code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
"files" , [
["full:README.md", "dataset_infos.json"],
["empty:README.md", "dataset_infos.json"],
["dataset_infos.json"],
["full:README.md"],
] , )
def lowerCAmelCase_ ( __lowerCamelCase , __lowerCamelCase ):
__snake_case : Union[str, Any] = tmp_path_factory.mktemp("dset_infos_dir" )
if "full:README.md" in files:
with open(dataset_infos_dir / "README.md" , "w" ) as f:
f.write("---\ndataset_info:\n dataset_size: 42\n---" )
if "empty:README.md" in files:
with open(dataset_infos_dir / "README.md" , "w" ) as f:
f.write("" )
# we want to support dataset_infos.json for backward compatibility
if "dataset_infos.json" in files:
with open(dataset_infos_dir / "dataset_infos.json" , "w" ) as f:
f.write("{\"default\": {\"dataset_size\": 42}}" )
__snake_case : Union[str, Any] = DatasetInfosDict.from_directory(__lowerCamelCase )
assert dataset_infos
assert dataset_infos["default"].dataset_size == 4_2
@pytest.mark.parametrize(
"dataset_info" , [
DatasetInfo(),
DatasetInfo(
description="foo" , features=Features({"a": Value("int32" )} ) , builder_name="builder" , config_name="config" , version="1.0.0" , splits=[{"name": "train"}] , download_size=4_2 , ),
] , )
def lowerCAmelCase_ ( __lowerCamelCase , __lowerCamelCase ):
__snake_case : int = str(__lowerCamelCase )
dataset_info.write_to_directory(__lowerCamelCase )
__snake_case : Optional[Any] = DatasetInfo.from_directory(__lowerCamelCase )
assert dataset_info == reloaded
assert os.path.exists(os.path.join(__lowerCamelCase , "dataset_info.json" ) )
def lowerCAmelCase_ ( ):
__snake_case : Optional[int] = DatasetInfo(
description="foo" , citation="bar" , homepage="https://foo.bar" , license="CC0" , features=Features({"a": Value("int32" )} ) , post_processed={} , supervised_keys=() , task_templates=[] , builder_name="builder" , config_name="config" , version="1.0.0" , splits=[{"name": "train", "num_examples": 4_2}] , download_checksums={} , download_size=1_3_3_7 , post_processing_size=4_4_2 , dataset_size=1_2_3_4 , size_in_bytes=1_3_3_7 + 4_4_2 + 1_2_3_4 , )
__snake_case : Optional[int] = dataset_info._to_yaml_dict()
assert sorted(__lowerCamelCase ) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML )
for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
assert key in dataset_info_yaml_dict
assert isinstance(dataset_info_yaml_dict[key] , (list, dict, int, str) )
__snake_case : Dict = yaml.safe_dump(__lowerCamelCase )
__snake_case : Optional[Any] = yaml.safe_load(__lowerCamelCase )
assert dataset_info_yaml_dict == reloaded
def lowerCAmelCase_ ( ):
__snake_case : List[Any] = DatasetInfo()
__snake_case : Tuple = dataset_info._to_yaml_dict()
assert dataset_info_yaml_dict == {}
@pytest.mark.parametrize(
"dataset_infos_dict" , [
DatasetInfosDict(),
DatasetInfosDict({"default": DatasetInfo()} ),
DatasetInfosDict({"my_config_name": DatasetInfo()} ),
DatasetInfosDict(
{
"default": DatasetInfo(
description="foo" , features=Features({"a": Value("int32" )} ) , builder_name="builder" , config_name="config" , version="1.0.0" , splits=[{"name": "train"}] , download_size=4_2 , )
} ),
DatasetInfosDict(
{
"v1": DatasetInfo(dataset_size=4_2 ),
"v2": DatasetInfo(dataset_size=1_3_3_7 ),
} ),
] , )
def lowerCAmelCase_ ( __lowerCamelCase , __lowerCamelCase ):
__snake_case : Tuple = str(__lowerCamelCase )
dataset_infos_dict.write_to_directory(__lowerCamelCase )
__snake_case : Any = DatasetInfosDict.from_directory(__lowerCamelCase )
# the config_name of the dataset_infos_dict take over the attribute
for config_name, dataset_info in dataset_infos_dict.items():
__snake_case : Dict = config_name
# the yaml representation doesn't include fields like description or citation
# so we just test that we can recover what we can from the yaml
__snake_case : Union[str, Any] = DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict() )
assert dataset_infos_dict == reloaded
if dataset_infos_dict:
assert os.path.exists(os.path.join(__lowerCamelCase , "README.md" ) )
| 81 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__UpperCAmelCase = {
'configuration_bridgetower': [
'BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'BridgeTowerConfig',
'BridgeTowerTextConfig',
'BridgeTowerVisionConfig',
],
'processing_bridgetower': ['BridgeTowerProcessor'],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = ['BridgeTowerImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
'BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST',
'BridgeTowerForContrastiveLearning',
'BridgeTowerForImageAndTextRetrieval',
'BridgeTowerForMaskedLM',
'BridgeTowerModel',
'BridgeTowerPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_bridgetower import (
BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
BridgeTowerConfig,
BridgeTowerTextConfig,
BridgeTowerVisionConfig,
)
from .processing_bridgetower import BridgeTowerProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_bridgetower import BridgeTowerImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_bridgetower import (
BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
BridgeTowerForContrastiveLearning,
BridgeTowerForImageAndTextRetrieval,
BridgeTowerForMaskedLM,
BridgeTowerModel,
BridgeTowerPreTrainedModel,
)
else:
import sys
__UpperCAmelCase = _LazyModule(__name__, globals()['__file__'], _import_structure)
| 65 | 0 |
"""simple docstring"""
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
UpperCAmelCase_ = 1.5
UpperCAmelCase_ = int(factor * num_class_images )
UpperCAmelCase_ = ClipClient(
url="https://knn.laion.ai/knn-service" , indice_name="laion_400m" , num_images=lowerCAmelCase__ , aesthetic_weight=0.1 )
os.makedirs(f"""{class_data_dir}/images""" , exist_ok=lowerCAmelCase__ )
if len(list(Path(f"""{class_data_dir}/images""" ).iterdir() ) ) >= num_class_images:
return
while True:
UpperCAmelCase_ = client.query(text=lowerCAmelCase__ )
if len(lowerCAmelCase__ ) >= factor * num_class_images or num_images > 1e4:
break
else:
UpperCAmelCase_ = int(factor * num_images )
UpperCAmelCase_ = ClipClient(
url="https://knn.laion.ai/knn-service" , indice_name="laion_400m" , num_images=lowerCAmelCase__ , aesthetic_weight=0.1 , )
UpperCAmelCase_ = 0
UpperCAmelCase_ = 0
UpperCAmelCase_ = tqdm(desc="downloading real regularization images" , total=lowerCAmelCase__ )
with open(f"""{class_data_dir}/caption.txt""" , "w" ) as fa, open(f"""{class_data_dir}/urls.txt""" , "w" ) as fa, open(
f"""{class_data_dir}/images.txt""" , "w" ) as fa:
while total < num_class_images:
UpperCAmelCase_ = class_images[count]
count += 1
try:
UpperCAmelCase_ = requests.get(images["url"] )
if img.status_code == 200:
UpperCAmelCase_ = Image.open(BytesIO(img.content ) )
with open(f"""{class_data_dir}/images/{total}.jpg""" , "wb" ) as f:
f.write(img.content )
fa.write(images["caption"] + "\n" )
fa.write(images["url"] + "\n" )
fa.write(f"""{class_data_dir}/images/{total}.jpg""" + "\n" )
total += 1
pbar.update(1 )
else:
continue
except Exception:
continue
return
def a__ ( ):
UpperCAmelCase_ = argparse.ArgumentParser("" , add_help=lowerCAmelCase__ )
parser.add_argument("--class_prompt" , help="text prompt to retrieve images" , required=lowerCAmelCase__ , type=lowerCAmelCase__ )
parser.add_argument("--class_data_dir" , help="path to save images" , required=lowerCAmelCase__ , type=lowerCAmelCase__ )
parser.add_argument("--num_class_images" , help="number of images to download" , default=200 , type=lowerCAmelCase__ )
return parser.parse_args()
if __name__ == "__main__":
lowerCamelCase = parse_args()
retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)
| 82 |
"""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 __lowercase ( __lowerCamelCase ):
snake_case_ = ["""input_features""", """is_longer"""]
def __init__( self : str ,A : Union[str, Any]=64 ,A : Tuple=48_000 ,A : Dict=480 ,A : List[str]=10 ,A : str=1_024 ,A : Any=0.0 ,A : Optional[int]=False ,A : float = 0 ,A : float = 14_000 ,A : int = None ,A : str = "fusion" ,A : str = "repeatpad" ,**A : List[Any] ,):
'''simple docstring'''
super().__init__(
feature_size=A ,sampling_rate=A ,padding_value=A ,return_attention_mask=A ,**A ,)
UpperCAmelCase__ : List[Any] = top_db
UpperCAmelCase__ : Union[str, Any] = truncation
UpperCAmelCase__ : Optional[int] = padding
UpperCAmelCase__ : List[Any] = fft_window_size
UpperCAmelCase__ : Optional[Any] = (fft_window_size >> 1) + 1
UpperCAmelCase__ : Any = hop_length
UpperCAmelCase__ : List[str] = max_length_s
UpperCAmelCase__ : List[Any] = max_length_s * sampling_rate
UpperCAmelCase__ : List[Any] = sampling_rate
UpperCAmelCase__ : Optional[int] = frequency_min
UpperCAmelCase__ : Tuple = frequency_max
UpperCAmelCase__ : 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=A ,mel_scale="""htk""" ,)
UpperCAmelCase__ : 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 __lowercase ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = copy.deepcopy(self.__dict__ )
UpperCAmelCase__ : 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 __lowercase ( self : List[str] ,A : np.array ,A : Optional[np.array] = None ):
'''simple docstring'''
UpperCAmelCase__ : Dict = 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 : Optional[Any] ,A : Union[str, Any] ,A : int ,A : Optional[int] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[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
UpperCAmelCase__ : List[str] = [0]
if len(ranges[2] ) == 0:
# if the audio is too short, we just use the first chunk
UpperCAmelCase__ : int = [0]
# randomly choose index for each part
UpperCAmelCase__ : Tuple = np.random.choice(ranges[0] )
UpperCAmelCase__ : Tuple = np.random.choice(ranges[1] )
UpperCAmelCase__ : str = np.random.choice(ranges[2] )
UpperCAmelCase__ : List[str] = mel[idx_front : idx_front + chunk_frames, :]
UpperCAmelCase__ : List[str] = mel[idx_middle : idx_middle + chunk_frames, :]
UpperCAmelCase__ : Dict = mel[idx_back : idx_back + chunk_frames, :]
UpperCAmelCase__ : Optional[Any] = torch.tensor(mel[None, None, :] )
UpperCAmelCase__ : int = torch.nn.functional.interpolate(
A ,size=[chunk_frames, 64] ,mode="""bilinear""" ,align_corners=A )
UpperCAmelCase__ : Dict = mel_shrink[0][0].numpy()
UpperCAmelCase__ : Dict = np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back] ,axis=0 )
return mel_fusion
def __lowercase ( self : Any ,A : np.array ,A : Optional[int] ,A : Any ,A : Tuple ):
'''simple docstring'''
if waveform.shape[0] > max_length:
if truncation == "rand_trunc":
UpperCAmelCase__ : int = True
# random crop to max_length (for compatibility) -> this should be handled by self.pad
UpperCAmelCase__ : str = len(A ) - max_length
UpperCAmelCase__ : Optional[Any] = np.random.randint(0 ,overflow + 1 )
UpperCAmelCase__ : Optional[int] = waveform[idx : idx + max_length]
UpperCAmelCase__ : Any = self._np_extract_fbank_features(A ,self.mel_filters_slaney )[None, :]
elif truncation == "fusion":
UpperCAmelCase__ : Tuple = self._np_extract_fbank_features(A ,self.mel_filters )
UpperCAmelCase__ : Optional[int] = max_length // self.hop_length + 1 # the +1 related to how the spectrogram is computed
UpperCAmelCase__ : 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.
UpperCAmelCase__ : List[Any] = np.stack([mel, mel, mel, mel] ,axis=0 )
UpperCAmelCase__ : Any = False
else:
UpperCAmelCase__ : Union[str, Any] = self._random_mel_fusion(A ,A ,A )
UpperCAmelCase__ : List[str] = True
else:
raise NotImplementedError(f"data_truncating {truncation} not implemented" )
else:
UpperCAmelCase__ : Optional[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":
UpperCAmelCase__ : str = int(max_length / len(A ) )
UpperCAmelCase__ : int = np.stack(np.tile(A ,n_repeat + 1 ) )[:max_length]
if padding == "repeatpad":
UpperCAmelCase__ : List[Any] = int(max_length / len(A ) )
UpperCAmelCase__ : str = np.stack(np.tile(A ,A ) )
UpperCAmelCase__ : Optional[Any] = np.pad(A ,(0, max_length - waveform.shape[0]) ,mode="""constant""" ,constant_values=0 )
if truncation == "fusion":
UpperCAmelCase__ : int = self._np_extract_fbank_features(A ,self.mel_filters )
UpperCAmelCase__ : List[Any] = np.stack([input_mel, input_mel, input_mel, input_mel] ,axis=0 )
else:
UpperCAmelCase__ : Any = self._np_extract_fbank_features(A ,self.mel_filters_slaney )[None, :]
return input_mel, longer
def __call__( self : str ,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 : List[str] ,):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = truncation if truncation is not None else self.truncation
UpperCAmelCase__ : Dict = 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__ : Optional[int] = 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__ : List[str] = is_batched_numpy or (
isinstance(A ,(list, tuple) ) and (isinstance(raw_speech[0] ,(np.ndarray, tuple, list) ))
)
if is_batched:
UpperCAmelCase__ : str = [np.asarray(A ,dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(A ,np.ndarray ):
UpperCAmelCase__ : Any = np.asarray(A ,dtype=np.floataa )
elif isinstance(A ,np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
UpperCAmelCase__ : str = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
UpperCAmelCase__ : Optional[Any] = [np.asarray(A )]
# convert to mel spectrogram, truncate and pad if needed.
UpperCAmelCase__ : Tuple = [
self._get_input_mel(A ,max_length if max_length else self.nb_max_samples ,A ,A )
for waveform in raw_speech
]
UpperCAmelCase__ : Optional[int] = []
UpperCAmelCase__ : 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
UpperCAmelCase__ : List[str] = np.random.randint(0 ,len(A ) )
UpperCAmelCase__ : int = True
if isinstance(input_mel[0] ,A ):
UpperCAmelCase__ : Tuple = [np.asarray(A ,dtype=np.floataa ) for feature in input_mel]
# is_longer is a list of bool
UpperCAmelCase__ : List[str] = [[longer] for longer in is_longer]
UpperCAmelCase__ : List[Any] = {"""input_features""": input_mel, """is_longer""": is_longer}
UpperCAmelCase__ : str = BatchFeature(A )
if return_tensors is not None:
UpperCAmelCase__ : int = input_features.convert_to_tensors(A )
return input_features
| 65 | 0 |
"""simple docstring"""
import argparse
import torch
from transformers import (
UniSpeechSatConfig,
UniSpeechSatForAudioFrameClassification,
UniSpeechSatForSequenceClassification,
UniSpeechSatForXVector,
WavaVecaFeatureExtractor,
logging,
)
logging.set_verbosity_info()
lowerCAmelCase__ = logging.get_logger(__name__)
def snake_case_ ( A_ : List[Any], A_ : int, A_ : List[Any] ):
'''simple docstring'''
_lowerCamelCase : Optional[int] = UniSpeechSatForSequenceClassification.from_pretrained(A_, config=A_ )
_lowerCamelCase : List[str] = downstream_dict['''projector.weight''']
_lowerCamelCase : Union[str, Any] = downstream_dict['''projector.bias''']
_lowerCamelCase : Union[str, Any] = downstream_dict['''model.post_net.linear.weight''']
_lowerCamelCase : int = downstream_dict['''model.post_net.linear.bias''']
return model
def snake_case_ ( A_ : List[str], A_ : Optional[Any], A_ : Dict ):
'''simple docstring'''
_lowerCamelCase : int = UniSpeechSatForAudioFrameClassification.from_pretrained(A_, config=A_ )
_lowerCamelCase : Dict = downstream_dict['''model.linear.weight''']
_lowerCamelCase : int = downstream_dict['''model.linear.bias''']
return model
def snake_case_ ( A_ : int, A_ : str, A_ : Tuple ):
'''simple docstring'''
_lowerCamelCase : List[Any] = UniSpeechSatForXVector.from_pretrained(A_, config=A_ )
_lowerCamelCase : Tuple = downstream_dict['''connector.weight''']
_lowerCamelCase : Any = downstream_dict['''connector.bias''']
for i, kernel_size in enumerate(hf_config.tdnn_kernel ):
_lowerCamelCase : Dict = downstream_dict[
F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
]
_lowerCamelCase : int = downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']
_lowerCamelCase : str = downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
_lowerCamelCase : Optional[Any] = downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
_lowerCamelCase : List[str] = downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
_lowerCamelCase : int = downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
_lowerCamelCase : Dict = downstream_dict['''objective.W''']
return model
@torch.no_grad()
def snake_case_ ( A_ : Union[str, Any], A_ : Optional[int], A_ : Union[str, Any], A_ : str ):
'''simple docstring'''
_lowerCamelCase : Optional[Any] = torch.load(A_, map_location='''cpu''' )
_lowerCamelCase : str = checkpoint['''Downstream''']
_lowerCamelCase : Any = UniSpeechSatConfig.from_pretrained(A_ )
_lowerCamelCase : Tuple = WavaVecaFeatureExtractor.from_pretrained(
A_, return_attention_mask=A_, do_normalize=A_ )
_lowerCamelCase : Tuple = hf_config.architectures[0]
if arch.endswith('''ForSequenceClassification''' ):
_lowerCamelCase : Union[str, Any] = convert_classification(A_, A_, A_ )
elif arch.endswith('''ForAudioFrameClassification''' ):
_lowerCamelCase : int = convert_diarization(A_, A_, A_ )
elif arch.endswith('''ForXVector''' ):
_lowerCamelCase : Tuple = convert_xvector(A_, A_, A_ )
else:
raise NotImplementedError(F'''S3PRL weights conversion is not supported for {arch}''' )
if hf_config.use_weighted_layer_sum:
_lowerCamelCase : Tuple = checkpoint['''Featurizer''']['''weights''']
hf_feature_extractor.save_pretrained(A_ )
hf_model.save_pretrained(A_ )
if __name__ == "__main__":
lowerCAmelCase__ = argparse.ArgumentParser()
parser.add_argument(
'''--base_model_name''', default=None, type=str, help='''Name of the huggingface pretrained base model.'''
)
parser.add_argument('''--config_path''', default=None, type=str, help='''Path to the huggingface classifier config.''')
parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to the s3prl checkpoint.''')
parser.add_argument('''--model_dump_path''', default=None, type=str, help='''Path to the final converted model.''')
lowerCAmelCase__ = parser.parse_args()
convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
| 83 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, 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 DonutImageProcessor
class __lowercase ( unittest.TestCase ):
def __init__( self : Union[str, Any] ,A : Union[str, Any] ,A : Dict=7 ,A : Optional[int]=3 ,A : List[str]=18 ,A : Union[str, Any]=30 ,A : Tuple=400 ,A : Dict=True ,A : List[str]=None ,A : str=True ,A : Optional[Any]=False ,A : Optional[Any]=True ,A : List[str]=True ,A : Optional[int]=[0.5, 0.5, 0.5] ,A : List[str]=[0.5, 0.5, 0.5] ,):
'''simple docstring'''
UpperCAmelCase__ : str = parent
UpperCAmelCase__ : List[str] = batch_size
UpperCAmelCase__ : List[str] = num_channels
UpperCAmelCase__ : Union[str, Any] = image_size
UpperCAmelCase__ : List[Any] = min_resolution
UpperCAmelCase__ : Optional[int] = max_resolution
UpperCAmelCase__ : str = do_resize
UpperCAmelCase__ : Tuple = size if size is not None else {"""height""": 18, """width""": 20}
UpperCAmelCase__ : List[str] = do_thumbnail
UpperCAmelCase__ : Optional[int] = do_align_axis
UpperCAmelCase__ : Union[str, Any] = do_pad
UpperCAmelCase__ : Tuple = do_normalize
UpperCAmelCase__ : Optional[Any] = image_mean
UpperCAmelCase__ : List[Any] = image_std
def __lowercase ( self : Optional[int] ):
'''simple docstring'''
return {
"do_resize": self.do_resize,
"size": self.size,
"do_thumbnail": self.do_thumbnail,
"do_align_long_axis": self.do_align_axis,
"do_pad": self.do_pad,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
}
@require_torch
@require_vision
class __lowercase ( __lowerCamelCase , unittest.TestCase ):
snake_case_ = DonutImageProcessor if is_vision_available() else None
def __lowercase ( self : str ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = DonutImageProcessingTester(self )
@property
def __lowercase ( self : Dict ):
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def __lowercase ( self : Any ):
'''simple docstring'''
UpperCAmelCase__ : Any = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(A ,"""do_resize""" ) )
self.assertTrue(hasattr(A ,"""size""" ) )
self.assertTrue(hasattr(A ,"""do_thumbnail""" ) )
self.assertTrue(hasattr(A ,"""do_align_long_axis""" ) )
self.assertTrue(hasattr(A ,"""do_pad""" ) )
self.assertTrue(hasattr(A ,"""do_normalize""" ) )
self.assertTrue(hasattr(A ,"""image_mean""" ) )
self.assertTrue(hasattr(A ,"""image_std""" ) )
def __lowercase ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size ,{"""height""": 18, """width""": 20} )
UpperCAmelCase__ : str = self.image_processing_class.from_dict(self.image_processor_dict ,size=42 )
self.assertEqual(image_processor.size ,{"""height""": 42, """width""": 42} )
# Previous config had dimensions in (width, height) order
UpperCAmelCase__ : str = self.image_processing_class.from_dict(self.image_processor_dict ,size=(42, 84) )
self.assertEqual(image_processor.size ,{"""height""": 84, """width""": 42} )
def __lowercase ( self : Dict ):
'''simple docstring'''
pass
@is_flaky()
def __lowercase ( self : int ):
'''simple docstring'''
# Initialize image_processing
UpperCAmelCase__ : Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
UpperCAmelCase__ : Dict = prepare_image_inputs(self.image_processor_tester ,equal_resolution=A )
for image in image_inputs:
self.assertIsInstance(A ,Image.Image )
# Test not batched input
UpperCAmelCase__ : int = image_processing(image_inputs[0] ,return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) ,)
# Test batched
UpperCAmelCase__ : Tuple = image_processing(A ,return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) ,)
@is_flaky()
def __lowercase ( self : List[str] ):
'''simple docstring'''
# Initialize image_processing
UpperCAmelCase__ : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
UpperCAmelCase__ : Dict = prepare_image_inputs(self.image_processor_tester ,equal_resolution=A ,numpify=A )
for image in image_inputs:
self.assertIsInstance(A ,np.ndarray )
# Test not batched input
UpperCAmelCase__ : List[str] = image_processing(image_inputs[0] ,return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) ,)
# Test batched
UpperCAmelCase__ : Optional[int] = image_processing(A ,return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) ,)
@is_flaky()
def __lowercase ( self : Any ):
'''simple docstring'''
# Initialize image_processing
UpperCAmelCase__ : int = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
UpperCAmelCase__ : int = prepare_image_inputs(self.image_processor_tester ,equal_resolution=A ,torchify=A )
for image in image_inputs:
self.assertIsInstance(A ,torch.Tensor )
# Test not batched input
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.size["""height"""],
self.image_processor_tester.size["""width"""],
) ,)
# Test batched
UpperCAmelCase__ : List[Any] = image_processing(A ,return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) ,)
| 65 | 0 |
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
if digit_amount > 0:
return round(number - int(__SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE )
return number - int(__SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
print(decimal_isolate(1.53, 0))
print(decimal_isolate(35.345, 1))
print(decimal_isolate(35.345, 2))
print(decimal_isolate(35.345, 3))
print(decimal_isolate(-14.789, 3))
print(decimal_isolate(0, 2))
print(decimal_isolate(-14.123, 1))
print(decimal_isolate(-14.123, 2))
print(decimal_isolate(-14.123, 3))
| 84 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
's-JoL/Open-Llama-V1': 'https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json',
}
class __lowercase ( __lowerCamelCase ):
snake_case_ = """open-llama"""
def __init__( self : Dict ,A : str=100_000 ,A : str=4_096 ,A : Optional[Any]=11_008 ,A : Tuple=32 ,A : str=32 ,A : Optional[int]="silu" ,A : List[Any]=2_048 ,A : str=0.0_2 ,A : Optional[int]=1e-6 ,A : int=True ,A : Tuple=0 ,A : str=1 ,A : Any=2 ,A : Optional[Any]=False ,A : int=True ,A : Any=0.1 ,A : Optional[Any]=0.1 ,A : Optional[Any]=True ,A : Union[str, Any]=True ,A : Tuple=None ,**A : Optional[int] ,):
'''simple docstring'''
UpperCAmelCase__ : str = vocab_size
UpperCAmelCase__ : List[str] = max_position_embeddings
UpperCAmelCase__ : Union[str, Any] = hidden_size
UpperCAmelCase__ : Tuple = intermediate_size
UpperCAmelCase__ : Optional[int] = num_hidden_layers
UpperCAmelCase__ : Any = num_attention_heads
UpperCAmelCase__ : str = hidden_act
UpperCAmelCase__ : Optional[Any] = initializer_range
UpperCAmelCase__ : Optional[int] = rms_norm_eps
UpperCAmelCase__ : Any = use_cache
UpperCAmelCase__ : Optional[Any] = kwargs.pop(
"""use_memorry_efficient_attention""" ,A )
UpperCAmelCase__ : Any = hidden_dropout_prob
UpperCAmelCase__ : str = attention_dropout_prob
UpperCAmelCase__ : Optional[int] = use_stable_embedding
UpperCAmelCase__ : Tuple = shared_input_output_embedding
UpperCAmelCase__ : Tuple = rope_scaling
self._rope_scaling_validation()
super().__init__(
pad_token_id=A ,bos_token_id=A ,eos_token_id=A ,tie_word_embeddings=A ,**A ,)
def __lowercase ( self : Optional[Any] ):
'''simple docstring'''
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling ,A ) or len(self.rope_scaling ) != 2:
raise ValueError(
"""`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, """
f"got {self.rope_scaling}" )
UpperCAmelCase__ : List[Any] = self.rope_scaling.get("""type""" ,A )
UpperCAmelCase__ : int = self.rope_scaling.get("""factor""" ,A )
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
raise ValueError(
f"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}" )
if rope_scaling_factor is None or not isinstance(A ,A ) or rope_scaling_factor <= 1.0:
raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}" )
| 65 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
SCREAMING_SNAKE_CASE__ : Any = {
"configuration_bloom": ["BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP", "BloomConfig", "BloomOnnxConfig"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ : List[str] = ["BloomTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ : List[str] = [
"BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST",
"BloomForCausalLM",
"BloomModel",
"BloomPreTrainedModel",
"BloomForSequenceClassification",
"BloomForTokenClassification",
"BloomForQuestionAnswering",
]
if TYPE_CHECKING:
from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bloom_fast import BloomTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_bloom import (
BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
BloomForCausalLM,
BloomForQuestionAnswering,
BloomForSequenceClassification,
BloomForTokenClassification,
BloomModel,
BloomPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE__ : Tuple = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 85 |
"""simple docstring"""
from collections.abc import Callable
class __lowercase :
def __init__( self : Tuple ,A : Callable | None = None ):
'''simple docstring'''
# Stores actual heap items.
UpperCAmelCase__ : list = []
# Stores indexes of each item for supporting updates and deletion.
UpperCAmelCase__ : dict = {}
# Stores current size of heap.
UpperCAmelCase__ : Any = 0
# Stores function used to evaluate the score of an item on which basis ordering
# will be done.
UpperCAmelCase__ : int = key or (lambda A : x)
def __lowercase ( self : Union[str, Any] ,A : int ):
'''simple docstring'''
return int((i - 1) / 2 ) if i > 0 else None
def __lowercase ( self : Tuple ,A : int ):
'''simple docstring'''
UpperCAmelCase__ : Any = int(2 * i + 1 )
return left if 0 < left < self.size else None
def __lowercase ( self : Any ,A : int ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = int(2 * i + 2 )
return right if 0 < right < self.size else None
def __lowercase ( self : List[Any] ,A : int ,A : int ):
'''simple docstring'''
UpperCAmelCase__ , UpperCAmelCase__ : int = (
self.pos_map[self.arr[j][0]],
self.pos_map[self.arr[i][0]],
)
# Then swap the items in the list.
UpperCAmelCase__ , UpperCAmelCase__ : Tuple = self.arr[j], self.arr[i]
def __lowercase ( self : Optional[int] ,A : int ,A : int ):
'''simple docstring'''
return self.arr[i][1] < self.arr[j][1]
def __lowercase ( self : Optional[int] ,A : int ):
'''simple docstring'''
UpperCAmelCase__ : int = self._left(A )
UpperCAmelCase__ : Dict = self._right(A )
UpperCAmelCase__ : Optional[int] = i
if left is not None and not self._cmp(A ,A ):
UpperCAmelCase__ : List[Any] = left
if right is not None and not self._cmp(A ,A ):
UpperCAmelCase__ : List[Any] = right
return valid_parent
def __lowercase ( self : int ,A : int ):
'''simple docstring'''
UpperCAmelCase__ : int = self._parent(A )
while parent is not None and not self._cmp(A ,A ):
self._swap(A ,A )
UpperCAmelCase__ , UpperCAmelCase__ : int = parent, self._parent(A )
def __lowercase ( self : str ,A : int ):
'''simple docstring'''
UpperCAmelCase__ : Any = self._get_valid_parent(A )
while valid_parent != index:
self._swap(A ,A )
UpperCAmelCase__ , UpperCAmelCase__ : Optional[int] = valid_parent, self._get_valid_parent(A )
def __lowercase ( self : Optional[Any] ,A : int ,A : int ):
'''simple docstring'''
if item not in self.pos_map:
return
UpperCAmelCase__ : Tuple = self.pos_map[item]
UpperCAmelCase__ : Dict = [item, self.key(A )]
# Make sure heap is right in both up and down direction.
# Ideally only one of them will make any change.
self._heapify_up(A )
self._heapify_down(A )
def __lowercase ( self : List[Any] ,A : int ):
'''simple docstring'''
if item not in self.pos_map:
return
UpperCAmelCase__ : Any = self.pos_map[item]
del self.pos_map[item]
UpperCAmelCase__ : Dict = self.arr[self.size - 1]
UpperCAmelCase__ : List[Any] = index
self.size -= 1
# Make sure heap is right in both up and down direction. Ideally only one
# of them will make any change- so no performance loss in calling both.
if self.size > index:
self._heapify_up(A )
self._heapify_down(A )
def __lowercase ( self : str ,A : int ,A : int ):
'''simple docstring'''
UpperCAmelCase__ : Dict = len(self.arr )
if arr_len == self.size:
self.arr.append([item, self.key(A )] )
else:
UpperCAmelCase__ : List[str] = [item, self.key(A )]
UpperCAmelCase__ : Union[str, Any] = self.size
self.size += 1
self._heapify_up(self.size - 1 )
def __lowercase ( self : str ):
'''simple docstring'''
return self.arr[0] if self.size else None
def __lowercase ( self : Dict ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = self.get_top()
if top_item_tuple:
self.delete_item(top_item_tuple[0] )
return top_item_tuple
def lowerCAmelCase ( ):
'''simple docstring'''
if __name__ == "__main__":
import doctest
doctest.testmod()
| 65 | 0 |
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed
__a :Optional[Any] = 'true'
def __snake_case ( __UpperCamelCase : Any ,__UpperCamelCase : List[Any]=82 ,__UpperCamelCase : Dict=16 ):
"""simple docstring"""
set_seed(42 )
A_ = RegressionModel()
A_ = deepcopy(__UpperCamelCase )
A_ = RegressionDataset(length=__UpperCamelCase )
A_ = DataLoader(__UpperCamelCase ,batch_size=__UpperCamelCase )
model.to(accelerator.device )
A_ , A_ = accelerator.prepare(__UpperCamelCase ,__UpperCamelCase )
return model, ddp_model, dataloader
def __snake_case ( __UpperCamelCase : Accelerator ,__UpperCamelCase : Dict=False ):
"""simple docstring"""
A_ = AutoTokenizer.from_pretrained("hf-internal-testing/mrpc-bert-base-cased" )
A_ = load_dataset("glue" ,"mrpc" ,split="validation" )
def tokenize_function(__UpperCamelCase : Optional[Any] ):
A_ = tokenizer(examples["sentence1"] ,examples["sentence2"] ,truncation=__UpperCamelCase ,max_length=__UpperCamelCase )
return outputs
with accelerator.main_process_first():
A_ = dataset.map(
__UpperCamelCase ,batched=__UpperCamelCase ,remove_columns=["idx", "sentence1", "sentence2"] ,)
A_ = tokenized_datasets.rename_column("label" ,"labels" )
def collate_fn(__UpperCamelCase : Union[str, Any] ):
if use_longest:
return tokenizer.pad(__UpperCamelCase ,padding="longest" ,return_tensors="pt" )
return tokenizer.pad(__UpperCamelCase ,padding="max_length" ,max_length=128 ,return_tensors="pt" )
return DataLoader(__UpperCamelCase ,shuffle=__UpperCamelCase ,collate_fn=__UpperCamelCase ,batch_size=16 )
def __snake_case ( __UpperCamelCase : Optional[Any] ,__UpperCamelCase : str ):
"""simple docstring"""
A_ = Accelerator(dispatch_batches=__UpperCamelCase ,split_batches=__UpperCamelCase )
A_ = get_dataloader(__UpperCamelCase ,not dispatch_batches )
A_ = AutoModelForSequenceClassification.from_pretrained(
"hf-internal-testing/mrpc-bert-base-cased" ,return_dict=__UpperCamelCase )
A_ , A_ = accelerator.prepare(__UpperCamelCase ,__UpperCamelCase )
return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator
def __snake_case ( __UpperCamelCase : Dict ,__UpperCamelCase : int ,__UpperCamelCase : Optional[Any] ):
"""simple docstring"""
A_ = []
for batch in dataloader:
A_ , A_ = batch.values()
with torch.no_grad():
A_ = model(__UpperCamelCase )
A_ , A_ = accelerator.gather_for_metrics((logit, target) )
logits_and_targets.append((logit, target) )
A_ , A_ = [], []
for logit, targ in logits_and_targets:
logits.append(__UpperCamelCase )
targs.append(__UpperCamelCase )
A_ , A_ = torch.cat(__UpperCamelCase ), torch.cat(__UpperCamelCase )
return logits, targs
def __snake_case ( __UpperCamelCase : Accelerator ,__UpperCamelCase : Dict=82 ,__UpperCamelCase : List[Any]=False ,__UpperCamelCase : Dict=False ,__UpperCamelCase : Optional[int]=16 ):
"""simple docstring"""
A_ , A_ , A_ = get_basic_setup(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
A_ , A_ = generate_predictions(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
assert (
len(__UpperCamelCase ) == num_samples
), f'''Unexpected number of inputs:\n Expected: {num_samples}\n Actual: {len(__UpperCamelCase )}'''
def __snake_case ( __UpperCamelCase : bool = False ,__UpperCamelCase : bool = False ):
"""simple docstring"""
A_ = evaluate.load("glue" ,"mrpc" )
A_ , A_ = get_mrpc_setup(__UpperCamelCase ,__UpperCamelCase )
# First do baseline
A_ , A_ , A_ = setup["no"]
model.to(__UpperCamelCase )
model.eval()
for batch in dataloader:
batch.to(__UpperCamelCase )
with torch.inference_mode():
A_ = model(**__UpperCamelCase )
A_ = outputs.logits.argmax(dim=-1 )
metric.add_batch(predictions=__UpperCamelCase ,references=batch["labels"] )
A_ = metric.compute()
# Then do distributed
A_ , A_ , A_ = setup["ddp"]
model.eval()
for batch in dataloader:
with torch.inference_mode():
A_ = model(**__UpperCamelCase )
A_ = outputs.logits.argmax(dim=-1 )
A_ = batch["labels"]
A_ , A_ = accelerator.gather_for_metrics((preds, references) )
metric.add_batch(predictions=__UpperCamelCase ,references=__UpperCamelCase )
A_ = metric.compute()
for key in "accuracy f1".split():
assert math.isclose(
baseline[key] ,distributed[key] ), f'''Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n'''
def __snake_case ( ):
"""simple docstring"""
A_ = Accelerator(split_batches=__UpperCamelCase ,dispatch_batches=__UpperCamelCase )
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_warning()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# These are a bit slower so they should only be ran on the GPU or TPU
if torch.cuda.is_available() or is_tpu_available():
if accelerator.is_local_main_process:
print("**Testing gather_for_metrics**" )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
if accelerator.is_local_main_process:
print(f'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`''' )
test_mrpc(__UpperCamelCase ,__UpperCamelCase )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print("**Test torch metrics**" )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
A_ = Accelerator(split_batches=__UpperCamelCase ,dispatch_batches=__UpperCamelCase )
if accelerator.is_local_main_process:
print(f'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99''' )
test_torch_metrics(__UpperCamelCase ,99 )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print("**Test last batch is not dropped when perfectly divisible**" )
A_ = Accelerator()
test_torch_metrics(__UpperCamelCase ,512 )
accelerator.state._reset_state()
def __snake_case ( __UpperCamelCase : List[Any] ):
"""simple docstring"""
main()
if __name__ == "__main__":
main() | 86 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import PaddingStrategy, TensorType
from ....utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
class __lowercase ( __lowerCamelCase ):
snake_case_ = ["""input_features""", """attention_mask"""]
def __init__( self : Any ,A : str=80 ,A : Optional[int]=16_000 ,A : int=0.0 ,A : str=10 ,A : Any=25 ,A : str="hamming_window" ,A : int=3_2_7_6_8.0 ,A : List[str]=0.9_7 ,A : Optional[int]=1.0 ,A : Optional[Any]=True ,A : Tuple=True ,A : Any=False ,**A : int ,):
'''simple docstring'''
super().__init__(feature_size=A ,sampling_rate=A ,padding_value=A ,**A )
UpperCAmelCase__ : str = feature_size
UpperCAmelCase__ : int = sampling_rate
UpperCAmelCase__ : int = padding_value
UpperCAmelCase__ : Dict = hop_length
UpperCAmelCase__ : int = win_length
UpperCAmelCase__ : Dict = frame_signal_scale
UpperCAmelCase__ : Dict = preemphasis_coeff
UpperCAmelCase__ : str = mel_floor
UpperCAmelCase__ : Any = normalize_means
UpperCAmelCase__ : str = normalize_vars
UpperCAmelCase__ : int = win_function
UpperCAmelCase__ : List[Any] = return_attention_mask
UpperCAmelCase__ : str = win_length * sampling_rate // 1_000
UpperCAmelCase__ : List[Any] = hop_length * sampling_rate // 1_000
UpperCAmelCase__ : int = optimal_fft_length(self.sample_size )
UpperCAmelCase__ : List[Any] = (self.n_fft // 2) + 1
def __lowercase ( self : Union[str, Any] ,A : np.array ):
'''simple docstring'''
if self.win_function == "hamming_window":
UpperCAmelCase__ : Any = window_function(window_length=self.sample_size ,name=self.win_function ,periodic=A )
else:
UpperCAmelCase__ : Any = window_function(window_length=self.sample_size ,name=self.win_function )
UpperCAmelCase__ : Union[str, Any] = mel_filter_bank(
num_frequency_bins=self.n_freqs ,num_mel_filters=self.feature_size ,min_frequency=0.0 ,max_frequency=self.sampling_rate / 2.0 ,sampling_rate=self.sampling_rate ,)
UpperCAmelCase__ : Optional[Any] = spectrogram(
one_waveform * self.frame_signal_scale ,window=A ,frame_length=self.sample_size ,hop_length=self.sample_stride ,fft_length=self.n_fft ,center=A ,preemphasis=self.preemphasis_coeff ,mel_filters=A ,mel_floor=self.mel_floor ,log_mel="""log""" ,)
return msfc_features.T
def __lowercase ( self : str ,A : Any ,A : Optional[int] ,A : str ):
'''simple docstring'''
# make sure we normalize float32 arrays
if self.normalize_means:
UpperCAmelCase__ : Optional[Any] = x[:input_length].mean(axis=0 )
UpperCAmelCase__ : Any = np.subtract(A ,A )
if self.normalize_vars:
UpperCAmelCase__ : str = x[:input_length].std(axis=0 )
UpperCAmelCase__ : Optional[int] = np.divide(A ,A )
if input_length < x.shape[0]:
UpperCAmelCase__ : int = padding_value
# make sure array is in float32
UpperCAmelCase__ : str = x.astype(np.floataa )
return x
def __lowercase ( self : Union[str, Any] ,A : List[np.ndarray] ,A : Optional[np.ndarray] = None ):
'''simple docstring'''
UpperCAmelCase__ : Any = attention_mask.sum(-1 ) if attention_mask is not None else [x.shape[0] for x in input_features]
return [self._normalize_one(A ,A ,self.padding_value ) for x, n in zip(A ,A )]
def __call__( self : Union[str, Any] ,A : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] ,A : Union[bool, str, PaddingStrategy] = False ,A : Optional[int] = None ,A : bool = False ,A : Optional[int] = None ,A : Optional[bool] = None ,A : Optional[Union[str, TensorType]] = None ,A : Optional[int] = None ,**A : Tuple ,):
'''simple docstring'''
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 `raw_speech` 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.""" )
UpperCAmelCase__ : 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}" )
UpperCAmelCase__ : Any = is_batched_numpy or (
isinstance(A ,(list, tuple) ) and (isinstance(raw_speech[0] ,(np.ndarray, tuple, list) ))
)
if is_batched:
UpperCAmelCase__ : List[str] = [np.asarray(A ,dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(A ,np.ndarray ):
UpperCAmelCase__ : Union[str, Any] = np.asarray(A ,dtype=np.floataa )
elif isinstance(A ,np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
UpperCAmelCase__ : Optional[int] = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
UpperCAmelCase__ : Optional[Any] = [raw_speech]
# extract fbank features
UpperCAmelCase__ : Tuple = [self._extract_mfsc_features(A ) for one_waveform in raw_speech]
# convert into correct format for padding
UpperCAmelCase__ : str = BatchFeature({"""input_features""": features} )
UpperCAmelCase__ : Optional[Any] = self.pad(
A ,padding=A ,max_length=A ,truncation=A ,pad_to_multiple_of=A ,return_attention_mask=A ,**A ,)
# make sure list is in array format
UpperCAmelCase__ : Tuple = padded_inputs.get("""input_features""" )
if isinstance(input_features[0] ,A ):
UpperCAmelCase__ : Union[str, Any] = [np.asarray(A ,dtype=np.floataa ) for feature in input_features]
UpperCAmelCase__ : Dict = padded_inputs.get("""attention_mask""" )
if attention_mask is not None:
UpperCAmelCase__ : str = [np.asarray(A ,dtype=np.intaa ) for array in attention_mask]
if self.normalize_means or self.normalize_vars:
UpperCAmelCase__ : Union[str, Any] = (
np.array(A ,dtype=np.intaa )
if self._get_padding_strategies(A ,max_length=A ) is not PaddingStrategy.DO_NOT_PAD
and padding
else None
)
UpperCAmelCase__ : Any = self.normalize(
padded_inputs["""input_features"""] ,attention_mask=A )
if return_tensors is not None:
UpperCAmelCase__ : Union[str, Any] = padded_inputs.convert_to_tensors(A )
return padded_inputs
| 65 | 0 |
import inspect
import unittest
from transformers import ConvNextConfig
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_backbone_common import BackboneTesterMixin
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 ConvNextBackbone, ConvNextForImageClassification, ConvNextModel
from transformers.models.convnext.modeling_convnext import CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class UpperCamelCase_ :
'''simple docstring'''
def __init__( self : str , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : int=13 , UpperCAmelCase__ : Dict=32 , UpperCAmelCase__ : Any=3 , UpperCAmelCase__ : Optional[Any]=4 , UpperCAmelCase__ : int=[10, 20, 30, 40] , UpperCAmelCase__ : Tuple=[2, 2, 3, 2] , UpperCAmelCase__ : Union[str, Any]=True , UpperCAmelCase__ : Dict=True , UpperCAmelCase__ : List[str]=37 , UpperCAmelCase__ : Optional[Any]="gelu" , UpperCAmelCase__ : Optional[int]=10 , UpperCAmelCase__ : str=0.02 , UpperCAmelCase__ : Optional[Any]=["stage2", "stage3", "stage4"] , UpperCAmelCase__ : Tuple=[2, 3, 4] , UpperCAmelCase__ : Any=None , ) ->Dict:
