion''' )
pipe.to(lowerCamelCase )
pipe.set_progress_bar_config(disable=lowerCamelCase )
lowerCamelCase__ : int =load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg''' )
lowerCamelCase__ : Dict =torch.manual_seed(0 )
lowerCamelCase__ : str =pipe(
image=lowerCamelCase, generator=lowerCamelCase, guidance_scale=7.5, num_inference_steps=50, output_type='''numpy''', ).images
lowerCamelCase__ : Dict =image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
lowerCamelCase__ : List[Any] =np.array([0.0_441, 0.0_469, 0.0_507, 0.0_575, 0.0_632, 0.0_650, 0.0_865, 0.0_909, 0.0_945] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 272 | 0 |
import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def _a ( *SCREAMING_SNAKE_CASE : str ) -> Dict:
"""simple docstring"""
if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
__lowerCAmelCase: Dict = list(SCREAMING_SNAKE_CASE )
for i in range(len(SCREAMING_SNAKE_CASE ) ):
__lowerCAmelCase: List[Any] = 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 _a ( SCREAMING_SNAKE_CASE : Exception ) -> bool:
"""simple docstring"""
__lowerCAmelCase: Tuple = [
'CUDA out of memory.', # CUDA OOM
'cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.', # CUDNN SNAFU
'DefaultCPUAllocator: can\'t allocate memory', # CPU OOM
]
if isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) and len(exception.args ) == 1:
return any(err in exception.args[0] for err in _statements )
return False
def _a ( SCREAMING_SNAKE_CASE : callable = None , SCREAMING_SNAKE_CASE : int = 1_28 ) -> Union[str, Any]:
"""simple docstring"""
if function is None:
return functools.partial(SCREAMING_SNAKE_CASE , starting_batch_size=SCREAMING_SNAKE_CASE )
__lowerCAmelCase: Dict = starting_batch_size
def decorator(*SCREAMING_SNAKE_CASE : str , **SCREAMING_SNAKE_CASE : int ):
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: str = list(inspect.signature(SCREAMING_SNAKE_CASE ).parameters.keys() )
# Guard against user error
if len(SCREAMING_SNAKE_CASE ) < (len(SCREAMING_SNAKE_CASE ) + 1):
__lowerCAmelCase: Dict = ', '.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(SCREAMING_SNAKE_CASE , *SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
except Exception as e:
if should_reduce_batch_size(SCREAMING_SNAKE_CASE ):
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
| 322 |
import re
import time
from typing import Optional
import IPython.display as disp
from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length
def _a ( SCREAMING_SNAKE_CASE : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
__lowerCAmelCase: Union[str, Any] = int(SCREAMING_SNAKE_CASE )
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase: List[str] = t // 36_00, (t // 60) % 60, t % 60
return f'''{h}:{m:02d}:{s:02d}''' if h != 0 else f'''{m:02d}:{s:02d}'''
def _a ( SCREAMING_SNAKE_CASE : Dict , SCREAMING_SNAKE_CASE : Optional[Any] , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : Dict , SCREAMING_SNAKE_CASE : str=3_00 ) -> int:
"""simple docstring"""
return f'''
<div>
{prefix}
<progress value=\'{value}\' max=\'{total}\' style=\'width:{width}px; height:20px; vertical-align: middle;\'></progress>
{label}
</div>
'''
def _a ( SCREAMING_SNAKE_CASE : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
__lowerCAmelCase: List[str] = '<table border="1" class="dataframe">\n'
html_code += """ <thead>\n <tr style="text-align: left;">\n"""
for i in items[0]:
html_code += f''' <th>{i}</th>\n'''
html_code += " </tr>\n </thead>\n <tbody>\n"
for line in items[1:]:
html_code += " <tr>\n"
for elt in line:
__lowerCAmelCase: List[Any] = f'''{elt:.6f}''' if isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) else str(SCREAMING_SNAKE_CASE )
html_code += f''' <td>{elt}</td>\n'''
html_code += " </tr>\n"
html_code += " </tbody>\n</table><p>"
return html_code
class A_ :
_lowercase : str = 5
_lowercase : str = 0.2
def __init__( self : Union[str, Any] , UpperCAmelCase : int , UpperCAmelCase : Optional[str] = None , UpperCAmelCase : bool = True , UpperCAmelCase : Optional["NotebookTrainingTracker"] = None , UpperCAmelCase : int = 3_0_0 , ) -> List[Any]:
__lowerCAmelCase: List[str] = total
__lowerCAmelCase: Optional[int] = '' if prefix is None else prefix
__lowerCAmelCase: int = leave
__lowerCAmelCase: List[str] = parent
__lowerCAmelCase: Optional[Any] = width
__lowerCAmelCase: List[str] = None
__lowerCAmelCase: Dict = None
__lowerCAmelCase: List[str] = None
def UpperCAmelCase ( self : Union[str, Any] , UpperCAmelCase : int , UpperCAmelCase : bool = False , UpperCAmelCase : str = None ) -> Optional[int]:
__lowerCAmelCase: int = value
if comment is not None:
__lowerCAmelCase: Any = comment
if self.last_value is None:
__lowerCAmelCase: List[Any] = time.time()
__lowerCAmelCase: Any = value
__lowerCAmelCase: List[str] = None
__lowerCAmelCase: Dict = self.warmup
__lowerCAmelCase: List[str] = 1
self.update_bar(UpperCAmelCase )
elif value <= self.last_value and not force_update:
return
elif force_update or self.first_calls > 0 or value >= min(self.last_value + self.wait_for , self.total ):
if self.first_calls > 0:
self.first_calls -= 1
__lowerCAmelCase: Union[str, Any] = time.time()
__lowerCAmelCase: str = current_time - self.start_time
# We could have value = self.start_value if the update is called twixe with the same start value.
if value > self.start_value:
__lowerCAmelCase: Dict = self.elapsed_time / (value - self.start_value)
else:
__lowerCAmelCase: int = None
if value >= self.total:
__lowerCAmelCase: Any = self.total
__lowerCAmelCase: str = None
if not self.leave:
self.close()
elif self.average_time_per_item is not None:
__lowerCAmelCase: List[str] = self.average_time_per_item * (self.total - value)
self.update_bar(UpperCAmelCase )
__lowerCAmelCase: Tuple = value
__lowerCAmelCase: int = current_time
if self.average_time_per_item is None:
__lowerCAmelCase: Optional[int] = 1
else:
__lowerCAmelCase: Optional[Any] = max(int(self.update_every / self.average_time_per_item ) , 1 )
def UpperCAmelCase ( self : int , UpperCAmelCase : Any , UpperCAmelCase : List[Any]=None ) -> Union[str, Any]:
__lowerCAmelCase: int = ' ' * (len(str(self.total ) ) - len(str(UpperCAmelCase ) )) + str(UpperCAmelCase )
if self.elapsed_time is None:
__lowerCAmelCase: Dict = F'''[{spaced_value}/{self.total} : < :'''
elif self.predicted_remaining is None:
__lowerCAmelCase: str = F'''[{spaced_value}/{self.total} {format_time(self.elapsed_time )}'''
else:
__lowerCAmelCase: Any = (
F'''[{spaced_value}/{self.total} {format_time(self.elapsed_time )} <'''
F''' {format_time(self.predicted_remaining )}'''
)
self.label += F''', {1/self.average_time_per_item:.2f} it/s'''
self.label += "]" if self.comment is None or len(self.comment ) == 0 else F''', {self.comment}]'''
self.display()
def UpperCAmelCase ( self : Any ) -> Optional[Any]:
__lowerCAmelCase: Any = html_progress_bar(self.value , self.total , self.prefix , self.label , self.width )
if self.parent is not None:
# If this is a child bar, the parent will take care of the display.
self.parent.display()
return
if self.output is None:
__lowerCAmelCase: Tuple = disp.display(disp.HTML(self.html_code ) , display_id=UpperCAmelCase )
else:
self.output.update(disp.HTML(self.html_code ) )
def UpperCAmelCase ( self : str ) -> Optional[Any]:
if self.parent is None and self.output is not None:
self.output.update(disp.HTML('' ) )
class A_ ( snake_case__ ):
def __init__( self : Tuple , UpperCAmelCase : Dict , UpperCAmelCase : List[Any]=None ) -> Any:
super().__init__(UpperCAmelCase )
__lowerCAmelCase: Tuple = None if column_names is None else [column_names]
__lowerCAmelCase: Union[str, Any] = None
def UpperCAmelCase ( self : Union[str, Any] ) -> Any:
__lowerCAmelCase: str = html_progress_bar(self.value , self.total , self.prefix , self.label , self.width )
if self.inner_table is not None:
self.html_code += text_to_html_table(self.inner_table )
if self.child_bar is not None:
self.html_code += self.child_bar.html_code
if self.output is None:
__lowerCAmelCase: Optional[Any] = disp.display(disp.HTML(self.html_code ) , display_id=UpperCAmelCase )
else:
self.output.update(disp.HTML(self.html_code ) )
def UpperCAmelCase ( self : Tuple , UpperCAmelCase : List[Any] ) -> Dict:
if self.inner_table is None:
__lowerCAmelCase: List[str] = [list(values.keys() ), list(values.values() )]
else:
__lowerCAmelCase: Any = self.inner_table[0]
if len(self.inner_table ) == 1:
# We give a chance to update the column names at the first iteration
for key in values.keys():
if key not in columns:
columns.append(UpperCAmelCase )
__lowerCAmelCase: List[Any] = columns
self.inner_table.append([values[c] for c in columns] )
def UpperCAmelCase ( self : Dict , UpperCAmelCase : int , UpperCAmelCase : List[Any]=None , UpperCAmelCase : List[str]=3_0_0 ) -> List[Any]:
__lowerCAmelCase: Union[str, Any] = NotebookProgressBar(UpperCAmelCase , prefix=UpperCAmelCase , parent=self , width=UpperCAmelCase )
return self.child_bar
def UpperCAmelCase ( self : Optional[int] ) -> Optional[int]:
__lowerCAmelCase: Tuple = None
self.display()
class A_ ( snake_case__ ):
def __init__( self : Any ) -> List[str]:
__lowerCAmelCase: int = None
__lowerCAmelCase: Optional[int] = None
__lowerCAmelCase: str = False
def UpperCAmelCase ( self : Tuple , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Optional[int] , UpperCAmelCase : Any , **UpperCAmelCase : Tuple ) -> str:
__lowerCAmelCase: Tuple = 'Epoch' if args.evaluation_strategy == IntervalStrategy.EPOCH else 'Step'
__lowerCAmelCase: Optional[int] = 0
__lowerCAmelCase: Any = 0
__lowerCAmelCase: Tuple = [self.first_column] + ['Training Loss']
if args.evaluation_strategy != IntervalStrategy.NO:
column_names.append('Validation Loss' )
__lowerCAmelCase: List[Any] = NotebookTrainingTracker(state.max_steps , UpperCAmelCase )
def UpperCAmelCase ( self : Optional[int] , UpperCAmelCase : Tuple , UpperCAmelCase : Any , UpperCAmelCase : Optional[Any] , **UpperCAmelCase : Union[str, Any] ) -> Any:
__lowerCAmelCase: Union[str, Any] = int(state.epoch ) if int(state.epoch ) == state.epoch else F'''{state.epoch:.2f}'''
self.training_tracker.update(
state.global_step + 1 , comment=F'''Epoch {epoch}/{state.num_train_epochs}''' , force_update=self._force_next_update , )
__lowerCAmelCase: Any = False
def UpperCAmelCase ( self : List[Any] , UpperCAmelCase : str , UpperCAmelCase : Optional[int] , UpperCAmelCase : Optional[Any] , UpperCAmelCase : int=None , **UpperCAmelCase : Dict ) -> List[Any]:
if not has_length(UpperCAmelCase ):
return
if self.prediction_bar is None:
if self.training_tracker is not None:
__lowerCAmelCase: int = self.training_tracker.add_child(len(UpperCAmelCase ) )
else:
__lowerCAmelCase: List[str] = NotebookProgressBar(len(UpperCAmelCase ) )
self.prediction_bar.update(1 )
else:
self.prediction_bar.update(self.prediction_bar.value + 1 )
def UpperCAmelCase ( self : Union[str, Any] , UpperCAmelCase : Dict , UpperCAmelCase : Dict , UpperCAmelCase : Optional[Any] , **UpperCAmelCase : int ) -> Union[str, Any]:
if self.prediction_bar is not None:
self.prediction_bar.close()
__lowerCAmelCase: Any = None
def UpperCAmelCase ( self : str , UpperCAmelCase : Tuple , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : int=None , **UpperCAmelCase : Optional[Any] ) -> Optional[Any]:
# Only for when there is no evaluation
if args.evaluation_strategy == IntervalStrategy.NO and "loss" in logs:
__lowerCAmelCase: Union[str, Any] = {'Training Loss': logs['loss']}
# First column is necessarily Step sine we're not in epoch eval strategy
__lowerCAmelCase: Dict = state.global_step
self.training_tracker.write_line(UpperCAmelCase )
def UpperCAmelCase ( self : int , UpperCAmelCase : List[Any] , UpperCAmelCase : List[str] , UpperCAmelCase : Optional[int] , UpperCAmelCase : Tuple=None , **UpperCAmelCase : int ) -> List[str]:
if self.training_tracker is not None:
__lowerCAmelCase: Dict = {'Training Loss': 'No log', 'Validation Loss': 'No log'}
for log in reversed(state.log_history ):
if "loss" in log:
__lowerCAmelCase: List[str] = log['loss']
break
if self.first_column == "Epoch":
__lowerCAmelCase: int = int(state.epoch )
else:
__lowerCAmelCase: Tuple = state.global_step
__lowerCAmelCase: Optional[int] = 'eval'
for k in metrics:
if k.endswith('_loss' ):
__lowerCAmelCase: Union[str, Any] = re.sub(R'\_loss$' , '' , UpperCAmelCase )
__lowerCAmelCase: Optional[Any] = metrics.pop('total_flos' , UpperCAmelCase )
__lowerCAmelCase: str = metrics.pop('epoch' , UpperCAmelCase )
__lowerCAmelCase: int = metrics.pop(F'''{metric_key_prefix}_runtime''' , UpperCAmelCase )
__lowerCAmelCase: List[Any] = metrics.pop(F'''{metric_key_prefix}_samples_per_second''' , UpperCAmelCase )
__lowerCAmelCase: List[str] = metrics.pop(F'''{metric_key_prefix}_steps_per_second''' , UpperCAmelCase )
__lowerCAmelCase: Tuple = metrics.pop(F'''{metric_key_prefix}_jit_compilation_time''' , UpperCAmelCase )
for k, v in metrics.items():
if k == F'''{metric_key_prefix}_loss''':
__lowerCAmelCase: Tuple = v
else:
__lowerCAmelCase: int = k.split('_' )
__lowerCAmelCase: List[Any] = ' '.join([part.capitalize() for part in splits[1:]] )
__lowerCAmelCase: List[Any] = v
self.training_tracker.write_line(UpperCAmelCase )
self.training_tracker.remove_child()
__lowerCAmelCase: List[str] = None
# Evaluation takes a long time so we should force the next update.
__lowerCAmelCase: str = True
def UpperCAmelCase ( self : int , UpperCAmelCase : int , UpperCAmelCase : Tuple , UpperCAmelCase : Optional[int] , **UpperCAmelCase : Optional[int] ) -> Optional[int]:
self.training_tracker.update(
state.global_step , comment=F'''Epoch {int(state.epoch )}/{state.num_train_epochs}''' , force_update=UpperCAmelCase )
__lowerCAmelCase: Union[str, Any] = None
| 322 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A : Any = {
'configuration_jukebox': [
'JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP',
'JukeboxConfig',
'JukeboxPriorConfig',
'JukeboxVQVAEConfig',
],
'tokenization_jukebox': ['JukeboxTokenizer'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Optional[int] = [
'JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST',
'JukeboxModel',
'JukeboxPreTrainedModel',
'JukeboxVQVAE',
'JukeboxPrior',
]
if TYPE_CHECKING:
from .configuration_jukebox import (
JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP,
JukeboxConfig,
JukeboxPriorConfig,
JukeboxVQVAEConfig,
)
from .tokenization_jukebox import JukeboxTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_jukebox import (
JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST,
JukeboxModel,
JukeboxPreTrainedModel,
JukeboxPrior,
JukeboxVQVAE,
)
else:
import sys
A : List[str] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 359 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A : Optional[Any] = logging.get_logger(__name__)
A : 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 A ( UpperCAmelCase__ ):
'''simple docstring'''
A__ = '''convbert'''
def __init__(self : str , _UpperCAmelCase : Union[str, Any]=3_0522 , _UpperCAmelCase : Dict=768 , _UpperCAmelCase : Optional[int]=12 , _UpperCAmelCase : List[str]=12 , _UpperCAmelCase : str=3072 , _UpperCAmelCase : Tuple="gelu" , _UpperCAmelCase : Optional[int]=0.1 , _UpperCAmelCase : int=0.1 , _UpperCAmelCase : str=512 , _UpperCAmelCase : Dict=2 , _UpperCAmelCase : int=0.02 , _UpperCAmelCase : Tuple=1E-1_2 , _UpperCAmelCase : Dict=1 , _UpperCAmelCase : Optional[Any]=0 , _UpperCAmelCase : List[str]=2 , _UpperCAmelCase : str=768 , _UpperCAmelCase : List[Any]=2 , _UpperCAmelCase : Optional[Any]=9 , _UpperCAmelCase : List[Any]=1 , _UpperCAmelCase : Union[str, Any]=None , **_UpperCAmelCase : List[Any] , ) -> List[str]:
"""simple docstring"""
super().__init__(
pad_token_id=_UpperCAmelCase , bos_token_id=_UpperCAmelCase , eos_token_id=_UpperCAmelCase , **_UpperCAmelCase , )
lowercase__ = vocab_size
lowercase__ = hidden_size
lowercase__ = num_hidden_layers
lowercase__ = num_attention_heads
lowercase__ = intermediate_size
lowercase__ = hidden_act
lowercase__ = hidden_dropout_prob
lowercase__ = attention_probs_dropout_prob
lowercase__ = max_position_embeddings
lowercase__ = type_vocab_size
lowercase__ = initializer_range
lowercase__ = layer_norm_eps
lowercase__ = embedding_size
lowercase__ = head_ratio
lowercase__ = conv_kernel_size
lowercase__ = num_groups
lowercase__ = classifier_dropout
class A ( UpperCAmelCase__ ):
'''simple docstring'''
@property
def lowerCamelCase__ (self : Any ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
if self.task == "multiple-choice":
lowercase__ = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
lowercase__ = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
("""token_type_ids""", dynamic_axis),
] )
| 146 | 0 |
"""simple docstring"""
def _lowercase ( __snake_case ,__snake_case ) -> int:
return int((input_a, input_a).count(1 ) != 0 )
def _lowercase ( ) -> None:
assert or_gate(0 ,0 ) == 0
assert or_gate(0 ,1 ) == 1
assert or_gate(1 ,0 ) == 1
assert or_gate(1 ,1 ) == 1
if __name__ == "__main__":
print(or_gate(0, 1))
print(or_gate(1, 0))
print(or_gate(0, 0))
print(or_gate(1, 1)) | 269 |
"""simple docstring"""
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
from transformers.models.ta.modeling_flax_ta import shift_tokens_right
@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class A__ ( unittest.TestCase ):
'''simple docstring'''
@slow
def _SCREAMING_SNAKE_CASE ( self: Union[str, Any]) -> str:
"""simple docstring"""
__lowerCAmelCase : Optional[Any] = FlaxMTaForConditionalGeneration.from_pretrained("google/mt5-small")
__lowerCAmelCase : Optional[Any] = AutoTokenizer.from_pretrained("google/mt5-small")
__lowerCAmelCase : Tuple = tokenizer("Hello there" , return_tensors="np").input_ids
__lowerCAmelCase : Dict = tokenizer("Hi I am" , return_tensors="np").input_ids
__lowerCAmelCase : str = shift_tokens_right(_SCREAMING_SNAKE_CASE , model.config.pad_token_id , model.config.decoder_start_token_id)
__lowerCAmelCase : Optional[int] = model(_SCREAMING_SNAKE_CASE , decoder_input_ids=_SCREAMING_SNAKE_CASE).logits
__lowerCAmelCase : int = optax.softmax_cross_entropy(_SCREAMING_SNAKE_CASE , onehot(_SCREAMING_SNAKE_CASE , logits.shape[-1])).mean()
__lowerCAmelCase : List[str] = -(labels.shape[-1] * loss.item())
__lowerCAmelCase : str = -84.9127
self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4) | 269 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__SCREAMING_SNAKE_CASE : List[Any] = {
'''configuration_layoutlmv2''': ['''LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LayoutLMv2Config'''],
'''processing_layoutlmv2''': ['''LayoutLMv2Processor'''],
'''tokenization_layoutlmv2''': ['''LayoutLMv2Tokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE : Tuple = ['''LayoutLMv2TokenizerFast''']
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE : Any = ['''LayoutLMv2FeatureExtractor''']
__SCREAMING_SNAKE_CASE : Optional[Any] = ['''LayoutLMv2ImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE : str = [
'''LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''LayoutLMv2ForQuestionAnswering''',
'''LayoutLMv2ForSequenceClassification''',
'''LayoutLMv2ForTokenClassification''',
'''LayoutLMv2Layer''',
'''LayoutLMv2Model''',
'''LayoutLMv2PreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
from .processing_layoutlmva import LayoutLMvaProcessor
from .tokenization_layoutlmva import LayoutLMvaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_layoutlmva import (
LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
LayoutLMvaForQuestionAnswering,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaLayer,
LayoutLMvaModel,
LayoutLMvaPreTrainedModel,
)
else:
import sys
__SCREAMING_SNAKE_CASE : Dict = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 370 |
"""simple docstring"""
import os
import sys
__SCREAMING_SNAKE_CASE : Dict = os.path.join(os.path.dirname(__file__), '''src''')
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
AutoModelForSequenceClassification,
AutoTokenizer,
add_start_docstrings,
)
__SCREAMING_SNAKE_CASE : List[Any] = [
'''torch''',
'''numpy''',
'''tokenizers''',
'''filelock''',
'''requests''',
'''tqdm''',
'''regex''',
'''sentencepiece''',
'''sacremoses''',
'''importlib_metadata''',
'''huggingface_hub''',
]
@add_start_docstrings(AutoConfig.__doc__ )
def lowerCAmelCase_( *lowercase_ : Any , **lowercase_ : Optional[Any] ) -> Optional[Any]:
return AutoConfig.from_pretrained(*lowercase_ , **lowercase_ )
@add_start_docstrings(AutoTokenizer.__doc__ )
def lowerCAmelCase_( *lowercase_ : List[Any] , **lowercase_ : List[Any] ) -> Tuple:
return AutoTokenizer.from_pretrained(*lowercase_ , **lowercase_ )
@add_start_docstrings(AutoModel.__doc__ )
def lowerCAmelCase_( *lowercase_ : Optional[Any] , **lowercase_ : Optional[Any] ) -> int:
return AutoModel.from_pretrained(*lowercase_ , **lowercase_ )
@add_start_docstrings(AutoModelForCausalLM.__doc__ )
def lowerCAmelCase_( *lowercase_ : Dict , **lowercase_ : List[Any] ) -> int:
return AutoModelForCausalLM.from_pretrained(*lowercase_ , **lowercase_ )
@add_start_docstrings(AutoModelForMaskedLM.__doc__ )
def lowerCAmelCase_( *lowercase_ : str , **lowercase_ : List[Any] ) -> Any:
return AutoModelForMaskedLM.from_pretrained(*lowercase_ , **lowercase_ )
@add_start_docstrings(AutoModelForSequenceClassification.__doc__ )
def lowerCAmelCase_( *lowercase_ : Tuple , **lowercase_ : List[Any] ) -> Dict:
return AutoModelForSequenceClassification.from_pretrained(*lowercase_ , **lowercase_ )
@add_start_docstrings(AutoModelForQuestionAnswering.__doc__ )
def lowerCAmelCase_( *lowercase_ : List[str] , **lowercase_ : List[str] ) -> str:
return AutoModelForQuestionAnswering.from_pretrained(*lowercase_ , **lowercase_ )
| 73 | 0 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import (
BaseOutput,
OptionalDependencyNotAvailable,
is_flax_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_onnx_available,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
@dataclass
class __magic_name__ ( _UpperCamelCase ):
"""simple docstring"""
__UpperCamelCase = 42
__UpperCamelCase = 42
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .pipeline_cycle_diffusion import CycleDiffusionPipeline
from .pipeline_stable_diffusion import StableDiffusionPipeline
from .pipeline_stable_diffusion_attend_and_excite import StableDiffusionAttendAndExcitePipeline
from .pipeline_stable_diffusion_imgaimg import StableDiffusionImgaImgPipeline
from .pipeline_stable_diffusion_inpaint import StableDiffusionInpaintPipeline
from .pipeline_stable_diffusion_inpaint_legacy import StableDiffusionInpaintPipelineLegacy
from .pipeline_stable_diffusion_instruct_pixapix import StableDiffusionInstructPixaPixPipeline
from .pipeline_stable_diffusion_latent_upscale import StableDiffusionLatentUpscalePipeline
from .pipeline_stable_diffusion_ldmad import StableDiffusionLDMaDPipeline
from .pipeline_stable_diffusion_model_editing import StableDiffusionModelEditingPipeline
from .pipeline_stable_diffusion_panorama import StableDiffusionPanoramaPipeline
from .pipeline_stable_diffusion_paradigms import StableDiffusionParadigmsPipeline
from .pipeline_stable_diffusion_sag import StableDiffusionSAGPipeline
from .pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
from .pipeline_stable_unclip import StableUnCLIPPipeline
from .pipeline_stable_unclip_imgaimg import StableUnCLIPImgaImgPipeline
from .safety_checker import StableDiffusionSafetyChecker
from .stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
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 StableDiffusionImageVariationPipeline
else:
from .pipeline_stable_diffusion_image_variation import StableDiffusionImageVariationPipeline
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.26.0''')):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import (
StableDiffusionDepthaImgPipeline,
StableDiffusionDiffEditPipeline,
StableDiffusionPixaPixZeroPipeline,
)
else:
from .pipeline_stable_diffusion_depthaimg import StableDiffusionDepthaImgPipeline
from .pipeline_stable_diffusion_diffedit import StableDiffusionDiffEditPipeline
from .pipeline_stable_diffusion_pixapix_zero import StableDiffusionPixaPixZeroPipeline
try:
if not (
is_torch_available()
and is_transformers_available()
and is_k_diffusion_available()
and is_k_diffusion_version('''>=''', '''0.0.12''')
):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_and_k_diffusion_objects import * # noqa F403
else:
from .pipeline_stable_diffusion_k_diffusion import StableDiffusionKDiffusionPipeline
try:
if not (is_transformers_available() and is_onnx_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_onnx_objects import * # noqa F403
else:
from .pipeline_onnx_stable_diffusion import OnnxStableDiffusionPipeline, StableDiffusionOnnxPipeline
from .pipeline_onnx_stable_diffusion_imgaimg import OnnxStableDiffusionImgaImgPipeline
from .pipeline_onnx_stable_diffusion_inpaint import OnnxStableDiffusionInpaintPipeline
from .pipeline_onnx_stable_diffusion_inpaint_legacy import OnnxStableDiffusionInpaintPipelineLegacy
from .pipeline_onnx_stable_diffusion_upscale import OnnxStableDiffusionUpscalePipeline
if is_transformers_available() and is_flax_available():
import flax
@flax.struct.dataclass
class __magic_name__ ( _UpperCamelCase ):
"""simple docstring"""
__UpperCamelCase = 42
__UpperCamelCase = 42
from ...schedulers.scheduling_pndm_flax import PNDMSchedulerState
from .pipeline_flax_stable_diffusion import FlaxStableDiffusionPipeline
from .pipeline_flax_stable_diffusion_imgaimg import FlaxStableDiffusionImgaImgPipeline
from .pipeline_flax_stable_diffusion_inpaint import FlaxStableDiffusionInpaintPipeline
from .safety_checker_flax import FlaxStableDiffusionSafetyChecker
| 300 |
"""simple docstring"""
A = 9.80665
def __A ( a_ :float , a_ :float , a_ :float = g) -> float:
if fluid_density <= 0:
raise ValueError('''Impossible fluid density''')
if volume < 0:
raise ValueError('''Impossible Object volume''')
if gravity <= 0:
raise ValueError('''Impossible Gravity''')
return fluid_density * gravity * volume
if __name__ == "__main__":
import doctest
# run doctest
doctest.testmod() | 160 | 0 |
from io import BytesIO
from typing import List, Union
import requests
from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_decord_available():
import numpy as np
from decord import VideoReader
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING
SCREAMING_SNAKE_CASE_:str = logging.get_logger(__name__)
@add_end_docstrings(SCREAMING_SNAKE_CASE__ )
class SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
def __init__( self, *lowerCamelCase__, **lowerCamelCase__ ):
super().__init__(*lowerCamelCase__, **lowerCamelCase__ )
requires_backends(self, """decord""" )
self.check_model_type(lowerCamelCase__ )
def _lowerCAmelCase ( self, lowerCamelCase__=None, lowerCamelCase__=None, lowerCamelCase__=None ):
A : List[Any] = {}
if frame_sampling_rate is not None:
A : Union[str, Any] = frame_sampling_rate
if num_frames is not None:
A : str = num_frames
A : Tuple = {}
if top_k is not None:
A : Dict = top_k
return preprocess_params, {}, postprocess_params
def __call__( self, lowerCamelCase__, **lowerCamelCase__ ):
return super().__call__(lowerCamelCase__, **lowerCamelCase__ )
def _lowerCAmelCase ( self, lowerCamelCase__, lowerCamelCase__=None, lowerCamelCase__=1 ):
if num_frames is None:
A : List[Any] = self.model.config.num_frames
if video.startswith("""http://""" ) or video.startswith("""https://""" ):
A : Any = BytesIO(requests.get(lowerCamelCase__ ).content )
A : Any = VideoReader(lowerCamelCase__ )
videoreader.seek(0 )
A : int = 0
A : Optional[int] = num_frames * frame_sampling_rate - 1
A : Optional[Any] = np.linspace(lowerCamelCase__, lowerCamelCase__, num=lowerCamelCase__, dtype=np.intaa )
A : List[Any] = videoreader.get_batch(lowerCamelCase__ ).asnumpy()
A : Dict = list(lowerCamelCase__ )
A : Union[str, Any] = self.image_processor(lowerCamelCase__, return_tensors=self.framework )
return model_inputs
def _lowerCAmelCase ( self, lowerCamelCase__ ):
A : Optional[Any] = self.model(**lowerCamelCase__ )
return model_outputs
def _lowerCAmelCase ( self, lowerCamelCase__, lowerCamelCase__=5 ):
if top_k > self.model.config.num_labels:
A : int = self.model.config.num_labels
if self.framework == "pt":
A : List[Any] = model_outputs.logits.softmax(-1 )[0]
A : Union[str, Any] = probs.topk(lowerCamelCase__ )
else:
raise ValueError(f'''Unsupported framework: {self.framework}''' )
A : Optional[int] = scores.tolist()
A : List[str] = ids.tolist()
return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(lowerCamelCase__, lowerCamelCase__ )]
| 370 |
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 SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
'''simple docstring'''
def _lowerCAmelCase ( self ):
A : str = tempfile.mkdtemp()
A : Any = [
"""[UNK]""",
"""[CLS]""",
"""[SEP]""",
"""[PAD]""",
"""[MASK]""",
"""的""",
"""价""",
"""格""",
"""是""",
"""15""",
"""便""",
"""alex""",
"""##andra""",
"""，""",
"""。""",
"""-""",
"""t""",
"""shirt""",
]
A : Any = 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] ) )
A : Any = {
"""do_resize""": True,
"""size""": {"""height""": 224, """width""": 224},
"""do_center_crop""": True,
"""crop_size""": {"""height""": 18, """width""": 18},
"""do_normalize""": True,
"""image_mean""": [0.4814_5466, 0.457_8275, 0.4082_1073],
"""image_std""": [0.2686_2954, 0.2613_0258, 0.2757_7711],
"""do_convert_rgb""": True,
}
A : int = os.path.join(self.tmpdirname, lowerCamelCase__ )
with open(self.image_processor_file, """w""", encoding="""utf-8""" ) as fp:
json.dump(lowerCamelCase__, lowerCamelCase__ )
def _lowerCAmelCase ( self, **lowerCamelCase__ ):
return BertTokenizer.from_pretrained(self.tmpdirname, **lowerCamelCase__ )
def _lowerCAmelCase ( self, **lowerCamelCase__ ):
return BertTokenizerFast.from_pretrained(self.tmpdirname, **lowerCamelCase__ )
def _lowerCAmelCase ( self, **lowerCamelCase__ ):
return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname, **lowerCamelCase__ )
def _lowerCAmelCase ( self ):
shutil.rmtree(self.tmpdirname )
def _lowerCAmelCase ( self ):
A : Optional[int] = [np.random.randint(255, size=(3, 30, 400), dtype=np.uinta )]
A : List[Any] = [Image.fromarray(np.moveaxis(lowerCamelCase__, 0, -1 ) ) for x in image_inputs]
return image_inputs
def _lowerCAmelCase ( self ):
A : List[Any] = self.get_tokenizer()
A : Dict = self.get_rust_tokenizer()
A : List[Any] = self.get_image_processor()
A : int = ChineseCLIPProcessor(tokenizer=lowerCamelCase__, image_processor=lowerCamelCase__ )
processor_slow.save_pretrained(self.tmpdirname )
A : Optional[Any] = ChineseCLIPProcessor.from_pretrained(self.tmpdirname, use_fast=lowerCamelCase__ )
A : str = ChineseCLIPProcessor(tokenizer=lowerCamelCase__, image_processor=lowerCamelCase__ )
processor_fast.save_pretrained(self.tmpdirname )
A : Tuple = 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, lowerCamelCase__ )
self.assertIsInstance(processor_fast.tokenizer, lowerCamelCase__ )
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, lowerCamelCase__ )
self.assertIsInstance(processor_fast.image_processor, lowerCamelCase__ )
def _lowerCAmelCase ( self ):
A : str = ChineseCLIPProcessor(tokenizer=self.get_tokenizer(), image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
A : Optional[int] = self.get_tokenizer(cls_token="""(CLS)""", sep_token="""(SEP)""" )
A : Optional[int] = self.get_image_processor(do_normalize=lowerCamelCase__ )
A : Optional[int] = ChineseCLIPProcessor.from_pretrained(
self.tmpdirname, cls_token="""(CLS)""", sep_token="""(SEP)""", do_normalize=lowerCamelCase__ )
self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer, lowerCamelCase__ )
self.assertEqual(processor.image_processor.to_json_string(), image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor, lowerCamelCase__ )
def _lowerCAmelCase ( self ):
A : Tuple = self.get_image_processor()
A : List[str] = self.get_tokenizer()
A : List[str] = ChineseCLIPProcessor(tokenizer=lowerCamelCase__, image_processor=lowerCamelCase__ )
A : Any = self.prepare_image_inputs()
A : Union[str, Any] = image_processor(lowerCamelCase__, return_tensors="""np""" )
A : str = processor(images=lowerCamelCase__, 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 ):
A : List[str] = self.get_image_processor()
A : Tuple = self.get_tokenizer()
A : str = ChineseCLIPProcessor(tokenizer=lowerCamelCase__, image_processor=lowerCamelCase__ )
A : Any = """Alexandra，T-shirt的价格是15便士。"""
A : Optional[Any] = processor(text=lowerCamelCase__ )
A : int = tokenizer(lowerCamelCase__ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key], encoded_processor[key] )
def _lowerCAmelCase ( self ):
A : Dict = self.get_image_processor()
A : List[str] = self.get_tokenizer()
A : int = ChineseCLIPProcessor(tokenizer=lowerCamelCase__, image_processor=lowerCamelCase__ )
A : str = """Alexandra，T-shirt的价格是15便士。"""
A : Dict = self.prepare_image_inputs()
A : Optional[int] = processor(text=lowerCamelCase__, images=lowerCamelCase__ )
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(lowerCamelCase__ ):
processor()
def _lowerCAmelCase ( self ):
A : Union[str, Any] = self.get_image_processor()
A : List[str] = self.get_tokenizer()
A : List[str] = ChineseCLIPProcessor(tokenizer=lowerCamelCase__, image_processor=lowerCamelCase__ )
A : int = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
A : List[Any] = processor.batch_decode(lowerCamelCase__ )
A : str = tokenizer.batch_decode(lowerCamelCase__ )
self.assertListEqual(lowerCamelCase__, lowerCamelCase__ )
def _lowerCAmelCase ( self ):
A : List[str] = self.get_image_processor()
A : List[str] = self.get_tokenizer()
A : Any = ChineseCLIPProcessor(tokenizer=lowerCamelCase__, image_processor=lowerCamelCase__ )
A : List[Any] = """Alexandra，T-shirt的价格是15便士。"""
A : Optional[Any] = self.prepare_image_inputs()
A : str = processor(text=lowerCamelCase__, images=lowerCamelCase__ )
self.assertListEqual(list(inputs.keys() ), processor.model_input_names )
| 115 | 0 |
from __future__ import annotations
from collections import namedtuple
def lowercase ( SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : float ) -> tuple:
_snake_case : int = namedtuple("""result""" , """name value""" )
if (voltage, current, power).count(0 ) != 1:
raise ValueError("""Only one argument must be 0""" )
elif power < 0:
raise ValueError(
"""Power cannot be negative in any electrical/electronics system""" )
elif voltage == 0:
return result("""voltage""" , power / current )
elif current == 0:
return result("""current""" , power / voltage )
elif power == 0:
return result("""power""" , float(round(abs(voltage * current ) , 2 ) ) )
else:
raise ValueError("""Exactly one argument must be 0""" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 317 | """simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : Union[str, Any]= logging.get_logger(__name__)
_a : str= {
"alibaba-damo/mgp-str-base": "https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json",
}
class UpperCamelCase ( lowercase ):
UpperCAmelCase : List[str] = """mgp-str"""
def __init__(self : List[Any] , _A : Dict=[32, 1_28] , _A : Any=4 , _A : int=3 , _A : Any=27 , _A : List[str]=38 , _A : str=5_02_57 , _A : Optional[int]=3_05_22 , _A : Union[str, Any]=7_68 , _A : Tuple=12 , _A : List[str]=12 , _A : List[str]=4.0 , _A : Optional[int]=True , _A : Optional[Any]=False , _A : Dict=1E-5 , _A : Optional[int]=0.0 , _A : str=0.0 , _A : int=0.0 , _A : str=False , _A : List[Any]=0.02 , **_A : Union[str, Any] , ) -> Tuple:
super().__init__(**_A)
__snake_case : Union[str, Any] = image_size
__snake_case : Optional[int] = patch_size
__snake_case : int = num_channels
__snake_case : int = max_token_length
__snake_case : List[Any] = num_character_labels
__snake_case : Optional[int] = num_bpe_labels
__snake_case : Optional[Any] = num_wordpiece_labels
__snake_case : int = hidden_size
__snake_case : List[Any] = num_hidden_layers
__snake_case : List[str] = num_attention_heads
__snake_case : Any = mlp_ratio
__snake_case : List[str] = distilled
__snake_case : List[Any] = layer_norm_eps
__snake_case : List[Any] = drop_rate
__snake_case : Optional[int] = qkv_bias
__snake_case : Optional[int] = attn_drop_rate
__snake_case : int = drop_path_rate
__snake_case : List[str] = output_aa_attentions
__snake_case : Optional[Any] = initializer_range
| 172 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_a = {
'configuration_blip_2': [
'BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Blip2Config',
'Blip2QFormerConfig',
'Blip2VisionConfig',
],
'processing_blip_2': ['Blip2Processor'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_a = [
'BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST',
'Blip2Model',
'Blip2QFormerModel',
'Blip2PreTrainedModel',
'Blip2ForConditionalGeneration',
'Blip2VisionModel',
]
if TYPE_CHECKING:
from .configuration_blip_a import (
BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
BlipaConfig,
BlipaQFormerConfig,
BlipaVisionConfig,
)
from .processing_blip_a import BlipaProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blip_a import (
BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST,
BlipaForConditionalGeneration,
BlipaModel,
BlipaPreTrainedModel,
BlipaQFormerModel,
BlipaVisionModel,
)
else:
import sys
_a = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 361 |
"""simple docstring"""
# 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
_a = '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 _A ( ) -> Tuple:
'''simple docstring'''
__lowercase = _ask_options(
"In which compute environment are you running?", ["This machine", "AWS (Amazon SageMaker)"], _convert_compute_environment, )
if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
__lowercase = get_sagemaker_input()
else:
__lowercase = get_cluster_input()
return config
def _A ( UpperCamelCase_ : Union[str, Any]=None) -> Union[str, Any]:
'''simple docstring'''
if subparsers is not None:
__lowercase = subparsers.add_parser("config", description=UpperCamelCase_)
else:
__lowercase = argparse.ArgumentParser("Accelerate config command", description=UpperCamelCase_)
parser.add_argument(
"--config_file", default=UpperCamelCase_, 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=UpperCamelCase_)
return parser
def _A ( UpperCamelCase_ : Dict) -> str:
'''simple docstring'''
__lowercase = get_user_input()
if args.config_file is not None:
__lowercase = args.config_file
else:
if not os.path.isdir(UpperCamelCase_):
os.makedirs(UpperCamelCase_)
__lowercase = default_yaml_config_file
if config_file.endswith(".json"):
config.to_json_file(UpperCamelCase_)
else:
config.to_yaml_file(UpperCamelCase_)
print(F"""accelerate configuration saved at {config_file}""")
def _A ( ) -> Optional[Any]:
'''simple docstring'''
__lowercase = config_command_parser()
__lowercase = parser.parse_args()
config_command(UpperCamelCase_)
if __name__ == "__main__":
main()
| 144 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase_ = {
"configuration_blip_2": [
"BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Blip2Config",
"Blip2QFormerConfig",
"Blip2VisionConfig",
],
"processing_blip_2": ["Blip2Processor"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase_ = [
"BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST",
"Blip2Model",
"Blip2QFormerModel",
"Blip2PreTrainedModel",
"Blip2ForConditionalGeneration",
"Blip2VisionModel",
]
if TYPE_CHECKING:
from .configuration_blip_a import (
BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
BlipaConfig,
BlipaQFormerConfig,
BlipaVisionConfig,
)
from .processing_blip_a import BlipaProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blip_a import (
BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST,
BlipaForConditionalGeneration,
BlipaModel,
BlipaPreTrainedModel,
BlipaQFormerModel,
BlipaVisionModel,
)
else:
import sys
lowerCamelCase_ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) | 191 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
"microsoft/beit-base-patch16-224-pt22k": (
"https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json"
),
# See all BEiT models at https://huggingface.co/models?filter=beit
}
class _SCREAMING_SNAKE_CASE( A ):
SCREAMING_SNAKE_CASE_ : str = '''beit'''
def __init__( self ,SCREAMING_SNAKE_CASE__=81_92 ,SCREAMING_SNAKE_CASE__=7_68 ,SCREAMING_SNAKE_CASE__=12 ,SCREAMING_SNAKE_CASE__=12 ,SCREAMING_SNAKE_CASE__=30_72 ,SCREAMING_SNAKE_CASE__="gelu" ,SCREAMING_SNAKE_CASE__=0.0 ,SCREAMING_SNAKE_CASE__=0.0 ,SCREAMING_SNAKE_CASE__=0.0_2 ,SCREAMING_SNAKE_CASE__=1E-12 ,SCREAMING_SNAKE_CASE__=2_24 ,SCREAMING_SNAKE_CASE__=16 ,SCREAMING_SNAKE_CASE__=3 ,SCREAMING_SNAKE_CASE__=False ,SCREAMING_SNAKE_CASE__=False ,SCREAMING_SNAKE_CASE__=False ,SCREAMING_SNAKE_CASE__=False ,SCREAMING_SNAKE_CASE__=0.1 ,SCREAMING_SNAKE_CASE__=0.1 ,SCREAMING_SNAKE_CASE__=True ,SCREAMING_SNAKE_CASE__=[3, 5, 7, 11] ,SCREAMING_SNAKE_CASE__=[1, 2, 3, 6] ,SCREAMING_SNAKE_CASE__=True ,SCREAMING_SNAKE_CASE__=0.4 ,SCREAMING_SNAKE_CASE__=2_56 ,SCREAMING_SNAKE_CASE__=1 ,SCREAMING_SNAKE_CASE__=False ,SCREAMING_SNAKE_CASE__=2_55 ,**SCREAMING_SNAKE_CASE__ ,) -> Optional[Any]:
"""simple docstring"""
super().__init__(**SCREAMING_SNAKE_CASE__ )
__SCREAMING_SNAKE_CASE :Any = vocab_size
__SCREAMING_SNAKE_CASE :Dict = hidden_size
__SCREAMING_SNAKE_CASE :Union[str, Any] = num_hidden_layers
__SCREAMING_SNAKE_CASE :List[str] = num_attention_heads
__SCREAMING_SNAKE_CASE :Optional[Any] = intermediate_size
__SCREAMING_SNAKE_CASE :Optional[int] = hidden_act
__SCREAMING_SNAKE_CASE :Tuple = hidden_dropout_prob
__SCREAMING_SNAKE_CASE :Optional[Any] = attention_probs_dropout_prob
__SCREAMING_SNAKE_CASE :Optional[Any] = initializer_range
__SCREAMING_SNAKE_CASE :str = layer_norm_eps
__SCREAMING_SNAKE_CASE :int = image_size
__SCREAMING_SNAKE_CASE :Tuple = patch_size
__SCREAMING_SNAKE_CASE :Any = num_channels
__SCREAMING_SNAKE_CASE :Any = use_mask_token
__SCREAMING_SNAKE_CASE :Union[str, Any] = use_absolute_position_embeddings
__SCREAMING_SNAKE_CASE :Union[str, Any] = use_relative_position_bias
__SCREAMING_SNAKE_CASE :Union[str, Any] = use_shared_relative_position_bias
__SCREAMING_SNAKE_CASE :List[str] = layer_scale_init_value
__SCREAMING_SNAKE_CASE :Optional[Any] = drop_path_rate
__SCREAMING_SNAKE_CASE :str = use_mean_pooling
# decode head attributes (semantic segmentation)
__SCREAMING_SNAKE_CASE :Dict = out_indices
__SCREAMING_SNAKE_CASE :Optional[int] = pool_scales
# auxiliary head attributes (semantic segmentation)
__SCREAMING_SNAKE_CASE :Optional[int] = use_auxiliary_head
__SCREAMING_SNAKE_CASE :Union[str, Any] = auxiliary_loss_weight
__SCREAMING_SNAKE_CASE :Dict = auxiliary_channels
__SCREAMING_SNAKE_CASE :Optional[int] = auxiliary_num_convs
__SCREAMING_SNAKE_CASE :List[str] = auxiliary_concat_input
__SCREAMING_SNAKE_CASE :List[Any] = semantic_loss_ignore_index
class _SCREAMING_SNAKE_CASE( A ):
SCREAMING_SNAKE_CASE_ : List[Any] = version.parse('''1.11''' )
@property
def _UpperCamelCase ( self ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def _UpperCamelCase ( self ) -> float:
"""simple docstring"""
return 1E-4 | 191 | 1 |
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida
from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
from huggingface_hub.utils import (
EntryNotFoundError,
RepositoryNotFoundError,
RevisionNotFoundError,
is_jinja_available,
)
from packaging import version
from requests import HTTPError
from .. import __version__
from .constants import (
DEPRECATED_REVISION_ARGS,
DIFFUSERS_CACHE,
HUGGINGFACE_CO_RESOLVE_ENDPOINT,
SAFETENSORS_WEIGHTS_NAME,
WEIGHTS_NAME,
)
from .import_utils import (
ENV_VARS_TRUE_VALUES,
_flax_version,
_jax_version,
_onnxruntime_version,
_torch_version,
is_flax_available,
is_onnx_available,
is_torch_available,
)
from .logging import get_logger
UpperCAmelCase__ = get_logger(__name__)
UpperCAmelCase__ = Path(__file__).parent / "model_card_template.md"
UpperCAmelCase__ = uuida().hex
UpperCAmelCase__ = os.getenv("HF_HUB_OFFLINE", "").upper() in ENV_VARS_TRUE_VALUES
UpperCAmelCase__ = os.getenv("DISABLE_TELEMETRY", "").upper() in ENV_VARS_TRUE_VALUES
UpperCAmelCase__ = HUGGINGFACE_CO_RESOLVE_ENDPOINT + "/api/telemetry/"
def _a ( a :Union[Dict, str, None] = None ) -> str:
a = F"""diffusers/{__version__}; python/{sys.version.split()[0]}; session_id/{SESSION_ID}"""
if DISABLE_TELEMETRY or HF_HUB_OFFLINE:
return ua + "; telemetry/off"
if is_torch_available():
ua += F"""; torch/{_torch_version}"""
if is_flax_available():
ua += F"""; jax/{_jax_version}"""
ua += F"""; flax/{_flax_version}"""
if is_onnx_available():
ua += F"""; onnxruntime/{_onnxruntime_version}"""
# CI will set this value to True
if os.environ.get('''DIFFUSERS_IS_CI''' , '''''' ).upper() in ENV_VARS_TRUE_VALUES:
ua += "; is_ci/true"
if isinstance(a , a ):
ua += "; " + "; ".join(F"""{k}/{v}""" for k, v in user_agent.items() )
elif isinstance(a , a ):
ua += "; " + user_agent
return ua
def _a ( a :str , a :Optional[str] = None , a :Optional[str] = None ) -> Optional[Any]:
if token is None:
a = HfFolder.get_token()
if organization is None:
a = whoami(a )['''name''']
return F"""{username}/{model_id}"""
else:
return F"""{organization}/{model_id}"""
def _a ( a :Optional[Any] , a :str ) -> str:
if not is_jinja_available():
raise ValueError(
'''Modelcard rendering is based on Jinja templates.'''
''' Please make sure to have `jinja` installed before using `create_model_card`.'''
''' To install it, please run `pip install Jinja2`.''' )
if hasattr(a , '''local_rank''' ) and args.local_rank not in [-1, 0]:
return
a = args.hub_token if hasattr(a , '''hub_token''' ) else None
a = get_full_repo_name(a , token=a )
a = ModelCard.from_template(
card_data=ModelCardData( # Card metadata object that will be converted to YAML block
language='''en''' , license='''apache-2.0''' , library_name='''diffusers''' , tags=[] , datasets=args.dataset_name , metrics=[] , ) , template_path=a , model_name=a , repo_name=a , dataset_name=args.dataset_name if hasattr(a , '''dataset_name''' ) else None , learning_rate=args.learning_rate , train_batch_size=args.train_batch_size , eval_batch_size=args.eval_batch_size , gradient_accumulation_steps=(
args.gradient_accumulation_steps if hasattr(a , '''gradient_accumulation_steps''' ) else None
) , adam_betaa=args.adam_betaa if hasattr(a , '''adam_beta1''' ) else None , adam_betaa=args.adam_betaa if hasattr(a , '''adam_beta2''' ) else None , adam_weight_decay=args.adam_weight_decay if hasattr(a , '''adam_weight_decay''' ) else None , adam_epsilon=args.adam_epsilon if hasattr(a , '''adam_epsilon''' ) else None , lr_scheduler=args.lr_scheduler if hasattr(a , '''lr_scheduler''' ) else None , lr_warmup_steps=args.lr_warmup_steps if hasattr(a , '''lr_warmup_steps''' ) else None , ema_inv_gamma=args.ema_inv_gamma if hasattr(a , '''ema_inv_gamma''' ) else None , ema_power=args.ema_power if hasattr(a , '''ema_power''' ) else None , ema_max_decay=args.ema_max_decay if hasattr(a , '''ema_max_decay''' ) else None , mixed_precision=args.mixed_precision , )
a = os.path.join(args.output_dir , '''README.md''' )
model_card.save(a )
def _a ( a :Optional[str] , a :Optional[str] = None ) -> int:
if resolved_file is None or commit_hash is not None:
return commit_hash
a = str(Path(a ).as_posix() )
a = re.search(r'''snapshots/([^/]+)/''' , a )
if search is None:
return None
a = search.groups()[0]
return commit_hash if REGEX_COMMIT_HASH.match(a ) else None
# Old default cache path, potentially to be migrated.
# This logic was more or less taken from `transformers`, with the following differences:
# - Diffusers doesn't use custom environment variables to specify the cache path.
# - There is no need to migrate the cache format, just move the files to the new location.
UpperCAmelCase__ = os.path.expanduser(
os.getenv("HF_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "huggingface"))
)
UpperCAmelCase__ = os.path.join(hf_cache_home, "diffusers")
def _a ( a :Optional[str] = None , a :Optional[str] = None ) -> None:
if new_cache_dir is None:
a = DIFFUSERS_CACHE
if old_cache_dir is None:
a = old_diffusers_cache
a = Path(a ).expanduser()
a = Path(a ).expanduser()
for old_blob_path in old_cache_dir.glob('''**/blobs/*''' ):
if old_blob_path.is_file() and not old_blob_path.is_symlink():
a = new_cache_dir / old_blob_path.relative_to(a )
new_blob_path.parent.mkdir(parents=a , exist_ok=a )
os.replace(a , a )
try:
os.symlink(a , a )
except OSError:
logger.warning(
'''Could not create symlink between old cache and new cache. If you use an older version of diffusers again, files will be re-downloaded.''' )
# At this point, old_cache_dir contains symlinks to the new cache (it can still be used).
UpperCAmelCase__ = os.path.join(DIFFUSERS_CACHE, "version_diffusers_cache.txt")
if not os.path.isfile(cache_version_file):
UpperCAmelCase__ = 0
else:
with open(cache_version_file) as f:
try:
UpperCAmelCase__ = int(f.read())
except ValueError:
UpperCAmelCase__ = 0
if cache_version < 1:
UpperCAmelCase__ = os.path.isdir(old_diffusers_cache) and len(os.listdir(old_diffusers_cache)) > 0
if old_cache_is_not_empty:
logger.warning(
"The cache for model files in Diffusers v0.14.0 has moved to a new location. Moving your "
"existing cached models. This is a one-time operation, you can interrupt it or run it "
"later by calling `diffusers.utils.hub_utils.move_cache()`."
)
try:
move_cache()
except Exception as e:
UpperCAmelCase__ = "\n".join(traceback.format_tb(e.__traceback__))
logger.error(
f"""There was a problem when trying to move your cache:\n\n{trace}\n{e.__class__.__name__}: {e}\n\nPlease """
"file an issue at https://github.com/huggingface/diffusers/issues/new/choose, copy paste this whole "
"message and we will do our best to help."
)
if cache_version < 1:
try:
os.makedirs(DIFFUSERS_CACHE, exist_ok=True)
with open(cache_version_file, "w") as f:
f.write("1")
except Exception:
logger.warning(
f"""There was a problem when trying to write in your cache folder ({DIFFUSERS_CACHE}). Please, ensure """
"the directory exists and can be written to."
)
def _a ( a :str , a :Optional[str] = None ) -> str:
if variant is not None:
a = weights_name.split('''.''' )
a = splits[:-1] + [variant] + splits[-1:]
a = '''.'''.join(a )
return weights_name
def _a ( a :Dict , *,
a :Tuple , a :Any , a :Any , a :Dict , a :str , a :List[str] , a :List[str] , a :Optional[int] , a :Any , a :List[Any] , a :Optional[Any]=None , ) -> Any:
a = str(a )
if os.path.isfile(a ):
return pretrained_model_name_or_path
elif os.path.isdir(a ):
if os.path.isfile(os.path.join(a , a ) ):
# Load from a PyTorch checkpoint
a = os.path.join(a , a )
return model_file
elif subfolder is not None and os.path.isfile(
os.path.join(a , a , a ) ):
a = os.path.join(a , a , a )
return model_file
else:
raise EnvironmentError(
F"""Error no file named {weights_name} found in directory {pretrained_model_name_or_path}.""" )
else:
# 1. First check if deprecated way of loading from branches is used
if (
revision in DEPRECATED_REVISION_ARGS
and (weights_name == WEIGHTS_NAME or weights_name == SAFETENSORS_WEIGHTS_NAME)
and version.parse(version.parse(a ).base_version ) >= version.parse('''0.20.0''' )
):
try:
a = hf_hub_download(
a , filename=_add_variant(a , a ) , cache_dir=a , force_download=a , proxies=a , resume_download=a , local_files_only=a , use_auth_token=a , user_agent=a , subfolder=a , revision=revision or commit_hash , )
warnings.warn(
F"""Loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'` is deprecated. Loading instead from `revision='main'` with `variant={revision}`. Loading model variants via `revision='{revision}'` will be removed in diffusers v1. Please use `variant='{revision}'` instead.""" , a , )
return model_file
except: # noqa: E722
warnings.warn(
F"""You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'`. This behavior is deprecated and will be removed in diffusers v1. One should use `variant='{revision}'` instead. However, it appears that {pretrained_model_name_or_path} currently does not have a {_add_variant(a , a )} file in the 'main' branch of {pretrained_model_name_or_path}. \n The Diffusers team and community would be very grateful if you could open an issue: https://github.com/huggingface/diffusers/issues/new with the title '{pretrained_model_name_or_path} is missing {_add_variant(a , a )}' so that the correct variant file can be added.""" , a , )
try:
# 2. Load model file as usual
a = hf_hub_download(
a , filename=a , cache_dir=a , force_download=a , proxies=a , resume_download=a , local_files_only=a , use_auth_token=a , user_agent=a , subfolder=a , revision=revision or commit_hash , )
return model_file
except RepositoryNotFoundError:
raise EnvironmentError(
F"""{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier """
'''listed on \'https://huggingface.co/models\'\nIf this is a private repository, make sure to pass a '''
'''token having permission to this repo with `use_auth_token` or log in with `huggingface-cli '''
'''login`.''' )
except RevisionNotFoundError:
raise EnvironmentError(
F"""{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for """
'''this model name. Check the model page at '''
F"""'https://huggingface.co/{pretrained_model_name_or_path}' for available revisions.""" )
except EntryNotFoundError:
raise EnvironmentError(
F"""{pretrained_model_name_or_path} does not appear to have a file named {weights_name}.""" )
except HTTPError as err:
raise EnvironmentError(
F"""There was a specific connection error when trying to load {pretrained_model_name_or_path}:\n{err}""" )
except ValueError:
raise EnvironmentError(
F"""We couldn't connect to '{HUGGINGFACE_CO_RESOLVE_ENDPOINT}' to load this model, couldn't find it"""
F""" in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a"""
F""" directory containing a file named {weights_name} or"""
''' \nCheckout your internet connection or see how to run the library in'''
''' offline mode at \'https://huggingface.co/docs/diffusers/installation#offline-mode\'.''' )
except EnvironmentError:
raise EnvironmentError(
F"""Can't load the model for '{pretrained_model_name_or_path}'. If you were trying to load it from """
'''\'https://huggingface.co/models\', make sure you don\'t have a local directory with the same name. '''
F"""Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a directory """
F"""containing a file named {weights_name}""" )
| 26 |
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
UpperCAmelCase__ = logging.get_logger("transformers.models.speecht5")
def _a ( a :Optional[Any] , a :Tuple , a :Dict ) -> List[str]:
hf_model.apply_weight_norm()
a = checkpoint['''input_conv.weight_g''']
a = checkpoint['''input_conv.weight_v''']
a = checkpoint['''input_conv.bias''']
for i in range(len(config.upsample_rates ) ):
a = checkpoint[F"""upsamples.{i}.1.weight_g"""]
a = checkpoint[F"""upsamples.{i}.1.weight_v"""]
a = checkpoint[F"""upsamples.{i}.1.bias"""]
for i in range(len(config.upsample_rates ) * len(config.resblock_kernel_sizes ) ):
for j in range(len(config.resblock_dilation_sizes ) ):
a = checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_g"""]
a = checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_v"""]
a = checkpoint[F"""blocks.{i}.convs1.{j}.1.bias"""]
a = checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_g"""]
a = checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_v"""]
a = checkpoint[F"""blocks.{i}.convs2.{j}.1.bias"""]
a = checkpoint['''output_conv.1.weight_g''']
a = checkpoint['''output_conv.1.weight_v''']
a = checkpoint['''output_conv.1.bias''']
hf_model.remove_weight_norm()
@torch.no_grad()
def _a ( a :List[str] , a :Union[str, Any] , a :Dict , a :Dict=None , a :List[Any]=None , ) -> int:
if config_path is not None:
a = SpeechTaHifiGanConfig.from_pretrained(a )
else:
a = SpeechTaHifiGanConfig()
a = SpeechTaHifiGan(a )
a = torch.load(a )
load_weights(orig_checkpoint['''model''']['''generator'''] , a , a )
a = np.load(a )
a = stats[0].reshape(-1 )
a = stats[1].reshape(-1 )
a = torch.from_numpy(a ).float()
a = torch.from_numpy(a ).float()
model.save_pretrained(a )
if repo_id:
print('''Pushing to the hub...''' )
model.push_to_hub(a )
if __name__ == "__main__":
UpperCAmelCase__ = argparse.ArgumentParser()
parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to original checkpoint")
parser.add_argument("--stats_path", required=True, default=None, type=str, help="Path to stats.npy file")
parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
parser.add_argument(
"--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model."