'''simple docstring'''
A__ = parent
A__ = batch_size
A__ = image_size
A__ = num_channels
A__ = num_stages
A__ = hidden_sizes
A__ = depths
A__ = is_training
A__ = use_labels
A__ = intermediate_size
A__ = hidden_act
A__ = num_labels
A__ = initializer_range
A__ = out_features
A__ = out_indices
A__ = scope
def SCREAMING_SNAKE_CASE ( self : List[Any]) ->Union[str, Any]:
'''simple docstring'''
A__ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
A__ = None
if self.use_labels:
A__ = ids_tensor([self.batch_size] , self.num_labels)
A__ = self.get_config()
return config, pixel_values, labels
def SCREAMING_SNAKE_CASE ( self : Union[str, Any]) ->Dict:
'''simple docstring'''
return ConvNextConfig(
num_channels=self.num_channels , hidden_sizes=self.hidden_sizes , depths=self.depths , num_stages=self.num_stages , hidden_act=self.hidden_act , is_decoder=UpperCAmelCase__ , initializer_range=self.initializer_range , out_features=self.out_features , out_indices=self.out_indices , num_labels=self.num_labels , )
def SCREAMING_SNAKE_CASE ( self : Tuple , UpperCAmelCase__ : Dict , UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : Dict) ->Optional[int]:
'''simple docstring'''
A__ = ConvNextModel(config=UpperCAmelCase__)
model.to(UpperCAmelCase__)
model.eval()
A__ = model(UpperCAmelCase__)
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def SCREAMING_SNAKE_CASE ( self : List[str] , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : str , UpperCAmelCase__ : List[str]) ->str:
'''simple docstring'''
A__ = ConvNextForImageClassification(UpperCAmelCase__)
model.to(UpperCAmelCase__)
model.eval()
A__ = model(UpperCAmelCase__ , labels=UpperCAmelCase__)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels))
def SCREAMING_SNAKE_CASE ( self : Tuple , UpperCAmelCase__ : Tuple , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : str) ->Optional[Any]:
'''simple docstring'''
A__ = ConvNextBackbone(config=UpperCAmelCase__)
model.to(UpperCAmelCase__)
model.eval()
A__ = model(UpperCAmelCase__)
# verify hidden states
self.parent.assertEqual(len(result.feature_maps) , len(config.out_features))
self.parent.assertListEqual(list(result.feature_maps[0].shape) , [self.batch_size, self.hidden_sizes[1], 4, 4])
# verify channels
self.parent.assertEqual(len(model.channels) , len(config.out_features))
self.parent.assertListEqual(model.channels , config.hidden_sizes[1:])
# verify backbone works with out_features=None
A__ = None
A__ = ConvNextBackbone(config=UpperCAmelCase__)
model.to(UpperCAmelCase__)
model.eval()
A__ = model(UpperCAmelCase__)
# verify feature maps
self.parent.assertEqual(len(result.feature_maps) , 1)
self.parent.assertListEqual(list(result.feature_maps[0].shape) , [self.batch_size, self.hidden_sizes[-1], 1, 1])
# verify channels
self.parent.assertEqual(len(model.channels) , 1)
self.parent.assertListEqual(model.channels , [config.hidden_sizes[-1]])
def SCREAMING_SNAKE_CASE ( self : Dict) ->Optional[int]:
'''simple docstring'''
A__ = self.prepare_config_and_inputs()
A__ , A__ , A__ = config_and_inputs
A__ = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class UpperCamelCase_ ( UpperCAmelCase__ , UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase__ = (
(
ConvNextModel,
ConvNextForImageClassification,
ConvNextBackbone,
)
if is_torch_available()
else ()
)
UpperCAmelCase__ = (
{'''feature-extraction''': ConvNextModel, '''image-classification''': ConvNextForImageClassification}
if is_torch_available()
else {}
)
UpperCAmelCase__ = True
UpperCAmelCase__ = False
UpperCAmelCase__ = False
UpperCAmelCase__ = False
UpperCAmelCase__ = False
def SCREAMING_SNAKE_CASE ( self : Dict) ->Any:
'''simple docstring'''
A__ = ConvNextModelTester(self)
A__ = ConfigTester(self , config_class=UpperCAmelCase__ , has_text_modality=UpperCAmelCase__ , hidden_size=37)
def SCREAMING_SNAKE_CASE ( self : int) ->Dict:
'''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 SCREAMING_SNAKE_CASE ( self : Tuple) ->str:
'''simple docstring'''
return
@unittest.skip(reason='''ConvNext does not use inputs_embeds''')
def SCREAMING_SNAKE_CASE ( self : str) ->str:
'''simple docstring'''
pass
@unittest.skip(reason='''ConvNext does not support input and output embeddings''')
def SCREAMING_SNAKE_CASE ( self : Tuple) ->List[Any]:
'''simple docstring'''
pass
@unittest.skip(reason='''ConvNext does not use feedforward chunking''')
def SCREAMING_SNAKE_CASE ( self : int) ->List[Any]:
'''simple docstring'''
pass
def SCREAMING_SNAKE_CASE ( self : List[str]) ->List[Any]:
'''simple docstring'''
A__ , A__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A__ = model_class(UpperCAmelCase__)
A__ = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
A__ = [*signature.parameters.keys()]
A__ = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , UpperCAmelCase__)
def SCREAMING_SNAKE_CASE ( self : int) ->List[str]:
'''simple docstring'''
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCAmelCase__)
def SCREAMING_SNAKE_CASE ( self : List[Any]) ->int:
'''simple docstring'''
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*UpperCAmelCase__)
def SCREAMING_SNAKE_CASE ( self : Any) ->Tuple:
'''simple docstring'''
def check_hidden_states_output(UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : Tuple , UpperCAmelCase__ : int):
A__ = model_class(UpperCAmelCase__)
model.to(UpperCAmelCase__)
model.eval()
with torch.no_grad():
A__ = model(**self._prepare_for_class(UpperCAmelCase__ , UpperCAmelCase__))
A__ = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
A__ = self.model_tester.num_stages
self.assertEqual(len(UpperCAmelCase__) , expected_num_stages + 1)
# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:]) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
A__ , A__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A__ = True
check_hidden_states_output(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
A__ = True
check_hidden_states_output(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__)
def SCREAMING_SNAKE_CASE ( self : Tuple) ->int:
'''simple docstring'''
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase__)
@slow
def SCREAMING_SNAKE_CASE ( self : List[str]) ->Optional[int]:
'''simple docstring'''
for model_name in CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A__ = ConvNextModel.from_pretrained(UpperCAmelCase__)
self.assertIsNotNone(UpperCAmelCase__)
def SCREAMING_SNAKE_CASE ( ) -> Tuple:
"""simple docstring"""
A__ = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class UpperCamelCase_ ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def SCREAMING_SNAKE_CASE ( self : Optional[Any]) ->Tuple:
'''simple docstring'''
return AutoImageProcessor.from_pretrained('''facebook/convnext-tiny-224''') if is_vision_available() else None
@slow
def SCREAMING_SNAKE_CASE ( self : List[Any]) ->Dict:
'''simple docstring'''
A__ = ConvNextForImageClassification.from_pretrained('''facebook/convnext-tiny-224''').to(UpperCAmelCase__)
A__ = self.default_image_processor
A__ = prepare_img()
A__ = image_processor(images=UpperCAmelCase__ , return_tensors='''pt''').to(UpperCAmelCase__)
# forward pass
with torch.no_grad():
A__ = model(**UpperCAmelCase__)
# verify the logits
A__ = torch.Size((1, 1_000))
self.assertEqual(outputs.logits.shape , UpperCAmelCase__)
A__ = torch.tensor([-0.0260, -0.4739, 0.1911]).to(UpperCAmelCase__)
self.assertTrue(torch.allclose(outputs.logits[0, :3] , UpperCAmelCase__ , atol=1e-4))
@require_torch
class UpperCamelCase_ ( unittest.TestCase , UpperCAmelCase__ ):
'''simple docstring'''
UpperCAmelCase__ = (ConvNextBackbone,) if is_torch_available() else ()
UpperCAmelCase__ = ConvNextConfig
UpperCAmelCase__ = False
def SCREAMING_SNAKE_CASE ( self : Any) ->Optional[int]:
'''simple docstring'''
A__ = ConvNextModelTester(self)
| 87 |
"""simple docstring"""
from math import factorial
def lowerCAmelCase ( __UpperCamelCase = 100 ):
'''simple docstring'''
return sum(int(__UpperCamelCase ) for x in str(factorial(__UpperCamelCase ) ) )
if __name__ == "__main__":
print(solution(int(input('Enter the Number: ').strip())))
| 65 | 0 |
"""simple docstring"""
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 lowercase__ :
def __init__( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=2 , SCREAMING_SNAKE_CASE=True , SCREAMING_SNAKE_CASE=False , SCREAMING_SNAKE_CASE=10 , SCREAMING_SNAKE_CASE=3 , SCREAMING_SNAKE_CASE=32 * 8 , SCREAMING_SNAKE_CASE=32 * 8 , SCREAMING_SNAKE_CASE=4 , SCREAMING_SNAKE_CASE=64 , ) -> Optional[int]:
_lowerCamelCase : List[str] = parent
_lowerCamelCase : List[Any] = batch_size
_lowerCamelCase : Tuple = is_training
_lowerCamelCase : Tuple = use_auxiliary_loss
_lowerCamelCase : Any = num_queries
_lowerCamelCase : List[str] = num_channels
_lowerCamelCase : List[str] = min_size
_lowerCamelCase : Tuple = max_size
_lowerCamelCase : str = num_labels
_lowerCamelCase : Any = hidden_dim
_lowerCamelCase : Dict = hidden_dim
def UpperCamelCase_ ( self) -> List[str]:
_lowerCamelCase : Union[str, Any] = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]).to(
SCREAMING_SNAKE_CASE)
_lowerCamelCase : List[Any] = torch.ones([self.batch_size, self.min_size, self.max_size] , device=SCREAMING_SNAKE_CASE)
_lowerCamelCase : Union[str, Any] = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=SCREAMING_SNAKE_CASE) > 0.5
).float()
_lowerCamelCase : Dict = (torch.rand((self.batch_size, self.num_labels) , device=SCREAMING_SNAKE_CASE) > 0.5).long()
_lowerCamelCase : Optional[int] = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def UpperCamelCase_ ( self) -> str:
_lowerCamelCase : List[str] = MaskaFormerConfig(
hidden_size=self.hidden_dim , )
_lowerCamelCase : Any = self.num_queries
_lowerCamelCase : int = self.num_labels
_lowerCamelCase : int = [1, 1, 1, 1]
_lowerCamelCase : Any = self.num_channels
_lowerCamelCase : Optional[Any] = 64
_lowerCamelCase : str = 128
_lowerCamelCase : Optional[Any] = self.hidden_dim
_lowerCamelCase : Any = self.hidden_dim
_lowerCamelCase : List[Any] = self.hidden_dim
return config
def UpperCamelCase_ ( self) -> Any:
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase : List[str] = self.prepare_config_and_inputs()
_lowerCamelCase : str = {"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask}
return config, inputs_dict
def UpperCamelCase_ ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE) -> Optional[int]:
_lowerCamelCase : str = output.encoder_hidden_states
_lowerCamelCase : int = output.pixel_decoder_hidden_states
_lowerCamelCase : Optional[int] = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(SCREAMING_SNAKE_CASE) , len(config.backbone_config.depths))
self.parent.assertTrue(len(SCREAMING_SNAKE_CASE) , len(config.backbone_config.depths))
self.parent.assertTrue(len(SCREAMING_SNAKE_CASE) , config.decoder_layers)
def UpperCamelCase_ ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=False) -> List[str]:
with torch.no_grad():
_lowerCamelCase : Optional[int] = MaskaFormerModel(config=SCREAMING_SNAKE_CASE)
model.to(SCREAMING_SNAKE_CASE)
model.eval()
_lowerCamelCase : Optional[int] = model(pixel_values=SCREAMING_SNAKE_CASE , pixel_mask=SCREAMING_SNAKE_CASE)
_lowerCamelCase : List[str] = model(SCREAMING_SNAKE_CASE , output_hidden_states=SCREAMING_SNAKE_CASE)
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(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE)
def UpperCamelCase_ ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE) -> str:
_lowerCamelCase : str = MaskaFormerForUniversalSegmentation(config=SCREAMING_SNAKE_CASE)
model.to(SCREAMING_SNAKE_CASE)
model.eval()
def comm_check_on_output(SCREAMING_SNAKE_CASE):
# 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 : List[Any] = model(pixel_values=SCREAMING_SNAKE_CASE , pixel_mask=SCREAMING_SNAKE_CASE)
_lowerCamelCase : List[Any] = model(SCREAMING_SNAKE_CASE)
comm_check_on_output(SCREAMING_SNAKE_CASE)
_lowerCamelCase : Optional[Any] = model(
pixel_values=SCREAMING_SNAKE_CASE , pixel_mask=SCREAMING_SNAKE_CASE , mask_labels=SCREAMING_SNAKE_CASE , class_labels=SCREAMING_SNAKE_CASE)
comm_check_on_output(SCREAMING_SNAKE_CASE)
self.parent.assertTrue(result.loss is not None)
self.parent.assertEqual(result.loss.shape , torch.Size([1]))
@require_torch
class lowercase__ ( A_ ,A_ ,unittest.TestCase ):
__UpperCAmelCase = (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
__UpperCAmelCase = {'''feature-extraction''': MaskaFormerModel} if is_torch_available() else {}
__UpperCAmelCase = False
__UpperCAmelCase = False
__UpperCAmelCase = False
__UpperCAmelCase = False
def UpperCamelCase_ ( self) -> Dict:
_lowerCamelCase : Optional[int] = MaskaFormerModelTester(self)
_lowerCamelCase : Union[str, Any] = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE , has_text_modality=SCREAMING_SNAKE_CASE)
def UpperCamelCase_ ( self) -> List[str]:
self.config_tester.run_common_tests()
def UpperCamelCase_ ( self) -> int:
_lowerCamelCase , _lowerCamelCase : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskaformer_model(SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE , output_hidden_states=SCREAMING_SNAKE_CASE)
def UpperCamelCase_ ( self) -> Tuple:
_lowerCamelCase : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*SCREAMING_SNAKE_CASE)
@unittest.skip(reason="""Mask2Former does not use inputs_embeds""")
def UpperCamelCase_ ( self) -> Optional[int]:
pass
@unittest.skip(reason="""Mask2Former does not have a get_input_embeddings method""")
def UpperCamelCase_ ( self) -> Tuple:
pass
@unittest.skip(reason="""Mask2Former is not a generative model""")
def UpperCamelCase_ ( self) -> List[Any]:
pass
@unittest.skip(reason="""Mask2Former does not use token embeddings""")
def UpperCamelCase_ ( self) -> Any:
pass
@require_torch_multi_gpu
@unittest.skip(
reason="""Mask2Former has some layers using `add_module` which doesn't work well with `nn.DataParallel`""")
def UpperCamelCase_ ( self) -> Dict:
pass
@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""")
def UpperCamelCase_ ( self) -> Optional[int]:
pass
def UpperCamelCase_ ( self) -> Optional[Any]:
_lowerCamelCase , _lowerCamelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowerCamelCase : Dict = model_class(SCREAMING_SNAKE_CASE)
_lowerCamelCase : Any = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_lowerCamelCase : str = [*signature.parameters.keys()]
_lowerCamelCase : int = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , SCREAMING_SNAKE_CASE)
@slow
def UpperCamelCase_ ( self) -> Optional[int]:
for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
_lowerCamelCase : Optional[int] = MaskaFormerModel.from_pretrained(SCREAMING_SNAKE_CASE)
self.assertIsNotNone(SCREAMING_SNAKE_CASE)
def UpperCamelCase_ ( self) -> Optional[Any]:
_lowerCamelCase : Dict = (self.model_tester.min_size,) * 2
_lowerCamelCase : str = {
"""pixel_values""": torch.randn((2, 3, *size) , device=SCREAMING_SNAKE_CASE),
"""mask_labels""": torch.randn((2, 10, *size) , device=SCREAMING_SNAKE_CASE),
"""class_labels""": torch.zeros(2 , 10 , device=SCREAMING_SNAKE_CASE).long(),
}
_lowerCamelCase : List[str] = self.model_tester.get_config()
_lowerCamelCase : Tuple = MaskaFormerForUniversalSegmentation(SCREAMING_SNAKE_CASE).to(SCREAMING_SNAKE_CASE)
_lowerCamelCase : Union[str, Any] = model(**SCREAMING_SNAKE_CASE)
self.assertTrue(outputs.loss is not None)
def UpperCamelCase_ ( self) -> Tuple:
_lowerCamelCase , _lowerCamelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskaformer_model(SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE , output_hidden_states=SCREAMING_SNAKE_CASE)
def UpperCamelCase_ ( self) -> Optional[int]:
_lowerCamelCase , _lowerCamelCase : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowerCamelCase : str = model_class(SCREAMING_SNAKE_CASE).to(SCREAMING_SNAKE_CASE)
_lowerCamelCase : Union[str, Any] = model(**SCREAMING_SNAKE_CASE , output_attentions=SCREAMING_SNAKE_CASE)
self.assertTrue(outputs.attentions is not None)
def UpperCamelCase_ ( self) -> Optional[Any]:
if not self.model_tester.is_training:
return
_lowerCamelCase : Any = self.all_model_classes[1]
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
_lowerCamelCase : List[Any] = model_class(SCREAMING_SNAKE_CASE)
model.to(SCREAMING_SNAKE_CASE)
model.train()
_lowerCamelCase : int = model(SCREAMING_SNAKE_CASE , mask_labels=SCREAMING_SNAKE_CASE , class_labels=SCREAMING_SNAKE_CASE).loss
loss.backward()
def UpperCamelCase_ ( self) -> Optional[Any]:
_lowerCamelCase : Any = self.all_model_classes[1]
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase : int = self.model_tester.prepare_config_and_inputs()
_lowerCamelCase : int = True
_lowerCamelCase : Optional[Any] = True
_lowerCamelCase : str = model_class(SCREAMING_SNAKE_CASE).to(SCREAMING_SNAKE_CASE)
model.train()
_lowerCamelCase : List[Any] = model(SCREAMING_SNAKE_CASE , mask_labels=SCREAMING_SNAKE_CASE , class_labels=SCREAMING_SNAKE_CASE)
_lowerCamelCase : Tuple = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
_lowerCamelCase : int = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
_lowerCamelCase : str = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
_lowerCamelCase : Optional[int] = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=SCREAMING_SNAKE_CASE)
self.assertIsNotNone(encoder_hidden_states.grad)
self.assertIsNotNone(pixel_decoder_hidden_states.grad)
self.assertIsNotNone(transformer_decoder_hidden_states.grad)
self.assertIsNotNone(attentions.grad)
UpperCAmelCase = 1e-4
def _snake_case ( ):
"""simple docstring"""
_lowerCamelCase : List[str] = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_vision
@slow
class lowercase__ ( unittest.TestCase ):
@cached_property
def UpperCamelCase_ ( self) -> int:
return "facebook/mask2former-swin-small-coco-instance"
@cached_property
def UpperCamelCase_ ( self) -> Union[str, Any]:
return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints) if is_vision_available() else None
def UpperCamelCase_ ( self) -> Optional[Any]:
_lowerCamelCase : Tuple = MaskaFormerModel.from_pretrained(self.model_checkpoints).to(SCREAMING_SNAKE_CASE)
_lowerCamelCase : str = self.default_image_processor
_lowerCamelCase : List[str] = prepare_img()
_lowerCamelCase : Union[str, Any] = image_processor(SCREAMING_SNAKE_CASE , return_tensors="""pt""").to(SCREAMING_SNAKE_CASE)
_lowerCamelCase : Union[str, Any] = 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(SCREAMING_SNAKE_CASE , (1, 3, 384, 384))
with torch.no_grad():
_lowerCamelCase : Dict = model(**SCREAMING_SNAKE_CASE)
_lowerCamelCase : List[Any] = 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(SCREAMING_SNAKE_CASE)
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , SCREAMING_SNAKE_CASE , atol=SCREAMING_SNAKE_CASE))
_lowerCamelCase : Any = 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(SCREAMING_SNAKE_CASE)
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , SCREAMING_SNAKE_CASE , atol=SCREAMING_SNAKE_CASE))
_lowerCamelCase : Dict = 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(SCREAMING_SNAKE_CASE)
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , SCREAMING_SNAKE_CASE , atol=SCREAMING_SNAKE_CASE))
def UpperCamelCase_ ( self) -> Any:
_lowerCamelCase : Optional[Any] = MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints).to(SCREAMING_SNAKE_CASE).eval()
_lowerCamelCase : Optional[Any] = self.default_image_processor
_lowerCamelCase : Any = prepare_img()
_lowerCamelCase : Dict = image_processor(SCREAMING_SNAKE_CASE , return_tensors="""pt""").to(SCREAMING_SNAKE_CASE)
_lowerCamelCase : Union[str, Any] = 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(SCREAMING_SNAKE_CASE , (1, 3, 384, 384))
with torch.no_grad():
_lowerCamelCase : List[str] = model(**SCREAMING_SNAKE_CASE)
# masks_queries_logits
_lowerCamelCase : str = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4))
_lowerCamelCase : Any = [
[-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 : List[Any] = torch.tensor(SCREAMING_SNAKE_CASE).to(SCREAMING_SNAKE_CASE)
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , SCREAMING_SNAKE_CASE , atol=SCREAMING_SNAKE_CASE))
# class_queries_logits
_lowerCamelCase : List[str] = outputs.class_queries_logits
self.assertEqual(class_queries_logits.shape , (1, model.config.num_queries, model.config.num_labels + 1))
_lowerCamelCase : Optional[Any] = 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(SCREAMING_SNAKE_CASE)
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , SCREAMING_SNAKE_CASE , atol=SCREAMING_SNAKE_CASE))
def UpperCamelCase_ ( self) -> int:
_lowerCamelCase : Tuple = MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints).to(SCREAMING_SNAKE_CASE).eval()
_lowerCamelCase : str = self.default_image_processor
_lowerCamelCase : Tuple = image_processor(
[np.zeros((3, 800, 1333)), np.zeros((3, 800, 1333))] , segmentation_maps=[np.zeros((384, 384)).astype(np.floataa), np.zeros((384, 384)).astype(np.floataa)] , return_tensors="""pt""" , )
_lowerCamelCase : Optional[Any] = inputs["""pixel_values"""].to(SCREAMING_SNAKE_CASE)
_lowerCamelCase : Any = [el.to(SCREAMING_SNAKE_CASE) for el in inputs["""mask_labels"""]]
_lowerCamelCase : Union[str, Any] = [el.to(SCREAMING_SNAKE_CASE) for el in inputs["""class_labels"""]]
with torch.no_grad():
_lowerCamelCase : Any = model(**SCREAMING_SNAKE_CASE)
self.assertTrue(outputs.loss is not None)
| 88 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.distilbert.modeling_flax_distilbert import (
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertModel,
)
class __lowercase ( unittest.TestCase ):
def __init__( self : Union[str, Any] ,A : Optional[int] ,A : int=13 ,A : Tuple=7 ,A : Dict=True ,A : Optional[int]=True ,A : Tuple=True ,A : str=True ,A : Any=99 ,A : Tuple=32 ,A : Dict=5 ,A : Optional[int]=4 ,A : Dict=37 ,A : Any="gelu" ,A : Any=0.1 ,A : Optional[int]=0.1 ,A : Union[str, Any]=512 ,A : Any=16 ,A : List[str]=2 ,A : List[Any]=0.0_2 ,A : Optional[int]=4 ,):
'''simple docstring'''
UpperCAmelCase__ : Dict = parent
UpperCAmelCase__ : Any = batch_size
UpperCAmelCase__ : List[Any] = seq_length
UpperCAmelCase__ : Optional[int] = is_training
UpperCAmelCase__ : Optional[Any] = use_attention_mask
UpperCAmelCase__ : int = use_token_type_ids
UpperCAmelCase__ : int = use_labels
UpperCAmelCase__ : Any = vocab_size
UpperCAmelCase__ : Union[str, Any] = hidden_size
UpperCAmelCase__ : int = num_hidden_layers
UpperCAmelCase__ : int = num_attention_heads
UpperCAmelCase__ : Dict = intermediate_size
UpperCAmelCase__ : Any = hidden_act
UpperCAmelCase__ : Union[str, Any] = hidden_dropout_prob
UpperCAmelCase__ : Any = attention_probs_dropout_prob
UpperCAmelCase__ : str = max_position_embeddings
UpperCAmelCase__ : List[Any] = type_vocab_size
UpperCAmelCase__ : List[str] = type_sequence_label_size
UpperCAmelCase__ : List[Any] = initializer_range
UpperCAmelCase__ : List[Any] = num_choices
def __lowercase ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
UpperCAmelCase__ : List[str] = None
if self.use_attention_mask:
UpperCAmelCase__ : str = random_attention_mask([self.batch_size, self.seq_length] )
UpperCAmelCase__ : int = DistilBertConfig(
vocab_size=self.vocab_size ,dim=self.hidden_size ,n_layers=self.num_hidden_layers ,n_heads=self.num_attention_heads ,hidden_dim=self.intermediate_size ,hidden_act=self.hidden_act ,dropout=self.hidden_dropout_prob ,attention_dropout=self.attention_probs_dropout_prob ,max_position_embeddings=self.max_position_embeddings ,initializer_range=self.initializer_range ,tie_weights_=A ,)
return config, input_ids, attention_mask
def __lowercase ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = self.prepare_config_and_inputs()
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ : Any = config_and_inputs
UpperCAmelCase__ : str = {"""input_ids""": input_ids, """attention_mask""": attention_mask}
return config, inputs_dict
@require_flax
class __lowercase ( __lowerCamelCase , unittest.TestCase ):
snake_case_ = (
(
FlaxDistilBertModel,
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def __lowercase ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = FlaxDistilBertModelTester(self )
@slow
def __lowercase ( self : Optional[Any] ):
'''simple docstring'''
for model_class_name in self.all_model_classes:
UpperCAmelCase__ : Union[str, Any] = model_class_name.from_pretrained("""distilbert-base-uncased""" )
UpperCAmelCase__ : List[Any] = model(np.ones((1, 1) ) )
self.assertIsNotNone(A )
@require_flax
class __lowercase ( unittest.TestCase ):
@slow
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = FlaxDistilBertModel.from_pretrained("""distilbert-base-uncased""" )
UpperCAmelCase__ : List[Any] = np.array([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]] )
UpperCAmelCase__ : str = np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
UpperCAmelCase__ : Dict = model(A ,attention_mask=A )[0]
UpperCAmelCase__ : List[Any] = (1, 11, 768)
self.assertEqual(output.shape ,A )
UpperCAmelCase__ : Any = np.array([[[-0.1_6_3_9, 0.3_2_9_9, 0.1_6_4_8], [-0.1_7_4_6, 0.3_2_8_9, 0.1_7_1_0], [-0.1_8_8_4, 0.3_3_5_7, 0.1_8_1_0]]] )
self.assertTrue(jnp.allclose(output[:, 1:4, 1:4] ,A ,atol=1e-4 ) )
| 65 | 0 |
def UpperCamelCase_( lowerCamelCase_ ) -> set:
_lowercase : Optional[Any] = set()
# edges = list of graph's edges
_lowercase : List[Any] = get_edges(lowerCamelCase_ )
# While there are still elements in edges list, take an arbitrary edge
# (from_node, to_node) and add his extremity to chosen_vertices and then
# remove all arcs adjacent to the from_node and to_node
while edges:
_lowercase , _lowercase : str = edges.pop()
chosen_vertices.add(lowerCamelCase_ )
chosen_vertices.add(lowerCamelCase_ )
for edge in edges.copy():
if from_node in edge or to_node in edge:
edges.discard(lowerCamelCase_ )
return chosen_vertices
def UpperCamelCase_( lowerCamelCase_ ) -> set:
_lowercase : List[Any] = set()
for from_node, to_nodes in graph.items():
for to_node in to_nodes:
edges.add((from_node, to_node) )
return edges
if __name__ == "__main__":
import doctest
doctest.testmod()
# graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
# print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")
| 89 |
"""simple docstring"""
__UpperCAmelCase = frozenset(
[
'prompt',
'height',
'width',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
'cross_attention_kwargs',
]
)
__UpperCAmelCase = frozenset(['prompt', 'negative_prompt'])
__UpperCAmelCase = frozenset([])
__UpperCAmelCase = frozenset(['image'])
__UpperCAmelCase = frozenset(
[
'image',
'height',
'width',
'guidance_scale',
]
)
__UpperCAmelCase = frozenset(['image'])
__UpperCAmelCase = frozenset(
[
'prompt',
'image',
'height',
'width',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
]
)
__UpperCAmelCase = frozenset(['prompt', 'image', 'negative_prompt'])
__UpperCAmelCase = frozenset(
[
# Text guided image variation with an image mask
'prompt',
'image',
'mask_image',
'height',
'width',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
]
)
__UpperCAmelCase = frozenset(['prompt', 'image', 'mask_image', 'negative_prompt'])
__UpperCAmelCase = frozenset(
[
# image variation with an image mask
'image',
'mask_image',
'height',
'width',
'guidance_scale',
]
)
__UpperCAmelCase = frozenset(['image', 'mask_image'])
__UpperCAmelCase = frozenset(
[
'example_image',
'image',
'mask_image',
'height',
'width',
'guidance_scale',
]
)
__UpperCAmelCase = frozenset(['example_image', 'image', 'mask_image'])
__UpperCAmelCase = frozenset(['class_labels'])
__UpperCAmelCase = frozenset(['class_labels'])
__UpperCAmelCase = frozenset(['batch_size'])
__UpperCAmelCase = frozenset([])
__UpperCAmelCase = frozenset(['batch_size'])
__UpperCAmelCase = frozenset([])
__UpperCAmelCase = frozenset(
[
'prompt',
'audio_length_in_s',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
'cross_attention_kwargs',
]
)
__UpperCAmelCase = frozenset(['prompt', 'negative_prompt'])
__UpperCAmelCase = frozenset(['input_tokens'])
__UpperCAmelCase = frozenset(['input_tokens'])
| 65 | 0 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class a__ :
'''simple docstring'''
def __init__( self , lowerCamelCase_=2 , lowerCamelCase_=3 , lowerCamelCase_=64 , lowerCamelCase_=None ) -> Dict:
lowerCAmelCase__ = np.random.default_rng(lowerCamelCase_ )
lowerCAmelCase__ = length
lowerCAmelCase__ = rng.normal(size=(length,) ).astype(np.floataa )
lowerCAmelCase__ = a * self.x + b + rng.normal(scale=0.1 , size=(length,) ).astype(np.floataa )
def __len__( self ) -> Any:
return self.length
def __getitem__( self , lowerCamelCase_ ) -> List[str]:
return {"x": self.x[i], "y": self.y[i]}
class a__ ( torch.nn.Module ):
'''simple docstring'''
def __init__( self , lowerCamelCase_=0 , lowerCamelCase_=0 , lowerCamelCase_=False ) -> List[Any]:
super().__init__()
lowerCAmelCase__ = torch.nn.Parameter(torch.tensor([2, 3] ).float() )
lowerCAmelCase__ = torch.nn.Parameter(torch.tensor([2, 3] ).float() )
lowerCAmelCase__ = True
def __SCREAMING_SNAKE_CASE ( self , lowerCamelCase_=None ) -> Optional[Any]:
if self.first_batch:
print(F"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}""" )
lowerCAmelCase__ = False
return x * self.a[0] + self.b[0]
class a__ ( torch.nn.Module ):
'''simple docstring'''
def __init__( self , lowerCamelCase_=0 , lowerCamelCase_=0 , lowerCamelCase_=False ) -> Any:
super().__init__()
lowerCAmelCase__ = torch.nn.Parameter(torch.tensor(lowerCamelCase_ ).float() )
lowerCAmelCase__ = torch.nn.Parameter(torch.tensor(lowerCamelCase_ ).float() )
lowerCAmelCase__ = True
def __SCREAMING_SNAKE_CASE ( self , lowerCamelCase_=None ) -> Any:
if self.first_batch:
print(F"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}""" )
lowerCAmelCase__ = False
return x * self.a + self.b
def _snake_case ( A , A = 16 ) -> Any:
from datasets import load_dataset
from transformers import AutoTokenizer
lowerCAmelCase__ = AutoTokenizer.from_pretrained('''bert-base-cased''' )
lowerCAmelCase__ = {'''train''': '''tests/test_samples/MRPC/train.csv''', '''validation''': '''tests/test_samples/MRPC/dev.csv'''}
lowerCAmelCase__ = load_dataset('''csv''' , data_files=A )
lowerCAmelCase__ = datasets['''train'''].unique('''label''' )
lowerCAmelCase__ = {v: i for i, v in enumerate(A )}
def tokenize_function(A ):
# max_length=None => use the model max length (it's actually the default)
lowerCAmelCase__ = tokenizer(
examples['''sentence1'''] , examples['''sentence2'''] , truncation=A , max_length=A , padding='''max_length''' )
if "label" in examples:
lowerCAmelCase__ = [label_to_id[l] for l in examples['''label''']]
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
lowerCAmelCase__ = datasets.map(
A , batched=A , remove_columns=['''sentence1''', '''sentence2''', '''label'''] , )
def collate_fn(A ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(A , padding='''max_length''' , max_length=128 , return_tensors='''pt''' )
return tokenizer.pad(A , padding='''longest''' , return_tensors='''pt''' )
# Instantiate dataloaders.
lowerCAmelCase__ = DataLoader(tokenized_datasets['''train'''] , shuffle=A , collate_fn=A , batch_size=2 )
lowerCAmelCase__ = DataLoader(tokenized_datasets['''validation'''] , shuffle=A , collate_fn=A , batch_size=1 )
return train_dataloader, eval_dataloader | 90 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProcessor
from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.testing_utils import require_pyctcdecode, require_torch, require_torchaudio, slow
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_pyctcdecode_available, is_torch_available
from ..wavaveca.test_feature_extraction_wavaveca import floats_list
if is_pyctcdecode_available():
from huggingface_hub import snapshot_download
from pyctcdecode import BeamSearchDecoderCTC
from transformers.models.wavaveca_with_lm import WavaVecaProcessorWithLM
from transformers.models.wavaveca_with_lm.processing_wavaveca_with_lm import WavaVecaDecoderWithLMOutput
if is_torch_available():
from transformers import WavaVecaForCTC
@require_pyctcdecode
class __lowercase ( unittest.TestCase ):
def __lowercase ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase__ : Dict = """| <pad> <unk> <s> </s> a b c d e f g h i j k""".split()
UpperCAmelCase__ : Tuple = dict(zip(A ,range(len(A ) ) ) )
UpperCAmelCase__ : Optional[Any] = {
"""unk_token""": """<unk>""",
"""bos_token""": """<s>""",
"""eos_token""": """</s>""",
}
UpperCAmelCase__ : int = {
"""feature_size""": 1,
"""padding_value""": 0.0,
"""sampling_rate""": 16_000,
"""return_attention_mask""": False,
"""do_normalize""": True,
}
UpperCAmelCase__ : Optional[int] = tempfile.mkdtemp()
UpperCAmelCase__ : Optional[int] = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["""vocab_file"""] )
UpperCAmelCase__ : Tuple = os.path.join(self.tmpdirname ,A )
with open(self.vocab_file ,"""w""" ,encoding="""utf-8""" ) as fp:
fp.write(json.dumps(A ) + """\n""" )
with open(self.feature_extraction_file ,"""w""" ,encoding="""utf-8""" ) as fp:
fp.write(json.dumps(A ) + """\n""" )
# load decoder from hub
UpperCAmelCase__ : int = """hf-internal-testing/ngram-beam-search-decoder"""
def __lowercase ( self : str ,**A : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = self.add_kwargs_tokens_map.copy()
kwargs.update(A )
return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname ,**A )
def __lowercase ( self : List[str] ,**A : Dict ):
'''simple docstring'''
return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname ,**A )
def __lowercase ( self : Any ,**A : List[Any] ):
'''simple docstring'''
return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name ,**A )
def __lowercase ( self : Any ):
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def __lowercase ( self : str ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = self.get_tokenizer()
UpperCAmelCase__ : Dict = self.get_feature_extractor()
UpperCAmelCase__ : str = self.get_decoder()
UpperCAmelCase__ : Tuple = WavaVecaProcessorWithLM(tokenizer=A ,feature_extractor=A ,decoder=A )
processor.save_pretrained(self.tmpdirname )
UpperCAmelCase__ : str = WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname )
# tokenizer
self.assertEqual(processor.tokenizer.get_vocab() ,tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer ,A )
# feature extractor
self.assertEqual(processor.feature_extractor.to_json_string() ,feature_extractor.to_json_string() )
self.assertIsInstance(processor.feature_extractor ,A )
# decoder
self.assertEqual(processor.decoder._alphabet.labels ,decoder._alphabet.labels )
self.assertEqual(
processor.decoder.model_container[decoder._model_key]._unigram_set ,decoder.model_container[decoder._model_key]._unigram_set ,)
self.assertIsInstance(processor.decoder ,A )
def __lowercase ( self : int ):
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = WavaVecaProcessorWithLM(
tokenizer=self.get_tokenizer() ,feature_extractor=self.get_feature_extractor() ,decoder=self.get_decoder() )
processor.save_pretrained(self.tmpdirname )
# make sure that error is thrown when decoder alphabet doesn't match
UpperCAmelCase__ : Tuple = WavaVecaProcessorWithLM.from_pretrained(
self.tmpdirname ,alpha=5.0 ,beta=3.0 ,score_boundary=-7.0 ,unk_score_offset=3 )
# decoder
self.assertEqual(processor.language_model.alpha ,5.0 )
self.assertEqual(processor.language_model.beta ,3.0 )
self.assertEqual(processor.language_model.score_boundary ,-7.0 )
self.assertEqual(processor.language_model.unk_score_offset ,3 )
def __lowercase ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase__ : int = self.get_tokenizer()
# add token to trigger raise
tokenizer.add_tokens(["""xx"""] )
with self.assertRaisesRegex(A ,"""include""" ):
WavaVecaProcessorWithLM(
tokenizer=A ,feature_extractor=self.get_feature_extractor() ,decoder=self.get_decoder() )
def __lowercase ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = self.get_feature_extractor()
UpperCAmelCase__ : Optional[Any] = self.get_tokenizer()
UpperCAmelCase__ : Any = self.get_decoder()
UpperCAmelCase__ : Union[str, Any] = WavaVecaProcessorWithLM(tokenizer=A ,feature_extractor=A ,decoder=A )
UpperCAmelCase__ : str = floats_list((3, 1_000) )
UpperCAmelCase__ : Optional[Any] = feature_extractor(A ,return_tensors="""np""" )
UpperCAmelCase__ : List[Any] = processor(A ,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 __lowercase ( self : int ):
'''simple docstring'''
UpperCAmelCase__ : int = self.get_feature_extractor()
UpperCAmelCase__ : Union[str, Any] = self.get_tokenizer()
UpperCAmelCase__ : Optional[int] = self.get_decoder()
UpperCAmelCase__ : List[Any] = WavaVecaProcessorWithLM(tokenizer=A ,feature_extractor=A ,decoder=A )
UpperCAmelCase__ : List[Any] = """This is a test string"""
UpperCAmelCase__ : int = processor(text=A )
UpperCAmelCase__ : Dict = tokenizer(A )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] ,encoded_processor[key] )
def __lowercase ( self : Tuple ,A : List[Any]=(2, 10, 16) ,A : Dict=77 ):
'''simple docstring'''
np.random.seed(A )
return np.random.rand(*A )
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ : Dict = self.get_feature_extractor()
UpperCAmelCase__ : Optional[Any] = self.get_tokenizer()
UpperCAmelCase__ : int = self.get_decoder()
UpperCAmelCase__ : Optional[Any] = WavaVecaProcessorWithLM(tokenizer=A ,feature_extractor=A ,decoder=A )
UpperCAmelCase__ : Dict = self._get_dummy_logits(shape=(10, 16) ,seed=13 )
UpperCAmelCase__ : Tuple = processor.decode(A )
UpperCAmelCase__ : Union[str, Any] = decoder.decode_beams(A )[0]
self.assertEqual(decoded_decoder[0] ,decoded_processor.text )
self.assertEqual("""</s> <s> </s>""" ,decoded_processor.text )
self.assertEqual(decoded_decoder[-2] ,decoded_processor.logit_score )
self.assertEqual(decoded_decoder[-1] ,decoded_processor.lm_score )
@parameterized.expand([[None], ["""fork"""], ["""spawn"""]] )
def __lowercase ( self : List[str] ,A : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = self.get_feature_extractor()
UpperCAmelCase__ : int = self.get_tokenizer()
UpperCAmelCase__ : List[Any] = self.get_decoder()
UpperCAmelCase__ : Dict = WavaVecaProcessorWithLM(tokenizer=A ,feature_extractor=A ,decoder=A )
UpperCAmelCase__ : Optional[Any] = self._get_dummy_logits()
# note: pool should be instantiated *after* Wav2Vec2ProcessorWithLM.
# otherwise, the LM won't be available to the pool's sub-processes.
# manual logic used to allow parameterized test for both pool=None and pool=Pool(...)
if pool_context is None:
UpperCAmelCase__ : List[str] = processor.batch_decode(A )
else:
with get_context(A ).Pool() as pool:
UpperCAmelCase__ : Union[str, Any] = processor.batch_decode(A ,A )
UpperCAmelCase__ : Optional[Any] = list(A )
with get_context("""fork""" ).Pool() as p:
UpperCAmelCase__ : Union[str, Any] = decoder.decode_beams_batch(A ,A )
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ : Optional[Any] = [], [], []
for beams in decoded_beams:
texts_decoder.append(beams[0][0] )
logit_scores_decoder.append(beams[0][-2] )
lm_scores_decoder.append(beams[0][-1] )
self.assertListEqual(A ,decoded_processor.text )
self.assertListEqual(["""<s> <s> </s>""", """<s> <s> <s>"""] ,decoded_processor.text )
self.assertListEqual(A ,decoded_processor.logit_score )
self.assertListEqual(A ,decoded_processor.lm_score )
def __lowercase ( self : int ):
'''simple docstring'''
UpperCAmelCase__ : Any = self.get_feature_extractor()
UpperCAmelCase__ : Tuple = self.get_tokenizer()
UpperCAmelCase__ : List[Any] = self.get_decoder()
UpperCAmelCase__ : Union[str, Any] = WavaVecaProcessorWithLM(tokenizer=A ,feature_extractor=A ,decoder=A )
UpperCAmelCase__ : Dict = self._get_dummy_logits()
UpperCAmelCase__ : Any = 15
UpperCAmelCase__ : Dict = -2_0.0
UpperCAmelCase__ : List[Any] = -4.0
UpperCAmelCase__ : Union[str, Any] = processor.batch_decode(
A ,beam_width=A ,beam_prune_logp=A ,token_min_logp=A ,)
UpperCAmelCase__ : List[str] = decoded_processor_out.text
UpperCAmelCase__ : List[str] = list(A )
with get_context("""fork""" ).Pool() as pool:
UpperCAmelCase__ : Tuple = decoder.decode_beams_batch(
A ,A ,beam_width=A ,beam_prune_logp=A ,token_min_logp=A ,)
UpperCAmelCase__ : List[Any] = [d[0][0] for d in decoded_decoder_out]
UpperCAmelCase__ : Any = [d[0][2] for d in decoded_decoder_out]
UpperCAmelCase__ : List[str] = [d[0][3] for d in decoded_decoder_out]
self.assertListEqual(A ,A )
self.assertListEqual(["""</s> <s> <s>""", """<s> <s> <s>"""] ,A )
self.assertTrue(np.array_equal(A ,decoded_processor_out.logit_score ) )
self.assertTrue(np.allclose([-2_0.0_5_4, -1_8.4_4_7] ,A ,atol=1e-3 ) )
self.assertTrue(np.array_equal(A ,decoded_processor_out.lm_score ) )
self.assertTrue(np.allclose([-1_5.5_5_4, -1_3.9_4_7_4] ,A ,atol=1e-3 ) )
def __lowercase ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = self.get_feature_extractor()
UpperCAmelCase__ : Optional[Any] = self.get_tokenizer()
UpperCAmelCase__ : int = self.get_decoder()
UpperCAmelCase__ : str = WavaVecaProcessorWithLM(tokenizer=A ,feature_extractor=A ,decoder=A )
UpperCAmelCase__ : Tuple = self._get_dummy_logits()
UpperCAmelCase__ : Tuple = 2.0
UpperCAmelCase__ : str = 5.0
UpperCAmelCase__ : Union[str, Any] = -2_0.0
UpperCAmelCase__ : Optional[Any] = True
UpperCAmelCase__ : str = processor.batch_decode(
A ,alpha=A ,beta=A ,unk_score_offset=A ,lm_score_boundary=A ,)
UpperCAmelCase__ : Any = decoded_processor_out.text
UpperCAmelCase__ : Union[str, Any] = list(A )
decoder.reset_params(
alpha=A ,beta=A ,unk_score_offset=A ,lm_score_boundary=A ,)
with get_context("""fork""" ).Pool() as pool:
UpperCAmelCase__ : List[Any] = decoder.decode_beams_batch(
A ,A ,)
UpperCAmelCase__ : Union[str, Any] = [d[0][0] for d in decoded_decoder_out]
self.assertListEqual(A ,A )
self.assertListEqual(["""<s> </s> <s> </s> </s>""", """</s> </s> <s> </s> </s>"""] ,A )
UpperCAmelCase__ : Union[str, Any] = processor.decoder.model_container[processor.decoder._model_key]
self.assertEqual(lm_model.alpha ,2.0 )
self.assertEqual(lm_model.beta ,5.0 )
self.assertEqual(lm_model.unk_score_offset ,-2_0.0 )
self.assertEqual(lm_model.score_boundary ,A )
def __lowercase ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Dict = WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm""" )
UpperCAmelCase__ : str = processor.decoder.model_container[processor.decoder._model_key]
UpperCAmelCase__ : Any = Path(language_model._kenlm_model.path.decode("""utf-8""" ) ).parent.parent.absolute()
UpperCAmelCase__ : Optional[int] = os.listdir(A )
UpperCAmelCase__ : List[Any] = ["""alphabet.json""", """language_model"""]
downloaded_decoder_files.sort()
expected_decoder_files.sort()