)
parser.add_argument(
"--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
)
UpperCAmelCase__ = parser.parse_args()
convert_hifigan_checkpoint(
args.checkpoint_path,
args.stats_path,
args.pytorch_dump_folder_path,
args.config_path,
args.push_to_hub,
)
| 26 | 1 |
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
def lowerCAmelCase_ ( __a ) -> int:
"""simple docstring"""
return 1.0 / (1.0 + np.exp(-_outputs ))
def lowerCAmelCase_ ( __a ) -> Tuple:
"""simple docstring"""
lowerCamelCase__: List[str] =np.max(_outputs , axis=-1 , keepdims=_SCREAMING_SNAKE_CASE )
lowerCamelCase__: List[Any] =np.exp(_outputs - maxes )
return shifted_exp / shifted_exp.sum(axis=-1 , keepdims=_SCREAMING_SNAKE_CASE )
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowercase_ = """sigmoid"""
lowercase_ = """softmax"""
lowercase_ = """none"""
@add_end_docstrings(
__SCREAMING_SNAKE_CASE , R"\n return_all_scores (`bool`, *optional*, defaults to `False`):\n Whether to return all prediction scores or just the one of the predicted class.\n function_to_apply (`str`, *optional*, defaults to `\"default\"`):\n The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:\n\n - `\"default\"`: if the model has a single label, will apply the sigmoid function on the output. If the model\n has several labels, will apply the softmax function on the output.\n - `\"sigmoid\"`: Applies the sigmoid function on the output.\n - `\"softmax\"`: Applies the softmax function on the output.\n - `\"none\"`: Does not apply any function on the output.\n " , )
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowercase_ = False
lowercase_ = ClassificationFunction.NONE
def __init__(self : Optional[Any] , **UpperCAmelCase_ : Tuple) ->Any:
'''simple docstring'''
super().__init__(**__UpperCAmelCase)
self.check_model_type(
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if self.framework == "tf"
else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING)
def SCREAMING_SNAKE_CASE_ (self : int , UpperCAmelCase_ : Any=None , UpperCAmelCase_ : Optional[int]=None , UpperCAmelCase_ : int="" , **UpperCAmelCase_ : Dict) ->int:
'''simple docstring'''
lowerCamelCase__: Optional[int] =tokenizer_kwargs
lowerCamelCase__: List[Any] ={}
if hasattr(self.model.config , "return_all_scores") and return_all_scores is None:
lowerCamelCase__: List[Any] =self.model.config.return_all_scores
if isinstance(__UpperCAmelCase , __UpperCAmelCase) or top_k is None:
lowerCamelCase__: int =top_k
lowerCamelCase__: Dict =False
elif return_all_scores is not None:
warnings.warn(
"`return_all_scores` is now deprecated, if want a similar functionality use `top_k=None` instead of"
" `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`." , __UpperCAmelCase , )
if return_all_scores:
lowerCamelCase__: List[Any] =None
else:
lowerCamelCase__: Union[str, Any] =1
if isinstance(__UpperCAmelCase , __UpperCAmelCase):
lowerCamelCase__: Union[str, Any] =ClassificationFunction[function_to_apply.upper()]
if function_to_apply is not None:
lowerCamelCase__: List[Any] =function_to_apply
return preprocess_params, {}, postprocess_params
def __call__(self : Dict , *UpperCAmelCase_ : str , **UpperCAmelCase_ : Any) ->Optional[Any]:
'''simple docstring'''
lowerCamelCase__: Union[str, Any] =super().__call__(*__UpperCAmelCase , **__UpperCAmelCase)
# TODO try and retrieve it in a nicer way from _sanitize_parameters.
lowerCamelCase__: Optional[Any] ='top_k' not in kwargs
if isinstance(args[0] , __UpperCAmelCase) and _legacy:
# This pipeline is odd, and return a list when single item is run
return [result]
else:
return result
def SCREAMING_SNAKE_CASE_ (self : List[Any] , UpperCAmelCase_ : Union[str, Any] , **UpperCAmelCase_ : int) ->str:
'''simple docstring'''
lowerCamelCase__: Dict =self.framework
if isinstance(__UpperCAmelCase , __UpperCAmelCase):
return self.tokenizer(**__UpperCAmelCase , return_tensors=__UpperCAmelCase , **__UpperCAmelCase)
elif isinstance(__UpperCAmelCase , __UpperCAmelCase) and len(__UpperCAmelCase) == 1 and isinstance(inputs[0] , __UpperCAmelCase) and len(inputs[0]) == 2:
# It used to be valid to use a list of list of list for text pairs, keeping this path for BC
return self.tokenizer(
text=inputs[0][0] , text_pair=inputs[0][1] , return_tensors=__UpperCAmelCase , **__UpperCAmelCase)
elif isinstance(__UpperCAmelCase , __UpperCAmelCase):
# This is likely an invalid usage of the pipeline attempting to pass text pairs.
raise ValueError(
"The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a"
" dictionary `{\"text\": \"My text\", \"text_pair\": \"My pair\"}` in order to send a text pair.")
return self.tokenizer(__UpperCAmelCase , return_tensors=__UpperCAmelCase , **__UpperCAmelCase)
def SCREAMING_SNAKE_CASE_ (self : Union[str, Any] , UpperCAmelCase_ : Optional[int]) ->str:
'''simple docstring'''
return self.model(**__UpperCAmelCase)
def SCREAMING_SNAKE_CASE_ (self : List[str] , UpperCAmelCase_ : str , UpperCAmelCase_ : str=None , UpperCAmelCase_ : Any=1 , UpperCAmelCase_ : List[Any]=True) ->List[str]:
'''simple docstring'''
if function_to_apply is None:
if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
lowerCamelCase__: str =ClassificationFunction.SIGMOID
elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
lowerCamelCase__: int =ClassificationFunction.SOFTMAX
elif hasattr(self.model.config , "function_to_apply") and function_to_apply is None:
lowerCamelCase__: Optional[Any] =self.model.config.function_to_apply
else:
lowerCamelCase__: Dict =ClassificationFunction.NONE
lowerCamelCase__: int =model_outputs['logits'][0]
lowerCamelCase__: Union[str, Any] =outputs.numpy()
if function_to_apply == ClassificationFunction.SIGMOID:
lowerCamelCase__: Dict =sigmoid(__UpperCAmelCase)
elif function_to_apply == ClassificationFunction.SOFTMAX:
lowerCamelCase__: int =softmax(__UpperCAmelCase)
elif function_to_apply == ClassificationFunction.NONE:
lowerCamelCase__: Tuple =outputs
else:
raise ValueError(F"""Unrecognized `function_to_apply` argument: {function_to_apply}""")
if top_k == 1 and _legacy:
return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}
lowerCamelCase__: Any =[
{'label': self.model.config.idalabel[i], 'score': score.item()} for i, score in enumerate(__UpperCAmelCase)
]
if not _legacy:
dict_scores.sort(key=lambda UpperCAmelCase_: x["score"] , reverse=__UpperCAmelCase)
if top_k is not None:
lowerCamelCase__: List[str] =dict_scores[:top_k]
return dict_scores
| 10 |
"""simple docstring"""
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class _lowerCAmelCase ( yaml.SafeLoader ):
"""simple docstring"""
def snake_case ( self , __UpperCAmelCase ):
'''simple docstring'''
lowerCAmelCase__ :List[Any] = [self.constructed_objects[key_node] for key_node, _ in node.value]
lowerCAmelCase__ :str = [tuple(__UpperCAmelCase ) if isinstance(__UpperCAmelCase , __UpperCAmelCase ) else key for key in keys]
lowerCAmelCase__ :Optional[int] = Counter(__UpperCAmelCase )
lowerCAmelCase__ :int = [key for key in counter if counter[key] > 1]
if duplicate_keys:
raise TypeError(F"Got duplicate yaml keys: {duplicate_keys}" )
def snake_case ( self , __UpperCAmelCase , __UpperCAmelCase=False ):
'''simple docstring'''
lowerCAmelCase__ :Union[str, Any] = super().construct_mapping(__UpperCAmelCase , deep=__UpperCAmelCase )
self._check_no_duplicates_on_constructed_node(__UpperCAmelCase )
return mapping
def __A (_SCREAMING_SNAKE_CASE ) ->Tuple[Optional[str], str]:
"""simple docstring"""
lowerCAmelCase__ :Optional[Any] = list(readme_content.splitlines() )
if full_content and full_content[0] == "---" and "---" in full_content[1:]:
lowerCAmelCase__ :Optional[int] = full_content[1:].index('---' ) + 1
lowerCAmelCase__ :Union[str, Any] = '\n'.join(full_content[1:sep_idx] )
return yamlblock, "\n".join(full_content[sep_idx + 1 :] )
return None, "\n".join(_SCREAMING_SNAKE_CASE )
class _lowerCAmelCase ( a ):
"""simple docstring"""
__magic_name__ :List[str] = {"""train_eval_index"""} # train-eval-index in the YAML metadata
@classmethod
def snake_case ( cls , __UpperCAmelCase ):
'''simple docstring'''
with open(__UpperCAmelCase , encoding='utf-8' ) as readme_file:
lowerCAmelCase__ , lowerCAmelCase__ :Union[str, Any] = _split_yaml_from_readme(readme_file.read() )
if yaml_string is not None:
return cls.from_yaml_string(__UpperCAmelCase )
else:
return cls()
def snake_case ( self , __UpperCAmelCase ):
'''simple docstring'''
if path.exists():
with open(__UpperCAmelCase , encoding='utf-8' ) as readme_file:
lowerCAmelCase__ :Optional[Any] = readme_file.read()
else:
lowerCAmelCase__ :Union[str, Any] = None
lowerCAmelCase__ :Union[str, Any] = self._to_readme(__UpperCAmelCase )
with open(__UpperCAmelCase , 'w' , encoding='utf-8' ) as readme_file:
readme_file.write(__UpperCAmelCase )
def snake_case ( self , __UpperCAmelCase = None ):
'''simple docstring'''
if readme_content is not None:
lowerCAmelCase__ , lowerCAmelCase__ :Optional[int] = _split_yaml_from_readme(__UpperCAmelCase )
lowerCAmelCase__ :Optional[Any] = '---\n' + self.to_yaml_string() + '---\n' + content
else:
lowerCAmelCase__ :str = '---\n' + self.to_yaml_string() + '---\n'
return full_content
@classmethod
def snake_case ( cls , __UpperCAmelCase ):
'''simple docstring'''
lowerCAmelCase__ :Dict = yaml.load(__UpperCAmelCase , Loader=_NoDuplicateSafeLoader ) or {}
# Convert the YAML keys to DatasetMetadata fields
lowerCAmelCase__ :int = {
(key.replace('-' , '_' ) if key.replace('-' , '_' ) in cls._FIELDS_WITH_DASHES else key): value
for key, value in metadata_dict.items()
}
return cls(**__UpperCAmelCase )
def snake_case ( self ):
'''simple docstring'''
return yaml.safe_dump(
{
(key.replace('_' , '-' ) if key in self._FIELDS_WITH_DASHES else key): value
for key, value in self.items()
} , sort_keys=__UpperCAmelCase , allow_unicode=__UpperCAmelCase , encoding='utf-8' , ).decode('utf-8' )
__A = {
"""image-classification""": [],
"""translation""": [],
"""image-segmentation""": [],
"""fill-mask""": [],
"""automatic-speech-recognition""": [],
"""token-classification""": [],
"""sentence-similarity""": [],
"""audio-classification""": [],
"""question-answering""": [],
"""summarization""": [],
"""zero-shot-classification""": [],
"""table-to-text""": [],
"""feature-extraction""": [],
"""other""": [],
"""multiple-choice""": [],
"""text-classification""": [],
"""text-to-image""": [],
"""text2text-generation""": [],
"""zero-shot-image-classification""": [],
"""tabular-classification""": [],
"""tabular-regression""": [],
"""image-to-image""": [],
"""tabular-to-text""": [],
"""unconditional-image-generation""": [],
"""text-retrieval""": [],
"""text-to-speech""": [],
"""object-detection""": [],
"""audio-to-audio""": [],
"""text-generation""": [],
"""conversational""": [],
"""table-question-answering""": [],
"""visual-question-answering""": [],
"""image-to-text""": [],
"""reinforcement-learning""": [],
"""voice-activity-detection""": [],
"""time-series-forecasting""": [],
"""document-question-answering""": [],
}
if __name__ == "__main__":
from argparse import ArgumentParser
__A = ArgumentParser(usage="""Validate the yaml metadata block of a README.md file.""")
ap.add_argument("""readme_filepath""")
__A = ap.parse_args()
__A = Path(args.readme_filepath)
__A = DatasetMetadata.from_readme(readme_filepath)
print(dataset_metadata)
dataset_metadata.to_readme(readme_filepath)
| 293 | 0 |
from __future__ import annotations
def UpperCAmelCase_ ( _A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = 2
SCREAMING_SNAKE_CASE__ = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(_A )
if n > 1:
factors.append(_A )
return factors
if __name__ == "__main__":
import doctest
doctest.testmod()
| 218 |
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def UpperCAmelCase_ ( _A , _A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = args.log_outputs
SCREAMING_SNAKE_CASE__ = '''_'''.join(args.dataset.split('''/''' ) + [args.config, args.split] )
# load metric
SCREAMING_SNAKE_CASE__ = load_metric('''wer''' )
SCREAMING_SNAKE_CASE__ = load_metric('''cer''' )
# compute metrics
SCREAMING_SNAKE_CASE__ = wer.compute(references=result['''target'''] , predictions=result['''prediction'''] )
SCREAMING_SNAKE_CASE__ = cer.compute(references=result['''target'''] , predictions=result['''prediction'''] )
# print & log results
SCREAMING_SNAKE_CASE__ = F'''WER: {wer_result}\nCER: {cer_result}'''
print(_A )
with open(F'''{dataset_id}_eval_results.txt''' , '''w''' ) as f:
f.write(_A )
# log all results in text file. Possibly interesting for analysis
if log_outputs is not None:
SCREAMING_SNAKE_CASE__ = F'''log_{dataset_id}_predictions.txt'''
SCREAMING_SNAKE_CASE__ = F'''log_{dataset_id}_targets.txt'''
with open(_A , '''w''' ) as p, open(_A , '''w''' ) as t:
# mapping function to write output
def write_to_file(_A , _A ):
p.write(F'''{i}''' + '''\n''' )
p.write(batch['''prediction'''] + '''\n''' )
t.write(F'''{i}''' + '''\n''' )
t.write(batch['''target'''] + '''\n''' )
result.map(_A , with_indices=_A )
def UpperCAmelCase_ ( _A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = '''[,?.!\-\;\:"“%‘”�—’…–]''' # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training
SCREAMING_SNAKE_CASE__ = re.sub(_A , '''''' , text.lower() )
# In addition, we can normalize the target text, e.g. removing new lines characters etc...
# note that order is important here!
SCREAMING_SNAKE_CASE__ = ['''\n\n''', '''\n''', ''' ''', ''' ''']
for t in token_sequences_to_ignore:
SCREAMING_SNAKE_CASE__ = ''' '''.join(text.split(_A ) )
return text
def UpperCAmelCase_ ( _A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = load_dataset(args.dataset , args.config , split=args.split , use_auth_token=_A )
# for testing: only process the first two examples as a test
# dataset = dataset.select(range(10))
# load processor
SCREAMING_SNAKE_CASE__ = AutoFeatureExtractor.from_pretrained(args.model_id )
SCREAMING_SNAKE_CASE__ = feature_extractor.sampling_rate
# resample audio
SCREAMING_SNAKE_CASE__ = dataset.cast_column('''audio''' , Audio(sampling_rate=_A ) )
# load eval pipeline
if args.device is None:
SCREAMING_SNAKE_CASE__ = 0 if torch.cuda.is_available() else -1
SCREAMING_SNAKE_CASE__ = pipeline('''automatic-speech-recognition''' , model=args.model_id , device=args.device )
# map function to decode audio
def map_to_pred(_A ):
SCREAMING_SNAKE_CASE__ = asr(
batch['''audio''']['''array'''] , chunk_length_s=args.chunk_length_s , stride_length_s=args.stride_length_s )
SCREAMING_SNAKE_CASE__ = prediction['''text''']
SCREAMING_SNAKE_CASE__ = normalize_text(batch['''sentence'''] )
return batch
# run inference on all examples
SCREAMING_SNAKE_CASE__ = dataset.map(_A , remove_columns=dataset.column_names )
# compute and log_results
# do not change function below
log_results(_A , _A )
if __name__ == "__main__":
_SCREAMING_SNAKE_CASE : List[str] = argparse.ArgumentParser()
parser.add_argument(
'''--model_id''', type=str, required=True, help='''Model identifier. Should be loadable with 🤗 Transformers'''
)
parser.add_argument(
'''--dataset''',
type=str,
required=True,
help='''Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets''',
)
parser.add_argument(
'''--config''', type=str, required=True, help='''Config of the dataset. *E.g.* `\'en\'` for Common Voice'''
)
parser.add_argument('''--split''', type=str, required=True, help='''Split of the dataset. *E.g.* `\'test\'`''')
parser.add_argument(
'''--chunk_length_s''', type=float, default=None, help='''Chunk length in seconds. Defaults to 5 seconds.'''
)
parser.add_argument(
'''--stride_length_s''', type=float, default=None, help='''Stride of the audio chunks. Defaults to 1 second.'''
)
parser.add_argument(
'''--log_outputs''', action='''store_true''', help='''If defined, write outputs to log file for analysis.'''
)
parser.add_argument(
'''--device''',
type=int,
default=None,
help='''The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.''',
)
_SCREAMING_SNAKE_CASE : List[Any] = parser.parse_args()
main(args)
| 218 | 1 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device
snake_case_ : Optional[Any] = False
class __snake_case ( unittest.TestCase ):
pass
@nightly
@require_torch_gpu
class __snake_case ( unittest.TestCase ):
def lowerCamelCase ( self : int):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase ( self : str):
"""simple docstring"""
UpperCAmelCase_ = VersatileDiffusionTextToImagePipeline.from_pretrained('''shi-labs/versatile-diffusion''')
# remove text_unet
pipe.remove_unused_weights()
pipe.to(_snake_case)
pipe.set_progress_bar_config(disable=_snake_case)
UpperCAmelCase_ = '''A painting of a squirrel eating a burger '''
UpperCAmelCase_ = torch.manual_seed(0)
UpperCAmelCase_ = pipe(
prompt=_snake_case , generator=_snake_case , guidance_scale=7.5 , num_inference_steps=2 , output_type='''numpy''').images
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(_snake_case)
UpperCAmelCase_ = VersatileDiffusionTextToImagePipeline.from_pretrained(_snake_case)
pipe.to(_snake_case)
pipe.set_progress_bar_config(disable=_snake_case)
UpperCAmelCase_ = generator.manual_seed(0)
UpperCAmelCase_ = pipe(
prompt=_snake_case , generator=_snake_case , guidance_scale=7.5 , num_inference_steps=2 , output_type='''numpy''').images
assert np.abs(image - new_image).sum() < 1e-5, "Models don't have the same forward pass"
def lowerCamelCase ( self : List[str]):
"""simple docstring"""
UpperCAmelCase_ = VersatileDiffusionTextToImagePipeline.from_pretrained(
'''shi-labs/versatile-diffusion''' , torch_dtype=torch.floataa)
pipe.to(_snake_case)
pipe.set_progress_bar_config(disable=_snake_case)
UpperCAmelCase_ = '''A painting of a squirrel eating a burger '''
UpperCAmelCase_ = torch.manual_seed(0)
UpperCAmelCase_ = pipe(
prompt=_snake_case , generator=_snake_case , guidance_scale=7.5 , num_inference_steps=50 , output_type='''numpy''').images
UpperCAmelCase_ = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
UpperCAmelCase_ = np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
| 51 |
from __future__ import annotations
from collections.abc import Callable
__UpperCAmelCase = list[list[float | int]]
def A__ ( __lowerCamelCase, __lowerCamelCase ):
SCREAMING_SNAKE_CASE_ = len(__lowerCamelCase )
SCREAMING_SNAKE_CASE_ = [[0 for _ in range(size + 1 )] for _ in range(__lowerCamelCase )]
SCREAMING_SNAKE_CASE_ = 42
SCREAMING_SNAKE_CASE_ = 42
SCREAMING_SNAKE_CASE_ = 42
SCREAMING_SNAKE_CASE_ = 42
SCREAMING_SNAKE_CASE_ = 42
SCREAMING_SNAKE_CASE_ = 42
for row in range(__lowerCamelCase ):
for col in range(__lowerCamelCase ):
SCREAMING_SNAKE_CASE_ = matrix[row][col]
SCREAMING_SNAKE_CASE_ = vector[row][0]
SCREAMING_SNAKE_CASE_ = 0
SCREAMING_SNAKE_CASE_ = 0
while row < size and col < size:
# pivoting
SCREAMING_SNAKE_CASE_ = max((abs(augmented[rowa][col] ), rowa) for rowa in range(__lowerCamelCase, __lowerCamelCase ) )[
1
]
if augmented[pivot_row][col] == 0:
col += 1
continue
else:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = augmented[pivot_row], augmented[row]
for rowa in range(row + 1, __lowerCamelCase ):
SCREAMING_SNAKE_CASE_ = augmented[rowa][col] / augmented[row][col]
SCREAMING_SNAKE_CASE_ = 0
for cola in range(col + 1, size + 1 ):
augmented[rowa][cola] -= augmented[row][cola] * ratio
row += 1
col += 1
# back substitution
for col in range(1, __lowerCamelCase ):
for row in range(__lowerCamelCase ):
SCREAMING_SNAKE_CASE_ = augmented[row][col] / augmented[col][col]
for cola in range(__lowerCamelCase, size + 1 ):
augmented[row][cola] -= augmented[col][cola] * ratio
# round to get rid of numbers like 2.000000000000004
return [
[round(augmented[row][size] / augmented[row][row], 10 )] for row in range(__lowerCamelCase )
]
def A__ ( __lowerCamelCase ):
SCREAMING_SNAKE_CASE_ = len(__lowerCamelCase )
SCREAMING_SNAKE_CASE_ = [[0 for _ in range(__lowerCamelCase )] for _ in range(__lowerCamelCase )]
SCREAMING_SNAKE_CASE_ = [[0] for _ in range(__lowerCamelCase )]
SCREAMING_SNAKE_CASE_ = 42
SCREAMING_SNAKE_CASE_ = 42
SCREAMING_SNAKE_CASE_ = 42
SCREAMING_SNAKE_CASE_ = 42
for x_val, y_val in enumerate(__lowerCamelCase ):
for col in range(__lowerCamelCase ):
SCREAMING_SNAKE_CASE_ = (x_val + 1) ** (size - col - 1)
SCREAMING_SNAKE_CASE_ = y_val
SCREAMING_SNAKE_CASE_ = solve(__lowerCamelCase, __lowerCamelCase )
def interpolated_func(__lowerCamelCase ) -> int:
return sum(
round(coeffs[x_val][0] ) * (var ** (size - x_val - 1))
for x_val in range(__lowerCamelCase ) )
return interpolated_func
def A__ ( __lowerCamelCase ):
return (
1
- variable
+ variable**2
- variable**3
+ variable**4
- variable**5
+ variable**6
- variable**7
+ variable**8
- variable**9
+ variable**10
)
def A__ ( __lowerCamelCase = question_function, __lowerCamelCase = 10 ):
SCREAMING_SNAKE_CASE_ = [func(__lowerCamelCase ) for x_val in range(1, order + 1 )]
SCREAMING_SNAKE_CASE_ = [
interpolate(data_points[:max_coeff] ) for max_coeff in range(1, order + 1 )
]
SCREAMING_SNAKE_CASE_ = 0
SCREAMING_SNAKE_CASE_ = 42
SCREAMING_SNAKE_CASE_ = 42
for poly in polynomials:
SCREAMING_SNAKE_CASE_ = 1
while func(__lowerCamelCase ) == poly(__lowerCamelCase ):
x_val += 1
ret += poly(__lowerCamelCase )
return ret
if __name__ == "__main__":
print(F"""{solution() = }""")
| 299 | 0 |
"""simple docstring"""
def _snake_case ( lowercase__ : float , lowercase__ : float , lowercase__ : int ) -> float:
'''simple docstring'''
if principal <= 0:
raise Exception("""Principal borrowed must be > 0""" )
if rate_per_annum < 0:
raise Exception("""Rate of interest must be >= 0""" )
if years_to_repay <= 0 or not isinstance(lowercase__ , lowercase__ ):
raise Exception("""Years to repay must be an integer > 0""" )
# Yearly rate is divided by 12 to get monthly rate
lowerCAmelCase_ :Any = rate_per_annum / 1_2
# Years to repay is multiplied by 12 to get number of payments as payment is monthly
lowerCAmelCase_ :Optional[int] = years_to_repay * 1_2
return (
principal
* rate_per_month
* (1 + rate_per_month) ** number_of_payments
/ ((1 + rate_per_month) ** number_of_payments - 1)
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 1 |
"""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
from accelerate.local_sgd import LocalSGD
########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to 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
#
########################################################################
__UpperCAmelCase = 16
__UpperCAmelCase = 32
def _snake_case ( lowercase__ : Accelerator , lowercase__ : int = 1_6 ) -> str:
'''simple docstring'''
lowerCAmelCase_ :List[str] = AutoTokenizer.from_pretrained("""bert-base-cased""" )
lowerCAmelCase_ :Optional[Any] = load_dataset("""glue""" , """mrpc""" )
def tokenize_function(lowercase__ : int ):
# max_length=None => use the model max length (it's actually the default)
lowerCAmelCase_ :Optional[int] = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=lowercase__ , max_length=lowercase__ )
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():
lowerCAmelCase_ :Optional[Any] = datasets.map(
lowercase__ , batched=lowercase__ , 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
lowerCAmelCase_ :str = tokenized_datasets.rename_column("""label""" , """labels""" )
def collate_fn(lowercase__ : Dict ):
# On TPU it's best to pad everything to the same length or training will be very slow.
lowerCAmelCase_ :int = 1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
lowerCAmelCase_ :List[Any] = 1_6
elif accelerator.mixed_precision != "no":
lowerCAmelCase_ :List[str] = 8
else:
lowerCAmelCase_ :Optional[int] = None
return tokenizer.pad(
lowercase__ , padding="""longest""" , max_length=lowercase__ , pad_to_multiple_of=lowercase__ , return_tensors="""pt""" , )
# Instantiate dataloaders.
lowerCAmelCase_ :Optional[Any] = DataLoader(
tokenized_datasets["""train"""] , shuffle=lowercase__ , collate_fn=lowercase__ , batch_size=lowercase__ )
lowerCAmelCase_ :List[Any] = DataLoader(
tokenized_datasets["""validation"""] , shuffle=lowercase__ , collate_fn=lowercase__ , batch_size=lowercase__ )
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
__UpperCAmelCase = mocked_dataloaders # noqa: F811
def _snake_case ( lowercase__ : List[Any] , lowercase__ : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
if os.environ.get("""TESTING_MOCKED_DATALOADERS""" , lowercase__ ) == "1":
lowerCAmelCase_ :Optional[Any] = 2
# New Code #
lowerCAmelCase_ :List[str] = int(args.gradient_accumulation_steps )
lowerCAmelCase_ :int = int(args.local_sgd_steps )
# Initialize accelerator
lowerCAmelCase_ :str = Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , gradient_accumulation_steps=lowercase__ )
if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
raise NotImplementedError("""LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)""" )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
lowerCAmelCase_ :int = config["""lr"""]
lowerCAmelCase_ :Union[str, Any] = int(config["""num_epochs"""] )
lowerCAmelCase_ :int = int(config["""seed"""] )
lowerCAmelCase_ :Union[str, Any] = int(config["""batch_size"""] )
lowerCAmelCase_ :Union[str, Any] = evaluate.load("""glue""" , """mrpc""" )
set_seed(lowercase__ )
lowerCAmelCase_ , lowerCAmelCase_ :Optional[int] = get_dataloaders(lowercase__ , lowercase__ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
lowerCAmelCase_ :Optional[int] = AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" , return_dict=lowercase__ )
# 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_ :Union[str, Any] = model.to(accelerator.device )
# Instantiate optimizer
lowerCAmelCase_ :Optional[Any] = AdamW(params=model.parameters() , lr=lowercase__ )
# Instantiate scheduler
lowerCAmelCase_ :Union[str, Any] = get_linear_schedule_with_warmup(
optimizer=lowercase__ , num_warmup_steps=1_0_0 , num_training_steps=(len(lowercase__ ) * 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.
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ :Union[str, Any] = accelerator.prepare(
lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ )
# Now we train the model
for epoch in range(lowercase__ ):
model.train()
with LocalSGD(
accelerator=lowercase__ , model=lowercase__ , local_sgd_steps=lowercase__ , enabled=local_sgd_steps is not None ) as local_sgd:
for step, batch in enumerate(lowercase__ ):
# 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(lowercase__ ):
lowerCAmelCase_ :str = model(**lowercase__ )
lowerCAmelCase_ :Optional[int] = output.loss
accelerator.backward(lowercase__ )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# LocalSGD-specific line
local_sgd.step()
model.eval()
for step, batch in enumerate(lowercase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
lowerCAmelCase_ :Optional[int] = model(**lowercase__ )
lowerCAmelCase_ :Optional[int] = outputs.logits.argmax(dim=-1 )
lowerCAmelCase_ , lowerCAmelCase_ :Union[str, Any] = accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
metric.add_batch(
predictions=lowercase__ , references=lowercase__ , )
lowerCAmelCase_ :Any = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f"""epoch {epoch}:""" , lowercase__ )
def _snake_case ( ) -> Tuple:
'''simple docstring'''
lowerCAmelCase_ :str = argparse.ArgumentParser(description="""Simple example of training script.""" )
parser.add_argument(
"""--mixed_precision""" , type=lowercase__ , default=lowercase__ , 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=lowercase__ , default=1 , help="""The number of minibatches to be ran before gradients are accumulated.""" , )
parser.add_argument(
"""--local_sgd_steps""" , type=lowercase__ , default=8 , help="""Number of local SGD steps or None to disable local SGD""" )
parser.add_argument("""--cpu""" , action="""store_true""" , help="""If passed, will train on the CPU.""" )
lowerCAmelCase_ :Optional[Any] = parser.parse_args()
lowerCAmelCase_ :Tuple = {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 1_6}
training_function(lowercase__ , lowercase__ )
if __name__ == "__main__":
main()
| 1 | 1 |
from __future__ import annotations
from decimal import Decimal
from math import * # noqa: F403
from sympy import diff
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = 10**-10 )-> float:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] =a
while True:
UpperCAmelCase : Union[str, Any] =Decimal(_A ) - (
Decimal(eval(_A ) ) / Decimal(eval(str(diff(_A ) ) ) ) # noqa: S307
)
# This number dictates the accuracy of the answer
if abs(eval(_A ) ) < precision: # noqa: S307
return float(_A )
# Let's Execute
if __name__ == "__main__":
# Find root of trigonometric function
# Find value of pi
print(f'The root of sin(x) = 0 is {newton_raphson("sin(x)", 2)}')
# Find root of polynomial
print(f'The root of x**2 - 5*x + 2 = 0 is {newton_raphson("x**2 - 5*x + 2", 0.4)}')
# Find Square Root of 5
print(f'The root of log(x) - 1 = 0 is {newton_raphson("log(x) - 1", 2)}')
# Exponential Roots
print(f'The root of exp(x) - 1 = 0 is {newton_raphson("exp(x) - 1", 0)}')
| 348 | '''simple docstring'''
def snake_case__ ( _A: str ) -> list[int]:
'''simple docstring'''
lowerCAmelCase = [0 for i in range(len(_A ) )]
# initialize interval's left pointer and right pointer
lowerCAmelCase , lowerCAmelCase = 0, 0
for i in range(1 , len(_A ) ):
# case when current index is inside the interval
if i <= right_pointer:
lowerCAmelCase = min(right_pointer - i + 1 , z_result[i - left_pointer] )
lowerCAmelCase = min_edge
while go_next(_A , _A , _A ):
z_result[i] += 1
# if new index's result gives us more right interval,
# we've to update left_pointer and right_pointer
if i + z_result[i] - 1 > right_pointer:
lowerCAmelCase , lowerCAmelCase = i, i + z_result[i] - 1
return z_result
def snake_case__ ( _A: int , _A: list[int] , _A: str ) -> bool:
'''simple docstring'''
return i + z_result[i] < len(_A ) and s[z_result[i]] == s[i + z_result[i]]
def snake_case__ ( _A: str , _A: str ) -> int:
'''simple docstring'''
lowerCAmelCase = 0
# concatenate 'pattern' and 'input_str' and call z_function
# with concatenated string
lowerCAmelCase = z_function(pattern + input_str )
for val in z_result:
# if value is greater then length of the pattern string
# that means this index is starting position of substring
# which is equal to pattern string
if val >= len(_A ):
answer += 1
return answer
if __name__ == "__main__":
import doctest
doctest.testmod()
| 272 | 0 |
'''simple docstring'''
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def lowercase (_A ):
"""simple docstring"""
return sum(param.float().sum() if 'encoder.embeddings' not in key else 0 for key, param in state_dict.items() )
def lowercase (_A , _A ):
"""simple docstring"""
_lowerCAmelCase : List[str] = {}
for key, value in state_dict.items():
if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
continue
_lowerCAmelCase : int = key.replace('heads.cmd.mim_head.cls.predictions' , 'mmm_image_head' )
_lowerCAmelCase : str = key.replace('heads.cmd.mlm_head.cls.predictions' , 'mmm_text_head' )
_lowerCAmelCase : Optional[Any] = key.replace('heads.cmd.itm_head.cls' , 'itm_head' )
_lowerCAmelCase : str = key.replace('heads.cmd.itm_head.pooler' , 'itm_head.pooler' )
_lowerCAmelCase : List[str] = key.replace('heads.cmd.clip_head.logit_scale' , 'flava.logit_scale' )
_lowerCAmelCase : int = key.replace('heads.fairseq_mlm.cls.predictions' , 'mlm_head' )
_lowerCAmelCase : str = key.replace('heads.imagenet.mim_head.cls.predictions' , 'mim_head' )
_lowerCAmelCase : Dict = key.replace('mm_text_projection' , 'flava.text_to_mm_projection' )
_lowerCAmelCase : int = key.replace('mm_image_projection' , 'flava.image_to_mm_projection' )
_lowerCAmelCase : Optional[Any] = key.replace('image_encoder.module' , 'flava.image_model' )
_lowerCAmelCase : Tuple = key.replace('text_encoder.module' , 'flava.text_model' )
_lowerCAmelCase : int = key.replace('mm_encoder.module.encoder.cls_token' , 'flava.multimodal_model.cls_token' )
_lowerCAmelCase : int = key.replace('mm_encoder.module' , 'flava.multimodal_model' )
_lowerCAmelCase : Optional[int] = key.replace('text_projection' , 'flava.text_projection' )
_lowerCAmelCase : Dict = key.replace('image_projection' , 'flava.image_projection' )
_lowerCAmelCase : Tuple = value.float()
for key, value in codebook_state_dict.items():
_lowerCAmelCase : str = value
return upgrade
@torch.no_grad()
def lowercase (_A , _A , _A , _A=None ):
"""simple docstring"""
if config_path is not None:
_lowerCAmelCase : Optional[Any] = FlavaConfig.from_pretrained(_A )
else:
_lowerCAmelCase : Optional[int] = FlavaConfig()
_lowerCAmelCase : Optional[int] = FlavaForPreTraining(_A ).eval()
_lowerCAmelCase : str = convert_dalle_checkpoint(_A , _A , save_checkpoint=_A )
if os.path.exists(_A ):
_lowerCAmelCase : Optional[Any] = torch.load(_A , map_location='cpu' )
else:
_lowerCAmelCase : Tuple = torch.hub.load_state_dict_from_url(_A , map_location='cpu' )
_lowerCAmelCase : List[Any] = upgrade_state_dict(_A , _A )
hf_model.load_state_dict(_A )
_lowerCAmelCase : str = hf_model.state_dict()
_lowerCAmelCase : str = count_parameters(_A )
_lowerCAmelCase : Any = count_parameters(_A ) + count_parameters(_A )
assert torch.allclose(_A , _A , atol=1E-3 )
hf_model.save_pretrained(_A )
if __name__ == "__main__":
lowerCAmelCase : 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 flava checkpoint""")
parser.add_argument("""--codebook_path""", default=None, type=str, help="""Path to flava codebook checkpoint""")
parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
lowerCAmelCase : Any = parser.parse_args()
convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)
| 25 |
'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging
lowerCAmelCase : Optional[Any] = logging.get_logger(__name__)
lowerCAmelCase : Optional[Any] = {
"""CarlCochet/trajectory-transformer-halfcheetah-medium-v2""": (
"""https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/resolve/main/config.json"""
),
# See all TrajectoryTransformer models at https://huggingface.co/models?filter=trajectory_transformer
}
class UpperCamelCase__ ( SCREAMING_SNAKE_CASE_ ):
"""simple docstring"""
__magic_name__ = "trajectory_transformer"
__magic_name__ = ["past_key_values"]
__magic_name__ = {
"hidden_size": "n_embd",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__( self , snake_case__=100 , snake_case__=5 , snake_case__=1 , snake_case__=1 , snake_case__=249 , snake_case__=6 , snake_case__=17 , snake_case__=25 , snake_case__=4 , snake_case__=4 , snake_case__=128 , snake_case__=0.1 , snake_case__=0.1 , snake_case__=0.1 , snake_case__=0.0006 , snake_case__=512 , snake_case__=0.02 , snake_case__=1E-12 , snake_case__=1 , snake_case__=True , snake_case__=1 , snake_case__=5_0256 , snake_case__=5_0256 , **snake_case__ , ):
'''simple docstring'''
_lowerCAmelCase : List[Any] = vocab_size
_lowerCAmelCase : Any = action_weight
_lowerCAmelCase : Optional[int] = reward_weight
_lowerCAmelCase : Union[str, Any] = value_weight
_lowerCAmelCase : List[str] = max_position_embeddings
_lowerCAmelCase : Tuple = block_size
_lowerCAmelCase : List[Any] = action_dim
_lowerCAmelCase : List[Any] = observation_dim
_lowerCAmelCase : Union[str, Any] = transition_dim
_lowerCAmelCase : Tuple = learning_rate
_lowerCAmelCase : int = n_layer
_lowerCAmelCase : Any = n_head
_lowerCAmelCase : Tuple = n_embd
_lowerCAmelCase : Optional[Any] = embd_pdrop
_lowerCAmelCase : Union[str, Any] = attn_pdrop
_lowerCAmelCase : Any = resid_pdrop
_lowerCAmelCase : Optional[Any] = initializer_range
_lowerCAmelCase : List[Any] = layer_norm_eps
_lowerCAmelCase : Union[str, Any] = kaiming_initializer_range
_lowerCAmelCase : List[Any] = use_cache
super().__init__(pad_token_id=snake_case__ , bos_token_id=snake_case__ , eos_token_id=snake_case__ , **snake_case__ )
| 25 | 1 |
def a ( A__ : int ) -> Union[str, Any]:
"""simple docstring"""
if a < 0:
raise ValueError('Input value must be a positive integer' )
elif isinstance(A__ , A__ ):
raise TypeError('Input value must be a \'int\' type' )
return bin(A__ ).count('1' )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 205 |
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 _a ( SCREAMING_SNAKE_CASE : Optional[Any] , SCREAMING_SNAKE_CASE : Any ):
"""simple docstring"""
UpperCamelCase__ : List[Any] = torch.load(SCREAMING_SNAKE_CASE , map_location='''cpu''' )
UpperCamelCase__ : str = chkpt['''model''']
# We have the base model one level deeper than the original XLM repository
UpperCamelCase__ : int = {}
for k, v in state_dict.items():
if "pred_layer" in k:
UpperCamelCase__ : Optional[int] = v
else:
UpperCamelCase__ : Tuple = v
UpperCamelCase__ : Union[str, Any] = chkpt['''params''']
UpperCamelCase__ : Optional[Any] = {n: v for n, v in config.items() if not isinstance(SCREAMING_SNAKE_CASE , (torch.FloatTensor, numpy.ndarray) )}
UpperCamelCase__ : Dict = chkpt['''dico_word2id''']
UpperCamelCase__ : Dict = {s + '''</w>''' if s.find('''@@''' ) == -1 and i > 13 else s.replace('''@@''' , '''''' ): i for s, i in vocab.items()}
# Save pytorch-model
UpperCamelCase__ : List[str] = pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
UpperCamelCase__ : Optional[Any] = pytorch_dump_folder_path + '''/''' + CONFIG_NAME
UpperCamelCase__ : Any = pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''vocab_file''']
print(F"Save PyTorch model to {pytorch_weights_dump_path}" )
torch.save(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
print(F"Save configuration file to {pytorch_config_dump_path}" )
with open(SCREAMING_SNAKE_CASE , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(SCREAMING_SNAKE_CASE , indent=2 ) + '''\n''' )
print(F"Save vocab file to {pytorch_config_dump_path}" )
with open(SCREAMING_SNAKE_CASE , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(SCREAMING_SNAKE_CASE , indent=2 ) + '''\n''' )
if __name__ == "__main__":
__UpperCamelCase : Optional[int] = 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."
)
__UpperCamelCase : Optional[int] = parser.parse_args()
convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)
| 146 | 0 |
"""simple docstring"""
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu
class lowerCAmelCase__ ( unittest.TestCase ):
'''simple docstring'''
def A_ ( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def A_ ( self ):
_lowerCamelCase : Dict = 1
_lowerCamelCase : Optional[int] = 3
_lowerCamelCase : str = (32, 32)
_lowerCamelCase : Optional[int] = floats_tensor((batch_size, num_channels) + sizes , rng=random.Random(0 ) ).to(lowercase )
return image
@property
def A_ ( self ):
torch.manual_seed(0 )
_lowerCamelCase : str = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') , up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D') , cross_attention_dim=32 , )
return model
@property
def A_ ( self ):
torch.manual_seed(0 )
_lowerCamelCase : Union[str, Any] = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] , up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] , latent_channels=4 , )
return model
@property
def A_ ( self ):
torch.manual_seed(0 )
_lowerCamelCase : List[Any] = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
return CLIPTextModel(lowercase )
@property
def A_ ( self ):
def extract(*lowercase , **lowercase ):
class lowerCAmelCase__ :
'''simple docstring'''
def __init__( self ):
_lowerCamelCase : Any = torch.ones([0] )
def A_ ( self , lowercase ):
self.pixel_values.to(lowercase )
return self
return Out()
return extract
def A_ ( self ):
_lowerCamelCase : int = 'cpu' # ensure determinism for the device-dependent torch.Generator
_lowerCamelCase : int = self.dummy_cond_unet
_lowerCamelCase : Union[str, Any] = DDIMScheduler(
beta_start=0.0_00_85 , beta_end=0.0_12 , beta_schedule='scaled_linear' , clip_sample=lowercase , set_alpha_to_one=lowercase , )
_lowerCamelCase : List[Any] = self.dummy_vae
_lowerCamelCase : int = self.dummy_text_encoder
_lowerCamelCase : Tuple = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
# make sure here that pndm scheduler skips prk
_lowerCamelCase : str = StableDiffusionPipeline(
unet=lowercase , scheduler=lowercase , vae=lowercase , text_encoder=lowercase , tokenizer=lowercase , safety_checker=lowercase , feature_extractor=self.dummy_extractor , )
_lowerCamelCase : Dict = sd_pipe.to(lowercase )
sd_pipe.set_progress_bar_config(disable=lowercase )
_lowerCamelCase : List[str] = 'A painting of a squirrel eating a burger'
_lowerCamelCase : str = torch.Generator(device=lowercase ).manual_seed(0 )
_lowerCamelCase : Dict = sd_pipe([prompt] , generator=lowercase , guidance_scale=6.0 , num_inference_steps=2 , output_type='np' )
_lowerCamelCase : List[str] = output.images
_lowerCamelCase : Optional[Any] = torch.Generator(device=lowercase ).manual_seed(0 )
_lowerCamelCase : Union[str, Any] = sd_pipe(
[prompt] , generator=lowercase , guidance_scale=6.0 , num_inference_steps=2 , output_type='np' , return_dict=lowercase , )[0]
_lowerCamelCase : str = image[0, -3:, -3:, -1]
_lowerCamelCase : Optional[int] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
_lowerCamelCase : str = np.array([0.57_56, 0.61_18, 0.50_05, 0.50_41, 0.54_71, 0.47_26, 0.49_76, 0.48_65, 0.48_64] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
def A_ ( self ):
_lowerCamelCase : List[Any] = 'cpu' # ensure determinism for the device-dependent torch.Generator
_lowerCamelCase : List[Any] = self.dummy_cond_unet
_lowerCamelCase : Tuple = PNDMScheduler(skip_prk_steps=lowercase )
_lowerCamelCase : List[str] = self.dummy_vae
_lowerCamelCase : List[str] = self.dummy_text_encoder
_lowerCamelCase : Union[str, Any] = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
# make sure here that pndm scheduler skips prk
_lowerCamelCase : str = StableDiffusionPipeline(
unet=lowercase , scheduler=lowercase , vae=lowercase , text_encoder=lowercase , tokenizer=lowercase , safety_checker=lowercase , feature_extractor=self.dummy_extractor , )
_lowerCamelCase : Optional[int] = sd_pipe.to(lowercase )
sd_pipe.set_progress_bar_config(disable=lowercase )
_lowerCamelCase : Tuple = 'A painting of a squirrel eating a burger'
_lowerCamelCase : int = torch.Generator(device=lowercase ).manual_seed(0 )
_lowerCamelCase : List[Any] = sd_pipe([prompt] , generator=lowercase , guidance_scale=6.0 , num_inference_steps=2 , output_type='np' )
_lowerCamelCase : Union[str, Any] = output.images
_lowerCamelCase : str = torch.Generator(device=lowercase ).manual_seed(0 )
_lowerCamelCase : Any = sd_pipe(
[prompt] , generator=lowercase , guidance_scale=6.0 , num_inference_steps=2 , output_type='np' , return_dict=lowercase , )[0]
_lowerCamelCase : List[Any] = image[0, -3:, -3:, -1]
_lowerCamelCase : List[str] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
_lowerCamelCase : str = np.array([0.51_25, 0.57_16, 0.48_28, 0.50_60, 0.56_50, 0.47_68, 0.51_85, 0.48_95, 0.49_93] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
def A_ ( self ):
_lowerCamelCase : Union[str, Any] = StableDiffusionPipeline.from_pretrained(
'hf-internal-testing/tiny-stable-diffusion-lms-pipe' , safety_checker=lowercase )
assert isinstance(lowercase , lowercase )
assert isinstance(pipe.scheduler , lowercase )
assert pipe.safety_checker is None
_lowerCamelCase : Any = pipe('example prompt' , num_inference_steps=2 ).images[0]
assert image is not None
# check that there's no error when saving a pipeline with one of the models being None
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(lowercase )
_lowerCamelCase : Dict = StableDiffusionPipeline.from_pretrained(lowercase )
# sanity check that the pipeline still works
assert pipe.safety_checker is None
_lowerCamelCase : Tuple = pipe('example prompt' , num_inference_steps=2 ).images[0]
assert image is not None
@unittest.skipIf(torch_device != 'cuda' , 'This test requires a GPU' )
def A_ ( self ):
_lowerCamelCase : int = self.dummy_cond_unet
_lowerCamelCase : Optional[int] = PNDMScheduler(skip_prk_steps=lowercase )
_lowerCamelCase : Any = self.dummy_vae
_lowerCamelCase : Union[str, Any] = self.dummy_text_encoder
_lowerCamelCase : Tuple = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
# put models in fp16
_lowerCamelCase : Optional[Any] = unet.half()
_lowerCamelCase : Any = vae.half()
_lowerCamelCase : Tuple = bert.half()
# make sure here that pndm scheduler skips prk
_lowerCamelCase : Tuple = StableDiffusionPipeline(
unet=lowercase , scheduler=lowercase , vae=lowercase , text_encoder=lowercase , tokenizer=lowercase , safety_checker=lowercase , feature_extractor=self.dummy_extractor , )
_lowerCamelCase : Optional[Any] = sd_pipe.to(lowercase )
sd_pipe.set_progress_bar_config(disable=lowercase )
_lowerCamelCase : Any = 'A painting of a squirrel eating a burger'
_lowerCamelCase : List[Any] = sd_pipe([prompt] , num_inference_steps=2 , output_type='np' ).images
assert image.shape == (1, 64, 64, 3)
@nightly
@require_torch_gpu
class lowerCAmelCase__ ( unittest.TestCase ):
'''simple docstring'''
def A_ ( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def A_ ( self ):
_lowerCamelCase : Dict = StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5' , safety_checker=lowercase )
_lowerCamelCase : Optional[Any] = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config )
_lowerCamelCase : Any = sd_pipe.to(lowercase )
sd_pipe.set_progress_bar_config(disable=lowercase )
_lowerCamelCase : Tuple = (
'portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle'
' coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with'
' anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and'
' children from bahnhof zoo, detailed '
)
_lowerCamelCase : Dict = 4003660346
_lowerCamelCase : str = 7
# without safety guidance (sld_guidance_scale = 0)
_lowerCamelCase : Dict = torch.manual_seed(lowercase )
_lowerCamelCase : List[Any] = sd_pipe(
[prompt] , generator=lowercase , guidance_scale=lowercase , num_inference_steps=50 , output_type='np' , width=512 , height=512 , sld_guidance_scale=0 , )
_lowerCamelCase : int = output.images
_lowerCamelCase : Optional[Any] = image[0, -3:, -3:, -1]
_lowerCamelCase : List[str] = [0.22_78, 0.22_31, 0.22_49, 0.23_33, 0.23_03, 0.18_85, 0.22_73, 0.21_44, 0.21_76]
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
# without safety guidance (strong configuration)
_lowerCamelCase : str = torch.manual_seed(lowercase )
_lowerCamelCase : Optional[int] = sd_pipe(
[prompt] , generator=lowercase , guidance_scale=lowercase , num_inference_steps=50 , output_type='np' , width=512 , height=512 , sld_guidance_scale=2000 , sld_warmup_steps=7 , sld_threshold=0.0_25 , sld_momentum_scale=0.5 , sld_mom_beta=0.7 , )
_lowerCamelCase : Dict = output.images
_lowerCamelCase : Tuple = image[0, -3:, -3:, -1]
_lowerCamelCase : List[str] = [0.23_83, 0.22_76, 0.2_36, 0.21_92, 0.21_86, 0.20_53, 0.19_71, 0.19_01, 0.17_19]
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def A_ ( self ):
_lowerCamelCase : List[Any] = StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5' , safety_checker=lowercase )
_lowerCamelCase : Dict = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config )
_lowerCamelCase : Dict = sd_pipe.to(lowercase )
sd_pipe.set_progress_bar_config(disable=lowercase )
_lowerCamelCase : int = 'padme amidala taking a bath artwork, safe for work, no nudity'
_lowerCamelCase : Tuple = 2734971755
_lowerCamelCase : Tuple = 7
_lowerCamelCase : Optional[int] = torch.manual_seed(lowercase )
_lowerCamelCase : Tuple = sd_pipe(
[prompt] , generator=lowercase , guidance_scale=lowercase , num_inference_steps=50 , output_type='np' , width=512 , height=512 , sld_guidance_scale=0 , )
_lowerCamelCase : int = output.images
_lowerCamelCase : Tuple = image[0, -3:, -3:, -1]
_lowerCamelCase : Any = [0.35_02, 0.36_22, 0.33_96, 0.36_42, 0.34_78, 0.33_18, 0.35, 0.33_48, 0.32_97]
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
_lowerCamelCase : Tuple = torch.manual_seed(lowercase )
_lowerCamelCase : Any = sd_pipe(
[prompt] , generator=lowercase , guidance_scale=lowercase , num_inference_steps=50 , output_type='np' , width=512 , height=512 , sld_guidance_scale=2000 , sld_warmup_steps=7 , sld_threshold=0.0_25 , sld_momentum_scale=0.5 , sld_mom_beta=0.7 , )
_lowerCamelCase : Union[str, Any] = output.images
_lowerCamelCase : Dict = image[0, -3:, -3:, -1]
_lowerCamelCase : Optional[int] = [0.55_31, 0.52_06, 0.48_95, 0.51_56, 0.51_82, 0.47_51, 0.48_02, 0.48_03, 0.44_43]
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def A_ ( self ):
_lowerCamelCase : Tuple = StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5' )
_lowerCamelCase : Union[str, Any] = sd_pipe.to(lowercase )
sd_pipe.set_progress_bar_config(disable=lowercase )
_lowerCamelCase : Optional[Any] = (
'the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c.'