# test that only decoder relevant files from
# https://huggingface.co/hf-internal-testing/processor_with_lm/tree/main
# are downloaded and none of the rest (e.g. README.md, ...)
self.assertListEqual(A ,A )
def __lowercase ( self : int ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = snapshot_download("""hf-internal-testing/processor_with_lm""" )
UpperCAmelCase__ : Union[str, Any] = WavaVecaProcessorWithLM.from_pretrained(A )
UpperCAmelCase__ : Tuple = processor.decoder.model_container[processor.decoder._model_key]
UpperCAmelCase__ : Optional[int] = Path(language_model._kenlm_model.path.decode("""utf-8""" ) ).parent.parent.absolute()
UpperCAmelCase__ : Tuple = os.listdir(A )
UpperCAmelCase__ : Dict = os.listdir(A )
local_decoder_files.sort()
expected_decoder_files.sort()
# test that both decoder form hub and local files in cache are the same
self.assertListEqual(A ,A )
def __lowercase ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm""" )
UpperCAmelCase__ : Tuple = AutoProcessor.from_pretrained("""hf-internal-testing/processor_with_lm""" )
UpperCAmelCase__ : Dict = floats_list((3, 1_000) )
UpperCAmelCase__ : List[str] = processor_wavaveca(A ,return_tensors="""np""" )
UpperCAmelCase__ : Dict = processor_auto(A ,return_tensors="""np""" )
for key in input_wavaveca.keys():
self.assertAlmostEqual(input_wavaveca[key].sum() ,input_auto[key].sum() ,delta=1e-2 )
UpperCAmelCase__ : List[str] = self._get_dummy_logits()
UpperCAmelCase__ : Tuple = processor_wavaveca.batch_decode(A )
UpperCAmelCase__ : List[str] = processor_auto.batch_decode(A )
self.assertListEqual(decoded_wavaveca.text ,decoded_auto.text )
def __lowercase ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase__ : Dict = self.get_feature_extractor()
UpperCAmelCase__ : Tuple = self.get_tokenizer()
UpperCAmelCase__ : List[Any] = self.get_decoder()
UpperCAmelCase__ : int = WavaVecaProcessorWithLM(tokenizer=A ,feature_extractor=A ,decoder=A )
self.assertListEqual(
processor.model_input_names ,feature_extractor.model_input_names ,msg="""`processor` and `feature_extractor` model input names do not match""" ,)
@staticmethod
def __lowercase ( A : Optional[Any] ,A : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = [d[key] for d in offsets]
return retrieved_list
def __lowercase ( self : Dict ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm""" )
UpperCAmelCase__ : Dict = self._get_dummy_logits()[0]
UpperCAmelCase__ : List[str] = processor.decode(A ,output_word_offsets=A )
# check Wav2Vec2CTCTokenizerOutput keys for word
self.assertEqual(len(outputs.keys() ) ,4 )
self.assertTrue("""text""" in outputs )
self.assertTrue("""word_offsets""" in outputs )
self.assertTrue(isinstance(A ,A ) )
self.assertEqual(""" """.join(self.get_from_offsets(outputs["""word_offsets"""] ,"""word""" ) ) ,outputs.text )
self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""] ,"""word""" ) ,["""<s>""", """<s>""", """</s>"""] )
self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""] ,"""start_offset""" ) ,[0, 2, 4] )
self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""] ,"""end_offset""" ) ,[1, 3, 5] )
def __lowercase ( self : Dict ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm""" )
UpperCAmelCase__ : int = self._get_dummy_logits()
UpperCAmelCase__ : Any = processor.batch_decode(A ,output_word_offsets=A )
# check Wav2Vec2CTCTokenizerOutput keys for word
self.assertEqual(len(outputs.keys() ) ,4 )
self.assertTrue("""text""" in outputs )
self.assertTrue("""word_offsets""" in outputs )
self.assertTrue(isinstance(A ,A ) )
self.assertListEqual(
[""" """.join(self.get_from_offsets(A ,"""word""" ) ) for o in outputs["""word_offsets"""]] ,outputs.text )
self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""][0] ,"""word""" ) ,["""<s>""", """<s>""", """</s>"""] )
self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""][0] ,"""start_offset""" ) ,[0, 2, 4] )
self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""][0] ,"""end_offset""" ) ,[1, 3, 5] )
@slow
@require_torch
@require_torchaudio
def __lowercase ( self : Tuple ):
'''simple docstring'''
import torch
UpperCAmelCase__ : Any = load_dataset("""common_voice""" ,"""en""" ,split="""train""" ,streaming=A )
UpperCAmelCase__ : Tuple = ds.cast_column("""audio""" ,datasets.Audio(sampling_rate=16_000 ) )
UpperCAmelCase__ : Tuple = iter(A )
UpperCAmelCase__ : Optional[int] = next(A )
UpperCAmelCase__ : List[Any] = AutoProcessor.from_pretrained("""patrickvonplaten/wav2vec2-base-100h-with-lm""" )
UpperCAmelCase__ : Tuple = WavaVecaForCTC.from_pretrained("""patrickvonplaten/wav2vec2-base-100h-with-lm""" )
# compare to filename `common_voice_en_100038.mp3` of dataset viewer on https://huggingface.co/datasets/common_voice/viewer/en/train
UpperCAmelCase__ : Tuple = processor(sample["""audio"""]["""array"""] ,return_tensors="""pt""" ).input_values
with torch.no_grad():
UpperCAmelCase__ : Union[str, Any] = model(A ).logits.cpu().numpy()
UpperCAmelCase__ : Any = processor.decode(logits[0] ,output_word_offsets=A )
UpperCAmelCase__ : str = model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
UpperCAmelCase__ : Union[str, Any] = [
{
"""start_time""": d["""start_offset"""] * time_offset,
"""end_time""": d["""end_offset"""] * time_offset,
"""word""": d["""word"""],
}
for d in output["""word_offsets"""]
]
UpperCAmelCase__ : Dict = """WHY DOES MILISANDRA LOOK LIKE SHE WANTS TO CONSUME JOHN SNOW ON THE RIVER AT THE WALL"""
# output words
self.assertEqual(""" """.join(self.get_from_offsets(A ,"""word""" ) ) ,A )
self.assertEqual(""" """.join(self.get_from_offsets(A ,"""word""" ) ) ,output.text )
# output times
UpperCAmelCase__ : str = torch.tensor(self.get_from_offsets(A ,"""start_time""" ) )
UpperCAmelCase__ : List[Any] = torch.tensor(self.get_from_offsets(A ,"""end_time""" ) )
# fmt: off
UpperCAmelCase__ : Union[str, Any] = torch.tensor([1.4_1_9_9, 1.6_5_9_9, 2.2_5_9_9, 3.0, 3.2_4, 3.5_9_9_9, 3.7_9_9_9, 4.0_9_9_9, 4.2_6, 4.9_4, 5.2_8, 5.6_5_9_9, 5.7_8, 5.9_4, 6.3_2, 6.5_3_9_9, 6.6_5_9_9] )
UpperCAmelCase__ : List[Any] = torch.tensor([1.5_3_9_9, 1.8_9_9_9, 2.9, 3.1_6, 3.5_3_9_9, 3.7_2, 4.0_1_9_9, 4.1_7_9_9, 4.7_6, 5.1_5_9_9, 5.5_5_9_9, 5.6_9_9_9, 5.8_6, 6.1_9_9_9, 6.3_8, 6.6_1_9_9, 6.9_4] )
# fmt: on
self.assertTrue(torch.allclose(A ,A ,atol=0.0_1 ) )
self.assertTrue(torch.allclose(A ,A ,atol=0.0_1 ) )
| 65 | 0 |
"""simple docstring"""
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class lowerCAmelCase_ ( _lowercase , unittest.TestCase ):
'''simple docstring'''
_lowerCamelCase: Dict = '''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''
def _SCREAMING_SNAKE_CASE ( self : Any ,A_ : List[str]=0 ) -> str:
A = floats_tensor((1, 3, 128, 128) ,rng=random.Random(A_ ) )
A = np.random.RandomState(A_ )
A = {
'prompt': 'A painting of a squirrel eating a burger',
'image': image,
'generator': generator,
'num_inference_steps': 3,
'strength': 0.75,
'guidance_scale': 7.5,
'output_type': 'numpy',
}
return inputs
def _SCREAMING_SNAKE_CASE ( self : Dict ) -> Tuple:
A = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint ,provider='CPUExecutionProvider' )
pipe.set_progress_bar_config(disable=A_ )
A = self.get_dummy_inputs()
A = pipe(**A_ ).images
A = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 128, 128, 3)
A = np.array([0.6_96_43, 0.5_84_84, 0.5_03_14, 0.5_87_60, 0.5_53_68, 0.5_96_43, 0.5_15_29, 0.4_12_17, 0.4_90_87] )
assert np.abs(image_slice - expected_slice ).max() < 1e-1
def _SCREAMING_SNAKE_CASE ( self : List[Any] ) -> Any:
A = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint ,provider='CPUExecutionProvider' )
A = PNDMScheduler.from_config(pipe.scheduler.config ,skip_prk_steps=A_ )
pipe.set_progress_bar_config(disable=A_ )
A = self.get_dummy_inputs()
A = pipe(**A_ ).images
A = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
A = np.array([0.6_17_37, 0.5_46_42, 0.5_31_83, 0.5_44_65, 0.5_27_42, 0.6_05_25, 0.4_99_69, 0.4_06_55, 0.4_81_54] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> int:
A = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint ,provider='CPUExecutionProvider' )
A = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=A_ )
# warmup pass to apply optimizations
A = pipe(**self.get_dummy_inputs() )
A = self.get_dummy_inputs()
A = pipe(**A_ ).images
A = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
A = np.array([0.5_27_61, 0.5_99_77, 0.4_90_33, 0.4_96_19, 0.5_42_82, 0.5_03_11, 0.4_76_00, 0.4_09_18, 0.4_52_03] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> Tuple:
A = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint ,provider='CPUExecutionProvider' )
A = EulerDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=A_ )
A = self.get_dummy_inputs()
A = pipe(**A_ ).images
A = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
A = np.array([0.5_29_11, 0.6_00_04, 0.4_92_29, 0.4_98_05, 0.5_45_02, 0.5_06_80, 0.4_77_77, 0.4_10_28, 0.4_53_04] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> str:
A = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint ,provider='CPUExecutionProvider' )
A = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=A_ )
A = self.get_dummy_inputs()
A = pipe(**A_ ).images
A = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
A = np.array([0.5_29_11, 0.6_00_04, 0.4_92_29, 0.4_98_05, 0.5_45_02, 0.5_06_80, 0.4_77_77, 0.4_10_28, 0.4_53_04] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def _SCREAMING_SNAKE_CASE ( self : int ) -> Tuple:
A = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint ,provider='CPUExecutionProvider' )
A = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=A_ )
A = self.get_dummy_inputs()
A = pipe(**A_ ).images
A = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
A = np.array([0.6_53_31, 0.5_82_77, 0.4_82_04, 0.5_60_59, 0.5_36_65, 0.5_62_35, 0.5_09_69, 0.4_00_09, 0.4_65_52] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
@nightly
@require_onnxruntime
@require_torch_gpu
class lowerCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
@property
def _SCREAMING_SNAKE_CASE ( self : int ) -> Union[str, Any]:
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def _SCREAMING_SNAKE_CASE ( self : str ) -> List[Any]:
A = ort.SessionOptions()
A = False
return options
def _SCREAMING_SNAKE_CASE ( self : Tuple ) -> str:
A = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/img2img/sketch-mountains-input.jpg' )
A = init_image.resize((768, 512) )
# using the PNDM scheduler by default
A = OnnxStableDiffusionImgaImgPipeline.from_pretrained(
'CompVis/stable-diffusion-v1-4' ,revision='onnx' ,safety_checker=A_ ,feature_extractor=A_ ,provider=self.gpu_provider ,sess_options=self.gpu_options ,)
pipe.set_progress_bar_config(disable=A_ )
A = 'A fantasy landscape, trending on artstation'
A = np.random.RandomState(0 )
A = pipe(
prompt=A_ ,image=A_ ,strength=0.75 ,guidance_scale=7.5 ,num_inference_steps=10 ,generator=A_ ,output_type='np' ,)
A = output.images
A = images[0, 255:258, 383:386, -1]
assert images.shape == (1, 512, 768, 3)
A = np.array([0.49_09, 0.50_59, 0.53_72, 0.46_23, 0.48_76, 0.50_49, 0.48_20, 0.49_56, 0.50_19] )
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2e-2
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> Optional[Any]:
A = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/img2img/sketch-mountains-input.jpg' )
A = init_image.resize((768, 512) )
A = LMSDiscreteScheduler.from_pretrained(
'runwayml/stable-diffusion-v1-5' ,subfolder='scheduler' ,revision='onnx' )
A = OnnxStableDiffusionImgaImgPipeline.from_pretrained(
'runwayml/stable-diffusion-v1-5' ,revision='onnx' ,scheduler=A_ ,safety_checker=A_ ,feature_extractor=A_ ,provider=self.gpu_provider ,sess_options=self.gpu_options ,)
pipe.set_progress_bar_config(disable=A_ )
A = 'A fantasy landscape, trending on artstation'
A = np.random.RandomState(0 )
A = pipe(
prompt=A_ ,image=A_ ,strength=0.75 ,guidance_scale=7.5 ,num_inference_steps=20 ,generator=A_ ,output_type='np' ,)
A = output.images
A = images[0, 255:258, 383:386, -1]
assert images.shape == (1, 512, 768, 3)
A = np.array([0.80_43, 0.9_26, 0.95_81, 0.81_19, 0.89_54, 0.9_13, 0.72_09, 0.74_63, 0.74_31] )
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2e-2 | 91 |
"""simple docstring"""
from sklearn.metrics import fa_score
import datasets
__UpperCAmelCase = '\nThe F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:\nF1 = 2 * (precision * recall) / (precision + recall)\n'
__UpperCAmelCase = '\nArgs:\n predictions (`list` of `int`): Predicted labels.\n references (`list` of `int`): Ground truth labels.\n labels (`list` of `int`): The set of labels to include when `average` is not set to `\'binary\'`, and the order of the labels if `average` is `None`. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class. Labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in `predictions` and `references` are used in sorted order. Defaults to None.\n pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.\n average (`string`): This parameter is required for multiclass/multilabel targets. If set to `None`, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `\'binary\'`.\n\n - \'binary\': Only report results for the class specified by `pos_label`. This is applicable only if the classes found in `predictions` and `references` are binary.\n - \'micro\': Calculate metrics globally by counting the total true positives, false negatives and false positives.\n - \'macro\': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.\n - \'weighted\': Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `\'macro\'` to account for label imbalance. This option can result in an F-score that is not between precision and recall.\n - \'samples\': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).\n sample_weight (`list` of `float`): Sample weights Defaults to None.\n\nReturns:\n f1 (`float` or `array` of `float`): F1 score or list of f1 scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher f1 scores are better.\n\nExamples:\n\n Example 1-A simple binary example\n >>> f1_metric = datasets.load_metric("f1")\n >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])\n >>> print(results)\n {\'f1\': 0.5}\n\n Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.\n >>> f1_metric = datasets.load_metric("f1")\n >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)\n >>> print(round(results[\'f1\'], 2))\n 0.67\n\n Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.\n >>> f1_metric = datasets.load_metric("f1")\n >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], sample_weight=[0.9, 0.5, 3.9, 1.2, 0.3])\n >>> print(round(results[\'f1\'], 2))\n 0.35\n\n Example 4-A multiclass example, with different values for the `average` input.\n >>> predictions = [0, 2, 1, 0, 0, 1]\n >>> references = [0, 1, 2, 0, 1, 2]\n >>> results = f1_metric.compute(predictions=predictions, references=references, average="macro")\n >>> print(round(results[\'f1\'], 2))\n 0.27\n >>> results = f1_metric.compute(predictions=predictions, references=references, average="micro")\n >>> print(round(results[\'f1\'], 2))\n 0.33\n >>> results = f1_metric.compute(predictions=predictions, references=references, average="weighted")\n >>> print(round(results[\'f1\'], 2))\n 0.27\n >>> results = f1_metric.compute(predictions=predictions, references=references, average=None)\n >>> print(results)\n {\'f1\': array([0.8, 0. , 0. ])}\n'
__UpperCAmelCase = '\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __lowercase ( datasets.Metric ):
def __lowercase ( self : List[Any] ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION ,citation=_CITATION ,inputs_description=_KWARGS_DESCRIPTION ,features=datasets.Features(
{
"""predictions""": datasets.Sequence(datasets.Value("""int32""" ) ),
"""references""": datasets.Sequence(datasets.Value("""int32""" ) ),
}
if self.config_name == """multilabel"""
else {
"""predictions""": datasets.Value("""int32""" ),
"""references""": datasets.Value("""int32""" ),
} ) ,reference_urls=["""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html"""] ,)
def __lowercase ( self : Union[str, Any] ,A : List[str] ,A : List[Any] ,A : Optional[Any]=None ,A : List[str]=1 ,A : Optional[Any]="binary" ,A : Any=None ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = fa_score(
A ,A ,labels=A ,pos_label=A ,average=A ,sample_weight=A )
return {"f1": float(A ) if score.size == 1 else score}
| 65 | 0 |
'''simple docstring'''
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_video_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import VivitImageProcessor
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __init__( self : List[str] , UpperCAmelCase__ : str , UpperCAmelCase__ : Tuple=7 , UpperCAmelCase__ : str=3 , UpperCAmelCase__ : Union[str, Any]=10 , UpperCAmelCase__ : int=18 , UpperCAmelCase__ : Optional[int]=30 , UpperCAmelCase__ : Dict=400 , UpperCAmelCase__ : Union[str, Any]=True , UpperCAmelCase__ : int=None , UpperCAmelCase__ : Optional[int]=True , UpperCAmelCase__ : str=[0.5, 0.5, 0.5] , UpperCAmelCase__ : Optional[Any]=[0.5, 0.5, 0.5] , UpperCAmelCase__ : List[Any]=None , ):
'''simple docstring'''
lowercase : Optional[Any] =size if size is not None else {'''shortest_edge''': 18}
lowercase : Union[str, Any] =crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
lowercase : List[Any] =parent
lowercase : Optional[Any] =batch_size
lowercase : str =num_channels
lowercase : Any =num_frames
lowercase : Any =image_size
lowercase : Optional[Any] =min_resolution
lowercase : Tuple =max_resolution
lowercase : Any =do_resize
lowercase : Optional[int] =size
lowercase : int =do_normalize
lowercase : List[str] =image_mean
lowercase : Union[str, Any] =image_std
lowercase : int =crop_size
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"size": self.size,
"crop_size": self.crop_size,
}
@require_torch
@require_vision
class __SCREAMING_SNAKE_CASE ( lowercase__ , unittest.TestCase ):
lowerCamelCase_ = VivitImageProcessor if is_vision_available() else None
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
lowercase : Optional[int] =VivitImageProcessingTester(self )
@property
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
lowercase : int =self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(UpperCAmelCase__ , '''image_mean''' ) )
self.assertTrue(hasattr(UpperCAmelCase__ , '''image_std''' ) )
self.assertTrue(hasattr(UpperCAmelCase__ , '''do_normalize''' ) )
self.assertTrue(hasattr(UpperCAmelCase__ , '''do_resize''' ) )
self.assertTrue(hasattr(UpperCAmelCase__ , '''do_center_crop''' ) )
self.assertTrue(hasattr(UpperCAmelCase__ , '''size''' ) )
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
lowercase : Dict =self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''shortest_edge''': 18} )
self.assertEqual(image_processor.crop_size , {'''height''': 18, '''width''': 18} )
lowercase : Dict =self.image_processing_class.from_dict(self.image_processor_dict , size=42 , crop_size=84 )
self.assertEqual(image_processor.size , {'''shortest_edge''': 42} )
self.assertEqual(image_processor.crop_size , {'''height''': 84, '''width''': 84} )
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
# Initialize image_processing
lowercase : Optional[int] =self.image_processing_class(**self.image_processor_dict )
# create random PIL videos
lowercase : Tuple =prepare_video_inputs(self.image_processor_tester , equal_resolution=UpperCAmelCase__ )
for video in video_inputs:
self.assertIsInstance(UpperCAmelCase__ , UpperCAmelCase__ )
self.assertIsInstance(video[0] , Image.Image )
# Test not batched input
lowercase : Union[str, Any] =image_processing(video_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_videos.shape , (
1,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
lowercase : Optional[int] =image_processing(UpperCAmelCase__ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_videos.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
# Initialize image_processing
lowercase : List[str] =self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
lowercase : List[Any] =prepare_video_inputs(self.image_processor_tester , equal_resolution=UpperCAmelCase__ , numpify=UpperCAmelCase__ )
for video in video_inputs:
self.assertIsInstance(UpperCAmelCase__ , UpperCAmelCase__ )
self.assertIsInstance(video[0] , np.ndarray )
# Test not batched input
lowercase : Tuple =image_processing(video_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_videos.shape , (
1,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
lowercase : str =image_processing(UpperCAmelCase__ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_videos.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def lowerCamelCase_ ( self : Any ):
'''simple docstring'''
# Initialize image_processing
lowercase : Optional[Any] =self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
lowercase : Optional[int] =prepare_video_inputs(self.image_processor_tester , equal_resolution=UpperCAmelCase__ , torchify=UpperCAmelCase__ )
for video in video_inputs:
self.assertIsInstance(UpperCAmelCase__ , UpperCAmelCase__ )
self.assertIsInstance(video[0] , torch.Tensor )
# Test not batched input
lowercase : str =image_processing(video_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_videos.shape , (
1,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
lowercase : Any =image_processing(UpperCAmelCase__ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_videos.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
| 92 |
"""simple docstring"""
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available
if is_sentencepiece_available():
from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json
from ...test_tokenization_common import TokenizerTesterMixin
__UpperCAmelCase = get_tests_dir('fixtures/test_sentencepiece.model')
__UpperCAmelCase = {'target_lang': 'fi', 'source_lang': 'en'}
__UpperCAmelCase = '>>zh<<'
__UpperCAmelCase = 'Helsinki-NLP/'
if is_torch_available():
__UpperCAmelCase = 'pt'
elif is_tf_available():
__UpperCAmelCase = 'tf'
else:
__UpperCAmelCase = 'jax'
@require_sentencepiece
class __lowercase ( __lowerCamelCase , unittest.TestCase ):
snake_case_ = MarianTokenizer
snake_case_ = False
snake_case_ = True
def __lowercase ( self : Optional[int] ):
'''simple docstring'''
super().setUp()
UpperCAmelCase__ : Optional[Any] = ["""</s>""", """<unk>""", """▁This""", """▁is""", """▁a""", """▁t""", """est""", """\u0120""", """<pad>"""]
UpperCAmelCase__ : int = dict(zip(A ,range(len(A ) ) ) )
UpperCAmelCase__ : Optional[int] = Path(self.tmpdirname )
save_json(A ,save_dir / VOCAB_FILES_NAMES["""vocab"""] )
save_json(A ,save_dir / VOCAB_FILES_NAMES["""tokenizer_config_file"""] )
if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
copyfile(A ,save_dir / VOCAB_FILES_NAMES["""source_spm"""] )
copyfile(A ,save_dir / VOCAB_FILES_NAMES["""target_spm"""] )
UpperCAmelCase__ : Dict = MarianTokenizer.from_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname )
def __lowercase ( self : List[Any] ,**A : List[Any] ):
'''simple docstring'''
return MarianTokenizer.from_pretrained(self.tmpdirname ,**A )
def __lowercase ( self : Union[str, Any] ,A : Tuple ):
'''simple docstring'''
return (
"This is a test",
"This is a test",
)
def __lowercase ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = """</s>"""
UpperCAmelCase__ : int = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(A ) ,A )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(A ) ,A )
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ : Dict = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] ,"""</s>""" )
self.assertEqual(vocab_keys[1] ,"""<unk>""" )
self.assertEqual(vocab_keys[-1] ,"""<pad>""" )
self.assertEqual(len(A ) ,9 )
def __lowercase ( self : Dict ):
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size ,9 )
def __lowercase ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = MarianTokenizer.from_pretrained(f"{ORG_NAME}opus-mt-en-de" )
UpperCAmelCase__ : List[str] = en_de_tokenizer(["""I am a small frog"""] ,return_tensors=A )
self.assertIsInstance(A ,A )
UpperCAmelCase__ : str = [38, 121, 14, 697, 38_848, 0]
self.assertListEqual(A ,batch.input_ids[0] )
UpperCAmelCase__ : Optional[Any] = tempfile.mkdtemp()
en_de_tokenizer.save_pretrained(A )
UpperCAmelCase__ : Tuple = [x.name for x in Path(A ).glob("""*""" )]
self.assertIn("""source.spm""" ,A )
MarianTokenizer.from_pretrained(A )
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = self.get_tokenizer()
UpperCAmelCase__ : Any = tok(
["""I am a small frog""" * 1_000, """I am a small frog"""] ,padding=A ,truncation=A ,return_tensors=A )
self.assertIsInstance(A ,A )
self.assertEqual(batch.input_ids.shape ,(2, 512) )
def __lowercase ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase__ : int = self.get_tokenizer()
UpperCAmelCase__ : Tuple = tok(["""I am a tiny frog""", """I am a small frog"""] ,padding=A ,return_tensors=A )
self.assertIsInstance(A ,A )
self.assertEqual(batch_smaller.input_ids.shape ,(2, 10) )
@slow
def __lowercase ( self : Dict ):
'''simple docstring'''
# fmt: off
UpperCAmelCase__ : Optional[int] = {"""input_ids""": [[43_495, 462, 20, 42_164, 1_369, 52, 464, 132, 1_703, 492, 13, 7_491, 38_999, 6, 8, 464, 132, 1_703, 492, 13, 4_669, 37_867, 13, 7_525, 27, 1_593, 988, 13, 33_972, 7_029, 6, 20, 8_251, 383, 2, 270, 5_866, 3_788, 2, 2_353, 8_251, 12_338, 2, 13_958, 387, 2, 3_629, 6_953, 188, 2_900, 2, 13_958, 8_011, 11_501, 23, 8_460, 4_073, 34_009, 20, 435, 11_439, 27, 8, 8_460, 4_073, 6_004, 20, 9_988, 375, 27, 33, 266, 1_945, 1_076, 1_350, 37_867, 3_288, 5, 577, 1_076, 4_374, 8, 5_082, 5, 26_453, 257, 556, 403, 2, 242, 132, 383, 316, 492, 8, 10_767, 6, 316, 304, 4_239, 3, 0], [148, 15_722, 19, 1_839, 12, 1_350, 13, 22_327, 5_082, 5_418, 47_567, 35_938, 59, 318, 19_552, 108, 2_183, 54, 14_976, 4_835, 32, 547, 1_114, 8, 315, 2_417, 5, 92, 19_088, 3, 0, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100], [36, 6_395, 12_570, 39_147, 11_597, 6, 266, 4, 45_405, 7_296, 3, 0, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100]], """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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=A ,model_name="""Helsinki-NLP/opus-mt-en-de""" ,revision="""1a8c2263da11e68e50938f97e10cd57820bd504c""" ,decode_kwargs={"""use_source_tokenizer""": True} ,)
def __lowercase ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = MarianTokenizer.from_pretrained("""hf-internal-testing/test-marian-two-vocabs""" )
UpperCAmelCase__ : Any = """Tämä on testi"""
UpperCAmelCase__ : int = """This is a test"""
UpperCAmelCase__ : List[str] = [76, 7, 2_047, 2]
UpperCAmelCase__ : Optional[Any] = [69, 12, 11, 940, 2]
UpperCAmelCase__ : List[str] = tokenizer(A ).input_ids
self.assertListEqual(A ,A )
UpperCAmelCase__ : Optional[int] = tokenizer(text_target=A ).input_ids
self.assertListEqual(A ,A )
UpperCAmelCase__ : int = tokenizer.decode(A ,skip_special_tokens=A )
self.assertEqual(A ,A )
| 65 | 0 |
"""simple docstring"""
# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union
__A = re.compile(R"""^(?P<major>\d+)""" R"""\.(?P<minor>\d+)""" R"""\.(?P<patch>\d+)$""")
@total_ordering
@dataclass
class _lowerCAmelCase :
"""simple docstring"""
__magic_name__ :str
__magic_name__ :Optional[str] = None
__magic_name__ :Optional[Union[str, int]] = None
__magic_name__ :Optional[Union[str, int]] = None
__magic_name__ :Optional[Union[str, int]] = None
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ :Tuple = _str_to_version_tuple(self.version_str )
def __repr__( self ):
'''simple docstring'''
return F"{self.tuple[0]}.{self.tuple[1]}.{self.tuple[2]}"
@property
def snake_case ( self ):
'''simple docstring'''
return self.major, self.minor, self.patch
def snake_case ( self , __UpperCAmelCase ):
'''simple docstring'''
if isinstance(__UpperCAmelCase , __UpperCAmelCase ):
return Version(__UpperCAmelCase )
elif isinstance(__UpperCAmelCase , __UpperCAmelCase ):
return other
raise TypeError(F"{other} (type {type(__UpperCAmelCase )}) cannot be compared to version." )
def __eq__( self , __UpperCAmelCase ):
'''simple docstring'''
try:
lowerCAmelCase__ :Optional[int] = self._validate_operand(__UpperCAmelCase )
except (TypeError, ValueError):
return False
else:
return self.tuple == other.tuple
def __lt__( self , __UpperCAmelCase ):
'''simple docstring'''
lowerCAmelCase__ :Union[str, Any] = self._validate_operand(__UpperCAmelCase )
return self.tuple < other.tuple
def __hash__( self ):
'''simple docstring'''
return hash(_version_tuple_to_str(self.tuple ) )
@classmethod
def snake_case ( cls , __UpperCAmelCase ):
'''simple docstring'''
lowerCAmelCase__ :List[str] = {f.name for f in dataclasses.fields(cls )}
return cls(**{k: v for k, v in dic.items() if k in field_names} )
def snake_case ( self ):
'''simple docstring'''
return self.version_str
def __A (_SCREAMING_SNAKE_CASE ) ->Dict:
"""simple docstring"""
lowerCAmelCase__ :Any = _VERSION_REG.match(_SCREAMING_SNAKE_CASE )
if not res:
raise ValueError(F"Invalid version '{version_str}'. Format should be x.y.z with {{x,y,z}} being digits." )
return tuple(int(_SCREAMING_SNAKE_CASE ) for v in [res.group('major' ), res.group('minor' ), res.group('patch' )] )
def __A (_SCREAMING_SNAKE_CASE ) ->int:
"""simple docstring"""
return ".".join(str(_SCREAMING_SNAKE_CASE ) for v in version_tuple )
| 93 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class __lowercase ( metaclass=__lowerCamelCase ):
snake_case_ = ["""onnx"""]
def __init__( self : int ,*A : List[str] ,**A : int ):
'''simple docstring'''
requires_backends(self ,["""onnx"""] )
@classmethod
def __lowercase ( cls : Optional[Any] ,*A : List[str] ,**A : Dict ):
'''simple docstring'''
requires_backends(cls ,["""onnx"""] )
@classmethod
def __lowercase ( cls : List[Any] ,*A : Optional[int] ,**A : int ):
'''simple docstring'''
requires_backends(cls ,["""onnx"""] )
| 65 | 0 |
'''simple docstring'''
from math import isqrt
def lowercase_ ( __A : int ) -> list[int]:
"""simple docstring"""
lowercase : Dict =[True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , __A , __A ):
lowercase : str =False
return [i for i in range(2 , __A ) if is_prime[i]]
def lowercase_ ( __A : int = 1_0**8 ) -> int:
"""simple docstring"""
lowercase : Dict =calculate_prime_numbers(max_number // 2 )
lowercase : str =0
lowercase : Optional[Any] =0
lowercase : Union[str, Any] =len(__A ) - 1
while left <= right:
while prime_numbers[left] * prime_numbers[right] >= max_number:
right -= 1
semiprimes_count += right - left + 1
left += 1
return semiprimes_count
if __name__ == "__main__":
print(f"""{solution() = }""")
| 94 |
"""simple docstring"""
from argparse import ArgumentParser
from .env import EnvironmentCommand
def lowerCAmelCase ( ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = ArgumentParser("""Diffusers CLI tool""" , usage="""diffusers-cli <command> [<args>]""" )
UpperCAmelCase__ : List[Any] = parser.add_subparsers(help="""diffusers-cli command helpers""" )
# Register commands
EnvironmentCommand.register_subcommand(__UpperCamelCase )
# Let's go
UpperCAmelCase__ : int = parser.parse_args()
if not hasattr(__UpperCamelCase , """func""" ):
parser.print_help()
exit(1 )
# Run
UpperCAmelCase__ : Union[str, Any] = args.func(__UpperCamelCase )
service.run()
if __name__ == "__main__":
main()
| 65 | 0 |
"""simple docstring"""
import functools
def snake_case ( A__ ,A__ ):
# Validation
if not isinstance(A__ ,A__ ) or not all(isinstance(A__ ,A__ ) for day in days ):
raise ValueError("The parameter days should be a list of integers" )
if len(A__ ) != 3 or not all(isinstance(A__ ,A__ ) for cost in costs ):
raise ValueError("The parameter costs should be a list of three integers" )
if len(A__ ) == 0:
return 0
if min(A__ ) <= 0:
raise ValueError("All days elements should be greater than 0" )
if max(A__ ) >= 3_66:
raise ValueError("All days elements should be less than 366" )
UpperCAmelCase_ : Dict = set(A__ )
@functools.cache
def dynamic_programming(A__ ) -> int:
if index > 3_65:
return 0
if index not in days_set:
return dynamic_programming(index + 1 )
return min(
costs[0] + dynamic_programming(index + 1 ) ,costs[1] + dynamic_programming(index + 7 ) ,costs[2] + dynamic_programming(index + 30 ) ,)
return dynamic_programming(1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 95 |
"""simple docstring"""
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
import os
# The slow tests are often failing with OOM error on GPU
# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
__UpperCAmelCase = 'platform'
import jax
import jax.numpy as jnp
import numpy as np
from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel
@require_flax
class __lowercase :
snake_case_ = PegasusConfig
snake_case_ = {}
snake_case_ = """gelu"""
def __init__( self : List[Any] ,A : int ,A : Optional[Any]=13 ,A : Dict=7 ,A : Dict=True ,A : Any=False ,A : Dict=99 ,A : int=32 ,A : Optional[int]=5 ,A : Union[str, Any]=4 ,A : Union[str, Any]=37 ,A : str=0.1 ,A : int=0.1 ,A : Optional[int]=20 ,A : Tuple=2 ,A : str=1 ,A : Optional[Any]=0 ,):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = parent
UpperCAmelCase__ : Union[str, Any] = batch_size
UpperCAmelCase__ : List[Any] = seq_length
UpperCAmelCase__ : int = is_training
UpperCAmelCase__ : Any = use_labels
UpperCAmelCase__ : int = vocab_size
UpperCAmelCase__ : Dict = hidden_size
UpperCAmelCase__ : Optional[Any] = num_hidden_layers
UpperCAmelCase__ : int = num_attention_heads
UpperCAmelCase__ : Any = intermediate_size
UpperCAmelCase__ : Optional[int] = hidden_dropout_prob
UpperCAmelCase__ : str = attention_probs_dropout_prob
UpperCAmelCase__ : str = max_position_embeddings
UpperCAmelCase__ : Union[str, Any] = eos_token_id
UpperCAmelCase__ : Union[str, Any] = pad_token_id
UpperCAmelCase__ : List[str] = bos_token_id
def __lowercase ( self : Dict ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = ids_tensor([self.batch_size, self.seq_length - 1] ,self.vocab_size ).clip(3 ,self.vocab_size )
UpperCAmelCase__ : List[str] = np.expand_dims(np.array([self.eos_token_id] * self.batch_size ) ,1 )
UpperCAmelCase__ : Any = np.concatenate([input_ids, eos_tensor] ,axis=1 )
UpperCAmelCase__ : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
UpperCAmelCase__ : str = 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 ,)
UpperCAmelCase__ : Optional[Any] = prepare_pegasus_inputs_dict(A ,A ,A )
return config, inputs_dict
def __lowercase ( self : Any ,A : Optional[int] ,A : str ,A : Optional[int] ):
'''simple docstring'''
UpperCAmelCase__ : Any = 20
UpperCAmelCase__ : Dict = model_class_name(A )
UpperCAmelCase__ : str = model.encode(inputs_dict["""input_ids"""] )
UpperCAmelCase__ , UpperCAmelCase__ : List[str] = (
inputs_dict["""decoder_input_ids"""],
inputs_dict["""decoder_attention_mask"""],
)
UpperCAmelCase__ : Union[str, Any] = model.init_cache(decoder_input_ids.shape[0] ,A ,A )
UpperCAmelCase__ : Union[str, Any] = jnp.ones((decoder_input_ids.shape[0], max_decoder_length) ,dtype="""i4""" )
UpperCAmelCase__ : str = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] ,(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) ,)
UpperCAmelCase__ : Optional[int] = model.decode(
decoder_input_ids[:, :-1] ,A ,decoder_attention_mask=A ,past_key_values=A ,decoder_position_ids=A ,)
UpperCAmelCase__ : Any = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] ,dtype="""i4""" )
UpperCAmelCase__ : int = model.decode(
decoder_input_ids[:, -1:] ,A ,decoder_attention_mask=A ,past_key_values=outputs_cache.past_key_values ,decoder_position_ids=A ,)
UpperCAmelCase__ : Dict = model.decode(A ,A )
UpperCAmelCase__ : str = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1e-3 ,msg=f"Max diff is {diff}" )
def __lowercase ( self : Optional[int] ,A : str ,A : Dict ,A : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ : Any = 20
UpperCAmelCase__ : str = model_class_name(A )
UpperCAmelCase__ : Any = model.encode(inputs_dict["""input_ids"""] )
UpperCAmelCase__ , UpperCAmelCase__ : Optional[int] = (
inputs_dict["""decoder_input_ids"""],
inputs_dict["""decoder_attention_mask"""],
)
UpperCAmelCase__ : Optional[int] = jnp.concatenate(
[
decoder_attention_mask,
jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1]) ),
] ,axis=-1 ,)
UpperCAmelCase__ : Union[str, Any] = model.init_cache(decoder_input_ids.shape[0] ,A ,A )
UpperCAmelCase__ : List[str] = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] ,(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) ,)
UpperCAmelCase__ : Union[str, Any] = model.decode(
decoder_input_ids[:, :-1] ,A ,decoder_attention_mask=A ,past_key_values=A ,decoder_position_ids=A ,)
UpperCAmelCase__ : int = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] ,dtype="""i4""" )
UpperCAmelCase__ : Dict = model.decode(
decoder_input_ids[:, -1:] ,A ,past_key_values=outputs_cache.past_key_values ,decoder_attention_mask=A ,decoder_position_ids=A ,)
UpperCAmelCase__ : Union[str, Any] = model.decode(A ,A ,decoder_attention_mask=A )
UpperCAmelCase__ : Union[str, 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}" )
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=None , __UpperCamelCase=None , ):
'''simple docstring'''
if attention_mask is None:
UpperCAmelCase__ : Union[str, Any] = np.not_equal(__UpperCamelCase , config.pad_token_id ).astype(np.inta )
if decoder_attention_mask is None:
UpperCAmelCase__ : Tuple = np.concatenate(
[
np.ones(decoder_input_ids[:, :1].shape , dtype=np.inta ),
np.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ).astype(np.inta ),
] , axis=-1 , )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
}
@require_flax
class __lowercase ( __lowerCamelCase , unittest.TestCase ):
snake_case_ = (
(
FlaxPegasusForConditionalGeneration,
FlaxPegasusModel,
)
if is_flax_available()
else ()
)
snake_case_ = (FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
snake_case_ = True
snake_case_ = False
snake_case_ = False
snake_case_ = False
def __lowercase ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase__ : int = FlaxPegasusModelTester(self )
UpperCAmelCase__ : Optional[Any] = ConfigTester(self ,config_class=A )
def __lowercase ( self : Tuple ):
'''simple docstring'''
self.config_tester.run_common_tests()
def __lowercase ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase__ , UpperCAmelCase__ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward(A ,A ,A )
def __lowercase ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase__ , UpperCAmelCase__ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward_with_attn_mask(A ,A ,A )
def __lowercase ( self : Any ):
'''simple docstring'''
UpperCAmelCase__ , UpperCAmelCase__ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
UpperCAmelCase__ : List[Any] = self._prepare_for_class(A ,A )
UpperCAmelCase__ : int = model_class(A )
@jax.jit
def encode_jitted(A : Optional[int] ,A : Union[str, Any]=None ,**A : Optional[Any] ):
return model.encode(input_ids=A ,attention_mask=A )
with self.subTest("""JIT Enabled""" ):
UpperCAmelCase__ : int = encode_jitted(**A ).to_tuple()
with self.subTest("""JIT Disabled""" ):
with jax.disable_jit():
UpperCAmelCase__ : Dict = encode_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 )
def __lowercase ( self : str ):
'''simple docstring'''
UpperCAmelCase__ , UpperCAmelCase__ : 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__ ):
UpperCAmelCase__ : Dict = model_class(A )
UpperCAmelCase__ : str = model.encode(inputs_dict["""input_ids"""] ,inputs_dict["""attention_mask"""] )
UpperCAmelCase__ : Dict = {
"""decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
"""decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
"""encoder_outputs""": encoder_outputs,
}
@jax.jit
def decode_jitted(A : List[Any] ,A : Any ,A : List[Any] ):
return model.decode(
decoder_input_ids=A ,decoder_attention_mask=A ,encoder_outputs=A ,)
with self.subTest("""JIT Enabled""" ):
UpperCAmelCase__ : Tuple = decode_jitted(**A ).to_tuple()
with self.subTest("""JIT Disabled""" ):
with jax.disable_jit():
UpperCAmelCase__ : str = decode_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 : List[Any] ):
'''simple docstring'''
for model_class_name in self.all_model_classes:
UpperCAmelCase__ : List[str] = model_class_name.from_pretrained("""google/pegasus-large""" ,from_pt=A )
UpperCAmelCase__ : Any = np.ones((1, 1) )
UpperCAmelCase__ : Optional[Any] = model(A )
self.assertIsNotNone(A )
@slow
def __lowercase ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase__ : Dict = FlaxPegasusForConditionalGeneration.from_pretrained("""google/pegasus-xsum""" )
UpperCAmelCase__ : Optional[Any] = PegasusTokenizer.from_pretrained("""google/pegasus-xsum""" )
UpperCAmelCase__ : Union[str, Any] = [
""" PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.""",
""" The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning 'Oh I think you're nominated'\", said Dappy.\"And I was like 'Oh yeah, which one?' And now we've got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it's best of us to come down and mingle with everyone and say hello to the cameras. And now we find we've got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn't be too disappointed if they didn't win this time around.\"At the end of the day we're grateful to be where we are in our careers.\"If it don't happen then it don't happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a re-working of The Killers' All These Things That I've Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year's Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" """,
]
UpperCAmelCase__ : str = [
"""California's largest electricity provider has turned off power to hundreds of thousands of customers.""",
"""Pop group N-Dubz have revealed they were surprised to get four nominations for this year's Mobo Awards.""",
]
UpperCAmelCase__ : str = tokenizer(A ,return_tensors="""np""" ,truncation=A ,max_length=512 ,padding=A )
UpperCAmelCase__ : Union[str, Any] = model.generate(**A ,num_beams=2 ).sequences
UpperCAmelCase__ : int = tokenizer.batch_decode(A ,skip_special_tokens=A )
assert tgt_text == decoded
| 65 | 0 |
"""simple docstring"""
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class __A ( unittest.TestCase ):
def lowerCamelCase__ ( self : Dict ) -> None:
__magic_name__: Any = Vector([1, 2, 3] )
self.assertEqual(x.component(0 ) , 1 )
self.assertEqual(x.component(2 ) , 3 )
__magic_name__: Optional[Any] = Vector()
def lowerCamelCase__ ( self : Optional[Any] ) -> None:
__magic_name__: Dict = Vector([0, 0, 0, 0, 0, 1] )
self.assertEqual(str(__snake_case ) , """(0,0,0,0,0,1)""" )
def lowerCamelCase__ ( self : Union[str, Any] ) -> None:
__magic_name__: Optional[Any] = Vector([1, 2, 3, 4] )
self.assertEqual(len(__snake_case ) , 4 )
def lowerCamelCase__ ( self : Optional[Any] ) -> None:
__magic_name__: Tuple = Vector([1, 2] )
__magic_name__: int = Vector([1, 2, 3, 4, 5] )
__magic_name__: int = Vector([0, 0, 0, 0, 0, 0, 0, 0, 0, 0] )
__magic_name__: Optional[Any] = Vector([1, -1, 1, -1, 2, -3, 4, -5] )
self.assertAlmostEqual(x.euclidean_length() , 2.236 , 3 )
self.assertAlmostEqual(y.euclidean_length() , 7.416 , 3 )
self.assertEqual(z.euclidean_length() , 0 )
self.assertAlmostEqual(w.euclidean_length() , 7.616 , 3 )
def lowerCamelCase__ ( self : List[Any] ) -> None:
__magic_name__: Any = Vector([1, 2, 3] )
__magic_name__: List[str] = Vector([1, 1, 1] )
self.assertEqual((x + y).component(0 ) , 2 )
self.assertEqual((x + y).component(1 ) , 3 )
self.assertEqual((x + y).component(2 ) , 4 )
def lowerCamelCase__ ( self : Optional[Any] ) -> None:
__magic_name__: List[str] = Vector([1, 2, 3] )
__magic_name__: Dict = Vector([1, 1, 1] )
self.assertEqual((x - y).component(0 ) , 0 )
self.assertEqual((x - y).component(1 ) , 1 )
self.assertEqual((x - y).component(2 ) , 2 )
def lowerCamelCase__ ( self : Union[str, Any] ) -> None:
__magic_name__: Union[str, Any] = Vector([1, 2, 3] )
__magic_name__: List[Any] = Vector([2, -1, 4] ) # for test of dot product
__magic_name__: Tuple = Vector([1, -2, -1] )
self.assertEqual(str(x * 3.0 ) , """(3.0,6.0,9.0)""" )
self.assertEqual((a * b) , 0 )
def lowerCamelCase__ ( self : Optional[int] ) -> None:
self.assertEqual(str(zero_vector(1_0 ) ).count("""0""" ) , 1_0 )
def lowerCamelCase__ ( self : List[str] ) -> None:
self.assertEqual(str(unit_basis_vector(3 , 1 ) ) , """(0,1,0)""" )
def lowerCamelCase__ ( self : Tuple ) -> None:
__magic_name__: int = Vector([1, 2, 3] )
__magic_name__: Union[str, Any] = Vector([1, 0, 1] )
self.assertEqual(str(axpy(2 , __snake_case , __snake_case ) ) , """(3,4,7)""" )
def lowerCamelCase__ ( self : Optional[int] ) -> None:
__magic_name__: List[Any] = Vector([1, 0, 0, 0, 0, 0] )
__magic_name__: Optional[Any] = x.copy()
self.assertEqual(str(__snake_case ) , str(__snake_case ) )
def lowerCamelCase__ ( self : str ) -> None:
__magic_name__: Any = Vector([1, 0, 0] )
x.change_component(0 , 0 )
x.change_component(1 , 1 )
self.assertEqual(str(__snake_case ) , """(0,1,0)""" )
def lowerCamelCase__ ( self : Optional[Any] ) -> None:
__magic_name__: str = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
self.assertEqual("""|1,2,3|\n|2,4,5|\n|6,7,8|\n""" , str(__snake_case ) )
def lowerCamelCase__ ( self : Union[str, Any] ) -> None:
__magic_name__: List[str] = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
__magic_name__: str = [[-3, -1_4, -1_0], [-5, -1_0, -5], [-2, -1, 0]]
for x in range(a.height() ):
for y in range(a.width() ):
self.assertEqual(minors[x][y] , a.minor(__snake_case , __snake_case ) )
def lowerCamelCase__ ( self : Optional[int] ) -> None:
__magic_name__: str = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
__magic_name__: int = [[-3, 1_4, -1_0], [5, -1_0, 5], [-2, 1, 0]]
for x in range(a.height() ):
for y in range(a.width() ):
self.assertEqual(cofactors[x][y] , a.cofactor(__snake_case , __snake_case ) )
def lowerCamelCase__ ( self : Dict ) -> None:
__magic_name__: Optional[Any] = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
self.assertEqual(-5 , a.determinant() )
def lowerCamelCase__ ( self : Tuple ) -> None:
__magic_name__: List[str] = Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]] , 3 , 3 )
__magic_name__: List[Any] = Vector([1, 2, 3] )
self.assertEqual("""(14,32,50)""" , str(a * x ) )
self.assertEqual("""|2,4,6|\n|8,10,12|\n|14,16,18|\n""" , str(a * 2 ) )
def lowerCamelCase__ ( self : Any ) -> None:
__magic_name__: Optional[int] = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
a.change_component(0 , 2 , 5 )
self.assertEqual("""|1,2,5|\n|2,4,5|\n|6,7,8|\n""" , str(__snake_case ) )
def lowerCamelCase__ ( self : Optional[Any] ) -> None:
__magic_name__: List[Any] = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
self.assertEqual(7 , a.component(2 , 1 ) , 0.01 )
def lowerCamelCase__ ( self : List[str] ) -> None:
__magic_name__: int = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
__magic_name__: List[str] = Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 1_0]] , 3 , 3 )
self.assertEqual("""|2,4,10|\n|4,8,10|\n|12,14,18|\n""" , str(a + b ) )
def lowerCamelCase__ ( self : Dict ) -> None:
__magic_name__: Any = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
__magic_name__: Optional[Any] = Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 1_0]] , 3 , 3 )
self.assertEqual("""|0,0,-4|\n|0,0,0|\n|0,0,-2|\n""" , str(a - b ) )
def lowerCamelCase__ ( self : List[Any] ) -> None:
self.assertEqual(
"""|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n""" , str(square_zero_matrix(5 ) ) , )
if __name__ == "__main__":
unittest.main()
| 96 |
"""simple docstring"""
def lowerCAmelCase ( __UpperCamelCase ):
'''simple docstring'''
if not isinstance(__UpperCamelCase , __UpperCamelCase ) or number < 0:
raise ValueError("""Input must be a non-negative integer""" )
UpperCAmelCase__ : Union[str, Any] = 0
while number:
# This way we arrive at next set bit (next 1) instead of looping
# through each bit and checking for 1s hence the
# loop won't run 32 times it will only run the number of `1` times
number &= number - 1
count += 1
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 65 | 0 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqLM,
AutoTokenizer,
HfArgumentParser,
MBartTokenizer,
MBartTokenizerFast,
set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
SeqaSeqDataCollator,
SeqaSeqDataset,
assert_all_frozen,
build_compute_metrics_fn,
check_output_dir,
freeze_embeds,
freeze_params,
lmap,
save_json,
use_task_specific_params,
write_txt_file,
)
__a = logging.getLogger(__name__)
@dataclass
class lowercase__:
"""simple docstring"""
a :str = field(
metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
a :Optional[str] = field(
default=UpperCAmelCase , metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
a :Optional[str] = field(
default=UpperCAmelCase , metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
a :Optional[str] = field(
default=UpperCAmelCase , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
a :bool = field(default=UpperCAmelCase , metadata={'help': 'Whether tp freeze the encoder.'} )
a :bool = field(default=UpperCAmelCase , metadata={'help': 'Whether to freeze the embeddings.'} )
@dataclass
class lowercase__:
"""simple docstring"""
a :str = field(
metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'} )
a :Optional[str] = field(
default='summarization' , metadata={'help': 'Task name, summarization (or summarization_{dataset} for pegasus) or translation'} , )
a :Optional[int] = field(
default=1_024 , metadata={
'help': (
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
a :Optional[int] = field(
default=128 , metadata={
'help': (
'The maximum total sequence length for target text after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
a :Optional[int] = field(
default=142 , metadata={
'help': (
'The maximum total sequence length for validation target text after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded. '
'This argument is also used to override the ``max_length`` param of ``model.generate``, which is used '
'during ``evaluate`` and ``predict``.'
)
} , )
a :Optional[int] = field(
default=142 , metadata={
'help': (
'The maximum total sequence length for test target text after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
a :Optional[int] = field(default=-1 , metadata={'help': '# training examples. -1 means use all.'} )
a :Optional[int] = field(default=-1 , metadata={'help': '# validation examples. -1 means use all.'} )
a :Optional[int] = field(default=-1 , metadata={'help': '# test examples. -1 means use all.'} )
a :Optional[str] = field(default=UpperCAmelCase , metadata={'help': 'Source language id for translation.'} )
a :Optional[str] = field(default=UpperCAmelCase , metadata={'help': 'Target language id for translation.'} )
a :Optional[int] = field(default=UpperCAmelCase , metadata={'help': '# num_beams to use for evaluation.'} )
a :bool = field(
default=UpperCAmelCase , metadata={'help': 'If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'} , )
def a ( snake_case__: Dict , snake_case__: Optional[int] , snake_case__: List[str] ):
'''simple docstring'''
logger.info(F'''***** {split} metrics *****''' )
for key in sorted(metrics.keys() ):
logger.info(F''' {key} = {metrics[key]}''' )
save_json(snake_case__ , os.path.join(snake_case__ , F'''{split}_results.json''' ) )
def a ( ):
'''simple docstring'''
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
lowercase_ = HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('''.json''' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
lowercase_ , lowercase_ , lowercase_ = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
lowercase_ , lowercase_ , lowercase_ = parser.parse_args_into_dataclasses()
check_output_dir(snake_case__ )
# Setup logging
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
'''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s''' , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED ) , training_args.fpaa , )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
logger.info('''Training/evaluation parameters %s''' , snake_case__ )
# Set seed
set_seed(training_args.seed )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
lowercase_ = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , )
lowercase_ = ('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
for p in extra_model_params:
if getattr(snake_case__ , snake_case__ , snake_case__ ):
assert hasattr(snake_case__ , snake_case__ ), F'''({config.__class__.__name__}) doesn\'t have a `{p}` attribute'''
setattr(snake_case__ , snake_case__ , getattr(snake_case__ , snake_case__ ) )
lowercase_ = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , )
lowercase_ = AutoModelForSeqaSeqLM.from_pretrained(
model_args.model_name_or_path , from_tf='''.ckpt''' in model_args.model_name_or_path , config=snake_case__ , cache_dir=model_args.cache_dir , )
# use task specific params
use_task_specific_params(snake_case__ , data_args.task )
# set num_beams for evaluation
if data_args.eval_beams is None:
lowercase_ = model.config.num_beams
# set decoder_start_token_id for MBart
if model.config.decoder_start_token_id is None and isinstance(snake_case__ , (MBartTokenizer, MBartTokenizerFast) ):
assert (
data_args.tgt_lang is not None and data_args.src_lang is not None
), "mBart requires --tgt_lang and --src_lang"
if isinstance(snake_case__ , snake_case__ ):
lowercase_ = tokenizer.lang_code_to_id[data_args.tgt_lang]
else:
lowercase_ = tokenizer.convert_tokens_to_ids(data_args.tgt_lang )
if model_args.freeze_embeds:
freeze_embeds(snake_case__ )
if model_args.freeze_encoder:
freeze_params(model.get_encoder() )
assert_all_frozen(model.get_encoder() )
lowercase_ = SeqaSeqDataset
# Get datasets
lowercase_ = (
dataset_class(
snake_case__ , type_path='''train''' , data_dir=data_args.data_dir , n_obs=data_args.n_train , max_target_length=data_args.max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or '''''' , )
if training_args.do_train
else None
)
lowercase_ = (
dataset_class(
snake_case__ , type_path='''val''' , data_dir=data_args.data_dir , n_obs=data_args.n_val , max_target_length=data_args.val_max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or '''''' , )
if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
else None
)
lowercase_ = (
dataset_class(
snake_case__ , type_path='''test''' , data_dir=data_args.data_dir , n_obs=data_args.n_test , max_target_length=data_args.test_max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or '''''' , )
if training_args.do_predict
else None
)
# Initialize our Trainer
lowercase_ = (
build_compute_metrics_fn(data_args.task , snake_case__ ) if training_args.predict_with_generate else None
)
lowercase_ = SeqaSeqTrainer(
model=snake_case__ , args=snake_case__ , data_args=snake_case__ , train_dataset=snake_case__ , eval_dataset=snake_case__ , data_collator=SeqaSeqDataCollator(
snake_case__ , snake_case__ , model.config.decoder_start_token_id , training_args.tpu_num_cores ) , compute_metrics=snake_case__ , tokenizer=snake_case__ , )
lowercase_ = {}
# Training
if training_args.do_train:
logger.info('''*** Train ***''' )
lowercase_ = trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
lowercase_ = train_result.metrics
lowercase_ = data_args.n_train
trainer.save_model() # this also saves the tokenizer
if trainer.is_world_process_zero():
handle_metrics('''train''' , snake_case__ , training_args.output_dir )
all_metrics.update(snake_case__ )
# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
trainer.state.save_to_json(os.path.join(training_args.output_dir , '''trainer_state.json''' ) )
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
if training_args.do_eval:
logger.info('''*** Evaluate ***''' )
lowercase_ = trainer.evaluate(metric_key_prefix='''val''' )
lowercase_ = data_args.n_val
lowercase_ = round(metrics['''val_loss'''] , 4 )
if trainer.is_world_process_zero():
handle_metrics('''val''' , snake_case__ , training_args.output_dir )
all_metrics.update(snake_case__ )
if training_args.do_predict:
logger.info('''*** Predict ***''' )
lowercase_ = trainer.predict(test_dataset=snake_case__ , metric_key_prefix='''test''' )
lowercase_ = test_output.metrics
lowercase_ = data_args.n_test
if trainer.is_world_process_zero():
lowercase_ = round(metrics['''test_loss'''] , 4 )
handle_metrics('''test''' , snake_case__ , training_args.output_dir )
all_metrics.update(snake_case__ )
if training_args.predict_with_generate:
lowercase_ = tokenizer.batch_decode(
test_output.predictions , skip_special_tokens=snake_case__ , clean_up_tokenization_spaces=snake_case__ )
lowercase_ = lmap(str.strip , snake_case__ )
write_txt_file(snake_case__ , os.path.join(training_args.output_dir , '''test_generations.txt''' ) )
if trainer.is_world_process_zero():
save_json(snake_case__ , os.path.join(training_args.output_dir , '''all_results.json''' ) )
return all_metrics
def a ( snake_case__: List[str] ):
'''simple docstring'''
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 97 |
"""simple docstring"""
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def lowerCAmelCase ( __UpperCamelCase = "isbn/0140328726" ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = olid.strip().strip("""/""" ) # Remove leading/trailing whitespace & slashes
if new_olid.count("""/""" ) != 1:
UpperCAmelCase__ : Dict = F"{olid} is not a valid Open Library olid"
raise ValueError(__UpperCamelCase )
return requests.get(F"https://openlibrary.org/{new_olid}.json" ).json()
def lowerCAmelCase ( __UpperCamelCase ):
'''simple docstring'''
UpperCAmelCase__ : Any = {
"""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__ : Dict = {better_key: ol_book_data[key] for key, better_key in desired_keys.items()}
UpperCAmelCase__ : str = [
get_openlibrary_data(author["""key"""] )["""name"""] for author in data["""Authors"""]
]
UpperCAmelCase__ : Dict = data["""First sentence"""]["""value"""]
for key, value in data.items():
if isinstance(__UpperCamelCase , __UpperCamelCase ):
UpperCAmelCase__ : Dict = """, """.join(__UpperCamelCase )
return data
if __name__ == "__main__":
import doctest
doctest.testmod()
while True:
__UpperCAmelCase = 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:
__UpperCAmelCase = 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}.")
| 65 | 0 |
'''simple docstring'''
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowercase__ : Dict = logging.get_logger(__name__)
lowercase__ : Tuple = {
'vocab_file': 'vocab.json',
'merges_file': 'merges.txt',
}
lowercase__ : Any = {
'vocab_file': {'ctrl': 'https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-vocab.json'},
'merges_file': {'ctrl': 'https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-merges.txt'},
}
lowercase__ : Union[str, Any] = {
'ctrl': 2_56,
}
lowercase__ : List[Any] = {
'Pregnancy': 16_86_29,
'Christianity': 76_75,
'Explain': 10_64_23,
'Fitness': 6_34_40,
'Saving': 6_31_63,
'Ask': 2_71_71,
'Ass': 9_59_85,
'Joke': 16_35_09,
'Questions': 4_56_22,
'Thoughts': 4_96_05,
'Retail': 5_23_42,
'Feminism': 16_43_38,
'Writing': 1_19_92,
'Atheism': 19_22_63,
'Netflix': 4_86_16,
'Computing': 3_96_39,
'Opinion': 4_32_13,
'Alone': 4_49_67,
'Funny': 5_89_17,
'Gaming': 4_03_58,
'Human': 40_88,
'India': 13_31,
'Joker': 7_71_38,
'Diet': 3_62_06,
'Legal': 1_18_59,
'Norman': 49_39,
'Tip': 7_26_89,
'Weight': 5_23_43,
'Movies': 4_62_73,
'Running': 2_34_25,
'Science': 20_90,
'Horror': 3_77_93,
'Confession': 6_05_72,
'Finance': 1_22_50,
'Politics': 1_63_60,
'Scary': 19_19_85,
'Support': 1_26_54,
'Technologies': 3_25_16,
'Teenage': 6_61_60,
'Event': 3_27_69,
'Learned': 6_74_60,
'Notion': 18_27_70,
'Wikipedia': 3_75_83,
'Books': 66_65,
'Extract': 7_60_50,
'Confessions': 10_27_01,
'Conspiracy': 7_59_32,
'Links': 6_36_74,
'Narcissus': 15_04_25,
'Relationship': 5_47_66,
'Relationships': 13_47_96,
'Reviews': 4_16_71,
'News': 42_56,
'Translation': 2_68_20,
'multilingual': 12_84_06,
}
def a__ ( lowercase : List[str] ) -> Dict:
"""simple docstring"""
_UpperCamelCase = set()
_UpperCamelCase = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
_UpperCamelCase = char
_UpperCamelCase = set(lowercase )
return pairs
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
_snake_case : Tuple = VOCAB_FILES_NAMES
_snake_case : List[Any] = PRETRAINED_VOCAB_FILES_MAP
_snake_case : Tuple = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_snake_case : List[Any] = CONTROL_CODES
def __init__( self : str , lowerCAmelCase__ : List[Any] , lowerCAmelCase__ : List[str] , lowerCAmelCase__ : Union[str, Any]="<unk>" , **lowerCAmelCase__ : Any ) -> Optional[Any]:
'''simple docstring'''
super().__init__(unk_token=lowerCAmelCase__ , **lowerCAmelCase__ )
with open(lowerCAmelCase__ , encoding='''utf-8''' ) as vocab_handle:
_UpperCamelCase = json.load(lowerCAmelCase__ )
_UpperCamelCase = {v: k for k, v in self.encoder.items()}
with open(lowerCAmelCase__ , encoding='''utf-8''' ) as merges_handle:
_UpperCamelCase = merges_handle.read().split('''\n''' )[1:-1]
_UpperCamelCase = [tuple(merge.split() ) for merge in merges]
_UpperCamelCase = dict(zip(lowerCAmelCase__ , range(len(lowerCAmelCase__ ) ) ) )
_UpperCamelCase = {}
@property
def snake_case__ ( self : Tuple ) -> Tuple:
'''simple docstring'''
return len(self.encoder )
def snake_case__ ( self : int ) -> int:
'''simple docstring'''
return dict(self.encoder , **self.added_tokens_encoder )
def snake_case__ ( self : List[Any] , lowerCAmelCase__ : Optional[int] ) -> Optional[int]:
'''simple docstring'''
if token in self.cache:
return self.cache[token]
_UpperCamelCase = tuple(lowerCAmelCase__ )
_UpperCamelCase = tuple(list(word[:-1] ) + [word[-1] + '''</w>'''] )
_UpperCamelCase = get_pairs(lowerCAmelCase__ )
if not pairs:
return token
while True:
_UpperCamelCase = min(lowerCAmelCase__ , key=lambda lowerCAmelCase__ : self.bpe_ranks.get(lowerCAmelCase__ , float('''inf''' ) ) )
if bigram not in self.bpe_ranks:
break
_UpperCamelCase , _UpperCamelCase = bigram
_UpperCamelCase = []
_UpperCamelCase = 0
while i < len(lowerCAmelCase__ ):
try:
_UpperCamelCase = word.index(lowerCAmelCase__ , lowerCAmelCase__ )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
_UpperCamelCase = j
if word[i] == first and i < len(lowerCAmelCase__ ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
_UpperCamelCase = tuple(lowerCAmelCase__ )
_UpperCamelCase = new_word
if len(lowerCAmelCase__ ) == 1:
break
else:
_UpperCamelCase = get_pairs(lowerCAmelCase__ )
_UpperCamelCase = '''@@ '''.join(lowerCAmelCase__ )
_UpperCamelCase = word[:-4]
_UpperCamelCase = word
return word
def snake_case__ ( self : Tuple , lowerCAmelCase__ : Optional[int] ) -> int:
'''simple docstring'''
_UpperCamelCase = []
_UpperCamelCase = re.findall(r'''\S+\n?''' , lowerCAmelCase__ )
for token in words:
split_tokens.extend(list(self.bpe(lowerCAmelCase__ ).split(''' ''' ) ) )
return split_tokens
def snake_case__ ( self : int , lowerCAmelCase__ : Optional[int] ) -> str:
'''simple docstring'''
return self.encoder.get(lowerCAmelCase__ , self.encoder.get(self.unk_token ) )
def snake_case__ ( self : Any , lowerCAmelCase__ : List[str] ) -> Optional[Any]:
'''simple docstring'''
return self.decoder.get(lowerCAmelCase__ , self.unk_token )
def snake_case__ ( self : Any , lowerCAmelCase__ : Tuple ) -> int:
'''simple docstring'''
_UpperCamelCase = ''' '''.join(lowerCAmelCase__ ).replace('''@@ ''' , '''''' ).strip()
return out_string
def snake_case__ ( self : Any , lowerCAmelCase__ : str , lowerCAmelCase__ : Optional[str] = None ) -> Tuple[str]:
'''simple docstring'''
if not os.path.isdir(lowerCAmelCase__ ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
_UpperCamelCase = os.path.join(
lowerCAmelCase__ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
_UpperCamelCase = os.path.join(
lowerCAmelCase__ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''] )
with open(lowerCAmelCase__ , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=lowerCAmelCase__ , ensure_ascii=lowerCAmelCase__ ) + '''\n''' )
_UpperCamelCase = 0
with open(lowerCAmelCase__ , '''w''' , encoding='''utf-8''' ) as writer:
writer.write('''#version: 0.2\n''' )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda lowerCAmelCase__ : kv[1] ):
if index != token_index:
logger.warning(
f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
''' Please check that the tokenizer is not corrupted!''' )
_UpperCamelCase = token_index
writer.write(''' '''.join(lowerCAmelCase__ ) + '''\n''' )
index += 1
return vocab_file, merge_file
# def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
# filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
# tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
# tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
# return ''.join(tokens_generated_so_far)
| 98 |
"""simple docstring"""
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from transformers import BartTokenizer, RagTokenizer, TaTokenizer
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=True , __UpperCamelCase="pt" ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = {"""add_prefix_space""": True} if isinstance(__UpperCamelCase , __UpperCamelCase ) and not line.startswith(""" """ ) else {}
UpperCAmelCase__ : List[str] = padding_side
return tokenizer(
[line] , max_length=__UpperCamelCase , padding="""max_length""" if pad_to_max_length else None , truncation=__UpperCamelCase , return_tensors=__UpperCamelCase , add_special_tokens=__UpperCamelCase , **__UpperCamelCase , )
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=None , ):
'''simple docstring'''
UpperCAmelCase__ : str = input_ids.ne(__UpperCamelCase ).any(dim=0 )
if attention_mask is None:
return input_ids[:, keep_column_mask]
else:
return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
class __lowercase ( __lowerCamelCase ):
def __init__( self : Tuple ,A : List[Any] ,A : Union[str, Any] ,A : Any ,A : Optional[int] ,A : Union[str, Any]="train" ,A : Tuple=None ,A : Union[str, Any]=None ,A : Tuple=None ,A : int="" ,):
'''simple docstring'''
super().__init__()
UpperCAmelCase__ : Optional[Any] = Path(A ).joinpath(type_path + """.source""" )
UpperCAmelCase__ : List[str] = Path(A ).joinpath(type_path + """.target""" )
UpperCAmelCase__ : Dict = self.get_char_lens(self.src_file )
UpperCAmelCase__ : int = max_source_length
UpperCAmelCase__ : List[str] = max_target_length
assert min(self.src_lens ) > 0, f"found empty line in {self.src_file}"
UpperCAmelCase__ : Dict = tokenizer
UpperCAmelCase__ : str = prefix
if n_obs is not None:
UpperCAmelCase__ : int = self.src_lens[:n_obs]
UpperCAmelCase__ : Any = src_lang
UpperCAmelCase__ : Any = tgt_lang
def __len__( self : Optional[Any] ):
'''simple docstring'''
return len(self.src_lens )
def __getitem__( self : Union[str, Any] ,A : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = index + 1 # linecache starts at 1
UpperCAmelCase__ : Tuple = self.prefix + linecache.getline(str(self.src_file ) ,A ).rstrip("""\n""" )
UpperCAmelCase__ : Dict = linecache.getline(str(self.tgt_file ) ,A ).rstrip("""\n""" )
assert source_line, f"empty source line for index {index}"
assert tgt_line, f"empty tgt line for index {index}"
# Need to add eos token manually for T5
if isinstance(self.tokenizer ,A ):
source_line += self.tokenizer.eos_token
tgt_line += self.tokenizer.eos_token
# Pad source and target to the right
UpperCAmelCase__ : str = (
self.tokenizer.question_encoder if isinstance(self.tokenizer ,A ) else self.tokenizer
)
UpperCAmelCase__ : Tuple = self.tokenizer.generator if isinstance(self.tokenizer ,A ) else self.tokenizer
UpperCAmelCase__ : Tuple = encode_line(A ,A ,self.max_source_length ,"""right""" )
UpperCAmelCase__ : Dict = encode_line(A ,A ,self.max_target_length ,"""right""" )
UpperCAmelCase__ : Optional[Any] = source_inputs["""input_ids"""].squeeze()
UpperCAmelCase__ : List[str] = target_inputs["""input_ids"""].squeeze()
UpperCAmelCase__ : Union[str, Any] = source_inputs["""attention_mask"""].squeeze()
return {
"input_ids": source_ids,
"attention_mask": src_mask,
"decoder_input_ids": target_ids,
}
@staticmethod
def __lowercase ( A : int ):
'''simple docstring'''
return [len(A ) for x in Path(A ).open().readlines()]
def __lowercase ( self : List[Any] ,A : Any ):
'''simple docstring'''
UpperCAmelCase__ : int = torch.stack([x["""input_ids"""] for x in batch] )
UpperCAmelCase__ : Union[str, Any] = torch.stack([x["""attention_mask"""] for x in batch] )
UpperCAmelCase__ : Any = torch.stack([x["""decoder_input_ids"""] for x in batch] )
UpperCAmelCase__ : List[Any] = (
self.tokenizer.generator.pad_token_id
if isinstance(self.tokenizer ,A )
else self.tokenizer.pad_token_id
)
UpperCAmelCase__ : Any = (
self.tokenizer.question_encoder.pad_token_id
if isinstance(self.tokenizer ,A )
else self.tokenizer.pad_token_id
)
UpperCAmelCase__ : str = trim_batch(A ,A )
UpperCAmelCase__ , UpperCAmelCase__ : Union[str, Any] = trim_batch(A ,A ,attention_mask=A )
UpperCAmelCase__ : List[str] = {
"""input_ids""": source_ids,
"""attention_mask""": source_mask,
"""decoder_input_ids""": y,
}
return batch
__UpperCAmelCase = getLogger(__name__)
def lowerCAmelCase ( __UpperCamelCase ):
'''simple docstring'''
return list(itertools.chain.from_iterable(__UpperCamelCase ) )
def lowerCAmelCase ( __UpperCamelCase ):
'''simple docstring'''
UpperCAmelCase__ : Dict = get_git_info()
save_json(__UpperCamelCase , os.path.join(__UpperCamelCase , """git_log.json""" ) )
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=4 , **__UpperCamelCase ):
'''simple docstring'''
with open(__UpperCamelCase , """w""" ) as f:
json.dump(__UpperCamelCase , __UpperCamelCase , indent=__UpperCamelCase , **__UpperCamelCase )
def lowerCAmelCase ( __UpperCamelCase ):
'''simple docstring'''
with open(__UpperCamelCase ) as f:
return json.load(__UpperCamelCase )
def lowerCAmelCase ( ):
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = git.Repo(search_parent_directories=__UpperCamelCase )
UpperCAmelCase__ : List[str] = {
"""repo_id""": str(__UpperCamelCase ),
"""repo_sha""": str(repo.head.object.hexsha ),
"""repo_branch""": str(repo.active_branch ),
"""hostname""": str(socket.gethostname() ),
}
return repo_infos
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase ):
'''simple docstring'''
return list(map(__UpperCamelCase , __UpperCamelCase ) )
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase ):
'''simple docstring'''
with open(__UpperCamelCase , """wb""" ) as f:
return pickle.dump(__UpperCamelCase , __UpperCamelCase )
def lowerCAmelCase ( __UpperCamelCase ):
'''simple docstring'''
def remove_articles(__UpperCamelCase ):
return re.sub(r"""\b(a|an|the)\b""" , """ """ , __UpperCamelCase )
def white_space_fix(__UpperCamelCase ):
return " ".join(text.split() )
def remove_punc(__UpperCamelCase ):
UpperCAmelCase__ : List[Any] = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(__UpperCamelCase ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(__UpperCamelCase ) ) ) )
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase ):
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = normalize_answer(__UpperCamelCase ).split()
UpperCAmelCase__ : Dict = normalize_answer(__UpperCamelCase ).split()
UpperCAmelCase__ : int = Counter(__UpperCamelCase ) & Counter(__UpperCamelCase )
UpperCAmelCase__ : List[str] = sum(common.values() )
if num_same == 0:
return 0
UpperCAmelCase__ : str = 1.0 * num_same / len(__UpperCamelCase )
UpperCAmelCase__ : Optional[int] = 1.0 * num_same / len(__UpperCamelCase )
UpperCAmelCase__ : Tuple = (2 * precision * recall) / (precision + recall)
return fa
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase ):
'''simple docstring'''
return normalize_answer(__UpperCamelCase ) == normalize_answer(__UpperCamelCase )
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase ):
'''simple docstring'''
assert len(__UpperCamelCase ) == len(__UpperCamelCase )
UpperCAmelCase__ : Union[str, Any] = 0
for hypo, pred in zip(__UpperCamelCase , __UpperCamelCase ):
em += exact_match_score(__UpperCamelCase , __UpperCamelCase )
if len(__UpperCamelCase ) > 0:
em /= len(__UpperCamelCase )
return {"em": em}
def lowerCAmelCase ( __UpperCamelCase ):
'''simple docstring'''
return model_prefix.startswith("""rag""" )
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = {p: p for p in extra_params}
# T5 models don't have `dropout` param, they have `dropout_rate` instead
UpperCAmelCase__ : str = """dropout_rate"""
for p in extra_params:
if getattr(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
if not hasattr(__UpperCamelCase , __UpperCamelCase ) and not hasattr(__UpperCamelCase , equivalent_param[p] ):
logger.info("""config doesn't have a `{}` attribute""".format(__UpperCamelCase ) )
delattr(__UpperCamelCase , __UpperCamelCase )
continue
UpperCAmelCase__ : Tuple = p if hasattr(__UpperCamelCase , __UpperCamelCase ) else equivalent_param[p]
setattr(__UpperCamelCase , __UpperCamelCase , getattr(__UpperCamelCase , __UpperCamelCase ) )
delattr(__UpperCamelCase , __UpperCamelCase )
return hparams, config
| 65 | 0 |
import argparse
import OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def a (lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
__a = OmegaConf.load(lowerCAmelCase__ )
__a = torch.load(lowerCAmelCase__ , map_location="""cpu""" )["""model"""]
__a = list(state_dict.keys() )
# extract state_dict for VQVAE
__a = {}
__a = """first_stage_model."""
for key in keys:
if key.startswith(lowerCAmelCase__ ):
__a = state_dict[key]
# extract state_dict for UNetLDM
__a = {}
__a = """model.diffusion_model."""
for key in keys:
if key.startswith(lowerCAmelCase__ ):
__a = state_dict[key]
__a = config.model.params.first_stage_config.params
__a = config.model.params.unet_config.params
__a = VQModel(**lowerCAmelCase__ ).eval()
vqvae.load_state_dict(lowerCAmelCase__ )
__a = UNetLDMModel(**lowerCAmelCase__ ).eval()
unet.load_state_dict(lowerCAmelCase__ )
__a = DDIMScheduler(
timesteps=config.model.params.timesteps , beta_schedule="""scaled_linear""" , beta_start=config.model.params.linear_start , beta_end=config.model.params.linear_end , clip_sample=lowerCAmelCase__ , )
__a = LDMPipeline(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
pipeline.save_pretrained(lowerCAmelCase__ )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE = argparse.ArgumentParser()
parser.add_argument('--checkpoint_path', type=str, required=True)
parser.add_argument('--config_path', type=str, required=True)
parser.add_argument('--output_path', type=str, required=True)
SCREAMING_SNAKE_CASE = parser.parse_args()
convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)
| 99 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetPipeline,
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 __lowercase ( __lowerCamelCase , unittest.TestCase ):
snake_case_ = KandinskyVaaControlnetPipeline
snake_case_ = ["""image_embeds""", """negative_image_embeds""", """hint"""]
snake_case_ = ["""image_embeds""", """negative_image_embeds""", """hint"""]
snake_case_ = [
"""generator""",
"""height""",
"""width""",
"""latents""",
"""guidance_scale""",
"""num_inference_steps""",
"""return_dict""",
"""guidance_scale""",
"""num_images_per_prompt""",
"""output_type""",
"""return_dict""",
]
snake_case_ = False
@property
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
return 32
@property
def __lowercase ( self : int ):
'''simple docstring'''
return 32
@property
def __lowercase ( self : Dict ):
'''simple docstring'''
return self.time_input_dim
@property
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
return self.time_input_dim * 4
@property
def __lowercase ( self : Any ):
'''simple docstring'''
return 100
@property
def __lowercase ( self : Any ):
'''simple docstring'''
torch.manual_seed(0 )
UpperCAmelCase__ : Tuple = {
"""in_channels""": 8,
# Out channels is double in channels because predicts mean and variance
"""out_channels""": 8,
"""addition_embed_type""": """image_hint""",
"""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,
}
UpperCAmelCase__ : int = UNetaDConditionModel(**A )
return model
@property
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
return {
"block_out_channels": [32, 32, 64, 64],
"down_block_types": [
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"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", "UpDecoderBlock2D", "UpDecoderBlock2D"],
"vq_embed_dim": 4,
}
@property
def __lowercase ( self : Dict ):
'''simple docstring'''
torch.manual_seed(0 )
UpperCAmelCase__ : str = VQModel(**self.dummy_movq_kwargs )
return model
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ : str = self.dummy_unet
UpperCAmelCase__ : List[Any] = self.dummy_movq
UpperCAmelCase__ : List[Any] = DDIMScheduler(
num_train_timesteps=1_000 ,beta_schedule="""linear""" ,beta_start=0.0_0_0_8_5 ,beta_end=0.0_1_2 ,clip_sample=A ,set_alpha_to_one=A ,steps_offset=1 ,prediction_type="""epsilon""" ,thresholding=A ,)
UpperCAmelCase__ : Optional[Any] = {
"""unet""": unet,
"""scheduler""": scheduler,
"""movq""": movq,
}
return components
def __lowercase ( self : str ,A : Optional[Any] ,A : Any=0 ):
'''simple docstring'''
UpperCAmelCase__ : str = floats_tensor((1, self.text_embedder_hidden_size) ,rng=random.Random(A ) ).to(A )
UpperCAmelCase__ : Union[str, Any] = floats_tensor((1, self.text_embedder_hidden_size) ,rng=random.Random(seed + 1 ) ).to(
A )
# create hint
UpperCAmelCase__ : int = floats_tensor((1, 3, 64, 64) ,rng=random.Random(A ) ).to(A )
if str(A ).startswith("""mps""" ):
UpperCAmelCase__ : Optional[int] = torch.manual_seed(A )
else:
UpperCAmelCase__ : Dict = torch.Generator(device=A ).manual_seed(A )
UpperCAmelCase__ : Dict = {
"""image_embeds""": image_embeds,
"""negative_image_embeds""": negative_image_embeds,
"""hint""": hint,
"""generator""": generator,
"""height""": 64,
"""width""": 64,
"""guidance_scale""": 4.0,
"""num_inference_steps""": 2,
"""output_type""": """np""",
}
return inputs
def __lowercase ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase__ : Dict = """cpu"""
UpperCAmelCase__ : List[Any] = self.get_dummy_components()
UpperCAmelCase__ : Union[str, Any] = self.pipeline_class(**A )
UpperCAmelCase__ : Optional[int] = pipe.to(A )
pipe.set_progress_bar_config(disable=A )
UpperCAmelCase__ : Optional[int] = pipe(**self.get_dummy_inputs(A ) )
UpperCAmelCase__ : Tuple = output.images
UpperCAmelCase__ : Dict = pipe(
**self.get_dummy_inputs(A ) ,return_dict=A ,)[0]
UpperCAmelCase__ : Tuple = image[0, -3:, -3:, -1]
UpperCAmelCase__ : Dict = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
UpperCAmelCase__ : Optional[int] = np.array(
[0.6_9_5_9_8_2_6, 0.8_6_8_2_7_9, 0.7_5_5_8_0_9_2, 0.6_8_7_6_9_4_6_7, 0.8_5_8_0_5_8_0_4, 0.6_5_9_7_7_4_9_6, 0.4_4_8_8_5_3_0_2, 0.5_9_5_9_1_1_1, 0.4_2_5_1_5_9_5] )
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 __lowercase ( unittest.TestCase ):
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __lowercase ( self : int ):
'''simple docstring'''
UpperCAmelCase__ : int = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy""" )
UpperCAmelCase__ : int = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/kandinskyv22/hint_image_cat.png""" )
UpperCAmelCase__ : int = torch.from_numpy(np.array(A ) ).float() / 2_5_5.0
UpperCAmelCase__ : Union[str, Any] = hint.permute(2 ,0 ,1 ).unsqueeze(0 )
UpperCAmelCase__ : List[str] = KandinskyVaaPriorPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-2-prior""" ,torch_dtype=torch.floataa )
pipe_prior.to(A )
UpperCAmelCase__ : List[Any] = KandinskyVaaControlnetPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-2-controlnet-depth""" ,torch_dtype=torch.floataa )
UpperCAmelCase__ : int = pipeline.to(A )
pipeline.set_progress_bar_config(disable=A )
UpperCAmelCase__ : Optional[Any] = """A robot, 4k photo"""
UpperCAmelCase__ : List[Any] = torch.Generator(device="""cuda""" ).manual_seed(0 )
UpperCAmelCase__ , UpperCAmelCase__ : Tuple = pipe_prior(
A ,generator=A ,num_inference_steps=5 ,negative_prompt="""""" ,).to_tuple()
UpperCAmelCase__ : List[str] = torch.Generator(device="""cuda""" ).manual_seed(0 )
UpperCAmelCase__ : int = pipeline(
image_embeds=A ,negative_image_embeds=A ,hint=A ,generator=A ,num_inference_steps=100 ,output_type="""np""" ,)
UpperCAmelCase__ : Any = output.images[0]
assert image.shape == (512, 512, 3)
assert_mean_pixel_difference(A ,A )
| 65 | 0 |
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 __snake_case ( lowerCAmelCase_ ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__ = int(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = t // 3_6_0_0, (t // 6_0) % 6_0, t % 6_0
return f'''{h}:{m:02d}:{s:02d}''' if h != 0 else f'''{m:02d}:{s:02d}'''
def __snake_case ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_=3_0_0 ) -> Dict:
# docstyle-ignore
return f'''
<div>
{prefix}
<progress value=\'{value}\' max=\'{total}\' style=\'width:{width}px; height:20px; vertical-align: middle;\'></progress>
{label}
</div>
'''
def __snake_case ( lowerCAmelCase_ ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__ = '''<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:
SCREAMING_SNAKE_CASE__ = f'''{elt:.6f}''' if isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) else str(lowerCAmelCase_ )
html_code += f''' <td>{elt}</td>\n'''
html_code += " </tr>\n"
html_code += " </tbody>\n</table><p>"
return html_code
class __snake_case :
'''simple docstring'''
lowerCamelCase__ : Tuple = 5
lowerCamelCase__ : str = 0.2
def __init__( self , A_ , A_ = None , A_ = True , A_ = None , A_ = 3_00 , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = total
SCREAMING_SNAKE_CASE__ = '''''' if prefix is None else prefix
SCREAMING_SNAKE_CASE__ = leave
SCREAMING_SNAKE_CASE__ = parent
SCREAMING_SNAKE_CASE__ = width
SCREAMING_SNAKE_CASE__ = None
SCREAMING_SNAKE_CASE__ = None
SCREAMING_SNAKE_CASE__ = None
def lowercase_ ( self , A_ , A_ = False , A_ = None ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = value
if comment is not None:
SCREAMING_SNAKE_CASE__ = comment
if self.last_value is None:
SCREAMING_SNAKE_CASE__ = SCREAMING_SNAKE_CASE__ = time.time()
SCREAMING_SNAKE_CASE__ = SCREAMING_SNAKE_CASE__ = value
SCREAMING_SNAKE_CASE__ = SCREAMING_SNAKE_CASE__ = None
SCREAMING_SNAKE_CASE__ = self.warmup
SCREAMING_SNAKE_CASE__ = 1
self.update_bar(A_ )
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
SCREAMING_SNAKE_CASE__ = time.time()
SCREAMING_SNAKE_CASE__ = 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:
SCREAMING_SNAKE_CASE__ = self.elapsed_time / (value - self.start_value)
else:
SCREAMING_SNAKE_CASE__ = None
if value >= self.total:
SCREAMING_SNAKE_CASE__ = self.total
SCREAMING_SNAKE_CASE__ = None
if not self.leave:
self.close()
elif self.average_time_per_item is not None:
SCREAMING_SNAKE_CASE__ = self.average_time_per_item * (self.total - value)
self.update_bar(A_ )
SCREAMING_SNAKE_CASE__ = value
SCREAMING_SNAKE_CASE__ = current_time
if self.average_time_per_item is None:
SCREAMING_SNAKE_CASE__ = 1
else:
SCREAMING_SNAKE_CASE__ = max(int(self.update_every / self.average_time_per_item ) , 1 )
def lowercase_ ( self , A_ , A_=None ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = ''' ''' * (len(str(self.total ) ) - len(str(A_ ) )) + str(A_ )
if self.elapsed_time is None:
SCREAMING_SNAKE_CASE__ = f'''[{spaced_value}/{self.total} : < :'''
elif self.predicted_remaining is None:
SCREAMING_SNAKE_CASE__ = f'''[{spaced_value}/{self.total} {format_time(self.elapsed_time )}'''
else:
SCREAMING_SNAKE_CASE__ = (
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 lowercase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = 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:
SCREAMING_SNAKE_CASE__ = disp.display(disp.HTML(self.html_code ) , display_id=A_ )
else:
self.output.update(disp.HTML(self.html_code ) )
def lowercase_ ( self ):
'''simple docstring'''
if self.parent is None and self.output is not None:
self.output.update(disp.HTML('''''' ) )
class __snake_case ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self , A_ , A_=None ):
'''simple docstring'''
super().__init__(A_ )
SCREAMING_SNAKE_CASE__ = None if column_names is None else [column_names]
SCREAMING_SNAKE_CASE__ = None
def lowercase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = 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:
SCREAMING_SNAKE_CASE__ = disp.display(disp.HTML(self.html_code ) , display_id=A_ )
else:
self.output.update(disp.HTML(self.html_code ) )
def lowercase_ ( self , A_ ):
'''simple docstring'''
if self.inner_table is None:
SCREAMING_SNAKE_CASE__ = [list(values.keys() ), list(values.values() )]
else:
SCREAMING_SNAKE_CASE__ = 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(A_ )
SCREAMING_SNAKE_CASE__ = columns
self.inner_table.append([values[c] for c in columns] )
def lowercase_ ( self , A_ , A_=None , A_=3_00 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = NotebookProgressBar(A_ , prefix=A_ , parent=self , width=A_ )
return self.child_bar
def lowercase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = None
self.display()
class __snake_case ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = None
SCREAMING_SNAKE_CASE__ = None
SCREAMING_SNAKE_CASE__ = False
def lowercase_ ( self , A_ , A_ , A_ , **A_ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = '''Epoch''' if args.evaluation_strategy == IntervalStrategy.EPOCH else '''Step'''
SCREAMING_SNAKE_CASE__ = 0
SCREAMING_SNAKE_CASE__ = 0
SCREAMING_SNAKE_CASE__ = [self.first_column] + ['''Training Loss''']
if args.evaluation_strategy != IntervalStrategy.NO:
column_names.append('''Validation Loss''' )
SCREAMING_SNAKE_CASE__ = NotebookTrainingTracker(state.max_steps , A_ )
def lowercase_ ( self , A_ , A_ , A_ , **A_ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = 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 , )
SCREAMING_SNAKE_CASE__ = False
def lowercase_ ( self , A_ , A_ , A_ , A_=None , **A_ ):
'''simple docstring'''
if not has_length(A_ ):
return
if self.prediction_bar is None:
if self.training_tracker is not None:
SCREAMING_SNAKE_CASE__ = self.training_tracker.add_child(len(A_ ) )
else:
SCREAMING_SNAKE_CASE__ = NotebookProgressBar(len(A_ ) )
self.prediction_bar.update(1 )
else:
self.prediction_bar.update(self.prediction_bar.value + 1 )
def lowercase_ ( self , A_ , A_ , A_ , **A_ ):
'''simple docstring'''
if self.prediction_bar is not None:
self.prediction_bar.close()
SCREAMING_SNAKE_CASE__ = None
def lowercase_ ( self , A_ , A_ , A_ , A_=None , **A_ ):
'''simple docstring'''
if args.evaluation_strategy == IntervalStrategy.NO and "loss" in logs:
SCREAMING_SNAKE_CASE__ = {'''Training Loss''': logs['''loss''']}
# First column is necessarily Step sine we're not in epoch eval strategy
SCREAMING_SNAKE_CASE__ = state.global_step
self.training_tracker.write_line(A_ )
def lowercase_ ( self , A_ , A_ , A_ , A_=None , **A_ ):
'''simple docstring'''
if self.training_tracker is not None:
SCREAMING_SNAKE_CASE__ = {'''Training Loss''': '''No log''', '''Validation Loss''': '''No log'''}
for log in reversed(state.log_history ):
if "loss" in log:
SCREAMING_SNAKE_CASE__ = log['''loss''']
break
if self.first_column == "Epoch":
SCREAMING_SNAKE_CASE__ = int(state.epoch )
else:
SCREAMING_SNAKE_CASE__ = state.global_step
SCREAMING_SNAKE_CASE__ = '''eval'''
for k in metrics:
if k.endswith('''_loss''' ):
SCREAMING_SNAKE_CASE__ = re.sub(r'''\_loss$''' , '''''' , A_ )
SCREAMING_SNAKE_CASE__ = metrics.pop('''total_flos''' , A_ )
SCREAMING_SNAKE_CASE__ = metrics.pop('''epoch''' , A_ )
SCREAMING_SNAKE_CASE__ = metrics.pop(f'''{metric_key_prefix}_runtime''' , A_ )
SCREAMING_SNAKE_CASE__ = metrics.pop(f'''{metric_key_prefix}_samples_per_second''' , A_ )
SCREAMING_SNAKE_CASE__ = metrics.pop(f'''{metric_key_prefix}_steps_per_second''' , A_ )
SCREAMING_SNAKE_CASE__ = metrics.pop(f'''{metric_key_prefix}_jit_compilation_time''' , A_ )
for k, v in metrics.items():
if k == f'''{metric_key_prefix}_loss''':
SCREAMING_SNAKE_CASE__ = v
else:
SCREAMING_SNAKE_CASE__ = k.split('''_''' )
SCREAMING_SNAKE_CASE__ = ''' '''.join([part.capitalize() for part in splits[1:]] )
SCREAMING_SNAKE_CASE__ = v
self.training_tracker.write_line(A_ )
self.training_tracker.remove_child()
SCREAMING_SNAKE_CASE__ = None
# Evaluation takes a long time so we should force the next update.
SCREAMING_SNAKE_CASE__ = True
def lowercase_ ( self , A_ , A_ , A_ , **A_ ):
'''simple docstring'''
self.training_tracker.update(
state.global_step , comment=f'''Epoch {int(state.epoch )}/{state.num_train_epochs}''' , force_update=A_ )
SCREAMING_SNAKE_CASE__ = None
| 100 |
"""simple docstring"""
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHECKING:
from ... import PreTrainedTokenizerBase, TensorType
__UpperCAmelCase = logging.get_logger(__name__)
class __lowercase ( __lowerCamelCase ):
snake_case_ = """vision-encoder-decoder"""
snake_case_ = True
def __init__( self : List[Any] ,**A : Union[str, Any] ):
'''simple docstring'''
super().__init__(**A )
if "encoder" not in kwargs or "decoder" not in kwargs:
raise ValueError(
f"A configuraton of type {self.model_type} cannot be instantiated because "
f"not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}" )
UpperCAmelCase__ : int = kwargs.pop("""encoder""" )
UpperCAmelCase__ : int = encoder_config.pop("""model_type""" )
UpperCAmelCase__ : str = kwargs.pop("""decoder""" )
UpperCAmelCase__ : Dict = decoder_config.pop("""model_type""" )
UpperCAmelCase__ : List[Any] = AutoConfig.for_model(A ,**A )
UpperCAmelCase__ : Any = AutoConfig.for_model(A ,**A )
UpperCAmelCase__ : Union[str, Any] = True
@classmethod
def __lowercase ( cls : List[Any] ,A : PretrainedConfig ,A : PretrainedConfig ,**A : Tuple ):
'''simple docstring'''
logger.info("""Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config""" )
UpperCAmelCase__ : Union[str, Any] = True
UpperCAmelCase__ : List[Any] = True
return cls(encoder=encoder_config.to_dict() ,decoder=decoder_config.to_dict() ,**A )
def __lowercase ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = copy.deepcopy(self.__dict__ )
UpperCAmelCase__ : Dict = self.encoder.to_dict()
UpperCAmelCase__ : Any = self.decoder.to_dict()
UpperCAmelCase__ : Dict = self.__class__.model_type
return output
class __lowercase ( __lowerCamelCase ):
snake_case_ = version.parse("""1.11""" )
@property
def __lowercase ( self : Optional[int] ):
'''simple docstring'''
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def __lowercase ( self : List[Any] ):
'''simple docstring'''
return 1e-4
@property
def __lowercase ( self : List[Any] ):
'''simple docstring'''
return OrderedDict({"""last_hidden_state""": {0: """batch""", 1: """encoder_sequence"""}} )
class __lowercase ( __lowerCamelCase ):
@property
def __lowercase ( self : Any ):
'''simple docstring'''
UpperCAmelCase__ : int = OrderedDict()
UpperCAmelCase__ : Dict = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
UpperCAmelCase__ : Optional[Any] = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
UpperCAmelCase__ : List[str] = {0: """batch""", 1: """encoder_sequence"""}
return common_inputs
def __lowercase ( self : Dict ,A : "PreTrainedTokenizerBase" ,A : int = -1 ,A : int = -1 ,A : bool = False ,A : Optional["TensorType"] = None ,):
'''simple docstring'''
import torch
UpperCAmelCase__ : int = OrderedDict()
UpperCAmelCase__ : List[Any] = super().generate_dummy_inputs(
A ,batch_size=A ,seq_length=A ,is_pair=A ,framework=A )
UpperCAmelCase__ , UpperCAmelCase__ : int = dummy_input["""input_ids"""].shape
UpperCAmelCase__ : int = (batch, encoder_sequence, self._config.encoder_hidden_size)
UpperCAmelCase__ : Tuple = dummy_input.pop("""input_ids""" )
UpperCAmelCase__ : Optional[int] = dummy_input.pop("""attention_mask""" )
UpperCAmelCase__ : Dict = torch.zeros(A )
return common_inputs
class __lowercase ( __lowerCamelCase ):
@property
def __lowercase ( self : str ):
'''simple docstring'''
pass
def __lowercase ( self : Any ,A : PretrainedConfig ):
'''simple docstring'''
return VisionEncoderDecoderEncoderOnnxConfig(A )
def __lowercase ( self : Dict ,A : PretrainedConfig ,A : PretrainedConfig ,A : str = "default" ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = encoder_config.hidden_size
return VisionEncoderDecoderDecoderOnnxConfig(A ,A )
| 65 | 0 |
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
lowerCAmelCase__ : Optional[int] =_symbol_database.Default()
lowerCAmelCase__ : Optional[int] =_descriptor_pool.Default().AddSerializedFile(
b'\n\x19sentencepiece_model.proto\x12\rsentencepiece"\x80\x0c\n\x0bTrainerSpec\x12\r\n\x05input\x18\x01 \x03(\t\x12\x14\n\x0cinput_format\x18\x07 \x01(\t\x12\x14\n\x0cmodel_prefix\x18\x02 \x01(\t\x12\x41\n\nmodel_type\x18\x03 \x01(\x0e\x32$.sentencepiece.TrainerSpec.ModelType:\x07UNIGRAM\x12\x18\n\nvocab_size\x18\x04 \x01(\x05:\x04\x38\x30\x30\x30\x12\x17\n\x0f\x61\x63\x63\x65pt_language\x18\x05 \x03(\t\x12 \n\x15self_test_sample_size\x18\x06 \x01(\x05:\x01\x30\x12*\n\x1b\x65nable_differential_privacy\x18\x32 \x01(\x08:\x05\x66\x61lse\x12+\n differential_privacy_noise_level\x18\x33 \x01(\x02:\x01\x30\x12\x32\n\'differential_privacy_clipping_threshold\x18\x34 \x01(\x04:\x01\x30\x12"\n\x12\x63haracter_coverage\x18\n \x01(\x02:\x06\x30.9995\x12\x1e\n\x13input_sentence_size\x18\x0b \x01(\x04:\x01\x30\x12$\n\x16shuffle_input_sentence\x18\x13 \x01(\x08:\x04true\x12 \n\x14mining_sentence_size\x18\x0c \x01(\x05\x42\x02\x18\x01\x12"\n\x16training_sentence_size\x18\r \x01(\x05\x42\x02\x18\x01\x12(\n\x17seed_sentencepiece_size\x18\x0e \x01(\x05:\x07\x31\x30\x30\x30\x30\x30\x30\x12\x1e\n\x10shrinking_factor\x18\x0f \x01(\x02:\x04\x30.75\x12!\n\x13max_sentence_length\x18\x12 \x01(\x05:\x04\x34\x31\x39\x32\x12\x17\n\x0bnum_threads\x18\x10 \x01(\x05:\x02\x31\x36\x12\x1d\n\x12num_sub_iterations\x18\x11 \x01(\x05:\x01\x32\x12$\n\x18max_sentencepiece_length\x18\x14 \x01(\x05:\x02\x31\x36\x12%\n\x17split_by_unicode_script\x18\x15 \x01(\x08:\x04true\x12\x1d\n\x0fsplit_by_number\x18\x17 \x01(\x08:\x04true\x12!\n\x13split_by_whitespace\x18\x16 \x01(\x08:\x04true\x12)\n\x1atreat_whitespace_as_suffix\x18\x18 \x01(\x08:\x05\x66\x61lse\x12+\n\x1c\x61llow_whitespace_only_pieces\x18\x1a \x01(\x08:\x05\x66\x61lse\x12\x1b\n\x0csplit_digits\x18\x19 \x01(\x08:\x05\x66\x61lse\x12#\n\x19pretokenization_delimiter\x18\x35 \x01(\t:\x00\x12\x17\n\x0f\x63ontrol_symbols\x18\x1e \x03(\t\x12\x1c\n\x14user_defined_symbols\x18\x1f \x03(\t\x12\x16\n\x0erequired_chars\x18$ \x01(\t\x12\x1c\n\rbyte_fallback\x18# \x01(\x08:\x05\x66\x61lse\x12+\n\x1dvocabulary_output_piece_score\x18 \x01(\x08:\x04true\x12\x1e\n\x10hard_vocab_limit\x18! \x01(\x08:\x04true\x12\x1c\n\ruse_all_vocab\x18" \x01(\x08:\x05\x66\x61lse\x12\x11\n\x06unk_id\x18( \x01(\x05:\x01\x30\x12\x11\n\x06\x62os_id\x18) \x01(\x05:\x01\x31\x12\x11\n\x06\x65os_id\x18* \x01(\x05:\x01\x32\x12\x12\n\x06pad_id\x18+ \x01(\x05:\x02-1\x12\x18\n\tunk_piece\x18- \x01(\t:\x05<unk>\x12\x16\n\tbos_piece\x18. \x01(\t:\x03<s>\x12\x17\n\teos_piece\x18/ \x01(\t:\x04</s>\x12\x18\n\tpad_piece\x18\x30 \x01(\t:\x05<pad>\x12\x1a\n\x0bunk_surface\x18, \x01(\t:\x05 \xe2\x81\x87 \x12+\n\x1ctrain_extremely_large_corpus\x18\x31 \x01(\x08:\x05\x66\x61lse"5\n\tModelType\x12\x0b\n\x07UNIGRAM\x10\x01\x12\x07\n\x03\x42PE\x10\x02\x12\x08\n\x04WORD\x10\x03\x12\x08\n\x04\x43HAR\x10\x04*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xd1\x01\n\x0eNormalizerSpec\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1c\n\x14precompiled_charsmap\x18\x02 \x01(\x0c\x12\x1e\n\x10\x61\x64\x64_dummy_prefix\x18\x03 \x01(\x08:\x04true\x12&\n\x18remove_extra_whitespaces\x18\x04 \x01(\x08:\x04true\x12 \n\x12\x65scape_whitespaces\x18\x05 \x01(\x08:\x04true\x12\x1e\n\x16normalization_rule_tsv\x18\x06 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"y\n\x0cSelfTestData\x12\x33\n\x07samples\x18\x01 \x03(\x0b\x32".sentencepiece.SelfTestData.Sample\x1a)\n\x06Sample\x12\r\n\x05input\x18\x01 \x01(\t\x12\x10\n\x08\x65xpected\x18\x02 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xfe\x03\n\nModelProto\x12\x37\n\x06pieces\x18\x01 \x03(\x0b\x32\'.sentencepiece.ModelProto.SentencePiece\x12\x30\n\x0ctrainer_spec\x18\x02 \x01(\x0b\x32\x1a.sentencepiece.TrainerSpec\x12\x36\n\x0fnormalizer_spec\x18\x03 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x12\x33\n\x0eself_test_data\x18\x04 \x01(\x0b\x32\x1b.sentencepiece.SelfTestData\x12\x38\n\x11\x64\x65normalizer_spec\x18\x05 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x1a\xd2\x01\n\rSentencePiece\x12\r\n\x05piece\x18\x01 \x01(\t\x12\r\n\x05score\x18\x02 \x01(\x02\x12\x42\n\x04type\x18\x03 \x01(\x0e\x32,.sentencepiece.ModelProto.SentencePiece.Type:\x06NORMAL"T\n\x04Type\x12\n\n\x06NORMAL\x10\x01\x12\x0b\n\x07UNKNOWN\x10\x02\x12\x0b\n\x07\x43ONTROL\x10\x03\x12\x10\n\x0cUSER_DEFINED\x10\x04\x12\x08\n\x04\x42YTE\x10\x06\x12\n\n\x06UNUSED\x10\x05*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\x42\x02H\x03'
)
lowerCAmelCase__ : Any =globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'sentencepiece_model_pb2', _globals)
if _descriptor._USE_C_DESCRIPTORS is False:
lowerCAmelCase__ : List[Any] =None
lowerCAmelCase__ : Any =b'H\003'
# (generated by protobuf compiler, but `_TRAINERSPEC` is not defined)
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._serialized_options = b"\030\001"
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._serialized_options = b"\030\001"
lowerCAmelCase__ : str =45
lowerCAmelCase__ : List[str] =15_81
lowerCAmelCase__ : Optional[int] =15_17
lowerCAmelCase__ : Any =15_70
lowerCAmelCase__ : Optional[Any] =15_84
lowerCAmelCase__ : List[str] =17_93
lowerCAmelCase__ : int =17_95
lowerCAmelCase__ : Optional[int] =19_16
lowerCAmelCase__ : str =18_64
lowerCAmelCase__ : int =19_05
lowerCAmelCase__ : Union[str, Any] =19_19
lowerCAmelCase__ : Any =24_29
lowerCAmelCase__ : Union[str, Any] =22_08
lowerCAmelCase__ : List[Any] =24_18
lowerCAmelCase__ : Optional[int] =23_23
lowerCAmelCase__ : int =24_07
# @@protoc_insertion_point(module_scope)
| 101 |
"""simple docstring"""
import requests
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = {"""Content-Type""": """application/json"""}
UpperCAmelCase__ : Optional[Any] = requests.post(__UpperCamelCase , json={"""text""": message_body} , headers=__UpperCamelCase )
if response.status_code != 200:
UpperCAmelCase__ : Any = (
"""Request to slack returned an error """
F"{response.status_code}, the response is:\n{response.text}"
)
raise ValueError(__UpperCamelCase )
if __name__ == "__main__":
# Set the slack url to the one provided by Slack when you create the webhook at
# https://my.slack.com/services/new/incoming-webhook/
send_slack_message('<YOUR MESSAGE BODY>', '<SLACK CHANNEL URL>')
| 65 | 0 |
"""simple docstring"""
import qiskit
def UpperCamelCase (SCREAMING_SNAKE_CASE = 2 ):
UpperCamelCase : List[Any] = qubits
# Using Aer's simulator
UpperCamelCase : int = qiskit.Aer.get_backend("""aer_simulator""" )
# Creating a Quantum Circuit acting on the q register
UpperCamelCase : Dict = qiskit.QuantumCircuit(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
# Adding a H gate on qubit 0 (now q0 in superposition)
circuit.h(0 )
for i in range(1 , SCREAMING_SNAKE_CASE ):
# Adding CX (CNOT) gate
circuit.cx(i - 1 , SCREAMING_SNAKE_CASE )
# Mapping the quantum measurement to the classical bits
circuit.measure(list(range(SCREAMING_SNAKE_CASE ) ) , list(range(SCREAMING_SNAKE_CASE ) ) )
# Now measuring any one qubit would affect other qubits to collapse
# their super position and have same state as the measured one.
# Executing the circuit on the simulator
UpperCamelCase : Optional[int] = qiskit.execute(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , shots=1000 )
return job.result().get_counts(SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
print(f'''Total count for various states are: {quantum_entanglement(3)}''')
| 102 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class __lowercase ( __lowerCamelCase , unittest.TestCase ):
snake_case_ = CTRLTokenizer
snake_case_ = False
snake_case_ = False
def __lowercase ( self : List[str] ):
'''simple docstring'''
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
UpperCAmelCase__ : List[Any] = ["""adapt""", """re@@""", """a@@""", """apt""", """c@@""", """t""", """<unk>"""]
UpperCAmelCase__ : Optional[int] = dict(zip(A ,range(len(A ) ) ) )
UpperCAmelCase__ : List[Any] = ["""#version: 0.2""", """a p""", """ap t</w>""", """r e""", """a d""", """ad apt</w>""", """"""]
UpperCAmelCase__ : int = {"""unk_token""": """<unk>"""}
UpperCAmelCase__ : Optional[Any] = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["""vocab_file"""] )
UpperCAmelCase__ : str = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["""merges_file"""] )
with open(self.vocab_file ,"""w""" ,encoding="""utf-8""" ) as fp:
fp.write(json.dumps(A ) + """\n""" )
with open(self.merges_file ,"""w""" ,encoding="""utf-8""" ) as fp:
fp.write("""\n""".join(A ) )
def __lowercase ( self : int ,**A : Dict ):
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return CTRLTokenizer.from_pretrained(self.tmpdirname ,**A )
def __lowercase ( self : List[Any] ,A : Any ):
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = """adapt react readapt apt"""
UpperCAmelCase__ : Any = """adapt react readapt apt"""
return input_text, output_text
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = CTRLTokenizer(self.vocab_file ,self.merges_file ,**self.special_tokens_map )
UpperCAmelCase__ : Tuple = """adapt react readapt apt"""
UpperCAmelCase__ : Optional[int] = """adapt re@@ a@@ c@@ t re@@ adapt apt""".split()
UpperCAmelCase__ : Dict = tokenizer.tokenize(A )
self.assertListEqual(A ,A )
UpperCAmelCase__ : Any = tokens + [tokenizer.unk_token]
UpperCAmelCase__ : Dict = [0, 1, 2, 4, 5, 1, 0, 3, 6]
self.assertListEqual(tokenizer.convert_tokens_to_ids(A ) ,A )
| 65 | 0 |
"""simple docstring"""
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
A__ : str = ['''image_processor''', '''tokenizer''']
A__ : Tuple = '''BlipImageProcessor'''
A__ : str = ('''BertTokenizer''', '''BertTokenizerFast''')
def __init__( self : Any , __lowerCamelCase : List[Any] , __lowerCamelCase : Any ):
"""simple docstring"""
_snake_case = False
super().__init__(__lowerCamelCase , __lowerCamelCase )
_snake_case = self.image_processor
def __call__( self : Optional[int] , __lowerCamelCase : ImageInput = None , __lowerCamelCase : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = 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 : bool = False , __lowerCamelCase : bool = False , __lowerCamelCase : bool = False , __lowerCamelCase : bool = False , __lowerCamelCase : bool = False , __lowerCamelCase : bool = True , __lowerCamelCase : Optional[Union[str, TensorType]] = None , **__lowerCamelCase : List[str] , ):
"""simple docstring"""
if images is None and text is None:
raise ValueError('''You have to specify either images or text.''' )
# Get only text
if images is None:
_snake_case = self.tokenizer
_snake_case = self.tokenizer(
text=__lowerCamelCase , add_special_tokens=__lowerCamelCase , padding=__lowerCamelCase , truncation=__lowerCamelCase , max_length=__lowerCamelCase , stride=__lowerCamelCase , pad_to_multiple_of=__lowerCamelCase , return_attention_mask=__lowerCamelCase , return_overflowing_tokens=__lowerCamelCase , return_special_tokens_mask=__lowerCamelCase , return_offsets_mapping=__lowerCamelCase , return_token_type_ids=__lowerCamelCase , return_length=__lowerCamelCase , verbose=__lowerCamelCase , return_tensors=__lowerCamelCase , **__lowerCamelCase , )
return text_encoding
# add pixel_values
_snake_case = self.image_processor(__lowerCamelCase , return_tensors=__lowerCamelCase )
if text is not None:
_snake_case = self.tokenizer(
text=__lowerCamelCase , add_special_tokens=__lowerCamelCase , padding=__lowerCamelCase , truncation=__lowerCamelCase , max_length=__lowerCamelCase , stride=__lowerCamelCase , pad_to_multiple_of=__lowerCamelCase , return_attention_mask=__lowerCamelCase , return_overflowing_tokens=__lowerCamelCase , return_special_tokens_mask=__lowerCamelCase , return_offsets_mapping=__lowerCamelCase , return_token_type_ids=__lowerCamelCase , return_length=__lowerCamelCase , verbose=__lowerCamelCase , return_tensors=__lowerCamelCase , **__lowerCamelCase , )
else:
_snake_case = None
if text_encoding is not None:
encoding_image_processor.update(__lowerCamelCase )
return encoding_image_processor
def __UpperCAmelCase ( self : Dict , *__lowerCamelCase : List[str] , **__lowerCamelCase : Optional[Any] ):
"""simple docstring"""
return self.tokenizer.batch_decode(*__lowerCamelCase , **__lowerCamelCase )
def __UpperCAmelCase ( self : List[str] , *__lowerCamelCase : Optional[Any] , **__lowerCamelCase : Any ):
"""simple docstring"""
return self.tokenizer.decode(*__lowerCamelCase , **__lowerCamelCase )
@property
def __UpperCAmelCase ( self : str ):
"""simple docstring"""
_snake_case = self.tokenizer.model_input_names
_snake_case = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
| 103 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__UpperCAmelCase = {
'configuration_bridgetower': [
'BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'BridgeTowerConfig',
'BridgeTowerTextConfig',
'BridgeTowerVisionConfig',
],
'processing_bridgetower': ['BridgeTowerProcessor'],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = ['BridgeTowerImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
'BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST',
'BridgeTowerForContrastiveLearning',
'BridgeTowerForImageAndTextRetrieval',
'BridgeTowerForMaskedLM',
'BridgeTowerModel',
'BridgeTowerPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_bridgetower import (
BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
BridgeTowerConfig,
BridgeTowerTextConfig,
BridgeTowerVisionConfig,
)
from .processing_bridgetower import BridgeTowerProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_bridgetower import BridgeTowerImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_bridgetower import (
BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
BridgeTowerForContrastiveLearning,
BridgeTowerForImageAndTextRetrieval,
BridgeTowerForMaskedLM,
BridgeTowerModel,
BridgeTowerPreTrainedModel,
)
else:
import sys
__UpperCAmelCase = _LazyModule(__name__, globals()['__file__'], _import_structure)
| 65 | 0 |
"""simple docstring"""
def _lowerCamelCase ( UpperCAmelCase_ : int = 200 ) -> int:
"""simple docstring"""
A__ = [1, 2, 5, 10, 20, 50, 100, 200]
A__ = [0] * (pence + 1)
A__ = 1 # base case: 1 way to make 0 pence
for coin in coins:
for i in range(UpperCAmelCase_, pence + 1, 1 ):
number_of_ways[i] += number_of_ways[i - coin]
return number_of_ways[pence]
if __name__ == "__main__":
assert solution(200) == 7_3682
| 104 |
"""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 __lowercase ( __lowerCamelCase ):
snake_case_ = ["""input_features""", """is_longer"""]
def __init__( self : str ,A : Union[str, Any]=64 ,A : Tuple=48_000 ,A : Dict=480 ,A : List[str]=10 ,A : str=1_024 ,A : Any=0.0 ,A : Optional[int]=False ,A : float = 0 ,A : float = 14_000 ,A : int = None ,A : str = "fusion" ,A : str = "repeatpad" ,**A : List[Any] ,):
'''simple docstring'''
super().__init__(
feature_size=A ,sampling_rate=A ,padding_value=A ,return_attention_mask=A ,**A ,)
UpperCAmelCase__ : List[Any] = top_db
UpperCAmelCase__ : Union[str, Any] = truncation
UpperCAmelCase__ : Optional[int] = padding
UpperCAmelCase__ : List[Any] = fft_window_size
UpperCAmelCase__ : Optional[Any] = (fft_window_size >> 1) + 1
UpperCAmelCase__ : Any = hop_length
UpperCAmelCase__ : List[str] = max_length_s
UpperCAmelCase__ : List[Any] = max_length_s * sampling_rate
UpperCAmelCase__ : List[Any] = sampling_rate
UpperCAmelCase__ : Optional[int] = frequency_min
UpperCAmelCase__ : Tuple = frequency_max
UpperCAmelCase__ : 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=A ,mel_scale="""htk""" ,)
UpperCAmelCase__ : 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 __lowercase ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = copy.deepcopy(self.__dict__ )
UpperCAmelCase__ : 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 __lowercase ( self : List[str] ,A : np.array ,A : Optional[np.array] = None ):
'''simple docstring'''
UpperCAmelCase__ : Dict = 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 : Optional[Any] ,A : Union[str, Any] ,A : int ,A : Optional[int] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[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
UpperCAmelCase__ : List[str] = [0]
if len(ranges[2] ) == 0:
# if the audio is too short, we just use the first chunk
UpperCAmelCase__ : int = [0]
# randomly choose index for each part
UpperCAmelCase__ : Tuple = np.random.choice(ranges[0] )
UpperCAmelCase__ : Tuple = np.random.choice(ranges[1] )
UpperCAmelCase__ : str = np.random.choice(ranges[2] )
UpperCAmelCase__ : List[str] = mel[idx_front : idx_front + chunk_frames, :]
UpperCAmelCase__ : List[str] = mel[idx_middle : idx_middle + chunk_frames, :]
UpperCAmelCase__ : Dict = mel[idx_back : idx_back + chunk_frames, :]
UpperCAmelCase__ : Optional[Any] = torch.tensor(mel[None, None, :] )
UpperCAmelCase__ : int = torch.nn.functional.interpolate(
A ,size=[chunk_frames, 64] ,mode="""bilinear""" ,align_corners=A )
UpperCAmelCase__ : Dict = mel_shrink[0][0].numpy()
UpperCAmelCase__ : Dict = np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back] ,axis=0 )
return mel_fusion
def __lowercase ( self : Any ,A : np.array ,A : Optional[int] ,A : Any ,A : Tuple ):
'''simple docstring'''
if waveform.shape[0] > max_length:
if truncation == "rand_trunc":
UpperCAmelCase__ : int = True
# random crop to max_length (for compatibility) -> this should be handled by self.pad
UpperCAmelCase__ : str = len(A ) - max_length
UpperCAmelCase__ : Optional[Any] = np.random.randint(0 ,overflow + 1 )
UpperCAmelCase__ : Optional[int] = waveform[idx : idx + max_length]
UpperCAmelCase__ : Any = self._np_extract_fbank_features(A ,self.mel_filters_slaney )[None, :]
elif truncation == "fusion":
UpperCAmelCase__ : Tuple = self._np_extract_fbank_features(A ,self.mel_filters )
UpperCAmelCase__ : Optional[int] = max_length // self.hop_length + 1 # the +1 related to how the spectrogram is computed
UpperCAmelCase__ : 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.