' leyendecker'
)
_lowerCamelCase : int = 1044355234
_lowerCamelCase : Any = 12
_lowerCamelCase : List[Any] = torch.manual_seed(lowercase )
_lowerCamelCase : Tuple = sd_pipe(
[prompt] , generator=lowercase , guidance_scale=lowercase , num_inference_steps=50 , output_type='np' , width=512 , height=512 , sld_guidance_scale=0 , )
_lowerCamelCase : Union[str, Any] = output.images
_lowerCamelCase : List[str] = image[0, -3:, -3:, -1]
_lowerCamelCase : Union[str, Any] = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] )
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-7
_lowerCamelCase : Union[str, Any] = torch.manual_seed(lowercase )
_lowerCamelCase : Dict = sd_pipe(
[prompt] , generator=lowercase , guidance_scale=lowercase , num_inference_steps=50 , output_type='np' , width=512 , height=512 , sld_guidance_scale=2000 , sld_warmup_steps=7 , sld_threshold=0.0_25 , sld_momentum_scale=0.5 , sld_mom_beta=0.7 , )
_lowerCamelCase : str = output.images
_lowerCamelCase : List[Any] = image[0, -3:, -3:, -1]
_lowerCamelCase : int = np.array([0.58_18, 0.62_85, 0.68_35, 0.60_19, 0.6_25, 0.67_54, 0.60_96, 0.63_34, 0.65_61] )
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2 | 12 |
"""simple docstring"""
from __future__ import annotations
import unittest
import numpy as np
from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import GPTaTokenizer, TFOPTForCausalLM, TFOPTModel
def _snake_case ( lowercase__ , lowercase__ , lowercase__=None , lowercase__=None ):
if attention_mask is None:
_lowerCamelCase : List[str] = tf.cast(tf.math.not_equal(lowercase__ , config.pad_token_id ) , tf.inta )
return {"input_ids": input_ids, "attention_mask": attention_mask}
@require_tf
class lowerCAmelCase__ :
'''simple docstring'''
lowerCamelCase__ = OPTConfig
lowerCamelCase__ = {}
lowerCamelCase__ = """gelu"""
def __init__( self , lowercase , lowercase=13 , lowercase=7 , lowercase=True , lowercase=False , lowercase=99 , lowercase=16 , lowercase=2 , lowercase=4 , lowercase=4 , lowercase="gelu" , lowercase=0.1 , lowercase=0.1 , lowercase=20 , lowercase=2 , lowercase=1 , lowercase=0 , lowercase=16 , lowercase=16 , ):
_lowerCamelCase : Tuple = parent
_lowerCamelCase : Any = batch_size
_lowerCamelCase : Tuple = seq_length
_lowerCamelCase : str = is_training
_lowerCamelCase : Optional[int] = use_labels
_lowerCamelCase : List[Any] = vocab_size
_lowerCamelCase : Dict = hidden_size
_lowerCamelCase : str = num_hidden_layers
_lowerCamelCase : Optional[int] = num_attention_heads
_lowerCamelCase : Any = intermediate_size
_lowerCamelCase : Dict = hidden_act
_lowerCamelCase : Any = hidden_dropout_prob
_lowerCamelCase : List[str] = attention_probs_dropout_prob
_lowerCamelCase : Optional[Any] = max_position_embeddings
_lowerCamelCase : List[Any] = eos_token_id
_lowerCamelCase : Tuple = pad_token_id
_lowerCamelCase : List[str] = bos_token_id
_lowerCamelCase : Optional[int] = embed_dim
_lowerCamelCase : List[str] = word_embed_proj_dim
_lowerCamelCase : Any = False
def A_ ( self ):
_lowerCamelCase : Optional[int] = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
_lowerCamelCase : Optional[int] = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
_lowerCamelCase : str = tf.concat([input_ids, eos_tensor] , axis=1 )
_lowerCamelCase : Tuple = self.config_cls(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_id=self.eos_token_id , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , embed_dim=self.embed_dim , word_embed_proj_dim=self.word_embed_proj_dim , is_encoder_decoder=lowercase , **self.config_updates , )
_lowerCamelCase : int = prepare_opt_inputs_dict(lowercase , lowercase )
return config, inputs_dict
def A_ ( self , lowercase , lowercase ):
_lowerCamelCase : Optional[Any] = TFOPTModel(config=lowercase )
_lowerCamelCase : Optional[Any] = inputs_dict['input_ids']
_lowerCamelCase : str = input_ids[:1, :]
_lowerCamelCase : Dict = inputs_dict['attention_mask'][:1, :]
_lowerCamelCase : Optional[Any] = 1
# first forward pass
_lowerCamelCase : Any = model(lowercase , attention_mask=lowercase , use_cache=lowercase )
_lowerCamelCase, _lowerCamelCase : List[str] = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
_lowerCamelCase : Optional[Any] = ids_tensor((self.batch_size, 3) , config.vocab_size )
_lowerCamelCase : Optional[Any] = tf.cast(ids_tensor((self.batch_size, 3) , 2 ) , tf.inta )
# append to next input_ids and
_lowerCamelCase : List[Any] = tf.concat([input_ids, next_tokens] , axis=-1 )
_lowerCamelCase : Optional[int] = tf.concat([attention_mask, next_attn_mask] , axis=-1 )
_lowerCamelCase : Optional[Any] = model(lowercase , attention_mask=lowercase )[0]
_lowerCamelCase : List[str] = model(lowercase , attention_mask=lowercase , past_key_values=lowercase )[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1] )
# select random slice
_lowerCamelCase : Any = int(ids_tensor((1,) , output_from_past.shape[-1] ) )
_lowerCamelCase : Optional[int] = output_from_no_past[:, -3:, random_slice_idx]
_lowerCamelCase : List[str] = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(lowercase , lowercase , rtol=1E-3 )
@require_tf
class lowerCAmelCase__ ( lowercase, lowercase, unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ = (TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
lowerCamelCase__ = (TFOPTForCausalLM,) if is_tf_available() else ()
lowerCamelCase__ = (
{"""feature-extraction""": TFOPTModel, """text-generation""": TFOPTForCausalLM} if is_tf_available() else {}
)
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = 10
def A_ ( self ):
_lowerCamelCase : int = TFOPTModelTester(self )
_lowerCamelCase : Tuple = ConfigTester(self , config_class=lowercase )
def A_ ( self ):
self.config_tester.run_common_tests()
def A_ ( self ):
_lowerCamelCase : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*lowercase )
def A_ ( self ):
_lowerCamelCase, _lowerCamelCase : Any = self.model_tester.prepare_config_and_inputs_for_common()
def _get_word_embedding_weight(lowercase , lowercase ):
if hasattr(lowercase , 'weight' ):
return embedding_layer.weight
else:
# Here we build the word embeddings weights if not exists.
# And then we retry to get the attribute once built.
model.build()
if hasattr(lowercase , 'weight' ):
return embedding_layer.weight
else:
return None
for model_class in self.all_model_classes:
for size in [config.vocab_size - 10, config.vocab_size + 10]:
# build the embeddings
_lowerCamelCase : Optional[int] = model_class(config=lowercase )
_lowerCamelCase : int = _get_word_embedding_weight(lowercase , model.get_input_embeddings() )
_lowerCamelCase : Tuple = _get_word_embedding_weight(lowercase , model.get_output_embeddings() )
# reshape the embeddings
model.resize_token_embeddings(lowercase )
_lowerCamelCase : str = _get_word_embedding_weight(lowercase , model.get_input_embeddings() )
_lowerCamelCase : Any = _get_word_embedding_weight(lowercase , model.get_output_embeddings() )
# check that the resized embeddings size matches the desired size.
_lowerCamelCase : Union[str, Any] = size if size is not None else config.vocab_size
self.assertEqual(new_input_embeddings.shape[0] , lowercase )
# check that weights remain the same after resizing
_lowerCamelCase : int = True
for pa, pa in zip(old_input_embeddings.value() , new_input_embeddings.value() ):
if tf.math.reduce_sum(tf.math.abs(pa - pa ) ) > 0:
_lowerCamelCase : Optional[Any] = False
self.assertTrue(lowercase )
if old_output_embeddings is not None and new_output_embeddings is not None:
self.assertEqual(new_output_embeddings.shape[0] , lowercase )
_lowerCamelCase : Dict = True
for pa, pa in zip(old_output_embeddings.value() , new_output_embeddings.value() ):
if tf.math.reduce_sum(tf.math.abs(pa - pa ) ) > 0:
_lowerCamelCase : Union[str, Any] = False
self.assertTrue(lowercase )
def _snake_case ( lowercase__ ):
return tf.constant(lowercase__ , dtype=tf.intaa )
@require_tf
class lowerCAmelCase__ ( unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ = 99
def A_ ( self ):
_lowerCamelCase : Tuple = tf.ones((4, 1) , dtype=tf.intaa ) * 2
_lowerCamelCase : Tuple = tf.concat([ids_tensor((4, 6) , self.vocab_size - 3 ) + 3, eos_column_vector] , axis=1 )
_lowerCamelCase : int = input_ids.shape[0]
_lowerCamelCase : List[Any] = OPTConfig(
vocab_size=self.vocab_size , hidden_size=24 , num_hidden_layers=2 , num_attention_heads=2 , ffn_dim=32 , max_position_embeddings=48 , eos_token_id=2 , pad_token_id=1 , bos_token_id=0 , )
return config, input_ids, batch_size
@require_sentencepiece
@require_tf
class lowerCAmelCase__ ( unittest.TestCase ):
'''simple docstring'''
@slow
def A_ ( self ):
_lowerCamelCase : Tuple = TFOPTModel.from_pretrained('facebook/opt-350m' )
_lowerCamelCase : List[Any] = _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]] )
_lowerCamelCase : List[str] = tf.not_equal(lowercase , model.config.pad_token_id )
with tf.GradientTape():
_lowerCamelCase : List[str] = model(input_ids=lowercase , attention_mask=lowercase ).last_hidden_state
_lowerCamelCase : Optional[Any] = (1, 11, 512)
self.assertEqual(output.shape , lowercase )
_lowerCamelCase : List[str] = tf.constant(
[[-0.28_73, -1.92_18, -0.30_33], [-1.27_10, -0.13_38, -0.19_02], [0.40_95, 0.12_14, -1.31_21]] )
self.assertTrue(np.allclose(output[:, :3, :3] , lowercase , atol=4E-3 ) )
_lowerCamelCase : List[str] = tf.function(lowercase , jit_compile=lowercase )
_lowerCamelCase : Union[str, Any] = xla_generate(lowercase , lowercase )[0]
self.assertTrue(np.allclose(output[:, :3, :3] , lowercase , atol=4E-2 ) )
@require_tf
@slow
class lowerCAmelCase__ ( unittest.TestCase ):
'''simple docstring'''
def A_ ( self ):
super().setUp()
_lowerCamelCase : List[Any] = 'facebook/opt-350m'
def A_ ( self ):
_lowerCamelCase : int = TFOPTForCausalLM.from_pretrained(self.path_model )
_lowerCamelCase : List[Any] = GPTaTokenizer.from_pretrained(self.path_model )
_lowerCamelCase : List[str] = [
'Today is a beautiful day and I want to',
'In the city of',
'Paris is the capital of France and',
'Computers and mobile phones have taken',
]
# verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
_lowerCamelCase : List[Any] = tokenizer(lowercase , return_tensors='tf' , padding=lowercase , add_special_tokens=lowercase )
_lowerCamelCase : Optional[int] = tf.math.reduce_mean(model(inputs.input_ids , attention_mask=inputs.attention_mask )[0] , axis=-1 )
_lowerCamelCase : Any = tf.constant(
[
[1.38_51, -13.89_23, -10.52_29, -10.75_33, -0.23_09, -10.23_84, -0.53_65, -9.09_47, -5.16_70],
[-4.70_73, -10.62_76, -3.94_15, -21.52_42, -0.28_22, -0.28_22, -0.28_22, -0.28_22, -0.28_22],
[0.62_47, -3.42_29, -8.91_79, -1.42_97, -14.16_50, 1.41_46, -9.02_18, -0.27_03, -0.27_03],
[6.47_83, -1.99_13, -10.79_26, -2.33_36, 1.50_92, -0.99_74, -6.82_13, 1.34_77, 1.34_77],
] )
self.assertTrue(np.allclose(lowercase , lowercase , atol=1E-4 ) )
_lowerCamelCase : Tuple = tf.function(lowercase , jit_compile=lowercase )
_lowerCamelCase : List[Any] = tf.math.reduce_mean(xla_generate(inputs.input_ids , attention_mask=inputs.attention_mask )[0] , axis=-1 )
self.assertTrue(np.allclose(lowercase , lowercase , atol=1E-4 ) )
@require_tf
@slow
class lowerCAmelCase__ ( unittest.TestCase ):
'''simple docstring'''
@property
def A_ ( self ):
return [
"Today is a beautiful day and I want",
"In the city of",
"Paris is the capital of France and",
"Computers and mobile phones have taken",
]
def A_ ( self ):
_lowerCamelCase : str = 'facebook/opt-125m'
_lowerCamelCase : Dict = [
'Today is a beautiful day and I want to',
'In the city of New York, the city',
'Paris is the capital of France and the capital',
'Computers and mobile phones have taken over the',
]
_lowerCamelCase : Optional[int] = []
_lowerCamelCase : Optional[int] = GPTaTokenizer.from_pretrained(lowercase )
_lowerCamelCase : Dict = TFOPTForCausalLM.from_pretrained(lowercase )
for prompt in self.prompts:
_lowerCamelCase : int = tokenizer(lowercase , return_tensors='tf' ).input_ids
_lowerCamelCase : int = model.generate(lowercase , max_length=10 )
_lowerCamelCase : Any = tokenizer.batch_decode(lowercase , skip_special_tokens=lowercase )
predicted_outputs += generated_string
self.assertListEqual(lowercase , lowercase )
def A_ ( self ):
_lowerCamelCase : List[Any] = 'facebook/opt-350m'
_lowerCamelCase : int = GPTaTokenizer.from_pretrained(lowercase )
_lowerCamelCase : Optional[int] = TFOPTForCausalLM.from_pretrained(lowercase )
_lowerCamelCase : Any = 'left'
# use different length sentences to test batching
_lowerCamelCase : Optional[int] = [
'Hello, my dog is a little',
'Today, I',
]
_lowerCamelCase : Dict = tokenizer(lowercase , return_tensors='tf' , padding=lowercase )
_lowerCamelCase : int = inputs['input_ids']
_lowerCamelCase : Tuple = model.generate(input_ids=lowercase , attention_mask=inputs['attention_mask'] )
_lowerCamelCase : Optional[int] = tokenizer(sentences[0] , return_tensors='tf' ).input_ids
_lowerCamelCase : Union[str, Any] = model.generate(input_ids=lowercase )
_lowerCamelCase : Dict = inputs_non_padded.shape[-1] - tf.math.reduce_sum(
tf.cast(inputs['attention_mask'][-1] , tf.intaa ) )
_lowerCamelCase : int = tokenizer(sentences[1] , return_tensors='tf' ).input_ids
_lowerCamelCase : Union[str, Any] = model.generate(input_ids=lowercase , max_length=model.config.max_length - num_paddings )
_lowerCamelCase : List[Any] = tokenizer.batch_decode(lowercase , skip_special_tokens=lowercase )
_lowerCamelCase : Union[str, Any] = tokenizer.decode(output_non_padded[0] , skip_special_tokens=lowercase )
_lowerCamelCase : Optional[Any] = tokenizer.decode(output_padded[0] , skip_special_tokens=lowercase )
_lowerCamelCase : Optional[Any] = [
'Hello, my dog is a little bit of a dork.\nI\'m a little bit',
'Today, I was in the middle of a conversation with a friend about the',
]
self.assertListEqual(lowercase , lowercase )
self.assertListEqual(lowercase , [non_padded_sentence, padded_sentence] )
def A_ ( self ):
_lowerCamelCase : Tuple = 'facebook/opt-350m'
_lowerCamelCase : List[Any] = [
'Today is a beautiful day and I want to',
'In the city of San Francisco, the city',
'Paris is the capital of France and the capital',
'Computers and mobile phones have taken over the',
]
_lowerCamelCase : Optional[int] = []
_lowerCamelCase : Optional[Any] = GPTaTokenizer.from_pretrained(lowercase )
_lowerCamelCase : Optional[Any] = TFOPTForCausalLM.from_pretrained(lowercase )
for prompt in self.prompts:
_lowerCamelCase : List[Any] = tokenizer(lowercase , return_tensors='tf' ).input_ids
_lowerCamelCase : Optional[Any] = model.generate(lowercase , max_length=10 )
_lowerCamelCase : Dict = tokenizer.batch_decode(lowercase , skip_special_tokens=lowercase )
predicted_outputs += generated_string
self.assertListEqual(lowercase , lowercase ) | 12 | 1 |
"""simple docstring"""
def lowercase ( A_ )-> bool:
'''simple docstring'''
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 40 |
from __future__ import annotations
import math
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ , lowerCamelCase__ ) -> list:
if len(lowerCamelCase__ ) != 2 or len(a[0] ) != 2 or len(lowerCamelCase__ ) != 2 or len(b[0] ) != 2:
raise Exception('Matrices are not 2x2' )
__lowerCamelCase : Optional[int] = [
[a[0][0] * b[0][0] + a[0][1] * b[1][0], a[0][0] * b[0][1] + a[0][1] * b[1][1]],
[a[1][0] * b[0][0] + a[1][1] * b[1][0], a[1][0] * b[0][1] + a[1][1] * b[1][1]],
]
return new_matrix
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ , lowerCamelCase__ ) -> Optional[int]:
return [
[matrix_a[row][col] + matrix_b[row][col] for col in range(len(matrix_a[row] ) )]
for row in range(len(lowerCamelCase__ ) )
]
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ , lowerCamelCase__ ) -> List[str]:
return [
[matrix_a[row][col] - matrix_b[row][col] for col in range(len(matrix_a[row] ) )]
for row in range(len(lowerCamelCase__ ) )
]
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ ) -> tuple[list, list, list, list]:
if len(lowerCamelCase__ ) % 2 != 0 or len(a[0] ) % 2 != 0:
raise Exception('Odd matrices are not supported!' )
__lowerCamelCase : Tuple = len(lowerCamelCase__ )
__lowerCamelCase : List[Any] = matrix_length // 2
__lowerCamelCase : Dict = [[a[i][j] for j in range(lowerCamelCase__ , lowerCamelCase__ )] for i in range(lowerCamelCase__ )]
__lowerCamelCase : str = [
[a[i][j] for j in range(lowerCamelCase__ , lowerCamelCase__ )] for i in range(lowerCamelCase__ , lowerCamelCase__ )
]
__lowerCamelCase : Dict = [[a[i][j] for j in range(lowerCamelCase__ )] for i in range(lowerCamelCase__ )]
__lowerCamelCase : Optional[Any] = [[a[i][j] for j in range(lowerCamelCase__ )] for i in range(lowerCamelCase__ , lowerCamelCase__ )]
return top_left, top_right, bot_left, bot_right
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ ) -> tuple[int, int]:
return len(lowerCamelCase__ ), len(matrix[0] )
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ ) -> None:
print('\n'.join(str(lowerCamelCase__ ) for line in matrix ) )
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ , lowerCamelCase__ ) -> list:
if matrix_dimensions(lowerCamelCase__ ) == (2, 2):
return default_matrix_multiplication(lowerCamelCase__ , lowerCamelCase__ )
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase : Optional[Any] = split_matrix(lowerCamelCase__ )
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase : Optional[Any] = split_matrix(lowerCamelCase__ )
__lowerCamelCase : str = actual_strassen(lowerCamelCase__ , matrix_subtraction(lowerCamelCase__ , lowerCamelCase__ ) )
__lowerCamelCase : List[str] = actual_strassen(matrix_addition(lowerCamelCase__ , lowerCamelCase__ ) , lowerCamelCase__ )
__lowerCamelCase : List[Any] = actual_strassen(matrix_addition(lowerCamelCase__ , lowerCamelCase__ ) , lowerCamelCase__ )
__lowerCamelCase : Tuple = actual_strassen(lowerCamelCase__ , matrix_subtraction(lowerCamelCase__ , lowerCamelCase__ ) )
__lowerCamelCase : Optional[int] = actual_strassen(matrix_addition(lowerCamelCase__ , lowerCamelCase__ ) , matrix_addition(lowerCamelCase__ , lowerCamelCase__ ) )
__lowerCamelCase : Dict = actual_strassen(matrix_subtraction(lowerCamelCase__ , lowerCamelCase__ ) , matrix_addition(lowerCamelCase__ , lowerCamelCase__ ) )
__lowerCamelCase : Tuple = actual_strassen(matrix_subtraction(lowerCamelCase__ , lowerCamelCase__ ) , matrix_addition(lowerCamelCase__ , lowerCamelCase__ ) )
__lowerCamelCase : Dict = matrix_addition(matrix_subtraction(matrix_addition(lowerCamelCase__ , lowerCamelCase__ ) , lowerCamelCase__ ) , lowerCamelCase__ )
__lowerCamelCase : Tuple = matrix_addition(lowerCamelCase__ , lowerCamelCase__ )
__lowerCamelCase : List[str] = matrix_addition(lowerCamelCase__ , lowerCamelCase__ )
__lowerCamelCase : Any = matrix_subtraction(matrix_subtraction(matrix_addition(lowerCamelCase__ , lowerCamelCase__ ) , lowerCamelCase__ ) , lowerCamelCase__ )
# construct the new matrix from our 4 quadrants
__lowerCamelCase : List[Any] = []
for i in range(len(lowerCamelCase__ ) ):
new_matrix.append(top_left[i] + top_right[i] )
for i in range(len(lowerCamelCase__ ) ):
new_matrix.append(bot_left[i] + bot_right[i] )
return new_matrix
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ , lowerCamelCase__ ) -> list:
if matrix_dimensions(lowerCamelCase__ )[1] != matrix_dimensions(lowerCamelCase__ )[0]:
__lowerCamelCase : Any = (
'Unable to multiply these matrices, please check the dimensions.\n'
F"Matrix A: {matrixa}\n"
F"Matrix B: {matrixa}"
)
raise Exception(lowerCamelCase__ )
__lowerCamelCase : str = matrix_dimensions(lowerCamelCase__ )
__lowerCamelCase : List[str] = matrix_dimensions(lowerCamelCase__ )
if dimensiona[0] == dimensiona[1] and dimensiona[0] == dimensiona[1]:
return [matrixa, matrixa]
__lowerCamelCase : str = max(*lowerCamelCase__ , *lowerCamelCase__ )
__lowerCamelCase : List[str] = int(math.pow(2 , math.ceil(math.loga(lowerCamelCase__ ) ) ) )
__lowerCamelCase : Any = matrixa
__lowerCamelCase : int = matrixa
# Adding zeros to the matrices so that the arrays dimensions are the same and also
# power of 2
for i in range(0 , lowerCamelCase__ ):
if i < dimensiona[0]:
for _ in range(dimensiona[1] , lowerCamelCase__ ):
new_matrixa[i].append(0 )
else:
new_matrixa.append([0] * maxim )
if i < dimensiona[0]:
for _ in range(dimensiona[1] , lowerCamelCase__ ):
new_matrixa[i].append(0 )
else:
new_matrixa.append([0] * maxim )
__lowerCamelCase : List[str] = actual_strassen(lowerCamelCase__ , lowerCamelCase__ )
# Removing the additional zeros
for i in range(0 , lowerCamelCase__ ):
if i < dimensiona[0]:
for _ in range(dimensiona[1] , lowerCamelCase__ ):
final_matrix[i].pop()
else:
final_matrix.pop()
return final_matrix
if __name__ == "__main__":
a =[
[2, 3, 4, 5],
[6, 4, 3, 1],
[2, 3, 6, 7],
[3, 1, 2, 4],
[2, 3, 4, 5],
[6, 4, 3, 1],
[2, 3, 6, 7],
[3, 1, 2, 4],
[2, 3, 4, 5],
[6, 2, 3, 1],
]
a =[[0, 2, 1, 1], [16, 2, 3, 3], [2, 2, 7, 7], [13, 11, 22, 4]]
print(strassen(matrixa, matrixa))
| 73 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionInstructPixaPixPipeline,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class __SCREAMING_SNAKE_CASE ( lowerCAmelCase_ ,lowerCAmelCase_ ,lowerCAmelCase_ ,unittest.TestCase ):
_UpperCAmelCase : List[Any] = StableDiffusionInstructPixaPixPipeline
_UpperCAmelCase : Any = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width", "cross_attention_kwargs"}
_UpperCAmelCase : Optional[int] = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
_UpperCAmelCase : Tuple = IMAGE_TO_IMAGE_IMAGE_PARAMS
_UpperCAmelCase : Any = IMAGE_TO_IMAGE_IMAGE_PARAMS
def __lowerCamelCase ( self : str ) ->Union[str, Any]:
torch.manual_seed(0 )
lowerCamelCase__ : List[Any] = UNetaDConditionModel(
block_out_channels=(3_2, 6_4) , layers_per_block=2 , sample_size=3_2 , in_channels=8 , out_channels=4 , down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') , up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') , cross_attention_dim=3_2 , )
lowerCamelCase__ : int = PNDMScheduler(skip_prk_steps=A )
torch.manual_seed(0 )
lowerCamelCase__ : str = AutoencoderKL(
block_out_channels=[3_2, 6_4] , in_channels=3 , out_channels=3 , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] , latent_channels=4 , )
torch.manual_seed(0 )
lowerCamelCase__ : str = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=3_2 , intermediate_size=3_7 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_0_0_0 , )
lowerCamelCase__ : List[Any] = CLIPTextModel(A )
lowerCamelCase__ : str = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
lowerCamelCase__ : Dict = {
'''unet''': unet,
'''scheduler''': scheduler,
'''vae''': vae,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''safety_checker''': None,
'''feature_extractor''': None,
}
return components
def __lowerCamelCase ( self : Dict , A : Any , A : Tuple=0 ) ->List[Any]:
lowerCamelCase__ : Dict = floats_tensor((1, 3, 3_2, 3_2) , rng=random.Random(A ) ).to(A )
lowerCamelCase__ : Union[str, Any] = image.cpu().permute(0 , 2 , 3 , 1 )[0]
lowerCamelCase__ : List[str] = Image.fromarray(np.uinta(A ) ).convert('''RGB''' )
if str(A ).startswith('''mps''' ):
lowerCamelCase__ : Any = torch.manual_seed(A )
else:
lowerCamelCase__ : int = torch.Generator(device=A ).manual_seed(A )
lowerCamelCase__ : Dict = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''image''': image,
'''generator''': generator,
'''num_inference_steps''': 2,
'''guidance_scale''': 6.0,
'''image_guidance_scale''': 1,
'''output_type''': '''numpy''',
}
return inputs
def __lowerCamelCase ( self : List[Any] ) ->Dict:
lowerCamelCase__ : List[Any] = '''cpu''' # ensure determinism for the device-dependent torch.Generator
lowerCamelCase__ : Any = self.get_dummy_components()
lowerCamelCase__ : List[str] = StableDiffusionInstructPixaPixPipeline(**A )
lowerCamelCase__ : List[Any] = sd_pipe.to(A )
sd_pipe.set_progress_bar_config(disable=A )
lowerCamelCase__ : Optional[int] = self.get_dummy_inputs(A )
lowerCamelCase__ : Any = sd_pipe(**A ).images
lowerCamelCase__ : Optional[int] = image[0, -3:, -3:, -1]
assert image.shape == (1, 3_2, 3_2, 3)
lowerCamelCase__ : List[str] = np.array([0.75_26, 0.37_50, 0.45_47, 0.61_17, 0.58_66, 0.50_16, 0.43_27, 0.56_42, 0.48_15] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def __lowerCamelCase ( self : Optional[int] ) ->List[str]:
lowerCamelCase__ : int = '''cpu''' # ensure determinism for the device-dependent torch.Generator
lowerCamelCase__ : Any = self.get_dummy_components()
lowerCamelCase__ : Optional[int] = StableDiffusionInstructPixaPixPipeline(**A )
lowerCamelCase__ : str = sd_pipe.to(A )
sd_pipe.set_progress_bar_config(disable=A )
lowerCamelCase__ : Tuple = self.get_dummy_inputs(A )
lowerCamelCase__ : Dict = '''french fries'''
lowerCamelCase__ : Dict = sd_pipe(**A , negative_prompt=A )
lowerCamelCase__ : Dict = output.images
lowerCamelCase__ : Optional[int] = image[0, -3:, -3:, -1]
assert image.shape == (1, 3_2, 3_2, 3)
lowerCamelCase__ : Dict = np.array([0.75_11, 0.36_42, 0.45_53, 0.62_36, 0.57_97, 0.50_13, 0.43_43, 0.56_11, 0.48_31] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def __lowerCamelCase ( self : Any ) ->int:
lowerCamelCase__ : Union[str, Any] = '''cpu''' # ensure determinism for the device-dependent torch.Generator
lowerCamelCase__ : Optional[Any] = self.get_dummy_components()
lowerCamelCase__ : Any = StableDiffusionInstructPixaPixPipeline(**A )
lowerCamelCase__ : List[Any] = sd_pipe.to(A )
sd_pipe.set_progress_bar_config(disable=A )
lowerCamelCase__ : Optional[int] = self.get_dummy_inputs(A )
lowerCamelCase__ : Dict = [inputs['''prompt''']] * 2
lowerCamelCase__ : int = np.array(inputs['''image'''] ).astype(np.floataa ) / 2_55.0
lowerCamelCase__ : str = torch.from_numpy(A ).unsqueeze(0 ).to(A )
lowerCamelCase__ : List[Any] = image / 2 + 0.5
lowerCamelCase__ : int = image.permute(0 , 3 , 1 , 2 )
lowerCamelCase__ : str = image.repeat(2 , 1 , 1 , 1 )
lowerCamelCase__ : Optional[Any] = sd_pipe(**A ).images
lowerCamelCase__ : List[Any] = image[-1, -3:, -3:, -1]
assert image.shape == (2, 3_2, 3_2, 3)
lowerCamelCase__ : List[str] = np.array([0.58_12, 0.57_48, 0.52_22, 0.59_08, 0.56_95, 0.71_74, 0.68_04, 0.55_23, 0.55_79] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def __lowerCamelCase ( self : Optional[int] ) ->List[str]:
lowerCamelCase__ : Dict = '''cpu''' # ensure determinism for the device-dependent torch.Generator
lowerCamelCase__ : List[str] = self.get_dummy_components()
lowerCamelCase__ : Optional[Any] = EulerAncestralDiscreteScheduler(
beta_start=0.0_00_85 , beta_end=0.0_12 , beta_schedule='''scaled_linear''' )
lowerCamelCase__ : Union[str, Any] = StableDiffusionInstructPixaPixPipeline(**A )
lowerCamelCase__ : Any = sd_pipe.to(A )
sd_pipe.set_progress_bar_config(disable=A )
lowerCamelCase__ : List[Any] = self.get_dummy_inputs(A )
lowerCamelCase__ : int = sd_pipe(**A ).images
lowerCamelCase__ : Optional[Any] = image[0, -3:, -3:, -1]
lowerCamelCase__ : List[str] = [round(A , 4 ) for x in image_slice.flatten().tolist()]
print(''','''.join([str(A ) for x in slice] ) )
assert image.shape == (1, 3_2, 3_2, 3)
lowerCamelCase__ : List[Any] = np.array([0.74_17, 0.38_42, 0.47_32, 0.57_76, 0.58_91, 0.51_39, 0.40_52, 0.56_73, 0.49_86] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def __lowerCamelCase ( self : Optional[int] ) ->Dict:
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
def __lowerCamelCase ( self : Dict ) ->Optional[int]:
lowerCamelCase__ : Any = self.get_dummy_components()
lowerCamelCase__ : str = StableDiffusionInstructPixaPixPipeline(**A )
lowerCamelCase__ : Dict = VaeImageProcessor(do_resize=A , do_normalize=A )
lowerCamelCase__ : Tuple = pipe.to(A )
pipe.set_progress_bar_config(disable=A )
lowerCamelCase__ : int = pipe(**self.get_dummy_inputs_by_type(A , input_image_type='''pt''' ) )[0]
lowerCamelCase__ : int = components['''vae''']
lowerCamelCase__ : Any = self.get_dummy_inputs_by_type(A , input_image_type='''pt''' )
for image_param in self.image_latents_params:
if image_param in inputs.keys():
lowerCamelCase__ : List[Any] = vae.encode(inputs[image_param] ).latent_dist.mode()
lowerCamelCase__ : Union[str, Any] = pipe(**A )[0]
lowerCamelCase__ : Any = np.abs(out - out_latents_inputs ).max()
self.assertLess(A , 1e-4 , '''passing latents as image input generate different result from passing image''' )
@slow
@require_torch_gpu
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __lowerCamelCase ( self : List[Any] ) ->Dict:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __lowerCamelCase ( self : str , A : Optional[int]=0 ) ->Dict:
lowerCamelCase__ : Tuple = torch.manual_seed(A )
lowerCamelCase__ : Tuple = load_image(
'''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg''' )
lowerCamelCase__ : int = {
'''prompt''': '''turn him into a cyborg''',
'''image''': image,
'''generator''': generator,
'''num_inference_steps''': 3,
'''guidance_scale''': 7.5,
'''image_guidance_scale''': 1.0,
'''output_type''': '''numpy''',
}
return inputs
def __lowerCamelCase ( self : Dict ) ->int:
lowerCamelCase__ : Union[str, Any] = StableDiffusionInstructPixaPixPipeline.from_pretrained(
'''timbrooks/instruct-pix2pix''' , safety_checker=A )
pipe.to(A )
pipe.set_progress_bar_config(disable=A )
pipe.enable_attention_slicing()
lowerCamelCase__ : Tuple = self.get_inputs()
lowerCamelCase__ : int = pipe(**A ).images
lowerCamelCase__ : int = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 5_1_2, 5_1_2, 3)
lowerCamelCase__ : Tuple = np.array([0.59_02, 0.60_15, 0.60_27, 0.59_83, 0.60_92, 0.60_61, 0.57_65, 0.57_85, 0.55_55] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def __lowerCamelCase ( self : List[str] ) ->List[Any]:
lowerCamelCase__ : List[str] = StableDiffusionInstructPixaPixPipeline.from_pretrained(
'''timbrooks/instruct-pix2pix''' , safety_checker=A )
lowerCamelCase__ : Tuple = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.to(A )
pipe.set_progress_bar_config(disable=A )
pipe.enable_attention_slicing()
lowerCamelCase__ : List[str] = self.get_inputs()
lowerCamelCase__ : int = pipe(**A ).images
lowerCamelCase__ : Tuple = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 5_1_2, 5_1_2, 3)
lowerCamelCase__ : List[str] = np.array([0.65_78, 0.68_17, 0.69_72, 0.67_61, 0.68_56, 0.69_16, 0.64_28, 0.65_16, 0.63_01] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def __lowerCamelCase ( self : Optional[Any] ) ->List[Any]:
lowerCamelCase__ : int = StableDiffusionInstructPixaPixPipeline.from_pretrained(
'''timbrooks/instruct-pix2pix''' , safety_checker=A )
lowerCamelCase__ : Any = DDIMScheduler.from_config(pipe.scheduler.config )
pipe.to(A )
pipe.set_progress_bar_config(disable=A )
pipe.enable_attention_slicing()
lowerCamelCase__ : Tuple = self.get_inputs()
lowerCamelCase__ : Any = pipe(**A ).images
lowerCamelCase__ : List[str] = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 5_1_2, 5_1_2, 3)
lowerCamelCase__ : Optional[Any] = np.array([0.38_28, 0.38_34, 0.38_18, 0.37_92, 0.38_65, 0.37_52, 0.37_92, 0.38_47, 0.37_53] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def __lowerCamelCase ( self : List[Any] ) ->List[Any]:
lowerCamelCase__ : str = 0
def callback_fn(A : int , A : int , A : torch.FloatTensor ) -> None:
lowerCamelCase__ : Optional[Any] = True
nonlocal number_of_steps
number_of_steps += 1
if step == 1:
lowerCamelCase__ : int = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 6_4, 6_4)
lowerCamelCase__ : Any = latents[0, -3:, -3:, -1]
lowerCamelCase__ : Tuple = np.array([-0.24_63, -0.46_44, -0.97_56, 1.51_76, 1.44_14, 0.78_66, 0.98_97, 0.85_21, 0.79_83] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2
elif step == 2:
lowerCamelCase__ : Union[str, Any] = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 6_4, 6_4)
lowerCamelCase__ : List[str] = latents[0, -3:, -3:, -1]
lowerCamelCase__ : int = np.array([-0.26_44, -0.46_26, -0.96_53, 1.51_76, 1.45_51, 0.76_86, 0.98_05, 0.84_52, 0.81_15] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2
lowerCamelCase__ : Union[str, Any] = False
lowerCamelCase__ : Tuple = StableDiffusionInstructPixaPixPipeline.from_pretrained(
'''timbrooks/instruct-pix2pix''' , safety_checker=A , torch_dtype=torch.floataa )
lowerCamelCase__ : int = pipe.to(A )
pipe.set_progress_bar_config(disable=A )
pipe.enable_attention_slicing()
lowerCamelCase__ : Optional[int] = self.get_inputs()
pipe(**A , callback=A , callback_steps=1 )
assert callback_fn.has_been_called
assert number_of_steps == 3
def __lowerCamelCase ( self : Any ) ->List[str]:
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
lowerCamelCase__ : Any = StableDiffusionInstructPixaPixPipeline.from_pretrained(
'''timbrooks/instruct-pix2pix''' , safety_checker=A , torch_dtype=torch.floataa )
lowerCamelCase__ : List[str] = pipe.to(A )
pipe.set_progress_bar_config(disable=A )
pipe.enable_attention_slicing(1 )
pipe.enable_sequential_cpu_offload()
lowerCamelCase__ : Tuple = self.get_inputs()
lowerCamelCase__ : List[Any] = pipe(**A )
lowerCamelCase__ : Dict = torch.cuda.max_memory_allocated()
# make sure that less than 2.2 GB is allocated
assert mem_bytes < 2.2 * 1_0**9
def __lowerCamelCase ( self : str ) ->Optional[int]:
lowerCamelCase__ : Union[str, Any] = self.get_inputs()
# resize to resolution that is divisible by 8 but not 16 or 32
lowerCamelCase__ : Any = inputs['''image'''].resize((5_0_4, 5_0_4) )
lowerCamelCase__ : Dict = '''timbrooks/instruct-pix2pix'''
lowerCamelCase__ : List[str] = StableDiffusionInstructPixaPixPipeline.from_pretrained(
A , safety_checker=A , )
pipe.to(A )
pipe.set_progress_bar_config(disable=A )
pipe.enable_attention_slicing()
lowerCamelCase__ : List[str] = pipe(**A )
lowerCamelCase__ : List[str] = output.images[0]
lowerCamelCase__ : Tuple = image[2_5_5:2_5_8, 3_8_3:3_8_6, -1]
assert image.shape == (5_0_4, 5_0_4, 3)
lowerCamelCase__ : str = np.array([0.27_26, 0.25_29, 0.26_64, 0.26_55, 0.26_41, 0.26_42, 0.25_91, 0.26_49, 0.25_90] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-3
| 265 |
def _a ( UpperCAmelCase ) -> bool:
"""simple docstring"""
return str(UpperCAmelCase ) == str(UpperCAmelCase )[::-1]
def _a ( UpperCAmelCase ) -> int:
"""simple docstring"""
return int(UpperCAmelCase ) + int(str(UpperCAmelCase )[::-1] )
def _a ( UpperCAmelCase = 10000 ) -> int:
"""simple docstring"""
lowerCamelCase__ : Tuple = []
for num in range(1 , UpperCAmelCase ):
lowerCamelCase__ : List[str] = 0
lowerCamelCase__ : Union[str, Any] = num
while iterations < 50:
lowerCamelCase__ : Dict = sum_reverse(UpperCAmelCase )
iterations += 1
if is_palindrome(UpperCAmelCase ):
break
else:
lychrel_nums.append(UpperCAmelCase )
return len(UpperCAmelCase )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 265 | 1 |
'''simple docstring'''
lowercase__ = {
"joule": 1.0,
"kilojoule": 1000,
"megajoule": 1000000,
"gigajoule": 1000000000,
"wattsecond": 1.0,
"watthour": 3600,
"kilowatthour": 3600000,
"newtonmeter": 1.0,
"calorie_nutr": 4186.8,
"kilocalorie_nutr": 4186800.00,
"electronvolt": 1.6_0_2_1_7_6_6_3_4e-1_9,
"britishthermalunit_it": 1055.05585,
"footpound": 1.355818,
}
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ):
if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
UpperCAmelCase : Any = (
F"""Incorrect \'from_type\' or \'to_type\' value: {from_type!r}, {to_type!r}\n"""
F"""Valid values are: {", ".join(_UpperCamelCase )}"""
)
raise ValueError(_UpperCamelCase )
return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 151 |
"""simple docstring"""
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
UpperCAmelCase : str = {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 lowerCamelCase__ ( nn.Module ):
"""simple docstring"""
def __init__( self : Union[str, Any] , UpperCamelCase : Optional[int] ):
'''simple docstring'''
super().__init__()
__UpperCAmelCase : Union[str, Any] = torchvision.models.resnetaaa(pretrained=UpperCamelCase )
__UpperCAmelCase : Union[str, Any] = list(model.children() )[:-2]
__UpperCAmelCase : Tuple = nn.Sequential(*UpperCamelCase )
__UpperCAmelCase : Tuple = nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds] )
def lowerCamelCase__ ( self : List[Any] , UpperCamelCase : List[str] ):
'''simple docstring'''
__UpperCAmelCase : int = self.pool(self.model(UpperCamelCase ) )
__UpperCAmelCase : str = torch.flatten(UpperCamelCase , start_dim=2 )
__UpperCAmelCase : Union[str, Any] = out.transpose(1 , 2 ).contiguous()
return out # BxNx2048
class lowerCamelCase__ ( A ):
"""simple docstring"""
def __init__( self : str , UpperCamelCase : str , UpperCamelCase : Tuple , UpperCamelCase : str , UpperCamelCase : Any , UpperCamelCase : Optional[int] ):
'''simple docstring'''
__UpperCAmelCase : Optional[Any] = [json.loads(UpperCamelCase ) for l in open(UpperCamelCase )]
__UpperCAmelCase : Optional[Any] = os.path.dirname(UpperCamelCase )
__UpperCAmelCase : List[Any] = tokenizer
__UpperCAmelCase : List[str] = labels
__UpperCAmelCase : Optional[int] = len(UpperCamelCase )
__UpperCAmelCase : Union[str, Any] = max_seq_length
__UpperCAmelCase : Tuple = transforms
def __len__( self : Tuple ):
'''simple docstring'''
return len(self.data )
def __getitem__( self : int , UpperCamelCase : Any ):
'''simple docstring'''
__UpperCAmelCase : Union[str, Any] = torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""] , add_special_tokens=UpperCamelCase ) )
__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase : Dict = sentence[0], sentence[1:-1], sentence[-1]
__UpperCAmelCase : Union[str, Any] = sentence[: self.max_seq_length]
__UpperCAmelCase : Tuple = torch.zeros(self.n_classes )
__UpperCAmelCase : str = 1
__UpperCAmelCase : List[str] = Image.open(os.path.join(self.data_dir , self.data[index]["""img"""] ) ).convert("""RGB""" )
__UpperCAmelCase : Any = self.transforms(UpperCamelCase )
return {
"image_start_token": start_token,
"image_end_token": end_token,
"sentence": sentence,
"image": image,
"label": label,
}
def lowerCamelCase__ ( self : Tuple ):
'''simple docstring'''
__UpperCAmelCase : int = Counter()
for row in self.data:
label_freqs.update(row["""label"""] )
return label_freqs
def lowerCamelCase ( _UpperCamelCase : Union[str, Any] ) -> Any:
'''simple docstring'''
__UpperCAmelCase : Union[str, Any] = [len(row["""sentence"""] ) for row in batch]
__UpperCAmelCase ,__UpperCAmelCase : Union[str, Any] = len(_UpperCamelCase ), max(_UpperCamelCase )
__UpperCAmelCase : int = torch.zeros(_UpperCamelCase , _UpperCamelCase , dtype=torch.long )
__UpperCAmelCase : Any = torch.zeros(_UpperCamelCase , _UpperCamelCase , dtype=torch.long )
for i_batch, (input_row, length) in enumerate(zip(_UpperCamelCase , _UpperCamelCase ) ):
__UpperCAmelCase : List[str] = input_row["""sentence"""]
__UpperCAmelCase : Optional[Any] = 1
__UpperCAmelCase : int = torch.stack([row["""image"""] for row in batch] )
__UpperCAmelCase : Optional[int] = torch.stack([row["""label"""] for row in batch] )
__UpperCAmelCase : int = torch.stack([row["""image_start_token"""] for row in batch] )
__UpperCAmelCase : int = 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 ( ) -> List[str]:
'''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 ( ) -> Dict:
'''simple docstring'''
return transforms.Compose(
[
transforms.Resize(2_5_6 ),
transforms.CenterCrop(2_2_4 ),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.46_777_044, 0.44_531_429, 0.40_661_017] , std=[0.12_221_994, 0.12_145_835, 0.14_380_469] , ),
] )
| 115 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import _LazyModule
_snake_case = {'tokenization_wav2vec2_phoneme': ['Wav2Vec2PhonemeCTCTokenizer']}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
_snake_case = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 363 |
"""simple docstring"""
from math import factorial
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
if successes > trials:
raise ValueError("""successes must be lower or equal to trials""" )
if trials < 0 or successes < 0:
raise ValueError("""the function is defined for non-negative integers""" )
if not isinstance(UpperCamelCase__ , UpperCamelCase__ ) or not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
raise ValueError("""the function is defined for non-negative integers""" )
if not 0 < prob < 1:
raise ValueError("""prob has to be in range of 1 - 0""" )
_a : Optional[int] = (prob**successes) * ((1 - prob) ** (trials - successes))
# Calculate the binomial coefficient: n! / k!(n-k)!
_a : Optional[int] = float(factorial(UpperCamelCase__ ) )
coefficient /= factorial(UpperCamelCase__ ) * factorial(trials - successes )
return probability * coefficient
if __name__ == "__main__":
from doctest import testmod
testmod()
print('Probability of 2 successes out of 4 trails')
print('with probability of 0.75 is:', end=' ')
print(binomial_distribution(2, 4, 0.75))
| 324 | 0 |
import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple
import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
from torch import nn
from torch.utils.data import DataLoader
from transformers import MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import (
ROUGE_KEYS,
LegacySeqaSeqDataset,
SeqaSeqDataset,
assert_all_frozen,
calculate_bleu,
calculate_rouge,
check_output_dir,
flatten_list,
freeze_embeds,
freeze_params,
get_git_info,
label_smoothed_nll_loss,
lmap,
pickle_save,
save_git_info,
save_json,
use_task_specific_params,
)
# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import BaseTransformer, add_generic_args, generic_train # noqa
lowerCAmelCase_ = logging.getLogger(__name__)
class snake_case_ ( __A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[int] = "summarization"
SCREAMING_SNAKE_CASE : Union[str, Any] = ["loss"]
SCREAMING_SNAKE_CASE : List[Any] = ROUGE_KEYS
SCREAMING_SNAKE_CASE : List[Any] = "rouge2"
def __init__( self : int , _UpperCamelCase : Any , **_UpperCamelCase : List[Any] ) ->List[Any]:
if hparams.sortish_sampler and hparams.gpus > 1:
snake_case_ = False
elif hparams.max_tokens_per_batch is not None:
if hparams.gpus > 1:
raise NotImplementedError('''Dynamic Batch size does not work for multi-gpu training''' )
if hparams.sortish_sampler:
raise ValueError('''--sortish_sampler and --max_tokens_per_batch may not be used simultaneously''' )
super().__init__(_UpperCamelCase , num_labels=_UpperCamelCase , mode=self.mode , **_UpperCamelCase )
use_task_specific_params(self.model , '''summarization''' )
save_git_info(self.hparams.output_dir )
snake_case_ = Path(self.output_dir ) / '''metrics.json'''
snake_case_ = Path(self.output_dir ) / '''hparams.pkl'''
pickle_save(self.hparams , self.hparams_save_path )
snake_case_ = 0
snake_case_ = defaultdict(_UpperCamelCase )
snake_case_ = self.config.model_type
snake_case_ = self.config.tgt_vocab_size if self.model_type == '''fsmt''' else self.config.vocab_size
snake_case_ = {
"data_dir": self.hparams.data_dir,
"max_source_length": self.hparams.max_source_length,
"prefix": self.model.config.prefix or "",
}
snake_case_ = {
'''train''': self.hparams.n_train,
'''val''': self.hparams.n_val,
'''test''': self.hparams.n_test,
}
snake_case_ = {k: v if v >= 0 else None for k, v in n_observations_per_split.items()}
snake_case_ = {
'''train''': self.hparams.max_target_length,
'''val''': self.hparams.val_max_target_length,
'''test''': self.hparams.test_max_target_length,
}
assert self.target_lens["train"] <= self.target_lens["val"], f'''target_lens: {self.target_lens}'''
assert self.target_lens["train"] <= self.target_lens["test"], f'''target_lens: {self.target_lens}'''
if self.hparams.freeze_embeds:
freeze_embeds(self.model )
if self.hparams.freeze_encoder:
freeze_params(self.model.get_encoder() )
assert_all_frozen(self.model.get_encoder() )
snake_case_ = get_git_info()['''repo_sha''']
snake_case_ = hparams.num_workers
snake_case_ = None # default to config
if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer , _UpperCamelCase ):
snake_case_ = self.tokenizer.lang_code_to_id[hparams.tgt_lang]
snake_case_ = self.decoder_start_token_id
snake_case_ = (
SeqaSeqDataset if hasattr(self.tokenizer , '''prepare_seq2seq_batch''' ) else LegacySeqaSeqDataset
)
snake_case_ = False
snake_case_ = self.model.config.num_beams if self.hparams.eval_beams is None else self.hparams.eval_beams
if self.hparams.eval_max_gen_length is not None:
snake_case_ = self.hparams.eval_max_gen_length
else:
snake_case_ = self.model.config.max_length
snake_case_ = self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric
def snake_case__( self : List[Any] , _UpperCamelCase : Dict[str, torch.Tensor] ) ->Dict[str, List[str]]:
snake_case_ = {
k: self.tokenizer.batch_decode(v.tolist() ) if '''mask''' not in k else v.shape for k, v in batch.items()
}
save_json(_UpperCamelCase , Path(self.output_dir ) / '''text_batch.json''' )
save_json({k: v.tolist() for k, v in batch.items()} , Path(self.output_dir ) / '''tok_batch.json''' )
snake_case_ = True
return readable_batch
def snake_case__( self : Tuple , _UpperCamelCase : Union[str, Any] , **_UpperCamelCase : List[str] ) ->List[Any]:
return self.model(_UpperCamelCase , **_UpperCamelCase )
def snake_case__( self : int , _UpperCamelCase : List[int] ) ->List[Any]:
snake_case_ = self.tokenizer.batch_decode(
_UpperCamelCase , skip_special_tokens=_UpperCamelCase , clean_up_tokenization_spaces=_UpperCamelCase )
return lmap(str.strip , _UpperCamelCase )
def snake_case__( self : List[Any] , _UpperCamelCase : dict ) ->Tuple:
snake_case_ = self.tokenizer.pad_token_id
snake_case_, snake_case_ = batch['''input_ids'''], batch['''attention_mask''']
snake_case_ = batch['''labels''']
if isinstance(self.model , _UpperCamelCase ):
snake_case_ = self.model._shift_right(_UpperCamelCase )
else:
snake_case_ = shift_tokens_right(_UpperCamelCase , _UpperCamelCase )
if not self.already_saved_batch: # This would be slightly better if it only happened on rank zero
snake_case_ = decoder_input_ids
self.save_readable_batch(_UpperCamelCase )
snake_case_ = self(_UpperCamelCase , attention_mask=_UpperCamelCase , decoder_input_ids=_UpperCamelCase , use_cache=_UpperCamelCase )
snake_case_ = outputs['''logits''']
if self.hparams.label_smoothing == 0:
# Same behavior as modeling_bart.py, besides ignoring pad_token_id
snake_case_ = nn.CrossEntropyLoss(ignore_index=_UpperCamelCase )
assert lm_logits.shape[-1] == self.vocab_size
snake_case_ = ce_loss_fct(lm_logits.view(-1 , lm_logits.shape[-1] ) , tgt_ids.view(-1 ) )
else:
snake_case_ = nn.functional.log_softmax(_UpperCamelCase , dim=-1 )
snake_case_, snake_case_ = label_smoothed_nll_loss(
_UpperCamelCase , _UpperCamelCase , self.hparams.label_smoothing , ignore_index=_UpperCamelCase )
return (loss,)
@property
def snake_case__( self : Tuple ) ->int:
return self.tokenizer.pad_token_id
def snake_case__( self : Dict , _UpperCamelCase : List[str] , _UpperCamelCase : str ) ->Dict:
snake_case_ = self._step(_UpperCamelCase )
snake_case_ = dict(zip(self.loss_names , _UpperCamelCase ) )
# tokens per batch
snake_case_ = batch['''input_ids'''].ne(self.pad ).sum() + batch['''labels'''].ne(self.pad ).sum()
snake_case_ = batch['''input_ids'''].shape[0]
snake_case_ = batch['''input_ids'''].eq(self.pad ).sum()
snake_case_ = batch['''input_ids'''].eq(self.pad ).float().mean()
# TODO(SS): make a wandb summary metric for this
return {"loss": loss_tensors[0], "log": logs}
def snake_case__( self : List[Any] , _UpperCamelCase : Optional[int] , _UpperCamelCase : Dict ) ->Dict:
return self._generative_step(_UpperCamelCase )
def snake_case__( self : Optional[Any] , _UpperCamelCase : str , _UpperCamelCase : Optional[Any]="val" ) ->Dict:
self.step_count += 1
snake_case_ = {k: torch.stack([x[k] for x in outputs] ).mean() for k in self.loss_names}
snake_case_ = losses['''loss''']
snake_case_ = {
k: np.array([x[k] for x in outputs] ).mean() for k in self.metric_names + ['''gen_time''', '''gen_len''']
}
snake_case_ = (
generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
)
snake_case_ = torch.tensor(_UpperCamelCase ).type_as(_UpperCamelCase )
generative_metrics.update({k: v.item() for k, v in losses.items()} )
losses.update(_UpperCamelCase )
snake_case_ = {f'''{prefix}_avg_{k}''': x for k, x in losses.items()}
snake_case_ = self.step_count
self.metrics[prefix].append(_UpperCamelCase ) # callback writes this to self.metrics_save_path
snake_case_ = flatten_list([x['''preds'''] for x in outputs] )
return {
"log": all_metrics,
"preds": preds,
f'''{prefix}_loss''': loss,
f'''{prefix}_{self.val_metric}''': metric_tensor,
}
def snake_case__( self : int , _UpperCamelCase : Optional[int] , _UpperCamelCase : List[str] ) ->Dict:
return calculate_rouge(_UpperCamelCase , _UpperCamelCase )
def snake_case__( self : str , _UpperCamelCase : dict ) ->dict:
snake_case_ = time.time()
# parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
snake_case_ = self.model.generate(
batch['''input_ids'''] , attention_mask=batch['''attention_mask'''] , use_cache=_UpperCamelCase , decoder_start_token_id=self.decoder_start_token_id , num_beams=self.eval_beams , max_length=self.eval_max_length , )
snake_case_ = (time.time() - ta) / batch['''input_ids'''].shape[0]
snake_case_ = self.ids_to_clean_text(_UpperCamelCase )
snake_case_ = self.ids_to_clean_text(batch['''labels'''] )
snake_case_ = self._step(_UpperCamelCase )
snake_case_ = dict(zip(self.loss_names , _UpperCamelCase ) )
snake_case_ = self.calc_generative_metrics(_UpperCamelCase , _UpperCamelCase )
snake_case_ = np.mean(lmap(_UpperCamelCase , _UpperCamelCase ) )
base_metrics.update(gen_time=_UpperCamelCase , gen_len=_UpperCamelCase , preds=_UpperCamelCase , target=_UpperCamelCase , **_UpperCamelCase )
return base_metrics
def snake_case__( self : Tuple , _UpperCamelCase : str , _UpperCamelCase : Dict ) ->Optional[int]:
return self._generative_step(_UpperCamelCase )
def snake_case__( self : Optional[int] , _UpperCamelCase : Optional[int] ) ->List[Any]:
return self.validation_epoch_end(_UpperCamelCase , prefix='''test''' )
def snake_case__( self : Union[str, Any] , _UpperCamelCase : Any ) ->SeqaSeqDataset:
snake_case_ = self.n_obs[type_path]
snake_case_ = self.target_lens[type_path]
snake_case_ = self.dataset_class(
self.tokenizer , type_path=_UpperCamelCase , n_obs=_UpperCamelCase , max_target_length=_UpperCamelCase , **self.dataset_kwargs , )
return dataset
def snake_case__( self : Optional[int] , _UpperCamelCase : str , _UpperCamelCase : int , _UpperCamelCase : bool = False ) ->DataLoader:
snake_case_ = self.get_dataset(_UpperCamelCase )
if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
snake_case_ = dataset.make_sortish_sampler(_UpperCamelCase , distributed=self.hparams.gpus > 1 )
return DataLoader(
_UpperCamelCase , batch_size=_UpperCamelCase , collate_fn=dataset.collate_fn , shuffle=_UpperCamelCase , num_workers=self.num_workers , sampler=_UpperCamelCase , )
elif self.hparams.max_tokens_per_batch is not None and type_path != "test" and type_path != "val":
snake_case_ = dataset.make_dynamic_sampler(
self.hparams.max_tokens_per_batch , distributed=self.hparams.gpus > 1 )
return DataLoader(
_UpperCamelCase , batch_sampler=_UpperCamelCase , collate_fn=dataset.collate_fn , num_workers=self.num_workers , )
else:
return DataLoader(
_UpperCamelCase , batch_size=_UpperCamelCase , collate_fn=dataset.collate_fn , shuffle=_UpperCamelCase , num_workers=self.num_workers , sampler=_UpperCamelCase , )
def snake_case__( self : List[str] ) ->DataLoader:
snake_case_ = self.get_dataloader('''train''' , batch_size=self.hparams.train_batch_size , shuffle=_UpperCamelCase )
return dataloader
def snake_case__( self : Union[str, Any] ) ->DataLoader:
return self.get_dataloader('''val''' , batch_size=self.hparams.eval_batch_size )
def snake_case__( self : Tuple ) ->DataLoader:
return self.get_dataloader('''test''' , batch_size=self.hparams.eval_batch_size )
@staticmethod
def snake_case__( _UpperCamelCase : List[Any] , _UpperCamelCase : Optional[int] ) ->Tuple:
BaseTransformer.add_model_specific_args(_UpperCamelCase , _UpperCamelCase )
add_generic_args(_UpperCamelCase , _UpperCamelCase )
parser.add_argument(
'''--max_source_length''' , default=1_0_2_4 , type=_UpperCamelCase , help=(
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
) , )
parser.add_argument(
'''--max_target_length''' , default=5_6 , type=_UpperCamelCase , help=(
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
) , )
parser.add_argument(
'''--val_max_target_length''' , default=1_4_2 , type=_UpperCamelCase , help=(
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
) , )
parser.add_argument(
'''--test_max_target_length''' , default=1_4_2 , type=_UpperCamelCase , help=(
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
) , )
parser.add_argument('''--freeze_encoder''' , action='''store_true''' )
parser.add_argument('''--freeze_embeds''' , action='''store_true''' )
parser.add_argument('''--sortish_sampler''' , action='''store_true''' , default=_UpperCamelCase )
parser.add_argument('''--overwrite_output_dir''' , action='''store_true''' , default=_UpperCamelCase )
parser.add_argument('''--max_tokens_per_batch''' , type=_UpperCamelCase , default=_UpperCamelCase )
parser.add_argument('''--logger_name''' , type=_UpperCamelCase , choices=['''default''', '''wandb''', '''wandb_shared'''] , default='''default''' )
parser.add_argument('''--n_train''' , type=_UpperCamelCase , default=-1 , required=_UpperCamelCase , help='''# examples. -1 means use all.''' )
parser.add_argument('''--n_val''' , type=_UpperCamelCase , default=5_0_0 , required=_UpperCamelCase , help='''# examples. -1 means use all.''' )
parser.add_argument('''--n_test''' , type=_UpperCamelCase , default=-1 , required=_UpperCamelCase , help='''# examples. -1 means use all.''' )
parser.add_argument(
'''--task''' , type=_UpperCamelCase , default='''summarization''' , required=_UpperCamelCase , help='''# examples. -1 means use all.''' )
parser.add_argument('''--label_smoothing''' , type=_UpperCamelCase , default=0.0 , required=_UpperCamelCase )
parser.add_argument('''--src_lang''' , type=_UpperCamelCase , default='''''' , required=_UpperCamelCase )
parser.add_argument('''--tgt_lang''' , type=_UpperCamelCase , default='''''' , required=_UpperCamelCase )
parser.add_argument('''--eval_beams''' , type=_UpperCamelCase , default=_UpperCamelCase , required=_UpperCamelCase )
parser.add_argument(
'''--val_metric''' , type=_UpperCamelCase , default=_UpperCamelCase , required=_UpperCamelCase , choices=['''bleu''', '''rouge2''', '''loss''', None] )
parser.add_argument('''--eval_max_gen_length''' , type=_UpperCamelCase , default=_UpperCamelCase , help='''never generate more than n tokens''' )
parser.add_argument('''--save_top_k''' , type=_UpperCamelCase , default=1 , required=_UpperCamelCase , help='''How many checkpoints to save''' )
parser.add_argument(
'''--early_stopping_patience''' , type=_UpperCamelCase , default=-1 , required=_UpperCamelCase , help=(
'''-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So'''
''' val_check_interval will effect it.'''