UpperCAmelCase__ : List[Any] = np.stack([mel, mel, mel, mel] ,axis=0 )
UpperCAmelCase__ : Any = False
else:
UpperCAmelCase__ : Union[str, Any] = self._random_mel_fusion(A ,A ,A )
UpperCAmelCase__ : List[str] = True
else:
raise NotImplementedError(f"data_truncating {truncation} not implemented" )
else:
UpperCAmelCase__ : Optional[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":
UpperCAmelCase__ : str = int(max_length / len(A ) )
UpperCAmelCase__ : int = np.stack(np.tile(A ,n_repeat + 1 ) )[:max_length]
if padding == "repeatpad":
UpperCAmelCase__ : List[Any] = int(max_length / len(A ) )
UpperCAmelCase__ : str = np.stack(np.tile(A ,A ) )
UpperCAmelCase__ : Optional[Any] = np.pad(A ,(0, max_length - waveform.shape[0]) ,mode="""constant""" ,constant_values=0 )
if truncation == "fusion":
UpperCAmelCase__ : int = self._np_extract_fbank_features(A ,self.mel_filters )
UpperCAmelCase__ : List[Any] = np.stack([input_mel, input_mel, input_mel, input_mel] ,axis=0 )
else:
UpperCAmelCase__ : Any = self._np_extract_fbank_features(A ,self.mel_filters_slaney )[None, :]
return input_mel, longer
def __call__( self : str ,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 : List[str] ,):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = truncation if truncation is not None else self.truncation
UpperCAmelCase__ : Dict = 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__ : Optional[int] = 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__ : List[str] = is_batched_numpy or (
isinstance(A ,(list, tuple) ) and (isinstance(raw_speech[0] ,(np.ndarray, tuple, list) ))
)
if is_batched:
UpperCAmelCase__ : str = [np.asarray(A ,dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(A ,np.ndarray ):
UpperCAmelCase__ : Any = np.asarray(A ,dtype=np.floataa )
elif isinstance(A ,np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
UpperCAmelCase__ : str = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
UpperCAmelCase__ : Optional[Any] = [np.asarray(A )]
# convert to mel spectrogram, truncate and pad if needed.
UpperCAmelCase__ : Tuple = [
self._get_input_mel(A ,max_length if max_length else self.nb_max_samples ,A ,A )
for waveform in raw_speech
]
UpperCAmelCase__ : Optional[int] = []
UpperCAmelCase__ : 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
UpperCAmelCase__ : List[str] = np.random.randint(0 ,len(A ) )
UpperCAmelCase__ : int = True
if isinstance(input_mel[0] ,A ):
UpperCAmelCase__ : Tuple = [np.asarray(A ,dtype=np.floataa ) for feature in input_mel]
# is_longer is a list of bool
UpperCAmelCase__ : List[str] = [[longer] for longer in is_longer]
UpperCAmelCase__ : List[Any] = {"""input_features""": input_mel, """is_longer""": is_longer}
UpperCAmelCase__ : str = BatchFeature(A )
if return_tensors is not None:
UpperCAmelCase__ : int = input_features.convert_to_tensors(A )
return input_features
| 65 | 0 |
import fire
from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer
def __UpperCAmelCase ( lowerCamelCase_ : str , lowerCamelCase_ : str , **lowerCamelCase_ : List[str] ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : str = AutoConfig.from_pretrained(lowerCamelCase_ , **lowerCamelCase_ )
SCREAMING_SNAKE_CASE_ : Optional[int] = AutoModelForSeqaSeqLM.from_config(lowerCamelCase_ )
model.save_pretrained(lowerCamelCase_ )
AutoTokenizer.from_pretrained(lowerCamelCase_ ).save_pretrained(lowerCamelCase_ )
return model
if __name__ == "__main__":
fire.Fire(save_randomly_initialized_version)
| 105 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, 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 DonutImageProcessor
class __lowercase ( unittest.TestCase ):
def __init__( self : Union[str, Any] ,A : Union[str, Any] ,A : Dict=7 ,A : Optional[int]=3 ,A : List[str]=18 ,A : Union[str, Any]=30 ,A : Tuple=400 ,A : Dict=True ,A : List[str]=None ,A : str=True ,A : Optional[Any]=False ,A : Optional[Any]=True ,A : List[str]=True ,A : Optional[int]=[0.5, 0.5, 0.5] ,A : List[str]=[0.5, 0.5, 0.5] ,):
'''simple docstring'''
UpperCAmelCase__ : str = parent
UpperCAmelCase__ : List[str] = batch_size
UpperCAmelCase__ : List[str] = num_channels
UpperCAmelCase__ : Union[str, Any] = image_size
UpperCAmelCase__ : List[Any] = min_resolution
UpperCAmelCase__ : Optional[int] = max_resolution
UpperCAmelCase__ : str = do_resize
UpperCAmelCase__ : Tuple = size if size is not None else {"""height""": 18, """width""": 20}
UpperCAmelCase__ : List[str] = do_thumbnail
UpperCAmelCase__ : Optional[int] = do_align_axis
UpperCAmelCase__ : Union[str, Any] = do_pad
UpperCAmelCase__ : Tuple = do_normalize
UpperCAmelCase__ : Optional[Any] = image_mean
UpperCAmelCase__ : List[Any] = image_std
def __lowercase ( self : Optional[int] ):
'''simple docstring'''
return {
"do_resize": self.do_resize,
"size": self.size,
"do_thumbnail": self.do_thumbnail,
"do_align_long_axis": self.do_align_axis,
"do_pad": self.do_pad,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
}
@require_torch
@require_vision
class __lowercase ( __lowerCamelCase , unittest.TestCase ):
snake_case_ = DonutImageProcessor if is_vision_available() else None
def __lowercase ( self : str ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = DonutImageProcessingTester(self )
@property
def __lowercase ( self : Dict ):
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def __lowercase ( self : Any ):
'''simple docstring'''
UpperCAmelCase__ : Any = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(A ,"""do_resize""" ) )
self.assertTrue(hasattr(A ,"""size""" ) )
self.assertTrue(hasattr(A ,"""do_thumbnail""" ) )
self.assertTrue(hasattr(A ,"""do_align_long_axis""" ) )
self.assertTrue(hasattr(A ,"""do_pad""" ) )
self.assertTrue(hasattr(A ,"""do_normalize""" ) )
self.assertTrue(hasattr(A ,"""image_mean""" ) )
self.assertTrue(hasattr(A ,"""image_std""" ) )
def __lowercase ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size ,{"""height""": 18, """width""": 20} )
UpperCAmelCase__ : str = self.image_processing_class.from_dict(self.image_processor_dict ,size=42 )
self.assertEqual(image_processor.size ,{"""height""": 42, """width""": 42} )
# Previous config had dimensions in (width, height) order
UpperCAmelCase__ : str = self.image_processing_class.from_dict(self.image_processor_dict ,size=(42, 84) )
self.assertEqual(image_processor.size ,{"""height""": 84, """width""": 42} )
def __lowercase ( self : Dict ):
'''simple docstring'''
pass
@is_flaky()
def __lowercase ( self : int ):
'''simple docstring'''
# Initialize image_processing
UpperCAmelCase__ : Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
UpperCAmelCase__ : Dict = prepare_image_inputs(self.image_processor_tester ,equal_resolution=A )
for image in image_inputs:
self.assertIsInstance(A ,Image.Image )
# Test not batched input
UpperCAmelCase__ : int = image_processing(image_inputs[0] ,return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) ,)
# Test batched
UpperCAmelCase__ : Tuple = image_processing(A ,return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) ,)
@is_flaky()
def __lowercase ( self : List[str] ):
'''simple docstring'''
# Initialize image_processing
UpperCAmelCase__ : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
UpperCAmelCase__ : Dict = prepare_image_inputs(self.image_processor_tester ,equal_resolution=A ,numpify=A )
for image in image_inputs:
self.assertIsInstance(A ,np.ndarray )
# Test not batched input
UpperCAmelCase__ : List[str] = image_processing(image_inputs[0] ,return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) ,)
# Test batched
UpperCAmelCase__ : Optional[int] = image_processing(A ,return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) ,)
@is_flaky()
def __lowercase ( self : Any ):
'''simple docstring'''
# Initialize image_processing
UpperCAmelCase__ : int = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
UpperCAmelCase__ : int = prepare_image_inputs(self.image_processor_tester ,equal_resolution=A ,torchify=A )
for image in image_inputs:
self.assertIsInstance(A ,torch.Tensor )
# Test not batched input
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.size["""height"""],
self.image_processor_tester.size["""width"""],
) ,)
# Test batched
UpperCAmelCase__ : List[Any] = image_processing(A ,return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) ,)
| 65 | 0 |
import logging
from transformers import PretrainedConfig
__snake_case :int =logging.getLogger(__name__)
__snake_case :Tuple ={
'bertabs-finetuned-cnndm': 'https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json',
}
class lowerCAmelCase__ ( _lowerCamelCase ):
A_ : Dict = 'bertabs'
def __init__( self : Optional[int] , __UpperCamelCase : int=30_522 , __UpperCamelCase : Tuple=512 , __UpperCamelCase : List[Any]=6 , __UpperCamelCase : Tuple=512 , __UpperCamelCase : Dict=8 , __UpperCamelCase : List[Any]=512 , __UpperCamelCase : Dict=0.2 , __UpperCamelCase : Optional[Any]=6 , __UpperCamelCase : Union[str, Any]=768 , __UpperCamelCase : List[Any]=8 , __UpperCamelCase : Optional[int]=2_048 , __UpperCamelCase : Tuple=0.2 , **__UpperCamelCase : Any , ) -> Union[str, Any]:
super().__init__(**__UpperCamelCase )
A = vocab_size
A = max_pos
A = enc_layers
A = enc_hidden_size
A = enc_heads
A = enc_ff_size
A = enc_dropout
A = dec_layers
A = dec_hidden_size
A = dec_heads
A = dec_ff_size
A = dec_dropout | 106 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
's-JoL/Open-Llama-V1': 'https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json',
}
class __lowercase ( __lowerCamelCase ):
snake_case_ = """open-llama"""
def __init__( self : Dict ,A : str=100_000 ,A : str=4_096 ,A : Optional[Any]=11_008 ,A : Tuple=32 ,A : str=32 ,A : Optional[int]="silu" ,A : List[Any]=2_048 ,A : str=0.0_2 ,A : Optional[int]=1e-6 ,A : int=True ,A : Tuple=0 ,A : str=1 ,A : Any=2 ,A : Optional[Any]=False ,A : int=True ,A : Any=0.1 ,A : Optional[Any]=0.1 ,A : Optional[Any]=True ,A : Union[str, Any]=True ,A : Tuple=None ,**A : Optional[int] ,):
'''simple docstring'''
UpperCAmelCase__ : str = vocab_size
UpperCAmelCase__ : List[str] = max_position_embeddings
UpperCAmelCase__ : Union[str, Any] = hidden_size
UpperCAmelCase__ : Tuple = intermediate_size
UpperCAmelCase__ : Optional[int] = num_hidden_layers
UpperCAmelCase__ : Any = num_attention_heads
UpperCAmelCase__ : str = hidden_act
UpperCAmelCase__ : Optional[Any] = initializer_range
UpperCAmelCase__ : Optional[int] = rms_norm_eps
UpperCAmelCase__ : Any = use_cache
UpperCAmelCase__ : Optional[Any] = kwargs.pop(
"""use_memorry_efficient_attention""" ,A )
UpperCAmelCase__ : Any = hidden_dropout_prob
UpperCAmelCase__ : str = attention_dropout_prob
UpperCAmelCase__ : Optional[int] = use_stable_embedding
UpperCAmelCase__ : Tuple = shared_input_output_embedding
UpperCAmelCase__ : Tuple = rope_scaling
self._rope_scaling_validation()
super().__init__(
pad_token_id=A ,bos_token_id=A ,eos_token_id=A ,tie_word_embeddings=A ,**A ,)
def __lowercase ( self : Optional[Any] ):
'''simple docstring'''
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling ,A ) or len(self.rope_scaling ) != 2:
raise ValueError(
"""`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, """
f"got {self.rope_scaling}" )
UpperCAmelCase__ : List[Any] = self.rope_scaling.get("""type""" ,A )
UpperCAmelCase__ : int = self.rope_scaling.get("""factor""" ,A )
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
raise ValueError(
f"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}" )
if rope_scaling_factor is None or not isinstance(A ,A ) or rope_scaling_factor <= 1.0:
raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}" )
| 65 | 0 |
'''simple docstring'''
import os
import pytest
from attr import dataclass
_UpperCAmelCase : Optional[Any] = '''us-east-1''' # defaults region
@dataclass
class lowercase_ :
"""simple docstring"""
__lowerCAmelCase = 42
__lowerCAmelCase = "arn:aws:iam::558105141721:role/sagemaker_execution_role"
__lowerCAmelCase = {
"task_name": "mnli",
"per_device_train_batch_size": 1_6,
"per_device_eval_batch_size": 1_6,
"do_train": True,
"do_eval": True,
"do_predict": True,
"output_dir": "/opt/ml/model",
"overwrite_output_dir": True,
"max_steps": 5_0_0,
"save_steps": 5_5_0_0,
}
__lowerCAmelCase = {**hyperparameters, "max_steps": 1_0_0_0}
@property
def __UpperCAmelCase ( self : List[Any] ) -> str:
if self.framework == "pytorch":
return [
{"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
{"Name": "eval_accuracy", "Regex": r"eval_accuracy.*=\D*(.*?)$"},
{"Name": "eval_loss", "Regex": r"eval_loss.*=\D*(.*?)$"},
]
else:
return [
{"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
{"Name": "eval_accuracy", "Regex": r"loss.*=\D*(.*?)]?$"},
{"Name": "eval_loss", "Regex": r"sparse_categorical_accuracy.*=\D*(.*?)]?$"},
]
@property
def __UpperCAmelCase ( self : List[str] ) -> str:
return f'{self.framework}-transfromers-test'
@property
def __UpperCAmelCase ( self : Any ) -> str:
return f'./tests/sagemaker/scripts/{self.framework}'
@property
def __UpperCAmelCase ( self : Optional[int] ) -> str:
if self.framework == "pytorch":
return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04"
else:
return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.6.1-gpu-py37-cu110-ubuntu18.04"
@pytest.fixture(scope='class' )
def _SCREAMING_SNAKE_CASE ( __snake_case : Any ):
_A = SageMakerTestEnvironment(framework=request.cls.framework )
| 107 |
"""simple docstring"""
from collections.abc import Callable
class __lowercase :
def __init__( self : Tuple ,A : Callable | None = None ):
'''simple docstring'''
# Stores actual heap items.
UpperCAmelCase__ : list = []
# Stores indexes of each item for supporting updates and deletion.
UpperCAmelCase__ : dict = {}
# Stores current size of heap.
UpperCAmelCase__ : Any = 0
# Stores function used to evaluate the score of an item on which basis ordering
# will be done.
UpperCAmelCase__ : int = key or (lambda A : x)
def __lowercase ( self : Union[str, Any] ,A : int ):
'''simple docstring'''
return int((i - 1) / 2 ) if i > 0 else None
def __lowercase ( self : Tuple ,A : int ):
'''simple docstring'''
UpperCAmelCase__ : Any = int(2 * i + 1 )
return left if 0 < left < self.size else None
def __lowercase ( self : Any ,A : int ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = int(2 * i + 2 )
return right if 0 < right < self.size else None
def __lowercase ( self : List[Any] ,A : int ,A : int ):
'''simple docstring'''
UpperCAmelCase__ , UpperCAmelCase__ : int = (
self.pos_map[self.arr[j][0]],
self.pos_map[self.arr[i][0]],
)
# Then swap the items in the list.
UpperCAmelCase__ , UpperCAmelCase__ : Tuple = self.arr[j], self.arr[i]
def __lowercase ( self : Optional[int] ,A : int ,A : int ):
'''simple docstring'''
return self.arr[i][1] < self.arr[j][1]
def __lowercase ( self : Optional[int] ,A : int ):
'''simple docstring'''
UpperCAmelCase__ : int = self._left(A )
UpperCAmelCase__ : Dict = self._right(A )
UpperCAmelCase__ : Optional[int] = i
if left is not None and not self._cmp(A ,A ):
UpperCAmelCase__ : List[Any] = left
if right is not None and not self._cmp(A ,A ):
UpperCAmelCase__ : List[Any] = right
return valid_parent
def __lowercase ( self : int ,A : int ):
'''simple docstring'''
UpperCAmelCase__ : int = self._parent(A )
while parent is not None and not self._cmp(A ,A ):
self._swap(A ,A )
UpperCAmelCase__ , UpperCAmelCase__ : int = parent, self._parent(A )
def __lowercase ( self : str ,A : int ):
'''simple docstring'''
UpperCAmelCase__ : Any = self._get_valid_parent(A )
while valid_parent != index:
self._swap(A ,A )
UpperCAmelCase__ , UpperCAmelCase__ : Optional[int] = valid_parent, self._get_valid_parent(A )
def __lowercase ( self : Optional[Any] ,A : int ,A : int ):
'''simple docstring'''
if item not in self.pos_map:
return
UpperCAmelCase__ : Tuple = self.pos_map[item]
UpperCAmelCase__ : Dict = [item, self.key(A )]
# Make sure heap is right in both up and down direction.
# Ideally only one of them will make any change.
self._heapify_up(A )
self._heapify_down(A )
def __lowercase ( self : List[Any] ,A : int ):
'''simple docstring'''
if item not in self.pos_map:
return
UpperCAmelCase__ : Any = self.pos_map[item]
del self.pos_map[item]
UpperCAmelCase__ : Dict = self.arr[self.size - 1]
UpperCAmelCase__ : List[Any] = index
self.size -= 1
# Make sure heap is right in both up and down direction. Ideally only one
# of them will make any change- so no performance loss in calling both.
if self.size > index:
self._heapify_up(A )
self._heapify_down(A )
def __lowercase ( self : str ,A : int ,A : int ):
'''simple docstring'''
UpperCAmelCase__ : Dict = len(self.arr )
if arr_len == self.size:
self.arr.append([item, self.key(A )] )
else:
UpperCAmelCase__ : List[str] = [item, self.key(A )]
UpperCAmelCase__ : Union[str, Any] = self.size
self.size += 1
self._heapify_up(self.size - 1 )
def __lowercase ( self : str ):
'''simple docstring'''
return self.arr[0] if self.size else None
def __lowercase ( self : Dict ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = self.get_top()
if top_item_tuple:
self.delete_item(top_item_tuple[0] )
return top_item_tuple
def lowerCAmelCase ( ):
'''simple docstring'''
if __name__ == "__main__":
import doctest
doctest.testmod()
| 65 | 0 |
# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
ControlNetModel,
DDIMScheduler,
StableDiffusionControlNetImgaImgPipeline,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import (
PipelineKarrasSchedulerTesterMixin,
PipelineLatentTesterMixin,
PipelineTesterMixin,
)
enable_full_determinism()
class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , unittest.TestCase ):
'''simple docstring'''
_lowerCamelCase = StableDiffusionControlNetImgaImgPipeline
_lowerCamelCase = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
_lowerCamelCase = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
_lowerCamelCase = IMAGE_TO_IMAGE_IMAGE_PARAMS.union({'''control_image'''} )
_lowerCamelCase = IMAGE_TO_IMAGE_IMAGE_PARAMS
def lowerCamelCase ( self : str ) -> List[str]:
"""simple docstring"""
torch.manual_seed(0 )
_UpperCAmelCase = 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 , )
torch.manual_seed(0 )
_UpperCAmelCase = ControlNetModel(
block_out_channels=(32, 64) , layers_per_block=2 , in_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , cross_attention_dim=32 , conditioning_embedding_out_channels=(16, 32) , )
torch.manual_seed(0 )
_UpperCAmelCase = DDIMScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , clip_sample=lowerCamelCase , set_alpha_to_one=lowerCamelCase , )
torch.manual_seed(0 )
_UpperCAmelCase = 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 )
_UpperCAmelCase = 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 , )
_UpperCAmelCase = CLIPTextModel(lowerCamelCase )
_UpperCAmelCase = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
_UpperCAmelCase = {
"""unet""": unet,
"""controlnet""": controlnet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""safety_checker""": None,
"""feature_extractor""": None,
}
return components
def lowerCamelCase ( self : Optional[int] , lowerCamelCase : Optional[int] , lowerCamelCase : int=0 ) -> Any:
"""simple docstring"""
if str(lowerCamelCase ).startswith("""mps""" ):
_UpperCAmelCase = torch.manual_seed(lowerCamelCase )
else:
_UpperCAmelCase = torch.Generator(device=lowerCamelCase ).manual_seed(lowerCamelCase )
_UpperCAmelCase = 2
_UpperCAmelCase = randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) , generator=lowerCamelCase , device=torch.device(lowerCamelCase ) , )
_UpperCAmelCase = floats_tensor(control_image.shape , rng=random.Random(lowerCamelCase ) ).to(lowerCamelCase )
_UpperCAmelCase = image.cpu().permute(0 , 2 , 3 , 1 )[0]
_UpperCAmelCase = Image.fromarray(np.uinta(lowerCamelCase ) ).convert("""RGB""" ).resize((64, 64) )
_UpperCAmelCase = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
"""output_type""": """numpy""",
"""image""": image,
"""control_image""": control_image,
}
return inputs
def lowerCamelCase ( self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3 )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def lowerCamelCase ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3 )
def lowerCamelCase ( self : Any ) -> Tuple:
"""simple docstring"""
self._test_inference_batch_single_identical(expected_max_diff=2E-3 )
class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase , UpperCAmelCase , unittest.TestCase ):
'''simple docstring'''
_lowerCamelCase = StableDiffusionControlNetImgaImgPipeline
_lowerCamelCase = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
_lowerCamelCase = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
_lowerCamelCase = frozenset([] ) # TO_DO: add image_params once refactored VaeImageProcessor.preprocess
def lowerCamelCase ( self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
torch.manual_seed(0 )
_UpperCAmelCase = 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 , )
torch.manual_seed(0 )
def init_weights(lowerCamelCase : Dict ):
if isinstance(lowerCamelCase , torch.nn.Convad ):
torch.nn.init.normal(m.weight )
m.bias.data.fill_(1.0 )
_UpperCAmelCase = ControlNetModel(
block_out_channels=(32, 64) , layers_per_block=2 , in_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , cross_attention_dim=32 , conditioning_embedding_out_channels=(16, 32) , )
controlneta.controlnet_down_blocks.apply(lowerCamelCase )
torch.manual_seed(0 )
_UpperCAmelCase = ControlNetModel(
block_out_channels=(32, 64) , layers_per_block=2 , in_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , cross_attention_dim=32 , conditioning_embedding_out_channels=(16, 32) , )
controlneta.controlnet_down_blocks.apply(lowerCamelCase )
torch.manual_seed(0 )
_UpperCAmelCase = DDIMScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , clip_sample=lowerCamelCase , set_alpha_to_one=lowerCamelCase , )
torch.manual_seed(0 )
_UpperCAmelCase = 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 )
_UpperCAmelCase = 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 , )
_UpperCAmelCase = CLIPTextModel(lowerCamelCase )
_UpperCAmelCase = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
_UpperCAmelCase = MultiControlNetModel([controlneta, controlneta] )
_UpperCAmelCase = {
"""unet""": unet,
"""controlnet""": controlnet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""safety_checker""": None,
"""feature_extractor""": None,
}
return components
def lowerCamelCase ( self : Any , lowerCamelCase : Optional[Any] , lowerCamelCase : Optional[int]=0 ) -> Dict:
"""simple docstring"""
if str(lowerCamelCase ).startswith("""mps""" ):
_UpperCAmelCase = torch.manual_seed(lowerCamelCase )
else:
_UpperCAmelCase = torch.Generator(device=lowerCamelCase ).manual_seed(lowerCamelCase )
_UpperCAmelCase = 2
_UpperCAmelCase = [
randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) , generator=lowerCamelCase , device=torch.device(lowerCamelCase ) , ),
randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) , generator=lowerCamelCase , device=torch.device(lowerCamelCase ) , ),
]
_UpperCAmelCase = floats_tensor(control_image[0].shape , rng=random.Random(lowerCamelCase ) ).to(lowerCamelCase )
_UpperCAmelCase = image.cpu().permute(0 , 2 , 3 , 1 )[0]
_UpperCAmelCase = Image.fromarray(np.uinta(lowerCamelCase ) ).convert("""RGB""" ).resize((64, 64) )
_UpperCAmelCase = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
"""output_type""": """numpy""",
"""image""": image,
"""control_image""": control_image,
}
return inputs
def lowerCamelCase ( self : Union[str, Any] ) -> str:
"""simple docstring"""
_UpperCAmelCase = self.get_dummy_components()
_UpperCAmelCase = self.pipeline_class(**lowerCamelCase )
pipe.to(lowerCamelCase )
_UpperCAmelCase = 10.0
_UpperCAmelCase = 4
_UpperCAmelCase = self.get_dummy_inputs(lowerCamelCase )
_UpperCAmelCase = steps
_UpperCAmelCase = scale
_UpperCAmelCase = pipe(**lowerCamelCase )[0]
_UpperCAmelCase = self.get_dummy_inputs(lowerCamelCase )
_UpperCAmelCase = steps
_UpperCAmelCase = scale
_UpperCAmelCase = pipe(**lowerCamelCase , control_guidance_start=0.1 , control_guidance_end=0.2 )[0]
_UpperCAmelCase = self.get_dummy_inputs(lowerCamelCase )
_UpperCAmelCase = steps
_UpperCAmelCase = scale
_UpperCAmelCase = pipe(**lowerCamelCase , control_guidance_start=[0.1, 0.3] , control_guidance_end=[0.2, 0.7] )[0]
_UpperCAmelCase = self.get_dummy_inputs(lowerCamelCase )
_UpperCAmelCase = steps
_UpperCAmelCase = scale
_UpperCAmelCase = pipe(**lowerCamelCase , control_guidance_start=0.4 , control_guidance_end=[0.5, 0.8] )[0]
# make sure that all outputs are different
assert np.sum(np.abs(output_a - output_a ) ) > 1E-3
assert np.sum(np.abs(output_a - output_a ) ) > 1E-3
assert np.sum(np.abs(output_a - output_a ) ) > 1E-3
def lowerCamelCase ( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3 )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def lowerCamelCase ( self : List[str] ) -> List[str]:
"""simple docstring"""
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3 )
def lowerCamelCase ( self : Tuple ) -> Dict:
"""simple docstring"""
self._test_inference_batch_single_identical(expected_max_diff=2E-3 )
def lowerCamelCase ( self : Dict ) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase = self.get_dummy_components()
_UpperCAmelCase = self.pipeline_class(**lowerCamelCase )
pipe.to(lowerCamelCase )
pipe.set_progress_bar_config(disable=lowerCamelCase )
with tempfile.TemporaryDirectory() as tmpdir:
try:
# save_pretrained is not implemented for Multi-ControlNet
pipe.save_pretrained(lowerCamelCase )
except NotImplementedError:
pass
@slow
@require_torch_gpu
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase ( self : Any ) -> Dict:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase ( self : Tuple ) -> int:
"""simple docstring"""
_UpperCAmelCase = ControlNetModel.from_pretrained("""lllyasviel/sd-controlnet-canny""" )
_UpperCAmelCase = StableDiffusionControlNetImgaImgPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , safety_checker=lowerCamelCase , controlnet=lowerCamelCase )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=lowerCamelCase )
_UpperCAmelCase = torch.Generator(device="""cpu""" ).manual_seed(0 )
_UpperCAmelCase = """evil space-punk bird"""
_UpperCAmelCase = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png""" ).resize((512, 512) )
_UpperCAmelCase = load_image(
"""https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png""" ).resize((512, 512) )
_UpperCAmelCase = pipe(
lowerCamelCase , lowerCamelCase , control_image=lowerCamelCase , generator=lowerCamelCase , output_type="""np""" , num_inference_steps=50 , strength=0.6 , )
_UpperCAmelCase = output.images[0]
assert image.shape == (512, 512, 3)
_UpperCAmelCase = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/img2img.npy""" )
assert np.abs(expected_image - image ).max() < 9E-2 | 108 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import PaddingStrategy, TensorType
from ....utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
class __lowercase ( __lowerCamelCase ):
snake_case_ = ["""input_features""", """attention_mask"""]
def __init__( self : Any ,A : str=80 ,A : Optional[int]=16_000 ,A : int=0.0 ,A : str=10 ,A : Any=25 ,A : str="hamming_window" ,A : int=3_2_7_6_8.0 ,A : List[str]=0.9_7 ,A : Optional[int]=1.0 ,A : Optional[Any]=True ,A : Tuple=True ,A : Any=False ,**A : int ,):
'''simple docstring'''
super().__init__(feature_size=A ,sampling_rate=A ,padding_value=A ,**A )
UpperCAmelCase__ : str = feature_size
UpperCAmelCase__ : int = sampling_rate
UpperCAmelCase__ : int = padding_value
UpperCAmelCase__ : Dict = hop_length
UpperCAmelCase__ : int = win_length
UpperCAmelCase__ : Dict = frame_signal_scale
UpperCAmelCase__ : Dict = preemphasis_coeff
UpperCAmelCase__ : str = mel_floor
UpperCAmelCase__ : Any = normalize_means
UpperCAmelCase__ : str = normalize_vars
UpperCAmelCase__ : int = win_function
UpperCAmelCase__ : List[Any] = return_attention_mask
UpperCAmelCase__ : str = win_length * sampling_rate // 1_000
UpperCAmelCase__ : List[Any] = hop_length * sampling_rate // 1_000
UpperCAmelCase__ : int = optimal_fft_length(self.sample_size )
UpperCAmelCase__ : List[Any] = (self.n_fft // 2) + 1
def __lowercase ( self : Union[str, Any] ,A : np.array ):
'''simple docstring'''
if self.win_function == "hamming_window":
UpperCAmelCase__ : Any = window_function(window_length=self.sample_size ,name=self.win_function ,periodic=A )
else:
UpperCAmelCase__ : Any = window_function(window_length=self.sample_size ,name=self.win_function )
UpperCAmelCase__ : Union[str, Any] = mel_filter_bank(
num_frequency_bins=self.n_freqs ,num_mel_filters=self.feature_size ,min_frequency=0.0 ,max_frequency=self.sampling_rate / 2.0 ,sampling_rate=self.sampling_rate ,)
UpperCAmelCase__ : Optional[Any] = spectrogram(
one_waveform * self.frame_signal_scale ,window=A ,frame_length=self.sample_size ,hop_length=self.sample_stride ,fft_length=self.n_fft ,center=A ,preemphasis=self.preemphasis_coeff ,mel_filters=A ,mel_floor=self.mel_floor ,log_mel="""log""" ,)
return msfc_features.T
def __lowercase ( self : str ,A : Any ,A : Optional[int] ,A : str ):
'''simple docstring'''
# make sure we normalize float32 arrays
if self.normalize_means:
UpperCAmelCase__ : Optional[Any] = x[:input_length].mean(axis=0 )
UpperCAmelCase__ : Any = np.subtract(A ,A )
if self.normalize_vars:
UpperCAmelCase__ : str = x[:input_length].std(axis=0 )
UpperCAmelCase__ : Optional[int] = np.divide(A ,A )
if input_length < x.shape[0]:
UpperCAmelCase__ : int = padding_value
# make sure array is in float32
UpperCAmelCase__ : str = x.astype(np.floataa )
return x
def __lowercase ( self : Union[str, Any] ,A : List[np.ndarray] ,A : Optional[np.ndarray] = None ):
'''simple docstring'''
UpperCAmelCase__ : Any = attention_mask.sum(-1 ) if attention_mask is not None else [x.shape[0] for x in input_features]
return [self._normalize_one(A ,A ,self.padding_value ) for x, n in zip(A ,A )]
def __call__( self : Union[str, Any] ,A : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] ,A : Union[bool, str, PaddingStrategy] = False ,A : Optional[int] = None ,A : bool = False ,A : Optional[int] = None ,A : Optional[bool] = None ,A : Optional[Union[str, TensorType]] = None ,A : Optional[int] = None ,**A : Tuple ,):
'''simple docstring'''
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 `raw_speech` 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.""" )
UpperCAmelCase__ : 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}" )
UpperCAmelCase__ : Any = is_batched_numpy or (
isinstance(A ,(list, tuple) ) and (isinstance(raw_speech[0] ,(np.ndarray, tuple, list) ))
)
if is_batched:
UpperCAmelCase__ : List[str] = [np.asarray(A ,dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(A ,np.ndarray ):
UpperCAmelCase__ : Union[str, Any] = np.asarray(A ,dtype=np.floataa )
elif isinstance(A ,np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
UpperCAmelCase__ : Optional[int] = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
UpperCAmelCase__ : Optional[Any] = [raw_speech]
# extract fbank features
UpperCAmelCase__ : Tuple = [self._extract_mfsc_features(A ) for one_waveform in raw_speech]
# convert into correct format for padding
UpperCAmelCase__ : str = BatchFeature({"""input_features""": features} )
UpperCAmelCase__ : Optional[Any] = self.pad(
A ,padding=A ,max_length=A ,truncation=A ,pad_to_multiple_of=A ,return_attention_mask=A ,**A ,)
# make sure list is in array format
UpperCAmelCase__ : Tuple = padded_inputs.get("""input_features""" )
if isinstance(input_features[0] ,A ):
UpperCAmelCase__ : Union[str, Any] = [np.asarray(A ,dtype=np.floataa ) for feature in input_features]
UpperCAmelCase__ : Dict = padded_inputs.get("""attention_mask""" )
if attention_mask is not None:
UpperCAmelCase__ : str = [np.asarray(A ,dtype=np.intaa ) for array in attention_mask]
if self.normalize_means or self.normalize_vars:
UpperCAmelCase__ : Union[str, Any] = (
np.array(A ,dtype=np.intaa )
if self._get_padding_strategies(A ,max_length=A ) is not PaddingStrategy.DO_NOT_PAD
and padding
else None
)
UpperCAmelCase__ : Any = self.normalize(
padded_inputs["""input_features"""] ,attention_mask=A )
if return_tensors is not None:
UpperCAmelCase__ : Union[str, Any] = padded_inputs.convert_to_tensors(A )
return padded_inputs
| 65 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
a = logging.get_logger(__name__)
class __a ( _snake_case ):
def __init__( self : Tuple ,*lowerCamelCase : Optional[int] ,**lowerCamelCase : Union[str, Any] ):
'''simple docstring'''
warnings.warn(
"""The class ImageGPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
""" Please use ImageGPTImageProcessor instead.""" ,lowerCamelCase ,)
super().__init__(*lowerCamelCase ,**lowerCamelCase )
| 109 |
"""simple docstring"""
from math import factorial
def lowerCAmelCase ( __UpperCamelCase = 100 ):
'''simple docstring'''
return sum(int(__UpperCamelCase ) for x in str(factorial(__UpperCamelCase ) ) )
if __name__ == "__main__":
print(solution(int(input('Enter the Number: ').strip())))
| 65 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCamelCase__ = {
'configuration_encodec': [
'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
'EncodecConfig',
],
'feature_extraction_encodec': ['EncodecFeatureExtractor'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ = [
'ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST',
'EncodecModel',
'EncodecPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_encodec import (
ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
EncodecConfig,
)
from .feature_extraction_encodec import EncodecFeatureExtractor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_encodec import (
ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
EncodecModel,
EncodecPreTrainedModel,
)
else:
import sys
UpperCamelCase__ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 110 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.distilbert.modeling_flax_distilbert import (
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertModel,
)
class __lowercase ( unittest.TestCase ):
def __init__( self : Union[str, Any] ,A : Optional[int] ,A : int=13 ,A : Tuple=7 ,A : Dict=True ,A : Optional[int]=True ,A : Tuple=True ,A : str=True ,A : Any=99 ,A : Tuple=32 ,A : Dict=5 ,A : Optional[int]=4 ,A : Dict=37 ,A : Any="gelu" ,A : Any=0.1 ,A : Optional[int]=0.1 ,A : Union[str, Any]=512 ,A : Any=16 ,A : List[str]=2 ,A : List[Any]=0.0_2 ,A : Optional[int]=4 ,):
'''simple docstring'''
UpperCAmelCase__ : Dict = parent
UpperCAmelCase__ : Any = batch_size
UpperCAmelCase__ : List[Any] = seq_length
UpperCAmelCase__ : Optional[int] = is_training
UpperCAmelCase__ : Optional[Any] = use_attention_mask
UpperCAmelCase__ : int = use_token_type_ids
UpperCAmelCase__ : int = use_labels
UpperCAmelCase__ : Any = vocab_size
UpperCAmelCase__ : Union[str, Any] = hidden_size
UpperCAmelCase__ : int = num_hidden_layers
UpperCAmelCase__ : int = num_attention_heads
UpperCAmelCase__ : Dict = intermediate_size
UpperCAmelCase__ : Any = hidden_act
UpperCAmelCase__ : Union[str, Any] = hidden_dropout_prob
UpperCAmelCase__ : Any = attention_probs_dropout_prob
UpperCAmelCase__ : str = max_position_embeddings
UpperCAmelCase__ : List[Any] = type_vocab_size
UpperCAmelCase__ : List[str] = type_sequence_label_size
UpperCAmelCase__ : List[Any] = initializer_range
UpperCAmelCase__ : List[Any] = num_choices
def __lowercase ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
UpperCAmelCase__ : List[str] = None
if self.use_attention_mask:
UpperCAmelCase__ : str = random_attention_mask([self.batch_size, self.seq_length] )
UpperCAmelCase__ : int = DistilBertConfig(
vocab_size=self.vocab_size ,dim=self.hidden_size ,n_layers=self.num_hidden_layers ,n_heads=self.num_attention_heads ,hidden_dim=self.intermediate_size ,hidden_act=self.hidden_act ,dropout=self.hidden_dropout_prob ,attention_dropout=self.attention_probs_dropout_prob ,max_position_embeddings=self.max_position_embeddings ,initializer_range=self.initializer_range ,tie_weights_=A ,)
return config, input_ids, attention_mask
def __lowercase ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = self.prepare_config_and_inputs()
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ : Any = config_and_inputs
UpperCAmelCase__ : str = {"""input_ids""": input_ids, """attention_mask""": attention_mask}
return config, inputs_dict
@require_flax
class __lowercase ( __lowerCamelCase , unittest.TestCase ):
snake_case_ = (
(
FlaxDistilBertModel,
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def __lowercase ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = FlaxDistilBertModelTester(self )
@slow
def __lowercase ( self : Optional[Any] ):
'''simple docstring'''
for model_class_name in self.all_model_classes:
UpperCAmelCase__ : Union[str, Any] = model_class_name.from_pretrained("""distilbert-base-uncased""" )
UpperCAmelCase__ : List[Any] = model(np.ones((1, 1) ) )
self.assertIsNotNone(A )
@require_flax
class __lowercase ( unittest.TestCase ):
@slow
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = FlaxDistilBertModel.from_pretrained("""distilbert-base-uncased""" )
UpperCAmelCase__ : List[Any] = np.array([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]] )
UpperCAmelCase__ : str = np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
UpperCAmelCase__ : Dict = model(A ,attention_mask=A )[0]
UpperCAmelCase__ : List[Any] = (1, 11, 768)
self.assertEqual(output.shape ,A )
UpperCAmelCase__ : Any = np.array([[[-0.1_6_3_9, 0.3_2_9_9, 0.1_6_4_8], [-0.1_7_4_6, 0.3_2_8_9, 0.1_7_1_0], [-0.1_8_8_4, 0.3_3_5_7, 0.1_8_1_0]]] )
self.assertTrue(jnp.allclose(output[:, 1:4, 1:4] ,A ,atol=1e-4 ) )
| 65 | 0 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=__lowerCamelCase )
class __lowerCAmelCase ( __lowerCamelCase ):
"""simple docstring"""
A__ : Optional[int] = field(default="image-classification" , metadata={"include_in_asdict_even_if_is_default": True} )
A__ : Optional[int] = Features({"image": Image()} )
A__ : int = Features({"labels": ClassLabel} )
A__ : Tuple = "image"
A__ : Tuple = "labels"
def _a ( self : Dict , _snake_case : str ):
"""simple docstring"""
if self.label_column not in features:
raise ValueError(F'''Column {self.label_column} is not present in features.''' )
if not isinstance(features[self.label_column] , _snake_case ):
raise ValueError(F'''Column {self.label_column} is not a ClassLabel.''' )
A__ = copy.deepcopy(self )
A__ = self.label_schema.copy()
A__ = features[self.label_column]
A__ = label_schema
return task_template
@property
def _a ( self : int ):
"""simple docstring"""
return {
self.image_column: "image",
self.label_column: "labels",
}
| 9 |
"""simple docstring"""
__UpperCAmelCase = frozenset(
[
'prompt',
'height',
'width',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
'cross_attention_kwargs',
]
)
__UpperCAmelCase = frozenset(['prompt', 'negative_prompt'])
__UpperCAmelCase = frozenset([])
__UpperCAmelCase = frozenset(['image'])
__UpperCAmelCase = frozenset(
[
'image',
'height',
'width',
'guidance_scale',
]
)
__UpperCAmelCase = frozenset(['image'])
__UpperCAmelCase = frozenset(
[
'prompt',
'image',
'height',
'width',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
]
)
__UpperCAmelCase = frozenset(['prompt', 'image', 'negative_prompt'])
__UpperCAmelCase = frozenset(
[
# Text guided image variation with an image mask
'prompt',
'image',
'mask_image',
'height',
'width',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
]
)
__UpperCAmelCase = frozenset(['prompt', 'image', 'mask_image', 'negative_prompt'])
__UpperCAmelCase = frozenset(
[
# image variation with an image mask
'image',
'mask_image',
'height',
'width',
'guidance_scale',
]
)
__UpperCAmelCase = frozenset(['image', 'mask_image'])
__UpperCAmelCase = frozenset(
[
'example_image',
'image',
'mask_image',
'height',
'width',
'guidance_scale',
]
)
__UpperCAmelCase = frozenset(['example_image', 'image', 'mask_image'])
__UpperCAmelCase = frozenset(['class_labels'])
__UpperCAmelCase = frozenset(['class_labels'])
__UpperCAmelCase = frozenset(['batch_size'])
__UpperCAmelCase = frozenset([])
__UpperCAmelCase = frozenset(['batch_size'])
__UpperCAmelCase = frozenset([])
__UpperCAmelCase = frozenset(
[
'prompt',
'audio_length_in_s',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
'cross_attention_kwargs',
]
)
__UpperCAmelCase = frozenset(['prompt', 'negative_prompt'])
__UpperCAmelCase = frozenset(['input_tokens'])
__UpperCAmelCase = frozenset(['input_tokens'])
| 65 | 0 |
from math import factorial
def lowercase_ (A : Tuple = 1_0_0 ):
return sum(int(__UpperCamelCase ) for x in str(factorial(__UpperCamelCase ) ) )
if __name__ == "__main__":
print(solution(int(input("Enter the Number: ").strip())))
| 478 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProcessor
from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.testing_utils import require_pyctcdecode, require_torch, require_torchaudio, slow
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_pyctcdecode_available, is_torch_available
from ..wavaveca.test_feature_extraction_wavaveca import floats_list
if is_pyctcdecode_available():
from huggingface_hub import snapshot_download
from pyctcdecode import BeamSearchDecoderCTC
from transformers.models.wavaveca_with_lm import WavaVecaProcessorWithLM
from transformers.models.wavaveca_with_lm.processing_wavaveca_with_lm import WavaVecaDecoderWithLMOutput
if is_torch_available():
from transformers import WavaVecaForCTC
@require_pyctcdecode
class __lowercase ( unittest.TestCase ):
def __lowercase ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase__ : Dict = """| <pad> <unk> <s> </s> a b c d e f g h i j k""".split()
UpperCAmelCase__ : Tuple = dict(zip(A ,range(len(A ) ) ) )
UpperCAmelCase__ : Optional[Any] = {
"""unk_token""": """<unk>""",
"""bos_token""": """<s>""",
"""eos_token""": """</s>""",
}
UpperCAmelCase__ : int = {
"""feature_size""": 1,
"""padding_value""": 0.0,
"""sampling_rate""": 16_000,
"""return_attention_mask""": False,
"""do_normalize""": True,
}
UpperCAmelCase__ : Optional[int] = tempfile.mkdtemp()
UpperCAmelCase__ : Optional[int] = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["""vocab_file"""] )
UpperCAmelCase__ : Tuple = os.path.join(self.tmpdirname ,A )
with open(self.vocab_file ,"""w""" ,encoding="""utf-8""" ) as fp:
fp.write(json.dumps(A ) + """\n""" )
with open(self.feature_extraction_file ,"""w""" ,encoding="""utf-8""" ) as fp:
fp.write(json.dumps(A ) + """\n""" )
# load decoder from hub
UpperCAmelCase__ : int = """hf-internal-testing/ngram-beam-search-decoder"""
def __lowercase ( self : str ,**A : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = self.add_kwargs_tokens_map.copy()
kwargs.update(A )
return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname ,**A )
def __lowercase ( self : List[str] ,**A : Dict ):
'''simple docstring'''
return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname ,**A )
def __lowercase ( self : Any ,**A : List[Any] ):
'''simple docstring'''
return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name ,**A )
def __lowercase ( self : Any ):
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def __lowercase ( self : str ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = self.get_tokenizer()
UpperCAmelCase__ : Dict = self.get_feature_extractor()
UpperCAmelCase__ : str = self.get_decoder()
UpperCAmelCase__ : Tuple = WavaVecaProcessorWithLM(tokenizer=A ,feature_extractor=A ,decoder=A )
processor.save_pretrained(self.tmpdirname )
UpperCAmelCase__ : str = WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname )
# tokenizer
self.assertEqual(processor.tokenizer.get_vocab() ,tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer ,A )
# feature extractor
self.assertEqual(processor.feature_extractor.to_json_string() ,feature_extractor.to_json_string() )
self.assertIsInstance(processor.feature_extractor ,A )
# decoder
self.assertEqual(processor.decoder._alphabet.labels ,decoder._alphabet.labels )
self.assertEqual(
processor.decoder.model_container[decoder._model_key]._unigram_set ,decoder.model_container[decoder._model_key]._unigram_set ,)
self.assertIsInstance(processor.decoder ,A )
def __lowercase ( self : int ):
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = WavaVecaProcessorWithLM(
tokenizer=self.get_tokenizer() ,feature_extractor=self.get_feature_extractor() ,decoder=self.get_decoder() )
processor.save_pretrained(self.tmpdirname )
# make sure that error is thrown when decoder alphabet doesn't match
UpperCAmelCase__ : Tuple = WavaVecaProcessorWithLM.from_pretrained(
self.tmpdirname ,alpha=5.0 ,beta=3.0 ,score_boundary=-7.0 ,unk_score_offset=3 )
# decoder
self.assertEqual(processor.language_model.alpha ,5.0 )
self.assertEqual(processor.language_model.beta ,3.0 )
self.assertEqual(processor.language_model.score_boundary ,-7.0 )
self.assertEqual(processor.language_model.unk_score_offset ,3 )
def __lowercase ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase__ : int = self.get_tokenizer()
# add token to trigger raise
tokenizer.add_tokens(["""xx"""] )
with self.assertRaisesRegex(A ,"""include""" ):
WavaVecaProcessorWithLM(
tokenizer=A ,feature_extractor=self.get_feature_extractor() ,decoder=self.get_decoder() )
def __lowercase ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = self.get_feature_extractor()
UpperCAmelCase__ : Optional[Any] = self.get_tokenizer()
UpperCAmelCase__ : Any = self.get_decoder()
UpperCAmelCase__ : Union[str, Any] = WavaVecaProcessorWithLM(tokenizer=A ,feature_extractor=A ,decoder=A )
UpperCAmelCase__ : str = floats_list((3, 1_000) )
UpperCAmelCase__ : Optional[Any] = feature_extractor(A ,return_tensors="""np""" )
UpperCAmelCase__ : List[Any] = processor(A ,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 __lowercase ( self : int ):
'''simple docstring'''
UpperCAmelCase__ : int = self.get_feature_extractor()
UpperCAmelCase__ : Union[str, Any] = self.get_tokenizer()
UpperCAmelCase__ : Optional[int] = self.get_decoder()
UpperCAmelCase__ : List[Any] = WavaVecaProcessorWithLM(tokenizer=A ,feature_extractor=A ,decoder=A )
UpperCAmelCase__ : List[Any] = """This is a test string"""
UpperCAmelCase__ : int = processor(text=A )
UpperCAmelCase__ : Dict = tokenizer(A )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] ,encoded_processor[key] )
def __lowercase ( self : Tuple ,A : List[Any]=(2, 10, 16) ,A : Dict=77 ):
'''simple docstring'''
np.random.seed(A )
return np.random.rand(*A )
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ : Dict = self.get_feature_extractor()
UpperCAmelCase__ : Optional[Any] = self.get_tokenizer()
UpperCAmelCase__ : int = self.get_decoder()
UpperCAmelCase__ : Optional[Any] = WavaVecaProcessorWithLM(tokenizer=A ,feature_extractor=A ,decoder=A )
UpperCAmelCase__ : Dict = self._get_dummy_logits(shape=(10, 16) ,seed=13 )
UpperCAmelCase__ : Tuple = processor.decode(A )
UpperCAmelCase__ : Union[str, Any] = decoder.decode_beams(A )[0]
self.assertEqual(decoded_decoder[0] ,decoded_processor.text )
self.assertEqual("""</s> <s> </s>""" ,decoded_processor.text )
self.assertEqual(decoded_decoder[-2] ,decoded_processor.logit_score )
self.assertEqual(decoded_decoder[-1] ,decoded_processor.lm_score )
@parameterized.expand([[None], ["""fork"""], ["""spawn"""]] )
def __lowercase ( self : List[str] ,A : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = self.get_feature_extractor()
UpperCAmelCase__ : int = self.get_tokenizer()
UpperCAmelCase__ : List[Any] = self.get_decoder()
UpperCAmelCase__ : Dict = WavaVecaProcessorWithLM(tokenizer=A ,feature_extractor=A ,decoder=A )
UpperCAmelCase__ : Optional[Any] = self._get_dummy_logits()