) , )
return parser
class snake_case_ ( __A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[int] = "translation"
SCREAMING_SNAKE_CASE : Dict = ["loss"]
SCREAMING_SNAKE_CASE : str = ["bleu"]
SCREAMING_SNAKE_CASE : List[str] = "bleu"
def __init__( self : Optional[Any] , _UpperCamelCase : Any , **_UpperCamelCase : Any ) ->Optional[int]:
super().__init__(_UpperCamelCase , **_UpperCamelCase )
snake_case_ = hparams.src_lang
snake_case_ = hparams.tgt_lang
def snake_case__( self : List[Any] , _UpperCamelCase : Optional[Any] , _UpperCamelCase : Optional[Any] ) ->dict:
return calculate_bleu(_UpperCamelCase , _UpperCamelCase )
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=None ):
Path(args.output_dir ).mkdir(exist_ok=SCREAMING_SNAKE_CASE__ )
check_output_dir(SCREAMING_SNAKE_CASE__ , expected_items=3 )
if model is None:
if "summarization" in args.task:
snake_case_ = SummarizationModule(SCREAMING_SNAKE_CASE__ )
else:
snake_case_ = TranslationModule(SCREAMING_SNAKE_CASE__ )
snake_case_ = Path(args.data_dir ).name
if (
args.logger_name == "default"
or args.fast_dev_run
or str(args.output_dir ).startswith('''/tmp''' )
or str(args.output_dir ).startswith('''/var''' )
):
snake_case_ = True # don't pollute wandb logs unnecessarily
elif args.logger_name == "wandb":
from pytorch_lightning.loggers import WandbLogger
snake_case_ = os.environ.get('''WANDB_PROJECT''' , SCREAMING_SNAKE_CASE__ )
snake_case_ = WandbLogger(name=model.output_dir.name , project=SCREAMING_SNAKE_CASE__ )
elif args.logger_name == "wandb_shared":
from pytorch_lightning.loggers import WandbLogger
snake_case_ = WandbLogger(name=model.output_dir.name , project=F'''hf_{dataset}''' )
if args.early_stopping_patience >= 0:
snake_case_ = get_early_stopping_callback(model.val_metric , args.early_stopping_patience )
else:
snake_case_ = False
snake_case_ = args.val_metric == '''loss'''
snake_case_ = generic_train(
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , logging_callback=SeqaSeqLoggingCallback() , checkpoint_callback=get_checkpoint_callback(
args.output_dir , model.val_metric , args.save_top_k , SCREAMING_SNAKE_CASE__ ) , early_stopping_callback=SCREAMING_SNAKE_CASE__ , logger=SCREAMING_SNAKE_CASE__ , )
pickle_save(model.hparams , model.output_dir / '''hparams.pkl''' )
if not args.do_predict:
return model
snake_case_ = ''''''
snake_case_ = sorted(glob.glob(os.path.join(args.output_dir , '''*.ckpt''' ) , recursive=SCREAMING_SNAKE_CASE__ ) )
if checkpoints:
snake_case_ = checkpoints[-1]
snake_case_ = checkpoints[-1]
trainer.logger.log_hyperparams(model.hparams )
# test() without a model tests using the best checkpoint automatically
trainer.test()
return model
if __name__ == "__main__":
lowerCAmelCase_ = argparse.ArgumentParser()
lowerCAmelCase_ = pl.Trainer.add_argparse_args(parser)
lowerCAmelCase_ = SummarizationModule.add_model_specific_args(parser, os.getcwd())
lowerCAmelCase_ = parser.parse_args()
main(args) | 8 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__ : int = logging.get_logger(__name__)
A__ : List[str] = {
'google/pix2struct-textcaps-base': (
'https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json'
),
}
class lowercase__ ( snake_case__ ):
_UpperCAmelCase :List[Any] = "pix2struct_text_model"
_UpperCAmelCase :str = ["past_key_values"]
_UpperCAmelCase :str = {
"hidden_size": "hidden_size",
"num_attention_heads": "num_heads",
"num_hidden_layers": "num_layers",
}
def __init__( self : Dict , snake_case__ : Any=5_0244 , snake_case__ : Optional[int]=768 , snake_case__ : Dict=64 , snake_case__ : List[str]=2048 , snake_case__ : Dict=12 , snake_case__ : Any=12 , snake_case__ : Dict=32 , snake_case__ : int=128 , snake_case__ : List[str]=0.1 , snake_case__ : Optional[int]=1E-6 , snake_case__ : Any=1.0 , snake_case__ : int="gelu_new" , snake_case__ : Optional[Any]=0 , snake_case__ : Any=False , snake_case__ : Any=0 , snake_case__ : Any=1 , snake_case__ : Optional[int]=False , snake_case__ : Tuple=True , **snake_case__ : Any , ):
lowerCamelCase_ : List[str] =vocab_size
lowerCamelCase_ : Tuple =hidden_size
lowerCamelCase_ : Optional[int] =d_kv
lowerCamelCase_ : List[Any] =d_ff
lowerCamelCase_ : Tuple =num_layers
lowerCamelCase_ : Optional[int] =num_heads
lowerCamelCase_ : Any =relative_attention_num_buckets
lowerCamelCase_ : Optional[int] =relative_attention_max_distance
lowerCamelCase_ : List[Any] =dropout_rate
lowerCamelCase_ : str =layer_norm_epsilon
lowerCamelCase_ : int =initializer_factor
lowerCamelCase_ : str =use_cache
lowerCamelCase_ : int =eos_token_id
lowerCamelCase_ : Optional[Any] =decoder_start_token_id
# for backwards compatibility
lowerCamelCase_ : Optional[Any] =dense_act_fn
super().__init__(
pad_token_id=snake_case__ , eos_token_id=snake_case__ , decoder_start_token_id=snake_case__ , tie_word_embeddings=snake_case__ , is_decoder=snake_case__ , **snake_case__ , )
@classmethod
def UpperCAmelCase__ ( cls : Tuple , snake_case__ : Union[str, os.PathLike] , **snake_case__ : str ):
cls._set_token_in_kwargs(snake_case__ )
lowerCamelCase_ , lowerCamelCase_ : Any =cls.get_config_dict(snake_case__ , **snake_case__ )
# get the text config dict if we are loading from Pix2StructConfig
if config_dict.get("model_type" ) == "pix2struct":
lowerCamelCase_ : List[Any] =config_dict["text_config"]
if "model_type" in config_dict and hasattr(cls , "model_type" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F"""You are using a model of type {config_dict['model_type']} to instantiate a model of type """
F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""" )
return cls.from_dict(snake_case__ , **snake_case__ )
class lowercase__ ( snake_case__ ):
_UpperCAmelCase :List[Any] = "pix2struct_vision_model"
def __init__( self : Optional[int] , snake_case__ : Tuple=768 , snake_case__ : str=768 , snake_case__ : Union[str, Any]=2048 , snake_case__ : Tuple=64 , snake_case__ : List[Any]=12 , snake_case__ : Dict=12 , snake_case__ : int="gelu_new" , snake_case__ : str=1E-6 , snake_case__ : int=0.0 , snake_case__ : int=0.0 , snake_case__ : Dict=1E-10 , snake_case__ : Tuple=1.0 , snake_case__ : int=4096 , snake_case__ : Tuple=32 , snake_case__ : List[str]=128 , **snake_case__ : List[Any] , ):
super().__init__(**snake_case__ )
lowerCamelCase_ : int =hidden_size
lowerCamelCase_ : List[Any] =patch_embed_hidden_size
lowerCamelCase_ : Tuple =d_ff
lowerCamelCase_ : List[Any] =dropout_rate
lowerCamelCase_ : Dict =num_hidden_layers
lowerCamelCase_ : List[str] =num_attention_heads
lowerCamelCase_ : Optional[Any] =initializer_range
lowerCamelCase_ : int =initializer_factor
lowerCamelCase_ : Any =attention_dropout
lowerCamelCase_ : List[str] =layer_norm_eps
lowerCamelCase_ : int =dense_act_fn
lowerCamelCase_ : Optional[Any] =seq_len
lowerCamelCase_ : Optional[int] =relative_attention_num_buckets
lowerCamelCase_ : Optional[int] =relative_attention_max_distance
lowerCamelCase_ : Dict =d_kv
@classmethod
def UpperCAmelCase__ ( cls : Optional[Any] , snake_case__ : Union[str, os.PathLike] , **snake_case__ : Dict ):
cls._set_token_in_kwargs(snake_case__ )
lowerCamelCase_ , lowerCamelCase_ : Dict =cls.get_config_dict(snake_case__ , **snake_case__ )
# get the vision config dict if we are loading from Pix2StructConfig
if config_dict.get("model_type" ) == "pix2struct":
lowerCamelCase_ : List[Any] =config_dict["vision_config"]
if "model_type" in config_dict and hasattr(cls , "model_type" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F"""You are using a model of type {config_dict['model_type']} to instantiate a model of type """
F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""" )
return cls.from_dict(snake_case__ , **snake_case__ )
class lowercase__ ( snake_case__ ):
_UpperCAmelCase :str = "pix2struct"
_UpperCAmelCase :List[str] = True
def __init__( self : Tuple , snake_case__ : List[Any]=None , snake_case__ : Optional[Any]=None , snake_case__ : List[str]=1.0 , snake_case__ : List[Any]=0.02 , snake_case__ : List[Any]=False , snake_case__ : int=False , snake_case__ : Any=True , **snake_case__ : List[Any] , ):
super().__init__(tie_word_embeddings=snake_case__ , is_encoder_decoder=snake_case__ , **snake_case__ )
if text_config is None:
lowerCamelCase_ : Dict ={}
logger.info("text_config is None. Initializing the Pix2StructTextConfig with default values." )
if vision_config is None:
lowerCamelCase_ : int ={}
logger.info("vision_config is None. Initializing the Pix2StructVisionConfig with default values." )
lowerCamelCase_ : Any =PixaStructTextConfig(**snake_case__ )
lowerCamelCase_ : Optional[Any] =PixaStructVisionConfig(**snake_case__ )
lowerCamelCase_ : str =self.text_config.decoder_start_token_id
lowerCamelCase_ : Optional[int] =self.text_config.pad_token_id
lowerCamelCase_ : List[Any] =self.text_config.eos_token_id
lowerCamelCase_ : int =initializer_factor
lowerCamelCase_ : Optional[Any] =initializer_range
lowerCamelCase_ : Any =self.initializer_range
lowerCamelCase_ : List[Any] =self.initializer_range
lowerCamelCase_ : List[str] =is_vqa
@classmethod
def UpperCAmelCase__ ( cls : List[str] , snake_case__ : PixaStructTextConfig , snake_case__ : PixaStructVisionConfig , **snake_case__ : Optional[int] ):
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **snake_case__ )
def UpperCAmelCase__ ( self : Any ):
lowerCamelCase_ : Tuple =copy.deepcopy(self.__dict__ )
lowerCamelCase_ : Dict =self.text_config.to_dict()
lowerCamelCase_ : Union[str, Any] =self.vision_config.to_dict()
lowerCamelCase_ : Dict =self.__class__.model_type
return output
| 144 | 0 |
import argparse
import os
import shutil
from pathlib import Path
import onnx
import torch
from packaging import version
from torch.onnx import export
from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline
_lowerCAmelCase : List[str] = version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def UpperCamelCase_( _snake_case : Optional[int] , _snake_case : tuple , _snake_case : Path , _snake_case : Any , _snake_case : Any , _snake_case : Union[str, Any] , _snake_case : Union[str, Any] , _snake_case : int=False , ):
"""simple docstring"""
output_path.parent.mkdir(parents=_snake_case , exist_ok=_snake_case )
# PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
# so we check the torch version for backwards compatibility
if is_torch_less_than_1_11:
export(
_snake_case , _snake_case , f=output_path.as_posix() , input_names=_snake_case , output_names=_snake_case , dynamic_axes=_snake_case , do_constant_folding=_snake_case , use_external_data_format=_snake_case , enable_onnx_checker=_snake_case , opset_version=_snake_case , )
else:
export(
_snake_case , _snake_case , f=output_path.as_posix() , input_names=_snake_case , output_names=_snake_case , dynamic_axes=_snake_case , do_constant_folding=_snake_case , opset_version=_snake_case , )
@torch.no_grad()
def UpperCamelCase_( _snake_case : str , _snake_case : str , _snake_case : int , _snake_case : bool = False ):
"""simple docstring"""
__a =torch.floataa if fpaa else torch.floataa
if fpaa and torch.cuda.is_available():
__a ='cuda'
elif fpaa and not torch.cuda.is_available():
raise ValueError('`float16` model export is only supported on GPUs with CUDA' )
else:
__a ='cpu'
__a =StableDiffusionPipeline.from_pretrained(_snake_case , torch_dtype=_snake_case ).to(_snake_case )
__a =Path(_snake_case )
# TEXT ENCODER
__a =pipeline.text_encoder.config.max_position_embeddings
__a =pipeline.text_encoder.config.hidden_size
__a =pipeline.tokenizer(
'A sample prompt' , padding='max_length' , max_length=pipeline.tokenizer.model_max_length , truncation=_snake_case , return_tensors='pt' , )
onnx_export(
pipeline.text_encoder , model_args=(text_input.input_ids.to(device=_snake_case , dtype=torch.intaa )) , output_path=output_path / 'text_encoder' / 'model.onnx' , ordered_input_names=['input_ids'] , output_names=['last_hidden_state', 'pooler_output'] , dynamic_axes={
'input_ids': {0: 'batch', 1: 'sequence'},
} , opset=_snake_case , )
del pipeline.text_encoder
# UNET
__a =pipeline.unet.config.in_channels
__a =pipeline.unet.config.sample_size
__a =output_path / 'unet' / 'model.onnx'
onnx_export(
pipeline.unet , model_args=(
torch.randn(2 , _snake_case , _snake_case , _snake_case ).to(device=_snake_case , dtype=_snake_case ),
torch.randn(2 ).to(device=_snake_case , dtype=_snake_case ),
torch.randn(2 , _snake_case , _snake_case ).to(device=_snake_case , dtype=_snake_case ),
False,
) , output_path=_snake_case , ordered_input_names=['sample', 'timestep', 'encoder_hidden_states', 'return_dict'] , output_names=['out_sample'] , dynamic_axes={
'sample': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
'timestep': {0: 'batch'},
'encoder_hidden_states': {0: 'batch', 1: 'sequence'},
} , opset=_snake_case , use_external_data_format=_snake_case , )
__a =str(unet_path.absolute().as_posix() )
__a =os.path.dirname(_snake_case )
__a =onnx.load(_snake_case )
# clean up existing tensor files
shutil.rmtree(_snake_case )
os.mkdir(_snake_case )
# collate external tensor files into one
onnx.save_model(
_snake_case , _snake_case , save_as_external_data=_snake_case , all_tensors_to_one_file=_snake_case , location='weights.pb' , convert_attribute=_snake_case , )
del pipeline.unet
# VAE ENCODER
__a =pipeline.vae
__a =vae_encoder.config.in_channels
__a =vae_encoder.config.sample_size
# need to get the raw tensor output (sample) from the encoder
__a =lambda _snake_case , _snake_case : vae_encoder.encode(_snake_case , _snake_case )[0].sample()
onnx_export(
_snake_case , model_args=(
torch.randn(1 , _snake_case , _snake_case , _snake_case ).to(device=_snake_case , dtype=_snake_case ),
False,
) , output_path=output_path / 'vae_encoder' / 'model.onnx' , ordered_input_names=['sample', 'return_dict'] , output_names=['latent_sample'] , dynamic_axes={
'sample': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
} , opset=_snake_case , )
# VAE DECODER
__a =pipeline.vae
__a =vae_decoder.config.latent_channels
__a =vae_decoder.config.out_channels
# forward only through the decoder part
__a =vae_encoder.decode
onnx_export(
_snake_case , model_args=(
torch.randn(1 , _snake_case , _snake_case , _snake_case ).to(device=_snake_case , dtype=_snake_case ),
False,
) , output_path=output_path / 'vae_decoder' / 'model.onnx' , ordered_input_names=['latent_sample', 'return_dict'] , output_names=['sample'] , dynamic_axes={
'latent_sample': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
} , opset=_snake_case , )
del pipeline.vae
# SAFETY CHECKER
if pipeline.safety_checker is not None:
__a =pipeline.safety_checker
__a =safety_checker.config.vision_config.num_channels
__a =safety_checker.config.vision_config.image_size
__a =safety_checker.forward_onnx
onnx_export(
pipeline.safety_checker , model_args=(
torch.randn(
1 , _snake_case , _snake_case , _snake_case , ).to(device=_snake_case , dtype=_snake_case ),
torch.randn(1 , _snake_case , _snake_case , _snake_case ).to(device=_snake_case , dtype=_snake_case ),
) , output_path=output_path / 'safety_checker' / 'model.onnx' , ordered_input_names=['clip_input', 'images'] , output_names=['out_images', 'has_nsfw_concepts'] , dynamic_axes={
'clip_input': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
'images': {0: 'batch', 1: 'height', 2: 'width', 3: 'channels'},
} , opset=_snake_case , )
del pipeline.safety_checker
__a =OnnxRuntimeModel.from_pretrained(output_path / 'safety_checker' )
__a =pipeline.feature_extractor
else:
__a =None
__a =None
__a =OnnxStableDiffusionPipeline(
vae_encoder=OnnxRuntimeModel.from_pretrained(output_path / 'vae_encoder' ) , vae_decoder=OnnxRuntimeModel.from_pretrained(output_path / 'vae_decoder' ) , text_encoder=OnnxRuntimeModel.from_pretrained(output_path / 'text_encoder' ) , tokenizer=pipeline.tokenizer , unet=OnnxRuntimeModel.from_pretrained(output_path / 'unet' ) , scheduler=pipeline.scheduler , safety_checker=_snake_case , feature_extractor=_snake_case , requires_safety_checker=safety_checker is not None , )
onnx_pipeline.save_pretrained(_snake_case )
print('ONNX pipeline saved to' , _snake_case )
del pipeline
del onnx_pipeline
__a =OnnxStableDiffusionPipeline.from_pretrained(_snake_case , provider='CPUExecutionProvider' )
print('ONNX pipeline is loadable' )
if __name__ == "__main__":
_lowerCAmelCase : Any = argparse.ArgumentParser()
parser.add_argument(
"--model_path",
type=str,
required=True,
help="Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).",
)
parser.add_argument("--output_path", type=str, required=True, help="Path to the output model.")
parser.add_argument(
"--opset",
default=14,
type=int,
help="The version of the ONNX operator set to use.",
)
parser.add_argument("--fp16", action="store_true", default=False, help="Export the models in `float16` mode")
_lowerCAmelCase : List[str] = parser.parse_args()
convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
| 308 |
import unittest
from transformers import XLMConfig, 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMWithLMHeadModel,
)
from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST
class __magic_name__ :
def __init__( self , __snake_case , __snake_case=13 , __snake_case=7 , __snake_case=True , __snake_case=True , __snake_case=True , __snake_case=True , __snake_case=True , __snake_case=False , __snake_case=False , __snake_case=False , __snake_case=2 , __snake_case=99 , __snake_case=0 , __snake_case=32 , __snake_case=5 , __snake_case=4 , __snake_case=0.1 , __snake_case=0.1 , __snake_case=512 , __snake_case=2 , __snake_case=0.02 , __snake_case=2 , __snake_case=4 , __snake_case="last" , __snake_case=True , __snake_case=None , __snake_case=0 , ) -> Optional[Any]:
'''simple docstring'''
__a =parent
__a =batch_size
__a =seq_length
__a =is_training
__a =use_input_lengths
__a =use_token_type_ids
__a =use_labels
__a =gelu_activation
__a =sinusoidal_embeddings
__a =causal
__a =asm
__a =n_langs
__a =vocab_size
__a =n_special
__a =hidden_size
__a =num_hidden_layers
__a =num_attention_heads
__a =hidden_dropout_prob
__a =attention_probs_dropout_prob
__a =max_position_embeddings
__a =type_sequence_label_size
__a =initializer_range
__a =num_labels
__a =num_choices
__a =summary_type
__a =use_proj
__a =scope
__a =bos_token_id
def __magic_name__ ( self ) -> Any:
'''simple docstring'''
__a =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__a =random_attention_mask([self.batch_size, self.seq_length] )
__a =None
if self.use_input_lengths:
__a =(
ids_tensor([self.batch_size] , vocab_size=2 ) + self.seq_length - 2
) # small variation of seq_length
__a =None
if self.use_token_type_ids:
__a =ids_tensor([self.batch_size, self.seq_length] , self.n_langs )
__a =None
__a =None
__a =None
if self.use_labels:
__a =ids_tensor([self.batch_size] , self.type_sequence_label_size )
__a =ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__a =ids_tensor([self.batch_size] , 2 ).float()
__a =ids_tensor([self.batch_size] , self.num_choices )
__a =self.get_config()
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def __magic_name__ ( self ) -> Any:
'''simple docstring'''
return XLMConfig(
vocab_size=self.vocab_size , n_special=self.n_special , emb_dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , gelu_activation=self.gelu_activation , sinusoidal_embeddings=self.sinusoidal_embeddings , asm=self.asm , causal=self.causal , n_langs=self.n_langs , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , summary_type=self.summary_type , use_proj=self.use_proj , num_labels=self.num_labels , bos_token_id=self.bos_token_id , )
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , ) -> List[Any]:
'''simple docstring'''
__a =XLMModel(config=__snake_case )
model.to(__snake_case )
model.eval()
__a =model(__snake_case , lengths=__snake_case , langs=__snake_case )
__a =model(__snake_case , langs=__snake_case )
__a =model(__snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , ) -> Optional[int]:
'''simple docstring'''
__a =XLMWithLMHeadModel(__snake_case )
model.to(__snake_case )
model.eval()
__a =model(__snake_case , token_type_ids=__snake_case , labels=__snake_case )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , ) -> Dict:
'''simple docstring'''
__a =XLMForQuestionAnsweringSimple(__snake_case )
model.to(__snake_case )
model.eval()
__a =model(__snake_case )
__a =model(__snake_case , start_positions=__snake_case , end_positions=__snake_case )
__a =outputs
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 __magic_name__ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , ) -> List[Any]:
'''simple docstring'''
__a =XLMForQuestionAnswering(__snake_case )
model.to(__snake_case )
model.eval()
__a =model(__snake_case )
__a =model(
__snake_case , start_positions=__snake_case , end_positions=__snake_case , cls_index=__snake_case , is_impossible=__snake_case , p_mask=__snake_case , )
__a =model(
__snake_case , start_positions=__snake_case , end_positions=__snake_case , cls_index=__snake_case , is_impossible=__snake_case , )
((__a) , ) =result_with_labels.to_tuple()
__a =model(__snake_case , start_positions=__snake_case , end_positions=__snake_case )
((__a) , ) =result_with_labels.to_tuple()
self.parent.assertEqual(result_with_labels.loss.shape , () )
self.parent.assertEqual(result.start_top_log_probs.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(result.start_top_index.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(
result.end_top_log_probs.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(
result.end_top_index.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(result.cls_logits.shape , (self.batch_size,) )
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , ) -> Optional[Any]:
'''simple docstring'''
__a =XLMForSequenceClassification(__snake_case )
model.to(__snake_case )
model.eval()
__a =model(__snake_case )
__a =model(__snake_case , labels=__snake_case )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , ) -> Any:
'''simple docstring'''
__a =self.num_labels
__a =XLMForTokenClassification(__snake_case )
model.to(__snake_case )
model.eval()
__a =model(__snake_case , attention_mask=__snake_case , labels=__snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , ) -> Tuple:
'''simple docstring'''
__a =self.num_choices
__a =XLMForMultipleChoice(config=__snake_case )
model.to(__snake_case )
model.eval()
__a =input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__a =token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__a =input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__a =model(
__snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , labels=__snake_case , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __magic_name__ ( self ) -> Union[str, Any]:
'''simple docstring'''
__a =self.prepare_config_and_inputs()
(
(
__a
) , (
__a
) , (
__a
) , (
__a
) , (
__a
) , (
__a
) , (
__a
) , (
__a
) , (
__a
) ,
) =config_and_inputs
__a ={'input_ids': input_ids, 'token_type_ids': token_type_ids, 'lengths': input_lengths}
return config, inputs_dict
@require_torch
class __magic_name__ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
SCREAMING_SNAKE_CASE = (
(
XLMModel,
XLMWithLMHeadModel,
XLMForQuestionAnswering,
XLMForSequenceClassification,
XLMForQuestionAnsweringSimple,
XLMForTokenClassification,
XLMForMultipleChoice,
)
if is_torch_available()
else ()
)
SCREAMING_SNAKE_CASE = (
(XLMWithLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Check other models whether language generation is also applicable
SCREAMING_SNAKE_CASE = (
{
'feature-extraction': XLMModel,
'fill-mask': XLMWithLMHeadModel,
'question-answering': XLMForQuestionAnsweringSimple,
'text-classification': XLMForSequenceClassification,
'text-generation': XLMWithLMHeadModel,
'token-classification': XLMForTokenClassification,
'zero-shot': XLMForSequenceClassification,
}
if is_torch_available()
else {}
)
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case ) -> int:
'''simple docstring'''
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith('Fast' )
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case=False ) -> str:
'''simple docstring'''
__a =super()._prepare_for_class(__snake_case , __snake_case , return_labels=__snake_case )
if return_labels:
if model_class.__name__ == "XLMForQuestionAnswering":
__a =torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__snake_case )
__a =torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__snake_case )
return inputs_dict
def __magic_name__ ( self ) -> Tuple:
'''simple docstring'''
__a =XLMModelTester(self )
__a =ConfigTester(self , config_class=__snake_case , emb_dim=37 )
def __magic_name__ ( self ) -> str:
'''simple docstring'''
self.config_tester.run_common_tests()
def __magic_name__ ( self ) -> List[str]:
'''simple docstring'''
__a =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_model(*__snake_case )
def __magic_name__ ( self ) -> List[str]:
'''simple docstring'''
__a =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_lm_head(*__snake_case )
def __magic_name__ ( self ) -> List[str]:
'''simple docstring'''
__a =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_simple_qa(*__snake_case )
def __magic_name__ ( self ) -> Optional[int]:
'''simple docstring'''
__a =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_qa(*__snake_case )
def __magic_name__ ( self ) -> Any:
'''simple docstring'''
__a =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_sequence_classif(*__snake_case )
def __magic_name__ ( self ) -> Optional[int]:
'''simple docstring'''
__a =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_token_classif(*__snake_case )
def __magic_name__ ( self ) -> Any:
'''simple docstring'''
__a =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_for_multiple_choice(*__snake_case )
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case=False , __snake_case=1 ) -> Optional[Any]:
'''simple docstring'''
self.assertIsInstance(__snake_case , __snake_case )
self.assertListEqual(
[isinstance(__snake_case , __snake_case ) for iter_attentions in attentions] , [True] * len(__snake_case ) )
self.assertEqual(len(__snake_case ) , (max_length - min_length) * num_beam_groups )
for idx, iter_attentions in enumerate(__snake_case ):
# adds PAD dummy token
__a =min_length + idx + 1
__a =min_length + idx + 1
__a =(
batch_size * num_beam_groups,
config.num_attention_heads,
tgt_len,
src_len,
)
# check attn size
self.assertListEqual(
[layer_attention.shape for layer_attention in iter_attentions] , [expected_shape] * len(__snake_case ) )
def __magic_name__ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case=False , __snake_case=1 ) -> Dict:
'''simple docstring'''
self.assertIsInstance(__snake_case , __snake_case )
self.assertListEqual(
[isinstance(__snake_case , __snake_case ) for iter_hidden_states in hidden_states] , [True] * len(__snake_case ) , )
self.assertEqual(len(__snake_case ) , (max_length - min_length) * num_beam_groups )
for idx, iter_hidden_states in enumerate(__snake_case ):
# adds PAD dummy token
__a =min_length + idx + 1
__a =(batch_size * num_beam_groups, seq_len, config.hidden_size)
# check hidden size
self.assertListEqual(
[layer_hidden_states.shape for layer_hidden_states in iter_hidden_states] , [expected_shape] * len(__snake_case ) , )
pass
@slow
def __magic_name__ ( self ) -> Union[str, Any]:
'''simple docstring'''
for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__a =XLMModel.from_pretrained(__snake_case )
self.assertIsNotNone(__snake_case )
@require_torch
class __magic_name__ ( unittest.TestCase ):
@slow
def __magic_name__ ( self ) -> Tuple:
'''simple docstring'''
__a =XLMWithLMHeadModel.from_pretrained('xlm-mlm-en-2048' )
model.to(__snake_case )
__a =torch.tensor([[14, 447]] , dtype=torch.long , device=__snake_case ) # the president
__a =[
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
] # the president the president the president the president the president the president the president the president the president the president
# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
__a =model.generate(__snake_case , do_sample=__snake_case )
self.assertListEqual(output_ids[0].cpu().numpy().tolist() , __snake_case )
| 308 | 1 |
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
#
########################################################################
_snake_case = 16
_snake_case = 32
def lowerCAmelCase_ ( snake_case_,snake_case_ = 16 ):
_A : List[Any] = AutoTokenizer.from_pretrained("""bert-base-cased""" )
_A : Any = load_dataset("""glue""","""mrpc""" )
def tokenize_function(snake_case_ ):
# max_length=None => use the model max length (it's actually the default)
_A : int = tokenizer(examples["""sentence1"""],examples["""sentence2"""],truncation=snake_case_,max_length=snake_case_ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
_A : Dict = datasets.map(
snake_case_,batched=snake_case_,remove_columns=["""idx""", """sentence1""", """sentence2"""],)
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
_A : List[Any] = tokenized_datasets.rename_column("""label""","""labels""" )
def collate_fn(snake_case_ ):
# On TPU it's best to pad everything to the same length or training will be very slow.
_A : 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":
_A : Any = 16
elif accelerator.mixed_precision != "no":
_A : int = 8
else:
_A : str = None
return tokenizer.pad(
snake_case_,padding="""longest""",max_length=snake_case_,pad_to_multiple_of=snake_case_,return_tensors="""pt""",)
# Instantiate dataloaders.
_A : Dict = DataLoader(
tokenized_datasets["""train"""],shuffle=snake_case_,collate_fn=snake_case_,batch_size=snake_case_ )
_A : str = DataLoader(
tokenized_datasets["""validation"""],shuffle=snake_case_,collate_fn=snake_case_,batch_size=snake_case_ )
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
_snake_case = mocked_dataloaders # noqa: F811
def lowerCAmelCase_ ( snake_case_,snake_case_ ):
# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""",snake_case_ ) == "1":
_A : Optional[Any] = 2
# New Code #
_A : str = int(args.gradient_accumulation_steps )
# Initialize accelerator
_A : Dict = Accelerator(
cpu=args.cpu,mixed_precision=args.mixed_precision,gradient_accumulation_steps=snake_case_ )
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
_A : int = config["""lr"""]
_A : str = int(config["""num_epochs"""] )
_A : Tuple = int(config["""seed"""] )
_A : Optional[Any] = int(config["""batch_size"""] )
_A : int = evaluate.load("""glue""","""mrpc""" )
set_seed(snake_case_ )
_A , _A : Dict = get_dataloaders(snake_case_,snake_case_ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
_A : Dict = AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""",return_dict=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).
_A : List[Any] = model.to(accelerator.device )
# Instantiate optimizer
_A : Any = AdamW(params=model.parameters(),lr=snake_case_ )
# Instantiate scheduler
_A : Optional[Any] = get_linear_schedule_with_warmup(
optimizer=snake_case_,num_warmup_steps=100,num_training_steps=(len(snake_case_ ) * 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.
_A , _A , _A , _A , _A : List[Any] = accelerator.prepare(
snake_case_,snake_case_,snake_case_,snake_case_,snake_case_ )
# Now we train the model
for epoch in range(snake_case_ ):
model.train()
for step, batch in enumerate(snake_case_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
# 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(snake_case_ ):
_A : List[Any] = model(**snake_case_ )
_A : Dict = output.loss
accelerator.backward(snake_case_ )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(snake_case_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
_A : Dict = model(**snake_case_ )
_A : Any = outputs.logits.argmax(dim=-1 )
_A , _A : Tuple = accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
metric.add_batch(
predictions=snake_case_,references=snake_case_,)
_A : int = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f'''epoch {epoch}:''',snake_case_ )
def lowerCAmelCase_ ( ):
_A : Any = argparse.ArgumentParser(description="""Simple example of training script.""" )
parser.add_argument(
"""--mixed_precision""",type=snake_case_,default=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.""",)
# New Code #
parser.add_argument(
"""--gradient_accumulation_steps""",type=snake_case_,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.""" )
_A : Tuple = parser.parse_args()
_A : List[str] = {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
training_function(snake_case_,snake_case_ )
if __name__ == "__main__":
main()
| 26 |
from __future__ import absolute_import, division, print_function, unicode_literals
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.roberta.modeling_roberta import (
ROBERTA_INPUTS_DOCSTRING,
ROBERTA_START_DOCSTRING,
RobertaEmbeddings,
)
from .modeling_highway_bert import BertPreTrainedModel, DeeBertModel, HighwayException, entropy
@add_start_docstrings(
"The RoBERTa Model transformer with early exiting (DeeRoBERTa). ",UpperCamelCase__,)
class lowercase ( UpperCamelCase__ ):
_a = RobertaConfig
_a = "roberta"
def __init__( self , _a ) -> Optional[int]:
super().__init__(_a )
_A : Union[str, Any] = RobertaEmbeddings(_a )
self.init_weights()
@add_start_docstrings(
"RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,\n also takes care of multi-layer training. ",UpperCamelCase__,)
class lowercase ( UpperCamelCase__ ):
_a = RobertaConfig
_a = "roberta"
def __init__( self , _a ) -> str:
super().__init__(_a )
_A : Any = config.num_labels
_A : Dict = config.num_hidden_layers
_A : List[str] = DeeRobertaModel(_a )
_A : int = nn.Dropout(config.hidden_dropout_prob )
_A : int = nn.Linear(config.hidden_size , self.config.num_labels )
@add_start_docstrings_to_model_forward(_a )
def a__ ( self , _a=None , _a=None , _a=None , _a=None , _a=None , _a=None , _a=None , _a=-1 , _a=False , ) -> Any:
_A : Optional[int] = self.num_layers
try:
_A : List[str] = self.roberta(
_a , attention_mask=_a , token_type_ids=_a , position_ids=_a , head_mask=_a , inputs_embeds=_a , )
_A : List[str] = outputs[1]
_A : List[str] = self.dropout(_a )
_A : Optional[Any] = self.classifier(_a )
_A : List[Any] = (logits,) + outputs[2:] # add hidden states and attention if they are here
except HighwayException as e:
_A : List[Any] = e.message
_A : Optional[int] = e.exit_layer
_A : Optional[int] = outputs[0]
if not self.training:
_A : int = entropy(_a )
_A : int = []
_A : int = []
if labels is not None:
if self.num_labels == 1:
# We are doing regression
_A : Union[str, Any] = MSELoss()
_A : Tuple = loss_fct(logits.view(-1 ) , labels.view(-1 ) )
else:
_A : List[Any] = CrossEntropyLoss()
_A : Dict = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
# work with highway exits
_A : Optional[Any] = []
for highway_exit in outputs[-1]:
_A : Tuple = highway_exit[0]
if not self.training:
highway_logits_all.append(_a )
highway_entropy.append(highway_exit[2] )
if self.num_labels == 1:
# We are doing regression
_A : List[str] = MSELoss()
_A : Optional[int] = loss_fct(highway_logits.view(-1 ) , labels.view(-1 ) )
else:
_A : List[Any] = CrossEntropyLoss()
_A : Tuple = loss_fct(highway_logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
highway_losses.append(_a )
if train_highway:
_A : Dict = (sum(highway_losses[:-1] ),) + outputs
# exclude the final highway, of course
else:
_A : int = (loss,) + outputs
if not self.training:
_A : Optional[Any] = outputs + ((original_entropy, highway_entropy), exit_layer)
if output_layer >= 0:
_A : Union[str, Any] = (
(outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
) # use the highway of the last layer
return outputs # (loss), logits, (hidden_states), (attentions), entropy
| 26 | 1 |
"""simple docstring"""
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class A__ ( unittest.TestCase ):
"""simple docstring"""
def __init__( self , lowercase , lowercase=3 , lowercase=32 , lowercase=3 , lowercase=10 , lowercase=[10, 20, 30, 40] , lowercase=[1, 1, 2, 1] , lowercase=True , lowercase=True , lowercase="relu" , lowercase=3 , lowercase=None , ) -> Optional[Any]:
'''simple docstring'''
a__ : List[Any] = parent
a__ : List[str] = batch_size
a__ : Dict = image_size
a__ : Any = num_channels
a__ : Union[str, Any] = embeddings_size
a__ : Dict = hidden_sizes
a__ : List[str] = depths
a__ : int = is_training
a__ : Tuple = use_labels
a__ : List[Any] = hidden_act
a__ : Tuple = num_labels
a__ : Optional[int] = scope
a__ : str = len(lowercase)
def __lowercase ( self) -> int:
'''simple docstring'''
a__ : str = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
a__ : List[str] = self.get_config()
return config, pixel_values
def __lowercase ( self) -> int:
'''simple docstring'''
return RegNetConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , image_size=self.image_size , )
def __lowercase ( self , lowercase , lowercase) -> Optional[int]:
'''simple docstring'''
a__ : int = FlaxRegNetModel(config=lowercase)
a__ : Tuple = model(lowercase)
# Output shape (b, c, h, w)
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def __lowercase ( self , lowercase , lowercase) -> str:
'''simple docstring'''
a__ : Any = self.num_labels
a__ : int = FlaxRegNetForImageClassification(config=lowercase)
a__ : str = model(lowercase)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels))
def __lowercase ( self) -> List[str]:
'''simple docstring'''
a__ : List[str] = self.prepare_config_and_inputs()
a__ : Any = config_and_inputs
a__ : Optional[int] = {'pixel_values': pixel_values}
return config, inputs_dict
@require_flax
class A__ ( __UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
__A : Tuple = (FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()
__A : Dict = False
__A : str = False
__A : Optional[int] = False
def __lowercase ( self) -> None:
'''simple docstring'''
a__ : Dict = FlaxRegNetModelTester(self)
a__ : Optional[int] = ConfigTester(self , config_class=lowercase , has_text_modality=lowercase)
def __lowercase ( self) -> str:
'''simple docstring'''
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def __lowercase ( self) -> Optional[int]:
'''simple docstring'''
return
def __lowercase ( self) -> List[str]:
'''simple docstring'''
a__ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowercase)
def __lowercase ( self) -> List[str]:
'''simple docstring'''
a__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowercase)
@unittest.skip(reason='RegNet does not use inputs_embeds')
def __lowercase ( self) -> Optional[int]:
'''simple docstring'''
pass
@unittest.skip(reason='RegNet does not support input and output embeddings')
def __lowercase ( self) -> List[str]:
'''simple docstring'''
pass
def __lowercase ( self) -> Optional[Any]:
'''simple docstring'''
a__ : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a__ : Dict = model_class(lowercase)
a__ : List[str] = inspect.signature(model.__call__)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
a__ : List[str] = [*signature.parameters.keys()]
a__ : int = ['pixel_values']
self.assertListEqual(arg_names[:1] , lowercase)
def __lowercase ( self) -> str:
'''simple docstring'''
def check_hidden_states_output(lowercase , lowercase , lowercase):
a__ : str = model_class(lowercase)
a__ : str = model(**self._prepare_for_class(lowercase , lowercase))
a__ : Any = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
a__ : List[Any] = self.model_tester.num_stages
self.assertEqual(len(lowercase) , expected_num_stages + 1)
a__ : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a__ : List[str] = True
check_hidden_states_output(lowercase , lowercase , lowercase)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
a__ : Optional[Any] = True
check_hidden_states_output(lowercase , lowercase , lowercase)
def __lowercase ( self) -> Optional[int]:
'''simple docstring'''
a__ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
a__ : List[str] = self._prepare_for_class(lowercase , lowercase)
a__ : str = model_class(lowercase)
@jax.jit
def model_jitted(lowercase , **lowercase):
return model(pixel_values=lowercase , **lowercase)
with self.subTest('JIT Enabled'):
a__ : int = model_jitted(**lowercase).to_tuple()
with self.subTest('JIT Disabled'):
with jax.disable_jit():
a__ : int = model_jitted(**lowercase).to_tuple()
self.assertEqual(len(lowercase) , len(lowercase))
for jitted_output, output in zip(lowercase , lowercase):
self.assertEqual(jitted_output.shape , output.shape)
def A_ ( ) -> Dict:
a__ : Optional[int] = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_flax
class A__ ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def __lowercase ( self) -> Optional[Any]:
'''simple docstring'''
return AutoImageProcessor.from_pretrained('facebook/regnet-y-040') if is_vision_available() else None
@slow
def __lowercase ( self) -> List[Any]:
'''simple docstring'''
a__ : Union[str, Any] = FlaxRegNetForImageClassification.from_pretrained('facebook/regnet-y-040')
a__ : Union[str, Any] = self.default_image_processor
a__ : Optional[int] = prepare_img()
a__ : List[str] = image_processor(images=lowercase , return_tensors='np')
a__ : Dict = model(**lowercase)
# verify the logits
a__ : Optional[Any] = (1, 1000)
self.assertEqual(outputs.logits.shape , lowercase)
a__ : Tuple = jnp.array([-0.41_80, -1.50_51, -3.48_36])
self.assertTrue(jnp.allclose(outputs.logits[0, :3] , lowercase , atol=1e-4))
| 357 |
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
lowercase : Union[str, Any] = data_utils.TransfoXLTokenizer
lowercase : Optional[int] = data_utils.TransfoXLCorpus
lowercase : List[Any] = data_utils
lowercase : Tuple = data_utils
def A_ ( A__ , A__ , A__ , A__ ) -> Optional[Any]:
if transfo_xl_dataset_file:
# Convert a pre-processed corpus (see original TensorFlow repo)
with open(A__ , 'rb' ) as fp:
a__ : int = pickle.load(A__ , encoding='latin1' )
# Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
a__ : int = pytorch_dump_folder_path + '/' + VOCAB_FILES_NAMES['pretrained_vocab_file']
print(F'Save vocabulary to {pytorch_vocab_dump_path}' )
a__ : List[Any] = corpus.vocab.__dict__
torch.save(A__ , A__ )
a__ : Dict = corpus.__dict__
corpus_dict_no_vocab.pop('vocab' , A__ )
a__ : Optional[int] = pytorch_dump_folder_path + '/' + CORPUS_NAME
print(F'Save dataset to {pytorch_dataset_dump_path}' )
torch.save(A__ , A__ )
if tf_checkpoint_path:
# Convert a pre-trained TensorFlow model
a__ : Union[str, Any] = os.path.abspath(A__ )
a__ : Optional[Any] = os.path.abspath(A__ )
print(F'Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.' )
# Initialise PyTorch model
if transfo_xl_config_file == "":
a__ : Dict = TransfoXLConfig()
else:
a__ : Dict = TransfoXLConfig.from_json_file(A__ )
print(F'Building PyTorch model from configuration: {config}' )
a__ : Optional[int] = TransfoXLLMHeadModel(A__ )
a__ : int = load_tf_weights_in_transfo_xl(A__ , A__ , A__ )
# Save pytorch-model
a__ : Any = os.path.join(A__ , A__ )
a__ : Dict = os.path.join(A__ , A__ )
print(F'Save PyTorch model to {os.path.abspath(A__ )}' )
torch.save(model.state_dict() , A__ )
print(F'Save configuration file to {os.path.abspath(A__ )}' )
with open(A__ , 'w' , encoding='utf-8' ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
lowercase : List[Any] = argparse.ArgumentParser()
parser.add_argument(
"""--pytorch_dump_folder_path""",
default=None,
type=str,
required=True,
help="""Path to the folder to store the PyTorch model or dataset/vocab.""",
)
parser.add_argument(
"""--tf_checkpoint_path""",
default="""""",
type=str,
help="""An optional path to a TensorFlow checkpoint path to be converted.""",
)
parser.add_argument(
"""--transfo_xl_config_file""",
default="""""",
type=str,
help=(
"""An optional config json file corresponding to the pre-trained BERT model. \n"""
"""This specifies the model architecture."""
),
)
parser.add_argument(
"""--transfo_xl_dataset_file""",
default="""""",
type=str,
help="""An optional dataset file to be converted in a vocabulary.""",
)
lowercase : Any = parser.parse_args()
convert_transfo_xl_checkpoint_to_pytorch(
args.tf_checkpoint_path,
args.transfo_xl_config_file,
args.pytorch_dump_folder_path,
args.transfo_xl_dataset_file,
)
| 225 | 0 |
import math
import flax.linen as nn
import jax.numpy as jnp
def UpperCamelCase_( _snake_case : jnp.ndarray , _snake_case : int , _snake_case : float = 1 , _snake_case : float = 1 , _snake_case : float = 1.0e4 , _snake_case : bool = False , _snake_case : float = 1.0 , ):
"""simple docstring"""
assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
assert embedding_dim % 2 == 0, F'Embedding dimension {embedding_dim} should be even'
__a =float(embedding_dim // 2 )
__a =math.log(max_timescale / min_timescale ) / (num_timescales - freq_shift)
__a =min_timescale * jnp.exp(jnp.arange(_snake_case , dtype=jnp.floataa ) * -log_timescale_increment )
__a =jnp.expand_dims(_snake_case , 1 ) * jnp.expand_dims(_snake_case , 0 )
# scale embeddings
__a =scale * emb
if flip_sin_to_cos:
__a =jnp.concatenate([jnp.cos(_snake_case ), jnp.sin(_snake_case )] , axis=1 )
else:
__a =jnp.concatenate([jnp.sin(_snake_case ), jnp.cos(_snake_case )] , axis=1 )
__a =jnp.reshape(_snake_case , [jnp.shape(_snake_case )[0], embedding_dim] )
return signal
class __magic_name__ ( nn.Module ):
SCREAMING_SNAKE_CASE = 3_2
SCREAMING_SNAKE_CASE = jnp.floataa
@nn.compact
def __call__( self , __snake_case ) -> Any:
'''simple docstring'''
__a =nn.Dense(self.time_embed_dim , dtype=self.dtype , name='linear_1' )(__snake_case )
__a =nn.silu(__snake_case )
__a =nn.Dense(self.time_embed_dim , dtype=self.dtype , name='linear_2' )(__snake_case )
return temb
class __magic_name__ ( nn.Module ):
SCREAMING_SNAKE_CASE = 3_2
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = 1
@nn.compact
def __call__( self , __snake_case ) -> Dict:
'''simple docstring'''
return get_sinusoidal_embeddings(
__snake_case , embedding_dim=self.dim , flip_sin_to_cos=self.flip_sin_to_cos , freq_shift=self.freq_shift )
| 218 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
_lowerCAmelCase : List[str] = logging.get_logger(__name__)
_lowerCAmelCase : Union[str, Any] = {
"Salesforce/instruct-blip-flan-t5": "https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json",
}
class __magic_name__ ( lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE = 'instructblip_vision_model'
def __init__( self , __snake_case=1408 , __snake_case=6144 , __snake_case=39 , __snake_case=16 , __snake_case=224 , __snake_case=14 , __snake_case="gelu" , __snake_case=1e-6 , __snake_case=0.0 , __snake_case=1e-10 , __snake_case=True , **__snake_case , ) -> str:
'''simple docstring'''
super().__init__(**__snake_case )
__a =hidden_size
__a =intermediate_size
__a =num_hidden_layers
__a =num_attention_heads
__a =patch_size
__a =image_size
__a =initializer_range
__a =attention_dropout
__a =layer_norm_eps
__a =hidden_act
__a =qkv_bias
@classmethod
def __magic_name__ ( cls , __snake_case , **__snake_case ) -> "PretrainedConfig":
'''simple docstring'''
cls._set_token_in_kwargs(__snake_case )
__a , __a =cls.get_config_dict(__snake_case , **__snake_case )
# get the vision config dict if we are loading from InstructBlipConfig
if config_dict.get('model_type' ) == "instructblip":
__a =config_dict['vision_config']
if "model_type" in config_dict and hasattr(cls , 'model_type' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(__snake_case , **__snake_case )
class __magic_name__ ( lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE = 'instructblip_qformer'
def __init__( self , __snake_case=3_0522 , __snake_case=768 , __snake_case=12 , __snake_case=12 , __snake_case=3072 , __snake_case="gelu" , __snake_case=0.1 , __snake_case=0.1 , __snake_case=512 , __snake_case=0.02 , __snake_case=1e-12 , __snake_case=0 , __snake_case="absolute" , __snake_case=2 , __snake_case=1408 , **__snake_case , ) -> List[str]:
'''simple docstring'''
super().__init__(pad_token_id=__snake_case , **__snake_case )
__a =vocab_size
__a =hidden_size
__a =num_hidden_layers
__a =num_attention_heads
__a =hidden_act
__a =intermediate_size
__a =hidden_dropout_prob
__a =attention_probs_dropout_prob
__a =max_position_embeddings
__a =initializer_range
__a =layer_norm_eps
__a =position_embedding_type
__a =cross_attention_frequency
__a =encoder_hidden_size
@classmethod
def __magic_name__ ( cls , __snake_case , **__snake_case ) -> "PretrainedConfig":
'''simple docstring'''
cls._set_token_in_kwargs(__snake_case )
__a , __a =cls.get_config_dict(__snake_case , **__snake_case )
# get the qformer config dict if we are loading from InstructBlipConfig
if config_dict.get('model_type' ) == "instructblip":
__a =config_dict['qformer_config']
if "model_type" in config_dict and hasattr(cls , 'model_type' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(__snake_case , **__snake_case )
class __magic_name__ ( lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE = 'instructblip'
SCREAMING_SNAKE_CASE = True
def __init__( self , __snake_case=None , __snake_case=None , __snake_case=None , __snake_case=32 , **__snake_case ) -> str:
'''simple docstring'''
super().__init__(**__snake_case )
if vision_config is None:
__a ={}
logger.info('vision_config is None. initializing the InstructBlipVisionConfig with default values.' )
if qformer_config is None:
__a ={}
logger.info('qformer_config is None. Initializing the InstructBlipQFormerConfig with default values.' )
if text_config is None:
__a ={}
logger.info('text_config is None. Initializing the text config with default values (`OPTConfig`).' )
__a =InstructBlipVisionConfig(**__snake_case )
__a =InstructBlipQFormerConfig(**__snake_case )
__a =text_config['model_type'] if 'model_type' in text_config else 'opt'
__a =CONFIG_MAPPING[text_model_type](**__snake_case )
__a =self.text_config.tie_word_embeddings
__a =self.text_config.is_encoder_decoder
__a =num_query_tokens
__a =self.vision_config.hidden_size
__a =self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
__a =1.0
__a =0.02
@classmethod
def __magic_name__ ( cls , __snake_case , __snake_case , __snake_case , **__snake_case , ) -> Optional[Any]:
'''simple docstring'''
return cls(
vision_config=vision_config.to_dict() , qformer_config=qformer_config.to_dict() , text_config=text_config.to_dict() , **__snake_case , )
def __magic_name__ ( self ) -> List[Any]:
'''simple docstring'''
__a =copy.deepcopy(self.__dict__ )
__a =self.vision_config.to_dict()
__a =self.qformer_config.to_dict()
__a =self.text_config.to_dict()
__a =self.__class__.model_type
return output
| 218 | 1 |
"""simple docstring"""
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging
__UpperCAmelCase = logging.get_logger(__name__)
class _SCREAMING_SNAKE_CASE ( A__ ):
UpperCAmelCase_ :Any = ["input_features", "is_longer"]
def __init__( self , __A=64 , __A=4_8000 , __A=480 , __A=10 , __A=1024 , __A=0.0 , __A=False , __A = 0 , __A = 1_4000 , __A = None , __A = "fusion" , __A = "repeatpad" , **__A , ) -> int:
super().__init__(
feature_size=__A , sampling_rate=__A , padding_value=__A , return_attention_mask=__A , **__A , )
lowerCAmelCase_ :Optional[int] = top_db
lowerCAmelCase_ :str = truncation
lowerCAmelCase_ :Any = padding
lowerCAmelCase_ :int = fft_window_size
lowerCAmelCase_ :int = (fft_window_size >> 1) + 1
lowerCAmelCase_ :Any = hop_length
lowerCAmelCase_ :int = max_length_s
lowerCAmelCase_ :Dict = max_length_s * sampling_rate
lowerCAmelCase_ :str = sampling_rate
lowerCAmelCase_ :int = frequency_min
lowerCAmelCase_ :Optional[int] = frequency_max
lowerCAmelCase_ :int = mel_filter_bank(
num_frequency_bins=self.nb_frequency_bins , num_mel_filters=__A , min_frequency=__A , max_frequency=__A , sampling_rate=__A , norm=__A , mel_scale="""htk""" , )
lowerCAmelCase_ :List[str] = mel_filter_bank(
num_frequency_bins=self.nb_frequency_bins , num_mel_filters=__A , min_frequency=__A , max_frequency=__A , sampling_rate=__A , norm="""slaney""" , mel_scale="""slaney""" , )
def __lowerCAmelCase ( self ) -> Dict[str, Any]:
lowerCAmelCase_ :List[str] = copy.deepcopy(self.__dict__ )
lowerCAmelCase_ :Tuple = self.__class__.__name__
if "mel_filters" in output:
del output["mel_filters"]
if "mel_filters_slaney" in output:
del output["mel_filters_slaney"]
return output
def __lowerCAmelCase ( self , __A , __A = None ) -> np.ndarray:
lowerCAmelCase_ :Tuple = spectrogram(
__A , window_function(self.fft_window_size , """hann""" ) , frame_length=self.fft_window_size , hop_length=self.hop_length , power=2.0 , mel_filters=__A , log_mel="""dB""" , )
return log_mel_spectrogram.T
def __lowerCAmelCase ( self , __A , __A , __A ) -> Optional[int]:
lowerCAmelCase_ :int = np.array_split(list(range(0 , total_frames - chunk_frames + 1 ) ) , 3 )
if len(ranges[1] ) == 0:
# if the audio is too short, we just use the first chunk
lowerCAmelCase_ :List[str] = [0]
if len(ranges[2] ) == 0:
# if the audio is too short, we just use the first chunk
lowerCAmelCase_ :Any = [0]
# randomly choose index for each part
lowerCAmelCase_ :Tuple = np.random.choice(ranges[0] )
lowerCAmelCase_ :List[Any] = np.random.choice(ranges[1] )
lowerCAmelCase_ :str = np.random.choice(ranges[2] )
lowerCAmelCase_ :str = mel[idx_front : idx_front + chunk_frames, :]
lowerCAmelCase_ :List[Any] = mel[idx_middle : idx_middle + chunk_frames, :]
lowerCAmelCase_ :int = mel[idx_back : idx_back + chunk_frames, :]
lowerCAmelCase_ :Union[str, Any] = torch.tensor(mel[None, None, :] )
lowerCAmelCase_ :Dict = torch.nn.functional.interpolate(
__A , size=[chunk_frames, 64] , mode="""bilinear""" , align_corners=__A )
lowerCAmelCase_ :List[str] = mel_shrink[0][0].numpy()
lowerCAmelCase_ :Optional[Any] = np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back] , axis=0 )
return mel_fusion
def __lowerCAmelCase ( self , __A , __A , __A , __A ) -> np.array:
if waveform.shape[0] > max_length:
if truncation == "rand_trunc":
lowerCAmelCase_ :Union[str, Any] = True
# random crop to max_length (for compatibility) -> this should be handled by self.pad
lowerCAmelCase_ :List[Any] = len(__A ) - max_length
lowerCAmelCase_ :Any = np.random.randint(0 , overflow + 1 )
lowerCAmelCase_ :Optional[int] = waveform[idx : idx + max_length]
lowerCAmelCase_ :Optional[Any] = self._np_extract_fbank_features(__A , self.mel_filters_slaney )[None, :]
elif truncation == "fusion":
lowerCAmelCase_ :List[str] = self._np_extract_fbank_features(__A , self.mel_filters )
lowerCAmelCase_ :Dict = max_length // self.hop_length + 1 # the +1 related to how the spectrogram is computed
lowerCAmelCase_ :Optional[int] = mel.shape[0]
if chunk_frames == total_frames:
# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
# In this case, we just use the whole audio.
lowerCAmelCase_ :Optional[int] = np.stack([mel, mel, mel, mel] , axis=0 )
lowerCAmelCase_ :Dict = False
else:
lowerCAmelCase_ :Tuple = self._random_mel_fusion(__A , __A , __A )
lowerCAmelCase_ :Dict = True
else:
raise NotImplementedError(f"""data_truncating {truncation} not implemented""" )
else:
lowerCAmelCase_ :Union[str, Any] = False
# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
if waveform.shape[0] < max_length:
if padding == "repeat":
lowerCAmelCase_ :Optional[Any] = int(max_length / len(__A ) )
lowerCAmelCase_ :List[Any] = np.stack(np.tile(__A , n_repeat + 1 ) )[:max_length]
if padding == "repeatpad":
lowerCAmelCase_ :Optional[int] = int(max_length / len(__A ) )
lowerCAmelCase_ :int = np.stack(np.tile(__A , __A ) )
lowerCAmelCase_ :List[Any] = np.pad(__A , (0, max_length - waveform.shape[0]) , mode="""constant""" , constant_values=0 )
if truncation == "fusion":
lowerCAmelCase_ :int = self._np_extract_fbank_features(__A , self.mel_filters )
lowerCAmelCase_ :Any = np.stack([input_mel, input_mel, input_mel, input_mel] , axis=0 )
else:
lowerCAmelCase_ :int = self._np_extract_fbank_features(__A , self.mel_filters_slaney )[None, :]
return input_mel, longer
def __call__( self , __A , __A = None , __A = None , __A = None , __A = None , __A = None , **__A , ) -> BatchFeature:
lowerCAmelCase_ :Union[str, Any] = truncation if truncation is not None else self.truncation
lowerCAmelCase_ :Optional[Any] = padding if padding else self.padding
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
f"""The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"""
f""" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"""
f""" was sampled with {self.sampling_rate} and not {sampling_rate}.""" )
else:
logger.warning(
"""It is strongly recommended to pass the `sampling_rate` argument to this function. """
"""Failing to do so can result in silent errors that might be hard to debug.""" )
lowerCAmelCase_ :Optional[Any] = isinstance(__A , np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(f"""Only mono-channel audio is supported for input to {self}""" )
lowerCAmelCase_ :Optional[Any] = is_batched_numpy or (
isinstance(__A , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
lowerCAmelCase_ :int = [np.asarray(__A , dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(__A , np.ndarray ):
lowerCAmelCase_ :List[str] = np.asarray(__A , dtype=np.floataa )
elif isinstance(__A , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
lowerCAmelCase_ :int = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
lowerCAmelCase_ :Optional[int] = [np.asarray(__A )]
# convert to mel spectrogram, truncate and pad if needed.
lowerCAmelCase_ :Tuple = [
self._get_input_mel(__A , max_length if max_length else self.nb_max_samples , __A , __A )
for waveform in raw_speech
]
lowerCAmelCase_ :Dict = []
lowerCAmelCase_ :Tuple = []
for mel, longer in padded_inputs:
input_mel.append(__A )
is_longer.append(__A )
if truncation == "fusion" and sum(__A ) == 0:
# if no audio is longer than 10s, then randomly select one audio to be longer
lowerCAmelCase_ :str = np.random.randint(0 , len(__A ) )
lowerCAmelCase_ :List[str] = True
if isinstance(input_mel[0] , __A ):
lowerCAmelCase_ :str = [np.asarray(__A , dtype=np.floataa ) for feature in input_mel]
# is_longer is a list of bool
lowerCAmelCase_ :Dict = [[longer] for longer in is_longer]
lowerCAmelCase_ :List[Any] = {"""input_features""": input_mel, """is_longer""": is_longer}
lowerCAmelCase_ :Tuple = BatchFeature(__A )
if return_tensors is not None:
lowerCAmelCase_ :Optional[int] = input_features.convert_to_tensors(__A )
return input_features
| 1 |
"""simple docstring"""
def _snake_case ( lowercase__ : list , lowercase__ : list , lowercase__ : int , lowercase__ : int , lowercase__ : int ) -> int:
'''simple docstring'''
if index == number_of_items:
return 0
lowerCAmelCase_ :Any = 0
lowerCAmelCase_ :str = 0
lowerCAmelCase_ :Dict = knapsack(lowercase__ , lowercase__ , lowercase__ , lowercase__ , index + 1 )
if weights[index] <= max_weight:
lowerCAmelCase_ :str = values[index] + knapsack(
lowercase__ , lowercase__ , lowercase__ , max_weight - weights[index] , index + 1 )
return max(lowercase__ , lowercase__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 1 | 1 |
'''simple docstring'''
def lowerCAmelCase_ ( snake_case_ : float , snake_case_ : float , snake_case_ : int ) -> float:
'''simple docstring'''
if principal <= 0:
raise Exception("Principal borrowed must be > 0" )
if rate_per_annum < 0:
raise Exception("Rate of interest must be >= 0" )
if years_to_repay <= 0 or not isinstance(snake_case_ , snake_case_ ):
raise Exception("Years to repay must be an integer > 0" )
# Yearly rate is divided by 12 to get monthly rate
UpperCAmelCase_ = rate_per_annum / 12
# Years to repay is multiplied by 12 to get number of payments as payment is monthly
UpperCAmelCase_ = years_to_repay * 12
return (
principal
* rate_per_month
* (1 + rate_per_month) ** number_of_payments
/ ((1 + rate_per_month) ** number_of_payments - 1)
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 1 | '''simple docstring'''
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed
def lowerCAmelCase_ ( snake_case_ : Dict , snake_case_ : Union[str, Any] , snake_case_ : Optional[Any] , snake_case_ : List[Any] ) -> List[Any]:
'''simple docstring'''
for param, grad_param in zip(model_a.parameters() , model_b.parameters() ):
if not param.requires_grad:
continue
if not did_step:
# Grads should not be in sync
assert (
torch.allclose(param.grad , grad_param.grad ) is False
), f"""Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})"""
else:
# Grads should be in sync
assert (
torch.allclose(param.grad , grad_param.grad ) is True
), f"""Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})"""
def lowerCAmelCase_ ( snake_case_ : Any , snake_case_ : Tuple , snake_case_ : Any , snake_case_ : List[Any] , snake_case_ : str=True ) -> Optional[Any]:
'''simple docstring'''
model.train()
UpperCAmelCase_ = model(snake_case_ )
UpperCAmelCase_ = F.mse_loss(snake_case_ , target.to(output.device ) )
if not do_backward:
loss /= accelerator.gradient_accumulation_steps
loss.backward()
else:
accelerator.backward(snake_case_ )
def lowerCAmelCase_ ( snake_case_ : Optional[Any] , snake_case_ : Any=False ) -> Dict:
'''simple docstring'''
set_seed(42 )
UpperCAmelCase_ = RegressionModel()
UpperCAmelCase_ = deepcopy(snake_case_ )
UpperCAmelCase_ = RegressionDataset(length=80 )
UpperCAmelCase_ = DataLoader(snake_case_ , batch_size=16 )
model.to(accelerator.device )
if sched:
UpperCAmelCase_ = AdamW(params=model.parameters() , lr=1E-3 )
UpperCAmelCase_ = AdamW(params=ddp_model.parameters() , lr=1E-3 )
UpperCAmelCase_ = LambdaLR(snake_case_ , lr_lambda=lambda snake_case_ : epoch**0.65 )
UpperCAmelCase_ = LambdaLR(snake_case_ , lr_lambda=lambda snake_case_ : epoch**0.65 )
# Make a copy of `model`
if sched:
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = accelerator.prepare(snake_case_ , snake_case_ , snake_case_ , snake_case_ )
else:
UpperCAmelCase_ , UpperCAmelCase_ = accelerator.prepare(snake_case_ , snake_case_ )
if sched:
return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
return model, ddp_model, dataloader
def lowerCAmelCase_ ( snake_case_ : Any ) -> int:
'''simple docstring'''
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = get_training_setup(snake_case_ )
# Use a single batch
UpperCAmelCase_ , UpperCAmelCase_ = next(iter(snake_case_ ) ).values()
for iteration in range(3 ):
# Gather the distributed inputs and targs for the base model
UpperCAmelCase_ , UpperCAmelCase_ = accelerator.gather((ddp_input, ddp_target) )
UpperCAmelCase_ , UpperCAmelCase_ = input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
step_model(snake_case_ , snake_case_ , snake_case_ , snake_case_ )
# Do "gradient accumulation" (noop)
if iteration % 2 == 0:
# Accumulate grads locally
with accelerator.no_sync(snake_case_ ):
step_model(snake_case_ , snake_case_ , snake_case_ , snake_case_ )
else:
# Sync grads
step_model(snake_case_ , snake_case_ , snake_case_ , snake_case_ )
# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
check_model_parameters(snake_case_ , snake_case_ , snake_case_ , snake_case_ )
for param, ddp_param in zip(model.parameters() , ddp_model.parameters() ):
if not param.requires_grad:
continue
assert torch.allclose(
param.grad , ddp_param.grad ), f"""Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""
# Shuffle ddp_input on each iteration
torch.manual_seed(13_37 + iteration )
UpperCAmelCase_ = ddp_input[torch.randperm(len(snake_case_ ) )]
def lowerCAmelCase_ ( snake_case_ : Tuple ) -> str:
'''simple docstring'''
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = get_training_setup(snake_case_ )
# Use a single batch
UpperCAmelCase_ , UpperCAmelCase_ = next(iter(snake_case_ ) ).values()
for iteration in range(3 ):
# Gather the distributed inputs and targs for the base model
UpperCAmelCase_ , UpperCAmelCase_ = accelerator.gather((ddp_input, ddp_target) )
UpperCAmelCase_ , UpperCAmelCase_ = input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
step_model(snake_case_ , snake_case_ , snake_case_ , snake_case_ )
# Do "gradient accumulation" (noop)
if iteration % 2 == 0:
# Accumulate grads locally
with accelerator.no_sync(snake_case_ ):
step_model(snake_case_ , snake_case_ , snake_case_ , snake_case_ )
else:
# Sync grads
step_model(snake_case_ , snake_case_ , snake_case_ , snake_case_ )
# DDP model and model should only be in sync when not (iteration % 2 == 0)
for param, ddp_param in zip(model.parameters() , ddp_model.parameters() ):
if not param.requires_grad:
continue
if iteration % 2 == 0:
# Grads should not be in sync
assert (
torch.allclose(param.grad , ddp_param.grad ) is False
), f"""Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"""
else:
# Grads should be in sync
assert (
torch.allclose(param.grad , ddp_param.grad ) is True
), f"""Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""
# Shuffle ddp_input on each iteration
torch.manual_seed(13_37 + iteration )
UpperCAmelCase_ = ddp_input[torch.randperm(len(snake_case_ ) )]
def lowerCAmelCase_ ( snake_case_ : Optional[int]=False , snake_case_ : str=False ) -> List[str]:
'''simple docstring'''
UpperCAmelCase_ = Accelerator(
split_batches=snake_case_ , dispatch_batches=snake_case_ , gradient_accumulation_steps=2 )
# Test that context manager behaves properly
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = get_training_setup(snake_case_ )
for iteration, batch in enumerate(snake_case_ ):
UpperCAmelCase_ , UpperCAmelCase_ = batch.values()
# Gather the distributed inputs and targs for the base model
UpperCAmelCase_ , UpperCAmelCase_ = accelerator.gather((ddp_input, ddp_target) )
UpperCAmelCase_ , UpperCAmelCase_ = input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
step_model(snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ )
# Do "gradient accumulation" (noop)
with accelerator.accumulate(snake_case_ ):
step_model(snake_case_ , snake_case_ , snake_case_ , snake_case_ )
# DDP model and model should only be in sync when not (iteration % 2 == 0)
for param, ddp_param in zip(model.parameters() , ddp_model.parameters() ):
if not param.requires_grad:
continue
if ((iteration + 1) % 2 == 0) or (iteration == len(snake_case_ ) - 1):
# Grads should be in sync
assert (
torch.allclose(param.grad , ddp_param.grad ) is True
), f"""Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""
else:
# Grads should not be in sync
assert (
torch.allclose(param.grad , ddp_param.grad ) is False
), f"""Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"""
# Shuffle ddp_input on each iteration
torch.manual_seed(13_37 + iteration )
UpperCAmelCase_ = ddp_input[torch.randperm(len(snake_case_ ) )]
GradientState._reset_state()
def lowerCAmelCase_ ( snake_case_ : Optional[Any]=False , snake_case_ : Tuple=False ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase_ = Accelerator(
split_batches=snake_case_ , dispatch_batches=snake_case_ , gradient_accumulation_steps=2 )
# Test that context manager behaves properly
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = get_training_setup(snake_case_ , snake_case_ )
for iteration, batch in enumerate(snake_case_ ):
UpperCAmelCase_ , UpperCAmelCase_ = batch.values()
# Gather the distributed inputs and targs for the base model
UpperCAmelCase_ , UpperCAmelCase_ = accelerator.gather((ddp_input, ddp_target) )
UpperCAmelCase_ , UpperCAmelCase_ = input.to(accelerator.device ), target.to(accelerator.device )
# Perform our initial ground truth step in non "DDP"
model.train()
ddp_model.train()
step_model(snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ )
opt.step()
if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(snake_case_ )):
if split_batches:
sched.step()
else:
for _ in range(accelerator.num_processes ):
sched.step()
opt.zero_grad()
# Perform gradient accumulation under wrapper
with accelerator.accumulate(snake_case_ ):
step_model(snake_case_ , snake_case_ , snake_case_ , snake_case_ )
ddp_opt.step()
ddp_sched.step()
ddp_opt.zero_grad()
# Learning rates should be the same
assert (
opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
), f"""Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]["lr"]}\nDDP opt: {ddp_opt.param_groups[0]["lr"]}\n"""
UpperCAmelCase_ = (((iteration + 1) % 2) == 0) or ((iteration + 1) == len(snake_case_ ))
if accelerator.num_processes > 1:
check_model_parameters(snake_case_ , snake_case_ , snake_case_ , snake_case_ )
# Shuffle ddp_input on each iteration
torch.manual_seed(13_37 + iteration )
GradientState._reset_state()
def lowerCAmelCase_ ( ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase_ = Accelerator()
UpperCAmelCase_ = RegressionDataset(length=80 )
UpperCAmelCase_ = DataLoader(snake_case_ , batch_size=16 )
UpperCAmelCase_ = RegressionDataset(length=96 )
UpperCAmelCase_ = DataLoader(snake_case_ , batch_size=16 )
UpperCAmelCase_ , UpperCAmelCase_ = accelerator.prepare(snake_case_ , snake_case_ )
assert accelerator.gradient_state.active_dataloader is None
for iteration, _ in enumerate(snake_case_ ):
assert id(accelerator.gradient_state.active_dataloader ) == id(snake_case_ )
if iteration < len(snake_case_ ) - 1:
assert not accelerator.gradient_state.end_of_dataloader
if iteration == 1:
for batch_num, _ in enumerate(snake_case_ ):
assert id(accelerator.gradient_state.active_dataloader ) == id(snake_case_ )
if batch_num < len(snake_case_ ) - 1:
assert not accelerator.gradient_state.end_of_dataloader
else:
assert accelerator.gradient_state.end_of_dataloader
else:
assert accelerator.gradient_state.end_of_dataloader
assert accelerator.gradient_state.active_dataloader is None
def lowerCAmelCase_ ( ) -> str:
'''simple docstring'''
UpperCAmelCase_ = Accelerator()
UpperCAmelCase_ = accelerator.state
if state.local_process_index == 0:
print("**Test `accumulate` gradient accumulation with dataloader break**" )
test_dataloader_break()
if state.distributed_type == DistributedType.NO:
if state.local_process_index == 0:
print("**Test NOOP `no_sync` context manager**" )
test_noop_sync(snake_case_ )
if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
if state.local_process_index == 0:
print("**Test Distributed `no_sync` context manager**" )
test_distributed_sync(snake_case_ )
if state.distributed_type == DistributedType.MULTI_GPU:
for split_batch in [True, False]:
for dispatch_batches in [True, False]:
if state.local_process_index == 0:
print(
"**Test `accumulate` gradient accumulation, " , f"""`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**""" , )
test_gradient_accumulation(snake_case_ , snake_case_ )
# Currently will break on torch 2.0 +, need to investigate why
if is_torch_version("<" , "2.0" ) or state.distributed_type == DistributedType.NO:
if state.local_process_index == 0:
print(
"**Test `accumulate` gradient accumulation with optimizer and scheduler, " , "`split_batches=False`, `dispatch_batches=False`**" , )
test_gradient_accumulation_with_opt_and_scheduler()
if state.distributed_type == DistributedType.MULTI_GPU:
for split_batch in [True, False]:
for dispatch_batches in [True, False]:
if not split_batch and not dispatch_batches:
continue
if state.local_process_index == 0:
print(
"**Test `accumulate` gradient accumulation with optimizer and scheduler, " , f"""`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**""" , )
test_gradient_accumulation_with_opt_and_scheduler(snake_case_ , snake_case_ )
def lowerCAmelCase_ ( snake_case_ : Dict ) -> int:
'''simple docstring'''
main()
if __name__ == "__main__":
main()
| 1 | 1 |
'''simple docstring'''
import collections
import importlib.util
import os
import re
from pathlib import Path
lowerCAmelCase : int ='src/transformers'
# Matches is_xxx_available()
lowerCAmelCase : Any =re.compile(r'''is\_([a-z_]*)_available()''')
# Catches a one-line _import_struct = {xxx}
lowerCAmelCase : Optional[Any] =re.compile(r'''^_import_structure\s+=\s+\{([^\}]+)\}''')
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
lowerCAmelCase : List[Any] =re.compile(r'''\s+"\S*":\s+\[([^\]]*)\]''')
# Catches a line if not is_foo_available
lowerCAmelCase : List[str] =re.compile(r'''^\s*if\s+not\s+is\_[a-z_]*\_available\(\)''')
# Catches a line _import_struct["bla"].append("foo")
lowerCAmelCase : Tuple =re.compile(r'''^\s*_import_structure\["\S*"\]\.append\("(\S*)"\)''')
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
lowerCAmelCase : str =re.compile(r'''^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]''')
# Catches a line with an object between quotes and a comma: "MyModel",
lowerCAmelCase : Any =re.compile('''^\s+"([^"]+)",''')
# Catches a line with objects between brackets only: ["foo", "bar"],
lowerCAmelCase : List[str] =re.compile('''^\s+\[([^\]]+)\]''')
# Catches a line with from foo import bar, bla, boo
lowerCAmelCase : str =re.compile(r'''\s+from\s+\S*\s+import\s+([^\(\s].*)\n''')
# Catches a line with try:
lowerCAmelCase : Optional[Any] =re.compile(r'''^\s*try:''')
# Catches a line with else:
lowerCAmelCase : Tuple =re.compile(r'''^\s*else:''')
def UpperCAmelCase_ ( __lowerCamelCase : Union[str, Any] ):
"""simple docstring"""
if _re_test_backend.search(snake_case_ ) is None:
return None
lowercase_ :Union[str, Any] = [b[0] for b in _re_backend.findall(snake_case_ )]
backends.sort()
return "_and_".join(snake_case_ )
def UpperCAmelCase_ ( __lowerCamelCase : Optional[Any] ):
"""simple docstring"""
with open(snake_case_ ,"r" ,encoding="utf-8" ,newline="\n" ) as f:
lowercase_ :Tuple = f.readlines()
lowercase_ :Dict = 0
while line_index < len(snake_case_ ) and not lines[line_index].startswith("_import_structure = {" ):
line_index += 1
# If this is a traditional init, just return.
if line_index >= len(snake_case_ ):
return None
# First grab the objects without a specific backend in _import_structure
lowercase_ :Dict = []
while not lines[line_index].startswith("if TYPE_CHECKING" ) and find_backend(lines[line_index] ) is None:
lowercase_ :Union[str, Any] = lines[line_index]
# If we have everything on a single line, let's deal with it.
if _re_one_line_import_struct.search(snake_case_ ):
lowercase_ :int = _re_one_line_import_struct.search(snake_case_ ).groups()[0]
lowercase_ :str = re.findall("\[([^\]]+)\]" ,snake_case_ )
for imp in imports:
objects.extend([obj[1:-1] for obj in imp.split(", " )] )
line_index += 1
continue
lowercase_ :List[Any] = _re_import_struct_key_value.search(snake_case_ )
if single_line_import_search is not None:
lowercase_ :Tuple = [obj[1:-1] for obj in single_line_import_search.groups()[0].split(", " ) if len(snake_case_ ) > 0]
objects.extend(snake_case_ )
elif line.startswith(" " * 8 + "\"" ):
objects.append(line[9:-3] )
line_index += 1
lowercase_ :Optional[Any] = {"none": objects}
# Let's continue with backend-specific objects in _import_structure
while not lines[line_index].startswith("if TYPE_CHECKING" ):
# If the line is an if not is_backend_available, we grab all objects associated.
lowercase_ :Tuple = find_backend(lines[line_index] )
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1] ) is None:
lowercase_ :Dict = None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index] ) is None:
line_index += 1
line_index += 1
lowercase_ :List[str] = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(" " * 4 ):
lowercase_ :Dict = lines[line_index]
if _re_import_struct_add_one.search(snake_case_ ) is not None:
objects.append(_re_import_struct_add_one.search(snake_case_ ).groups()[0] )
elif _re_import_struct_add_many.search(snake_case_ ) is not None:
lowercase_ :Optional[int] = _re_import_struct_add_many.search(snake_case_ ).groups()[0].split(", " )
lowercase_ :Any = [obj[1:-1] for obj in imports if len(snake_case_ ) > 0]
objects.extend(snake_case_ )
elif _re_between_brackets.search(snake_case_ ) is not None:
lowercase_ :List[Any] = _re_between_brackets.search(snake_case_ ).groups()[0].split(", " )
lowercase_ :Tuple = [obj[1:-1] for obj in imports if len(snake_case_ ) > 0]
objects.extend(snake_case_ )
elif _re_quote_object.search(snake_case_ ) is not None:
objects.append(_re_quote_object.search(snake_case_ ).groups()[0] )
elif line.startswith(" " * 8 + "\"" ):
objects.append(line[9:-3] )
elif line.startswith(" " * 12 + "\"" ):
objects.append(line[13:-3] )
line_index += 1
lowercase_ :int = objects
else:
line_index += 1
# At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
lowercase_ :int = []
while (
line_index < len(snake_case_ )
and find_backend(lines[line_index] ) is None
and not lines[line_index].startswith("else" )
):
lowercase_ :Optional[int] = lines[line_index]
lowercase_ :Optional[Any] = _re_import.search(snake_case_ )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(", " ) )
elif line.startswith(" " * 8 ):
objects.append(line[8:-2] )
line_index += 1
lowercase_ :List[str] = {"none": objects}
# Let's continue with backend-specific objects
while line_index < len(snake_case_ ):
# If the line is an if is_backend_available, we grab all objects associated.
lowercase_ :Union[str, Any] = find_backend(lines[line_index] )
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1] ) is None:
lowercase_ :Dict = None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index] ) is None:
line_index += 1
line_index += 1
lowercase_ :int = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(" " * 8 ):
lowercase_ :List[str] = lines[line_index]
lowercase_ :List[str] = _re_import.search(snake_case_ )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(", " ) )
elif line.startswith(" " * 12 ):
objects.append(line[12:-2] )
line_index += 1
lowercase_ :Optional[Any] = objects
else:
line_index += 1
return import_dict_objects, type_hint_objects
def UpperCAmelCase_ ( __lowerCamelCase : Dict ,__lowerCamelCase : int ):
"""simple docstring"""
def find_duplicates(__lowerCamelCase : Tuple ):
return [k for k, v in collections.Counter(snake_case_ ).items() if v > 1]
if list(import_dict_objects.keys() ) != list(type_hint_objects.keys() ):
return ["Both sides of the init do not have the same backends!"]
lowercase_ :List[Any] = []
for key in import_dict_objects.keys():
lowercase_ :int = find_duplicates(import_dict_objects[key] )
if duplicate_imports:
errors.append(F'Duplicate _import_structure definitions for: {duplicate_imports}' )
lowercase_ :Any = find_duplicates(type_hint_objects[key] )
if duplicate_type_hints:
errors.append(F'Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}' )
if sorted(set(import_dict_objects[key] ) ) != sorted(set(type_hint_objects[key] ) ):
lowercase_ :str = "base imports" if key == "none" else F'{key} backend'
errors.append(F'Differences for {name}:' )
for a in type_hint_objects[key]:
if a not in import_dict_objects[key]:
errors.append(F' {a} in TYPE_HINT but not in _import_structure.' )
for a in import_dict_objects[key]:
if a not in type_hint_objects[key]:
errors.append(F' {a} in _import_structure but not in TYPE_HINT.' )
return errors
def UpperCAmelCase_ ( ):
"""simple docstring"""
lowercase_ :Union[str, Any] = []
for root, _, files in os.walk(snake_case_ ):
if "__init__.py" in files:
lowercase_ :int = os.path.join(snake_case_ ,"__init__.py" )
lowercase_ :List[Any] = parse_init(snake_case_ )
if objects is not None:
lowercase_ :Optional[int] = analyze_results(*snake_case_ )
if len(snake_case_ ) > 0:
lowercase_ :int = F'Problem in {fname}, both halves do not define the same objects.\n{errors[0]}'
failures.append("\n".join(snake_case_ ) )
if len(snake_case_ ) > 0:
raise ValueError("\n\n".join(snake_case_ ) )
def UpperCAmelCase_ ( ):
"""simple docstring"""
lowercase_ :Optional[Any] = []
for path, directories, files in os.walk(snake_case_ ):
for folder in directories:
# Ignore private modules
if folder.startswith("_" ):
directories.remove(snake_case_ )
continue
# Ignore leftovers from branches (empty folders apart from pycache)
if len(list((Path(snake_case_ ) / folder).glob("*.py" ) ) ) == 0:
continue
lowercase_ :str = str((Path(snake_case_ ) / folder).relative_to(snake_case_ ) )
lowercase_ :Union[str, Any] = short_path.replace(os.path.sep ,"." )
submodules.append(snake_case_ )
for fname in files:
if fname == "__init__.py":
continue
lowercase_ :int = str((Path(snake_case_ ) / fname).relative_to(snake_case_ ) )
lowercase_ :List[Any] = short_path.replace(".py" ,"" ).replace(os.path.sep ,"." )
if len(submodule.split("." ) ) == 1:
submodules.append(snake_case_ )
return submodules
lowerCAmelCase : Any =[
'convert_pytorch_checkpoint_to_tf2',
'modeling_flax_pytorch_utils',
]
def UpperCAmelCase_ ( ):
"""simple docstring"""
lowercase_ :str = importlib.util.spec_from_file_location(
"transformers" ,os.path.join(snake_case_ ,"__init__.py" ) ,submodule_search_locations=[PATH_TO_TRANSFORMERS] ,)
lowercase_ :Optional[int] = spec.loader.load_module()
lowercase_ :Any = [
module
for module in get_transformers_submodules()
if module not in IGNORE_SUBMODULES and module not in transformers._import_structure.keys()
]
if len(snake_case_ ) > 0:
lowercase_ :Union[str, Any] = "\n".join(F'- {module}' for module in module_not_registered )
raise ValueError(
"The following submodules are not properly registered in the main init of Transformers:\n"
F'{list_of_modules}\n'
"Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value." )
if __name__ == "__main__":
check_all_inits()
check_submodules()
| 368 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowerCamelCase : str ,__lowerCamelCase : str ):
lowercase_ :Optional[Any] = len(__lowerCamelCase )
lowercase_ :List[Any] = []
for i in range(len(__lowerCamelCase ) - pat_len + 1 ):
lowercase_ :Dict = True
for j in range(__lowerCamelCase ):
if s[i + j] != pattern[j]:
lowercase_ :int = False
break
if match_found:
position.append(__lowerCamelCase )
return position
if __name__ == "__main__":
assert naive_pattern_search('''ABCDEFG''', '''DE''') == [3]
print(naive_pattern_search('''ABAAABCDBBABCDDEBCABC''', '''ABC'''))
| 147 | 0 |
"""simple docstring"""
import copy
import json
import os
import tempfile
from transformers import is_torch_available
from .test_configuration_utils import config_common_kwargs
class lowerCAmelCase_ (a__ ):
"""simple docstring"""
def __init__(self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=None , **SCREAMING_SNAKE_CASE__ ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = parent
SCREAMING_SNAKE_CASE__ : List[Any] = config_class
SCREAMING_SNAKE_CASE__ : int = has_text_modality
SCREAMING_SNAKE_CASE__ : Optional[Any] = kwargs
SCREAMING_SNAKE_CASE__ : Optional[Any] = common_properties
def __magic_name__ (self ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = self.config_class(**self.inputs_dict )
SCREAMING_SNAKE_CASE__ : Dict = (
["""hidden_size""", """num_attention_heads""", """num_hidden_layers"""]
if self.common_properties is None
else self.common_properties
)
# Add common fields for text models
if self.has_text_modality:
common_properties.extend(["""vocab_size"""] )
# Test that config has the common properties as getters
for prop in common_properties:
self.parent.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) , msg=F'''`{prop}` does not exist''' )
# Test that config has the common properties as setter
for idx, name in enumerate(SCREAMING_SNAKE_CASE__ ):
try:
setattr(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(
getattr(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) , SCREAMING_SNAKE_CASE__ , msg=F'''`{name} value {idx} expected, but was {getattr(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )}''' )
except NotImplementedError:
# Some models might not be able to implement setters for common_properties
# In that case, a NotImplementedError is raised
pass
# Test if config class can be called with Config(prop_name=..)
for idx, name in enumerate(SCREAMING_SNAKE_CASE__ ):
try:
SCREAMING_SNAKE_CASE__ : Tuple = self.config_class(**{name: idx} )
self.parent.assertEqual(
getattr(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) , SCREAMING_SNAKE_CASE__ , msg=F'''`{name} value {idx} expected, but was {getattr(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )}''' )
except NotImplementedError:
# Some models might not be able to implement setters for common_properties
# In that case, a NotImplementedError is raised
pass
def __magic_name__ (self ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.config_class(**self.inputs_dict )
SCREAMING_SNAKE_CASE__ : Any = json.loads(config.to_json_string() )
for key, value in self.inputs_dict.items():
self.parent.assertEqual(obj[key] , SCREAMING_SNAKE_CASE__ )
def __magic_name__ (self ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = self.config_class(**self.inputs_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
SCREAMING_SNAKE_CASE__ : Any = os.path.join(SCREAMING_SNAKE_CASE__ , """config.json""" )
config_first.to_json_file(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : str = self.config_class.from_json_file(SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(config_second.to_dict() , config_first.to_dict() )
def __magic_name__ (self ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = self.config_class(**self.inputs_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
config_first.save_pretrained(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Any = self.config_class.from_pretrained(SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(config_second.to_dict() , config_first.to_dict() )
def __magic_name__ (self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.config_class(**self.inputs_dict )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = """test"""
with tempfile.TemporaryDirectory() as tmpdirname:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = os.path.join(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
config_first.save_pretrained(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Any = self.config_class.from_pretrained(SCREAMING_SNAKE_CASE__ , subfolder=SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(config_second.to_dict() , config_first.to_dict() )
def __magic_name__ (self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.config_class(**self.inputs_dict , num_labels=5 )
self.parent.assertEqual(len(config.idalabel ) , 5 )
self.parent.assertEqual(len(config.labelaid ) , 5 )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 3
self.parent.assertEqual(len(config.idalabel ) , 3 )
self.parent.assertEqual(len(config.labelaid ) , 3 )
def __magic_name__ (self ) -> Any:
"""simple docstring"""
if self.config_class.is_composition:
return
SCREAMING_SNAKE_CASE__ : str = self.config_class()
self.parent.assertIsNotNone(SCREAMING_SNAKE_CASE__ )
def __magic_name__ (self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = copy.deepcopy(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.config_class(**SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : str = []
for key, value in config_common_kwargs.items():
if key == "torch_dtype":
if not is_torch_available():
continue
else:
import torch
if config.torch_dtype != torch.floataa:
wrong_values.append(("""torch_dtype""", config.torch_dtype, torch.floataa) )
elif getattr(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) != value:
wrong_values.append((key, getattr(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ), value) )
if len(SCREAMING_SNAKE_CASE__ ) > 0:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = """\n""".join([F'''- {v[0]}: got {v[1]} instead of {v[2]}''' for v in wrong_values] )
raise ValueError(F'''The following keys were not properly set in the config:\n{errors}''' )
def __magic_name__ (self ) -> Optional[Any]:
"""simple docstring"""
self.create_and_test_config_common_properties()
self.create_and_test_config_to_json_string()
self.create_and_test_config_to_json_file()
self.create_and_test_config_from_and_save_pretrained()
self.create_and_test_config_from_and_save_pretrained_subfolder()
self.create_and_test_config_with_num_labels()
self.check_config_can_be_init_without_params()
self.check_config_arguments_init()
| 25 |
"""simple docstring"""
import argparse
import json
import os
from tensorflow.core.protobuf.saved_model_pba import SavedModel
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
UpperCAmelCase__ : List[str] = '.'
# Internal TensorFlow ops that can be safely ignored (mostly specific to a saved model)
UpperCAmelCase__ : List[Any] = [
'Assert',
'AssignVariableOp',
'EmptyTensorList',
'MergeV2Checkpoints',
'ReadVariableOp',
'ResourceGather',
'RestoreV2',
'SaveV2',
'ShardedFilename',
'StatefulPartitionedCall',
'StaticRegexFullMatch',
'VarHandleOp',
]
def lowercase_ ( _snake_case ,_snake_case ,_snake_case ):
SCREAMING_SNAKE_CASE__ : List[str] = SavedModel()
SCREAMING_SNAKE_CASE__ : Dict = []
with open(os.path.join(_snake_case ,"""utils""" ,"""tf_ops""" ,"""onnx.json""" ) ) as f:
SCREAMING_SNAKE_CASE__ : Any = json.load(_snake_case )["""opsets"""]
for i in range(1 ,opset + 1 ):
onnx_ops.extend(onnx_opsets[str(_snake_case )] )
with open(_snake_case ,"""rb""" ) as f:
saved_model.ParseFromString(f.read() )
SCREAMING_SNAKE_CASE__ : List[str] = set()
# Iterate over every metagraph in case there is more than one (a saved model can contain multiple graphs)
for meta_graph in saved_model.meta_graphs:
# Add operations in the graph definition
model_op_names.update(node.op for node in meta_graph.graph_def.node )
# Go through the functions in the graph definition
for func in meta_graph.graph_def.library.function:
# Add operations in each function
model_op_names.update(node.op for node in func.node_def )
# Convert to list, sorted if you want
SCREAMING_SNAKE_CASE__ : int = sorted(_snake_case )
SCREAMING_SNAKE_CASE__ : Optional[Any] = []
for op in model_op_names:
if op not in onnx_ops and op not in INTERNAL_OPS:
incompatible_ops.append(_snake_case )
if strict and len(_snake_case ) > 0:
raise Exception(f'''Found the following incompatible ops for the opset {opset}:\n''' + incompatible_ops )
elif len(_snake_case ) > 0:
print(f'''Found the following incompatible ops for the opset {opset}:''' )
print(*_snake_case ,sep="""\n""" )
else:
print(f'''The saved model {saved_model_path} can properly be converted with ONNX.''' )
if __name__ == "__main__":
UpperCAmelCase__ : Optional[int] = argparse.ArgumentParser()
parser.add_argument('--saved_model_path', help='Path of the saved model to check (the .pb file).')
parser.add_argument(
'--opset', default=1_2, type=int, help='The ONNX opset against which the model has to be tested.'
)
parser.add_argument(
'--framework', choices=['onnx'], default='onnx', help='Frameworks against which to test the saved model.'
)
parser.add_argument(
'--strict', action='store_true', help='Whether make the checking strict (raise errors) or not (raise warnings)'
)
UpperCAmelCase__ : Dict = parser.parse_args()
if args.framework == "onnx":
onnx_compliancy(args.saved_model_path, args.strict, args.opset)
| 25 | 1 |
"""simple docstring"""
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_ ( lowerCAmelCase: int )-> Dict:
_snake_case : Tuple = [2, 2, 6, 2] if 'tiny' in model_name else [2, 2, 18, 2]
_snake_case : str = True if 'large' in model_name or 'huge' in model_name else False
_snake_case : Tuple = True if 'large' in model_name or 'huge' in model_name else False
_snake_case : Optional[Any] = 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:
_snake_case : Optional[Any] = [3, 3, 3, 3]
_snake_case : Any = [5, 5, 5, 5]
elif "fl4" in model_name:
_snake_case : Optional[Any] = [4, 4, 4, 4]
_snake_case : Optional[int] = [3, 3, 3, 3]
if "tiny" in model_name or "small" in model_name or "base" in model_name:
_snake_case : Tuple = [3, 3, 3, 3]
if "lrf" in model_name:
_snake_case : List[Any] = [3, 3, 3, 3]
else:
_snake_case : List[Any] = [2, 2, 2, 2]
if "tiny" in model_name:
_snake_case : Any = 96
elif "small" in model_name:
_snake_case : Tuple = 96
elif "base" in model_name:
_snake_case : Optional[int] = 1_28
elif "large" in model_name:
_snake_case : Any = 1_92
elif "xlarge" in model_name:
_snake_case : Tuple = 2_56
elif "huge" in model_name:
_snake_case : str = 3_52
# set label information
_snake_case : Optional[int] = 'huggingface/label-files'
if "large" in model_name or "huge" in model_name:
_snake_case : List[Any] = 'imagenet-22k-id2label.json'
else:
_snake_case : Union[str, Any] = 'imagenet-1k-id2label.json'
_snake_case : Optional[Any] = json.load(open(hf_hub_download(__lowerCamelCase , __lowerCamelCase , repo_type='dataset' ) , 'r' ) )
_snake_case : Optional[int] = {int(__lowerCamelCase ): v for k, v in idalabel.items()}
_snake_case : List[Any] = {v: k for k, v in idalabel.items()}
_snake_case : Union[str, Any] = 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_ ( lowerCAmelCase: Any )-> List[str]:
if "patch_embed.proj" in name:
_snake_case : int = name.replace('patch_embed.proj' , 'embeddings.patch_embeddings.projection' )
if "patch_embed.norm" in name:
_snake_case : str = name.replace('patch_embed.norm' , 'embeddings.norm' )
if "layers" in name:
_snake_case : Dict = 'encoder.' + name
if "encoder.layers" in name:
_snake_case : List[Any] = name.replace('encoder.layers' , 'encoder.stages' )
if "downsample.proj" in name:
_snake_case : Tuple = name.replace('downsample.proj' , 'downsample.projection' )
if "blocks" in name:
_snake_case : str = name.replace('blocks' , 'layers' )
if "modulation.f.weight" in name or "modulation.f.bias" in name:
_snake_case : Dict = name.replace('modulation.f' , 'modulation.projection_in' )
if "modulation.h.weight" in name or "modulation.h.bias" in name:
_snake_case : List[str] = name.replace('modulation.h' , 'modulation.projection_context' )
if "modulation.proj.weight" in name or "modulation.proj.bias" in name:
_snake_case : List[Any] = name.replace('modulation.proj' , 'modulation.projection_out' )
if name == "norm.weight":
_snake_case : Optional[Any] = 'layernorm.weight'
if name == "norm.bias":
_snake_case : str = 'layernorm.bias'
if "head" in name:
_snake_case : int = name.replace('head' , 'classifier' )
else:
_snake_case : str = 'focalnet.' + name
return name
def lowerCamelCase_ ( lowerCAmelCase: Union[str, Any] , lowerCAmelCase: Any , lowerCAmelCase: int=False )-> List[Any]:
# fmt: off
_snake_case : Optional[int] = {
'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
_snake_case : Dict = model_name_to_url[model_name]
print('Checkpoint URL: ' , __lowerCamelCase )
_snake_case : Optional[Any] = torch.hub.load_state_dict_from_url(__lowerCamelCase , map_location='cpu' )['model']
# rename keys
for key in state_dict.copy().keys():
_snake_case : Union[str, Any] = state_dict.pop(__lowerCamelCase )
_snake_case : List[Any] = val
_snake_case : Tuple = get_focalnet_config(__lowerCamelCase )
_snake_case : Optional[int] = FocalNetForImageClassification(__lowerCamelCase )
model.eval()
# load state dict
model.load_state_dict(__lowerCamelCase )
# verify conversion
_snake_case : str = 'http://images.cocodataset.org/val2017/000000039769.jpg'
_snake_case : str = 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 , )
_snake_case : Any = Image.open(requests.get(__lowerCamelCase , stream=__lowerCamelCase ).raw )
_snake_case : Dict = processor(images=__lowerCamelCase , return_tensors='pt' )
_snake_case : Union[str, Any] = transforms.Compose(
[
transforms.Resize(2_56 ),
transforms.CenterCrop(2_24 ),
transforms.ToTensor(),
transforms.Normalize(mean=[0.4_8_5, 0.4_5_6, 0.4_0_6] , std=[0.2_2_9, 0.2_2_4, 0.2_2_5] ),
] )
_snake_case : Tuple = image_transforms(__lowerCamelCase ).unsqueeze(0 )
# verify pixel_values
assert torch.allclose(inputs.pixel_values , __lowerCamelCase , atol=1E-4 )
_snake_case : Dict = model(**__lowerCamelCase )
_snake_case : int = 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":
_snake_case : Dict = torch.tensor([0.2_1_6_6, -0.4_3_6_8, 0.2_1_9_1] )
elif model_name == "focalnet-tiny-lrf":
_snake_case : List[Any] = torch.tensor([1.1_6_6_9, 0.0_1_2_5, -0.1_6_9_5] )
elif model_name == "focalnet-small":
_snake_case : Optional[Any] = torch.tensor([0.4_9_1_7, -0.0_4_3_0, 0.1_3_4_1] )
elif model_name == "focalnet-small-lrf":
_snake_case : int = torch.tensor([-0.2_5_8_8, -0.5_3_4_2, -0.2_3_3_1] )
elif model_name == "focalnet-base":
_snake_case : Union[str, Any] = torch.tensor([-0.1_6_5_5, -0.4_0_9_0, -0.1_7_3_0] )
elif model_name == "focalnet-base-lrf":
_snake_case : str = torch.tensor([0.5_3_0_6, -0.0_4_8_3, -0.3_9_2_8] )
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__":
lowerCAmelCase_ = 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.""",
)
lowerCAmelCase_ = parser.parse_args()
convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 368 |
from __future__ import annotations
def lowerCamelCase_ ( lowerCAmelCase: list , lowerCAmelCase: int | None = None , lowerCAmelCase: int | None = None )-> None:
if start is None:
_snake_case : int = 0
if end is None:
_snake_case : Optional[int] = len(lowerCAmelCase ) - 1
if start >= end:
return
_snake_case : int = (start + end) // 2
slowsort(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
slowsort(lowerCAmelCase , mid + 1 , lowerCAmelCase )
if sequence[end] < sequence[mid]:
_snake_case , _snake_case : str = sequence[mid], sequence[end]
slowsort(lowerCAmelCase , lowerCAmelCase , end - 1 )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 260 | 0 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu
class lowerCamelCase__( unittest.TestCase):
def lowerCAmelCase__ ( self: List[str] ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def lowerCAmelCase__ ( self: str ):
__lowerCamelCase = 1
__lowerCamelCase = 3
__lowerCamelCase = (32, 32)
__lowerCamelCase = floats_tensor((batch_size, num_channels) + sizes , rng=random.Random(0 ) ).to(UpperCamelCase_ )
return image
@property
def lowerCAmelCase__ ( self: List[str] ):
torch.manual_seed(0 )
__lowerCamelCase = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , cross_attention_dim=32 , )
return model
@property
def lowerCAmelCase__ ( self: Optional[Any] ):
torch.manual_seed(0 )
__lowerCamelCase = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , )
return model
@property
def lowerCAmelCase__ ( self: Any ):
torch.manual_seed(0 )
__lowerCamelCase = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , )
return CLIPTextModel(UpperCamelCase_ )
@property
def lowerCAmelCase__ ( self: Tuple ):
def extract(*UpperCamelCase_: Tuple , **UpperCamelCase_: Union[str, Any] ):
class lowerCamelCase__:
def __init__( self: Dict ):
__lowerCamelCase = torch.ones([0] )
def lowerCAmelCase__ ( self: Optional[int] , UpperCamelCase_: List[Any] ):
self.pixel_values.to(UpperCamelCase_ )
return self
return Out()
return extract
def lowerCAmelCase__ ( self: Dict ):
__lowerCamelCase = """cpu""" # ensure determinism for the device-dependent torch.Generator
__lowerCamelCase = self.dummy_cond_unet
__lowerCamelCase = DDIMScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , clip_sample=UpperCamelCase_ , set_alpha_to_one=UpperCamelCase_ , )
__lowerCamelCase = self.dummy_vae
__lowerCamelCase = self.dummy_text_encoder
__lowerCamelCase = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
# make sure here that pndm scheduler skips prk
__lowerCamelCase = StableDiffusionPipeline(
unet=UpperCamelCase_ , scheduler=UpperCamelCase_ , vae=UpperCamelCase_ , text_encoder=UpperCamelCase_ , tokenizer=UpperCamelCase_ , safety_checker=UpperCamelCase_ , feature_extractor=self.dummy_extractor , )
__lowerCamelCase = sd_pipe.to(UpperCamelCase_ )
sd_pipe.set_progress_bar_config(disable=UpperCamelCase_ )
__lowerCamelCase = """A painting of a squirrel eating a burger"""
__lowerCamelCase = torch.Generator(device=UpperCamelCase_ ).manual_seed(0 )
__lowerCamelCase = sd_pipe([prompt] , generator=UpperCamelCase_ , guidance_scale=6.0 , num_inference_steps=2 , output_type="""np""" )
__lowerCamelCase = output.images
__lowerCamelCase = torch.Generator(device=UpperCamelCase_ ).manual_seed(0 )
__lowerCamelCase = sd_pipe(
[prompt] , generator=UpperCamelCase_ , guidance_scale=6.0 , num_inference_steps=2 , output_type="""np""" , return_dict=UpperCamelCase_ , )[0]
__lowerCamelCase = image[0, -3:, -3:, -1]
__lowerCamelCase = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
__lowerCamelCase = np.array([0.5756, 0.6118, 0.5005, 0.5041, 0.5471, 0.4726, 0.4976, 0.4865, 0.4864] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
def lowerCAmelCase__ ( self: Optional[int] ):
__lowerCamelCase = """cpu""" # ensure determinism for the device-dependent torch.Generator
__lowerCamelCase = self.dummy_cond_unet
__lowerCamelCase = PNDMScheduler(skip_prk_steps=UpperCamelCase_ )
__lowerCamelCase = self.dummy_vae
__lowerCamelCase = self.dummy_text_encoder
__lowerCamelCase = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
# make sure here that pndm scheduler skips prk
__lowerCamelCase = StableDiffusionPipeline(
unet=UpperCamelCase_ , scheduler=UpperCamelCase_ , vae=UpperCamelCase_ , text_encoder=UpperCamelCase_ , tokenizer=UpperCamelCase_ , safety_checker=UpperCamelCase_ , feature_extractor=self.dummy_extractor , )
__lowerCamelCase = sd_pipe.to(UpperCamelCase_ )
sd_pipe.set_progress_bar_config(disable=UpperCamelCase_ )
__lowerCamelCase = """A painting of a squirrel eating a burger"""
__lowerCamelCase = torch.Generator(device=UpperCamelCase_ ).manual_seed(0 )
__lowerCamelCase = sd_pipe([prompt] , generator=UpperCamelCase_ , guidance_scale=6.0 , num_inference_steps=2 , output_type="""np""" )
__lowerCamelCase = output.images
__lowerCamelCase = torch.Generator(device=UpperCamelCase_ ).manual_seed(0 )
__lowerCamelCase = sd_pipe(
[prompt] , generator=UpperCamelCase_ , guidance_scale=6.0 , num_inference_steps=2 , output_type="""np""" , return_dict=UpperCamelCase_ , )[0]
__lowerCamelCase = image[0, -3:, -3:, -1]
__lowerCamelCase = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
__lowerCamelCase = np.array([0.5125, 0.5716, 0.4828, 0.5060, 0.5650, 0.4768, 0.5185, 0.4895, 0.4993] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
def lowerCAmelCase__ ( self: Optional[int] ):
__lowerCamelCase = StableDiffusionPipeline.from_pretrained(
"""hf-internal-testing/tiny-stable-diffusion-lms-pipe""" , safety_checker=UpperCamelCase_ )
assert isinstance(UpperCamelCase_ , UpperCamelCase_ )
assert isinstance(pipe.scheduler , UpperCamelCase_ )
assert pipe.safety_checker is None
__lowerCamelCase = pipe("""example prompt""" , num_inference_steps=2 ).images[0]
assert image is not None
# check that there's no error when saving a pipeline with one of the models being None
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(UpperCamelCase_ )
__lowerCamelCase = StableDiffusionPipeline.from_pretrained(UpperCamelCase_ )
# sanity check that the pipeline still works
assert pipe.safety_checker is None
__lowerCamelCase = pipe("""example prompt""" , num_inference_steps=2 ).images[0]
assert image is not None
@unittest.skipIf(torch_device != """cuda""" , """This test requires a GPU""" )
def lowerCAmelCase__ ( self: List[Any] ):
__lowerCamelCase = self.dummy_cond_unet
__lowerCamelCase = PNDMScheduler(skip_prk_steps=UpperCamelCase_ )
__lowerCamelCase = self.dummy_vae
__lowerCamelCase = self.dummy_text_encoder
__lowerCamelCase = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
# put models in fp16
__lowerCamelCase = unet.half()
__lowerCamelCase = vae.half()
__lowerCamelCase = bert.half()
# make sure here that pndm scheduler skips prk
__lowerCamelCase = StableDiffusionPipeline(
unet=UpperCamelCase_ , scheduler=UpperCamelCase_ , vae=UpperCamelCase_ , text_encoder=UpperCamelCase_ , tokenizer=UpperCamelCase_ , safety_checker=UpperCamelCase_ , feature_extractor=self.dummy_extractor , )
__lowerCamelCase = sd_pipe.to(UpperCamelCase_ )
sd_pipe.set_progress_bar_config(disable=UpperCamelCase_ )
__lowerCamelCase = """A painting of a squirrel eating a burger"""
__lowerCamelCase = sd_pipe([prompt] , num_inference_steps=2 , output_type="""np""" ).images
assert image.shape == (1, 64, 64, 3)
@nightly
@require_torch_gpu
class lowerCamelCase__( unittest.TestCase):
def lowerCAmelCase__ ( self: Any ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCAmelCase__ ( self: Tuple ):
__lowerCamelCase = StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5""" , safety_checker=UpperCamelCase_ )
__lowerCamelCase = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config )
__lowerCamelCase = sd_pipe.to(UpperCamelCase_ )
sd_pipe.set_progress_bar_config(disable=UpperCamelCase_ )
__lowerCamelCase = (
"""portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle"""
""" coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with"""
""" anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and"""
""" children from bahnhof zoo, detailed """
)
__lowerCamelCase = 40_03_66_03_46
__lowerCamelCase = 7
# without safety guidance (sld_guidance_scale = 0)
__lowerCamelCase = torch.manual_seed(UpperCamelCase_ )
__lowerCamelCase = sd_pipe(
[prompt] , generator=UpperCamelCase_ , guidance_scale=UpperCamelCase_ , num_inference_steps=50 , output_type="""np""" , width=5_12 , height=5_12 , sld_guidance_scale=0 , )
__lowerCamelCase = output.images
__lowerCamelCase = image[0, -3:, -3:, -1]
__lowerCamelCase = [0.2278, 0.2231, 0.2249, 0.2333, 0.2303, 0.1885, 0.2273, 0.2144, 0.2176]
assert image.shape == (1, 5_12, 5_12, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
# without safety guidance (strong configuration)
__lowerCamelCase = torch.manual_seed(UpperCamelCase_ )
__lowerCamelCase = sd_pipe(
[prompt] , generator=UpperCamelCase_ , guidance_scale=UpperCamelCase_ , num_inference_steps=50 , output_type="""np""" , width=5_12 , height=5_12 , sld_guidance_scale=20_00 , sld_warmup_steps=7 , sld_threshold=0.025 , sld_momentum_scale=0.5 , sld_mom_beta=0.7 , )
__lowerCamelCase = output.images
__lowerCamelCase = image[0, -3:, -3:, -1]
__lowerCamelCase = [0.2383, 0.2276, 0.236, 0.2192, 0.2186, 0.2053, 0.1971, 0.1901, 0.1719]
assert image.shape == (1, 5_12, 5_12, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def lowerCAmelCase__ ( self: Any ):
__lowerCamelCase = StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5""" , safety_checker=UpperCamelCase_ )
__lowerCamelCase = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config )
__lowerCamelCase = sd_pipe.to(UpperCamelCase_ )
sd_pipe.set_progress_bar_config(disable=UpperCamelCase_ )
__lowerCamelCase = """padme amidala taking a bath artwork, safe for work, no nudity"""
__lowerCamelCase = 27_34_97_17_55
__lowerCamelCase = 7
__lowerCamelCase = torch.manual_seed(UpperCamelCase_ )
__lowerCamelCase = sd_pipe(
[prompt] , generator=UpperCamelCase_ , guidance_scale=UpperCamelCase_ , num_inference_steps=50 , output_type="""np""" , width=5_12 , height=5_12 , sld_guidance_scale=0 , )
__lowerCamelCase = output.images
__lowerCamelCase = image[0, -3:, -3:, -1]
__lowerCamelCase = [0.3502, 0.3622, 0.3396, 0.3642, 0.3478, 0.3318, 0.35, 0.3348, 0.3297]
assert image.shape == (1, 5_12, 5_12, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
__lowerCamelCase = torch.manual_seed(UpperCamelCase_ )
__lowerCamelCase = sd_pipe(
[prompt] , generator=UpperCamelCase_ , guidance_scale=UpperCamelCase_ , num_inference_steps=50 , output_type="""np""" , width=5_12 , height=5_12 , sld_guidance_scale=20_00 , sld_warmup_steps=7 , sld_threshold=0.025 , sld_momentum_scale=0.5 , sld_mom_beta=0.7 , )
__lowerCamelCase = output.images
__lowerCamelCase = image[0, -3:, -3:, -1]
__lowerCamelCase = [0.5531, 0.5206, 0.4895, 0.5156, 0.5182, 0.4751, 0.4802, 0.4803, 0.4443]
assert image.shape == (1, 5_12, 5_12, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def lowerCAmelCase__ ( self: Dict ):
__lowerCamelCase = StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5""" )
__lowerCamelCase = sd_pipe.to(UpperCamelCase_ )
sd_pipe.set_progress_bar_config(disable=UpperCamelCase_ )
__lowerCamelCase = (
"""the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c."""
""" leyendecker"""
)
__lowerCamelCase = 10_44_35_52_34
__lowerCamelCase = 12
__lowerCamelCase = torch.manual_seed(UpperCamelCase_ )
__lowerCamelCase = sd_pipe(
[prompt] , generator=UpperCamelCase_ , guidance_scale=UpperCamelCase_ , num_inference_steps=50 , output_type="""np""" , width=5_12 , height=5_12 , sld_guidance_scale=0 , )
__lowerCamelCase = output.images
__lowerCamelCase = image[0, -3:, -3:, -1]
__lowerCamelCase = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] )
assert image.shape == (1, 5_12, 5_12, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-7
__lowerCamelCase = torch.manual_seed(UpperCamelCase_ )
__lowerCamelCase = sd_pipe(
[prompt] , generator=UpperCamelCase_ , guidance_scale=UpperCamelCase_ , num_inference_steps=50 , output_type="""np""" , width=5_12 , height=5_12 , sld_guidance_scale=20_00 , sld_warmup_steps=7 , sld_threshold=0.025 , sld_momentum_scale=0.5 , sld_mom_beta=0.7 , )
__lowerCamelCase = output.images
__lowerCamelCase = image[0, -3:, -3:, -1]
__lowerCamelCase = np.array([0.5818, 0.6285, 0.6835, 0.6019, 0.625, 0.6754, 0.6096, 0.6334, 0.6561] )
assert image.shape == (1, 5_12, 5_12, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 12 |
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils import logging
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
UpperCAmelCase_ = logging.get_logger(__name__) # pylint: disable=invalid-name
class lowerCamelCase__( __lowerCamelCase , __lowerCamelCase):
@register_to_config
def __init__( self: Optional[Any] , UpperCamelCase_: bool , UpperCamelCase_: Optional[int] = None , UpperCamelCase_: Optional[int] = None ):
super().__init__()
__lowerCamelCase = learnable
if self.learnable:
assert hidden_size is not None, "learnable=True requires `hidden_size` to be set"
assert length is not None, "learnable=True requires `length` to be set"
__lowerCamelCase = torch.zeros(UpperCamelCase_ , UpperCamelCase_ )
else:
__lowerCamelCase = None
__lowerCamelCase = torch.nn.Parameter(UpperCamelCase_ )
class lowerCamelCase__( __lowerCamelCase):
UpperCAmelCase__ : VQModel
UpperCAmelCase__ : CLIPTextModel
UpperCAmelCase__ : CLIPTokenizer
UpperCAmelCase__ : TransformeraDModel
UpperCAmelCase__ : LearnedClassifierFreeSamplingEmbeddings
UpperCAmelCase__ : VQDiffusionScheduler
def __init__( self: str , UpperCamelCase_: VQModel , UpperCamelCase_: CLIPTextModel , UpperCamelCase_: CLIPTokenizer , UpperCamelCase_: TransformeraDModel , UpperCamelCase_: VQDiffusionScheduler , UpperCamelCase_: LearnedClassifierFreeSamplingEmbeddings , ):
super().__init__()
self.register_modules(
vqvae=UpperCamelCase_ , transformer=UpperCamelCase_ , text_encoder=UpperCamelCase_ , tokenizer=UpperCamelCase_ , scheduler=UpperCamelCase_ , learned_classifier_free_sampling_embeddings=UpperCamelCase_ , )
def lowerCAmelCase__ ( self: str , UpperCamelCase_: Any , UpperCamelCase_: List[Any] , UpperCamelCase_: Any ):
__lowerCamelCase = len(UpperCamelCase_ ) if isinstance(UpperCamelCase_ , UpperCamelCase_ ) else 1
# get prompt text embeddings
__lowerCamelCase = self.tokenizer(
UpperCamelCase_ , padding="""max_length""" , max_length=self.tokenizer.model_max_length , return_tensors="""pt""" , )
__lowerCamelCase = text_inputs.input_ids
if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
__lowerCamelCase = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :] )
logger.warning(
"""The following part of your input was truncated because CLIP can only handle sequences up to"""
F' {self.tokenizer.model_max_length} tokens: {removed_text}' )
__lowerCamelCase = text_input_ids[:, : self.tokenizer.model_max_length]
__lowerCamelCase = self.text_encoder(text_input_ids.to(self.device ) )[0]
# NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
# While CLIP does normalize the pooled output of the text transformer when combining
# the image and text embeddings, CLIP does not directly normalize the last hidden state.
#
# CLIP normalizing the pooled output.
# https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053
__lowerCamelCase = prompt_embeds / prompt_embeds.norm(dim=-1 , keepdim=UpperCamelCase_ )
# duplicate text embeddings for each generation per prompt
__lowerCamelCase = prompt_embeds.repeat_interleave(UpperCamelCase_ , dim=0 )
if do_classifier_free_guidance:
if self.learned_classifier_free_sampling_embeddings.learnable:
__lowerCamelCase = self.learned_classifier_free_sampling_embeddings.embeddings
__lowerCamelCase = negative_prompt_embeds.unsqueeze(0 ).repeat(UpperCamelCase_ , 1 , 1 )
else:
__lowerCamelCase = [""""""] * batch_size
__lowerCamelCase = text_input_ids.shape[-1]
__lowerCamelCase = self.tokenizer(
UpperCamelCase_ , padding="""max_length""" , max_length=UpperCamelCase_ , truncation=UpperCamelCase_ , return_tensors="""pt""" , )
__lowerCamelCase = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# See comment for normalizing text embeddings
__lowerCamelCase = negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1 , keepdim=UpperCamelCase_ )
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
__lowerCamelCase = negative_prompt_embeds.shape[1]
__lowerCamelCase = negative_prompt_embeds.repeat(1 , UpperCamelCase_ , 1 )
__lowerCamelCase = negative_prompt_embeds.view(batch_size * num_images_per_prompt , UpperCamelCase_ , -1 )
# 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 = torch.cat([negative_prompt_embeds, prompt_embeds] )
return prompt_embeds
@torch.no_grad()
def __call__( self: Tuple , UpperCamelCase_: Union[str, List[str]] , UpperCamelCase_: int = 1_00 , UpperCamelCase_: float = 5.0 , UpperCamelCase_: float = 1.0 , UpperCamelCase_: int = 1 , UpperCamelCase_: Optional[Union[torch.Generator, List[torch.Generator]]] = None , UpperCamelCase_: Optional[torch.FloatTensor] = None , UpperCamelCase_: Optional[str] = "pil" , UpperCamelCase_: bool = True , UpperCamelCase_: Optional[Callable[[int, int, torch.FloatTensor], None]] = None , UpperCamelCase_: int = 1 , ):
if isinstance(UpperCamelCase_ , UpperCamelCase_ ):
__lowerCamelCase = 1
elif isinstance(UpperCamelCase_ , UpperCamelCase_ ):
__lowerCamelCase = len(UpperCamelCase_ )
else:
raise ValueError(F'`prompt` has to be of type `str` or `list` but is {type(UpperCamelCase_ )}' )
__lowerCamelCase = batch_size * num_images_per_prompt
__lowerCamelCase = guidance_scale > 1.0
__lowerCamelCase = self._encode_prompt(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(UpperCamelCase_ , UpperCamelCase_ ) or callback_steps <= 0)
):
raise ValueError(
F'`callback_steps` has to be a positive integer but is {callback_steps} of type'
F' {type(UpperCamelCase_ )}.' )
# get the initial completely masked latents unless the user supplied it
__lowerCamelCase = (batch_size, self.transformer.num_latent_pixels)
if latents is None:
__lowerCamelCase = self.transformer.num_vector_embeds - 1
__lowerCamelCase = torch.full(UpperCamelCase_ , UpperCamelCase_ ).to(self.device )
else:
if latents.shape != latents_shape:
raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {latents_shape}' )
if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any():
raise ValueError(
"""Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0,"""
F' {self.transformer.num_vector_embeds - 1} (inclusive).' )
__lowerCamelCase = latents.to(self.device )
# set timesteps
self.scheduler.set_timesteps(UpperCamelCase_ , device=self.device )
__lowerCamelCase = self.scheduler.timesteps.to(self.device )
__lowerCamelCase = latents
for i, t in enumerate(self.progress_bar(UpperCamelCase_ ) ):
# expand the sample if we are doing classifier free guidance
__lowerCamelCase = torch.cat([sample] * 2 ) if do_classifier_free_guidance else sample
# predict the un-noised image
# model_output == `log_p_x_0`
__lowerCamelCase = self.transformer(UpperCamelCase_ , encoder_hidden_states=UpperCamelCase_ , timestep=UpperCamelCase_ ).sample
if do_classifier_free_guidance:
__lowerCamelCase, __lowerCamelCase = model_output.chunk(2 )
__lowerCamelCase = model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
model_output -= torch.logsumexp(UpperCamelCase_ , dim=1 , keepdim=UpperCamelCase_ )
__lowerCamelCase = self.truncate(UpperCamelCase_ , UpperCamelCase_ )
# remove `log(0)`'s (`-inf`s)
__lowerCamelCase = model_output.clamp(-70 )
# compute the previous noisy sample x_t -> x_t-1
__lowerCamelCase = self.scheduler.step(UpperCamelCase_ , timestep=UpperCamelCase_ , sample=UpperCamelCase_ , generator=UpperCamelCase_ ).prev_sample
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
__lowerCamelCase = self.vqvae.config.vq_embed_dim
__lowerCamelCase = (batch_size, self.transformer.height, self.transformer.width, embedding_channels)
__lowerCamelCase = self.vqvae.quantize.get_codebook_entry(UpperCamelCase_ , shape=UpperCamelCase_ )
__lowerCamelCase = self.vqvae.decode(UpperCamelCase_ , force_not_quantize=UpperCamelCase_ ).sample
__lowerCamelCase = (image / 2 + 0.5).clamp(0 , 1 )
__lowerCamelCase = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
__lowerCamelCase = self.numpy_to_pil(UpperCamelCase_ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=UpperCamelCase_ )
def lowerCAmelCase__ ( self: Union[str, Any] , UpperCamelCase_: torch.FloatTensor , UpperCamelCase_: float ):
__lowerCamelCase, __lowerCamelCase = torch.sort(UpperCamelCase_ , 1 , descending=UpperCamelCase_ )
__lowerCamelCase = torch.exp(UpperCamelCase_ )
__lowerCamelCase = sorted_p_x_0.cumsum(dim=1 ) < truncation_rate
# Ensure that at least the largest probability is not zeroed out
__lowerCamelCase = torch.full_like(keep_mask[:, 0:1, :] , UpperCamelCase_ )
__lowerCamelCase = torch.cat((all_true, keep_mask) , dim=1 )
__lowerCamelCase = keep_mask[:, :-1, :]
__lowerCamelCase = keep_mask.gather(1 , indices.argsort(1 ) )
__lowerCamelCase = log_p_x_0.clone()
__lowerCamelCase = -torch.inf # -inf = log(0)
return rv
| 12 | 1 |
def __UpperCamelCase ( UpperCAmelCase = 6008_5147_5143 ):
try:
lowercase__ : Tuple = int(__A )
except (TypeError, ValueError):
raise TypeError('''Parameter n must be int or castable to int.''' )
if n <= 0:
raise ValueError('''Parameter n must be greater than or equal to one.''' )
lowercase__ : List[Any] = 2
lowercase__ : List[str] = 0
if n == 2:
return 2
while n > 2:
while n % i != 0:
i += 1
lowercase__ : str = i
while n % i == 0:
lowercase__ : List[str] = n // i
i += 1
return int(__A )
if __name__ == "__main__":
print(F'{solution() = }')
| 357 | '''simple docstring'''
import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
from tensorflow.keras.optimizers import Adam
class UpperCAmelCase ( tf.keras.optimizers.schedules.LearningRateSchedule ):
'''simple docstring'''
def __init__( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = 1.0 , __lowerCAmelCase = None , ) -> Union[str, Any]:
super().__init__()
lowercase__ : Any = initial_learning_rate
lowercase__ : str = warmup_steps
lowercase__ : Dict = power
lowercase__ : int = decay_schedule_fn
lowercase__ : Dict = name
def __call__( self , __lowerCAmelCase ) -> int:
with tf.name_scope(self.name or '''WarmUp''' ) as name:
# Implements polynomial warmup. i.e., if global_step < warmup_steps, the
# learning rate will be `global_step/num_warmup_steps * init_lr`.
lowercase__ : Union[str, Any] = tf.cast(__lowerCAmelCase , tf.floataa )
lowercase__ : Optional[int] = tf.cast(self.warmup_steps , tf.floataa )
lowercase__ : Dict = global_step_float / warmup_steps_float
lowercase__ : Dict = self.initial_learning_rate * tf.math.pow(__lowerCAmelCase , self.power )
return tf.cond(
global_step_float < warmup_steps_float , lambda: warmup_learning_rate , lambda: self.decay_schedule_fn(step - self.warmup_steps ) , name=__lowerCAmelCase , )
def _lowerCAmelCase( self ) -> List[str]:
return {
"initial_learning_rate": self.initial_learning_rate,
"decay_schedule_fn": self.decay_schedule_fn,
"warmup_steps": self.warmup_steps,
"power": self.power,
"name": self.name,
}
def __UpperCamelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = 0.0 , UpperCAmelCase = 0.9 , UpperCAmelCase = 0.9_9_9 , UpperCAmelCase = 1E-8 , UpperCAmelCase = None , UpperCAmelCase = None , UpperCAmelCase = 0.0 , UpperCAmelCase = 1.0 , UpperCAmelCase = None , ):
lowercase__ : List[Any] = tf.keras.optimizers.schedules.PolynomialDecay(
initial_learning_rate=UpperCAmelCase , decay_steps=num_train_steps - num_warmup_steps , end_learning_rate=init_lr * min_lr_ratio , power=UpperCAmelCase , )
if num_warmup_steps:
lowercase__ : Optional[Any] = WarmUp(
initial_learning_rate=UpperCAmelCase , decay_schedule_fn=UpperCAmelCase , warmup_steps=UpperCAmelCase , )
if weight_decay_rate > 0.0:
lowercase__ : str = AdamWeightDecay(
learning_rate=UpperCAmelCase , weight_decay_rate=UpperCAmelCase , beta_a=UpperCAmelCase , beta_a=UpperCAmelCase , epsilon=UpperCAmelCase , clipnorm=UpperCAmelCase , global_clipnorm=UpperCAmelCase , exclude_from_weight_decay=['''LayerNorm''', '''layer_norm''', '''bias'''] , include_in_weight_decay=UpperCAmelCase , )
else:
lowercase__ : Any = tf.keras.optimizers.Adam(
learning_rate=UpperCAmelCase , beta_a=UpperCAmelCase , beta_a=UpperCAmelCase , epsilon=UpperCAmelCase , clipnorm=UpperCAmelCase , global_clipnorm=UpperCAmelCase , )
# We return the optimizer and the LR scheduler in order to better track the
# evolution of the LR independently of the optimizer.
return optimizer, lr_schedule
class UpperCAmelCase ( a__ ):
'''simple docstring'''
def __init__( self , __lowerCAmelCase = 0.0_0_1 , __lowerCAmelCase = 0.9 , __lowerCAmelCase = 0.9_9_9 , __lowerCAmelCase = 1E-7 , __lowerCAmelCase = False , __lowerCAmelCase = 0.0 , __lowerCAmelCase = None , __lowerCAmelCase = None , __lowerCAmelCase = "AdamWeightDecay" , **__lowerCAmelCase , ) -> str:
super().__init__(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase )
lowercase__ : int = weight_decay_rate
lowercase__ : Dict = include_in_weight_decay
lowercase__ : List[str] = exclude_from_weight_decay
@classmethod
def _lowerCAmelCase( cls , __lowerCAmelCase ) -> str:
lowercase__ : int = {'''WarmUp''': WarmUp}
return super(__lowerCAmelCase , cls ).from_config(__lowerCAmelCase , custom_objects=__lowerCAmelCase )
def _lowerCAmelCase( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> List[str]:
super(__lowerCAmelCase , self )._prepare_local(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
lowercase__ : Any = tf.constant(
self.weight_decay_rate , name='''adam_weight_decay_rate''' )
def _lowerCAmelCase( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Tuple:
lowercase__ : str = self._do_use_weight_decay(var.name )
if do_decay:
return var.assign_sub(
learning_rate * var * apply_state[(var.device, var.dtype.base_dtype)]['''weight_decay_rate'''] , use_locking=self._use_locking , )
return tf.no_op()
def _lowerCAmelCase( self , __lowerCAmelCase , __lowerCAmelCase=None , **__lowerCAmelCase ) -> List[Any]:
lowercase__ , lowercase__ : Any = list(zip(*__lowerCAmelCase ) )
return super(__lowerCAmelCase , self ).apply_gradients(zip(__lowerCAmelCase , __lowerCAmelCase ) , name=__lowerCAmelCase , **__lowerCAmelCase )
def _lowerCAmelCase( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Optional[int]:
if apply_state is None:
return self._decayed_lr_t[var_dtype], {}
lowercase__ : Union[str, Any] = apply_state or {}
lowercase__ : Optional[Any] = apply_state.get((var_device, var_dtype) )
if coefficients is None:
lowercase__ : Union[str, Any] = self._fallback_apply_state(__lowerCAmelCase , __lowerCAmelCase )
lowercase__ : Optional[int] = coefficients
return coefficients["lr_t"], {"apply_state": apply_state}
def _lowerCAmelCase( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=None ) -> Any:
lowercase__ , lowercase__ : Dict = self._get_lr(var.device , var.dtype.base_dtype , __lowerCAmelCase )
lowercase__ : List[str] = self._decay_weights_op(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
with tf.control_dependencies([decay] ):
return super(__lowerCAmelCase , self )._resource_apply_dense(__lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase )
def _lowerCAmelCase( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=None ) -> List[str]:
lowercase__ , lowercase__ : List[Any] = self._get_lr(var.device , var.dtype.base_dtype , __lowerCAmelCase )
lowercase__ : Union[str, Any] = self._decay_weights_op(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
with tf.control_dependencies([decay] ):
return super(__lowerCAmelCase , self )._resource_apply_sparse(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase )
def _lowerCAmelCase( self ) -> List[str]:
lowercase__ : Optional[Any] = super().get_config()
config.update({'''weight_decay_rate''': self.weight_decay_rate} )
return config
def _lowerCAmelCase( self , __lowerCAmelCase ) -> List[Any]:
if self.weight_decay_rate == 0:
return False
if self._include_in_weight_decay:
for r in self._include_in_weight_decay:
if re.search(__lowerCAmelCase , __lowerCAmelCase ) is not None:
return True
if self._exclude_from_weight_decay:
for r in self._exclude_from_weight_decay:
if re.search(__lowerCAmelCase , __lowerCAmelCase ) is not None:
return False
return True
class UpperCAmelCase ( a__ ):
'''simple docstring'''
def __init__( self ) -> List[Any]:
lowercase__ : List[str] = []
lowercase__ : Optional[int] = None
@property
def _lowerCAmelCase( self ) -> Optional[Any]:
if self._accum_steps is None:
lowercase__ : int = tf.Variable(
tf.constant(0 , dtype=tf.intaa ) , trainable=__lowerCAmelCase , synchronization=tf.VariableSynchronization.ON_READ , aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA , )
return self._accum_steps.value()
@property
def _lowerCAmelCase( self ) -> Union[str, Any]:
if not self._gradients:
raise ValueError('''The accumulator should be called first to initialize the gradients''' )
return [gradient.value() if gradient is not None else gradient for gradient in self._gradients]
def __call__( self , __lowerCAmelCase ) -> Optional[Any]:
if not self._gradients:
lowercase__ : str = self.step # Create the step variable.
self._gradients.extend(
[
tf.Variable(
tf.zeros_like(__lowerCAmelCase ) , trainable=__lowerCAmelCase , synchronization=tf.VariableSynchronization.ON_READ , aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA , )
if gradient is not None
else gradient
for gradient in gradients
] )
if len(__lowerCAmelCase ) != len(self._gradients ):
raise ValueError(F"""Expected {len(self._gradients )} gradients, but got {len(__lowerCAmelCase )}""" )
for accum_gradient, gradient in zip(self._gradients , __lowerCAmelCase ):
if accum_gradient is not None and gradient is not None:
accum_gradient.assign_add(__lowerCAmelCase )
self._accum_steps.assign_add(1 )
def _lowerCAmelCase( self ) -> Optional[Any]:
if not self._gradients:
return
self._accum_steps.assign(0 )
for gradient in self._gradients:
if gradient is not None:
gradient.assign(tf.zeros_like(__lowerCAmelCase ) )
| 214 | 0 |
'''simple docstring'''
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def __lowerCamelCase ( _lowercase , _lowercase ) -> Optional[int]:
UpperCAmelCase : int = args.log_outputs
UpperCAmelCase : List[Any] = """_""".join(args.dataset.split("""/""" ) + [args.config, args.split] )
# load metric
UpperCAmelCase : Dict = load_metric("""wer""" )
UpperCAmelCase : Optional[Any] = load_metric("""cer""" )
# compute metrics
UpperCAmelCase : str = wer.compute(references=result["""target"""] , predictions=result["""prediction"""] )
UpperCAmelCase : int = cer.compute(references=result["""target"""] , predictions=result["""prediction"""] )
# print & log results
UpperCAmelCase : int = F'''WER: {wer_result}\nCER: {cer_result}'''
print(_lowercase )
with open(F'''{dataset_id}_eval_results.txt''' , """w""" ) as f:
f.write(_lowercase )
# log all results in text file. Possibly interesting for analysis
if log_outputs is not None:
UpperCAmelCase : Dict = F'''log_{dataset_id}_predictions.txt'''
UpperCAmelCase : Tuple = F'''log_{dataset_id}_targets.txt'''
with open(_lowercase , """w""" ) as p, open(_lowercase , """w""" ) as t:
# mapping function to write output
def write_to_file(_lowercase , _lowercase ):
p.write(F'''{i}''' + """\n""" )
p.write(batch["""prediction"""] + """\n""" )
t.write(F'''{i}''' + """\n""" )
t.write(batch["""target"""] + """\n""" )
result.map(_lowercase , with_indices=_lowercase )
def __lowerCamelCase ( _lowercase ) -> str:
UpperCAmelCase : str = """[,?.!\-\;\:\"“%‘”�—’…–]""" # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training
UpperCAmelCase : List[str] = re.sub(_lowercase , """""" , text.lower() )
# In addition, we can normalize the target text, e.g. removing new lines characters etc...
# note that order is important here!
UpperCAmelCase : int = ["""\n\n""", """\n""", """ """, """ """]
for t in token_sequences_to_ignore:
UpperCAmelCase : List[Any] = """ """.join(text.split(_lowercase ) )
return text
def __lowerCamelCase ( _lowercase ) -> Tuple:
# load dataset
UpperCAmelCase : int = load_dataset(args.dataset , args.config , split=args.split , use_auth_token=_lowercase )
# for testing: only process the first two examples as a test
# dataset = dataset.select(range(10))
# load processor
UpperCAmelCase : List[Any] = AutoFeatureExtractor.from_pretrained(args.model_id )
UpperCAmelCase : List[Any] = feature_extractor.sampling_rate
# resample audio
UpperCAmelCase : Tuple = dataset.cast_column("""audio""" , Audio(sampling_rate=_lowercase ) )
# load eval pipeline
if args.device is None:
UpperCAmelCase : Tuple = 0 if torch.cuda.is_available() else -1
UpperCAmelCase : Union[str, Any] = pipeline("""automatic-speech-recognition""" , model=args.model_id , device=args.device )
# map function to decode audio
def map_to_pred(_lowercase ):
UpperCAmelCase : int = asr(
batch["""audio"""]["""array"""] , chunk_length_s=args.chunk_length_s , stride_length_s=args.stride_length_s )
UpperCAmelCase : Optional[Any] = prediction["""text"""]
UpperCAmelCase : Union[str, Any] = normalize_text(batch["""sentence"""] )
return batch
# run inference on all examples
UpperCAmelCase : List[str] = dataset.map(_lowercase , remove_columns=dataset.column_names )
# compute and log_results
# do not change function below
log_results(_lowercase , _lowercase )
if __name__ == "__main__":
a : Optional[Any] = argparse.ArgumentParser()
parser.add_argument(
"""--model_id""", type=str, required=True, help="""Model identifier. Should be loadable with 🤗 Transformers"""
)
parser.add_argument(
"""--dataset""",
type=str,
required=True,
help="""Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets""",
)
parser.add_argument(
"""--config""", type=str, required=True, help="""Config of the dataset. *E.g.* `'en'` for Common Voice"""
)
parser.add_argument("""--split""", type=str, required=True, help="""Split of the dataset. *E.g.* `'test'`""")
parser.add_argument(
"""--chunk_length_s""", type=float, default=None, help="""Chunk length in seconds. Defaults to 5 seconds."""
)
parser.add_argument(
"""--stride_length_s""", type=float, default=None, help="""Stride of the audio chunks. Defaults to 1 second."""
)
parser.add_argument(
"""--log_outputs""", action="""store_true""", help="""If defined, write outputs to log file for analysis."""
)
parser.add_argument(
"""--device""",
type=int,
default=None,
help="""The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.""",
)
a : Any = parser.parse_args()
main(args)
| 265 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a : Optional[int] = {
"""configuration_git""": ["""GIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GitConfig""", """GitVisionConfig"""],
"""processing_git""": ["""GitProcessor"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : Tuple = [
"""GIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""GitForCausalLM""",
"""GitModel""",
"""GitPreTrainedModel""",
"""GitVisionModel""",
]
if TYPE_CHECKING:
from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
from .processing_git import GitProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_git import (
GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
GitForCausalLM,
GitModel,
GitPreTrainedModel,
GitVisionModel,
)
else:
import sys
a : Tuple = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 265 | 1 |
def UpperCamelCase ( __lowerCamelCase : List[Any] ):
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise ValueError("Input series is not valid, valid series - [2, 4, 6]" )
if len(__lowerCAmelCase ) == 0:
raise ValueError("Input list must be a non empty list" )
if len(__lowerCAmelCase ) == 1:
return True
snake_case : Any = series[1] - series[0]
for index in range(len(__lowerCAmelCase ) - 1 ):
if series[index + 1] - series[index] != common_diff:
return False
return True
def UpperCamelCase ( __lowerCamelCase : Any ):
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise ValueError("Input series is not valid, valid series - [2, 4, 6]" )
if len(__lowerCAmelCase ) == 0:
raise ValueError("Input list must be a non empty list" )
snake_case : Any = 0
for val in series:
answer += val
return answer / len(__lowerCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 371 |
import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = """▁"""
__lowerCamelCase = {"""vocab_file""": """prophetnet.tokenizer"""}
__lowerCamelCase = {
"""vocab_file""": {
"""microsoft/xprophetnet-large-wiki100-cased""": (
"""https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer"""
),
}
}
__lowerCamelCase = {
"""microsoft/xprophetnet-large-wiki100-cased""": {"""do_lower_case""": False},
}
__lowerCamelCase = {
"""microsoft/xprophetnet-large-wiki100-cased""": 5_12,
}
def UpperCamelCase ( __lowerCamelCase : Dict ):
snake_case : Dict = collections.OrderedDict()
with open(__lowerCamelCase , "r" , encoding="utf-8" ) as reader:
snake_case : Any = reader.readlines()
for index, token in enumerate(__lowerCamelCase ):
snake_case : List[Any] = token.rstrip("\n" )
snake_case : int = index
return vocab
class UpperCAmelCase ( A_ ):
A__ : Tuple = VOCAB_FILES_NAMES
A__ : Optional[Any] = PRETRAINED_VOCAB_FILES_MAP
A__ : str = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A__ : int = ["input_ids", "attention_mask"]
def __init__(self : Any , snake_case__ : Dict , snake_case__ : List[Any]="[SEP]" , snake_case__ : Optional[int]="[SEP]" , snake_case__ : Union[str, Any]="[SEP]" , snake_case__ : List[Any]="[UNK]" , snake_case__ : List[str]="[PAD]" , snake_case__ : List[str]="[CLS]" , snake_case__ : List[Any]="[MASK]" , snake_case__ : Optional[Dict[str, Any]] = None , **snake_case__ : List[str] , ) -> None:
'''simple docstring'''
snake_case : Dict = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=snake_case__ , eos_token=snake_case__ , sep_token=snake_case__ , unk_token=snake_case__ , pad_token=snake_case__ , cls_token=snake_case__ , mask_token=snake_case__ , sp_model_kwargs=self.sp_model_kwargs , **snake_case__ , )
try:
import sentencepiece as spm
except ImportError:
logger.warning(
"You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"
" pip install sentencepiece" )
raise
snake_case : Optional[int] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(snake_case__ ) )
snake_case : Dict = vocab_file
# Original fairseq vocab and spm vocab must be "aligned":
# Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
# fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-'
# spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a'
# put special tokens and [unused] tokens into the vocab
snake_case : List[Any] = {"[PAD]": 0, "[CLS]": 1, "[SEP]": 2, "[UNK]": 3, "[MASK]": 4}
for i in range(10 ):
snake_case : Dict = f"""[unused{i}]"""
snake_case : List[str] = 5 + i
# The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
snake_case : Dict = 12
snake_case : List[str] = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
for k in self.fairseq_tokens_to_ids.keys():
self.unique_no_split_tokens.append(snake_case__ )
def __getstate__(self : str ) -> Union[str, Any]:
'''simple docstring'''
snake_case : str = self.__dict__.copy()
snake_case : Tuple = None
return state
def __setstate__(self : str , snake_case__ : Optional[Any] ) -> Optional[int]:
'''simple docstring'''
snake_case : Union[str, Any] = d
try:
import sentencepiece as spm
except ImportError:
logger.warning(
"You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"
" pip install sentencepiece" )
raise
# for backward compatibility
if not hasattr(self , "sp_model_kwargs" ):
snake_case : Dict = {}
snake_case : List[str] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def _SCREAMING_SNAKE_CASE (self : Tuple , snake_case__ : List[int] , snake_case__ : Optional[List[int]] = None , snake_case__ : bool = False ) -> List[int]:
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=snake_case__ , token_ids_a=snake_case__ , already_has_special_tokens=snake_case__ )
if token_ids_a is None:
return ([0] * len(snake_case__ )) + [1]
return ([0] * len(snake_case__ )) + [1] + ([0] * len(snake_case__ )) + [1]
def _SCREAMING_SNAKE_CASE (self : Optional[int] , snake_case__ : List[int] , snake_case__ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
snake_case : List[str] = [self.sep_token_id]
if token_ids_a is None:
return len(token_ids_a + sep ) * [0]
return len(token_ids_a + sep + sep + token_ids_a + sep ) * [0]
@property
def _SCREAMING_SNAKE_CASE (self : Any ) -> int:
'''simple docstring'''
return len(self.sp_model ) + self.fairseq_offset
def _SCREAMING_SNAKE_CASE (self : int ) -> Any:
'''simple docstring'''
snake_case : List[str] = {self.convert_ids_to_tokens(snake_case__ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def _SCREAMING_SNAKE_CASE (self : Optional[int] , snake_case__ : str ) -> str:
'''simple docstring'''
return self.sp_model.encode(snake_case__ , out_type=snake_case__ )
def _SCREAMING_SNAKE_CASE (self : Tuple , snake_case__ : Optional[int] ) -> Any:
'''simple docstring'''
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
snake_case : Optional[Any] = self.sp_model.PieceToId(snake_case__ )
# Need to return unknown token if the SP model returned 0
return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
def _SCREAMING_SNAKE_CASE (self : str , snake_case__ : Optional[int] ) -> int:
'''simple docstring'''
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset )
def _SCREAMING_SNAKE_CASE (self : Union[str, Any] , snake_case__ : Dict ) -> List[Any]:
'''simple docstring'''
snake_case : Dict = "".join(snake_case__ ).replace(snake_case__ , " " ).strip()
return out_string
def _SCREAMING_SNAKE_CASE (self : Tuple , snake_case__ : str , snake_case__ : Optional[str] = None ) -> Tuple[str]:
'''simple docstring'''
if not os.path.isdir(snake_case__ ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
snake_case : Dict = os.path.join(
snake_case__ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(snake_case__ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , snake_case__ )
elif not os.path.isfile(self.vocab_file ):
with open(snake_case__ , "wb" ) as fi:
snake_case : Tuple = self.sp_model.serialized_model_proto()
fi.write(snake_case__ )
return (out_vocab_file,)
def _SCREAMING_SNAKE_CASE (self : Any , snake_case__ : List[int] , snake_case__ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
if token_ids_a is None:
return token_ids_a + [self.sep_token_id]
snake_case : str = [self.sep_token_id]
return token_ids_a + sep + token_ids_a + sep
| 10 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import LevitImageProcessor
class __UpperCAmelCase ( unittest.TestCase ):
def __init__( self : Optional[int], __A : Dict, __A : Optional[int]=7, __A : List[Any]=3, __A : Optional[Any]=1_8, __A : Union[str, Any]=3_0, __A : Any=4_0_0, __A : Any=True, __A : Tuple=None, __A : str=True, __A : List[str]=None, __A : Optional[Any]=True, __A : str=[0.5, 0.5, 0.5], __A : int=[0.5, 0.5, 0.5], ):
UpperCAmelCase : List[Any] = size if size is not None else {'''shortest_edge''': 1_8}
UpperCAmelCase : Tuple = crop_size if crop_size is not None else {'''height''': 1_8, '''width''': 1_8}
UpperCAmelCase : Tuple = parent
UpperCAmelCase : Optional[int] = batch_size
UpperCAmelCase : Optional[Any] = num_channels
UpperCAmelCase : Tuple = image_size
UpperCAmelCase : Optional[int] = min_resolution
UpperCAmelCase : Optional[Any] = max_resolution
UpperCAmelCase : str = do_resize
UpperCAmelCase : Optional[Any] = size
UpperCAmelCase : Optional[Any] = do_center_crop
UpperCAmelCase : Tuple = crop_size
UpperCAmelCase : List[Any] = do_normalize
UpperCAmelCase : str = image_mean
UpperCAmelCase : List[Any] = image_std
def __magic_name__ ( self : Union[str, Any] ):
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"do_center_crop": self.do_center_crop,
"size": self.size,
"crop_size": self.crop_size,
}
@require_torch
@require_vision
class __UpperCAmelCase ( lowerCamelCase__ , unittest.TestCase ):
UpperCamelCase = LevitImageProcessor if is_vision_available() else None
def __magic_name__ ( self : int ):
UpperCAmelCase : int = LevitImageProcessingTester(self )
@property
def __magic_name__ ( self : Optional[int] ):
return self.image_processor_tester.prepare_image_processor_dict()
def __magic_name__ ( self : Tuple ):
UpperCAmelCase : Dict = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowerCAmelCase__, '''image_mean''' ) )
self.assertTrue(hasattr(lowerCAmelCase__, '''image_std''' ) )
self.assertTrue(hasattr(lowerCAmelCase__, '''do_normalize''' ) )
self.assertTrue(hasattr(lowerCAmelCase__, '''do_resize''' ) )
self.assertTrue(hasattr(lowerCAmelCase__, '''do_center_crop''' ) )
self.assertTrue(hasattr(lowerCAmelCase__, '''size''' ) )
def __magic_name__ ( self : str ):
UpperCAmelCase : Dict = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size, {'''shortest_edge''': 1_8} )
self.assertEqual(image_processor.crop_size, {'''height''': 1_8, '''width''': 1_8} )
UpperCAmelCase : int = self.image_processing_class.from_dict(self.image_processor_dict, size=4_2, crop_size=8_4 )
self.assertEqual(image_processor.size, {'''shortest_edge''': 4_2} )
self.assertEqual(image_processor.crop_size, {'''height''': 8_4, '''width''': 8_4} )
def __magic_name__ ( self : Optional[int] ):
pass
def __magic_name__ ( self : Dict ):
UpperCAmelCase : List[Any] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
UpperCAmelCase : Any = prepare_image_inputs(self.image_processor_tester, equal_resolution=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__, Image.Image )
# Test not batched input
UpperCAmelCase : List[Any] = image_processing(image_inputs[0], return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape, (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
), )
# Test batched
UpperCAmelCase : int = image_processing(lowerCAmelCase__, return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape, (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
), )
def __magic_name__ ( self : List[Any] ):
UpperCAmelCase : str = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
UpperCAmelCase : int = prepare_image_inputs(self.image_processor_tester, equal_resolution=lowerCAmelCase__, numpify=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__, np.ndarray )
# Test not batched input
UpperCAmelCase : Optional[Any] = image_processing(image_inputs[0], return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape, (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
), )
# Test batched
UpperCAmelCase : List[Any] = image_processing(lowerCAmelCase__, return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape, (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
), )
def __magic_name__ ( self : Optional[int] ):
UpperCAmelCase : str = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
UpperCAmelCase : List[str] = prepare_image_inputs(self.image_processor_tester, equal_resolution=lowerCAmelCase__, torchify=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__, torch.Tensor )
# Test not batched input
UpperCAmelCase : Tuple = 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
UpperCAmelCase : List[str] = image_processing(lowerCAmelCase__, return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape, (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
), )
| 336 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase__ : Tuple = {
'configuration_mctct': ['MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MCTCTConfig'],
'feature_extraction_mctct': ['MCTCTFeatureExtractor'],
'processing_mctct': ['MCTCTProcessor'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ : Tuple = [
'MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST',
'MCTCTForCTC',
'MCTCTModel',
'MCTCTPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
from .feature_extraction_mctct import MCTCTFeatureExtractor
from .processing_mctct import MCTCTProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel
else:
import sys
lowercase__ : str = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 324 | 0 |
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import SPIECE_UNDERLINE, is_sentencepiece_available
from transformers.models.speech_to_text import SpeechaTextTokenizer
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
__A =get_tests_dir('''fixtures/test_sentencepiece.model''')
if is_sentencepiece_available():
import sentencepiece as sp
__A =5
__A =1_0
@require_sentencepiece
@require_tokenizers
class _SCREAMING_SNAKE_CASE ( snake_case_ , unittest.TestCase ):
lowerCAmelCase__ = SpeechaTextTokenizer
lowerCAmelCase__ = False
lowerCAmelCase__ = True
def SCREAMING_SNAKE_CASE_( self ) -> Optional[int]:
super().setUp()
lowerCamelCase_ = sp.SentencePieceProcessor()
spm_model.Load(lowercase )
lowerCamelCase_ = ["<s>", "<pad>", "</s>", "<unk>"]
vocab += [spm_model.IdToPiece(id_ ) for id_ in range(len(lowercase ) )]
lowerCamelCase_ = dict(zip(lowercase , range(len(lowercase ) ) ) )
lowerCamelCase_ = Path(self.tmpdirname )
save_json(lowercase , save_dir / VOCAB_FILES_NAMES["vocab_file"] )
if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
copyfile(lowercase , save_dir / VOCAB_FILES_NAMES["spm_file"] )
lowerCamelCase_ = SpeechaTextTokenizer.from_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname )
def SCREAMING_SNAKE_CASE_( self ) -> Optional[int]:
lowerCamelCase_ = "<pad>"
lowerCamelCase_ = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase ) , lowercase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase ) , lowercase )
def SCREAMING_SNAKE_CASE_( self ) -> Any:
lowerCamelCase_ = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , "<s>" )
self.assertEqual(vocab_keys[1] , "<pad>" )
self.assertEqual(vocab_keys[-1] , "j" )
self.assertEqual(len(lowercase ) , 1001 )
def SCREAMING_SNAKE_CASE_( self ) -> Optional[Any]:
self.assertEqual(self.get_tokenizer().vocab_size , 1001 )
def SCREAMING_SNAKE_CASE_( self ) -> Optional[Any]:
lowerCamelCase_ = SpeechaTextTokenizer.from_pretrained(self.tmpdirname )
lowerCamelCase_ = tokenizer.tokenize("This is a test" )
self.assertListEqual(lowercase , ["▁This", "▁is", "▁a", "▁t", "est"] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(lowercase ) , [289, 50, 14, 174, 386] , )
lowerCamelCase_ = tokenizer.tokenize("I was born in 92000, and this is falsé." )
self.assertListEqual(
lowercase , [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", "."] , )
lowerCamelCase_ = tokenizer.convert_tokens_to_ids(lowercase )
self.assertListEqual(lowercase , [12, 25, 88, 59, 28, 23, 11, 4, 606, 351, 351, 351, 7, 16, 70, 50, 76, 84, 10, 4, 8] )
lowerCamelCase_ = tokenizer.convert_ids_to_tokens(lowercase )
self.assertListEqual(
lowercase , [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>", "."] , )
@slow
def SCREAMING_SNAKE_CASE_( self ) -> List[Any]:
# fmt: off
lowerCamelCase_ = {"input_ids": [[3791, 797, 31, 11, 64, 797, 31, 2429, 433, 12, 1176, 12, 20, 786, 915, 142, 2413, 240, 37, 3238, 797, 31, 11, 35, 93, 915, 142, 2413, 240, 37, 5540, 567, 1276, 93, 37, 610, 40, 62, 455, 657, 1042, 123, 780, 177, 37, 309, 241, 1298, 514, 20, 292, 2737, 114, 2469, 241, 85, 64, 302, 548, 528, 423, 4, 509, 406, 423, 37, 601, 4, 777, 302, 548, 528, 423, 284, 4, 3388, 511, 459, 4, 3555, 40, 321, 302, 705, 4, 3388, 511, 583, 326, 5, 5, 5, 62, 3310, 560, 177, 2680, 217, 1508, 32, 31, 853, 418, 64, 583, 511, 1605, 62, 35, 93, 560, 177, 2680, 217, 1508, 1521, 64, 583, 511, 519, 62, 20, 1515, 764, 20, 149, 261, 5625, 7972, 20, 5540, 567, 1276, 93, 3925, 1675, 11, 15, 802, 7972, 576, 217, 1508, 11, 35, 93, 1253, 2441, 15, 289, 652, 31, 416, 321, 3842, 115, 40, 911, 8, 476, 619, 4, 380, 142, 423, 335, 240, 35, 93, 264, 8, 11, 335, 569, 420, 163, 5, 2], [260, 548, 528, 423, 20, 451, 20, 2681, 1153, 3434, 20, 5540, 37, 567, 126, 1253, 2441, 3376, 449, 210, 431, 1563, 177, 767, 5540, 11, 1203, 472, 11, 2953, 685, 285, 364, 706, 1153, 20, 6799, 20, 2869, 20, 4464, 126, 40, 2429, 20, 1040, 866, 2664, 418, 20, 318, 20, 1726, 186, 20, 265, 522, 35, 93, 2191, 4634, 20, 1040, 12, 6799, 15, 228, 2356, 142, 31, 11, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [2575, 2666, 684, 1582, 1176, 12, 627, 149, 619, 20, 4902, 563, 11, 20, 149, 261, 3420, 2356, 174, 142, 4714, 131, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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=lowercase , model_name="facebook/s2t-small-mustc-en-de-st" , revision="a14f04cf0776c02f62a8cb800cf7909e15ea23ad" , )
@require_sentencepiece
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
lowerCAmelCase__ = 'valhalla/s2t_mustc_multilinguial_medium'
lowerCAmelCase__ = 'C\'est trop cool'
lowerCAmelCase__ = 'Esto es genial'
@classmethod
def SCREAMING_SNAKE_CASE_( cls ) -> Optional[Any]:
lowerCamelCase_ = SpeechaTextTokenizer.from_pretrained(cls.checkpoint_name )
return cls
def SCREAMING_SNAKE_CASE_( self ) -> List[Any]:
self.assertEqual(self.tokenizer.lang_code_to_id["pt"] , 4 )
self.assertEqual(self.tokenizer.lang_code_to_id["ru"] , 6 )
self.assertEqual(self.tokenizer.lang_code_to_id["it"] , 9 )
self.assertEqual(self.tokenizer.lang_code_to_id["de"] , 11 )
def SCREAMING_SNAKE_CASE_( self ) -> List[Any]:
self.assertEqual(self.tokenizer.vocab_size , 10000 )
def SCREAMING_SNAKE_CASE_( self ) -> Optional[Any]:
self.assertIn(lowercase , self.tokenizer.all_special_ids )
lowerCamelCase_ = [ES_CODE, 4, 1601, 47, 7647, 2]
lowerCamelCase_ = self.tokenizer.decode(lowercase , skip_special_tokens=lowercase )
lowerCamelCase_ = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=lowercase )
self.assertEqual(lowercase , lowercase )
self.assertNotIn(self.tokenizer.eos_token , lowercase )
def SCREAMING_SNAKE_CASE_( self ) -> List[Any]:
lowerCamelCase_ = "fr"
lowerCamelCase_ = self.tokenizer(self.french_text ).input_ids
self.assertEqual(encoded[0] , lowercase )
self.assertEqual(encoded[-1] , self.tokenizer.eos_token_id )
def SCREAMING_SNAKE_CASE_( self ) -> List[Any]:
lowerCamelCase_ = "fr"
self.assertListEqual(self.tokenizer.prefix_tokens , [FR_CODE] )
lowerCamelCase_ = "es"
self.assertListEqual(self.tokenizer.prefix_tokens , [ES_CODE] )
| 370 |
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__=False ):
lowerCamelCase_ = OmegaConf.load(lowerCamelCase__ )
if display:
print(yaml.dump(OmegaConf.to_container(lowerCamelCase__ ) ) )
return config
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__=None , lowerCamelCase__=None ):
if conf_path is None:
lowerCamelCase_ = "./model_checkpoints/vqgan_only.yaml"
lowerCamelCase_ = load_config(lowerCamelCase__ , display=lowerCamelCase__ )
lowerCamelCase_ = VQModel(**config.model.params )
if ckpt_path is None:
lowerCamelCase_ = "./model_checkpoints/vqgan_only.pt"
lowerCamelCase_ = torch.load(lowerCamelCase__ , map_location=lowerCamelCase__ )
if ".ckpt" in ckpt_path:
lowerCamelCase_ = sd["state_dict"]
model.load_state_dict(lowerCamelCase__ , strict=lowerCamelCase__ )
model.to(lowerCamelCase__ )
del sd
return model
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ ):
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ = model.encode(lowerCamelCase__ )
print(F'VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}' )
lowerCamelCase_ = model.decode(lowerCamelCase__ )
return xrec
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__=False ):
lowerCamelCase_ , lowerCamelCase_ = string.rsplit("." , 1 )
if reload:
lowerCamelCase_ = importlib.import_module(lowerCamelCase__ )
importlib.reload(lowerCamelCase__ )
return getattr(importlib.import_module(lowerCamelCase__ , package=lowerCamelCase__ ) , cls )
def lowerCamelCase_ ( lowerCamelCase__ ):
if "target" not in config:
raise KeyError("Expected key `target` to instantiate." )
return get_obj_from_str(config["target"] )(**config.get("params" , {} ) )
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__=True , lowerCamelCase__=True ):
lowerCamelCase_ = instantiate_from_config(lowerCamelCase__ )
if sd is not None:
model.load_state_dict(lowerCamelCase__ )
if gpu:
model.cuda()
if eval_mode:
model.eval()
return {"model": model}
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
# load the specified checkpoint
if ckpt:
lowerCamelCase_ = torch.load(lowerCamelCase__ , map_location="cpu" )
lowerCamelCase_ = pl_sd["global_step"]
print(F'loaded model from global step {global_step}.' )
else:
lowerCamelCase_ = {"state_dict": None}
lowerCamelCase_ = None
lowerCamelCase_ = load_model_from_config(config.model , pl_sd["state_dict"] , gpu=lowerCamelCase__ , eval_mode=lowerCamelCase__ )["model"]
return model, global_step
| 47 | 0 |
import argparse
import os
import shutil
from pathlib import Path
import onnx
import torch
from packaging import version
from torch.onnx import export
from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline
lowerCAmelCase_ = version.parse(version.parse(torch.__version__).base_version) < version.parse('1.11')
def snake_case( __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__=False , ) -> List[str]:
'''simple docstring'''
output_path.parent.mkdir(parents=__magic_name__ , exist_ok=__magic_name__ )
# PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
# so we check the torch version for backwards compatibility
if is_torch_less_than_1_11:
export(
__magic_name__ , __magic_name__ , f=output_path.as_posix() , input_names=__magic_name__ , output_names=__magic_name__ , dynamic_axes=__magic_name__ , do_constant_folding=__magic_name__ , use_external_data_format=__magic_name__ , enable_onnx_checker=__magic_name__ , opset_version=__magic_name__ , )
else:
export(
__magic_name__ , __magic_name__ , f=output_path.as_posix() , input_names=__magic_name__ , output_names=__magic_name__ , dynamic_axes=__magic_name__ , do_constant_folding=__magic_name__ , opset_version=__magic_name__ , )
@torch.no_grad()
def snake_case( __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ = False ) -> Optional[Any]:
'''simple docstring'''
lowercase : str = torch.floataa if fpaa else torch.floataa
if fpaa and torch.cuda.is_available():
lowercase : Union[str, Any] = '''cuda'''
elif fpaa and not torch.cuda.is_available():
raise ValueError('''`float16` model export is only supported on GPUs with CUDA''' )
else:
lowercase : int = '''cpu'''
lowercase : Any = StableDiffusionPipeline.from_pretrained(__magic_name__ , torch_dtype=__magic_name__ ).to(__magic_name__ )
lowercase : Tuple = Path(__magic_name__ )
# TEXT ENCODER
lowercase : List[Any] = pipeline.text_encoder.config.max_position_embeddings
lowercase : Optional[Any] = pipeline.text_encoder.config.hidden_size
lowercase : Dict = pipeline.tokenizer(
'''A sample prompt''' , padding='''max_length''' , max_length=pipeline.tokenizer.model_max_length , truncation=__magic_name__ , return_tensors='''pt''' , )
onnx_export(
pipeline.text_encoder , model_args=(text_input.input_ids.to(device=__magic_name__ , dtype=torch.intaa )) , output_path=output_path / '''text_encoder''' / '''model.onnx''' , ordered_input_names=['''input_ids'''] , output_names=['''last_hidden_state''', '''pooler_output'''] , dynamic_axes={
'''input_ids''': {0: '''batch''', 1: '''sequence'''},
} , opset=__magic_name__ , )
del pipeline.text_encoder
# UNET
lowercase : Tuple = pipeline.unet.config.in_channels
lowercase : str = pipeline.unet.config.sample_size
lowercase : str = output_path / '''unet''' / '''model.onnx'''
onnx_export(
pipeline.unet , model_args=(
torch.randn(2 , __magic_name__ , __magic_name__ , __magic_name__ ).to(device=__magic_name__ , dtype=__magic_name__ ),
torch.randn(2 ).to(device=__magic_name__ , dtype=__magic_name__ ),
torch.randn(2 , __magic_name__ , __magic_name__ ).to(device=__magic_name__ , dtype=__magic_name__ ),
False,
) , output_path=__magic_name__ , ordered_input_names=['''sample''', '''timestep''', '''encoder_hidden_states''', '''return_dict'''] , output_names=['''out_sample'''] , dynamic_axes={
'''sample''': {0: '''batch''', 1: '''channels''', 2: '''height''', 3: '''width'''},
'''timestep''': {0: '''batch'''},
'''encoder_hidden_states''': {0: '''batch''', 1: '''sequence'''},
} , opset=__magic_name__ , use_external_data_format=__magic_name__ , )
lowercase : List[str] = str(unet_path.absolute().as_posix() )
lowercase : Optional[int] = os.path.dirname(__magic_name__ )
lowercase : Optional[Any] = onnx.load(__magic_name__ )
# clean up existing tensor files
shutil.rmtree(__magic_name__ )
os.mkdir(__magic_name__ )
# collate external tensor files into one
onnx.save_model(
__magic_name__ , __magic_name__ , save_as_external_data=__magic_name__ , all_tensors_to_one_file=__magic_name__ , location='''weights.pb''' , convert_attribute=__magic_name__ , )
del pipeline.unet
# VAE ENCODER
lowercase : Union[str, Any] = pipeline.vae
lowercase : Tuple = vae_encoder.config.in_channels
lowercase : Any = vae_encoder.config.sample_size
# need to get the raw tensor output (sample) from the encoder
lowercase : int = lambda __magic_name__ , __magic_name__ : vae_encoder.encode(__magic_name__ , __magic_name__ )[0].sample()
onnx_export(
__magic_name__ , model_args=(
torch.randn(1 , __magic_name__ , __magic_name__ , __magic_name__ ).to(device=__magic_name__ , dtype=__magic_name__ ),
False,
) , output_path=output_path / '''vae_encoder''' / '''model.onnx''' , ordered_input_names=['''sample''', '''return_dict'''] , output_names=['''latent_sample'''] , dynamic_axes={
'''sample''': {0: '''batch''', 1: '''channels''', 2: '''height''', 3: '''width'''},
} , opset=__magic_name__ , )
# VAE DECODER
lowercase : int = pipeline.vae
lowercase : Union[str, Any] = vae_decoder.config.latent_channels
lowercase : List[str] = vae_decoder.config.out_channels
# forward only through the decoder part
lowercase : Optional[Any] = vae_encoder.decode
onnx_export(
__magic_name__ , model_args=(
torch.randn(1 , __magic_name__ , __magic_name__ , __magic_name__ ).to(device=__magic_name__ , dtype=__magic_name__ ),
False,
) , output_path=output_path / '''vae_decoder''' / '''model.onnx''' , ordered_input_names=['''latent_sample''', '''return_dict'''] , output_names=['''sample'''] , dynamic_axes={
'''latent_sample''': {0: '''batch''', 1: '''channels''', 2: '''height''', 3: '''width'''},
} , opset=__magic_name__ , )
del pipeline.vae
# SAFETY CHECKER
if pipeline.safety_checker is not None:
lowercase : Dict = pipeline.safety_checker
lowercase : Union[str, Any] = safety_checker.config.vision_config.num_channels
lowercase : str = safety_checker.config.vision_config.image_size
lowercase : Union[str, Any] = safety_checker.forward_onnx
onnx_export(
pipeline.safety_checker , model_args=(
torch.randn(
1 , __magic_name__ , __magic_name__ , __magic_name__ , ).to(device=__magic_name__ , dtype=__magic_name__ ),
torch.randn(1 , __magic_name__ , __magic_name__ , __magic_name__ ).to(device=__magic_name__ , dtype=__magic_name__ ),
) , output_path=output_path / '''safety_checker''' / '''model.onnx''' , ordered_input_names=['''clip_input''', '''images'''] , output_names=['''out_images''', '''has_nsfw_concepts'''] , dynamic_axes={
'''clip_input''': {0: '''batch''', 1: '''channels''', 2: '''height''', 3: '''width'''},
'''images''': {0: '''batch''', 1: '''height''', 2: '''width''', 3: '''channels'''},
} , opset=__magic_name__ , )
del pipeline.safety_checker
lowercase : Dict = OnnxRuntimeModel.from_pretrained(output_path / '''safety_checker''' )
lowercase : Dict = pipeline.feature_extractor
else:
lowercase : int = None
lowercase : List[Any] = None
lowercase : Dict = OnnxStableDiffusionPipeline(
vae_encoder=OnnxRuntimeModel.from_pretrained(output_path / '''vae_encoder''' ) , vae_decoder=OnnxRuntimeModel.from_pretrained(output_path / '''vae_decoder''' ) , text_encoder=OnnxRuntimeModel.from_pretrained(output_path / '''text_encoder''' ) , tokenizer=pipeline.tokenizer , unet=OnnxRuntimeModel.from_pretrained(output_path / '''unet''' ) , scheduler=pipeline.scheduler , safety_checker=__magic_name__ , feature_extractor=__magic_name__ , requires_safety_checker=safety_checker is not None , )
onnx_pipeline.save_pretrained(__magic_name__ )
print('''ONNX pipeline saved to''' , __magic_name__ )
del pipeline
del onnx_pipeline
lowercase : Optional[Any] = OnnxStableDiffusionPipeline.from_pretrained(__magic_name__ , provider='''CPUExecutionProvider''' )
print('''ONNX pipeline is loadable''' )
if __name__ == "__main__":
lowerCAmelCase_ = argparse.ArgumentParser()
parser.add_argument(
'--model_path',
type=str,
required=True,
help='Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).',
)
parser.add_argument('--output_path', type=str, required=True, help='Path to the output model.')
parser.add_argument(
'--opset',
default=14,
type=int,
help='The version of the ONNX operator set to use.',
)
parser.add_argument('--fp16', action='store_true', default=False, help='Export the models in `float16` mode')
lowerCAmelCase_ = parser.parse_args()
convert_models(args.model_path, args.output_path, args.opset, args.fpaa) | 308 |
import os
def snake_case( __magic_name__ = "input.txt" ) -> int:
'''simple docstring'''
with open(os.path.join(os.path.dirname(__magic_name__ ) , __magic_name__ ) ) as input_file:
lowercase : Any = [
[int(__magic_name__ ) for element in line.split(''',''' )]
for line in input_file.readlines()
]
lowercase : List[Any] = len(__magic_name__ )
lowercase : Any = len(matrix[0] )
lowercase : Tuple = [[-1 for _ in range(__magic_name__ )] for _ in range(__magic_name__ )]
for i in range(__magic_name__ ):
lowercase : str = matrix[i][0]
for j in range(1 , __magic_name__ ):
for i in range(__magic_name__ ):
lowercase : Any = minimal_path_sums[i][j - 1] + matrix[i][j]
for i in range(1 , __magic_name__ ):
lowercase : Any = min(
minimal_path_sums[i][j] , minimal_path_sums[i - 1][j] + matrix[i][j] )
for i in range(rows - 2 , -1 , -1 ):
lowercase : Any = min(
minimal_path_sums[i][j] , minimal_path_sums[i + 1][j] + matrix[i][j] )
return min(minimal_path_sums_row[-1] for minimal_path_sums_row in minimal_path_sums )
if __name__ == "__main__":
print(f'''{solution() = }''') | 308 | 1 |
'''simple docstring'''
import math
def UpperCamelCase_ ( A__ : int ):
'''simple docstring'''
lowerCAmelCase_ : int = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(A__ )
def UpperCamelCase_ ( A__ : float = 1 / 1_23_45 ):
'''simple docstring'''
lowerCAmelCase_ : int = 0
lowerCAmelCase_ : List[str] = 0
lowerCAmelCase_ : str = 3
while True:
lowerCAmelCase_ : Union[str, Any] = (integer**2 - 1) / 4
# if candidate is an integer, then there is a partition for k
if partition_candidate == int(A__ ):
lowerCAmelCase_ : Dict = int(A__ )
total_partitions += 1
if check_partition_perfect(A__ ):
perfect_partitions += 1
if perfect_partitions > 0:
if perfect_partitions / total_partitions < max_proportion:
return int(A__ )
integer += 1
if __name__ == "__main__":
print(F'''{solution() = }''')
| 89 |
'''simple docstring'''
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import ViTMAEForPreTraining, ViTMAEModel
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 __snake_case :
"""simple docstring"""
def __init__( self : Optional[Any] , lowerCamelCase : Tuple , lowerCamelCase : Tuple=13 , lowerCamelCase : Dict=30 , lowerCamelCase : Dict=2 , lowerCamelCase : Optional[int]=3 , lowerCamelCase : List[Any]=True , lowerCamelCase : Any=True , lowerCamelCase : str=32 , lowerCamelCase : Any=5 , lowerCamelCase : int=4 , lowerCamelCase : List[str]=37 , lowerCamelCase : Any="gelu" , lowerCamelCase : Optional[Any]=0.1 , lowerCamelCase : List[str]=0.1 , lowerCamelCase : str=10 , lowerCamelCase : Optional[Any]=0.02 , lowerCamelCase : List[str]=3 , lowerCamelCase : Union[str, Any]=0.6 , lowerCamelCase : List[Any]=None , ) -> Optional[int]:
lowerCAmelCase_ : Optional[Any] = parent
lowerCAmelCase_ : Optional[int] = batch_size
lowerCAmelCase_ : int = image_size
lowerCAmelCase_ : List[Any] = patch_size
lowerCAmelCase_ : int = num_channels
lowerCAmelCase_ : Any = is_training
lowerCAmelCase_ : Tuple = use_labels
lowerCAmelCase_ : Optional[Any] = hidden_size
lowerCAmelCase_ : List[Any] = num_hidden_layers
lowerCAmelCase_ : Optional[Any] = num_attention_heads
lowerCAmelCase_ : Dict = intermediate_size
lowerCAmelCase_ : Union[str, Any] = hidden_act
lowerCAmelCase_ : Union[str, Any] = hidden_dropout_prob
lowerCAmelCase_ : Any = attention_probs_dropout_prob
lowerCAmelCase_ : List[Any] = type_sequence_label_size
lowerCAmelCase_ : Dict = initializer_range
lowerCAmelCase_ : List[str] = mask_ratio
lowerCAmelCase_ : Tuple = scope
# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
# (we add 1 for the [CLS] token)
lowerCAmelCase_ : Union[str, Any] = (image_size // patch_size) ** 2
lowerCAmelCase_ : Any = int(math.ceil((1 - mask_ratio) * (num_patches + 1) ) )
def __lowercase ( self : Optional[int] ) -> str:
lowerCAmelCase_ : str = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowerCAmelCase_ : Optional[int] = None
if self.use_labels:
lowerCAmelCase_ : int = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowerCAmelCase_ : str = self.get_config()
return config, pixel_values, labels
def __lowercase ( self : Optional[int] ) -> Optional[int]:
return ViTMAEConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=lowerCamelCase , initializer_range=self.initializer_range , mask_ratio=self.mask_ratio , )
def __lowercase ( self : Any , lowerCamelCase : Any , lowerCamelCase : Union[str, Any] , lowerCamelCase : Dict ) -> Tuple:
lowerCAmelCase_ : Tuple = ViTMAEModel(config=lowerCamelCase )
model.to(lowerCamelCase )
model.eval()
lowerCAmelCase_ : Dict = model(lowerCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __lowercase ( self : List[str] , lowerCamelCase : Union[str, Any] , lowerCamelCase : List[str] , lowerCamelCase : Union[str, Any] ) -> Dict:
lowerCAmelCase_ : Tuple = ViTMAEForPreTraining(lowerCamelCase )
model.to(lowerCamelCase )
model.eval()
lowerCAmelCase_ : List[str] = model(lowerCamelCase )
lowerCAmelCase_ : int = (self.image_size // self.patch_size) ** 2
lowerCAmelCase_ : int = self.patch_size**2 * self.num_channels
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_patches, expected_num_channels) )
# test greyscale images
lowerCAmelCase_ : List[Any] = 1
lowerCAmelCase_ : List[str] = ViTMAEForPreTraining(lowerCamelCase )
model.to(lowerCamelCase )
model.eval()
lowerCAmelCase_ : Optional[Any] = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
lowerCAmelCase_ : Tuple = model(lowerCamelCase )
lowerCAmelCase_ : List[Any] = self.patch_size**2
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_patches, expected_num_channels) )
def __lowercase ( self : Optional[int] ) -> str:
lowerCAmelCase_ : Any = self.prepare_config_and_inputs()
lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ : List[Any] = config_and_inputs
lowerCAmelCase_ : Any = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class __snake_case ( _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,unittest.TestCase):
"""simple docstring"""
lowercase = (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
lowercase = {'feature-extraction': ViTMAEModel} if is_torch_available() else {}
lowercase = False
lowercase = False
lowercase = False
lowercase = False
def __lowercase ( self : Optional[Any] ) -> List[Any]:
lowerCAmelCase_ : Optional[int] = ViTMAEModelTester(self )
lowerCAmelCase_ : Optional[int] = ConfigTester(self , config_class=lowerCamelCase , has_text_modality=lowerCamelCase , hidden_size=37 )
def __lowercase ( self : Dict ) -> Tuple:
self.config_tester.run_common_tests()
@unittest.skip(reason="""ViTMAE does not use inputs_embeds""" )
def __lowercase ( self : Optional[int] ) -> Optional[int]:
pass
def __lowercase ( self : List[str] ) -> Tuple:
lowerCAmelCase_, lowerCAmelCase_ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase_ : List[str] = model_class(lowerCamelCase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
lowerCAmelCase_ : Tuple = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(lowerCamelCase , nn.Linear ) )
def __lowercase ( self : Optional[Any] ) -> Any:
lowerCAmelCase_, lowerCAmelCase_ : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase_ : Optional[int] = model_class(lowerCamelCase )
lowerCAmelCase_ : Optional[Any] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCAmelCase_ : Any = [*signature.parameters.keys()]
lowerCAmelCase_ : Optional[Any] = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , lowerCamelCase )
def __lowercase ( self : Tuple ) -> str:
lowerCAmelCase_ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCamelCase )
def __lowercase ( self : Optional[int] ) -> str:
lowerCAmelCase_ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*lowerCamelCase )
def __lowercase ( self : Optional[int] , lowerCamelCase : Dict , lowerCamelCase : List[Any] , lowerCamelCase : Optional[Any] ) -> str:
# make masks reproducible
np.random.seed(2 )
lowerCAmelCase_ : Tuple = int((pt_model.config.image_size // pt_model.config.patch_size) ** 2 )
lowerCAmelCase_ : Optional[int] = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
lowerCAmelCase_ : Optional[Any] = torch.from_numpy(lowerCamelCase )
# Add `noise` argument.
# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
lowerCAmelCase_ : int = pt_noise
super().check_pt_tf_models(lowerCamelCase , lowerCamelCase , lowerCamelCase )
def __lowercase ( self : int ) -> Dict:
lowerCAmelCase_, lowerCAmelCase_ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase_ : Optional[int] = model_class(lowerCamelCase )
model.to(lowerCamelCase )
model.eval()
# make random mask reproducible
torch.manual_seed(2 )
with torch.no_grad():
lowerCAmelCase_ : Any = model(**self._prepare_for_class(lowerCamelCase , lowerCamelCase ) )
lowerCAmelCase_ : Any = outputs[0].cpu().numpy()
lowerCAmelCase_ : List[str] = 0
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(lowerCamelCase )
lowerCAmelCase_ : int = model_class.from_pretrained(lowerCamelCase )
model.to(lowerCamelCase )
# make random mask reproducible
torch.manual_seed(2 )
with torch.no_grad():
lowerCAmelCase_ : str = model(**self._prepare_for_class(lowerCamelCase , lowerCamelCase ) )
# Make sure we don't have nans
lowerCAmelCase_ : Optional[Any] = after_outputs[0].cpu().numpy()
lowerCAmelCase_ : str = 0
lowerCAmelCase_ : List[Any] = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(lowerCamelCase , 1E-5 )
@unittest.skip(
reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
to get deterministic results.""" )
def __lowercase ( self : Optional[int] ) -> List[Any]:
pass
@unittest.skip(
reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
to get deterministic results.""" )
def __lowercase ( self : Union[str, Any] ) -> str:
pass
@unittest.skip(
reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
to get deterministic results.""" )
def __lowercase ( self : Optional[Any] ) -> Union[str, Any]:
pass
@unittest.skip(reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load""" )
def __lowercase ( self : Tuple ) -> Optional[Any]:
pass
@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
def __lowercase ( self : List[Any] ) -> str:
pass
@slow
def __lowercase ( self : List[str] ) -> List[Any]:
for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase_ : List[Any] = ViTMAEModel.from_pretrained(lowerCamelCase )
self.assertIsNotNone(lowerCamelCase )
def UpperCamelCase_ ( ):
'''simple docstring'''
lowerCAmelCase_ : List[str] = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
@require_vision
class __snake_case ( unittest.TestCase):
"""simple docstring"""
@cached_property
def __lowercase ( self : Union[str, Any] ) -> Union[str, Any]:
return ViTImageProcessor.from_pretrained("""facebook/vit-mae-base""" ) if is_vision_available() else None
@slow
def __lowercase ( self : int ) -> List[Any]:
# make random mask reproducible across the PT and TF model
np.random.seed(2 )
lowerCAmelCase_ : Dict = ViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base""" ).to(lowerCamelCase )
lowerCAmelCase_ : Union[str, Any] = self.default_image_processor
lowerCAmelCase_ : Union[str, Any] = prepare_img()
lowerCAmelCase_ : Dict = image_processor(images=lowerCamelCase , return_tensors="""pt""" ).to(lowerCamelCase )
# prepare a noise vector that will be also used for testing the TF model
# (this way we can ensure that the PT and TF models operate on the same inputs)
lowerCAmelCase_ : Optional[int] = ViTMAEConfig()
lowerCAmelCase_ : Optional[Any] = int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2 )
lowerCAmelCase_ : Optional[int] = np.random.uniform(size=(1, num_patches) )
# forward pass
with torch.no_grad():
lowerCAmelCase_ : str = model(**lowerCamelCase , noise=torch.from_numpy(lowerCamelCase ).to(device=lowerCamelCase ) )
# verify the logits
lowerCAmelCase_ : str = torch.Size((1, 1_96, 7_68) )
self.assertEqual(outputs.logits.shape , lowerCamelCase )
lowerCAmelCase_ : str = torch.tensor(
[[-0.0_548, -1.7_023, -0.9_325], [0.3_721, -0.5_670, -0.2_233], [0.8_235, -1.3_878, -0.3_524]] )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] , expected_slice.to(lowerCamelCase ) , atol=1E-4 ) )
| 89 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
a_ : List[str] = logging.get_logger(__name__)
class snake_case ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
def __init__( self , *UpperCamelCase , **UpperCamelCase ):
"""simple docstring"""
warnings.warn(
"The class PoolFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
" Please use PoolFormerImageProcessor instead." , _lowerCAmelCase , )
super().__init__(*_lowerCAmelCase , **_lowerCAmelCase )
| 55 |
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def UpperCAmelCase_ ( __UpperCAmelCase : bytes , __UpperCAmelCase : int ) -> np.array:
SCREAMING_SNAKE_CASE_ = f"{sampling_rate}"
SCREAMING_SNAKE_CASE_ = '1'
SCREAMING_SNAKE_CASE_ = 'f32le'
SCREAMING_SNAKE_CASE_ = [
'ffmpeg',
'-i',
'pipe:0',
'-ac',
ac,
'-ar',
ar,
'-f',
format_for_conversion,
'-hide_banner',
'-loglevel',
'quiet',
'pipe:1',
]
try:
with subprocess.Popen(__UpperCAmelCase , stdin=subprocess.PIPE , stdout=subprocess.PIPE ) as ffmpeg_process:
SCREAMING_SNAKE_CASE_ = ffmpeg_process.communicate(__UpperCAmelCase )
except FileNotFoundError as error:
raise ValueError('ffmpeg was not found but is required to load audio files from filename' ) from error
SCREAMING_SNAKE_CASE_ = output_stream[0]
SCREAMING_SNAKE_CASE_ = np.frombuffer(__UpperCAmelCase , np.floataa )
if audio.shape[0] == 0:
raise ValueError('Malformed soundfile' )
return audio
def UpperCAmelCase_ ( __UpperCAmelCase : int , __UpperCAmelCase : float , __UpperCAmelCase : str = "f32le" , ) -> int:
SCREAMING_SNAKE_CASE_ = f"{sampling_rate}"
SCREAMING_SNAKE_CASE_ = '1'
if format_for_conversion == "s16le":
SCREAMING_SNAKE_CASE_ = 2
elif format_for_conversion == "f32le":
SCREAMING_SNAKE_CASE_ = 4
else:
raise ValueError(f"Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`" )
SCREAMING_SNAKE_CASE_ = platform.system()
if system == "Linux":
SCREAMING_SNAKE_CASE_ = 'alsa'
SCREAMING_SNAKE_CASE_ = 'default'
elif system == "Darwin":
SCREAMING_SNAKE_CASE_ = 'avfoundation'
SCREAMING_SNAKE_CASE_ = ':0'
elif system == "Windows":
SCREAMING_SNAKE_CASE_ = 'dshow'
SCREAMING_SNAKE_CASE_ = 'default'
SCREAMING_SNAKE_CASE_ = [
'ffmpeg',
'-f',
format_,
'-i',
input_,
'-ac',
ac,
'-ar',
ar,
'-f',
format_for_conversion,
'-fflags',
'nobuffer',
'-hide_banner',
'-loglevel',
'quiet',
'pipe:1',
]
SCREAMING_SNAKE_CASE_ = int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
SCREAMING_SNAKE_CASE_ = _ffmpeg_stream(__UpperCAmelCase , __UpperCAmelCase )
for item in iterator:
yield item
def UpperCAmelCase_ ( __UpperCAmelCase : int , __UpperCAmelCase : float , __UpperCAmelCase : Optional[int] = None , __UpperCAmelCase : Optional[Union[Tuple[float, float], float]] = None , __UpperCAmelCase : str = "f32le" , ) -> Tuple:
if stream_chunk_s is not None:
SCREAMING_SNAKE_CASE_ = stream_chunk_s
else:
SCREAMING_SNAKE_CASE_ = chunk_length_s
SCREAMING_SNAKE_CASE_ = ffmpeg_microphone(__UpperCAmelCase , __UpperCAmelCase , format_for_conversion=__UpperCAmelCase )
if format_for_conversion == "s16le":
SCREAMING_SNAKE_CASE_ = np.intaa
SCREAMING_SNAKE_CASE_ = 2
elif format_for_conversion == "f32le":
SCREAMING_SNAKE_CASE_ = np.floataa
SCREAMING_SNAKE_CASE_ = 4
else:
raise ValueError(f"Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`" )
if stride_length_s is None:
SCREAMING_SNAKE_CASE_ = chunk_length_s / 6
SCREAMING_SNAKE_CASE_ = int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
if isinstance(__UpperCAmelCase , (int, float) ):
SCREAMING_SNAKE_CASE_ = [stride_length_s, stride_length_s]
SCREAMING_SNAKE_CASE_ = int(round(sampling_rate * stride_length_s[0] ) ) * size_of_sample
SCREAMING_SNAKE_CASE_ = int(round(sampling_rate * stride_length_s[1] ) ) * size_of_sample
SCREAMING_SNAKE_CASE_ = datetime.datetime.now()
SCREAMING_SNAKE_CASE_ = datetime.timedelta(seconds=__UpperCAmelCase )
for item in chunk_bytes_iter(__UpperCAmelCase , __UpperCAmelCase , stride=(stride_left, stride_right) , stream=__UpperCAmelCase ):
# Put everything back in numpy scale
SCREAMING_SNAKE_CASE_ = np.frombuffer(item['raw'] , dtype=__UpperCAmelCase )
SCREAMING_SNAKE_CASE_ = (
item['stride'][0] // size_of_sample,
item['stride'][1] // size_of_sample,
)
SCREAMING_SNAKE_CASE_ = sampling_rate
audio_time += delta
if datetime.datetime.now() > audio_time + 10 * delta:
# We're late !! SKIP
continue
yield item
def UpperCAmelCase_ ( __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : int , __UpperCAmelCase : Tuple[int, int] , __UpperCAmelCase : bool = False ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE_ = b''
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = stride
if stride_left + stride_right >= chunk_len:
raise ValueError(
f"Stride needs to be strictly smaller than chunk_len: ({stride_left}, {stride_right}) vs {chunk_len}" )
SCREAMING_SNAKE_CASE_ = 0
for raw in iterator:
acc += raw
if stream and len(__UpperCAmelCase ) < chunk_len:
SCREAMING_SNAKE_CASE_ = (_stride_left, 0)
yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
else:
while len(__UpperCAmelCase ) >= chunk_len:
# We are flushing the accumulator
SCREAMING_SNAKE_CASE_ = (_stride_left, stride_right)
SCREAMING_SNAKE_CASE_ = {'raw': acc[:chunk_len], 'stride': stride}
if stream:
SCREAMING_SNAKE_CASE_ = False
yield item
SCREAMING_SNAKE_CASE_ = stride_left
SCREAMING_SNAKE_CASE_ = acc[chunk_len - stride_left - stride_right :]
# Last chunk
if len(__UpperCAmelCase ) > stride_left:
SCREAMING_SNAKE_CASE_ = {'raw': acc, 'stride': (_stride_left, 0)}
if stream:
SCREAMING_SNAKE_CASE_ = False
yield item
def UpperCAmelCase_ ( __UpperCAmelCase : List[str] , __UpperCAmelCase : int ) -> Optional[Any]:
SCREAMING_SNAKE_CASE_ = 2**24 # 16Mo
try:
with subprocess.Popen(__UpperCAmelCase , stdout=subprocess.PIPE , bufsize=__UpperCAmelCase ) as ffmpeg_process:
while True:
SCREAMING_SNAKE_CASE_ = ffmpeg_process.stdout.read(__UpperCAmelCase )
if raw == b"":
break
yield raw
except FileNotFoundError as error:
raise ValueError('ffmpeg was not found but is required to stream audio files from filename' ) from error | 225 | 0 |
'''simple docstring'''
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
_SCREAMING_SNAKE_CASE : Tuple = TypeVar("T")
def UpperCamelCase_( snake_case : int ):
'''simple docstring'''
return (position - 1) // 2
def UpperCamelCase_( snake_case : int ):
'''simple docstring'''
return (2 * position) + 1
def UpperCamelCase_( snake_case : int ):
'''simple docstring'''
return (2 * position) + 2
class _snake_case ( Generic[T] ):
def __init__( self ) -> None:
'''simple docstring'''
snake_case_ = []
snake_case_ = {}
snake_case_ = 0
def __len__( self ) -> int:
'''simple docstring'''
return self.elements
def __repr__( self ) -> str:
'''simple docstring'''
return str(self.heap )
def lowerCAmelCase__ ( self ) -> bool:
'''simple docstring'''
return self.elements == 0
def lowerCAmelCase__ ( self , a__ , a__ ) -> None:
'''simple docstring'''
self.heap.append((elem, weight) )
snake_case_ = self.elements
self.elements += 1
self._bubble_up(a__ )
def lowerCAmelCase__ ( self ) -> T:
'''simple docstring'''
if self.elements > 1:
self._swap_nodes(0 , self.elements - 1 )
snake_case_ , snake_case_ = self.heap.pop()
del self.position_map[elem]
self.elements -= 1
if self.elements > 0:
snake_case_ , snake_case_ = self.heap[0]
self._bubble_down(a__ )
return elem
def lowerCAmelCase__ ( self , a__ , a__ ) -> None:
'''simple docstring'''
snake_case_ = self.position_map[elem]
snake_case_ = (elem, weight)
if position > 0:
snake_case_ = get_parent_position(a__ )
snake_case_ , snake_case_ = self.heap[parent_position]
if parent_weight > weight:
self._bubble_up(a__ )
else:
self._bubble_down(a__ )
else:
self._bubble_down(a__ )
def lowerCAmelCase__ ( self , a__ ) -> None:
'''simple docstring'''
snake_case_ = self.position_map[elem]
if curr_pos == 0:
return None
snake_case_ = get_parent_position(a__ )
snake_case_ , snake_case_ = self.heap[curr_pos]
snake_case_ , snake_case_ = self.heap[parent_position]
if parent_weight > weight:
self._swap_nodes(a__ , a__ )
return self._bubble_up(a__ )
return None
def lowerCAmelCase__ ( self , a__ ) -> None:
'''simple docstring'''
snake_case_ = self.position_map[elem]
snake_case_ , snake_case_ = self.heap[curr_pos]
snake_case_ = get_child_left_position(a__ )
snake_case_ = get_child_right_position(a__ )
if child_left_position < self.elements and child_right_position < self.elements:
snake_case_ , snake_case_ = self.heap[child_left_position]
snake_case_ , snake_case_ = self.heap[child_right_position]
if child_right_weight < child_left_weight and child_right_weight < weight:
self._swap_nodes(a__ , a__ )
return self._bubble_down(a__ )
if child_left_position < self.elements:
snake_case_ , snake_case_ = self.heap[child_left_position]
if child_left_weight < weight:
self._swap_nodes(a__ , a__ )
return self._bubble_down(a__ )
else:
return None
if child_right_position < self.elements:
snake_case_ , snake_case_ = self.heap[child_right_position]
if child_right_weight < weight:
self._swap_nodes(a__ , a__ )
return self._bubble_down(a__ )
return None
def lowerCAmelCase__ ( self , a__ , a__ ) -> None:
'''simple docstring'''
snake_case_ = self.heap[nodea_pos][0]
snake_case_ = self.heap[nodea_pos][0]
snake_case_ , snake_case_ = (
self.heap[nodea_pos],
self.heap[nodea_pos],
)
snake_case_ = nodea_pos
snake_case_ = nodea_pos
class _snake_case ( Generic[T] ):
def __init__( self ) -> None:
'''simple docstring'''
snake_case_ = {}
snake_case_ = 0
def __repr__( self ) -> str:
'''simple docstring'''
return str(self.connections )
def __len__( self ) -> int:
'''simple docstring'''
return self.nodes
def lowerCAmelCase__ ( self , a__ ) -> None:
'''simple docstring'''
if node not in self.connections:
snake_case_ = {}
self.nodes += 1
def lowerCAmelCase__ ( self , a__ , a__ , a__ ) -> None:
'''simple docstring'''
self.add_node(a__ )
self.add_node(a__ )
snake_case_ = weight
snake_case_ = weight
def UpperCamelCase_( snake_case : GraphUndirectedWeighted[T] , ):
'''simple docstring'''
snake_case_ = {node: maxsize for node in graph.connections}
snake_case_ = {node: None for node in graph.connections}
snake_case_ = MinPriorityQueue()
for node, weight in dist.items():
priority_queue.push(snake_case , snake_case )
if priority_queue.is_empty():
return dist, parent
# initialization
snake_case_ = priority_queue.extract_min()
snake_case_ = 0
for neighbour in graph.connections[node]:
if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
snake_case_ = dist[node] + graph.connections[node][neighbour]
priority_queue.update_key(snake_case , dist[neighbour] )
snake_case_ = node
# running prim's algorithm
while not priority_queue.is_empty():
snake_case_ = priority_queue.extract_min()
for neighbour in graph.connections[node]:
if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
snake_case_ = dist[node] + graph.connections[node][neighbour]
priority_queue.update_key(snake_case , dist[neighbour] )
snake_case_ = node
return dist, parent
| 92 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE : List[Any] = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE : Optional[int] = {
"edbeeching/decision-transformer-gym-hopper-medium": (
"https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json"
),
# See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}
class _snake_case ( lowercase_ ):
lowerCAmelCase_ : Dict = "decision_transformer"
lowerCAmelCase_ : List[Any] = ["past_key_values"]
lowerCAmelCase_ : Tuple = {
"max_position_embeddings": "n_positions",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__( self , a__=17 , a__=4 , a__=128 , a__=4_096 , a__=True , a__=1 , a__=1_024 , a__=3 , a__=1 , a__=None , a__="relu" , a__=0.1 , a__=0.1 , a__=0.1 , a__=1e-5 , a__=0.0_2 , a__=True , a__=True , a__=50_256 , a__=50_256 , a__=False , a__=False , **a__ , ) -> Optional[int]:
'''simple docstring'''
snake_case_ = state_dim
snake_case_ = act_dim
snake_case_ = hidden_size
snake_case_ = max_ep_len
snake_case_ = action_tanh
snake_case_ = vocab_size
snake_case_ = n_positions
snake_case_ = n_layer
snake_case_ = n_head
snake_case_ = n_inner
snake_case_ = activation_function
snake_case_ = resid_pdrop
snake_case_ = embd_pdrop
snake_case_ = attn_pdrop
snake_case_ = layer_norm_epsilon
snake_case_ = initializer_range
snake_case_ = scale_attn_weights
snake_case_ = use_cache
snake_case_ = scale_attn_by_inverse_layer_idx
snake_case_ = reorder_and_upcast_attn
snake_case_ = bos_token_id
snake_case_ = eos_token_id
super().__init__(bos_token_id=a__ , eos_token_id=a__ , **a__ )
| 92 | 1 |
'''simple docstring'''
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging
SCREAMING_SNAKE_CASE_: Union[str, Any] =logging.get_logger(__name__)
class __A ( UpperCamelCase__ ):
a__ : Optional[int] = ["""input_features""", """is_longer"""]
def __init__(self : Tuple , __a : Optional[int]=64 , __a : Union[str, Any]=48000 , __a : str=480 , __a : Optional[int]=10 , __a : List[Any]=1024 , __a : Optional[Any]=0.0 , __a : Tuple=False , __a : float = 0 , __a : float = 14000 , __a : int = None , __a : str = "fusion" , __a : str = "repeatpad" , **__a : Any , ):
super().__init__(
feature_size=__a , sampling_rate=__a , padding_value=__a , return_attention_mask=__a , **__a , )
UpperCAmelCase_ = top_db
UpperCAmelCase_ = truncation
UpperCAmelCase_ = padding
UpperCAmelCase_ = fft_window_size
UpperCAmelCase_ = (fft_window_size >> 1) + 1
UpperCAmelCase_ = hop_length
UpperCAmelCase_ = max_length_s
UpperCAmelCase_ = max_length_s * sampling_rate
UpperCAmelCase_ = sampling_rate
UpperCAmelCase_ = frequency_min
UpperCAmelCase_ = frequency_max
UpperCAmelCase_ = mel_filter_bank(
num_frequency_bins=self.nb_frequency_bins , num_mel_filters=__a , min_frequency=__a , max_frequency=__a , sampling_rate=__a , norm=__a , mel_scale="htk" , )
UpperCAmelCase_ = mel_filter_bank(
num_frequency_bins=self.nb_frequency_bins , num_mel_filters=__a , min_frequency=__a , max_frequency=__a , sampling_rate=__a , norm="slaney" , mel_scale="slaney" , )
def _lowercase (self : List[str] ):
UpperCAmelCase_ = copy.deepcopy(self.__dict__ )
UpperCAmelCase_ = self.__class__.__name__
if "mel_filters" in output:
del output["mel_filters"]
if "mel_filters_slaney" in output:
del output["mel_filters_slaney"]
return output
def _lowercase (self : str , __a : np.array , __a : Optional[np.array] = None ):
UpperCAmelCase_ = spectrogram(
__a , window_function(self.fft_window_size , "hann" ) , frame_length=self.fft_window_size , hop_length=self.hop_length , power=2.0 , mel_filters=__a , log_mel="dB" , )
return log_mel_spectrogram.T
def _lowercase (self : List[Any] , __a : Dict , __a : Optional[Any] , __a : Tuple ):
UpperCAmelCase_ = np.array_split(list(range(0 , total_frames - chunk_frames + 1 ) ) , 3 )
if len(ranges[1] ) == 0:
# if the audio is too short, we just use the first chunk
UpperCAmelCase_ = [0]
if len(ranges[2] ) == 0:
# if the audio is too short, we just use the first chunk
UpperCAmelCase_ = [0]
# randomly choose index for each part
UpperCAmelCase_ = np.random.choice(ranges[0] )
UpperCAmelCase_ = np.random.choice(ranges[1] )
UpperCAmelCase_ = np.random.choice(ranges[2] )
UpperCAmelCase_ = mel[idx_front : idx_front + chunk_frames, :]
UpperCAmelCase_ = mel[idx_middle : idx_middle + chunk_frames, :]
UpperCAmelCase_ = mel[idx_back : idx_back + chunk_frames, :]
UpperCAmelCase_ = torch.tensor(mel[None, None, :] )
UpperCAmelCase_ = torch.nn.functional.interpolate(
__a , size=[chunk_frames, 64] , mode="bilinear" , align_corners=__a )
UpperCAmelCase_ = mel_shrink[0][0].numpy()
UpperCAmelCase_ = np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back] , axis=0 )
return mel_fusion
def _lowercase (self : Optional[Any] , __a : np.array , __a : Any , __a : List[str] , __a : Optional[Any] ):
if waveform.shape[0] > max_length:
if truncation == "rand_trunc":
UpperCAmelCase_ = True
# random crop to max_length (for compatibility) -> this should be handled by self.pad
UpperCAmelCase_ = len(__a ) - max_length
UpperCAmelCase_ = np.random.randint(0 , overflow + 1 )
UpperCAmelCase_ = waveform[idx : idx + max_length]
UpperCAmelCase_ = self._np_extract_fbank_features(__a , self.mel_filters_slaney )[None, :]
elif truncation == "fusion":
UpperCAmelCase_ = self._np_extract_fbank_features(__a , self.mel_filters )
UpperCAmelCase_ = max_length // self.hop_length + 1 # the +1 related to how the spectrogram is computed
UpperCAmelCase_ = mel.shape[0]
if chunk_frames == total_frames:
# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
# In this case, we just use the whole audio.
UpperCAmelCase_ = np.stack([mel, mel, mel, mel] , axis=0 )
UpperCAmelCase_ = False
else:
UpperCAmelCase_ = self._random_mel_fusion(__a , __a , __a )
UpperCAmelCase_ = True
else:
raise NotImplementedError(f"""data_truncating {truncation} not implemented""" )
else:
UpperCAmelCase_ = False
# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
if waveform.shape[0] < max_length:
if padding == "repeat":
UpperCAmelCase_ = int(max_length / len(__a ) )
UpperCAmelCase_ = np.stack(np.tile(__a , n_repeat + 1 ) )[:max_length]
if padding == "repeatpad":
UpperCAmelCase_ = int(max_length / len(__a ) )
UpperCAmelCase_ = np.stack(np.tile(__a , __a ) )
UpperCAmelCase_ = np.pad(__a , (0, max_length - waveform.shape[0]) , mode="constant" , constant_values=0 )
if truncation == "fusion":
UpperCAmelCase_ = self._np_extract_fbank_features(__a , self.mel_filters )
UpperCAmelCase_ = np.stack([input_mel, input_mel, input_mel, input_mel] , axis=0 )
else:
UpperCAmelCase_ = self._np_extract_fbank_features(__a , self.mel_filters_slaney )[None, :]
return input_mel, longer
def __call__(self : Any , __a : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , __a : str = None , __a : Optional[str] = None , __a : Optional[int] = None , __a : Optional[int] = None , __a : Optional[Union[str, TensorType]] = None , **__a : int , ):
UpperCAmelCase_ = truncation if truncation is not None else self.truncation
UpperCAmelCase_ = padding if padding else self.padding
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
f"""The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"""
f""" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"""
f""" was sampled with {self.sampling_rate} and not {sampling_rate}.""" )
else:
logger.warning(
"It is strongly recommended to pass the `sampling_rate` argument to this function. "
"Failing to do so can result in silent errors that might be hard to debug." )
UpperCAmelCase_ = isinstance(__a , np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(f"""Only mono-channel audio is supported for input to {self}""" )
UpperCAmelCase_ = is_batched_numpy or (
isinstance(__a , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
UpperCAmelCase_ = [np.asarray(__a , dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(__a , np.ndarray ):
UpperCAmelCase_ = np.asarray(__a , dtype=np.floataa )
elif isinstance(__a , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
UpperCAmelCase_ = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
UpperCAmelCase_ = [np.asarray(__a )]
# convert to mel spectrogram, truncate and pad if needed.
UpperCAmelCase_ = [
self._get_input_mel(__a , max_length if max_length else self.nb_max_samples , __a , __a )
for waveform in raw_speech
]
UpperCAmelCase_ = []
UpperCAmelCase_ = []
for mel, longer in padded_inputs:
input_mel.append(__a )
is_longer.append(__a )
if truncation == "fusion" and sum(__a ) == 0:
# if no audio is longer than 10s, then randomly select one audio to be longer
UpperCAmelCase_ = np.random.randint(0 , len(__a ) )
UpperCAmelCase_ = True
if isinstance(input_mel[0] , __a ):
UpperCAmelCase_ = [np.asarray(__a , dtype=np.floataa ) for feature in input_mel]
# is_longer is a list of bool
UpperCAmelCase_ = [[longer] for longer in is_longer]
UpperCAmelCase_ = {"input_features": input_mel, "is_longer": is_longer}
UpperCAmelCase_ = BatchFeature(__a )
if return_tensors is not None:
UpperCAmelCase_ = input_features.convert_to_tensors(__a )
return input_features
| 1 | '''simple docstring'''
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_flax_available():
import jax
from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel
class __A ( unittest.TestCase ):
def __init__(self : str , __a : Optional[Any] , __a : Optional[Any]=13 , __a : int=30 , __a : Union[str, Any]=2 , __a : Dict=3 , __a : List[Any]=True , __a : Optional[Any]=True , __a : List[Any]=32 , __a : Any=5 , __a : str=4 , __a : Optional[int]=37 , __a : Optional[int]="gelu" , __a : List[str]=0.1 , __a : Tuple=0.1 , __a : List[str]=10 , __a : Optional[int]=0.02 , ):
UpperCAmelCase_ = parent
UpperCAmelCase_ = batch_size
UpperCAmelCase_ = image_size
UpperCAmelCase_ = patch_size
UpperCAmelCase_ = num_channels
UpperCAmelCase_ = is_training
UpperCAmelCase_ = use_labels
UpperCAmelCase_ = hidden_size
UpperCAmelCase_ = num_hidden_layers
UpperCAmelCase_ = num_attention_heads
UpperCAmelCase_ = intermediate_size
UpperCAmelCase_ = hidden_act
UpperCAmelCase_ = hidden_dropout_prob
UpperCAmelCase_ = attention_probs_dropout_prob
UpperCAmelCase_ = type_sequence_label_size
UpperCAmelCase_ = initializer_range
# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
UpperCAmelCase_ = (image_size // patch_size) ** 2
UpperCAmelCase_ = num_patches + 1
def _lowercase (self : Any ):
UpperCAmelCase_ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCAmelCase_ = ViTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=__a , initializer_range=self.initializer_range , )
return config, pixel_values
def _lowercase (self : Dict , __a : Any , __a : List[Any] ):
UpperCAmelCase_ = FlaxViTModel(config=__a )
UpperCAmelCase_ = model(__a )
# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
UpperCAmelCase_ = (self.image_size, self.image_size)
UpperCAmelCase_ = (self.patch_size, self.patch_size)
UpperCAmelCase_ = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, num_patches + 1, self.hidden_size) )
def _lowercase (self : Tuple , __a : str , __a : Any ):
UpperCAmelCase_ = self.type_sequence_label_size
UpperCAmelCase_ = FlaxViTForImageClassification(config=__a )
UpperCAmelCase_ = model(__a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
UpperCAmelCase_ = 1
UpperCAmelCase_ = FlaxViTForImageClassification(__a )
UpperCAmelCase_ = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
UpperCAmelCase_ = model(__a )
def _lowercase (self : Optional[Any] ):
UpperCAmelCase_ = self.prepare_config_and_inputs()
(
(
UpperCAmelCase_
) , (
UpperCAmelCase_
) ,
) = config_and_inputs
UpperCAmelCase_ = {"pixel_values": pixel_values}
return config, inputs_dict
@require_flax
class __A ( UpperCamelCase__ , unittest.TestCase ):
a__ : Tuple = (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()
def _lowercase (self : Any ):
UpperCAmelCase_ = FlaxViTModelTester(self )
UpperCAmelCase_ = ConfigTester(self , config_class=__a , has_text_modality=__a , hidden_size=37 )
def _lowercase (self : Tuple ):
self.config_tester.run_common_tests()
def _lowercase (self : str ):
UpperCAmelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__a )
def _lowercase (self : str ):
UpperCAmelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__a )
def _lowercase (self : Tuple ):
UpperCAmelCase_ , UpperCAmelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase_ = model_class(__a )
UpperCAmelCase_ = inspect.signature(model.__call__ )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase_ = [*signature.parameters.keys()]
UpperCAmelCase_ = ["pixel_values"]
self.assertListEqual(arg_names[:1] , __a )
def _lowercase (self : Optional[Any] ):
UpperCAmelCase_ , UpperCAmelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
UpperCAmelCase_ = self._prepare_for_class(__a , __a )
UpperCAmelCase_ = model_class(__a )
@jax.jit
def model_jitted(__a : Tuple , **__a : List[Any] ):
return model(pixel_values=__a , **__a )
with self.subTest("JIT Enabled" ):
UpperCAmelCase_ = model_jitted(**__a ).to_tuple()
with self.subTest("JIT Disabled" ):
with jax.disable_jit():
UpperCAmelCase_ = model_jitted(**__a ).to_tuple()
self.assertEqual(len(__a ) , len(__a ) )
for jitted_output, output in zip(__a , __a ):
self.assertEqual(jitted_output.shape , output.shape )
@slow
def _lowercase (self : Tuple ):
for model_class_name in self.all_model_classes:
UpperCAmelCase_ = model_class_name.from_pretrained("google/vit-base-patch16-224" )
UpperCAmelCase_ = model(np.ones((1, 3, 224, 224) ) )
self.assertIsNotNone(__a )
| 1 | 1 |
"""simple docstring"""
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class _UpperCAmelCase ( _a ):
a__ : str = CustomTokenizer
pass
| 368 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_lowercase : List[Any] = {
'configuration_mvp': ['MVP_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MvpConfig', 'MvpOnnxConfig'],
'tokenization_mvp': ['MvpTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase : int = ['MvpTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase : Union[str, Any] = [
'MVP_PRETRAINED_MODEL_ARCHIVE_LIST',
'MvpForCausalLM',
'MvpForConditionalGeneration',
'MvpForQuestionAnswering',
'MvpForSequenceClassification',
'MvpModel',
'MvpPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
from .tokenization_mvp import MvpTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mvp_fast import MvpTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mvp import (
MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
MvpForCausalLM,
MvpForConditionalGeneration,
MvpForQuestionAnswering,
MvpForSequenceClassification,
MvpModel,
MvpPreTrainedModel,
)
else:
import sys
_lowercase : Union[str, Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 86 | 0 |
def UpperCamelCase( __UpperCamelCase : str ):
return [
txt[:a] + txt[a].upper() + txt[a + 1 :]
for a in range(len(__UpperCamelCase ) )
if txt[a].isalpha()
]
if __name__ == "__main__":
__import__('''doctest''').testmod()
| 103 |
from torch import nn
def lowerCAmelCase_ (lowerCAmelCase__: Optional[int] ):
"""simple docstring"""
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise ValueError(F'Unsupported activation function: {act_fn}' )
| 147 | 0 |
'''simple docstring'''
from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch
@require_torch
@require_tf
class UpperCamelCase__ ( SCREAMING_SNAKE_CASE_ ):
"""simple docstring"""
def a ( self ):
'''simple docstring'''
_lowerCAmelCase : Tuple = SMALL_MODEL_IDENTIFIER
_lowerCAmelCase : Optional[int] = 'pt'
_lowerCAmelCase : Tuple = 'tf'
def a ( self , snake_case__ ):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = AutoModel.from_pretrained(self.test_model )
model_pt.save_pretrained(snake_case__ )
def a ( self , snake_case__ ):
'''simple docstring'''
_lowerCAmelCase : Tuple = TFAutoModel.from_pretrained(self.test_model , from_pt=snake_case__ )
model_tf.save_pretrained(snake_case__ )
def a ( self ):
'''simple docstring'''
_lowerCAmelCase : Tuple = 'mock_framework'
# Framework provided - return whatever the user provides
_lowerCAmelCase : Any = FeaturesManager.determine_framework(self.test_model , snake_case__ )
self.assertEqual(snake_case__ , snake_case__ )
# Local checkpoint and framework provided - return provided framework
# PyTorch checkpoint
with TemporaryDirectory() as local_pt_ckpt:
self._setup_pt_ckpt(snake_case__ )
_lowerCAmelCase : Dict = FeaturesManager.determine_framework(snake_case__ , snake_case__ )
self.assertEqual(snake_case__ , snake_case__ )
# TensorFlow checkpoint
with TemporaryDirectory() as local_tf_ckpt:
self._setup_tf_ckpt(snake_case__ )
_lowerCAmelCase : int = FeaturesManager.determine_framework(snake_case__ , snake_case__ )
self.assertEqual(snake_case__ , snake_case__ )
def a ( self ):
'''simple docstring'''
with TemporaryDirectory() as local_pt_ckpt:
self._setup_pt_ckpt(snake_case__ )
_lowerCAmelCase : Tuple = FeaturesManager.determine_framework(snake_case__ )
self.assertEqual(snake_case__ , self.framework_pt )
# TensorFlow checkpoint
with TemporaryDirectory() as local_tf_ckpt:
self._setup_tf_ckpt(snake_case__ )
_lowerCAmelCase : Optional[int] = FeaturesManager.determine_framework(snake_case__ )
self.assertEqual(snake_case__ , self.framework_tf )
# Invalid local checkpoint
with TemporaryDirectory() as local_invalid_ckpt:
with self.assertRaises(snake_case__ ):
_lowerCAmelCase : str = FeaturesManager.determine_framework(snake_case__ )
def a ( self ):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = MagicMock(return_value=snake_case__ )
with patch('transformers.onnx.features.is_tf_available' , snake_case__ ):
_lowerCAmelCase : Any = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(snake_case__ , self.framework_pt )
# PyTorch not in environment -> use TensorFlow
_lowerCAmelCase : Any = MagicMock(return_value=snake_case__ )
with patch('transformers.onnx.features.is_torch_available' , snake_case__ ):
_lowerCAmelCase : Union[str, Any] = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(snake_case__ , self.framework_tf )
# Both in environment -> use PyTorch
_lowerCAmelCase : int = MagicMock(return_value=snake_case__ )
_lowerCAmelCase : Optional[int] = MagicMock(return_value=snake_case__ )
with patch('transformers.onnx.features.is_tf_available' , snake_case__ ), patch(
'transformers.onnx.features.is_torch_available' , snake_case__ ):
_lowerCAmelCase : Dict = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(snake_case__ , self.framework_pt )
# Both not in environment -> raise error
_lowerCAmelCase : str = MagicMock(return_value=snake_case__ )
_lowerCAmelCase : Optional[Any] = MagicMock(return_value=snake_case__ )
with patch('transformers.onnx.features.is_tf_available' , snake_case__ ), patch(
'transformers.onnx.features.is_torch_available' , snake_case__ ):
with self.assertRaises(snake_case__ ):
_lowerCAmelCase : Any = FeaturesManager.determine_framework(self.test_model )
| 25 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
def lowercase (_A ):
"""simple docstring"""
if not postfix_notation:
return 0
_lowerCAmelCase : int = {'+', '-', '*', '/'}
_lowerCAmelCase : list[Any] = []
for token in postfix_notation:
if token in operations:
_lowerCAmelCase , _lowerCAmelCase : Tuple = stack.pop(), stack.pop()
if token == "+":
stack.append(a + b )
elif token == "-":
stack.append(a - b )
elif token == "*":
stack.append(a * b )
else:
if a * b < 0 and a % b != 0:
stack.append(a // b + 1 )
else:
stack.append(a // b )
else:
stack.append(int(_A ) )
return stack.pop()
if __name__ == "__main__":
import doctest
doctest.testmod()
| 25 | 1 |
from __future__ import annotations
import unittest
from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel
@require_tf
class _A :
UpperCamelCase__ : Dict = BlenderbotSmallConfig
UpperCamelCase__ : Tuple = {}
UpperCamelCase__ : str = '''gelu'''
def __init__( self : Dict , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : List[Any]=13 , __SCREAMING_SNAKE_CASE : Optional[int]=7 , __SCREAMING_SNAKE_CASE : Optional[Any]=True , __SCREAMING_SNAKE_CASE : List[Any]=False , __SCREAMING_SNAKE_CASE : Dict=99 , __SCREAMING_SNAKE_CASE : Optional[int]=32 , __SCREAMING_SNAKE_CASE : Tuple=2 , __SCREAMING_SNAKE_CASE : str=4 , __SCREAMING_SNAKE_CASE : int=37 , __SCREAMING_SNAKE_CASE : Tuple=0.1 , __SCREAMING_SNAKE_CASE : Tuple=0.1 , __SCREAMING_SNAKE_CASE : Optional[int]=20 , __SCREAMING_SNAKE_CASE : List[str]=2 , __SCREAMING_SNAKE_CASE : Optional[Any]=1 , __SCREAMING_SNAKE_CASE : List[Any]=0 , ):
'''simple docstring'''
__a = parent
__a = batch_size
__a = seq_length
__a = is_training
__a = use_labels
__a = vocab_size
__a = hidden_size
__a = num_hidden_layers
__a = num_attention_heads
__a = intermediate_size
__a = hidden_dropout_prob
__a = attention_probs_dropout_prob
__a = max_position_embeddings
__a = eos_token_id
__a = pad_token_id
__a = bos_token_id
def _lowerCamelCase ( self : Union[str, Any]):
'''simple docstring'''
__a = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size)
__a = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size) , 1)
__a = tf.concat([input_ids, eos_tensor] , axis=1)
__a = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
__a = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
__a = prepare_blenderbot_small_inputs_dict(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE)
return config, inputs_dict
def _lowerCamelCase ( self : Any , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : Any):
'''simple docstring'''
__a = TFBlenderbotSmallModel(config=__SCREAMING_SNAKE_CASE).get_decoder()
__a = inputs_dict['''input_ids''']
__a = input_ids[:1, :]
__a = inputs_dict['''attention_mask'''][:1, :]
__a = inputs_dict['''head_mask''']
__a = 1
# first forward pass
__a = model(__SCREAMING_SNAKE_CASE , attention_mask=__SCREAMING_SNAKE_CASE , head_mask=__SCREAMING_SNAKE_CASE , use_cache=__SCREAMING_SNAKE_CASE)
__a , __a = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
__a = ids_tensor((self.batch_size, 3) , config.vocab_size)
__a = tf.cast(ids_tensor((self.batch_size, 3) , 2) , tf.inta)
# append to next input_ids and
__a = tf.concat([input_ids, next_tokens] , axis=-1)
__a = tf.concat([attention_mask, next_attn_mask] , axis=-1)
__a = model(__SCREAMING_SNAKE_CASE , attention_mask=__SCREAMING_SNAKE_CASE)[0]
__a = model(__SCREAMING_SNAKE_CASE , attention_mask=__SCREAMING_SNAKE_CASE , past_key_values=__SCREAMING_SNAKE_CASE)[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1])
# select random slice
__a = int(ids_tensor((1,) , output_from_past.shape[-1]))
__a = output_from_no_past[:, -3:, random_slice_idx]
__a = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , rtol=1E-3)
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase=None , _UpperCAmelCase=None , _UpperCAmelCase=None , _UpperCAmelCase=None , _UpperCAmelCase=None , ):
if attention_mask is None:
__a = tf.cast(tf.math.not_equal(_UpperCAmelCase , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
__a = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
__a = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
__a = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
__a = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_tf
class _A ( __UpperCAmelCase ,__UpperCAmelCase ,unittest.TestCase ):
UpperCamelCase__ : Union[str, Any] = (
(TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
)
UpperCamelCase__ : Union[str, Any] = (TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
UpperCamelCase__ : str = (
{
'''conversational''': TFBlenderbotSmallForConditionalGeneration,
'''feature-extraction''': TFBlenderbotSmallModel,
'''summarization''': TFBlenderbotSmallForConditionalGeneration,
'''text2text-generation''': TFBlenderbotSmallForConditionalGeneration,
'''translation''': TFBlenderbotSmallForConditionalGeneration,
}
if is_tf_available()
else {}
)
UpperCamelCase__ : Optional[int] = True
UpperCamelCase__ : int = False
UpperCamelCase__ : str = False
def _lowerCamelCase ( self : int):
'''simple docstring'''
__a = TFBlenderbotSmallModelTester(self)
__a = ConfigTester(self , config_class=__SCREAMING_SNAKE_CASE)
def _lowerCamelCase ( self : str):
'''simple docstring'''
self.config_tester.run_common_tests()
def _lowerCamelCase ( self : List[str]):
'''simple docstring'''
__a = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*__SCREAMING_SNAKE_CASE)
@require_tokenizers
@require_tf
class _A ( unittest.TestCase ):
UpperCamelCase__ : Union[str, Any] = [
'''Social anxiety\nWow, I am never shy. Do you have anxiety?\nYes. I end up sweating and blushing and feel like '''
''' i\'m going to throw up.\nand why is that?'''
]
UpperCamelCase__ : str = '''facebook/blenderbot_small-90M'''
@cached_property
def _lowerCamelCase ( self : Optional[Any]):
'''simple docstring'''
return BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M''')
@cached_property
def _lowerCamelCase ( self : Optional[int]):
'''simple docstring'''
__a = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name)
return model
@slow
def _lowerCamelCase ( self : List[Any]):
'''simple docstring'''
__a = self.tokenizer(self.src_text , return_tensors='''tf''')
__a = self.model.generate(
model_inputs.input_ids , attention_mask=model_inputs.attention_mask , num_beams=2 , use_cache=__SCREAMING_SNAKE_CASE , )
__a = self.tokenizer.batch_decode(generated_ids.numpy() , skip_special_tokens=__SCREAMING_SNAKE_CASE)[0]
assert generated_words in (
"i don't know. i just feel like i'm going to throw up. it's not fun.",
"i'm not sure. i just feel like i've been feeling like i have to be in a certain place",
"i'm not sure. i just feel like i've been in a bad situation.",
)
| 49 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Tuple = logging.get_logger(__name__)
__A : Optional[Any] = {
"MIT/ast-finetuned-audioset-10-10-0.4593": (
"https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/config.json"
),
}
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = """audio-spectrogram-transformer"""
def __init__( self : int , __UpperCamelCase : Optional[Any]=7_6_8 , __UpperCamelCase : int=1_2 , __UpperCamelCase : List[Any]=1_2 , __UpperCamelCase : List[Any]=3_0_7_2 , __UpperCamelCase : Any="gelu" , __UpperCamelCase : Union[str, Any]=0.0 , __UpperCamelCase : Dict=0.0 , __UpperCamelCase : Optional[int]=0.0_2 , __UpperCamelCase : Union[str, Any]=1e-12 , __UpperCamelCase : Optional[Any]=1_6 , __UpperCamelCase : List[Any]=True , __UpperCamelCase : int=1_0 , __UpperCamelCase : Optional[int]=1_0 , __UpperCamelCase : str=1_0_2_4 , __UpperCamelCase : Optional[Any]=1_2_8 , **__UpperCamelCase : Any , )->Tuple:
super().__init__(**__UpperCamelCase )
_UpperCAmelCase = hidden_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = intermediate_size
_UpperCAmelCase = hidden_act
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = initializer_range
_UpperCAmelCase = layer_norm_eps
_UpperCAmelCase = patch_size
_UpperCAmelCase = qkv_bias
_UpperCAmelCase = frequency_stride
_UpperCAmelCase = time_stride
_UpperCAmelCase = max_length
_UpperCAmelCase = num_mel_bins
| 260 | 0 |
"""simple docstring"""
import baseaa
def A ( snake_case :str ) -> bytes:
return baseaa.aaaencode(string.encode('utf-8' ) )
def A ( snake_case :bytes ) -> str:
return baseaa.aaadecode(snake_case ).decode('utf-8' )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 263 |
"""simple docstring"""
import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
import regex
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_flax_available, is_tf_available, is_torch_available, logging
from ...utils.generic import _is_jax, _is_numpy
UpperCamelCase : Tuple = logging.get_logger(__name__)
UpperCamelCase : int = {
"artists_file": "artists.json",
"lyrics_file": "lyrics.json",
"genres_file": "genres.json",
}
UpperCamelCase : Optional[Any] = {
"artists_file": {
"jukebox": "https://huggingface.co/ArthurZ/jukebox/blob/main/artists.json",
},
"genres_file": {
"jukebox": "https://huggingface.co/ArthurZ/jukebox/blob/main/genres.json",
},
"lyrics_file": {
"jukebox": "https://huggingface.co/ArthurZ/jukebox/blob/main/lyrics.json",
},
}
UpperCamelCase : Any = {
"jukebox": 5_1_2,
}
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
lowercase = VOCAB_FILES_NAMES
lowercase = PRETRAINED_VOCAB_FILES_MAP
lowercase = PRETRAINED_LYRIC_TOKENS_SIZES
lowercase = ["input_ids", "attention_mask"]
def __init__( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=["v3", "v2", "v2"] , __UpperCAmelCase=512 , __UpperCAmelCase=5 , __UpperCAmelCase="<|endoftext|>" , **__UpperCAmelCase , ):
'''simple docstring'''
__UpperCamelCase = AddedToken(__UpperCAmelCase , lstrip=__UpperCAmelCase , rstrip=__UpperCAmelCase ) if isinstance(__UpperCAmelCase , __UpperCAmelCase ) else unk_token
super().__init__(
unk_token=__UpperCAmelCase , n_genres=__UpperCAmelCase , version=__UpperCAmelCase , max_n_lyric_tokens=__UpperCAmelCase , **__UpperCAmelCase , )
__UpperCamelCase = version
__UpperCamelCase = max_n_lyric_tokens
__UpperCamelCase = n_genres
with open(__UpperCAmelCase , encoding='utf-8' ) as vocab_handle:
__UpperCamelCase = json.load(__UpperCAmelCase )
with open(__UpperCAmelCase , encoding='utf-8' ) as vocab_handle:
__UpperCamelCase = json.load(__UpperCAmelCase )
with open(__UpperCAmelCase , encoding='utf-8' ) as vocab_handle:
__UpperCamelCase = json.load(__UpperCAmelCase )
__UpperCamelCase = R'[^A-Za-z0-9.,:;!?\-\'\"()\[\] \t\n]+'
# In v2, we had a n_vocab=80 and in v3 we missed + and so n_vocab=79 of characters.
if len(self.lyrics_encoder ) == 79:
__UpperCamelCase = oov.replace(R'\-\'' , R'\-+\'' )
__UpperCamelCase = regex.compile(__UpperCAmelCase )
__UpperCamelCase = {v: k for k, v in self.artists_encoder.items()}
__UpperCamelCase = {v: k for k, v in self.genres_encoder.items()}
__UpperCamelCase = {v: k for k, v in self.lyrics_encoder.items()}
@property
def UpperCAmelCase ( self ):
'''simple docstring'''
return len(self.artists_encoder ) + len(self.genres_encoder ) + len(self.lyrics_encoder )
def UpperCAmelCase ( self ):
'''simple docstring'''
return dict(self.artists_encoder , self.genres_encoder , self.lyrics_encoder )
def UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__UpperCamelCase = [self.artists_encoder.get(__UpperCAmelCase , 0 ) for artist in list_artists]
for genres in range(len(__UpperCAmelCase ) ):
__UpperCamelCase = [self.genres_encoder.get(__UpperCAmelCase , 0 ) for genre in list_genres[genres]]
__UpperCamelCase = list_genres[genres] + [-1] * (self.n_genres - len(list_genres[genres] ))
__UpperCamelCase = [[self.lyrics_encoder.get(__UpperCAmelCase , 0 ) for character in list_lyrics[0]], [], []]
return artists_id, list_genres, lyric_ids
def UpperCAmelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
return list(__UpperCAmelCase )
def UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase = self.prepare_for_tokenization(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
__UpperCamelCase = self._tokenize(__UpperCAmelCase )
return artist, genre, lyrics
def UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = False ):
'''simple docstring'''
for idx in range(len(self.version ) ):
if self.version[idx] == "v3":
__UpperCamelCase = artists[idx].lower()
__UpperCamelCase = [genres[idx].lower()]
else:
__UpperCamelCase = self._normalize(artists[idx] ) + '.v2'
__UpperCamelCase = [
self._normalize(__UpperCAmelCase ) + '.v2' for genre in genres[idx].split('_' )
] # split is for the full dictionary with combined genres
if self.version[0] == "v2":
__UpperCamelCase = regex.compile(R'[^A-Za-z0-9.,:;!?\-\'\"()\[\] \t\n]+' )
__UpperCamelCase = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789.,:;!?-+\'\"()[] \t\n'
__UpperCamelCase = {vocab[index]: index + 1 for index in range(len(__UpperCAmelCase ) )}
__UpperCamelCase = 0
__UpperCamelCase = len(__UpperCAmelCase ) + 1
__UpperCamelCase = self.vocab
__UpperCamelCase = {v: k for k, v in self.vocab.items()}
__UpperCamelCase = ''
else:
__UpperCamelCase = regex.compile(R'[^A-Za-z0-9.,:;!?\-+\'\"()\[\] \t\n]+' )
__UpperCamelCase = self._run_strip_accents(__UpperCAmelCase )
__UpperCamelCase = lyrics.replace('\\' , '\n' )
__UpperCamelCase = self.out_of_vocab.sub('' , __UpperCAmelCase ), [], []
return artists, genres, lyrics
def UpperCAmelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
__UpperCamelCase = unicodedata.normalize('NFD' , __UpperCAmelCase )
__UpperCamelCase = []
for char in text:
__UpperCamelCase = unicodedata.category(__UpperCAmelCase )
if cat == "Mn":
continue
output.append(__UpperCAmelCase )
return "".join(__UpperCAmelCase )
def UpperCAmelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
__UpperCamelCase = (
[chr(__UpperCAmelCase ) for i in range(ord('a' ) , ord('z' ) + 1 )]
+ [chr(__UpperCAmelCase ) for i in range(ord('A' ) , ord('Z' ) + 1 )]
+ [chr(__UpperCAmelCase ) for i in range(ord('0' ) , ord('9' ) + 1 )]
+ ['.']
)
__UpperCamelCase = frozenset(__UpperCAmelCase )
__UpperCamelCase = re.compile(R'_+' )
__UpperCamelCase = ''.join([c if c in accepted else '_' for c in text.lower()] )
__UpperCamelCase = pattern.sub('_' , __UpperCAmelCase ).strip('_' )
return text
def UpperCAmelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
return " ".join(__UpperCAmelCase )
def UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase = None , __UpperCAmelCase = False ):
'''simple docstring'''
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
__UpperCamelCase = TensorType(__UpperCAmelCase )
# Get a function reference for the correct framework
if tensor_type == TensorType.TENSORFLOW:
if not is_tf_available():
raise ImportError(
'Unable to convert output to TensorFlow tensors format, TensorFlow is not installed.' )
import tensorflow as tf
__UpperCamelCase = tf.constant
__UpperCamelCase = tf.is_tensor
elif tensor_type == TensorType.PYTORCH:
if not is_torch_available():
raise ImportError('Unable to convert output to PyTorch tensors format, PyTorch is not installed.' )
import torch
__UpperCamelCase = torch.tensor
__UpperCamelCase = torch.is_tensor
elif tensor_type == TensorType.JAX:
if not is_flax_available():
raise ImportError('Unable to convert output to JAX tensors format, JAX is not installed.' )
import jax.numpy as jnp # noqa: F811
__UpperCamelCase = jnp.array
__UpperCamelCase = _is_jax
else:
__UpperCamelCase = np.asarray
__UpperCamelCase = _is_numpy
# Do the tensor conversion in batch
try:
if prepend_batch_axis:
__UpperCamelCase = [inputs]
if not is_tensor(__UpperCAmelCase ):
__UpperCamelCase = as_tensor(__UpperCAmelCase )
except: # noqa E722
raise ValueError(
'Unable to create tensor, you should probably activate truncation and/or padding '
'with \'padding=True\' \'truncation=True\' to have batched tensors with the same length.' )
return inputs
def __call__( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase="" , __UpperCAmelCase="pt" ):
'''simple docstring'''
__UpperCamelCase = [0, 0, 0]
__UpperCamelCase = [artist] * len(self.version )
__UpperCamelCase = [genres] * len(self.version )
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase = self.tokenize(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase = self._convert_token_to_id(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
__UpperCamelCase = [-INFINITY] * len(full_tokens[-1] )
__UpperCamelCase = [
self.convert_to_tensors(
[input_ids + [artists_id[i]] + genres_ids[i] + full_tokens[i]] , tensor_type=__UpperCAmelCase )
for i in range(len(self.version ) )
]
return BatchEncoding({'input_ids': input_ids, 'attention_masks': attention_masks} )
def UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase = None ):
'''simple docstring'''
if not os.path.isdir(__UpperCAmelCase ):
logger.error(F'Vocabulary path ({save_directory}) should be a directory' )
return
__UpperCamelCase = os.path.join(
__UpperCAmelCase , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['artists_file'] )
with open(__UpperCAmelCase , 'w' , encoding='utf-8' ) as f:
f.write(json.dumps(self.artists_encoder , ensure_ascii=__UpperCAmelCase ) )
__UpperCamelCase = os.path.join(
__UpperCAmelCase , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['genres_file'] )
with open(__UpperCAmelCase , 'w' , encoding='utf-8' ) as f:
f.write(json.dumps(self.genres_encoder , ensure_ascii=__UpperCAmelCase ) )
__UpperCamelCase = os.path.join(
__UpperCAmelCase , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['lyrics_file'] )
with open(__UpperCAmelCase , 'w' , encoding='utf-8' ) as f:
f.write(json.dumps(self.lyrics_encoder , ensure_ascii=__UpperCAmelCase ) )
return (artists_file, genres_file, lyrics_file)
def UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__UpperCamelCase = self.artists_decoder.get(__UpperCAmelCase )
__UpperCamelCase = [self.genres_decoder.get(__UpperCAmelCase ) for genre in genres_index]
__UpperCamelCase = [self.lyrics_decoder.get(__UpperCAmelCase ) for character in lyric_index]
return artist, genres, lyrics
| 263 | 1 |
'''simple docstring'''
lowerCamelCase : 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
lowerCamelCase : Any = {value: key for key, value in MORSE_CODE_DICT.items()}
def _SCREAMING_SNAKE_CASE (A ) -> str:
"""simple docstring"""
return " ".join(MORSE_CODE_DICT[char] for char in message.upper() )
def _SCREAMING_SNAKE_CASE (A ) -> str:
"""simple docstring"""
return "".join(REVERSE_DICT[char] for char in message.split() )
def _SCREAMING_SNAKE_CASE () -> None:
"""simple docstring"""
lowercase__ = '''Morse code here!'''
print(A )
lowercase__ = encrypt(A )
print(A )
lowercase__ = decrypt(A )
print(A )
if __name__ == "__main__":
main()
| 2 |
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
snake_case_ = logging.get_logger(__name__)
def snake_case__ ( SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : Union[str, Any] ):
'''simple docstring'''
lowercase__ : List[str] = set()
lowercase__ : List[str] = []
def parse_line(SCREAMING_SNAKE_CASE_ : Union[str, Any] ):
for line in fp:
if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
lowercase__ : Optional[int] = line.decode('UTF-8' )
if "warnings summary (final)" in line:
continue
# This means we are outside the body of a warning
elif not line.startswith(' ' ):
# process a single warning and move it to `selected_warnings`.
if len(SCREAMING_SNAKE_CASE_ ) > 0:
lowercase__ : Optional[Any] = '\n'.join(SCREAMING_SNAKE_CASE_ )
# Only keep the warnings specified in `targets`
if any(f""": {x}: """ in warning for x in targets ):
selected_warnings.add(SCREAMING_SNAKE_CASE_ )
buffer.clear()
continue
else:
lowercase__ : Optional[Any] = line.strip()
buffer.append(SCREAMING_SNAKE_CASE_ )
if from_gh:
for filename in os.listdir(SCREAMING_SNAKE_CASE_ ):
lowercase__ : int = os.path.join(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if not os.path.isdir(SCREAMING_SNAKE_CASE_ ):
# read the file
if filename != "warnings.txt":
continue
with open(SCREAMING_SNAKE_CASE_ ) as fp:
parse_line(SCREAMING_SNAKE_CASE_ )
else:
try:
with zipfile.ZipFile(SCREAMING_SNAKE_CASE_ ) as z:
for filename in z.namelist():
if not os.path.isdir(SCREAMING_SNAKE_CASE_ ):
# read the file
if filename != "warnings.txt":
continue
with z.open(SCREAMING_SNAKE_CASE_ ) as fp:
parse_line(SCREAMING_SNAKE_CASE_ )
except Exception:
logger.warning(
f"""{artifact_path} is either an invalid zip file or something else wrong. This file is skipped.""" )
return selected_warnings
def snake_case__ ( SCREAMING_SNAKE_CASE_ : List[Any] , SCREAMING_SNAKE_CASE_ : Optional[Any] ):
'''simple docstring'''
lowercase__ : Optional[Any] = set()
lowercase__ : List[str] = [os.path.join(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) for p in os.listdir(SCREAMING_SNAKE_CASE_ ) if (p.endswith('.zip' ) or from_gh)]
for p in paths:
selected_warnings.update(extract_warnings_from_single_artifact(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
return selected_warnings
if __name__ == "__main__":
def snake_case__ ( SCREAMING_SNAKE_CASE_ : Any ):
'''simple docstring'''
return values.split(',' )
snake_case_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument('''--workflow_run_id''', type=str, required=True, help='''A GitHub Actions workflow run id.''')
parser.add_argument(
'''--output_dir''',
type=str,
required=True,
help='''Where to store the downloaded artifacts and other result files.''',
)
parser.add_argument('''--token''', default=None, type=str, help='''A token that has actions:read permission.''')
# optional parameters
parser.add_argument(
'''--targets''',
default='''DeprecationWarning,UserWarning,FutureWarning''',
type=list_str,
help='''Comma-separated list of target warning(s) which we want to extract.''',
)
parser.add_argument(
'''--from_gh''',
action='''store_true''',
help='''If running from a GitHub action workflow and collecting warnings from its artifacts.''',
)
snake_case_ = parser.parse_args()
snake_case_ = args.from_gh
if from_gh:
# The artifacts have to be downloaded using `actions/download-artifact@v3`
pass
else:
os.makedirs(args.output_dir, exist_ok=True)
# get download links
snake_case_ = get_artifacts_links(args.workflow_run_id, token=args.token)
with open(os.path.join(args.output_dir, '''artifacts.json'''), '''w''', encoding='''UTF-8''') as fp:
json.dump(artifacts, fp, ensure_ascii=False, indent=4)
# download artifacts
for idx, (name, url) in enumerate(artifacts.items()):
print(name)
print(url)
print('''=''' * 80)
download_artifact(name, url, args.output_dir, args.token)
# Be gentle to GitHub
time.sleep(1)
# extract warnings from artifacts
snake_case_ = extract_warnings(args.output_dir, args.targets)
snake_case_ = sorted(selected_warnings)
with open(os.path.join(args.output_dir, '''selected_warnings.json'''), '''w''', encoding='''UTF-8''') as fp:
json.dump(selected_warnings, fp, ensure_ascii=False, indent=4)
| 214 | 0 |
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import six # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring
import datasets
_lowercase : Dict ="\\n@inproceedings{lin-2004-rouge,\n title = \"{ROUGE}: A Package for Automatic Evaluation of Summaries\",\n author = \"Lin, Chin-Yew\",\n booktitle = \"Text Summarization Branches Out\",\n month = jul,\n year = \"2004\",\n address = \"Barcelona, Spain\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://www.aclweb.org/anthology/W04-1013\",\n pages = \"74--81\",\n}\n"
_lowercase : List[Any] ="\\nROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for\nevaluating automatic summarization and machine translation software in natural language processing.\nThe metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.\n\nNote that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.\n\nThis metrics is a wrapper around Google Research reimplementation of ROUGE:\nhttps://github.com/google-research/google-research/tree/master/rouge\n"
_lowercase : Union[str, Any] ="\nCalculates average rouge scores for a list of hypotheses and references\nArgs:\n predictions: list of predictions to score. Each prediction\n should be a string with tokens separated by spaces.\n references: list of reference for each prediction. Each\n reference should be a string with tokens separated by spaces.\n rouge_types: A list of rouge types to calculate.\n Valid names:\n `\"rouge{n}\"` (e.g. `\"rouge1\"`, `\"rouge2\"`) where: {n} is the n-gram based scoring,\n `\"rougeL\"`: Longest common subsequence based scoring.\n `\"rougeLSum\"`: rougeLsum splits text using `\"\n\"`.\n See details in https://github.com/huggingface/datasets/issues/617\n use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.\n use_aggregator: Return aggregates if this is set to True\nReturns:\n rouge1: rouge_1 (precision, recall, f1),\n rouge2: rouge_2 (precision, recall, f1),\n rougeL: rouge_l (precision, recall, f1),\n rougeLsum: rouge_lsum (precision, recall, f1)\nExamples:\n\n >>> rouge = datasets.load_metric(\'rouge\')\n >>> predictions = [\"hello there\", \"general kenobi\"]\n >>> references = [\"hello there\", \"general kenobi\"]\n >>> results = rouge.compute(predictions=predictions, references=references)\n >>> print(list(results.keys()))\n [\'rouge1\', \'rouge2\', \'rougeL\', \'rougeLsum\']\n >>> print(results[\"rouge1\"])\n AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))\n >>> print(results[\"rouge1\"].mid.fmeasure)\n 1.0\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class snake_case__ (datasets.Metric ):
"""simple docstring"""
def SCREAMING_SNAKE_CASE__( self ) -> Optional[Any]:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Value("""string""" , id="""sequence""" ),
"""references""": datasets.Value("""string""" , id="""sequence""" ),
} ) , codebase_urls=["""https://github.com/google-research/google-research/tree/master/rouge"""] , reference_urls=[
"""https://en.wikipedia.org/wiki/ROUGE_(metric)""",
"""https://github.com/google-research/google-research/tree/master/rouge""",
] , )
def SCREAMING_SNAKE_CASE__( self , __lowercase , __lowercase , __lowercase=None , __lowercase=True , __lowercase=False ) -> Any:
"""simple docstring"""
if rouge_types is None:
a__ : Optional[int] = ["rouge1", "rouge2", "rougeL", "rougeLsum"]
a__ : List[Any] = rouge_scorer.RougeScorer(rouge_types=_a , use_stemmer=_a )
if use_aggregator:
a__ : Optional[int] = scoring.BootstrapAggregator()
else:
a__ : Optional[Any] = []
for ref, pred in zip(_a , _a ):
a__ : Dict = scorer.score(_a , _a )
if use_aggregator:
aggregator.add_scores(_a )
else:
scores.append(_a )
if use_aggregator:
a__ : str = aggregator.aggregate()
else:
a__ : Dict = {}
for key in scores[0]:
a__ : List[str] = [score[key] for score in scores]
return result
| 370 |
from __future__ import annotations
import math
def lowerCAmelCase_ ( _lowercase : int) -> list[int]:
"""simple docstring"""
if num <= 0:
a__ : Tuple = F'''{num}: Invalid input, please enter a positive integer.'''
raise ValueError(_lowercase)
a__ : List[Any] = [True] * (num + 1)
a__ : List[str] = []
a__ : List[Any] = 2
a__ : Optional[int] = int(math.sqrt(_lowercase))
while start <= end:
# If start is a prime
if sieve[start] is True:
prime.append(_lowercase)
# Set multiples of start be False
for i in range(start * start , num + 1 , _lowercase):
if sieve[i] is True:
a__ : Optional[int] = False
start += 1
for j in range(end + 1 , num + 1):
if sieve[j] is True:
prime.append(_lowercase)
return prime
if __name__ == "__main__":
print(prime_sieve(int(input("Enter a positive integer: ").strip())))
| 266 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class __A ( SCREAMING_SNAKE_CASE_ , unittest.TestCase ):
_UpperCamelCase : List[Any] = KandinskyImgaImgPipeline
_UpperCamelCase : Optional[Any] = ["prompt", "image_embeds", "negative_image_embeds", "image"]
_UpperCamelCase : List[Any] = [
"prompt",
"negative_prompt",
"image_embeds",
"negative_image_embeds",
"image",
]
_UpperCamelCase : Dict = [
"generator",
"height",
"width",
"strength",
"guidance_scale",
"negative_prompt",
"num_inference_steps",
"return_dict",
"guidance_scale",
"num_images_per_prompt",
"output_type",
"return_dict",
]
_UpperCamelCase : Union[str, Any] = False
@property
def __A ( self ):
return 32
@property
def __A ( self ):
return 32
@property
def __A ( self ):
return self.time_input_dim
@property
def __A ( self ):
return self.time_input_dim * 4
@property
def __A ( self ):
return 100
@property
def __A ( self ):
_lowerCAmelCase : Optional[Any] = XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base""" )
return tokenizer
@property
def __A ( self ):
torch.manual_seed(0 )
_lowerCAmelCase : List[Any] = MCLIPConfig(
numDims=self.cross_attention_dim , transformerDimensions=self.text_embedder_hidden_size , hidden_size=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=1005 , )
_lowerCAmelCase : int = MultilingualCLIP(a__ )
_lowerCAmelCase : Union[str, Any] = text_encoder.eval()
return text_encoder
@property
def __A ( self ):
torch.manual_seed(0 )
_lowerCAmelCase : str = {
"""in_channels""": 4,
# Out channels is double in channels because predicts mean and variance
"""out_channels""": 8,
"""addition_embed_type""": """text_image""",
"""down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
"""up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
"""mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
"""block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
"""layers_per_block""": 1,
"""encoder_hid_dim""": self.text_embedder_hidden_size,
"""encoder_hid_dim_type""": """text_image_proj""",
"""cross_attention_dim""": self.cross_attention_dim,
"""attention_head_dim""": 4,
"""resnet_time_scale_shift""": """scale_shift""",
"""class_embed_type""": None,
}
_lowerCAmelCase : Optional[Any] = UNetaDConditionModel(**a__ )
return model
@property
def __A ( self ):
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def __A ( self ):
torch.manual_seed(0 )
_lowerCAmelCase : str = VQModel(**self.dummy_movq_kwargs )
return model
def __A ( self ):
_lowerCAmelCase : Union[str, Any] = self.dummy_text_encoder
_lowerCAmelCase : List[Any] = self.dummy_tokenizer
_lowerCAmelCase : int = self.dummy_unet
_lowerCAmelCase : Dict = self.dummy_movq
_lowerCAmelCase : Tuple = {
"""num_train_timesteps""": 1000,
"""beta_schedule""": """linear""",
"""beta_start""": 0.0_0_0_8_5,
"""beta_end""": 0.0_1_2,
"""clip_sample""": False,
"""set_alpha_to_one""": False,
"""steps_offset""": 0,
"""prediction_type""": """epsilon""",
"""thresholding""": False,
}
_lowerCAmelCase : Optional[Any] = DDIMScheduler(**a__ )
_lowerCAmelCase : List[Any] = {
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""unet""": unet,
"""scheduler""": scheduler,
"""movq""": movq,
}
return components
def __A ( self , a__ , a__=0 ):
_lowerCAmelCase : Optional[int] = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(a__ ) ).to(a__ )
_lowerCAmelCase : int = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(seed + 1 ) ).to(a__ )
# create init_image
_lowerCAmelCase : List[Any] = floats_tensor((1, 3, 64, 64) , rng=random.Random(a__ ) ).to(a__ )
_lowerCAmelCase : int = image.cpu().permute(0 , 2 , 3 , 1 )[0]
_lowerCAmelCase : List[Any] = Image.fromarray(np.uinta(a__ ) ).convert("""RGB""" ).resize((256, 256) )
if str(a__ ).startswith("""mps""" ):
_lowerCAmelCase : List[Any] = torch.manual_seed(a__ )
else:
_lowerCAmelCase : Tuple = torch.Generator(device=a__ ).manual_seed(a__ )
_lowerCAmelCase : Optional[Any] = {
"""prompt""": """horse""",
"""image""": init_image,
"""image_embeds""": image_embeds,
"""negative_image_embeds""": negative_image_embeds,
"""generator""": generator,
"""height""": 64,
"""width""": 64,
"""num_inference_steps""": 10,
"""guidance_scale""": 7.0,
"""strength""": 0.2,
"""output_type""": """np""",
}
return inputs
def __A ( self ):
_lowerCAmelCase : Any = """cpu"""
_lowerCAmelCase : int = self.get_dummy_components()
_lowerCAmelCase : int = self.pipeline_class(**a__ )
_lowerCAmelCase : Optional[int] = pipe.to(a__ )
pipe.set_progress_bar_config(disable=a__ )
_lowerCAmelCase : Tuple = pipe(**self.get_dummy_inputs(a__ ) )
_lowerCAmelCase : List[Any] = output.images
_lowerCAmelCase : Tuple = pipe(
**self.get_dummy_inputs(a__ ) , return_dict=a__ , )[0]
_lowerCAmelCase : Dict = image[0, -3:, -3:, -1]
_lowerCAmelCase : List[Any] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
_lowerCAmelCase : str = np.array(
[0.6_1_4_7_4_9_4_3, 0.6_0_7_3_5_3_9, 0.4_3_3_0_8_5_4_4, 0.5_9_2_8_2_6_9, 0.4_7_4_9_3_5_9_5, 0.4_6_7_5_5_9_7_3, 0.4_6_1_3_8_3_8, 0.4_5_3_6_8_7_9_7, 0.5_0_1_1_9_2_3_3] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
), F" expected_slice {expected_slice}, but got {image_slice.flatten()}"
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
), F" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"
@slow
@require_torch_gpu
class __A ( unittest.TestCase ):
def __A ( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __A ( self ):
_lowerCAmelCase : Dict = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/kandinsky/kandinsky_img2img_frog.npy""" )
_lowerCAmelCase : List[str] = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png""" )
_lowerCAmelCase : Union[str, Any] = """A red cartoon frog, 4k"""
_lowerCAmelCase : int = KandinskyPriorPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-1-prior""" , torch_dtype=torch.floataa )
pipe_prior.to(a__ )
_lowerCAmelCase : Tuple = KandinskyImgaImgPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-1""" , torch_dtype=torch.floataa )
_lowerCAmelCase : Any = pipeline.to(a__ )
pipeline.set_progress_bar_config(disable=a__ )
_lowerCAmelCase : Any = torch.Generator(device="""cpu""" ).manual_seed(0 )
_lowerCAmelCase , _lowerCAmelCase : Dict = pipe_prior(
a__ , generator=a__ , num_inference_steps=5 , negative_prompt="""""" , ).to_tuple()
_lowerCAmelCase : Union[str, Any] = pipeline(
a__ , image=a__ , image_embeds=a__ , negative_image_embeds=a__ , generator=a__ , num_inference_steps=100 , height=768 , width=768 , strength=0.2 , output_type="""np""" , )
_lowerCAmelCase : Dict = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(a__ , a__ )
| 44 |
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowercase_ = (DDPMParallelScheduler,)
def SCREAMING_SNAKE_CASE_ (self : Any , **UpperCAmelCase_ : Any) ->Any:
'''simple docstring'''
lowerCamelCase__: Any ={
"num_train_timesteps": 1_000,
"beta_start": 0.0001,
"beta_end": 0.02,
"beta_schedule": "linear",
"variance_type": "fixed_small",
"clip_sample": True,
}
config.update(**UpperCAmelCase_)
return config
def SCREAMING_SNAKE_CASE_ (self : int) ->Dict:
'''simple docstring'''
for timesteps in [1, 5, 100, 1_000]:
self.check_over_configs(num_train_timesteps=UpperCAmelCase_)
def SCREAMING_SNAKE_CASE_ (self : int) ->Optional[int]:
'''simple docstring'''
for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1] , [0.002, 0.02, 0.2, 2]):
self.check_over_configs(beta_start=UpperCAmelCase_ , beta_end=UpperCAmelCase_)
def SCREAMING_SNAKE_CASE_ (self : Optional[Any]) ->Any:
'''simple docstring'''
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=UpperCAmelCase_)
def SCREAMING_SNAKE_CASE_ (self : int) ->Optional[int]:
'''simple docstring'''
for variance in ["fixed_small", "fixed_large", "other"]:
self.check_over_configs(variance_type=UpperCAmelCase_)
def SCREAMING_SNAKE_CASE_ (self : Optional[int]) ->Optional[Any]:
'''simple docstring'''
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=UpperCAmelCase_)
def SCREAMING_SNAKE_CASE_ (self : Any) ->Tuple:
'''simple docstring'''
self.check_over_configs(thresholding=UpperCAmelCase_)
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(
thresholding=UpperCAmelCase_ , prediction_type=UpperCAmelCase_ , sample_max_value=UpperCAmelCase_ , )
def SCREAMING_SNAKE_CASE_ (self : Any) ->Optional[int]:
'''simple docstring'''
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(prediction_type=UpperCAmelCase_)
def SCREAMING_SNAKE_CASE_ (self : int) ->int:
'''simple docstring'''
for t in [0, 500, 999]:
self.check_over_forward(time_step=UpperCAmelCase_)
def SCREAMING_SNAKE_CASE_ (self : Optional[Any]) ->str:
'''simple docstring'''
lowerCamelCase__: Dict =self.scheduler_classes[0]
lowerCamelCase__: Tuple =self.get_scheduler_config()
lowerCamelCase__: Any =scheduler_class(**UpperCAmelCase_)
assert torch.sum(torch.abs(scheduler._get_variance(0) - 0.0)) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(487) - 0.0_0979)) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(999) - 0.02)) < 1E-5
def SCREAMING_SNAKE_CASE_ (self : Any) ->str:
'''simple docstring'''
lowerCamelCase__: int =self.scheduler_classes[0]
lowerCamelCase__: Tuple =self.get_scheduler_config()
lowerCamelCase__: Tuple =scheduler_class(**UpperCAmelCase_)
lowerCamelCase__: str =len(UpperCAmelCase_)
lowerCamelCase__: Optional[int] =self.dummy_model()
lowerCamelCase__: int =self.dummy_sample_deter
lowerCamelCase__: Union[str, Any] =self.dummy_sample_deter + 0.1
lowerCamelCase__: Optional[Any] =self.dummy_sample_deter - 0.1
lowerCamelCase__: Optional[Any] =samplea.shape[0]
lowerCamelCase__: List[Any] =torch.stack([samplea, samplea, samplea] , dim=0)
lowerCamelCase__: Union[str, Any] =torch.arange(UpperCAmelCase_)[0:3, None].repeat(1 , UpperCAmelCase_)
lowerCamelCase__: Optional[int] =model(samples.flatten(0 , 1) , timesteps.flatten(0 , 1))
lowerCamelCase__: Tuple =scheduler.batch_step_no_noise(UpperCAmelCase_ , timesteps.flatten(0 , 1) , samples.flatten(0 , 1))
lowerCamelCase__: List[str] =torch.sum(torch.abs(UpperCAmelCase_))
lowerCamelCase__: Any =torch.mean(torch.abs(UpperCAmelCase_))
assert abs(result_sum.item() - 1153.1833) < 1E-2
assert abs(result_mean.item() - 0.5005) < 1E-3
def SCREAMING_SNAKE_CASE_ (self : Optional[Any]) ->Union[str, Any]:
'''simple docstring'''
lowerCamelCase__: Any =self.scheduler_classes[0]
lowerCamelCase__: Optional[Any] =self.get_scheduler_config()
lowerCamelCase__: Optional[int] =scheduler_class(**UpperCAmelCase_)
lowerCamelCase__: Union[str, Any] =len(UpperCAmelCase_)
lowerCamelCase__: Union[str, Any] =self.dummy_model()
lowerCamelCase__: List[Any] =self.dummy_sample_deter
lowerCamelCase__: int =torch.manual_seed(0)
for t in reversed(range(UpperCAmelCase_)):
# 1. predict noise residual
lowerCamelCase__: Tuple =model(UpperCAmelCase_ , UpperCAmelCase_)
# 2. predict previous mean of sample x_t-1
lowerCamelCase__: Optional[Any] =scheduler.step(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , generator=UpperCAmelCase_).prev_sample
lowerCamelCase__: Any =pred_prev_sample
lowerCamelCase__: Any =torch.sum(torch.abs(UpperCAmelCase_))
lowerCamelCase__: List[str] =torch.mean(torch.abs(UpperCAmelCase_))
assert abs(result_sum.item() - 258.9606) < 1E-2
assert abs(result_mean.item() - 0.3372) < 1E-3
def SCREAMING_SNAKE_CASE_ (self : int) ->Any:
'''simple docstring'''
lowerCamelCase__: Tuple =self.scheduler_classes[0]
lowerCamelCase__: Any =self.get_scheduler_config(prediction_type="v_prediction")
lowerCamelCase__: Any =scheduler_class(**UpperCAmelCase_)
lowerCamelCase__: str =len(UpperCAmelCase_)
lowerCamelCase__: str =self.dummy_model()
lowerCamelCase__: str =self.dummy_sample_deter
lowerCamelCase__: Dict =torch.manual_seed(0)
for t in reversed(range(UpperCAmelCase_)):
# 1. predict noise residual
lowerCamelCase__: Union[str, Any] =model(UpperCAmelCase_ , UpperCAmelCase_)
# 2. predict previous mean of sample x_t-1
lowerCamelCase__: Dict =scheduler.step(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , generator=UpperCAmelCase_).prev_sample
lowerCamelCase__: List[str] =pred_prev_sample
lowerCamelCase__: List[Any] =torch.sum(torch.abs(UpperCAmelCase_))
lowerCamelCase__: Tuple =torch.mean(torch.abs(UpperCAmelCase_))
assert abs(result_sum.item() - 202.0296) < 1E-2
assert abs(result_mean.item() - 0.2631) < 1E-3
def SCREAMING_SNAKE_CASE_ (self : Tuple) ->Optional[int]:
'''simple docstring'''
lowerCamelCase__: str =self.scheduler_classes[0]
lowerCamelCase__: Union[str, Any] =self.get_scheduler_config()
lowerCamelCase__: Any =scheduler_class(**UpperCAmelCase_)
lowerCamelCase__: List[Any] =[100, 87, 50, 1, 0]
scheduler.set_timesteps(timesteps=UpperCAmelCase_)
lowerCamelCase__: Union[str, Any] =scheduler.timesteps
for i, timestep in enumerate(UpperCAmelCase_):
if i == len(UpperCAmelCase_) - 1:
lowerCamelCase__: Dict =-1
else:
lowerCamelCase__: Union[str, Any] =timesteps[i + 1]
lowerCamelCase__: Tuple =scheduler.previous_timestep(UpperCAmelCase_)
lowerCamelCase__: str =prev_t.item()
self.assertEqual(UpperCAmelCase_ , UpperCAmelCase_)
def SCREAMING_SNAKE_CASE_ (self : Union[str, Any]) ->Union[str, Any]:
'''simple docstring'''
lowerCamelCase__: Tuple =self.scheduler_classes[0]
lowerCamelCase__: List[Any] =self.get_scheduler_config()
lowerCamelCase__: Dict =scheduler_class(**UpperCAmelCase_)
lowerCamelCase__: Optional[Any] =[100, 87, 50, 51, 0]
with self.assertRaises(UpperCAmelCase_ , msg="`custom_timesteps` must be in descending order."):
scheduler.set_timesteps(timesteps=UpperCAmelCase_)
def SCREAMING_SNAKE_CASE_ (self : List[Any]) ->List[Any]:
'''simple docstring'''
lowerCamelCase__: Dict =self.scheduler_classes[0]
lowerCamelCase__: Any =self.get_scheduler_config()
lowerCamelCase__: int =scheduler_class(**UpperCAmelCase_)
lowerCamelCase__: Optional[int] =[100, 87, 50, 1, 0]
lowerCamelCase__: int =len(UpperCAmelCase_)
with self.assertRaises(UpperCAmelCase_ , msg="Can only pass one of `num_inference_steps` or `custom_timesteps`."):
scheduler.set_timesteps(num_inference_steps=UpperCAmelCase_ , timesteps=UpperCAmelCase_)
def SCREAMING_SNAKE_CASE_ (self : Optional[Any]) ->Any:
'''simple docstring'''
lowerCamelCase__: Tuple =self.scheduler_classes[0]
lowerCamelCase__: Optional[Any] =self.get_scheduler_config()
lowerCamelCase__: Optional[Any] =scheduler_class(**UpperCAmelCase_)
lowerCamelCase__: Dict =[scheduler.config.num_train_timesteps]
with self.assertRaises(
UpperCAmelCase_ , msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}" , ):
scheduler.set_timesteps(timesteps=UpperCAmelCase_)
| 10 | 0 |
'''simple docstring'''
def snake_case__ ( lowerCamelCase__ : list ) -> list:
if len(UpperCamelCase__ ) < 2:
return collection
def circle_sort_util(lowerCamelCase__ : list , lowerCamelCase__ : int , lowerCamelCase__ : int ) -> bool:
A_ : Optional[Any] = False
if low == high:
return swapped
A_ : Optional[Any] = low
A_ : Tuple = high
while left < right:
if collection[left] > collection[right]:
A_ ,A_ : Tuple = (
collection[right],
collection[left],
)
A_ : List[str] = True
left += 1
right -= 1
if left == right and collection[left] > collection[right + 1]:
A_ ,A_ : str = (
collection[right + 1],
collection[left],
)
A_ : List[Any] = True
A_ : Optional[Any] = low + int((high - low) / 2 )
A_ : Union[str, Any] = circle_sort_util(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
A_ : Any = circle_sort_util(UpperCamelCase__ , mid + 1 , UpperCamelCase__ )
return swapped or left_swap or right_swap
A_ : str = True
while is_not_sorted is True:
A_ : Tuple = circle_sort_util(UpperCamelCase__ , 0 , len(UpperCamelCase__ ) - 1 )
return collection
if __name__ == "__main__":
snake_case__ = input("""Enter numbers separated by a comma:\n""").strip()
snake_case__ = [int(item) for item in user_input.split(""",""")]
print(circle_sort(unsorted))
| 366 |
'''simple docstring'''
import pprint
import requests
snake_case__ = """https://zenquotes.io/api"""
def snake_case__ ( ) -> list:
return requests.get(API_ENDPOINT_URL + '''/today''' ).json()
def snake_case__ ( ) -> list:
return requests.get(API_ENDPOINT_URL + '''/random''' ).json()
if __name__ == "__main__":
snake_case__ = random_quotes()
pprint.pprint(response)
| 4 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase : str = logging.get_logger(__name__)
_lowerCAmelCase : List[Any] = {
"facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/config.json",
# See all XGLM models at https://huggingface.co/models?filter=xglm
}
class _UpperCamelCase ( A__ ):
UpperCAmelCase_ = """xglm"""
UpperCAmelCase_ = ["""past_key_values"""]
UpperCAmelCase_ = {
"""num_attention_heads""": """attention_heads""",
"""hidden_size""": """d_model""",
"""num_hidden_layers""": """num_layers""",
}
def __init__( self :Union[str, Any] , lowerCamelCase :Optional[Any]=25_6008 , lowerCamelCase :Union[str, Any]=2048 , lowerCamelCase :int=1024 , lowerCamelCase :int=4096 , lowerCamelCase :Dict=24 , lowerCamelCase :Tuple=16 , lowerCamelCase :List[Any]="gelu" , lowerCamelCase :Optional[Any]=0.1 , lowerCamelCase :List[Any]=0.1 , lowerCamelCase :int=0.0 , lowerCamelCase :Union[str, Any]=0.0 , lowerCamelCase :Tuple=0.02 , lowerCamelCase :Dict=True , lowerCamelCase :List[str]=True , lowerCamelCase :str=2 , lowerCamelCase :Tuple=1 , lowerCamelCase :Optional[Any]=0 , lowerCamelCase :Dict=2 , **lowerCamelCase :Union[str, Any] , ) -> Optional[int]:
UpperCAmelCase__ = vocab_size
UpperCAmelCase__ = max_position_embeddings
UpperCAmelCase__ = d_model
UpperCAmelCase__ = ffn_dim
UpperCAmelCase__ = num_layers
UpperCAmelCase__ = attention_heads
UpperCAmelCase__ = activation_function
UpperCAmelCase__ = dropout
UpperCAmelCase__ = attention_dropout
UpperCAmelCase__ = activation_dropout
UpperCAmelCase__ = layerdrop
UpperCAmelCase__ = init_std
UpperCAmelCase__ = scale_embedding # scale factor will be sqrt(d_model) if True
UpperCAmelCase__ = use_cache
super().__init__(
pad_token_id=_a , bos_token_id=_a , eos_token_id=_a , decoder_start_token_id=_a , **_a , )
| 169 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCamelCase : int = {"configuration_glpn": ["GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP", "GLPNConfig"]}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase : int = ["GLPNFeatureExtractor"]
lowerCamelCase : Optional[int] = ["GLPNImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase : Union[str, Any] = [
"GLPN_PRETRAINED_MODEL_ARCHIVE_LIST",
"GLPNForDepthEstimation",
"GLPNLayer",
"GLPNModel",
"GLPNPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_glpn import GLPNFeatureExtractor
from .image_processing_glpn import GLPNImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_glpn import (
GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
GLPNForDepthEstimation,
GLPNLayer,
GLPNModel,
GLPNPreTrainedModel,
)
else:
import sys
lowerCamelCase : List[Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 47 | 0 |
'''simple docstring'''
import inspect
import unittest
from math import floor
from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import CvtForImageClassification, CvtModel
from transformers.models.cvt.modeling_cvt import CVT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class A__ ( A__ ):
def A ( self : Dict ) -> List[str]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(_a , 'embed_dim' ) )
self.parent.assertTrue(hasattr(_a , 'num_heads' ) )
class A__ :
def __init__( self : Union[str, Any] , _a : Tuple , _a : int=13 , _a : Any=64 , _a : Optional[Any]=3 , _a : Union[str, Any]=[16, 48, 96] , _a : Optional[Any]=[1, 3, 6] , _a : int=[1, 2, 10] , _a : List[str]=[7, 3, 3] , _a : Any=[4, 2, 2] , _a : Any=[2, 1, 1] , _a : Optional[Any]=[2, 2, 2] , _a : str=[False, False, True] , _a : str=[0.0, 0.0, 0.0] , _a : Union[str, Any]=0.02 , _a : int=1e-12 , _a : Optional[Any]=True , _a : Optional[int]=True , _a : List[Any]=2 , ) -> int:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =parent
_SCREAMING_SNAKE_CASE =batch_size
_SCREAMING_SNAKE_CASE =image_size
_SCREAMING_SNAKE_CASE =patch_sizes
_SCREAMING_SNAKE_CASE =patch_stride
_SCREAMING_SNAKE_CASE =patch_padding
_SCREAMING_SNAKE_CASE =is_training
_SCREAMING_SNAKE_CASE =use_labels
_SCREAMING_SNAKE_CASE =num_labels
_SCREAMING_SNAKE_CASE =num_channels
_SCREAMING_SNAKE_CASE =embed_dim
_SCREAMING_SNAKE_CASE =num_heads
_SCREAMING_SNAKE_CASE =stride_kv
_SCREAMING_SNAKE_CASE =depth
_SCREAMING_SNAKE_CASE =cls_token
_SCREAMING_SNAKE_CASE =attention_drop_rate
_SCREAMING_SNAKE_CASE =initializer_range
_SCREAMING_SNAKE_CASE =layer_norm_eps
def A ( self : List[Any] ) -> List[Any]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_SCREAMING_SNAKE_CASE =None
if self.use_labels:
_SCREAMING_SNAKE_CASE =ids_tensor([self.batch_size] , self.num_labels )
_SCREAMING_SNAKE_CASE =self.get_config()
return config, pixel_values, labels
def A ( self : Dict ) -> Optional[Any]:
'''simple docstring'''
return CvtConfig(
image_size=self.image_size , num_labels=self.num_labels , num_channels=self.num_channels , embed_dim=self.embed_dim , num_heads=self.num_heads , patch_sizes=self.patch_sizes , patch_padding=self.patch_padding , patch_stride=self.patch_stride , stride_kv=self.stride_kv , depth=self.depth , cls_token=self.cls_token , attention_drop_rate=self.attention_drop_rate , initializer_range=self.initializer_range , )
def A ( self : Tuple , _a : List[Any] , _a : Tuple , _a : Any ) -> Any:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =CvtModel(config=_a )
model.to(_a )
model.eval()
_SCREAMING_SNAKE_CASE =model(_a )
_SCREAMING_SNAKE_CASE =(self.image_size, self.image_size)
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE =image_size[0], image_size[1]
for i in range(len(self.depth ) ):
_SCREAMING_SNAKE_CASE =floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
_SCREAMING_SNAKE_CASE =floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.embed_dim[-1], height, width) )
def A ( self : str , _a : List[str] , _a : Union[str, Any] , _a : List[Any] ) -> Union[str, Any]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =self.num_labels
_SCREAMING_SNAKE_CASE =CvtForImageClassification(_a )
model.to(_a )
model.eval()
_SCREAMING_SNAKE_CASE =model(_a , labels=_a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def A ( self : List[str] ) -> Tuple:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =self.prepare_config_and_inputs()
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE =config_and_inputs
_SCREAMING_SNAKE_CASE ={'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class A__ ( A__ , A__ , unittest.TestCase ):
A__ = (CvtModel, CvtForImageClassification) if is_torch_available() else ()
A__ = (
{'feature-extraction': CvtModel, 'image-classification': CvtForImageClassification}
if is_torch_available()
else {}
)
A__ = False
A__ = False
A__ = False
A__ = False
A__ = False
def A ( self : Union[str, Any] ) -> List[str]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =CvtModelTester(self )
_SCREAMING_SNAKE_CASE =ConfigTester(self , config_class=_a , has_text_modality=_a , hidden_size=37 )
def A ( self : Any ) -> Any:
'''simple docstring'''
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def A ( self : Any ) -> Optional[Any]:
'''simple docstring'''
return
@unittest.skip(reason='Cvt does not output attentions' )
def A ( self : List[str] ) -> Optional[int]:
'''simple docstring'''
pass
@unittest.skip(reason='Cvt does not use inputs_embeds' )
def A ( self : str ) -> Optional[Any]:
'''simple docstring'''
pass
@unittest.skip(reason='Cvt does not support input and output embeddings' )
def A ( self : Tuple ) -> Any:
'''simple docstring'''
pass
def A ( self : str ) -> Union[str, Any]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_SCREAMING_SNAKE_CASE =model_class(_a )
_SCREAMING_SNAKE_CASE =inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_SCREAMING_SNAKE_CASE =[*signature.parameters.keys()]
_SCREAMING_SNAKE_CASE =['pixel_values']
self.assertListEqual(arg_names[:1] , _a )
def A ( self : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_a )
def A ( self : Union[str, Any] ) -> Dict:
'''simple docstring'''
def check_hidden_states_output(_a : Optional[int] , _a : Optional[Any] , _a : str ):
_SCREAMING_SNAKE_CASE =model_class(_a )
model.to(_a )
model.eval()
with torch.no_grad():
_SCREAMING_SNAKE_CASE =model(**self._prepare_for_class(_a , _a ) )
_SCREAMING_SNAKE_CASE =outputs.hidden_states
_SCREAMING_SNAKE_CASE =len(self.model_tester.depth )
self.assertEqual(len(_a ) , _a )
# verify the first hidden states (first block)
self.assertListEqual(
list(hidden_states[0].shape[-3:] ) , [
self.model_tester.embed_dim[0],
self.model_tester.image_size // 4,
self.model_tester.image_size // 4,
] , )
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_SCREAMING_SNAKE_CASE =True
check_hidden_states_output(_a , _a , _a )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_SCREAMING_SNAKE_CASE =True
check_hidden_states_output(_a , _a , _a )
def A ( self : Tuple ) -> List[str]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_a )
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def A ( self : str ) -> List[Any]:
'''simple docstring'''
pass
@slow
def A ( self : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
for model_name in CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_SCREAMING_SNAKE_CASE =CvtModel.from_pretrained(_a )
self.assertIsNotNone(_a )
def _lowerCAmelCase ( ) -> Dict:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class A__ ( unittest.TestCase ):
@cached_property
def A ( self : Tuple ) -> List[str]:
'''simple docstring'''
return AutoImageProcessor.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
@slow
def A ( self : List[str] ) -> Optional[int]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =CvtForImageClassification.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(_a )
_SCREAMING_SNAKE_CASE =self.default_image_processor
_SCREAMING_SNAKE_CASE =prepare_img()
_SCREAMING_SNAKE_CASE =image_processor(images=_a , return_tensors='pt' ).to(_a )
# forward pass
with torch.no_grad():
_SCREAMING_SNAKE_CASE =model(**_a )
# verify the logits
_SCREAMING_SNAKE_CASE =torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , _a )
_SCREAMING_SNAKE_CASE =torch.tensor([0.92_85, 0.90_15, -0.31_50] ).to(_a )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _a , atol=1e-4 ) )
| 358 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCamelCase : Optional[int] = {
"configuration_efficientnet": [
"EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP",
"EfficientNetConfig",
"EfficientNetOnnxConfig",
]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase : str = ["EfficientNetImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase : Tuple = [
"EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST",
"EfficientNetForImageClassification",
"EfficientNetModel",
"EfficientNetPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_efficientnet import (
EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
EfficientNetConfig,
EfficientNetOnnxConfig,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_efficientnet import EfficientNetImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_efficientnet import (
EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST,
EfficientNetForImageClassification,
EfficientNetModel,
EfficientNetPreTrainedModel,
)
else:
import sys
lowerCamelCase : int = _LazyModule(__name__, globals()["__file__"], _import_structure)
| 114 | 0 |
'''simple docstring'''
from __future__ import annotations
def __lowerCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ ) -> list[str]:
if nth_term == "":
return [""]
_a : Optional[int] = int(lowerCAmelCase_ )
_a : int = int(lowerCAmelCase_ )
_a : list[str] = []
for temp in range(int(lowerCAmelCase_ ) ):
series.append(f"""1 / {pow(temp + 1 , int(lowerCAmelCase_ ) )}""" if series else '1' )
return series
if __name__ == "__main__":
import doctest
doctest.testmod()
__lowerCAmelCase = int(input('''Enter the last number (nth term) of the P-Series'''))
__lowerCAmelCase = int(input('''Enter the power for P-Series'''))
print('''Formula of P-Series => 1+1/2^p+1/3^p ..... 1/n^p''')
print(p_series(nth_term, power))
| 89 |
'''simple docstring'''
import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict
if TYPE_CHECKING:
import PIL.Image
from .features import FeatureType
__lowerCAmelCase = None
__lowerCAmelCase = '''<''' if sys.byteorder == '''little''' else '''>'''
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
__lowerCAmelCase = [
np.dtype('''|b1'''),
np.dtype('''|u1'''),
np.dtype('''<u2'''),
np.dtype('''>u2'''),
np.dtype('''<i2'''),
np.dtype('''>i2'''),
np.dtype('''<u4'''),
np.dtype('''>u4'''),
np.dtype('''<i4'''),
np.dtype('''>i4'''),
np.dtype('''<f4'''),
np.dtype('''>f4'''),
np.dtype('''<f8'''),
np.dtype('''>f8'''),
]
@dataclass
class __magic_name__ :
lowerCAmelCase : bool = True
lowerCAmelCase : Optional[str] = None
# Automatically constructed
lowerCAmelCase : ClassVar[str] = "PIL.Image.Image"
lowerCAmelCase : ClassVar[Any] = pa.struct({'bytes': pa.binary(), 'path': pa.string()} )
lowerCAmelCase : str = field(default='Image' , init=_UpperCamelCase , repr=_UpperCamelCase )
def __call__( self : Union[str, Any] ):
return self.pa_type
def __lowercase ( self : Any ,_UpperCAmelCase : Union[str, bytes, dict, np.ndarray, "PIL.Image.Image"] ):
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError('To support encoding images, please install \'Pillow\'.' )
if isinstance(_UpperCAmelCase ,_UpperCAmelCase ):
_a : Optional[Any] = np.array(_UpperCAmelCase )
if isinstance(_UpperCAmelCase ,_UpperCAmelCase ):
return {"path": value, "bytes": None}
elif isinstance(_UpperCAmelCase ,_UpperCAmelCase ):
return {"path": None, "bytes": value}
elif isinstance(_UpperCAmelCase ,np.ndarray ):
# convert the image array to PNG/TIFF bytes
return encode_np_array(_UpperCAmelCase )
elif isinstance(_UpperCAmelCase ,PIL.Image.Image ):
# convert the PIL image to bytes (default format is PNG/TIFF)
return encode_pil_image(_UpperCAmelCase )
elif value.get('path' ) is not None and os.path.isfile(value['path'] ):
# we set "bytes": None to not duplicate the data if they're already available locally
return {"bytes": None, "path": value.get('path' )}
elif value.get('bytes' ) is not None or value.get('path' ) is not None:
# store the image bytes, and path is used to infer the image format using the file extension
return {"bytes": value.get('bytes' ), "path": value.get('path' )}
else:
raise ValueError(
F"""An image sample should have one of 'path' or 'bytes' but they are missing or None in {value}.""" )
def __lowercase ( self : Optional[Any] ,_UpperCAmelCase : dict ,_UpperCAmelCase : Optional[int]=None ):
if not self.decode:
raise RuntimeError('Decoding is disabled for this feature. Please use Image(decode=True) instead.' )
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError('To support decoding images, please install \'Pillow\'.' )
if token_per_repo_id is None:
_a : Dict = {}
_a , _a : str = value['path'], value['bytes']
if bytes_ is None:
if path is None:
raise ValueError(F"""An image should have one of 'path' or 'bytes' but both are None in {value}.""" )
else:
if is_local_path(_UpperCAmelCase ):
_a : Any = PIL.Image.open(_UpperCAmelCase )
else:
_a : List[Any] = path.split('::' )[-1]
try:
_a : str = string_to_dict(_UpperCAmelCase ,config.HUB_DATASETS_URL )['repo_id']
_a : Optional[Any] = token_per_repo_id.get(_UpperCAmelCase )
except ValueError:
_a : int = None
with xopen(_UpperCAmelCase ,'rb' ,use_auth_token=_UpperCAmelCase ) as f:
_a : Tuple = BytesIO(f.read() )
_a : Union[str, Any] = PIL.Image.open(bytes_ )
else:
_a : Optional[int] = PIL.Image.open(BytesIO(bytes_ ) )
image.load() # to avoid "Too many open files" errors
return image
def __lowercase ( self : int ):
from .features import Value
return (
self
if self.decode
else {
"bytes": Value('binary' ),
"path": Value('string' ),
}
)
def __lowercase ( self : str ,_UpperCAmelCase : Union[pa.StringArray, pa.StructArray, pa.ListArray] ):
if pa.types.is_string(storage.type ):
_a : Union[str, Any] = pa.array([None] * len(_UpperCAmelCase ) ,type=pa.binary() )
_a : Union[str, Any] = pa.StructArray.from_arrays([bytes_array, storage] ,['bytes', 'path'] ,mask=storage.is_null() )
elif pa.types.is_binary(storage.type ):
_a : List[str] = pa.array([None] * len(_UpperCAmelCase ) ,type=pa.string() )
_a : Any = pa.StructArray.from_arrays([storage, path_array] ,['bytes', 'path'] ,mask=storage.is_null() )
elif pa.types.is_struct(storage.type ):
if storage.type.get_field_index('bytes' ) >= 0:
_a : Union[str, Any] = storage.field('bytes' )
else:
_a : Tuple = pa.array([None] * len(_UpperCAmelCase ) ,type=pa.binary() )
if storage.type.get_field_index('path' ) >= 0:
_a : Union[str, Any] = storage.field('path' )
else:
_a : Dict = pa.array([None] * len(_UpperCAmelCase ) ,type=pa.string() )
_a : Optional[Any] = pa.StructArray.from_arrays([bytes_array, path_array] ,['bytes', 'path'] ,mask=storage.is_null() )
elif pa.types.is_list(storage.type ):
_a : List[str] = pa.array(
[encode_np_array(np.array(_UpperCAmelCase ) )['bytes'] if arr is not None else None for arr in storage.to_pylist()] ,type=pa.binary() ,)
_a : int = pa.array([None] * len(_UpperCAmelCase ) ,type=pa.string() )
_a : Optional[Any] = pa.StructArray.from_arrays(
[bytes_array, path_array] ,['bytes', 'path'] ,mask=bytes_array.is_null() )
return array_cast(_UpperCAmelCase ,self.pa_type )
def __lowercase ( self : Dict ,_UpperCAmelCase : pa.StructArray ):
@no_op_if_value_is_null
def path_to_bytes(_UpperCAmelCase : Tuple ):
with xopen(_UpperCAmelCase ,'rb' ) as f:
_a : int = f.read()
return bytes_
_a : Any = pa.array(
[
(path_to_bytes(x['path'] ) if x['bytes'] is None else x['bytes']) if x is not None else None
for x in storage.to_pylist()
] ,type=pa.binary() ,)
_a : Optional[Any] = pa.array(
[os.path.basename(_UpperCAmelCase ) if path is not None else None for path in storage.field('path' ).to_pylist()] ,type=pa.string() ,)
_a : Dict = pa.StructArray.from_arrays([bytes_array, path_array] ,['bytes', 'path'] ,mask=bytes_array.is_null() )
return array_cast(_UpperCAmelCase ,self.pa_type )
def __lowerCamelCase ( ) -> List[str]:
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError('To support encoding images, please install \'Pillow\'.' )
global _IMAGE_COMPRESSION_FORMATS
if _IMAGE_COMPRESSION_FORMATS is None:
PIL.Image.init()
_a : Dict = list(set(PIL.Image.OPEN.keys() ) & set(PIL.Image.SAVE.keys() ) )
return _IMAGE_COMPRESSION_FORMATS
def __lowerCamelCase ( lowerCAmelCase_ ) -> bytes:
_a : Optional[int] = BytesIO()
if image.format in list_image_compression_formats():
_a : Optional[Any] = image.format
else:
_a : str = 'PNG' if image.mode in ['1', 'L', 'LA', 'RGB', 'RGBA'] else 'TIFF'
image.save(lowerCAmelCase_ , format=lowerCAmelCase_ )
return buffer.getvalue()
def __lowerCamelCase ( lowerCAmelCase_ ) -> dict:
if hasattr(lowerCAmelCase_ , 'filename' ) and image.filename != "":
return {"path": image.filename, "bytes": None}
else:
return {"path": None, "bytes": image_to_bytes(lowerCAmelCase_ )}
def __lowerCamelCase ( lowerCAmelCase_ ) -> dict:
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError('To support encoding images, please install \'Pillow\'.' )
_a : List[Any] = array.dtype
_a : Optional[int] = dtype.byteorder if dtype.byteorder != '=' else _NATIVE_BYTEORDER
_a : Union[str, Any] = dtype.kind
_a : Union[str, Any] = dtype.itemsize
_a : List[Any] = None
# Multi-channel array case (only np.dtype("|u1") is allowed)
if array.shape[2:]:
_a : Optional[int] = np.dtype('|u1' )
if dtype_kind not in ["u", "i"]:
raise TypeError(
f"""Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays.""" )
if dtype is not dest_dtype:
warnings.warn(f"""Downcasting array dtype {dtype} to {dest_dtype} to be compatible with 'Pillow'""" )
# Exact match
elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
_a : Union[str, Any] = dtype
else: # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
while dtype_itemsize >= 1:
_a : str = dtype_byteorder + dtype_kind + str(lowerCAmelCase_ )
_a : List[Any] = np.dtype(lowerCAmelCase_ )
if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
warnings.warn(f"""Downcasting array dtype {dtype} to {dest_dtype} to be compatible with 'Pillow'""" )
break
else:
dtype_itemsize //= 2
if dest_dtype is None:
raise TypeError(
f"""Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}""" )
_a : Union[str, Any] = PIL.Image.fromarray(array.astype(lowerCAmelCase_ ) )
return {"path": None, "bytes": image_to_bytes(lowerCAmelCase_ )}
def __lowerCamelCase ( lowerCAmelCase_ ) -> List[dict]:
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError('To support encoding images, please install \'Pillow\'.' )
if objs:
_a , _a : Optional[Any] = first_non_null_value(lowerCAmelCase_ )
if isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
if isinstance(lowerCAmelCase_ , np.ndarray ):
_a : List[str] = no_op_if_value_is_null(lowerCAmelCase_ )
return [obj_to_image_dict_func(lowerCAmelCase_ ) for obj in objs]
elif isinstance(lowerCAmelCase_ , PIL.Image.Image ):
_a : List[str] = no_op_if_value_is_null(lowerCAmelCase_ )
return [obj_to_image_dict_func(lowerCAmelCase_ ) for obj in objs]
else:
return objs
else:
return objs
| 89 | 1 |
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / '''utils'''))
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
_A = get_tests_dir('''fixtures''')
class lowercase_ ( unittest.TestCase ):
def lowerCamelCase_ ( self ):
"""simple docstring"""
UpperCamelCase_ = mock.Mock()
UpperCamelCase_ = 5_0_0
UpperCamelCase_ = {}
UpperCamelCase_ = HTTPError
UpperCamelCase_ = {}
# Download this model to make sure it's in the cache.
UpperCamelCase_ = ViTImageProcessor.from_pretrained("""hf-internal-testing/tiny-random-vit""" )
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch("""requests.Session.request""" , return_value=__snake_case ) as mock_head:
UpperCamelCase_ = ViTImageProcessor.from_pretrained("""hf-internal-testing/tiny-random-vit""" )
# This check we did call the fake head request
mock_head.assert_called()
def lowerCamelCase_ ( self ):
"""simple docstring"""
UpperCamelCase_ = ViTImageProcessor.from_pretrained(
"""https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json""" )
def lowerCamelCase_ ( self ):
"""simple docstring"""
with self.assertRaises(__snake_case ):
# config is in subfolder, the following should not work without specifying the subfolder
UpperCamelCase_ = AutoImageProcessor.from_pretrained("""hf-internal-testing/stable-diffusion-all-variants""" )
UpperCamelCase_ = AutoImageProcessor.from_pretrained(
"""hf-internal-testing/stable-diffusion-all-variants""" , subfolder="""feature_extractor""" )
self.assertIsNotNone(__snake_case )
@is_staging_test
class lowercase_ ( unittest.TestCase ):
@classmethod
def lowerCamelCase_ ( cls ):
"""simple docstring"""
UpperCamelCase_ = TOKEN
HfFolder.save_token(__snake_case )
@classmethod
def lowerCamelCase_ ( cls ):
"""simple docstring"""
try:
delete_repo(token=cls._token , repo_id="""test-image-processor""" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="""valid_org/test-image-processor-org""" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="""test-dynamic-image-processor""" )
except HTTPError:
pass
def lowerCamelCase_ ( self ):
"""simple docstring"""
UpperCamelCase_ = ViTImageProcessor.from_pretrained(__snake_case )
image_processor.push_to_hub("""test-image-processor""" , use_auth_token=self._token )
UpperCamelCase_ = ViTImageProcessor.from_pretrained(f'''{USER}/test-image-processor''' )
for k, v in image_processor.__dict__.items():
self.assertEqual(__snake_case , getattr(__snake_case , __snake_case ) )
# Reset repo
delete_repo(token=self._token , repo_id="""test-image-processor""" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(
__snake_case , repo_id="""test-image-processor""" , push_to_hub=__snake_case , use_auth_token=self._token )
UpperCamelCase_ = ViTImageProcessor.from_pretrained(f'''{USER}/test-image-processor''' )
for k, v in image_processor.__dict__.items():
self.assertEqual(__snake_case , getattr(__snake_case , __snake_case ) )
def lowerCamelCase_ ( self ):
"""simple docstring"""
UpperCamelCase_ = ViTImageProcessor.from_pretrained(__snake_case )
image_processor.push_to_hub("""valid_org/test-image-processor""" , use_auth_token=self._token )
UpperCamelCase_ = ViTImageProcessor.from_pretrained("""valid_org/test-image-processor""" )
for k, v in image_processor.__dict__.items():
self.assertEqual(__snake_case , getattr(__snake_case , __snake_case ) )
# Reset repo
delete_repo(token=self._token , repo_id="""valid_org/test-image-processor""" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(
__snake_case , repo_id="""valid_org/test-image-processor-org""" , push_to_hub=__snake_case , use_auth_token=self._token )
UpperCamelCase_ = ViTImageProcessor.from_pretrained("""valid_org/test-image-processor-org""" )
for k, v in image_processor.__dict__.items():
self.assertEqual(__snake_case , getattr(__snake_case , __snake_case ) )
def lowerCamelCase_ ( self ):
"""simple docstring"""
CustomImageProcessor.register_for_auto_class()
UpperCamelCase_ = CustomImageProcessor.from_pretrained(__snake_case )
image_processor.push_to_hub("""test-dynamic-image-processor""" , use_auth_token=self._token )
# This has added the proper auto_map field to the config
self.assertDictEqual(
image_processor.auto_map , {"""AutoImageProcessor""": """custom_image_processing.CustomImageProcessor"""} , )
UpperCamelCase_ = AutoImageProcessor.from_pretrained(
f'''{USER}/test-dynamic-image-processor''' , trust_remote_code=__snake_case )
# Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
self.assertEqual(new_image_processor.__class__.__name__ , """CustomImageProcessor""" )
| 353 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_DEFAULT_MEAN,
IMAGENET_DEFAULT_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
is_batched,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
_A = logging.get_logger(__name__)
class lowercase_ ( __SCREAMING_SNAKE_CASE ):
A__ : Union[str, Any] = ["""pixel_values"""]
def __init__( self , __UpperCamelCase = True , __UpperCamelCase = None , __UpperCamelCase = PILImageResampling.BICUBIC , __UpperCamelCase = True , __UpperCamelCase = True , __UpperCamelCase = 1 / 2_5_5 , __UpperCamelCase = None , __UpperCamelCase = True , __UpperCamelCase = None , __UpperCamelCase = None , **__UpperCamelCase , ):
"""simple docstring"""
super().__init__(**__UpperCamelCase )
UpperCamelCase_ = size if size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
UpperCamelCase_ = get_size_dict(__UpperCamelCase )
UpperCamelCase_ = crop_size if crop_size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
UpperCamelCase_ = get_size_dict(__UpperCamelCase , default_to_square=__UpperCamelCase , param_name="""crop_size""" )
UpperCamelCase_ = do_resize
UpperCamelCase_ = do_rescale
UpperCamelCase_ = do_normalize
UpperCamelCase_ = do_center_crop
UpperCamelCase_ = crop_size
UpperCamelCase_ = size
UpperCamelCase_ = resample
UpperCamelCase_ = rescale_factor
UpperCamelCase_ = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
UpperCamelCase_ = image_std if image_std is not None else IMAGENET_DEFAULT_STD
def lowerCamelCase_ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = PILImageResampling.BILINEAR , __UpperCamelCase = None , **__UpperCamelCase , ):
"""simple docstring"""
UpperCamelCase_ = get_size_dict(__UpperCamelCase )
if "shortest_edge" in size:
UpperCamelCase_ = get_resize_output_image_size(__UpperCamelCase , size=size["""shortest_edge"""] , default_to_square=__UpperCamelCase )
# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
elif "height" in size and "width" in size:
UpperCamelCase_ = (size["""height"""], size["""width"""])
else:
raise ValueError(f'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}''' )
return resize(__UpperCamelCase , size=__UpperCamelCase , resample=__UpperCamelCase , data_format=__UpperCamelCase , **__UpperCamelCase )
def lowerCamelCase_ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = None , **__UpperCamelCase , ):
"""simple docstring"""
UpperCamelCase_ = get_size_dict(__UpperCamelCase )
if "height" not in size or "width" not in size:
raise ValueError(f'''The `size` parameter must contain the keys (height, width). Got {size.keys()}''' )
return center_crop(__UpperCamelCase , size=(size["""height"""], size["""width"""]) , data_format=__UpperCamelCase , **__UpperCamelCase )
def lowerCamelCase_ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = None , **__UpperCamelCase ):
"""simple docstring"""
return rescale(__UpperCamelCase , scale=__UpperCamelCase , data_format=__UpperCamelCase , **__UpperCamelCase )
def lowerCamelCase_ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = None , **__UpperCamelCase , ):
"""simple docstring"""
return normalize(__UpperCamelCase , mean=__UpperCamelCase , std=__UpperCamelCase , data_format=__UpperCamelCase , **__UpperCamelCase )
def lowerCamelCase_ ( self , __UpperCamelCase , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = ChannelDimension.FIRST , **__UpperCamelCase , ):
"""simple docstring"""
UpperCamelCase_ = do_resize if do_resize is not None else self.do_resize
UpperCamelCase_ = do_rescale if do_rescale is not None else self.do_rescale
UpperCamelCase_ = do_normalize if do_normalize is not None else self.do_normalize
UpperCamelCase_ = do_center_crop if do_center_crop is not None else self.do_center_crop
UpperCamelCase_ = crop_size if crop_size is not None else self.crop_size
UpperCamelCase_ = get_size_dict(__UpperCamelCase , param_name="""crop_size""" , default_to_square=__UpperCamelCase )
UpperCamelCase_ = resample if resample is not None else self.resample
UpperCamelCase_ = rescale_factor if rescale_factor is not None else self.rescale_factor
UpperCamelCase_ = image_mean if image_mean is not None else self.image_mean
UpperCamelCase_ = image_std if image_std is not None else self.image_std
UpperCamelCase_ = size if size is not None else self.size
UpperCamelCase_ = get_size_dict(__UpperCamelCase )
if not is_batched(__UpperCamelCase ):
UpperCamelCase_ = [images]
if not valid_images(__UpperCamelCase ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
if do_resize and size is None:
raise ValueError("""Size must be specified if do_resize is True.""" )
if do_center_crop and crop_size is None:
raise ValueError("""Crop size must be specified if do_center_crop is True.""" )
if do_rescale and rescale_factor is None:
raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )
# All transformations expect numpy arrays.
UpperCamelCase_ = [to_numpy_array(__UpperCamelCase ) for image in images]
if do_resize:
UpperCamelCase_ = [self.resize(image=__UpperCamelCase , size=__UpperCamelCase , resample=__UpperCamelCase ) for image in images]
if do_center_crop:
UpperCamelCase_ = [self.center_crop(image=__UpperCamelCase , size=__UpperCamelCase ) for image in images]
if do_rescale:
UpperCamelCase_ = [self.rescale(image=__UpperCamelCase , scale=__UpperCamelCase ) for image in images]
if do_normalize:
UpperCamelCase_ = [self.normalize(image=__UpperCamelCase , mean=__UpperCamelCase , std=__UpperCamelCase ) for image in images]
UpperCamelCase_ = [to_channel_dimension_format(__UpperCamelCase , __UpperCamelCase ) for image in images]
UpperCamelCase_ = {"""pixel_values""": images}
return BatchFeature(data=__UpperCamelCase , tensor_type=__UpperCamelCase )
| 261 | 0 |
import argparse
import re
import torch
from CLAP import create_model
from transformers import AutoFeatureExtractor, ClapConfig, ClapModel
UpperCamelCase__ = {
"""text_branch""": """text_model""",
"""audio_branch""": """audio_model.audio_encoder""",
"""attn""": """attention.self""",
"""self.proj""": """output.dense""",
"""attention.self_mask""": """attn_mask""",
"""mlp.fc1""": """intermediate.dense""",
"""mlp.fc2""": """output.dense""",
"""norm1""": """layernorm_before""",
"""norm2""": """layernorm_after""",
"""bn0""": """batch_norm""",
}
UpperCamelCase__ = AutoFeatureExtractor.from_pretrained("""laion/clap-htsat-unfused""", truncation="""rand_trunc""")
def _a ( SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : str=False ):
__lowerCAmelCase , __lowerCAmelCase = create_model(
"HTSAT-tiny" , "roberta" , SCREAMING_SNAKE_CASE_ , precision="fp32" , device="cuda:0" if torch.cuda.is_available() else "cpu" , enable_fusion=SCREAMING_SNAKE_CASE_ , fusion_type="aff_2d" if enable_fusion else None , )
return model, model_cfg
def _a ( SCREAMING_SNAKE_CASE_ : Tuple ):
__lowerCAmelCase = {}
__lowerCAmelCase = R".*sequential.(\d+).*"
__lowerCAmelCase = R".*_projection.(\d+).*"
for key, value in state_dict.items():
# check if any key needs to be modified
for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
if key_to_modify in key:
__lowerCAmelCase = key.replace(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if re.match(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
# replace sequential layers with list
__lowerCAmelCase = re.match(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ).group(1 )
__lowerCAmelCase = key.replace(F"""sequential.{sequential_layer}.""" , F"""layers.{int(SCREAMING_SNAKE_CASE_ )//3}.linear.""" )
elif re.match(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
__lowerCAmelCase = int(re.match(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ).group(1 ) )
# Because in CLAP they use `nn.Sequential`...
__lowerCAmelCase = 1 if projecton_layer == 0 else 2
__lowerCAmelCase = key.replace(F"""_projection.{projecton_layer}.""" , F"""_projection.linear{transformers_projection_layer}.""" )
if "audio" and "qkv" in key:
# split qkv into query key and value
__lowerCAmelCase = value
__lowerCAmelCase = mixed_qkv.size(0 ) // 3
__lowerCAmelCase = mixed_qkv[:qkv_dim]
__lowerCAmelCase = mixed_qkv[qkv_dim : qkv_dim * 2]
__lowerCAmelCase = mixed_qkv[qkv_dim * 2 :]
__lowerCAmelCase = query_layer
__lowerCAmelCase = key_layer
__lowerCAmelCase = value_layer
else:
__lowerCAmelCase = value
return model_state_dict
def _a ( SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : Any=False ):
__lowerCAmelCase , __lowerCAmelCase = init_clap(SCREAMING_SNAKE_CASE_ , enable_fusion=SCREAMING_SNAKE_CASE_ )
clap_model.eval()
__lowerCAmelCase = clap_model.state_dict()
__lowerCAmelCase = rename_state_dict(SCREAMING_SNAKE_CASE_ )
__lowerCAmelCase = ClapConfig()
__lowerCAmelCase = enable_fusion
__lowerCAmelCase = ClapModel(SCREAMING_SNAKE_CASE_ )
# ignore the spectrogram embedding layer
model.load_state_dict(SCREAMING_SNAKE_CASE_ , strict=SCREAMING_SNAKE_CASE_ )
model.save_pretrained(SCREAMING_SNAKE_CASE_ )
transformers_config.save_pretrained(SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
UpperCamelCase__ = argparse.ArgumentParser()
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
parser.add_argument("""--enable_fusion""", action="""store_true""", help="""Whether to enable fusion or not""")
UpperCamelCase__ = parser.parse_args()
convert_clap_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.enable_fusion)
| 92 |
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def _a ( SCREAMING_SNAKE_CASE_ : Optional[int] ):
__lowerCAmelCase = filter(lambda SCREAMING_SNAKE_CASE_ : p.requires_grad , model.parameters() )
__lowerCAmelCase = sum([np.prod(p.size() ) for p in model_parameters] )
return params
UpperCamelCase__ = logging.getLogger(__name__)
def _a ( SCREAMING_SNAKE_CASE_ : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Any ):
if metric == "rouge2":
__lowerCAmelCase = "{val_avg_rouge2:.4f}-{step_count}"
elif metric == "bleu":
__lowerCAmelCase = "{val_avg_bleu:.4f}-{step_count}"
elif metric == "em":
__lowerCAmelCase = "{val_avg_em:.4f}-{step_count}"
else:
raise NotImplementedError(
F"""seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this"""
" function." )
__lowerCAmelCase = ModelCheckpoint(
dirpath=SCREAMING_SNAKE_CASE_ , filename=SCREAMING_SNAKE_CASE_ , monitor=F"""val_{metric}""" , mode="max" , save_top_k=3 , every_n_epochs=1 , )
return checkpoint_callback
def _a ( SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : Union[str, Any] ):
return EarlyStopping(
monitor=F"""val_{metric}""" , mode="min" if "loss" in metric else "max" , patience=SCREAMING_SNAKE_CASE_ , verbose=SCREAMING_SNAKE_CASE_ , )
class a__ ( pl.Callback ):
def __SCREAMING_SNAKE_CASE( self , _A , _A ):
"""simple docstring"""
__lowerCAmelCase = {f"""lr_group_{i}""": param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups )}
pl_module.logger.log_metrics(_A )
@rank_zero_only
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A , _A=True ):
"""simple docstring"""
logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****""" )
__lowerCAmelCase = trainer.callback_metrics
trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["log", "progress_bar", "preds"]} )
# Log results
__lowerCAmelCase = Path(pl_module.hparams.output_dir )
if type_path == "test":
__lowerCAmelCase = od / "test_results.txt"
__lowerCAmelCase = od / "test_generations.txt"
else:
# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
# If people want this it will be easy enough to add back.
__lowerCAmelCase = od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
__lowerCAmelCase = od / f"""{type_path}_generations/{trainer.global_step:05d}.txt"""
results_file.parent.mkdir(exist_ok=_A )
generations_file.parent.mkdir(exist_ok=_A )
with open(_A , "a+" ) as writer:
for key in sorted(_A ):
if key in ["log", "progress_bar", "preds"]:
continue
__lowerCAmelCase = metrics[key]
if isinstance(_A , torch.Tensor ):
__lowerCAmelCase = val.item()
__lowerCAmelCase = f"""{key}: {val:.6f}\n"""
writer.write(_A )
if not save_generations:
return
if "preds" in metrics:
__lowerCAmelCase = "\n".join(metrics["preds"] )
generations_file.open("w+" ).write(_A )
@rank_zero_only
def __SCREAMING_SNAKE_CASE( self , _A , _A ):
"""simple docstring"""
try:
__lowerCAmelCase = pl_module.model.model.num_parameters()
except AttributeError:
__lowerCAmelCase = pl_module.model.num_parameters()
__lowerCAmelCase = count_trainable_parameters(_A )
# mp stands for million parameters
trainer.logger.log_metrics({"n_params": npars, "mp": npars / 1E6, "grad_mp": n_trainable_pars / 1E6} )
@rank_zero_only
def __SCREAMING_SNAKE_CASE( self , _A , _A ):
"""simple docstring"""
save_json(pl_module.metrics , pl_module.metrics_save_path )
return self._write_logs(_A , _A , "test" )
@rank_zero_only
def __SCREAMING_SNAKE_CASE( self , _A , _A ):
"""simple docstring"""
save_json(pl_module.metrics , pl_module.metrics_save_path )
# Uncommenting this will save val generations
# return self._write_logs(trainer, pl_module, "valid")
| 92 | 1 |
'''simple docstring'''
import random
class _UpperCamelCase :
'''simple docstring'''
@staticmethod
def __lowerCamelCase ( _lowerCAmelCase : str):
'''simple docstring'''
__lowercase =[ord(_lowerCAmelCase) for i in text]
__lowercase =[]
__lowercase =[]
for i in plain:
__lowercase =random.randint(1 , 3_0_0)
__lowercase =(i + k) * k
cipher.append(_lowerCAmelCase)
key.append(_lowerCAmelCase)
return cipher, key
@staticmethod
def __lowerCamelCase ( _lowerCAmelCase : list[int] , _lowerCAmelCase : list[int]):
'''simple docstring'''
__lowercase =[]
for i in range(len(_lowerCAmelCase)):
__lowercase =int((cipher[i] - (key[i]) ** 2) / key[i])
plain.append(chr(_lowerCAmelCase))
return "".join(_lowerCAmelCase)
if __name__ == "__main__":
lowerCamelCase , lowerCamelCase = Onepad().encrypt("""Hello""")
print(c, k)
print(Onepad().decrypt(c, k))
| 48 |
'''simple docstring'''
import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
lowerCamelCase = {
"""cola""": 2,
"""mnli""": 3,
"""mrpc""": 2,
"""sst-2""": 2,
"""sts-b""": 1,
"""qqp""": 2,
"""qnli""": 2,
"""rte""": 2,
"""wnli""": 2,
}
logging.set_verbosity_info()
def _A ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase=None ):
"""simple docstring"""
__lowercase =XLNetConfig.from_json_file(_lowerCAmelCase )
__lowercase =finetuning_task.lower() if finetuning_task is not None else ''
if finetuning_task in GLUE_TASKS_NUM_LABELS:
print(f"""Building PyTorch XLNetForSequenceClassification model from configuration: {config}""" )
__lowercase =finetuning_task
__lowercase =GLUE_TASKS_NUM_LABELS[finetuning_task]
__lowercase =XLNetForSequenceClassification(_lowerCAmelCase )
elif "squad" in finetuning_task:
__lowercase =finetuning_task
__lowercase =XLNetForQuestionAnswering(_lowerCAmelCase )
else:
__lowercase =XLNetLMHeadModel(_lowerCAmelCase )
# Load weights from tf checkpoint
load_tf_weights_in_xlnet(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
# Save pytorch-model
__lowercase =os.path.join(_lowerCAmelCase , _lowerCAmelCase )
__lowercase =os.path.join(_lowerCAmelCase , _lowerCAmelCase )
print(f"""Save PyTorch model to {os.path.abspath(_lowerCAmelCase )}""" )
torch.save(model.state_dict() , _lowerCAmelCase )
print(f"""Save configuration file to {os.path.abspath(_lowerCAmelCase )}""" )
with open(_lowerCAmelCase , 'w' , encoding='utf-8' ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
lowerCamelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
)
parser.add_argument(
"""--xlnet_config_file""",
default=None,
type=str,
required=True,
help=(
"""The config json file corresponding to the pre-trained XLNet model. \n"""
"""This specifies the model architecture."""
),
)
parser.add_argument(
"""--pytorch_dump_folder_path""",
default=None,
type=str,
required=True,
help="""Path to the folder to store the PyTorch model or dataset/vocab.""",
)
parser.add_argument(
"""--finetuning_task""",
default=None,
type=str,
help="""Name of a task on which the XLNet TensorFlow model was fine-tuned""",
)
lowerCamelCase = parser.parse_args()
print(args)
convert_xlnet_checkpoint_to_pytorch(
args.tf_checkpoint_path, args.xlnet_config_file, args.pytorch_dump_folder_path, args.finetuning_task
)
| 48 | 1 |
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