# note: pool should be instantiated *after* Wav2Vec2ProcessorWithLM.
# otherwise, the LM won't be available to the pool's sub-processes.
# manual logic used to allow parameterized test for both pool=None and pool=Pool(...)
if pool_context is None:
UpperCAmelCase__ : List[str] = processor.batch_decode(A )
else:
with get_context(A ).Pool() as pool:
UpperCAmelCase__ : Union[str, Any] = processor.batch_decode(A ,A )
UpperCAmelCase__ : Optional[Any] = list(A )
with get_context("""fork""" ).Pool() as p:
UpperCAmelCase__ : Union[str, Any] = decoder.decode_beams_batch(A ,A )
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ : Optional[Any] = [], [], []
for beams in decoded_beams:
texts_decoder.append(beams[0][0] )
logit_scores_decoder.append(beams[0][-2] )
lm_scores_decoder.append(beams[0][-1] )
self.assertListEqual(A ,decoded_processor.text )
self.assertListEqual(["""<s> <s> </s>""", """<s> <s> <s>"""] ,decoded_processor.text )
self.assertListEqual(A ,decoded_processor.logit_score )
self.assertListEqual(A ,decoded_processor.lm_score )
def __lowercase ( self : int ):
'''simple docstring'''
UpperCAmelCase__ : Any = self.get_feature_extractor()
UpperCAmelCase__ : Tuple = self.get_tokenizer()
UpperCAmelCase__ : List[Any] = self.get_decoder()
UpperCAmelCase__ : Union[str, Any] = WavaVecaProcessorWithLM(tokenizer=A ,feature_extractor=A ,decoder=A )
UpperCAmelCase__ : Dict = self._get_dummy_logits()
UpperCAmelCase__ : Any = 15
UpperCAmelCase__ : Dict = -2_0.0
UpperCAmelCase__ : List[Any] = -4.0
UpperCAmelCase__ : Union[str, Any] = processor.batch_decode(
A ,beam_width=A ,beam_prune_logp=A ,token_min_logp=A ,)
UpperCAmelCase__ : List[str] = decoded_processor_out.text
UpperCAmelCase__ : List[str] = list(A )
with get_context("""fork""" ).Pool() as pool:
UpperCAmelCase__ : Tuple = decoder.decode_beams_batch(
A ,A ,beam_width=A ,beam_prune_logp=A ,token_min_logp=A ,)
UpperCAmelCase__ : List[Any] = [d[0][0] for d in decoded_decoder_out]
UpperCAmelCase__ : Any = [d[0][2] for d in decoded_decoder_out]
UpperCAmelCase__ : List[str] = [d[0][3] for d in decoded_decoder_out]
self.assertListEqual(A ,A )
self.assertListEqual(["""</s> <s> <s>""", """<s> <s> <s>"""] ,A )
self.assertTrue(np.array_equal(A ,decoded_processor_out.logit_score ) )
self.assertTrue(np.allclose([-2_0.0_5_4, -1_8.4_4_7] ,A ,atol=1e-3 ) )
self.assertTrue(np.array_equal(A ,decoded_processor_out.lm_score ) )
self.assertTrue(np.allclose([-1_5.5_5_4, -1_3.9_4_7_4] ,A ,atol=1e-3 ) )
def __lowercase ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = self.get_feature_extractor()
UpperCAmelCase__ : Optional[Any] = self.get_tokenizer()
UpperCAmelCase__ : int = self.get_decoder()
UpperCAmelCase__ : str = WavaVecaProcessorWithLM(tokenizer=A ,feature_extractor=A ,decoder=A )
UpperCAmelCase__ : Tuple = self._get_dummy_logits()
UpperCAmelCase__ : Tuple = 2.0
UpperCAmelCase__ : str = 5.0
UpperCAmelCase__ : Union[str, Any] = -2_0.0
UpperCAmelCase__ : Optional[Any] = True
UpperCAmelCase__ : str = processor.batch_decode(
A ,alpha=A ,beta=A ,unk_score_offset=A ,lm_score_boundary=A ,)
UpperCAmelCase__ : Any = decoded_processor_out.text
UpperCAmelCase__ : Union[str, Any] = list(A )
decoder.reset_params(
alpha=A ,beta=A ,unk_score_offset=A ,lm_score_boundary=A ,)
with get_context("""fork""" ).Pool() as pool:
UpperCAmelCase__ : List[Any] = decoder.decode_beams_batch(
A ,A ,)
UpperCAmelCase__ : Union[str, Any] = [d[0][0] for d in decoded_decoder_out]
self.assertListEqual(A ,A )
self.assertListEqual(["""<s> </s> <s> </s> </s>""", """</s> </s> <s> </s> </s>"""] ,A )
UpperCAmelCase__ : Union[str, Any] = processor.decoder.model_container[processor.decoder._model_key]
self.assertEqual(lm_model.alpha ,2.0 )
self.assertEqual(lm_model.beta ,5.0 )
self.assertEqual(lm_model.unk_score_offset ,-2_0.0 )
self.assertEqual(lm_model.score_boundary ,A )
def __lowercase ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Dict = WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm""" )
UpperCAmelCase__ : str = processor.decoder.model_container[processor.decoder._model_key]
UpperCAmelCase__ : Any = Path(language_model._kenlm_model.path.decode("""utf-8""" ) ).parent.parent.absolute()
UpperCAmelCase__ : Optional[int] = os.listdir(A )
UpperCAmelCase__ : List[Any] = ["""alphabet.json""", """language_model"""]
downloaded_decoder_files.sort()
expected_decoder_files.sort()
# test that only decoder relevant files from
# https://huggingface.co/hf-internal-testing/processor_with_lm/tree/main
# are downloaded and none of the rest (e.g. README.md, ...)
self.assertListEqual(A ,A )
def __lowercase ( self : int ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = snapshot_download("""hf-internal-testing/processor_with_lm""" )
UpperCAmelCase__ : Union[str, Any] = WavaVecaProcessorWithLM.from_pretrained(A )
UpperCAmelCase__ : Tuple = processor.decoder.model_container[processor.decoder._model_key]
UpperCAmelCase__ : Optional[int] = Path(language_model._kenlm_model.path.decode("""utf-8""" ) ).parent.parent.absolute()
UpperCAmelCase__ : Tuple = os.listdir(A )
UpperCAmelCase__ : Dict = os.listdir(A )
local_decoder_files.sort()
expected_decoder_files.sort()
# test that both decoder form hub and local files in cache are the same
self.assertListEqual(A ,A )
def __lowercase ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm""" )
UpperCAmelCase__ : Tuple = AutoProcessor.from_pretrained("""hf-internal-testing/processor_with_lm""" )
UpperCAmelCase__ : Dict = floats_list((3, 1_000) )
UpperCAmelCase__ : List[str] = processor_wavaveca(A ,return_tensors="""np""" )
UpperCAmelCase__ : Dict = processor_auto(A ,return_tensors="""np""" )
for key in input_wavaveca.keys():
self.assertAlmostEqual(input_wavaveca[key].sum() ,input_auto[key].sum() ,delta=1e-2 )
UpperCAmelCase__ : List[str] = self._get_dummy_logits()
UpperCAmelCase__ : Tuple = processor_wavaveca.batch_decode(A )
UpperCAmelCase__ : List[str] = processor_auto.batch_decode(A )
self.assertListEqual(decoded_wavaveca.text ,decoded_auto.text )
def __lowercase ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase__ : Dict = self.get_feature_extractor()
UpperCAmelCase__ : Tuple = self.get_tokenizer()
UpperCAmelCase__ : List[Any] = self.get_decoder()
UpperCAmelCase__ : int = WavaVecaProcessorWithLM(tokenizer=A ,feature_extractor=A ,decoder=A )
self.assertListEqual(
processor.model_input_names ,feature_extractor.model_input_names ,msg="""`processor` and `feature_extractor` model input names do not match""" ,)
@staticmethod
def __lowercase ( A : Optional[Any] ,A : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = [d[key] for d in offsets]
return retrieved_list
def __lowercase ( self : Dict ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm""" )
UpperCAmelCase__ : Dict = self._get_dummy_logits()[0]
UpperCAmelCase__ : List[str] = processor.decode(A ,output_word_offsets=A )
# check Wav2Vec2CTCTokenizerOutput keys for word
self.assertEqual(len(outputs.keys() ) ,4 )
self.assertTrue("""text""" in outputs )
self.assertTrue("""word_offsets""" in outputs )
self.assertTrue(isinstance(A ,A ) )
self.assertEqual(""" """.join(self.get_from_offsets(outputs["""word_offsets"""] ,"""word""" ) ) ,outputs.text )
self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""] ,"""word""" ) ,["""<s>""", """<s>""", """</s>"""] )
self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""] ,"""start_offset""" ) ,[0, 2, 4] )
self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""] ,"""end_offset""" ) ,[1, 3, 5] )
def __lowercase ( self : Dict ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm""" )
UpperCAmelCase__ : int = self._get_dummy_logits()
UpperCAmelCase__ : Any = processor.batch_decode(A ,output_word_offsets=A )
# check Wav2Vec2CTCTokenizerOutput keys for word
self.assertEqual(len(outputs.keys() ) ,4 )
self.assertTrue("""text""" in outputs )
self.assertTrue("""word_offsets""" in outputs )
self.assertTrue(isinstance(A ,A ) )
self.assertListEqual(
[""" """.join(self.get_from_offsets(A ,"""word""" ) ) for o in outputs["""word_offsets"""]] ,outputs.text )
self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""][0] ,"""word""" ) ,["""<s>""", """<s>""", """</s>"""] )
self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""][0] ,"""start_offset""" ) ,[0, 2, 4] )
self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""][0] ,"""end_offset""" ) ,[1, 3, 5] )
@slow
@require_torch
@require_torchaudio
def __lowercase ( self : Tuple ):
'''simple docstring'''
import torch
UpperCAmelCase__ : Any = load_dataset("""common_voice""" ,"""en""" ,split="""train""" ,streaming=A )
UpperCAmelCase__ : Tuple = ds.cast_column("""audio""" ,datasets.Audio(sampling_rate=16_000 ) )
UpperCAmelCase__ : Tuple = iter(A )
UpperCAmelCase__ : Optional[int] = next(A )
UpperCAmelCase__ : List[Any] = AutoProcessor.from_pretrained("""patrickvonplaten/wav2vec2-base-100h-with-lm""" )
UpperCAmelCase__ : Tuple = WavaVecaForCTC.from_pretrained("""patrickvonplaten/wav2vec2-base-100h-with-lm""" )
# compare to filename `common_voice_en_100038.mp3` of dataset viewer on https://huggingface.co/datasets/common_voice/viewer/en/train
UpperCAmelCase__ : Tuple = processor(sample["""audio"""]["""array"""] ,return_tensors="""pt""" ).input_values
with torch.no_grad():
UpperCAmelCase__ : Union[str, Any] = model(A ).logits.cpu().numpy()
UpperCAmelCase__ : Any = processor.decode(logits[0] ,output_word_offsets=A )
UpperCAmelCase__ : str = model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
UpperCAmelCase__ : Union[str, Any] = [
{
"""start_time""": d["""start_offset"""] * time_offset,
"""end_time""": d["""end_offset"""] * time_offset,
"""word""": d["""word"""],
}
for d in output["""word_offsets"""]
]
UpperCAmelCase__ : Dict = """WHY DOES MILISANDRA LOOK LIKE SHE WANTS TO CONSUME JOHN SNOW ON THE RIVER AT THE WALL"""
# output words
self.assertEqual(""" """.join(self.get_from_offsets(A ,"""word""" ) ) ,A )
self.assertEqual(""" """.join(self.get_from_offsets(A ,"""word""" ) ) ,output.text )
# output times
UpperCAmelCase__ : str = torch.tensor(self.get_from_offsets(A ,"""start_time""" ) )
UpperCAmelCase__ : List[Any] = torch.tensor(self.get_from_offsets(A ,"""end_time""" ) )
# fmt: off
UpperCAmelCase__ : Union[str, Any] = torch.tensor([1.4_1_9_9, 1.6_5_9_9, 2.2_5_9_9, 3.0, 3.2_4, 3.5_9_9_9, 3.7_9_9_9, 4.0_9_9_9, 4.2_6, 4.9_4, 5.2_8, 5.6_5_9_9, 5.7_8, 5.9_4, 6.3_2, 6.5_3_9_9, 6.6_5_9_9] )
UpperCAmelCase__ : List[Any] = torch.tensor([1.5_3_9_9, 1.8_9_9_9, 2.9, 3.1_6, 3.5_3_9_9, 3.7_2, 4.0_1_9_9, 4.1_7_9_9, 4.7_6, 5.1_5_9_9, 5.5_5_9_9, 5.6_9_9_9, 5.8_6, 6.1_9_9_9, 6.3_8, 6.6_1_9_9, 6.9_4] )
# fmt: on
self.assertTrue(torch.allclose(A ,A ,atol=0.0_1 ) )
self.assertTrue(torch.allclose(A ,A ,atol=0.0_1 ) )
| 65 | 0 |
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging
lowerCamelCase__ : Optional[Any] = logging.get_logger(__name__)
lowerCamelCase__ : int = """▁"""
lowerCamelCase__ : Dict = {
"""vocab_file""": """vocab.json""",
"""spm_file""": """sentencepiece.bpe.model""",
"""tokenizer_config_file""": """tokenizer_config.json""",
}
lowerCamelCase__ : List[Any] = {
"""vocab_file""": {
"""facebook/m2m100_418M""": """https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json""",
"""facebook/m2m100_1.2B""": """https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json""",
},
"""spm_file""": {
"""facebook/m2m100_418M""": """https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model""",
"""facebook/m2m100_1.2B""": """https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model""",
},
"""tokenizer_config_file""": {
"""facebook/m2m100_418M""": """https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json""",
"""facebook/m2m100_1.2B""": """https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json""",
},
}
lowerCamelCase__ : Optional[int] = {
"""facebook/m2m100_418M""": 1_0_2_4,
}
# fmt: off
lowerCamelCase__ : Dict = {
"""m2m100""": ["""af""", """am""", """ar""", """ast""", """az""", """ba""", """be""", """bg""", """bn""", """br""", """bs""", """ca""", """ceb""", """cs""", """cy""", """da""", """de""", """el""", """en""", """es""", """et""", """fa""", """ff""", """fi""", """fr""", """fy""", """ga""", """gd""", """gl""", """gu""", """ha""", """he""", """hi""", """hr""", """ht""", """hu""", """hy""", """id""", """ig""", """ilo""", """is""", """it""", """ja""", """jv""", """ka""", """kk""", """km""", """kn""", """ko""", """lb""", """lg""", """ln""", """lo""", """lt""", """lv""", """mg""", """mk""", """ml""", """mn""", """mr""", """ms""", """my""", """ne""", """nl""", """no""", """ns""", """oc""", """or""", """pa""", """pl""", """ps""", """pt""", """ro""", """ru""", """sd""", """si""", """sk""", """sl""", """so""", """sq""", """sr""", """ss""", """su""", """sv""", """sw""", """ta""", """th""", """tl""", """tn""", """tr""", """uk""", """ur""", """uz""", """vi""", """wo""", """xh""", """yi""", """yo""", """zh""", """zu"""],
"""wmt21""": ["""en""", """ha""", """is""", """ja""", """cs""", """ru""", """zh""", """de"""]
}
class __magic_name__ (__lowerCamelCase ):
'''simple docstring'''
__lowercase : Any = VOCAB_FILES_NAMES
__lowercase : Optional[int] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowercase : List[Any] = PRETRAINED_VOCAB_FILES_MAP
__lowercase : List[Any] = ['input_ids', 'attention_mask']
__lowercase : Optional[Any] = []
__lowercase : Any = []
def __init__( self:int , _a:List[Any] , _a:str , _a:List[Any]=None , _a:Dict=None , _a:str="<s>" , _a:int="</s>" , _a:List[Any]="</s>" , _a:Optional[Any]="<pad>" , _a:List[str]="<unk>" , _a:Optional[Any]="m2m100" , _a:Optional[Dict[str, Any]] = None , _a:List[str]=8 , **_a:Optional[Any] , ):
snake_case__ = {} if sp_model_kwargs is None else sp_model_kwargs
snake_case__ = language_codes
snake_case__ = FAIRSEQ_LANGUAGE_CODES[language_codes]
snake_case__ = {lang_code: F"""__{lang_code}__""" for lang_code in fairseq_language_code}
snake_case__ = kwargs.get('''additional_special_tokens''' , [] )
kwargs["additional_special_tokens"] += [
self.get_lang_token(_a )
for lang_code in fairseq_language_code
if self.get_lang_token(_a ) not in kwargs["additional_special_tokens"]
]
super().__init__(
src_lang=_a , tgt_lang=_a , bos_token=_a , eos_token=_a , sep_token=_a , unk_token=_a , pad_token=_a , language_codes=_a , sp_model_kwargs=self.sp_model_kwargs , num_madeup_words=_a , **_a , )
snake_case__ = vocab_file
snake_case__ = load_json(_a )
snake_case__ = {v: k for k, v in self.encoder.items()}
snake_case__ = spm_file
snake_case__ = load_spm(_a , self.sp_model_kwargs )
snake_case__ = len(self.encoder )
snake_case__ = {
self.get_lang_token(_a ): self.encoder_size + i for i, lang_code in enumerate(_a )
}
snake_case__ = {lang_code: self.encoder_size + i for i, lang_code in enumerate(_a )}
snake_case__ = {v: k for k, v in self.lang_token_to_id.items()}
snake_case__ = src_lang if src_lang is not None else """en"""
snake_case__ = tgt_lang
snake_case__ = self.get_lang_id(self._src_lang )
self.set_src_lang_special_tokens(self._src_lang )
snake_case__ = num_madeup_words
@property
def SCREAMING_SNAKE_CASE__ ( self:Union[str, Any] ):
return len(self.encoder ) + len(self.lang_token_to_id )
@property
def SCREAMING_SNAKE_CASE__ ( self:Dict ):
return self._src_lang
@src_lang.setter
def SCREAMING_SNAKE_CASE__ ( self:List[str] , _a:str ):
snake_case__ = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def SCREAMING_SNAKE_CASE__ ( self:Optional[Any] , _a:str ):
return self.sp_model.encode(_a , out_type=_a )
def SCREAMING_SNAKE_CASE__ ( self:List[str] , _a:Dict ):
if token in self.lang_token_to_id:
return self.lang_token_to_id[token]
return self.encoder.get(_a , self.encoder[self.unk_token] )
def SCREAMING_SNAKE_CASE__ ( self:Any , _a:int ):
if index in self.id_to_lang_token:
return self.id_to_lang_token[index]
return self.decoder.get(_a , self.unk_token )
def SCREAMING_SNAKE_CASE__ ( self:Tuple , _a:List[str] ):
snake_case__ = []
snake_case__ = """"""
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(_a ) + token
snake_case__ = []
else:
current_sub_tokens.append(_a )
out_string += self.sp_model.decode(_a )
return out_string.strip()
def SCREAMING_SNAKE_CASE__ ( self:Optional[int] , _a:List[int] , _a:Optional[List[int]] = None , _a:bool = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_a , token_ids_a=_a , already_has_special_tokens=_a )
snake_case__ = [1] * len(self.prefix_tokens )
snake_case__ = [1] * len(self.suffix_tokens )
if token_ids_a is None:
return prefix_ones + ([0] * len(_a )) + suffix_ones
return prefix_ones + ([0] * len(_a )) + ([0] * len(_a )) + suffix_ones
def SCREAMING_SNAKE_CASE__ ( self:Tuple , _a:List[int] , _a:Optional[List[int]] = None ):
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 SCREAMING_SNAKE_CASE__ ( self:Optional[Any] ):
snake_case__ = {self.convert_ids_to_tokens(_a ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self:Any ):
snake_case__ = self.__dict__.copy()
snake_case__ = None
return state
def __setstate__( self:int , _a:Dict ):
snake_case__ = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
snake_case__ = {}
snake_case__ = load_spm(self.spm_file , self.sp_model_kwargs )
def SCREAMING_SNAKE_CASE__ ( self:Any , _a:str , _a:Optional[str] = None ):
snake_case__ = Path(_a )
if not save_dir.is_dir():
raise OSError(F"""{save_directory} should be a directory""" )
snake_case__ = save_dir / (
(filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""vocab_file"""]
)
snake_case__ = save_dir / (
(filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""spm_file"""]
)
save_json(self.encoder , _a )
if os.path.abspath(self.spm_file ) != os.path.abspath(_a ) and os.path.isfile(self.spm_file ):
copyfile(self.spm_file , _a )
elif not os.path.isfile(self.spm_file ):
with open(_a , '''wb''' ) as fi:
snake_case__ = self.sp_model.serialized_model_proto()
fi.write(_a )
return (str(_a ), str(_a ))
def SCREAMING_SNAKE_CASE__ ( self:str , _a:List[str] , _a:str = "en" , _a:Optional[List[str]] = None , _a:str = "ro" , **_a:List[Any] , ):
snake_case__ = src_lang
snake_case__ = tgt_lang
self.set_src_lang_special_tokens(self.src_lang )
return super().prepare_seqaseq_batch(_a , _a , **_a )
def SCREAMING_SNAKE_CASE__ ( self:Any , _a:Union[str, Any] , _a:Optional[str] , _a:Optional[str] , **_a:List[Any] ):
if src_lang is None or tgt_lang is None:
raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model''' )
snake_case__ = src_lang
snake_case__ = self(_a , add_special_tokens=_a , **_a )
snake_case__ = self.get_lang_id(_a )
snake_case__ = tgt_lang_id
return inputs
def SCREAMING_SNAKE_CASE__ ( self:Union[str, Any] ):
self.set_src_lang_special_tokens(self.src_lang )
def SCREAMING_SNAKE_CASE__ ( self:List[Any] ):
self.set_tgt_lang_special_tokens(self.tgt_lang )
def SCREAMING_SNAKE_CASE__ ( self:int , _a:str ):
snake_case__ = self.get_lang_token(_a )
snake_case__ = self.lang_token_to_id[lang_token]
snake_case__ = [self.cur_lang_id]
snake_case__ = [self.eos_token_id]
def SCREAMING_SNAKE_CASE__ ( self:Dict , _a:str ):
snake_case__ = self.get_lang_token(_a )
snake_case__ = self.lang_token_to_id[lang_token]
snake_case__ = [self.cur_lang_id]
snake_case__ = [self.eos_token_id]
def SCREAMING_SNAKE_CASE__ ( self:int , _a:str ):
return self.lang_code_to_token[lang]
def SCREAMING_SNAKE_CASE__ ( self:Dict , _a:str ):
snake_case__ = self.get_lang_token(_a )
return self.lang_token_to_id[lang_token]
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase ) -> Any:
snake_case__ = sentencepiece.SentencePieceProcessor(**__UpperCamelCase )
spm.Load(str(__UpperCamelCase ) )
return spm
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase ) -> Optional[Any]:
with open(__UpperCamelCase , '''r''' ) as f:
return json.load(__UpperCamelCase )
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase ) -> Union[str, Any]:
with open(__UpperCamelCase , '''w''' ) as f:
json.dump(__UpperCamelCase , __UpperCamelCase , indent=2 )
| 33 |
"""simple docstring"""
from sklearn.metrics import fa_score
import datasets
__UpperCAmelCase = '\nThe F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:\nF1 = 2 * (precision * recall) / (precision + recall)\n'
__UpperCAmelCase = '\nArgs:\n predictions (`list` of `int`): Predicted labels.\n references (`list` of `int`): Ground truth labels.\n labels (`list` of `int`): The set of labels to include when `average` is not set to `\'binary\'`, and the order of the labels if `average` is `None`. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class. Labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in `predictions` and `references` are used in sorted order. Defaults to None.\n pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.\n average (`string`): This parameter is required for multiclass/multilabel targets. If set to `None`, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `\'binary\'`.\n\n - \'binary\': Only report results for the class specified by `pos_label`. This is applicable only if the classes found in `predictions` and `references` are binary.\n - \'micro\': Calculate metrics globally by counting the total true positives, false negatives and false positives.\n - \'macro\': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.\n - \'weighted\': Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `\'macro\'` to account for label imbalance. This option can result in an F-score that is not between precision and recall.\n - \'samples\': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).\n sample_weight (`list` of `float`): Sample weights Defaults to None.\n\nReturns:\n f1 (`float` or `array` of `float`): F1 score or list of f1 scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher f1 scores are better.\n\nExamples:\n\n Example 1-A simple binary example\n >>> f1_metric = datasets.load_metric("f1")\n >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])\n >>> print(results)\n {\'f1\': 0.5}\n\n Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.\n >>> f1_metric = datasets.load_metric("f1")\n >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)\n >>> print(round(results[\'f1\'], 2))\n 0.67\n\n Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.\n >>> f1_metric = datasets.load_metric("f1")\n >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], sample_weight=[0.9, 0.5, 3.9, 1.2, 0.3])\n >>> print(round(results[\'f1\'], 2))\n 0.35\n\n Example 4-A multiclass example, with different values for the `average` input.\n >>> predictions = [0, 2, 1, 0, 0, 1]\n >>> references = [0, 1, 2, 0, 1, 2]\n >>> results = f1_metric.compute(predictions=predictions, references=references, average="macro")\n >>> print(round(results[\'f1\'], 2))\n 0.27\n >>> results = f1_metric.compute(predictions=predictions, references=references, average="micro")\n >>> print(round(results[\'f1\'], 2))\n 0.33\n >>> results = f1_metric.compute(predictions=predictions, references=references, average="weighted")\n >>> print(round(results[\'f1\'], 2))\n 0.27\n >>> results = f1_metric.compute(predictions=predictions, references=references, average=None)\n >>> print(results)\n {\'f1\': array([0.8, 0. , 0. ])}\n'
__UpperCAmelCase = '\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __lowercase ( datasets.Metric ):
def __lowercase ( self : List[Any] ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION ,citation=_CITATION ,inputs_description=_KWARGS_DESCRIPTION ,features=datasets.Features(
{
"""predictions""": datasets.Sequence(datasets.Value("""int32""" ) ),
"""references""": datasets.Sequence(datasets.Value("""int32""" ) ),
}
if self.config_name == """multilabel"""
else {
"""predictions""": datasets.Value("""int32""" ),
"""references""": datasets.Value("""int32""" ),
} ) ,reference_urls=["""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html"""] ,)
def __lowercase ( self : Union[str, Any] ,A : List[str] ,A : List[Any] ,A : Optional[Any]=None ,A : List[str]=1 ,A : Optional[Any]="binary" ,A : Any=None ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = fa_score(
A ,A ,labels=A ,pos_label=A ,average=A ,sample_weight=A )
return {"f1": float(A ) if score.size == 1 else score}
| 65 | 0 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import CLIPSegProcessor, ViTImageProcessor
@require_vision
class __lowercase ( unittest.TestCase ):
"""simple docstring"""
def __magic_name__ ( self )-> int:
_SCREAMING_SNAKE_CASE = tempfile.mkdtemp()
# fmt: off
_SCREAMING_SNAKE_CASE = ["""l""", """o""", """w""", """e""", """r""", """s""", """t""", """i""", """d""", """n""", """lo""", """l</w>""", """w</w>""", """r</w>""", """t</w>""", """low</w>""", """er</w>""", """lowest</w>""", """newer</w>""", """wider""", """<unk>""", """<|startoftext|>""", """<|endoftext|>"""]
# fmt: on
_SCREAMING_SNAKE_CASE = dict(zip(A_ , range(len(A_ ) ) ) )
_SCREAMING_SNAKE_CASE = ["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
_SCREAMING_SNAKE_CASE = {"""unk_token""": """<unk>"""}
_SCREAMING_SNAKE_CASE = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
_SCREAMING_SNAKE_CASE = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['merges_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as fp:
fp.write(json.dumps(A_ ) + '\n' )
with open(self.merges_file , 'w' , encoding='utf-8' ) as fp:
fp.write('\n'.join(A_ ) )
_SCREAMING_SNAKE_CASE = {
"""do_resize""": True,
"""size""": 20,
"""do_center_crop""": True,
"""crop_size""": 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],
}
_SCREAMING_SNAKE_CASE = os.path.join(self.tmpdirname , A_ )
with open(self.image_processor_file , 'w' , encoding='utf-8' ) as fp:
json.dump(A_ , A_ )
def __magic_name__ ( self , **A_ )-> List[Any]:
return CLIPTokenizer.from_pretrained(self.tmpdirname , **A_ )
def __magic_name__ ( self , **A_ )-> str:
return CLIPTokenizerFast.from_pretrained(self.tmpdirname , **A_ )
def __magic_name__ ( self , **A_ )-> Tuple:
return ViTImageProcessor.from_pretrained(self.tmpdirname , **A_ )
def __magic_name__ ( self )-> Optional[Any]:
shutil.rmtree(self.tmpdirname )
def __magic_name__ ( self )-> Optional[int]:
_SCREAMING_SNAKE_CASE = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
_SCREAMING_SNAKE_CASE = [Image.fromarray(np.moveaxis(A_ , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def __magic_name__ ( self )-> Tuple:
_SCREAMING_SNAKE_CASE = self.get_tokenizer()
_SCREAMING_SNAKE_CASE = self.get_rust_tokenizer()
_SCREAMING_SNAKE_CASE = self.get_image_processor()
_SCREAMING_SNAKE_CASE = CLIPSegProcessor(tokenizer=A_ , image_processor=A_ )
processor_slow.save_pretrained(self.tmpdirname )
_SCREAMING_SNAKE_CASE = CLIPSegProcessor.from_pretrained(self.tmpdirname , use_fast=A_ )
_SCREAMING_SNAKE_CASE = CLIPSegProcessor(tokenizer=A_ , image_processor=A_ )
processor_fast.save_pretrained(self.tmpdirname )
_SCREAMING_SNAKE_CASE = CLIPSegProcessor.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 , A_ )
self.assertIsInstance(processor_fast.tokenizer , A_ )
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 , A_ )
self.assertIsInstance(processor_fast.image_processor , A_ )
def __magic_name__ ( self )-> Tuple:
_SCREAMING_SNAKE_CASE = CLIPSegProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
_SCREAMING_SNAKE_CASE = self.get_tokenizer(bos_token='(BOS)' , eos_token='(EOS)' )
_SCREAMING_SNAKE_CASE = self.get_image_processor(do_normalize=A_ , padding_value=1.0 )
_SCREAMING_SNAKE_CASE = CLIPSegProcessor.from_pretrained(
self.tmpdirname , bos_token='(BOS)' , eos_token='(EOS)' , do_normalize=A_ , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , A_ )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , A_ )
def __magic_name__ ( self )-> Optional[int]:
_SCREAMING_SNAKE_CASE = self.get_image_processor()
_SCREAMING_SNAKE_CASE = self.get_tokenizer()
_SCREAMING_SNAKE_CASE = CLIPSegProcessor(tokenizer=A_ , image_processor=A_ )
_SCREAMING_SNAKE_CASE = self.prepare_image_inputs()
_SCREAMING_SNAKE_CASE = image_processor(A_ , return_tensors='np' )
_SCREAMING_SNAKE_CASE = processor(images=A_ , 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 __magic_name__ ( self )-> Tuple:
_SCREAMING_SNAKE_CASE = self.get_image_processor()
_SCREAMING_SNAKE_CASE = self.get_tokenizer()
_SCREAMING_SNAKE_CASE = CLIPSegProcessor(tokenizer=A_ , image_processor=A_ )
_SCREAMING_SNAKE_CASE = """lower newer"""
_SCREAMING_SNAKE_CASE = processor(text=A_ )
_SCREAMING_SNAKE_CASE = tokenizer(A_ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def __magic_name__ ( self )-> Dict:
_SCREAMING_SNAKE_CASE = self.get_image_processor()
_SCREAMING_SNAKE_CASE = self.get_tokenizer()
_SCREAMING_SNAKE_CASE = CLIPSegProcessor(tokenizer=A_ , image_processor=A_ )
_SCREAMING_SNAKE_CASE = """lower newer"""
_SCREAMING_SNAKE_CASE = self.prepare_image_inputs()
_SCREAMING_SNAKE_CASE = processor(text=A_ , images=A_ )
self.assertListEqual(list(inputs.keys() ) , ['input_ids', 'attention_mask', 'pixel_values'] )
# test if it raises when no input is passed
with pytest.raises(A_ ):
processor()
def __magic_name__ ( self )-> Optional[Any]:
_SCREAMING_SNAKE_CASE = self.get_image_processor()
_SCREAMING_SNAKE_CASE = self.get_tokenizer()
_SCREAMING_SNAKE_CASE = CLIPSegProcessor(tokenizer=A_ , image_processor=A_ )
_SCREAMING_SNAKE_CASE = self.prepare_image_inputs()
_SCREAMING_SNAKE_CASE = self.prepare_image_inputs()
_SCREAMING_SNAKE_CASE = processor(images=A_ , visual_prompt=A_ )
self.assertListEqual(list(inputs.keys() ) , ['pixel_values', 'conditional_pixel_values'] )
# test if it raises when no input is passed
with pytest.raises(A_ ):
processor()
def __magic_name__ ( self )-> Tuple:
_SCREAMING_SNAKE_CASE = self.get_image_processor()
_SCREAMING_SNAKE_CASE = self.get_tokenizer()
_SCREAMING_SNAKE_CASE = CLIPSegProcessor(tokenizer=A_ , image_processor=A_ )
_SCREAMING_SNAKE_CASE = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
_SCREAMING_SNAKE_CASE = processor.batch_decode(A_ )
_SCREAMING_SNAKE_CASE = tokenizer.batch_decode(A_ )
self.assertListEqual(A_ , A_ )
| 605 |
"""simple docstring"""
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available
if is_sentencepiece_available():
from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json
from ...test_tokenization_common import TokenizerTesterMixin
__UpperCAmelCase = get_tests_dir('fixtures/test_sentencepiece.model')
__UpperCAmelCase = {'target_lang': 'fi', 'source_lang': 'en'}
__UpperCAmelCase = '>>zh<<'
__UpperCAmelCase = 'Helsinki-NLP/'
if is_torch_available():
__UpperCAmelCase = 'pt'
elif is_tf_available():
__UpperCAmelCase = 'tf'
else:
__UpperCAmelCase = 'jax'
@require_sentencepiece
class __lowercase ( __lowerCamelCase , unittest.TestCase ):
snake_case_ = MarianTokenizer
snake_case_ = False
snake_case_ = True
def __lowercase ( self : Optional[int] ):
'''simple docstring'''
super().setUp()
UpperCAmelCase__ : Optional[Any] = ["""</s>""", """<unk>""", """▁This""", """▁is""", """▁a""", """▁t""", """est""", """\u0120""", """<pad>"""]
UpperCAmelCase__ : int = dict(zip(A ,range(len(A ) ) ) )
UpperCAmelCase__ : Optional[int] = Path(self.tmpdirname )
save_json(A ,save_dir / VOCAB_FILES_NAMES["""vocab"""] )
save_json(A ,save_dir / VOCAB_FILES_NAMES["""tokenizer_config_file"""] )
if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
copyfile(A ,save_dir / VOCAB_FILES_NAMES["""source_spm"""] )
copyfile(A ,save_dir / VOCAB_FILES_NAMES["""target_spm"""] )
UpperCAmelCase__ : Dict = MarianTokenizer.from_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname )
def __lowercase ( self : List[Any] ,**A : List[Any] ):
'''simple docstring'''
return MarianTokenizer.from_pretrained(self.tmpdirname ,**A )
def __lowercase ( self : Union[str, Any] ,A : Tuple ):
'''simple docstring'''
return (
"This is a test",
"This is a test",
)
def __lowercase ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = """</s>"""
UpperCAmelCase__ : int = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(A ) ,A )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(A ) ,A )
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ : Dict = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] ,"""</s>""" )
self.assertEqual(vocab_keys[1] ,"""<unk>""" )
self.assertEqual(vocab_keys[-1] ,"""<pad>""" )
self.assertEqual(len(A ) ,9 )
def __lowercase ( self : Dict ):
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size ,9 )
def __lowercase ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = MarianTokenizer.from_pretrained(f"{ORG_NAME}opus-mt-en-de" )
UpperCAmelCase__ : List[str] = en_de_tokenizer(["""I am a small frog"""] ,return_tensors=A )
self.assertIsInstance(A ,A )
UpperCAmelCase__ : str = [38, 121, 14, 697, 38_848, 0]
self.assertListEqual(A ,batch.input_ids[0] )
UpperCAmelCase__ : Optional[Any] = tempfile.mkdtemp()
en_de_tokenizer.save_pretrained(A )
UpperCAmelCase__ : Tuple = [x.name for x in Path(A ).glob("""*""" )]
self.assertIn("""source.spm""" ,A )
MarianTokenizer.from_pretrained(A )
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = self.get_tokenizer()
UpperCAmelCase__ : Any = tok(
["""I am a small frog""" * 1_000, """I am a small frog"""] ,padding=A ,truncation=A ,return_tensors=A )
self.assertIsInstance(A ,A )
self.assertEqual(batch.input_ids.shape ,(2, 512) )
def __lowercase ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase__ : int = self.get_tokenizer()
UpperCAmelCase__ : Tuple = tok(["""I am a tiny frog""", """I am a small frog"""] ,padding=A ,return_tensors=A )
self.assertIsInstance(A ,A )
self.assertEqual(batch_smaller.input_ids.shape ,(2, 10) )
@slow
def __lowercase ( self : Dict ):
'''simple docstring'''
# fmt: off
UpperCAmelCase__ : Optional[int] = {"""input_ids""": [[43_495, 462, 20, 42_164, 1_369, 52, 464, 132, 1_703, 492, 13, 7_491, 38_999, 6, 8, 464, 132, 1_703, 492, 13, 4_669, 37_867, 13, 7_525, 27, 1_593, 988, 13, 33_972, 7_029, 6, 20, 8_251, 383, 2, 270, 5_866, 3_788, 2, 2_353, 8_251, 12_338, 2, 13_958, 387, 2, 3_629, 6_953, 188, 2_900, 2, 13_958, 8_011, 11_501, 23, 8_460, 4_073, 34_009, 20, 435, 11_439, 27, 8, 8_460, 4_073, 6_004, 20, 9_988, 375, 27, 33, 266, 1_945, 1_076, 1_350, 37_867, 3_288, 5, 577, 1_076, 4_374, 8, 5_082, 5, 26_453, 257, 556, 403, 2, 242, 132, 383, 316, 492, 8, 10_767, 6, 316, 304, 4_239, 3, 0], [148, 15_722, 19, 1_839, 12, 1_350, 13, 22_327, 5_082, 5_418, 47_567, 35_938, 59, 318, 19_552, 108, 2_183, 54, 14_976, 4_835, 32, 547, 1_114, 8, 315, 2_417, 5, 92, 19_088, 3, 0, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100], [36, 6_395, 12_570, 39_147, 11_597, 6, 266, 4, 45_405, 7_296, 3, 0, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100]], """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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=A ,model_name="""Helsinki-NLP/opus-mt-en-de""" ,revision="""1a8c2263da11e68e50938f97e10cd57820bd504c""" ,decode_kwargs={"""use_source_tokenizer""": True} ,)
def __lowercase ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = MarianTokenizer.from_pretrained("""hf-internal-testing/test-marian-two-vocabs""" )
UpperCAmelCase__ : Any = """Tämä on testi"""
UpperCAmelCase__ : int = """This is a test"""
UpperCAmelCase__ : List[str] = [76, 7, 2_047, 2]
UpperCAmelCase__ : Optional[Any] = [69, 12, 11, 940, 2]
UpperCAmelCase__ : List[str] = tokenizer(A ).input_ids
self.assertListEqual(A ,A )
UpperCAmelCase__ : Optional[int] = tokenizer(text_target=A ).input_ids
self.assertListEqual(A ,A )
UpperCAmelCase__ : int = tokenizer.decode(A ,skip_special_tokens=A )
self.assertEqual(A ,A )
| 65 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowerCAmelCase : List[Any] ={
"""configuration_mask2former""": [
"""MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Mask2FormerConfig""",
],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase : str =["""Mask2FormerImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase : Tuple =[
"""MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""Mask2FormerForUniversalSegmentation""",
"""Mask2FormerModel""",
"""Mask2FormerPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_maskaformer import MaskaFormerImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_maskaformer import (
MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
MaskaFormerForUniversalSegmentation,
MaskaFormerModel,
MaskaFormerPreTrainedModel,
)
else:
import sys
_lowerCAmelCase : Union[str, Any] =_LazyModule(__name__, globals()["""__file__"""], _import_structure) | 113 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class __lowercase ( metaclass=__lowerCamelCase ):
snake_case_ = ["""onnx"""]
def __init__( self : int ,*A : List[str] ,**A : int ):
'''simple docstring'''
requires_backends(self ,["""onnx"""] )
@classmethod
def __lowercase ( cls : Optional[Any] ,*A : List[str] ,**A : Dict ):
'''simple docstring'''
requires_backends(cls ,["""onnx"""] )
@classmethod
def __lowercase ( cls : List[Any] ,*A : Optional[int] ,**A : int ):
'''simple docstring'''
requires_backends(cls ,["""onnx"""] )
| 65 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/config.json'''}
class _UpperCAmelCase ( __lowerCamelCase ):
'''simple docstring'''
__A = '''openai-gpt'''
__A = {
'''max_position_embeddings''': '''n_positions''',
'''hidden_size''': '''n_embd''',
'''num_attention_heads''': '''n_head''',
'''num_hidden_layers''': '''n_layer''',
}
def __init__( self : int , lowercase_ : Any=40478 , lowercase_ : List[str]=512 , lowercase_ : Tuple=768 , lowercase_ : int=12 , lowercase_ : Union[str, Any]=12 , lowercase_ : int="gelu" , lowercase_ : List[str]=0.1 , lowercase_ : int=0.1 , lowercase_ : List[Any]=0.1 , lowercase_ : Optional[int]=1e-5 , lowercase_ : Optional[Any]=0.02 , lowercase_ : List[Any]="cls_index" , lowercase_ : List[str]=True , lowercase_ : Union[str, Any]=None , lowercase_ : int=True , lowercase_ : int=0.1 , **lowercase_ : Optional[int] , ) -> Dict:
"""simple docstring"""
_UpperCamelCase = vocab_size
_UpperCamelCase = n_positions
_UpperCamelCase = n_embd
_UpperCamelCase = n_layer
_UpperCamelCase = n_head
_UpperCamelCase = afn
_UpperCamelCase = resid_pdrop
_UpperCamelCase = embd_pdrop
_UpperCamelCase = attn_pdrop
_UpperCamelCase = layer_norm_epsilon
_UpperCamelCase = initializer_range
_UpperCamelCase = summary_type
_UpperCamelCase = summary_use_proj
_UpperCamelCase = summary_activation
_UpperCamelCase = summary_first_dropout
_UpperCamelCase = summary_proj_to_labels
super().__init__(**lowercase_)
| 547 |
"""simple docstring"""
from argparse import ArgumentParser
from .env import EnvironmentCommand
def lowerCAmelCase ( ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = ArgumentParser("""Diffusers CLI tool""" , usage="""diffusers-cli <command> [<args>]""" )
UpperCAmelCase__ : List[Any] = parser.add_subparsers(help="""diffusers-cli command helpers""" )
# Register commands
EnvironmentCommand.register_subcommand(__UpperCamelCase )
# Let's go
UpperCAmelCase__ : int = parser.parse_args()
if not hasattr(__UpperCamelCase , """func""" ):
parser.print_help()
exit(1 )
# Run
UpperCAmelCase__ : Union[str, Any] = args.func(__UpperCamelCase )
service.run()
if __name__ == "__main__":
main()
| 65 | 0 |
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE :Any = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE :Optional[Any] = {
'''vocab_file''': '''vocab.json''',
'''merges_file''': '''merges.txt''',
'''tokenizer_config_file''': '''tokenizer_config.json''',
}
SCREAMING_SNAKE_CASE :int = {
'''vocab_file''': {
'''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'''
},
'''merges_file''': {
'''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'''
},
'''tokenizer_config_file''': {
'''facebook/blenderbot_small-90M''': (
'''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'''
)
},
}
SCREAMING_SNAKE_CASE :Tuple = {'''facebook/blenderbot_small-90M''': 5_12}
def _lowerCAmelCase ( lowerCAmelCase_ :List[str] )->str:
'''simple docstring'''
snake_case_ = set()
snake_case_ = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
snake_case_ = char
snake_case_ = set(__UpperCamelCase )
return pairs
class __lowerCAmelCase ( __lowerCamelCase ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = VOCAB_FILES_NAMES
_SCREAMING_SNAKE_CASE = PRETRAINED_VOCAB_FILES_MAP
_SCREAMING_SNAKE_CASE = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_SCREAMING_SNAKE_CASE = ['input_ids', 'attention_mask']
def __init__( self : Optional[int] , _lowerCAmelCase : Tuple , _lowerCAmelCase : int , _lowerCAmelCase : int="__start__" , _lowerCAmelCase : Dict="__end__" , _lowerCAmelCase : int="__unk__" , _lowerCAmelCase : List[Any]="__null__" , **_lowerCAmelCase : Optional[int] , ) -> Union[str, Any]:
"""simple docstring"""
super().__init__(unk_token=_lowerCAmelCase , bos_token=_lowerCAmelCase , eos_token=_lowerCAmelCase , pad_token=_lowerCAmelCase , **_lowerCAmelCase )
with open(_lowerCAmelCase , encoding="utf-8" ) as vocab_handle:
snake_case_ = json.load(_lowerCAmelCase )
snake_case_ = {v: k for k, v in self.encoder.items()}
with open(_lowerCAmelCase , encoding="utf-8" ) as merges_handle:
snake_case_ = merges_handle.read().split("\n" )[1:-1]
snake_case_ = [tuple(merge.split() ) for merge in merges]
snake_case_ = dict(zip(_lowerCAmelCase , range(len(_lowerCAmelCase ) ) ) )
snake_case_ = {}
@property
def lowerCAmelCase__ ( self : Union[str, Any] ) -> str:
"""simple docstring"""
return len(self.encoder )
def lowerCAmelCase__ ( self : Any ) -> int:
"""simple docstring"""
return dict(self.encoder , **self.added_tokens_encoder )
def lowerCAmelCase__ ( self : str , _lowerCAmelCase : str ) -> int:
"""simple docstring"""
if token in self.cache:
return self.cache[token]
snake_case_ = re.sub("([.,!?()])" , R" \1" , _lowerCAmelCase )
snake_case_ = re.sub("(')" , R" \1 " , _lowerCAmelCase )
snake_case_ = re.sub(R"\s{2,}" , " " , _lowerCAmelCase )
if "\n" in token:
snake_case_ = token.replace("\n" , " __newln__" )
snake_case_ = token.split(" " )
snake_case_ = []
for token in tokens:
if not len(_lowerCAmelCase ):
continue
snake_case_ = token.lower()
snake_case_ = tuple(_lowerCAmelCase )
snake_case_ = tuple(list(word[:-1] ) + [word[-1] + "</w>"] )
snake_case_ = get_pairs(_lowerCAmelCase )
if not pairs:
words.append(_lowerCAmelCase )
continue
while True:
snake_case_ = min(_lowerCAmelCase , key=lambda _lowerCAmelCase : self.bpe_ranks.get(_lowerCAmelCase , float("inf" ) ) )
if bigram not in self.bpe_ranks:
break
snake_case_ = bigram
snake_case_ = []
snake_case_ = 0
while i < len(_lowerCAmelCase ):
try:
snake_case_ = word.index(_lowerCAmelCase , _lowerCAmelCase )
new_word.extend(word[i:j] )
snake_case_ = j
except ValueError:
new_word.extend(word[i:] )
break
if word[i] == first and i < len(_lowerCAmelCase ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
snake_case_ = tuple(_lowerCAmelCase )
snake_case_ = new_word
if len(_lowerCAmelCase ) == 1:
break
else:
snake_case_ = get_pairs(_lowerCAmelCase )
snake_case_ = """@@ """.join(_lowerCAmelCase )
snake_case_ = word[:-4]
snake_case_ = word
words.append(_lowerCAmelCase )
return " ".join(_lowerCAmelCase )
def lowerCAmelCase__ ( self : str , _lowerCAmelCase : str ) -> Tuple:
"""simple docstring"""
snake_case_ = []
snake_case_ = re.findall(R"\S+\n?" , _lowerCAmelCase )
for token in words:
split_tokens.extend(list(self.bpe(_lowerCAmelCase ).split(" " ) ) )
return split_tokens
def lowerCAmelCase__ ( self : Optional[Any] , _lowerCAmelCase : str ) -> List[str]:
"""simple docstring"""
snake_case_ = token.lower()
return self.encoder.get(_lowerCAmelCase , self.encoder.get(self.unk_token ) )
def lowerCAmelCase__ ( self : Tuple , _lowerCAmelCase : int ) -> Tuple:
"""simple docstring"""
return self.decoder.get(_lowerCAmelCase , self.unk_token )
def lowerCAmelCase__ ( self : Union[str, Any] , _lowerCAmelCase : List[str] ) -> int:
"""simple docstring"""
snake_case_ = """ """.join(_lowerCAmelCase ).replace("@@ " , "" ).strip()
return out_string
def lowerCAmelCase__ ( self : Tuple , _lowerCAmelCase : str , _lowerCAmelCase : Optional[str] = None ) -> Optional[int]:
"""simple docstring"""
if not os.path.isdir(_lowerCAmelCase ):
logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
return
snake_case_ = os.path.join(
_lowerCAmelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
snake_case_ = os.path.join(
_lowerCAmelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] )
with open(_lowerCAmelCase , "w" , encoding="utf-8" ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=_lowerCAmelCase , ensure_ascii=_lowerCAmelCase ) + "\n" )
snake_case_ = 0
with open(_lowerCAmelCase , "w" , encoding="utf-8" ) as writer:
writer.write("#version: 0.2\n" )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda _lowerCAmelCase : kv[1] ):
if index != token_index:
logger.warning(
F'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
" Please check that the tokenizer is not corrupted!" )
snake_case_ = token_index
writer.write(" ".join(_lowerCAmelCase ) + "\n" )
index += 1
return vocab_file, merge_file
| 283 |
"""simple docstring"""
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
import os
# The slow tests are often failing with OOM error on GPU
# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
__UpperCAmelCase = 'platform'
import jax
import jax.numpy as jnp
import numpy as np
from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel
@require_flax
class __lowercase :
snake_case_ = PegasusConfig
snake_case_ = {}
snake_case_ = """gelu"""
def __init__( self : List[Any] ,A : int ,A : Optional[Any]=13 ,A : Dict=7 ,A : Dict=True ,A : Any=False ,A : Dict=99 ,A : int=32 ,A : Optional[int]=5 ,A : Union[str, Any]=4 ,A : Union[str, Any]=37 ,A : str=0.1 ,A : int=0.1 ,A : Optional[int]=20 ,A : Tuple=2 ,A : str=1 ,A : Optional[Any]=0 ,):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = parent
UpperCAmelCase__ : Union[str, Any] = batch_size
UpperCAmelCase__ : List[Any] = seq_length
UpperCAmelCase__ : int = is_training
UpperCAmelCase__ : Any = use_labels
UpperCAmelCase__ : int = vocab_size
UpperCAmelCase__ : Dict = hidden_size
UpperCAmelCase__ : Optional[Any] = num_hidden_layers
UpperCAmelCase__ : int = num_attention_heads
UpperCAmelCase__ : Any = intermediate_size
UpperCAmelCase__ : Optional[int] = hidden_dropout_prob
UpperCAmelCase__ : str = attention_probs_dropout_prob
UpperCAmelCase__ : str = max_position_embeddings
UpperCAmelCase__ : Union[str, Any] = eos_token_id
UpperCAmelCase__ : Union[str, Any] = pad_token_id
UpperCAmelCase__ : List[str] = bos_token_id
def __lowercase ( self : Dict ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = ids_tensor([self.batch_size, self.seq_length - 1] ,self.vocab_size ).clip(3 ,self.vocab_size )
UpperCAmelCase__ : List[str] = np.expand_dims(np.array([self.eos_token_id] * self.batch_size ) ,1 )
UpperCAmelCase__ : Any = np.concatenate([input_ids, eos_tensor] ,axis=1 )
UpperCAmelCase__ : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
UpperCAmelCase__ : str = 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 ,)
UpperCAmelCase__ : Optional[Any] = prepare_pegasus_inputs_dict(A ,A ,A )
return config, inputs_dict
def __lowercase ( self : Any ,A : Optional[int] ,A : str ,A : Optional[int] ):
'''simple docstring'''
UpperCAmelCase__ : Any = 20
UpperCAmelCase__ : Dict = model_class_name(A )
UpperCAmelCase__ : str = model.encode(inputs_dict["""input_ids"""] )
UpperCAmelCase__ , UpperCAmelCase__ : List[str] = (
inputs_dict["""decoder_input_ids"""],
inputs_dict["""decoder_attention_mask"""],
)
UpperCAmelCase__ : Union[str, Any] = model.init_cache(decoder_input_ids.shape[0] ,A ,A )
UpperCAmelCase__ : Union[str, Any] = jnp.ones((decoder_input_ids.shape[0], max_decoder_length) ,dtype="""i4""" )
UpperCAmelCase__ : str = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] ,(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) ,)
UpperCAmelCase__ : Optional[int] = model.decode(
decoder_input_ids[:, :-1] ,A ,decoder_attention_mask=A ,past_key_values=A ,decoder_position_ids=A ,)
UpperCAmelCase__ : Any = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] ,dtype="""i4""" )
UpperCAmelCase__ : int = model.decode(
decoder_input_ids[:, -1:] ,A ,decoder_attention_mask=A ,past_key_values=outputs_cache.past_key_values ,decoder_position_ids=A ,)
UpperCAmelCase__ : Dict = model.decode(A ,A )
UpperCAmelCase__ : str = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1e-3 ,msg=f"Max diff is {diff}" )
def __lowercase ( self : Optional[int] ,A : str ,A : Dict ,A : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ : Any = 20
UpperCAmelCase__ : str = model_class_name(A )
UpperCAmelCase__ : Any = model.encode(inputs_dict["""input_ids"""] )
UpperCAmelCase__ , UpperCAmelCase__ : Optional[int] = (
inputs_dict["""decoder_input_ids"""],
inputs_dict["""decoder_attention_mask"""],
)
UpperCAmelCase__ : Optional[int] = jnp.concatenate(
[
decoder_attention_mask,
jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1]) ),
] ,axis=-1 ,)
UpperCAmelCase__ : Union[str, Any] = model.init_cache(decoder_input_ids.shape[0] ,A ,A )
UpperCAmelCase__ : List[str] = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] ,(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) ,)
UpperCAmelCase__ : Union[str, Any] = model.decode(
decoder_input_ids[:, :-1] ,A ,decoder_attention_mask=A ,past_key_values=A ,decoder_position_ids=A ,)
UpperCAmelCase__ : int = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] ,dtype="""i4""" )
UpperCAmelCase__ : Dict = model.decode(
decoder_input_ids[:, -1:] ,A ,past_key_values=outputs_cache.past_key_values ,decoder_attention_mask=A ,decoder_position_ids=A ,)
UpperCAmelCase__ : Union[str, Any] = model.decode(A ,A ,decoder_attention_mask=A )
UpperCAmelCase__ : Union[str, 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}" )
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=None , __UpperCamelCase=None , ):
'''simple docstring'''
if attention_mask is None:
UpperCAmelCase__ : Union[str, Any] = np.not_equal(__UpperCamelCase , config.pad_token_id ).astype(np.inta )
if decoder_attention_mask is None:
UpperCAmelCase__ : Tuple = np.concatenate(
[
np.ones(decoder_input_ids[:, :1].shape , dtype=np.inta ),
np.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ).astype(np.inta ),
] , axis=-1 , )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
}
@require_flax
class __lowercase ( __lowerCamelCase , unittest.TestCase ):
snake_case_ = (
(
FlaxPegasusForConditionalGeneration,
FlaxPegasusModel,
)
if is_flax_available()
else ()
)
snake_case_ = (FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
snake_case_ = True
snake_case_ = False
snake_case_ = False
snake_case_ = False
def __lowercase ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase__ : int = FlaxPegasusModelTester(self )
UpperCAmelCase__ : Optional[Any] = ConfigTester(self ,config_class=A )
def __lowercase ( self : Tuple ):
'''simple docstring'''
self.config_tester.run_common_tests()
def __lowercase ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase__ , UpperCAmelCase__ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward(A ,A ,A )
def __lowercase ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase__ , UpperCAmelCase__ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward_with_attn_mask(A ,A ,A )
def __lowercase ( self : Any ):
'''simple docstring'''
UpperCAmelCase__ , UpperCAmelCase__ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
UpperCAmelCase__ : List[Any] = self._prepare_for_class(A ,A )
UpperCAmelCase__ : int = model_class(A )
@jax.jit
def encode_jitted(A : Optional[int] ,A : Union[str, Any]=None ,**A : Optional[Any] ):
return model.encode(input_ids=A ,attention_mask=A )
with self.subTest("""JIT Enabled""" ):
UpperCAmelCase__ : int = encode_jitted(**A ).to_tuple()
with self.subTest("""JIT Disabled""" ):
with jax.disable_jit():
UpperCAmelCase__ : Dict = encode_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 )
def __lowercase ( self : str ):
'''simple docstring'''
UpperCAmelCase__ , UpperCAmelCase__ : 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__ ):
UpperCAmelCase__ : Dict = model_class(A )
UpperCAmelCase__ : str = model.encode(inputs_dict["""input_ids"""] ,inputs_dict["""attention_mask"""] )
UpperCAmelCase__ : Dict = {
"""decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
"""decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
"""encoder_outputs""": encoder_outputs,
}
@jax.jit
def decode_jitted(A : List[Any] ,A : Any ,A : List[Any] ):
return model.decode(
decoder_input_ids=A ,decoder_attention_mask=A ,encoder_outputs=A ,)
with self.subTest("""JIT Enabled""" ):
UpperCAmelCase__ : Tuple = decode_jitted(**A ).to_tuple()
with self.subTest("""JIT Disabled""" ):
with jax.disable_jit():
UpperCAmelCase__ : str = decode_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 : List[Any] ):
'''simple docstring'''
for model_class_name in self.all_model_classes:
UpperCAmelCase__ : List[str] = model_class_name.from_pretrained("""google/pegasus-large""" ,from_pt=A )
UpperCAmelCase__ : Any = np.ones((1, 1) )
UpperCAmelCase__ : Optional[Any] = model(A )
self.assertIsNotNone(A )
@slow
def __lowercase ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase__ : Dict = FlaxPegasusForConditionalGeneration.from_pretrained("""google/pegasus-xsum""" )
UpperCAmelCase__ : Optional[Any] = PegasusTokenizer.from_pretrained("""google/pegasus-xsum""" )
UpperCAmelCase__ : Union[str, Any] = [
""" PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.""",
""" The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning 'Oh I think you're nominated'\", said Dappy.\"And I was like 'Oh yeah, which one?' And now we've got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it's best of us to come down and mingle with everyone and say hello to the cameras. And now we find we've got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn't be too disappointed if they didn't win this time around.\"At the end of the day we're grateful to be where we are in our careers.\"If it don't happen then it don't happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a re-working of The Killers' All These Things That I've Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year's Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" """,
]
UpperCAmelCase__ : str = [
"""California's largest electricity provider has turned off power to hundreds of thousands of customers.""",
"""Pop group N-Dubz have revealed they were surprised to get four nominations for this year's Mobo Awards.""",
]
UpperCAmelCase__ : str = tokenizer(A ,return_tensors="""np""" ,truncation=A ,max_length=512 ,padding=A )
UpperCAmelCase__ : Union[str, Any] = model.generate(**A ,num_beams=2 ).sequences
UpperCAmelCase__ : int = tokenizer.batch_decode(A ,skip_special_tokens=A )
assert tgt_text == decoded
| 65 | 0 |
import json
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
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 transformers import OneFormerImageProcessor
from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput
if is_vision_available():
from PIL import Image
def UpperCamelCase_( snake_case__: List[Any] , snake_case__: Any="shi-labs/oneformer_demo" ) -> Any:
with open(hf_hub_download(__UpperCamelCase , __UpperCamelCase , repo_type='dataset' ) , 'r' ) as f:
UpperCAmelCase__ = json.load(__UpperCamelCase )
UpperCAmelCase__ = {}
UpperCAmelCase__ = []
UpperCAmelCase__ = []
for key, info in class_info.items():
UpperCAmelCase__ = info["""name"""]
class_names.append(info['name'] )
if info["isthing"]:
thing_ids.append(int(__UpperCamelCase ) )
UpperCAmelCase__ = thing_ids
UpperCAmelCase__ = class_names
return metadata
class lowercase ( unittest.TestCase ):
'''simple docstring'''
def __init__(self , __a , __a=7 , __a=3 , __a=30 , __a=400 , __a=None , __a=True , __a=True , __a=[0.5, 0.5, 0.5] , __a=[0.5, 0.5, 0.5] , __a=10 , __a=False , __a=255 , __a="shi-labs/oneformer_demo" , __a="ade20k_panoptic.json" , __a=10 , ) -> Dict:
"""simple docstring"""
UpperCAmelCase__ = parent
UpperCAmelCase__ = batch_size
UpperCAmelCase__ = num_channels
UpperCAmelCase__ = min_resolution
UpperCAmelCase__ = max_resolution
UpperCAmelCase__ = do_resize
UpperCAmelCase__ = {"""shortest_edge""": 32, """longest_edge""": 1333} if size is None else size
UpperCAmelCase__ = do_normalize
UpperCAmelCase__ = image_mean
UpperCAmelCase__ = image_std
UpperCAmelCase__ = class_info_file
UpperCAmelCase__ = prepare_metadata(__a , __a )
UpperCAmelCase__ = num_text
UpperCAmelCase__ = repo_path
# for the post_process_functions
UpperCAmelCase__ = 2
UpperCAmelCase__ = 10
UpperCAmelCase__ = 10
UpperCAmelCase__ = 3
UpperCAmelCase__ = 4
UpperCAmelCase__ = num_labels
UpperCAmelCase__ = do_reduce_labels
UpperCAmelCase__ = ignore_index
def UpperCamelCase__ (self ) -> List[str]:
"""simple docstring"""
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"num_labels": self.num_labels,
"do_reduce_labels": self.do_reduce_labels,
"ignore_index": self.ignore_index,
"class_info_file": self.class_info_file,
"metadata": self.metadata,
"num_text": self.num_text,
}
def UpperCamelCase__ (self , __a , __a=False ) -> Dict:
"""simple docstring"""
if not batched:
UpperCAmelCase__ = image_inputs[0]
if isinstance(__a , Image.Image ):
UpperCAmelCase__ = image.size
else:
UpperCAmelCase__ = image.shape[1], image.shape[2]
if w < h:
UpperCAmelCase__ = int(self.size['shortest_edge'] * h / w )
UpperCAmelCase__ = self.size["""shortest_edge"""]
elif w > h:
UpperCAmelCase__ = self.size["""shortest_edge"""]
UpperCAmelCase__ = int(self.size['shortest_edge'] * w / h )
else:
UpperCAmelCase__ = self.size["""shortest_edge"""]
UpperCAmelCase__ = self.size["""shortest_edge"""]
else:
UpperCAmelCase__ = []
for image in image_inputs:
UpperCAmelCase__ = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
UpperCAmelCase__ = max(__a , key=lambda __a : item[0] )[0]
UpperCAmelCase__ = max(__a , key=lambda __a : item[1] )[1]
return expected_height, expected_width
def UpperCamelCase__ (self ) -> Optional[int]:
"""simple docstring"""
return OneFormerForUniversalSegmentationOutput(
# +1 for null class
class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1) ) , masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width) ) , )
@require_torch
@require_vision
class lowercase ( __lowerCamelCase , unittest.TestCase ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
# only for test_image_processing_common.test_image_proc_to_json_string
__SCREAMING_SNAKE_CASE = image_processing_class
def UpperCamelCase__ (self ) -> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase__ = OneFormerImageProcessorTester(self )
@property
def UpperCamelCase__ (self ) -> Optional[Any]:
"""simple docstring"""
return self.image_processing_tester.prepare_image_processor_dict()
def UpperCamelCase__ (self ) -> int:
"""simple docstring"""
UpperCAmelCase__ = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__a , 'image_mean' ) )
self.assertTrue(hasattr(__a , 'image_std' ) )
self.assertTrue(hasattr(__a , 'do_normalize' ) )
self.assertTrue(hasattr(__a , 'do_resize' ) )
self.assertTrue(hasattr(__a , 'size' ) )
self.assertTrue(hasattr(__a , 'ignore_index' ) )
self.assertTrue(hasattr(__a , 'class_info_file' ) )
self.assertTrue(hasattr(__a , 'num_text' ) )
self.assertTrue(hasattr(__a , 'repo_path' ) )
self.assertTrue(hasattr(__a , 'metadata' ) )
self.assertTrue(hasattr(__a , 'do_reduce_labels' ) )
def UpperCamelCase__ (self ) -> Tuple:
"""simple docstring"""
pass
def UpperCamelCase__ (self ) -> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase__ = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
UpperCAmelCase__ = prepare_image_inputs(self.image_processing_tester , equal_resolution=__a )
for image in image_inputs:
self.assertIsInstance(__a , Image.Image )
# Test not batched input
UpperCAmelCase__ = image_processor(image_inputs[0] , ['semantic'] , return_tensors='pt' ).pixel_values
UpperCAmelCase__ = self.image_processing_tester.get_expected_values(__a )
self.assertEqual(
encoded_images.shape , (1, self.image_processing_tester.num_channels, expected_height, expected_width) , )
# Test batched
UpperCAmelCase__ = self.image_processing_tester.get_expected_values(__a , batched=__a )
UpperCAmelCase__ = image_processor(
__a , ['semantic'] * len(__a ) , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processing_tester.batch_size,
self.image_processing_tester.num_channels,
expected_height,
expected_width,
) , )
def UpperCamelCase__ (self ) -> int:
"""simple docstring"""
UpperCAmelCase__ = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
UpperCAmelCase__ = prepare_image_inputs(self.image_processing_tester , equal_resolution=__a , numpify=__a )
for image in image_inputs:
self.assertIsInstance(__a , np.ndarray )
# Test not batched input
UpperCAmelCase__ = image_processor(image_inputs[0] , ['semantic'] , return_tensors='pt' ).pixel_values
UpperCAmelCase__ = self.image_processing_tester.get_expected_values(__a )
self.assertEqual(
encoded_images.shape , (1, self.image_processing_tester.num_channels, expected_height, expected_width) , )
# Test batched
UpperCAmelCase__ = self.image_processing_tester.get_expected_values(__a , batched=__a )
UpperCAmelCase__ = image_processor(
__a , ['semantic'] * len(__a ) , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processing_tester.batch_size,
self.image_processing_tester.num_channels,
expected_height,
expected_width,
) , )
def UpperCamelCase__ (self ) -> str:
"""simple docstring"""
UpperCAmelCase__ = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
UpperCAmelCase__ = prepare_image_inputs(self.image_processing_tester , equal_resolution=__a , torchify=__a )
for image in image_inputs:
self.assertIsInstance(__a , torch.Tensor )
# Test not batched input
UpperCAmelCase__ = image_processor(image_inputs[0] , ['semantic'] , return_tensors='pt' ).pixel_values
UpperCAmelCase__ = self.image_processing_tester.get_expected_values(__a )
self.assertEqual(
encoded_images.shape , (1, self.image_processing_tester.num_channels, expected_height, expected_width) , )
# Test batched
UpperCAmelCase__ = self.image_processing_tester.get_expected_values(__a , batched=__a )
UpperCAmelCase__ = image_processor(
__a , ['semantic'] * len(__a ) , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processing_tester.batch_size,
self.image_processing_tester.num_channels,
expected_height,
expected_width,
) , )
def UpperCamelCase__ (self , __a=False , __a=False , __a="np" ) -> Tuple:
"""simple docstring"""
UpperCAmelCase__ = self.image_processing_class(**self.image_processor_dict )
# prepare image and target
UpperCAmelCase__ = self.image_processing_tester.num_labels
UpperCAmelCase__ = None
UpperCAmelCase__ = None
UpperCAmelCase__ = prepare_image_inputs(self.image_processing_tester , equal_resolution=__a )
if with_segmentation_maps:
UpperCAmelCase__ = num_labels
if is_instance_map:
UpperCAmelCase__ = list(range(__a ) ) * 2
UpperCAmelCase__ = dict(enumerate(__a ) )
UpperCAmelCase__ = [
np.random.randint(0 , high * 2 , (img.size[1], img.size[0]) ).astype(np.uinta ) for img in image_inputs
]
if segmentation_type == "pil":
UpperCAmelCase__ = [Image.fromarray(__a ) for annotation in annotations]
UpperCAmelCase__ = image_processor(
__a , ['semantic'] * len(__a ) , __a , return_tensors='pt' , instance_id_to_semantic_id=__a , pad_and_return_pixel_mask=__a , )
return inputs
def UpperCamelCase__ (self ) -> List[Any]:
"""simple docstring"""
pass
def UpperCamelCase__ (self ) -> str:
"""simple docstring"""
def common(__a=False , __a=None ):
UpperCAmelCase__ = self.comm_get_image_processor_inputs(
with_segmentation_maps=__a , is_instance_map=__a , segmentation_type=__a )
UpperCAmelCase__ = inputs["""mask_labels"""]
UpperCAmelCase__ = inputs["""class_labels"""]
UpperCAmelCase__ = inputs["""pixel_values"""]
UpperCAmelCase__ = inputs["""text_inputs"""]
# check the batch_size
for mask_label, class_label, text_input in zip(__a , __a , __a ):
self.assertEqual(mask_label.shape[0] , class_label.shape[0] )
# this ensure padding has happened
self.assertEqual(mask_label.shape[1:] , pixel_values.shape[2:] )
self.assertEqual(len(__a ) , self.image_processing_tester.num_text )
common()
common(is_instance_map=__a )
common(is_instance_map=__a , segmentation_type='pil' )
common(is_instance_map=__a , segmentation_type='pil' )
def UpperCamelCase__ (self ) -> str:
"""simple docstring"""
UpperCAmelCase__ = np.zeros((20, 50) )
UpperCAmelCase__ = 1
UpperCAmelCase__ = 1
UpperCAmelCase__ = 1
UpperCAmelCase__ = binary_mask_to_rle(__a )
self.assertEqual(len(__a ) , 4 )
self.assertEqual(rle[0] , 21 )
self.assertEqual(rle[1] , 45 )
def UpperCamelCase__ (self ) -> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase__ = self.image_processing_class(
num_labels=self.image_processing_tester.num_classes , max_seq_length=77 , task_seq_length=77 , class_info_file='ade20k_panoptic.json' , num_text=self.image_processing_tester.num_text , repo_path='shi-labs/oneformer_demo' , )
UpperCAmelCase__ = self.image_processing_tester.get_fake_oneformer_outputs()
UpperCAmelCase__ = fature_extractor.post_process_semantic_segmentation(__a )
self.assertEqual(len(__a ) , self.image_processing_tester.batch_size )
self.assertEqual(
segmentation[0].shape , (
self.image_processing_tester.height,
self.image_processing_tester.width,
) , )
UpperCAmelCase__ = [(1, 4) for i in range(self.image_processing_tester.batch_size )]
UpperCAmelCase__ = fature_extractor.post_process_semantic_segmentation(__a , target_sizes=__a )
self.assertEqual(segmentation[0].shape , target_sizes[0] )
def UpperCamelCase__ (self ) -> Tuple:
"""simple docstring"""
UpperCAmelCase__ = self.image_processing_class(
num_labels=self.image_processing_tester.num_classes , max_seq_length=77 , task_seq_length=77 , class_info_file='ade20k_panoptic.json' , num_text=self.image_processing_tester.num_text , repo_path='shi-labs/oneformer_demo' , )
UpperCAmelCase__ = self.image_processing_tester.get_fake_oneformer_outputs()
UpperCAmelCase__ = image_processor.post_process_instance_segmentation(__a , threshold=0 )
self.assertTrue(len(__a ) == self.image_processing_tester.batch_size )
for el in segmentation:
self.assertTrue('segmentation' in el )
self.assertTrue('segments_info' in el )
self.assertEqual(type(el['segments_info'] ) , __a )
self.assertEqual(
el['segmentation'].shape , (self.image_processing_tester.height, self.image_processing_tester.width) )
def UpperCamelCase__ (self ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase__ = self.image_processing_class(
num_labels=self.image_processing_tester.num_classes , max_seq_length=77 , task_seq_length=77 , class_info_file='ade20k_panoptic.json' , num_text=self.image_processing_tester.num_text , repo_path='shi-labs/oneformer_demo' , )
UpperCAmelCase__ = self.image_processing_tester.get_fake_oneformer_outputs()
UpperCAmelCase__ = image_processor.post_process_panoptic_segmentation(__a , threshold=0 )
self.assertTrue(len(__a ) == self.image_processing_tester.batch_size )
for el in segmentation:
self.assertTrue('segmentation' in el )
self.assertTrue('segments_info' in el )
self.assertEqual(type(el['segments_info'] ) , __a )
self.assertEqual(
el['segmentation'].shape , (self.image_processing_tester.height, self.image_processing_tester.width) )
| 146 |
"""simple docstring"""
def lowerCAmelCase ( __UpperCamelCase ):
'''simple docstring'''
if not isinstance(__UpperCamelCase , __UpperCamelCase ) or number < 0:
raise ValueError("""Input must be a non-negative integer""" )
UpperCAmelCase__ : Union[str, Any] = 0
while number:
# This way we arrive at next set bit (next 1) instead of looping
# through each bit and checking for 1s hence the
# loop won't run 32 times it will only run the number of `1` times
number &= number - 1
count += 1
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 65 | 0 |
"""simple docstring"""
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
FlaxCrossAttnDownBlockaD,
FlaxDownBlockaD,
FlaxUNetMidBlockaDCrossAttn,
)
@flax.struct.dataclass
class _UpperCamelCase ( __lowerCamelCase ):
'''simple docstring'''
__UpperCAmelCase : Optional[Any] =4_2
__UpperCAmelCase : Tuple =4_2
class _UpperCamelCase ( nn.Module ):
'''simple docstring'''
__UpperCAmelCase : int =4_2
__UpperCAmelCase : Dict =(1_6, 3_2, 9_6, 2_5_6)
__UpperCAmelCase : Optional[Any] =jnp.floataa
def snake_case ( self ):
__lowerCAmelCase = nn.Conv(
self.block_out_channels[0] , kernel_size=(3, 3) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
__lowerCAmelCase = []
for i in range(len(self.block_out_channels ) - 1 ):
__lowerCAmelCase = self.block_out_channels[i]
__lowerCAmelCase = self.block_out_channels[i + 1]
__lowerCAmelCase = nn.Conv(
__a , kernel_size=(3, 3) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
blocks.append(__a )
__lowerCAmelCase = nn.Conv(
__a , kernel_size=(3, 3) , strides=(2, 2) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
blocks.append(__a )
__lowerCAmelCase = blocks
__lowerCAmelCase = nn.Conv(
self.conditioning_embedding_channels , kernel_size=(3, 3) , padding=((1, 1), (1, 1)) , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
def __call__( self , __a ):
__lowerCAmelCase = self.conv_in(__a )
__lowerCAmelCase = nn.silu(__a )
for block in self.blocks:
__lowerCAmelCase = block(__a )
__lowerCAmelCase = nn.silu(__a )
__lowerCAmelCase = self.conv_out(__a )
return embedding
@flax_register_to_config
class _UpperCamelCase ( nn.Module ,__lowerCamelCase ,__lowerCamelCase ):
'''simple docstring'''
__UpperCAmelCase : Union[str, Any] =3_2
__UpperCAmelCase : List[str] =4
__UpperCAmelCase : Optional[int] =(
"""CrossAttnDownBlock2D""",
"""CrossAttnDownBlock2D""",
"""CrossAttnDownBlock2D""",
"""DownBlock2D""",
)
__UpperCAmelCase : Optional[Any] =False
__UpperCAmelCase : str =(3_2_0, 6_4_0, 1_2_8_0, 1_2_8_0)
__UpperCAmelCase : str =2
__UpperCAmelCase : Tuple =8
__UpperCAmelCase : List[Any] =None
__UpperCAmelCase : Any =1_2_8_0
__UpperCAmelCase : List[str] =0.0
__UpperCAmelCase : Tuple =False
__UpperCAmelCase : Tuple =jnp.floataa
__UpperCAmelCase : Dict =True
__UpperCAmelCase : List[Any] =0
__UpperCAmelCase : Dict ="""rgb"""
__UpperCAmelCase : Any =(1_6, 3_2, 9_6, 2_5_6)
def snake_case ( self , __a ):
__lowerCAmelCase = (1, self.in_channels, self.sample_size, self.sample_size)
__lowerCAmelCase = jnp.zeros(__a , dtype=jnp.floataa )
__lowerCAmelCase = jnp.ones((1,) , dtype=jnp.intaa )
__lowerCAmelCase = jnp.zeros((1, 1, self.cross_attention_dim) , dtype=jnp.floataa )
__lowerCAmelCase = (1, 3, self.sample_size * 8, self.sample_size * 8)
__lowerCAmelCase = jnp.zeros(__a , dtype=jnp.floataa )
__lowerCAmelCase = jax.random.split(__a )
__lowerCAmelCase = {"""params""": params_rng, """dropout""": dropout_rng}
return self.init(__a , __a , __a , __a , __a )["params"]
def snake_case ( self ):
__lowerCAmelCase = self.block_out_channels
__lowerCAmelCase = block_out_channels[0] * 4
# If `num_attention_heads` is not defined (which is the case for most models)
# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
# The reason for this behavior is to correct for incorrectly named variables that were introduced
# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
# which is why we correct for the naming here.
__lowerCAmelCase = self.num_attention_heads or self.attention_head_dim
# input
__lowerCAmelCase = nn.Conv(
block_out_channels[0] , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
# time
__lowerCAmelCase = FlaxTimesteps(
block_out_channels[0] , flip_sin_to_cos=self.flip_sin_to_cos , freq_shift=self.config.freq_shift )
__lowerCAmelCase = FlaxTimestepEmbedding(__a , dtype=self.dtype )
__lowerCAmelCase = FlaxControlNetConditioningEmbedding(
conditioning_embedding_channels=block_out_channels[0] , block_out_channels=self.conditioning_embedding_out_channels , )
__lowerCAmelCase = self.only_cross_attention
if isinstance(__a , __a ):
__lowerCAmelCase = (only_cross_attention,) * len(self.down_block_types )
if isinstance(__a , __a ):
__lowerCAmelCase = (num_attention_heads,) * len(self.down_block_types )
# down
__lowerCAmelCase = []
__lowerCAmelCase = []
__lowerCAmelCase = block_out_channels[0]
__lowerCAmelCase = nn.Conv(
__a , kernel_size=(1, 1) , padding="VALID" , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
controlnet_down_blocks.append(__a )
for i, down_block_type in enumerate(self.down_block_types ):
__lowerCAmelCase = output_channel
__lowerCAmelCase = block_out_channels[i]
__lowerCAmelCase = i == len(__a ) - 1
if down_block_type == "CrossAttnDownBlock2D":
__lowerCAmelCase = FlaxCrossAttnDownBlockaD(
in_channels=__a , out_channels=__a , dropout=self.dropout , num_layers=self.layers_per_block , num_attention_heads=num_attention_heads[i] , add_downsample=not is_final_block , use_linear_projection=self.use_linear_projection , only_cross_attention=only_cross_attention[i] , dtype=self.dtype , )
else:
__lowerCAmelCase = FlaxDownBlockaD(
in_channels=__a , out_channels=__a , dropout=self.dropout , num_layers=self.layers_per_block , add_downsample=not is_final_block , dtype=self.dtype , )
down_blocks.append(__a )
for _ in range(self.layers_per_block ):
__lowerCAmelCase = nn.Conv(
__a , kernel_size=(1, 1) , padding="VALID" , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
controlnet_down_blocks.append(__a )
if not is_final_block:
__lowerCAmelCase = nn.Conv(
__a , kernel_size=(1, 1) , padding="VALID" , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
controlnet_down_blocks.append(__a )
__lowerCAmelCase = down_blocks
__lowerCAmelCase = controlnet_down_blocks
# mid
__lowerCAmelCase = block_out_channels[-1]
__lowerCAmelCase = FlaxUNetMidBlockaDCrossAttn(
in_channels=__a , dropout=self.dropout , num_attention_heads=num_attention_heads[-1] , use_linear_projection=self.use_linear_projection , dtype=self.dtype , )
__lowerCAmelCase = nn.Conv(
__a , kernel_size=(1, 1) , padding="VALID" , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
def __call__( self , __a , __a , __a , __a , __a = 1.0 , __a = True , __a = False , ):
__lowerCAmelCase = self.controlnet_conditioning_channel_order
if channel_order == "bgr":
__lowerCAmelCase = jnp.flip(__a , axis=1 )
# 1. time
if not isinstance(__a , jnp.ndarray ):
__lowerCAmelCase = jnp.array([timesteps] , dtype=jnp.intaa )
elif isinstance(__a , jnp.ndarray ) and len(timesteps.shape ) == 0:
__lowerCAmelCase = timesteps.astype(dtype=jnp.floataa )
__lowerCAmelCase = jnp.expand_dims(__a , 0 )
__lowerCAmelCase = self.time_proj(__a )
__lowerCAmelCase = self.time_embedding(__a )
# 2. pre-process
__lowerCAmelCase = jnp.transpose(__a , (0, 2, 3, 1) )
__lowerCAmelCase = self.conv_in(__a )
__lowerCAmelCase = jnp.transpose(__a , (0, 2, 3, 1) )
__lowerCAmelCase = self.controlnet_cond_embedding(__a )
sample += controlnet_cond
# 3. down
__lowerCAmelCase = (sample,)
for down_block in self.down_blocks:
if isinstance(__a , __a ):
__lowerCAmelCase = down_block(__a , __a , __a , deterministic=not train )
else:
__lowerCAmelCase = down_block(__a , __a , deterministic=not train )
down_block_res_samples += res_samples
# 4. mid
__lowerCAmelCase = self.mid_block(__a , __a , __a , deterministic=not train )
# 5. contronet blocks
__lowerCAmelCase = ()
for down_block_res_sample, controlnet_block in zip(__a , self.controlnet_down_blocks ):
__lowerCAmelCase = controlnet_block(__a )
controlnet_down_block_res_samples += (down_block_res_sample,)
__lowerCAmelCase = controlnet_down_block_res_samples
__lowerCAmelCase = self.controlnet_mid_block(__a )
# 6. scaling
__lowerCAmelCase = [sample * conditioning_scale for sample in down_block_res_samples]
mid_block_res_sample *= conditioning_scale
if not return_dict:
return (down_block_res_samples, mid_block_res_sample)
return FlaxControlNetOutput(
down_block_res_samples=__a , mid_block_res_sample=__a )
| 636 |
"""simple docstring"""
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def lowerCAmelCase ( __UpperCamelCase = "isbn/0140328726" ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = olid.strip().strip("""/""" ) # Remove leading/trailing whitespace & slashes
if new_olid.count("""/""" ) != 1:
UpperCAmelCase__ : Dict = F"{olid} is not a valid Open Library olid"
raise ValueError(__UpperCamelCase )
return requests.get(F"https://openlibrary.org/{new_olid}.json" ).json()
def lowerCAmelCase ( __UpperCamelCase ):
'''simple docstring'''
UpperCAmelCase__ : Any = {
"""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__ : Dict = {better_key: ol_book_data[key] for key, better_key in desired_keys.items()}
UpperCAmelCase__ : str = [
get_openlibrary_data(author["""key"""] )["""name"""] for author in data["""Authors"""]
]
UpperCAmelCase__ : Dict = data["""First sentence"""]["""value"""]
for key, value in data.items():
if isinstance(__UpperCamelCase , __UpperCamelCase ):
UpperCAmelCase__ : Dict = """, """.join(__UpperCamelCase )
return data
if __name__ == "__main__":
import doctest
doctest.testmod()
while True:
__UpperCAmelCase = 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:
__UpperCAmelCase = 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}.")
| 65 | 0 |
"""simple docstring"""
def __A (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->int:
"""simple docstring"""
return int((input_a, input_a).count(0 ) != 0 )
def __A () ->str:
"""simple docstring"""
assert nand_gate(0 , 0 ) == 1
assert nand_gate(0 , 1 ) == 1
assert nand_gate(1 , 0 ) == 1
assert nand_gate(1 , 1 ) == 0
if __name__ == "__main__":
print(nand_gate(0, 0))
print(nand_gate(0, 1))
print(nand_gate(1, 0))
print(nand_gate(1, 1))
| 93 |
"""simple docstring"""
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from transformers import BartTokenizer, RagTokenizer, TaTokenizer
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=True , __UpperCamelCase="pt" ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = {"""add_prefix_space""": True} if isinstance(__UpperCamelCase , __UpperCamelCase ) and not line.startswith(""" """ ) else {}
UpperCAmelCase__ : List[str] = padding_side
return tokenizer(
[line] , max_length=__UpperCamelCase , padding="""max_length""" if pad_to_max_length else None , truncation=__UpperCamelCase , return_tensors=__UpperCamelCase , add_special_tokens=__UpperCamelCase , **__UpperCamelCase , )
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=None , ):
'''simple docstring'''
UpperCAmelCase__ : str = input_ids.ne(__UpperCamelCase ).any(dim=0 )
if attention_mask is None:
return input_ids[:, keep_column_mask]
else:
return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
class __lowercase ( __lowerCamelCase ):
def __init__( self : Tuple ,A : List[Any] ,A : Union[str, Any] ,A : Any ,A : Optional[int] ,A : Union[str, Any]="train" ,A : Tuple=None ,A : Union[str, Any]=None ,A : Tuple=None ,A : int="" ,):
'''simple docstring'''
super().__init__()
UpperCAmelCase__ : Optional[Any] = Path(A ).joinpath(type_path + """.source""" )
UpperCAmelCase__ : List[str] = Path(A ).joinpath(type_path + """.target""" )
UpperCAmelCase__ : Dict = self.get_char_lens(self.src_file )
UpperCAmelCase__ : int = max_source_length
UpperCAmelCase__ : List[str] = max_target_length
assert min(self.src_lens ) > 0, f"found empty line in {self.src_file}"
UpperCAmelCase__ : Dict = tokenizer
UpperCAmelCase__ : str = prefix
if n_obs is not None:
UpperCAmelCase__ : int = self.src_lens[:n_obs]
UpperCAmelCase__ : Any = src_lang
UpperCAmelCase__ : Any = tgt_lang
def __len__( self : Optional[Any] ):
'''simple docstring'''
return len(self.src_lens )
def __getitem__( self : Union[str, Any] ,A : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = index + 1 # linecache starts at 1
UpperCAmelCase__ : Tuple = self.prefix + linecache.getline(str(self.src_file ) ,A ).rstrip("""\n""" )
UpperCAmelCase__ : Dict = linecache.getline(str(self.tgt_file ) ,A ).rstrip("""\n""" )
assert source_line, f"empty source line for index {index}"
assert tgt_line, f"empty tgt line for index {index}"
# Need to add eos token manually for T5
if isinstance(self.tokenizer ,A ):
source_line += self.tokenizer.eos_token
tgt_line += self.tokenizer.eos_token
# Pad source and target to the right
UpperCAmelCase__ : str = (
self.tokenizer.question_encoder if isinstance(self.tokenizer ,A ) else self.tokenizer
)
UpperCAmelCase__ : Tuple = self.tokenizer.generator if isinstance(self.tokenizer ,A ) else self.tokenizer
UpperCAmelCase__ : Tuple = encode_line(A ,A ,self.max_source_length ,"""right""" )
UpperCAmelCase__ : Dict = encode_line(A ,A ,self.max_target_length ,"""right""" )
UpperCAmelCase__ : Optional[Any] = source_inputs["""input_ids"""].squeeze()
UpperCAmelCase__ : List[str] = target_inputs["""input_ids"""].squeeze()
UpperCAmelCase__ : Union[str, Any] = source_inputs["""attention_mask"""].squeeze()
return {
"input_ids": source_ids,
"attention_mask": src_mask,
"decoder_input_ids": target_ids,
}
@staticmethod
def __lowercase ( A : int ):
'''simple docstring'''
return [len(A ) for x in Path(A ).open().readlines()]
def __lowercase ( self : List[Any] ,A : Any ):
'''simple docstring'''
UpperCAmelCase__ : int = torch.stack([x["""input_ids"""] for x in batch] )
UpperCAmelCase__ : Union[str, Any] = torch.stack([x["""attention_mask"""] for x in batch] )
UpperCAmelCase__ : Any = torch.stack([x["""decoder_input_ids"""] for x in batch] )
UpperCAmelCase__ : List[Any] = (
self.tokenizer.generator.pad_token_id
if isinstance(self.tokenizer ,A )
else self.tokenizer.pad_token_id
)
UpperCAmelCase__ : Any = (
self.tokenizer.question_encoder.pad_token_id
if isinstance(self.tokenizer ,A )
else self.tokenizer.pad_token_id
)
UpperCAmelCase__ : str = trim_batch(A ,A )
UpperCAmelCase__ , UpperCAmelCase__ : Union[str, Any] = trim_batch(A ,A ,attention_mask=A )
UpperCAmelCase__ : List[str] = {
"""input_ids""": source_ids,
"""attention_mask""": source_mask,
"""decoder_input_ids""": y,
}
return batch
__UpperCAmelCase = getLogger(__name__)
def lowerCAmelCase ( __UpperCamelCase ):
'''simple docstring'''
return list(itertools.chain.from_iterable(__UpperCamelCase ) )
def lowerCAmelCase ( __UpperCamelCase ):
'''simple docstring'''
UpperCAmelCase__ : Dict = get_git_info()
save_json(__UpperCamelCase , os.path.join(__UpperCamelCase , """git_log.json""" ) )
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=4 , **__UpperCamelCase ):
'''simple docstring'''
with open(__UpperCamelCase , """w""" ) as f:
json.dump(__UpperCamelCase , __UpperCamelCase , indent=__UpperCamelCase , **__UpperCamelCase )
def lowerCAmelCase ( __UpperCamelCase ):
'''simple docstring'''
with open(__UpperCamelCase ) as f:
return json.load(__UpperCamelCase )
def lowerCAmelCase ( ):
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = git.Repo(search_parent_directories=__UpperCamelCase )
UpperCAmelCase__ : List[str] = {
"""repo_id""": str(__UpperCamelCase ),
"""repo_sha""": str(repo.head.object.hexsha ),
"""repo_branch""": str(repo.active_branch ),
"""hostname""": str(socket.gethostname() ),
}
return repo_infos
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase ):
'''simple docstring'''
return list(map(__UpperCamelCase , __UpperCamelCase ) )
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase ):
'''simple docstring'''
with open(__UpperCamelCase , """wb""" ) as f:
return pickle.dump(__UpperCamelCase , __UpperCamelCase )
def lowerCAmelCase ( __UpperCamelCase ):
'''simple docstring'''
def remove_articles(__UpperCamelCase ):
return re.sub(r"""\b(a|an|the)\b""" , """ """ , __UpperCamelCase )
def white_space_fix(__UpperCamelCase ):
return " ".join(text.split() )
def remove_punc(__UpperCamelCase ):
UpperCAmelCase__ : List[Any] = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(__UpperCamelCase ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(__UpperCamelCase ) ) ) )
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase ):
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = normalize_answer(__UpperCamelCase ).split()
UpperCAmelCase__ : Dict = normalize_answer(__UpperCamelCase ).split()
UpperCAmelCase__ : int = Counter(__UpperCamelCase ) & Counter(__UpperCamelCase )
UpperCAmelCase__ : List[str] = sum(common.values() )
if num_same == 0:
return 0
UpperCAmelCase__ : str = 1.0 * num_same / len(__UpperCamelCase )
UpperCAmelCase__ : Optional[int] = 1.0 * num_same / len(__UpperCamelCase )
UpperCAmelCase__ : Tuple = (2 * precision * recall) / (precision + recall)
return fa
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase ):
'''simple docstring'''
return normalize_answer(__UpperCamelCase ) == normalize_answer(__UpperCamelCase )
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase ):
'''simple docstring'''
assert len(__UpperCamelCase ) == len(__UpperCamelCase )
UpperCAmelCase__ : Union[str, Any] = 0
for hypo, pred in zip(__UpperCamelCase , __UpperCamelCase ):
em += exact_match_score(__UpperCamelCase , __UpperCamelCase )
if len(__UpperCamelCase ) > 0:
em /= len(__UpperCamelCase )
return {"em": em}
def lowerCAmelCase ( __UpperCamelCase ):
'''simple docstring'''
return model_prefix.startswith("""rag""" )
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = {p: p for p in extra_params}
# T5 models don't have `dropout` param, they have `dropout_rate` instead
UpperCAmelCase__ : str = """dropout_rate"""
for p in extra_params:
if getattr(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
if not hasattr(__UpperCamelCase , __UpperCamelCase ) and not hasattr(__UpperCamelCase , equivalent_param[p] ):
logger.info("""config doesn't have a `{}` attribute""".format(__UpperCamelCase ) )
delattr(__UpperCamelCase , __UpperCamelCase )
continue
UpperCAmelCase__ : Tuple = p if hasattr(__UpperCamelCase , __UpperCamelCase ) else equivalent_param[p]
setattr(__UpperCamelCase , __UpperCamelCase , getattr(__UpperCamelCase , __UpperCamelCase ) )
delattr(__UpperCamelCase , __UpperCamelCase )
return hparams, config
| 65 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Generator
def _UpperCamelCase ( ) -> Union[str, Any]:
lowerCamelCase_ = {}
lowerCamelCase_ = 2
while True:
lowerCamelCase_ = factor_map.pop(__UpperCamelCase ,__UpperCamelCase )
if factor:
lowerCamelCase_ = factor + prime
while x in factor_map:
x += factor
lowerCamelCase_ = factor
else:
lowerCamelCase_ = prime
yield prime
prime += 1
def _UpperCamelCase ( __UpperCamelCase = 1e10 ) -> Dict:
lowerCamelCase_ = sieve()
lowerCamelCase_ = 1
while True:
lowerCamelCase_ = next(__UpperCamelCase )
if (2 * prime * n) > limit:
return n
# Ignore the next prime as the reminder will be 2.
next(__UpperCamelCase )
n += 2
if __name__ == "__main__":
print(solution())
| 42 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetPipeline,
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 __lowercase ( __lowerCamelCase , unittest.TestCase ):
snake_case_ = KandinskyVaaControlnetPipeline
snake_case_ = ["""image_embeds""", """negative_image_embeds""", """hint"""]
snake_case_ = ["""image_embeds""", """negative_image_embeds""", """hint"""]
snake_case_ = [
"""generator""",
"""height""",
"""width""",
"""latents""",
"""guidance_scale""",
"""num_inference_steps""",
"""return_dict""",
"""guidance_scale""",
"""num_images_per_prompt""",
"""output_type""",
"""return_dict""",
]
snake_case_ = False
@property
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
return 32
@property
def __lowercase ( self : int ):
'''simple docstring'''
return 32
@property
def __lowercase ( self : Dict ):
'''simple docstring'''
return self.time_input_dim
@property
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
return self.time_input_dim * 4
@property
def __lowercase ( self : Any ):
'''simple docstring'''
return 100
@property
def __lowercase ( self : Any ):
'''simple docstring'''
torch.manual_seed(0 )
UpperCAmelCase__ : Tuple = {
"""in_channels""": 8,
# Out channels is double in channels because predicts mean and variance
"""out_channels""": 8,
"""addition_embed_type""": """image_hint""",
"""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,
}
UpperCAmelCase__ : int = UNetaDConditionModel(**A )
return model
@property
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
return {
"block_out_channels": [32, 32, 64, 64],
"down_block_types": [
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"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", "UpDecoderBlock2D", "UpDecoderBlock2D"],
"vq_embed_dim": 4,
}
@property
def __lowercase ( self : Dict ):
'''simple docstring'''
torch.manual_seed(0 )
UpperCAmelCase__ : str = VQModel(**self.dummy_movq_kwargs )
return model
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ : str = self.dummy_unet
UpperCAmelCase__ : List[Any] = self.dummy_movq
UpperCAmelCase__ : List[Any] = DDIMScheduler(
num_train_timesteps=1_000 ,beta_schedule="""linear""" ,beta_start=0.0_0_0_8_5 ,beta_end=0.0_1_2 ,clip_sample=A ,set_alpha_to_one=A ,steps_offset=1 ,prediction_type="""epsilon""" ,thresholding=A ,)
UpperCAmelCase__ : Optional[Any] = {
"""unet""": unet,
"""scheduler""": scheduler,
"""movq""": movq,
}
return components
def __lowercase ( self : str ,A : Optional[Any] ,A : Any=0 ):
'''simple docstring'''
UpperCAmelCase__ : str = floats_tensor((1, self.text_embedder_hidden_size) ,rng=random.Random(A ) ).to(A )
UpperCAmelCase__ : Union[str, Any] = floats_tensor((1, self.text_embedder_hidden_size) ,rng=random.Random(seed + 1 ) ).to(
A )
# create hint
UpperCAmelCase__ : int = floats_tensor((1, 3, 64, 64) ,rng=random.Random(A ) ).to(A )
if str(A ).startswith("""mps""" ):
UpperCAmelCase__ : Optional[int] = torch.manual_seed(A )
else:
UpperCAmelCase__ : Dict = torch.Generator(device=A ).manual_seed(A )
UpperCAmelCase__ : Dict = {
"""image_embeds""": image_embeds,
"""negative_image_embeds""": negative_image_embeds,
"""hint""": hint,
"""generator""": generator,
"""height""": 64,
"""width""": 64,
"""guidance_scale""": 4.0,
"""num_inference_steps""": 2,
"""output_type""": """np""",
}
return inputs
def __lowercase ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase__ : Dict = """cpu"""
UpperCAmelCase__ : List[Any] = self.get_dummy_components()
UpperCAmelCase__ : Union[str, Any] = self.pipeline_class(**A )
UpperCAmelCase__ : Optional[int] = pipe.to(A )
pipe.set_progress_bar_config(disable=A )
UpperCAmelCase__ : Optional[int] = pipe(**self.get_dummy_inputs(A ) )
UpperCAmelCase__ : Tuple = output.images
UpperCAmelCase__ : Dict = pipe(
**self.get_dummy_inputs(A ) ,return_dict=A ,)[0]
UpperCAmelCase__ : Tuple = image[0, -3:, -3:, -1]
UpperCAmelCase__ : Dict = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
UpperCAmelCase__ : Optional[int] = np.array(
[0.6_9_5_9_8_2_6, 0.8_6_8_2_7_9, 0.7_5_5_8_0_9_2, 0.6_8_7_6_9_4_6_7, 0.8_5_8_0_5_8_0_4, 0.6_5_9_7_7_4_9_6, 0.4_4_8_8_5_3_0_2, 0.5_9_5_9_1_1_1, 0.4_2_5_1_5_9_5] )
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 __lowercase ( unittest.TestCase ):
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __lowercase ( self : int ):
'''simple docstring'''
UpperCAmelCase__ : int = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy""" )
UpperCAmelCase__ : int = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/kandinskyv22/hint_image_cat.png""" )
UpperCAmelCase__ : int = torch.from_numpy(np.array(A ) ).float() / 2_5_5.0
UpperCAmelCase__ : Union[str, Any] = hint.permute(2 ,0 ,1 ).unsqueeze(0 )
UpperCAmelCase__ : List[str] = KandinskyVaaPriorPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-2-prior""" ,torch_dtype=torch.floataa )
pipe_prior.to(A )
UpperCAmelCase__ : List[Any] = KandinskyVaaControlnetPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-2-controlnet-depth""" ,torch_dtype=torch.floataa )
UpperCAmelCase__ : int = pipeline.to(A )
pipeline.set_progress_bar_config(disable=A )
UpperCAmelCase__ : Optional[Any] = """A robot, 4k photo"""
UpperCAmelCase__ : List[Any] = torch.Generator(device="""cuda""" ).manual_seed(0 )
UpperCAmelCase__ , UpperCAmelCase__ : Tuple = pipe_prior(
A ,generator=A ,num_inference_steps=5 ,negative_prompt="""""" ,).to_tuple()
UpperCAmelCase__ : List[str] = torch.Generator(device="""cuda""" ).manual_seed(0 )
UpperCAmelCase__ : int = pipeline(
image_embeds=A ,negative_image_embeds=A ,hint=A ,generator=A ,num_inference_steps=100 ,output_type="""np""" ,)
UpperCAmelCase__ : Any = output.images[0]
assert image.shape == (512, 512, 3)
assert_mean_pixel_difference(A ,A )
| 65 | 0 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
SCREAMING_SNAKE_CASE__ = {'''configuration_gpt_neox''': ['''GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoXConfig''']}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = ['''GPTNeoXTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = [
'''GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''GPTNeoXForCausalLM''',
'''GPTNeoXForQuestionAnswering''',
'''GPTNeoXForSequenceClassification''',
'''GPTNeoXForTokenClassification''',
'''GPTNeoXLayer''',
'''GPTNeoXModel''',
'''GPTNeoXPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_neox import (
GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTNeoXForCausalLM,
GPTNeoXForQuestionAnswering,
GPTNeoXForSequenceClassification,
GPTNeoXForTokenClassification,
GPTNeoXLayer,
GPTNeoXModel,
GPTNeoXPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE__ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 9 |
"""simple docstring"""
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHECKING:
from ... import PreTrainedTokenizerBase, TensorType
__UpperCAmelCase = logging.get_logger(__name__)
class __lowercase ( __lowerCamelCase ):
snake_case_ = """vision-encoder-decoder"""
snake_case_ = True
def __init__( self : List[Any] ,**A : Union[str, Any] ):
'''simple docstring'''
super().__init__(**A )
if "encoder" not in kwargs or "decoder" not in kwargs:
raise ValueError(
f"A configuraton of type {self.model_type} cannot be instantiated because "
f"not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}" )
UpperCAmelCase__ : int = kwargs.pop("""encoder""" )
UpperCAmelCase__ : int = encoder_config.pop("""model_type""" )
UpperCAmelCase__ : str = kwargs.pop("""decoder""" )
UpperCAmelCase__ : Dict = decoder_config.pop("""model_type""" )
UpperCAmelCase__ : List[Any] = AutoConfig.for_model(A ,**A )
UpperCAmelCase__ : Any = AutoConfig.for_model(A ,**A )
UpperCAmelCase__ : Union[str, Any] = True
@classmethod
def __lowercase ( cls : List[Any] ,A : PretrainedConfig ,A : PretrainedConfig ,**A : Tuple ):
'''simple docstring'''
logger.info("""Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config""" )
UpperCAmelCase__ : Union[str, Any] = True
UpperCAmelCase__ : List[Any] = True
return cls(encoder=encoder_config.to_dict() ,decoder=decoder_config.to_dict() ,**A )
def __lowercase ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = copy.deepcopy(self.__dict__ )
UpperCAmelCase__ : Dict = self.encoder.to_dict()
UpperCAmelCase__ : Any = self.decoder.to_dict()
UpperCAmelCase__ : Dict = self.__class__.model_type
return output
class __lowercase ( __lowerCamelCase ):
snake_case_ = version.parse("""1.11""" )
@property
def __lowercase ( self : Optional[int] ):
'''simple docstring'''
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def __lowercase ( self : List[Any] ):
'''simple docstring'''
return 1e-4
@property
def __lowercase ( self : List[Any] ):
'''simple docstring'''
return OrderedDict({"""last_hidden_state""": {0: """batch""", 1: """encoder_sequence"""}} )
class __lowercase ( __lowerCamelCase ):
@property
def __lowercase ( self : Any ):
'''simple docstring'''
UpperCAmelCase__ : int = OrderedDict()
UpperCAmelCase__ : Dict = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
UpperCAmelCase__ : Optional[Any] = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
UpperCAmelCase__ : List[str] = {0: """batch""", 1: """encoder_sequence"""}
return common_inputs
def __lowercase ( self : Dict ,A : "PreTrainedTokenizerBase" ,A : int = -1 ,A : int = -1 ,A : bool = False ,A : Optional["TensorType"] = None ,):
'''simple docstring'''
import torch
UpperCAmelCase__ : int = OrderedDict()
UpperCAmelCase__ : List[Any] = super().generate_dummy_inputs(
A ,batch_size=A ,seq_length=A ,is_pair=A ,framework=A )
UpperCAmelCase__ , UpperCAmelCase__ : int = dummy_input["""input_ids"""].shape
UpperCAmelCase__ : int = (batch, encoder_sequence, self._config.encoder_hidden_size)
UpperCAmelCase__ : Tuple = dummy_input.pop("""input_ids""" )
UpperCAmelCase__ : Optional[int] = dummy_input.pop("""attention_mask""" )
UpperCAmelCase__ : Dict = torch.zeros(A )
return common_inputs
class __lowercase ( __lowerCamelCase ):
@property
def __lowercase ( self : str ):
'''simple docstring'''
pass
def __lowercase ( self : Any ,A : PretrainedConfig ):
'''simple docstring'''
return VisionEncoderDecoderEncoderOnnxConfig(A )
def __lowercase ( self : Dict ,A : PretrainedConfig ,A : PretrainedConfig ,A : str = "default" ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = encoder_config.hidden_size
return VisionEncoderDecoderDecoderOnnxConfig(A ,A )
| 65 | 0 |
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
a_ :Any = get_logger(__name__)
a_ :Optional[Any] = Path(__file__).parent / "model_card_template.md"
a_ :List[str] = uuida().hex
a_ :str = os.getenv("HF_HUB_OFFLINE", "").upper() in ENV_VARS_TRUE_VALUES
a_ :Tuple = os.getenv("DISABLE_TELEMETRY", "").upper() in ENV_VARS_TRUE_VALUES
a_ :List[Any] = HUGGINGFACE_CO_RESOLVE_ENDPOINT + "/api/telemetry/"
def lowercase_ (A : Tuple = None ):
snake_case__ : List[Any] = 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(__UpperCamelCase , __UpperCamelCase ):
ua += "; " + "; ".join(F'''{k}/{v}''' for k, v in user_agent.items() )
elif isinstance(__UpperCamelCase , __UpperCamelCase ):
ua += "; " + user_agent
return ua
def lowercase_ (A : int , A : Tuple = None , A : List[Any] = None ):
if token is None:
snake_case__ : List[str] = HfFolder.get_token()
if organization is None:
snake_case__ : Optional[Any] = whoami(__UpperCamelCase )["""name"""]
return F'''{username}/{model_id}'''
else:
return F'''{organization}/{model_id}'''
def lowercase_ (A : Dict , A : List[Any] ):
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(__UpperCamelCase , 'local_rank' ) and args.local_rank not in [-1, 0]:
return
snake_case__ : str = args.hub_token if hasattr(__UpperCamelCase , 'hub_token' ) else None
snake_case__ : int = get_full_repo_name(__UpperCamelCase , token=__UpperCamelCase )
snake_case__ : Union[str, Any] = 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=__UpperCamelCase , model_name=__UpperCamelCase , repo_name=__UpperCamelCase , dataset_name=args.dataset_name if hasattr(__UpperCamelCase , '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(__UpperCamelCase , 'gradient_accumulation_steps' ) else None
) , adam_betaa=args.adam_betaa if hasattr(__UpperCamelCase , 'adam_beta1' ) else None , adam_betaa=args.adam_betaa if hasattr(__UpperCamelCase , 'adam_beta2' ) else None , adam_weight_decay=args.adam_weight_decay if hasattr(__UpperCamelCase , 'adam_weight_decay' ) else None , adam_epsilon=args.adam_epsilon if hasattr(__UpperCamelCase , 'adam_epsilon' ) else None , lr_scheduler=args.lr_scheduler if hasattr(__UpperCamelCase , 'lr_scheduler' ) else None , lr_warmup_steps=args.lr_warmup_steps if hasattr(__UpperCamelCase , 'lr_warmup_steps' ) else None , ema_inv_gamma=args.ema_inv_gamma if hasattr(__UpperCamelCase , 'ema_inv_gamma' ) else None , ema_power=args.ema_power if hasattr(__UpperCamelCase , 'ema_power' ) else None , ema_max_decay=args.ema_max_decay if hasattr(__UpperCamelCase , 'ema_max_decay' ) else None , mixed_precision=args.mixed_precision , )
snake_case__ : List[str] = os.path.join(args.output_dir , 'README.md' )
model_card.save(__UpperCamelCase )
def lowercase_ (A : Dict , A : Optional[Any] = None ):
if resolved_file is None or commit_hash is not None:
return commit_hash
snake_case__ : Any = str(Path(__UpperCamelCase ).as_posix() )
snake_case__ : Union[str, Any] = re.search(r'snapshots/([^/]+)/' , __UpperCamelCase )
if search is None:
return None
snake_case__ : Optional[Any] = search.groups()[0]
return commit_hash if REGEX_COMMIT_HASH.match(__UpperCamelCase ) 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.
a_ :str = os.path.expanduser(
os.getenv("HF_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "huggingface"))
)
a_ :Optional[Any] = os.path.join(hf_cache_home, "diffusers")
def lowercase_ (A : Tuple = None , A : Optional[Any] = None ):
if new_cache_dir is None:
snake_case__ : Dict = DIFFUSERS_CACHE
if old_cache_dir is None:
snake_case__ : Tuple = old_diffusers_cache
snake_case__ : List[Any] = Path(__UpperCamelCase ).expanduser()
snake_case__ : Optional[int] = Path(__UpperCamelCase ).expanduser()
for old_blob_path in old_cache_dir.glob('**/blobs/*' ):
if old_blob_path.is_file() and not old_blob_path.is_symlink():
snake_case__ : Optional[Any] = new_cache_dir / old_blob_path.relative_to(__UpperCamelCase )
new_blob_path.parent.mkdir(parents=__UpperCamelCase , exist_ok=__UpperCamelCase )
os.replace(__UpperCamelCase , __UpperCamelCase )
try:
os.symlink(__UpperCamelCase , __UpperCamelCase )
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).
a_ :List[str] = os.path.join(DIFFUSERS_CACHE, "version_diffusers_cache.txt")
if not os.path.isfile(cache_version_file):
a_ :List[str] = 0
else:
with open(cache_version_file) as f:
try:
a_ :Tuple = int(f.read())
except ValueError:
a_ :str = 0
if cache_version < 1:
a_ :Tuple = 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:
a_ :List[Any] = "\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 lowercase_ (A : List[Any] , A : List[Any] = None ):
if variant is not None:
snake_case__ : int = weights_name.split('.' )
snake_case__ : List[str] = splits[:-1] + [variant] + splits[-1:]
snake_case__ : List[Any] = """.""".join(__UpperCamelCase )
return weights_name
def lowercase_ (A : Dict , *,
A : Any , A : Optional[Any] , A : List[str] , A : Optional[Any] , A : List[Any] , A : Tuple , A : str , A : Tuple , A : Optional[int] , A : Union[str, Any] , A : str=None , ):
snake_case__ : Tuple = str(__UpperCamelCase )
if os.path.isfile(__UpperCamelCase ):
return pretrained_model_name_or_path
elif os.path.isdir(__UpperCamelCase ):
if os.path.isfile(os.path.join(__UpperCamelCase , __UpperCamelCase ) ):
# Load from a PyTorch checkpoint
snake_case__ : Tuple = os.path.join(__UpperCamelCase , __UpperCamelCase )
return model_file
elif subfolder is not None and os.path.isfile(
os.path.join(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase ) ):
snake_case__ : Optional[int] = os.path.join(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
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(__UpperCamelCase ).base_version ) >= version.parse('0.20.0' )
):
try:
snake_case__ : List[str] = hf_hub_download(
__UpperCamelCase , filename=_add_variant(__UpperCamelCase , __UpperCamelCase ) , cache_dir=__UpperCamelCase , force_download=__UpperCamelCase , proxies=__UpperCamelCase , resume_download=__UpperCamelCase , local_files_only=__UpperCamelCase , use_auth_token=__UpperCamelCase , user_agent=__UpperCamelCase , subfolder=__UpperCamelCase , 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.''' , __UpperCamelCase , )
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(__UpperCamelCase , __UpperCamelCase )} 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(__UpperCamelCase , __UpperCamelCase )}\' so that the correct variant file can be added.''' , __UpperCamelCase , )
try:
# 2. Load model file as usual
snake_case__ : List[Any] = hf_hub_download(
__UpperCamelCase , filename=__UpperCamelCase , cache_dir=__UpperCamelCase , force_download=__UpperCamelCase , proxies=__UpperCamelCase , resume_download=__UpperCamelCase , local_files_only=__UpperCamelCase , use_auth_token=__UpperCamelCase , user_agent=__UpperCamelCase , subfolder=__UpperCamelCase , 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}''' )
| 478 |
"""simple docstring"""
import requests
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = {"""Content-Type""": """application/json"""}
UpperCAmelCase__ : Optional[Any] = requests.post(__UpperCamelCase , json={"""text""": message_body} , headers=__UpperCamelCase )
if response.status_code != 200:
UpperCAmelCase__ : Any = (
"""Request to slack returned an error """
F"{response.status_code}, the response is:\n{response.text}"
)
raise ValueError(__UpperCamelCase )
if __name__ == "__main__":
# Set the slack url to the one provided by Slack when you create the webhook at
# https://my.slack.com/services/new/incoming-webhook/
send_slack_message('<YOUR MESSAGE BODY>', '<SLACK CHANNEL URL>')
| 65 | 0 |
import random
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase ) -> List[Any]:
snake_case__ = [], [], []
for element in data:
if element < pivot:
less.append(__UpperCamelCase )
elif element > pivot:
greater.append(__UpperCamelCase )
else:
equal.append(__UpperCamelCase )
return less, equal, greater
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase ) -> Optional[Any]:
if index >= len(__UpperCamelCase ) or index < 0:
return None
snake_case__ = items[random.randint(0 , len(__UpperCamelCase ) - 1 )]
snake_case__ = 0
snake_case__ = _partition(__UpperCamelCase , __UpperCamelCase )
snake_case__ = len(__UpperCamelCase )
snake_case__ = len(__UpperCamelCase )
# index is the pivot
if m <= index < m + count:
return pivot
# must be in smaller
elif m > index:
return quick_select(__UpperCamelCase , __UpperCamelCase )
# must be in larger
else:
return quick_select(__UpperCamelCase , index - (m + count) )
| 33 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class __lowercase ( __lowerCamelCase , unittest.TestCase ):
snake_case_ = CTRLTokenizer
snake_case_ = False
snake_case_ = False
def __lowercase ( self : List[str] ):
'''simple docstring'''
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
UpperCAmelCase__ : List[Any] = ["""adapt""", """re@@""", """a@@""", """apt""", """c@@""", """t""", """<unk>"""]
UpperCAmelCase__ : Optional[int] = dict(zip(A ,range(len(A ) ) ) )
UpperCAmelCase__ : List[Any] = ["""#version: 0.2""", """a p""", """ap t</w>""", """r e""", """a d""", """ad apt</w>""", """"""]
UpperCAmelCase__ : int = {"""unk_token""": """<unk>"""}
UpperCAmelCase__ : Optional[Any] = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["""vocab_file"""] )
UpperCAmelCase__ : str = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["""merges_file"""] )
with open(self.vocab_file ,"""w""" ,encoding="""utf-8""" ) as fp:
fp.write(json.dumps(A ) + """\n""" )
with open(self.merges_file ,"""w""" ,encoding="""utf-8""" ) as fp:
fp.write("""\n""".join(A ) )
def __lowercase ( self : int ,**A : Dict ):
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return CTRLTokenizer.from_pretrained(self.tmpdirname ,**A )
def __lowercase ( self : List[Any] ,A : Any ):
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = """adapt react readapt apt"""
UpperCAmelCase__ : Any = """adapt react readapt apt"""
return input_text, output_text
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = CTRLTokenizer(self.vocab_file ,self.merges_file ,**self.special_tokens_map )
UpperCAmelCase__ : Tuple = """adapt react readapt apt"""
UpperCAmelCase__ : Optional[int] = """adapt re@@ a@@ c@@ t re@@ adapt apt""".split()
UpperCAmelCase__ : Dict = tokenizer.tokenize(A )
self.assertListEqual(A ,A )
UpperCAmelCase__ : Any = tokens + [tokenizer.unk_token]
UpperCAmelCase__ : Dict = [0, 1, 2, 4, 5, 1, 0, 3, 6]
self.assertListEqual(tokenizer.convert_tokens_to_ids(A ) ,A )
| 65 | 0 |
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
snake_case : Dict = '\\n\n'
snake_case : List[str] = '\nPerplexity (PPL) is one of the most common metrics for evaluating language models.\nIt is defined as the exponentiated average negative log-likelihood of a sequence.\n\nFor more information, see https://huggingface.co/docs/transformers/perplexity\n'
snake_case : str = '\nArgs:\n model_id (str): model used for calculating Perplexity\n NOTE: Perplexity can only be calculated for causal language models.\n This includes models such as gpt2, causal variations of bert,\n causal versions of t5, and more (the full list can be found\n in the AutoModelForCausalLM documentation here:\n https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )\n\n input_texts (list of str): input text, each separate text snippet\n is one list entry.\n batch_size (int): the batch size to run texts through the model. Defaults to 16.\n add_start_token (bool): whether to add the start token to the texts,\n so the perplexity can include the probability of the first word. Defaults to True.\n device (str): device to run on, defaults to \'cuda\' when available\nReturns:\n perplexity: dictionary containing the perplexity scores for the texts\n in the input list, as well as the mean perplexity. If one of the input texts is\n longer than the max input length of the model, then it is truncated to the\n max length for the perplexity computation.\nExamples:\n Example 1:\n >>> perplexity = datasets.load_metric("perplexity")\n >>> input_texts = ["lorem ipsum", "Happy Birthday!", "Bienvenue"]\n >>> results = perplexity.compute(model_id=\'gpt2\',\n ... add_start_token=False,\n ... input_texts=input_texts) # doctest:+ELLIPSIS\n >>> print(list(results.keys()))\n [\'perplexities\', \'mean_perplexity\']\n >>> print(round(results["mean_perplexity"], 2))\n 78.22\n >>> print(round(results["perplexities"][0], 2))\n 11.11\n\n Example 2:\n >>> perplexity = datasets.load_metric("perplexity")\n >>> input_texts = datasets.load_dataset("wikitext",\n ... "wikitext-2-raw-v1",\n ... split="test")["text"][:50] # doctest:+ELLIPSIS\n [...]\n >>> input_texts = [s for s in input_texts if s!=\'\']\n >>> results = perplexity.compute(model_id=\'gpt2\',\n ... input_texts=input_texts) # doctest:+ELLIPSIS\n >>> print(list(results.keys()))\n [\'perplexities\', \'mean_perplexity\']\n >>> print(round(results["mean_perplexity"], 2))\n 60.35\n >>> print(round(results["perplexities"][0], 2))\n 81.12\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __lowercase ( datasets.Metric ):
"""simple docstring"""
def __magic_name__ ( self )-> Tuple:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'input_texts': datasets.Value('string' ),
} ) , reference_urls=['https://huggingface.co/docs/transformers/perplexity'] , )
def __magic_name__ ( self , A_ , A_ , A_ = 16 , A_ = True , A_=None )-> Tuple:
if device is not None:
assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
if device == "gpu":
_SCREAMING_SNAKE_CASE = """cuda"""
else:
_SCREAMING_SNAKE_CASE = """cuda""" if torch.cuda.is_available() else """cpu"""
_SCREAMING_SNAKE_CASE = AutoModelForCausalLM.from_pretrained(A_ )
_SCREAMING_SNAKE_CASE = model.to(A_ )
_SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained(A_ )
# if batch_size > 1 (which generally leads to padding being required), and
# if there is not an already assigned pad_token, assign an existing
# special token to also be the padding token
if tokenizer.pad_token is None and batch_size > 1:
_SCREAMING_SNAKE_CASE = list(tokenizer.special_tokens_map_extended.values() )
# check that the model already has at least one special token defined
assert (
len(A_ ) > 0
), "If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1."
# assign one of the special tokens to also be the pad token
tokenizer.add_special_tokens({'pad_token': existing_special_tokens[0]} )
if add_start_token:
# leave room for <BOS> token to be added:
assert (
tokenizer.bos_token is not None
), "Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False"
_SCREAMING_SNAKE_CASE = model.config.max_length - 1
else:
_SCREAMING_SNAKE_CASE = model.config.max_length
_SCREAMING_SNAKE_CASE = tokenizer(
A_ , add_special_tokens=A_ , padding=A_ , truncation=A_ , max_length=A_ , return_tensors='pt' , return_attention_mask=A_ , ).to(A_ )
_SCREAMING_SNAKE_CASE = encodings["""input_ids"""]
_SCREAMING_SNAKE_CASE = encodings["""attention_mask"""]
# check that each input is long enough:
if add_start_token:
assert torch.all(torch.ge(attn_masks.sum(1 ) , 1 ) ), "Each input text must be at least one token long."
else:
assert torch.all(
torch.ge(attn_masks.sum(1 ) , 2 ) ), "When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings."
_SCREAMING_SNAKE_CASE = []
_SCREAMING_SNAKE_CASE = CrossEntropyLoss(reduction='none' )
for start_index in logging.tqdm(range(0 , len(A_ ) , A_ ) ):
_SCREAMING_SNAKE_CASE = min(start_index + batch_size , len(A_ ) )
_SCREAMING_SNAKE_CASE = encoded_texts[start_index:end_index]
_SCREAMING_SNAKE_CASE = attn_masks[start_index:end_index]
if add_start_token:
_SCREAMING_SNAKE_CASE = torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0 ) ).to(A_ )
_SCREAMING_SNAKE_CASE = torch.cat([bos_tokens_tensor, encoded_batch] , dim=1 )
_SCREAMING_SNAKE_CASE = torch.cat(
[torch.ones(bos_tokens_tensor.size() , dtype=torch.intaa ).to(A_ ), attn_mask] , dim=1 )
_SCREAMING_SNAKE_CASE = encoded_batch
with torch.no_grad():
_SCREAMING_SNAKE_CASE = model(A_ , attention_mask=A_ ).logits
_SCREAMING_SNAKE_CASE = out_logits[..., :-1, :].contiguous()
_SCREAMING_SNAKE_CASE = labels[..., 1:].contiguous()
_SCREAMING_SNAKE_CASE = attn_mask[..., 1:].contiguous()
_SCREAMING_SNAKE_CASE = torch.expa(
(loss_fct(shift_logits.transpose(1 , 2 ) , A_ ) * shift_attention_mask_batch).sum(1 )
/ shift_attention_mask_batch.sum(1 ) )
ppls += perplexity_batch.tolist()
return {"perplexities": ppls, "mean_perplexity": np.mean(A_ )}
| 605 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__UpperCAmelCase = {
'configuration_bridgetower': [
'BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'BridgeTowerConfig',
'BridgeTowerTextConfig',
'BridgeTowerVisionConfig',
],
'processing_bridgetower': ['BridgeTowerProcessor'],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = ['BridgeTowerImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
'BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST',
'BridgeTowerForContrastiveLearning',
'BridgeTowerForImageAndTextRetrieval',
'BridgeTowerForMaskedLM',
'BridgeTowerModel',
'BridgeTowerPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_bridgetower import (
BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
BridgeTowerConfig,
BridgeTowerTextConfig,
BridgeTowerVisionConfig,
)
from .processing_bridgetower import BridgeTowerProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_bridgetower import BridgeTowerImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_bridgetower import (
BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
BridgeTowerForContrastiveLearning,
BridgeTowerForImageAndTextRetrieval,
BridgeTowerForMaskedLM,
BridgeTowerModel,
BridgeTowerPreTrainedModel,
)
else:
import sys
__UpperCAmelCase = _LazyModule(__name__, globals()['__file__'], _import_structure)
| 65 | 0 |
from functools import reduce
_lowerCAmelCase : List[Any] =(
"""73167176531330624919225119674426574742355349194934"""
"""96983520312774506326239578318016984801869478851843"""
"""85861560789112949495459501737958331952853208805511"""
"""12540698747158523863050715693290963295227443043557"""
"""66896648950445244523161731856403098711121722383113"""
"""62229893423380308135336276614282806444486645238749"""
"""30358907296290491560440772390713810515859307960866"""
"""70172427121883998797908792274921901699720888093776"""
"""65727333001053367881220235421809751254540594752243"""
"""52584907711670556013604839586446706324415722155397"""
"""53697817977846174064955149290862569321978468622482"""
"""83972241375657056057490261407972968652414535100474"""
"""82166370484403199890008895243450658541227588666881"""
"""16427171479924442928230863465674813919123162824586"""
"""17866458359124566529476545682848912883142607690042"""
"""24219022671055626321111109370544217506941658960408"""
"""07198403850962455444362981230987879927244284909188"""
"""84580156166097919133875499200524063689912560717606"""
"""05886116467109405077541002256983155200055935729725"""
"""71636269561882670428252483600823257530420752963450"""
)
def _A ( SCREAMING_SNAKE_CASE = N ):
return max(
# mypy cannot properly interpret reduce
int(reduce(lambda SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : str(int(__UpperCamelCase ) * int(__UpperCamelCase ) ) ,n[i : i + 1_3] ) )
for i in range(len(__UpperCamelCase ) - 1_2 ) )
if __name__ == "__main__":
print(F"{solution() = }") | 113 |
"""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 __lowercase ( __lowerCamelCase ):
snake_case_ = ["""input_features""", """is_longer"""]
def __init__( self : str ,A : Union[str, Any]=64 ,A : Tuple=48_000 ,A : Dict=480 ,A : List[str]=10 ,A : str=1_024 ,A : Any=0.0 ,A : Optional[int]=False ,A : float = 0 ,A : float = 14_000 ,A : int = None ,A : str = "fusion" ,A : str = "repeatpad" ,**A : List[Any] ,):
'''simple docstring'''
super().__init__(
feature_size=A ,sampling_rate=A ,padding_value=A ,return_attention_mask=A ,**A ,)
UpperCAmelCase__ : List[Any] = top_db
UpperCAmelCase__ : Union[str, Any] = truncation
UpperCAmelCase__ : Optional[int] = padding
UpperCAmelCase__ : List[Any] = fft_window_size
UpperCAmelCase__ : Optional[Any] = (fft_window_size >> 1) + 1
UpperCAmelCase__ : Any = hop_length
UpperCAmelCase__ : List[str] = max_length_s
UpperCAmelCase__ : List[Any] = max_length_s * sampling_rate
UpperCAmelCase__ : List[Any] = sampling_rate
UpperCAmelCase__ : Optional[int] = frequency_min
UpperCAmelCase__ : Tuple = frequency_max
UpperCAmelCase__ : 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=A ,mel_scale="""htk""" ,)
UpperCAmelCase__ : 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 __lowercase ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = copy.deepcopy(self.__dict__ )
UpperCAmelCase__ : 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 __lowercase ( self : List[str] ,A : np.array ,A : Optional[np.array] = None ):
'''simple docstring'''
UpperCAmelCase__ : Dict = 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 : Optional[Any] ,A : Union[str, Any] ,A : int ,A : Optional[int] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[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
UpperCAmelCase__ : List[str] = [0]
if len(ranges[2] ) == 0:
# if the audio is too short, we just use the first chunk
UpperCAmelCase__ : int = [0]
# randomly choose index for each part
UpperCAmelCase__ : Tuple = np.random.choice(ranges[0] )
UpperCAmelCase__ : Tuple = np.random.choice(ranges[1] )
UpperCAmelCase__ : str = np.random.choice(ranges[2] )
UpperCAmelCase__ : List[str] = mel[idx_front : idx_front + chunk_frames, :]
UpperCAmelCase__ : List[str] = mel[idx_middle : idx_middle + chunk_frames, :]
UpperCAmelCase__ : Dict = mel[idx_back : idx_back + chunk_frames, :]
UpperCAmelCase__ : Optional[Any] = torch.tensor(mel[None, None, :] )
UpperCAmelCase__ : int = torch.nn.functional.interpolate(
A ,size=[chunk_frames, 64] ,mode="""bilinear""" ,align_corners=A )
UpperCAmelCase__ : Dict = mel_shrink[0][0].numpy()
UpperCAmelCase__ : Dict = np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back] ,axis=0 )
return mel_fusion
def __lowercase ( self : Any ,A : np.array ,A : Optional[int] ,A : Any ,A : Tuple ):
'''simple docstring'''
if waveform.shape[0] > max_length:
if truncation == "rand_trunc":
UpperCAmelCase__ : int = True
# random crop to max_length (for compatibility) -> this should be handled by self.pad
UpperCAmelCase__ : str = len(A ) - max_length
UpperCAmelCase__ : Optional[Any] = np.random.randint(0 ,overflow + 1 )
UpperCAmelCase__ : Optional[int] = waveform[idx : idx + max_length]
UpperCAmelCase__ : Any = self._np_extract_fbank_features(A ,self.mel_filters_slaney )[None, :]
elif truncation == "fusion":
UpperCAmelCase__ : Tuple = self._np_extract_fbank_features(A ,self.mel_filters )
UpperCAmelCase__ : Optional[int] = max_length // self.hop_length + 1 # the +1 related to how the spectrogram is computed
UpperCAmelCase__ : 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.
UpperCAmelCase__ : List[Any] = np.stack([mel, mel, mel, mel] ,axis=0 )
UpperCAmelCase__ : Any = False
else:
UpperCAmelCase__ : Union[str, Any] = self._random_mel_fusion(A ,A ,A )
UpperCAmelCase__ : List[str] = True
else:
raise NotImplementedError(f"data_truncating {truncation} not implemented" )
else:
UpperCAmelCase__ : Optional[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":
UpperCAmelCase__ : str = int(max_length / len(A ) )
UpperCAmelCase__ : int = np.stack(np.tile(A ,n_repeat + 1 ) )[:max_length]
if padding == "repeatpad":
UpperCAmelCase__ : List[Any] = int(max_length / len(A ) )
UpperCAmelCase__ : str = np.stack(np.tile(A ,A ) )
UpperCAmelCase__ : Optional[Any] = np.pad(A ,(0, max_length - waveform.shape[0]) ,mode="""constant""" ,constant_values=0 )
if truncation == "fusion":
UpperCAmelCase__ : int = self._np_extract_fbank_features(A ,self.mel_filters )
UpperCAmelCase__ : List[Any] = np.stack([input_mel, input_mel, input_mel, input_mel] ,axis=0 )
else:
UpperCAmelCase__ : Any = self._np_extract_fbank_features(A ,self.mel_filters_slaney )[None, :]
return input_mel, longer
def __call__( self : str ,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 : List[str] ,):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = truncation if truncation is not None else self.truncation
UpperCAmelCase__ : Dict = 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__ : Optional[int] = 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__ : List[str] = is_batched_numpy or (
isinstance(A ,(list, tuple) ) and (isinstance(raw_speech[0] ,(np.ndarray, tuple, list) ))
)
if is_batched:
UpperCAmelCase__ : str = [np.asarray(A ,dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(A ,np.ndarray ):
UpperCAmelCase__ : Any = np.asarray(A ,dtype=np.floataa )
elif isinstance(A ,np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
UpperCAmelCase__ : str = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
UpperCAmelCase__ : Optional[Any] = [np.asarray(A )]
# convert to mel spectrogram, truncate and pad if needed.
UpperCAmelCase__ : Tuple = [
self._get_input_mel(A ,max_length if max_length else self.nb_max_samples ,A ,A )
for waveform in raw_speech
]
UpperCAmelCase__ : Optional[int] = []
UpperCAmelCase__ : 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
UpperCAmelCase__ : List[str] = np.random.randint(0 ,len(A ) )
UpperCAmelCase__ : int = True
if isinstance(input_mel[0] ,A ):
UpperCAmelCase__ : Tuple = [np.asarray(A ,dtype=np.floataa ) for feature in input_mel]
# is_longer is a list of bool
UpperCAmelCase__ : List[str] = [[longer] for longer in is_longer]
UpperCAmelCase__ : List[Any] = {"""input_features""": input_mel, """is_longer""": is_longer}
UpperCAmelCase__ : str = BatchFeature(A )
if return_tensors is not None:
UpperCAmelCase__ : int = input_features.convert_to_tensors(A )
return input_features
| 65 | 0 |
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
if is_vision_available():
from PIL import Image
else:
class _UpperCAmelCase :
'''simple docstring'''
@staticmethod
def __UpperCAmelCase ( *lowercase_ : Any , **lowercase_ : Union[str, Any]) -> Dict:
"""simple docstring"""
pass
def lowerCAmelCase__ ( a__ ) ->int:
'''simple docstring'''
_UpperCamelCase = hashlib.mda(image.tobytes() )
return m.hexdigest()[:10]
def lowerCAmelCase__ ( a__ ) ->Optional[int]:
'''simple docstring'''
_UpperCamelCase = np.array(__UpperCamelCase )
_UpperCamelCase = npimg.shape
return {"hash": hashimage(__UpperCamelCase ), "shape": shape}
@is_pipeline_test
@require_vision
@require_torch
class _UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
__A = dict(
(list(MODEL_FOR_MASK_GENERATION_MAPPING.items() ) if MODEL_FOR_MASK_GENERATION_MAPPING else []) )
__A = dict(
(list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items() ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else []) )
def __UpperCAmelCase ( self : Optional[Any] , lowercase_ : int , lowercase_ : Union[str, Any] , lowercase_ : Any) -> Tuple:
"""simple docstring"""
_UpperCamelCase = MaskGenerationPipeline(model=lowercase_ , image_processor=lowercase_)
return image_segmenter, [
"./tests/fixtures/tests_samples/COCO/000000039769.png",
"./tests/fixtures/tests_samples/COCO/000000039769.png",
]
def __UpperCAmelCase ( self : Optional[int] , lowercase_ : List[str] , lowercase_ : Dict) -> Any:
"""simple docstring"""
pass
@require_tf
@unittest.skip("Image segmentation not implemented in TF")
def __UpperCAmelCase ( self : List[str]) -> Any:
"""simple docstring"""
pass
@slow
@require_torch
def __UpperCAmelCase ( self : Union[str, Any]) -> Any:
"""simple docstring"""
_UpperCamelCase = pipeline("mask-generation" , model="facebook/sam-vit-huge")
_UpperCamelCase = image_segmenter("http://images.cocodataset.org/val2017/000000039769.jpg" , points_per_batch=256)
# Shortening by hashing
_UpperCamelCase = []
for i, o in enumerate(outputs["masks"]):
new_outupt += [{"mask": mask_to_test_readable(lowercase_), "scores": outputs["scores"][i]}]
# fmt: off
self.assertEqual(
nested_simplify(lowercase_ , decimals=4) , [
{"mask": {"hash": "115ad19f5f", "shape": (480, 640)}, "scores": 1.04_44},
{"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.0_21},
{"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.01_67},
{"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.01_32},
{"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.00_53},
{"mask": {"hash": "e2d0b7a0b7", "shape": (480, 640)}, "scores": 0.99_67},
{"mask": {"hash": "453c7844bd", "shape": (480, 640)}, "scores": 0.9_93},
{"mask": {"hash": "3d44f2926d", "shape": (480, 640)}, "scores": 0.99_09},
{"mask": {"hash": "64033ddc3f", "shape": (480, 640)}, "scores": 0.98_79},
{"mask": {"hash": "801064ff79", "shape": (480, 640)}, "scores": 0.98_34},
{"mask": {"hash": "6172f276ef", "shape": (480, 640)}, "scores": 0.97_16},
{"mask": {"hash": "b49e60e084", "shape": (480, 640)}, "scores": 0.96_12},
{"mask": {"hash": "a811e775fd", "shape": (480, 640)}, "scores": 0.95_99},
{"mask": {"hash": "a6a8ebcf4b", "shape": (480, 640)}, "scores": 0.95_52},
{"mask": {"hash": "9d8257e080", "shape": (480, 640)}, "scores": 0.95_32},
{"mask": {"hash": "32de6454a8", "shape": (480, 640)}, "scores": 0.95_16},
{"mask": {"hash": "af3d4af2c8", "shape": (480, 640)}, "scores": 0.94_99},
{"mask": {"hash": "3c6db475fb", "shape": (480, 640)}, "scores": 0.94_83},
{"mask": {"hash": "c290813fb9", "shape": (480, 640)}, "scores": 0.94_64},
{"mask": {"hash": "b6f0b8f606", "shape": (480, 640)}, "scores": 0.9_43},
{"mask": {"hash": "92ce16bfdf", "shape": (480, 640)}, "scores": 0.9_43},
{"mask": {"hash": "c749b25868", "shape": (480, 640)}, "scores": 0.94_08},
{"mask": {"hash": "efb6cab859", "shape": (480, 640)}, "scores": 0.93_35},
{"mask": {"hash": "1ff2eafb30", "shape": (480, 640)}, "scores": 0.93_26},
{"mask": {"hash": "788b798e24", "shape": (480, 640)}, "scores": 0.92_62},
{"mask": {"hash": "abea804f0e", "shape": (480, 640)}, "scores": 0.89_99},
{"mask": {"hash": "7b9e8ddb73", "shape": (480, 640)}, "scores": 0.89_86},
{"mask": {"hash": "cd24047c8a", "shape": (480, 640)}, "scores": 0.89_84},
{"mask": {"hash": "6943e6bcbd", "shape": (480, 640)}, "scores": 0.88_73},
{"mask": {"hash": "b5f47c9191", "shape": (480, 640)}, "scores": 0.88_71}
] , )
# fmt: on
@require_torch
@slow
def __UpperCAmelCase ( self : List[Any]) -> Dict:
"""simple docstring"""
_UpperCamelCase = """facebook/sam-vit-huge"""
_UpperCamelCase = pipeline("mask-generation" , model=lowercase_)
_UpperCamelCase = image_segmenter(
"http://images.cocodataset.org/val2017/000000039769.jpg" , pred_iou_thresh=1 , points_per_batch=256)
# Shortening by hashing
_UpperCamelCase = []
for i, o in enumerate(outputs["masks"]):
new_outupt += [{"mask": mask_to_test_readable(lowercase_), "scores": outputs["scores"][i]}]
self.assertEqual(
nested_simplify(lowercase_ , decimals=4) , [
{"mask": {"hash": "115ad19f5f", "shape": (480, 640)}, "scores": 1.04_44},
{"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.02_10},
{"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.01_67},
{"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.01_32},
{"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.00_53},
] , )
| 547 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, 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 DonutImageProcessor
class __lowercase ( unittest.TestCase ):
def __init__( self : Union[str, Any] ,A : Union[str, Any] ,A : Dict=7 ,A : Optional[int]=3 ,A : List[str]=18 ,A : Union[str, Any]=30 ,A : Tuple=400 ,A : Dict=True ,A : List[str]=None ,A : str=True ,A : Optional[Any]=False ,A : Optional[Any]=True ,A : List[str]=True ,A : Optional[int]=[0.5, 0.5, 0.5] ,A : List[str]=[0.5, 0.5, 0.5] ,):
'''simple docstring'''
UpperCAmelCase__ : str = parent
UpperCAmelCase__ : List[str] = batch_size
UpperCAmelCase__ : List[str] = num_channels
UpperCAmelCase__ : Union[str, Any] = image_size
UpperCAmelCase__ : List[Any] = min_resolution
UpperCAmelCase__ : Optional[int] = max_resolution
UpperCAmelCase__ : str = do_resize
UpperCAmelCase__ : Tuple = size if size is not None else {"""height""": 18, """width""": 20}
UpperCAmelCase__ : List[str] = do_thumbnail
UpperCAmelCase__ : Optional[int] = do_align_axis
UpperCAmelCase__ : Union[str, Any] = do_pad
UpperCAmelCase__ : Tuple = do_normalize
UpperCAmelCase__ : Optional[Any] = image_mean
UpperCAmelCase__ : List[Any] = image_std
def __lowercase ( self : Optional[int] ):
'''simple docstring'''
return {
"do_resize": self.do_resize,
"size": self.size,
"do_thumbnail": self.do_thumbnail,
"do_align_long_axis": self.do_align_axis,
"do_pad": self.do_pad,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
}
@require_torch
@require_vision
class __lowercase ( __lowerCamelCase , unittest.TestCase ):
snake_case_ = DonutImageProcessor if is_vision_available() else None
def __lowercase ( self : str ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = DonutImageProcessingTester(self )
@property
def __lowercase ( self : Dict ):
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def __lowercase ( self : Any ):
'''simple docstring'''
UpperCAmelCase__ : Any = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(A ,"""do_resize""" ) )
self.assertTrue(hasattr(A ,"""size""" ) )
self.assertTrue(hasattr(A ,"""do_thumbnail""" ) )
self.assertTrue(hasattr(A ,"""do_align_long_axis""" ) )
self.assertTrue(hasattr(A ,"""do_pad""" ) )
self.assertTrue(hasattr(A ,"""do_normalize""" ) )
self.assertTrue(hasattr(A ,"""image_mean""" ) )
self.assertTrue(hasattr(A ,"""image_std""" ) )
def __lowercase ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size ,{"""height""": 18, """width""": 20} )
UpperCAmelCase__ : str = self.image_processing_class.from_dict(self.image_processor_dict ,size=42 )
self.assertEqual(image_processor.size ,{"""height""": 42, """width""": 42} )
# Previous config had dimensions in (width, height) order
UpperCAmelCase__ : str = self.image_processing_class.from_dict(self.image_processor_dict ,size=(42, 84) )
self.assertEqual(image_processor.size ,{"""height""": 84, """width""": 42} )
def __lowercase ( self : Dict ):
'''simple docstring'''
pass
@is_flaky()
def __lowercase ( self : int ):
'''simple docstring'''
# Initialize image_processing
UpperCAmelCase__ : Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
UpperCAmelCase__ : Dict = prepare_image_inputs(self.image_processor_tester ,equal_resolution=A )
for image in image_inputs:
self.assertIsInstance(A ,Image.Image )
# Test not batched input
UpperCAmelCase__ : int = image_processing(image_inputs[0] ,return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) ,)
# Test batched
UpperCAmelCase__ : Tuple = image_processing(A ,return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) ,)
@is_flaky()
def __lowercase ( self : List[str] ):
'''simple docstring'''
# Initialize image_processing
UpperCAmelCase__ : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
UpperCAmelCase__ : Dict = prepare_image_inputs(self.image_processor_tester ,equal_resolution=A ,numpify=A )
for image in image_inputs:
self.assertIsInstance(A ,np.ndarray )
# Test not batched input
UpperCAmelCase__ : List[str] = image_processing(image_inputs[0] ,return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) ,)
# Test batched
UpperCAmelCase__ : Optional[int] = image_processing(A ,return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) ,)
@is_flaky()
def __lowercase ( self : Any ):
'''simple docstring'''
# Initialize image_processing
UpperCAmelCase__ : int = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
UpperCAmelCase__ : int = prepare_image_inputs(self.image_processor_tester ,equal_resolution=A ,torchify=A )
for image in image_inputs:
self.assertIsInstance(A ,torch.Tensor )
# Test not batched input
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.size["""height"""],
self.image_processor_tester.size["""width"""],
) ,)
# Test batched
UpperCAmelCase__ : List[Any] = image_processing(A ,return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) ,)
| 65 | 0 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@require_sentencepiece
@require_tokenizers
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@slow
def lowerCAmelCase__ ( self : Dict ) -> Optional[Any]:
"""simple docstring"""
snake_case_ = AutoModelForSeqaSeqLM.from_pretrained("google/mt5-small" , return_dict=_lowerCAmelCase ).to(_lowerCAmelCase )
snake_case_ = AutoTokenizer.from_pretrained("google/mt5-small" )
snake_case_ = tokenizer("Hello there" , return_tensors="pt" ).input_ids
snake_case_ = tokenizer("Hi I am" , return_tensors="pt" ).input_ids
snake_case_ = model(input_ids.to(_lowerCAmelCase ) , labels=labels.to(_lowerCAmelCase ) ).loss
snake_case_ = -(labels.shape[-1] * loss.item())
snake_case_ = -8_4.9_1_2_7
self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 1e-4 )
| 283 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
's-JoL/Open-Llama-V1': 'https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json',
}
class __lowercase ( __lowerCamelCase ):
snake_case_ = """open-llama"""
def __init__( self : Dict ,A : str=100_000 ,A : str=4_096 ,A : Optional[Any]=11_008 ,A : Tuple=32 ,A : str=32 ,A : Optional[int]="silu" ,A : List[Any]=2_048 ,A : str=0.0_2 ,A : Optional[int]=1e-6 ,A : int=True ,A : Tuple=0 ,A : str=1 ,A : Any=2 ,A : Optional[Any]=False ,A : int=True ,A : Any=0.1 ,A : Optional[Any]=0.1 ,A : Optional[Any]=True ,A : Union[str, Any]=True ,A : Tuple=None ,**A : Optional[int] ,):
'''simple docstring'''
UpperCAmelCase__ : str = vocab_size
UpperCAmelCase__ : List[str] = max_position_embeddings
UpperCAmelCase__ : Union[str, Any] = hidden_size
UpperCAmelCase__ : Tuple = intermediate_size
UpperCAmelCase__ : Optional[int] = num_hidden_layers
UpperCAmelCase__ : Any = num_attention_heads
UpperCAmelCase__ : str = hidden_act
UpperCAmelCase__ : Optional[Any] = initializer_range
UpperCAmelCase__ : Optional[int] = rms_norm_eps
UpperCAmelCase__ : Any = use_cache
UpperCAmelCase__ : Optional[Any] = kwargs.pop(
"""use_memorry_efficient_attention""" ,A )
UpperCAmelCase__ : Any = hidden_dropout_prob
UpperCAmelCase__ : str = attention_dropout_prob
UpperCAmelCase__ : Optional[int] = use_stable_embedding
UpperCAmelCase__ : Tuple = shared_input_output_embedding
UpperCAmelCase__ : Tuple = rope_scaling
self._rope_scaling_validation()
super().__init__(
pad_token_id=A ,bos_token_id=A ,eos_token_id=A ,tie_word_embeddings=A ,**A ,)
def __lowercase ( self : Optional[Any] ):
'''simple docstring'''
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling ,A ) or len(self.rope_scaling ) != 2:
raise ValueError(
"""`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, """
f"got {self.rope_scaling}" )
UpperCAmelCase__ : List[Any] = self.rope_scaling.get("""type""" ,A )
UpperCAmelCase__ : int = self.rope_scaling.get("""factor""" ,A )
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
raise ValueError(
f"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}" )
if rope_scaling_factor is None or not isinstance(A ,A ) or rope_scaling_factor <= 1.0:
raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}" )
| 65 | 0 |
class lowercase ( __lowerCamelCase ):
'''simple docstring'''
pass
class lowercase ( __lowerCamelCase ):
'''simple docstring'''
pass
class lowercase :
'''simple docstring'''
def __init__(self ) -> List[str]:
"""simple docstring"""
UpperCAmelCase__ = [
[],
[],
[],
]
def UpperCamelCase__ (self , __a , __a ) -> List[str]:
"""simple docstring"""
try:
if len(self.queues[priority] ) >= 100:
raise OverflowError('Maximum queue size is 100' )
self.queues[priority].append(__a )
except IndexError:
raise ValueError('Valid priorities are 0, 1, and 2' )
def UpperCamelCase__ (self ) -> Dict:
"""simple docstring"""
for queue in self.queues:
if queue:
return queue.pop(0 )
raise UnderFlowError('All queues are empty' )
def __str__(self ) -> str:
"""simple docstring"""
return "\n".join(F"Priority {i}: {q}" for i, q in enumerate(self.queues ) )
class lowercase :
'''simple docstring'''
def __init__(self ) -> List[str]:
"""simple docstring"""
UpperCAmelCase__ = []
def UpperCamelCase__ (self , __a ) -> Any:
"""simple docstring"""
if len(self.queue ) == 100:
raise OverFlowError('Maximum queue size is 100' )
self.queue.append(__a )
def UpperCamelCase__ (self ) -> Union[str, Any]:
"""simple docstring"""
if not self.queue:
raise UnderFlowError('The queue is empty' )
else:
UpperCAmelCase__ = min(self.queue )
self.queue.remove(__a )
return data
def __str__(self ) -> int:
"""simple docstring"""
return str(self.queue )
def UpperCamelCase_( ) -> Optional[Any]:
UpperCAmelCase__ = FixedPriorityQueue()
fpq.enqueue(0 , 10 )
fpq.enqueue(1 , 70 )
fpq.enqueue(0 , 1_00 )
fpq.enqueue(2 , 1 )
fpq.enqueue(2 , 5 )
fpq.enqueue(1 , 7 )
fpq.enqueue(2 , 4 )
fpq.enqueue(1 , 64 )
fpq.enqueue(0 , 1_28 )
print(__UpperCamelCase )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(__UpperCamelCase )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
def UpperCamelCase_( ) -> Dict:
UpperCAmelCase__ = ElementPriorityQueue()
epq.enqueue(10 )
epq.enqueue(70 )
epq.enqueue(1_00 )
epq.enqueue(1 )
epq.enqueue(5 )
epq.enqueue(7 )
epq.enqueue(4 )
epq.enqueue(64 )
epq.enqueue(1_28 )
print(__UpperCamelCase )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(__UpperCamelCase )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
if __name__ == "__main__":
fixed_priority_queue()
element_priority_queue()
| 146 |
"""simple docstring"""
from collections.abc import Callable
class __lowercase :
def __init__( self : Tuple ,A : Callable | None = None ):
'''simple docstring'''
# Stores actual heap items.
UpperCAmelCase__ : list = []
# Stores indexes of each item for supporting updates and deletion.
UpperCAmelCase__ : dict = {}
# Stores current size of heap.
UpperCAmelCase__ : Any = 0
# Stores function used to evaluate the score of an item on which basis ordering
# will be done.
UpperCAmelCase__ : int = key or (lambda A : x)
def __lowercase ( self : Union[str, Any] ,A : int ):
'''simple docstring'''
return int((i - 1) / 2 ) if i > 0 else None
def __lowercase ( self : Tuple ,A : int ):
'''simple docstring'''
UpperCAmelCase__ : Any = int(2 * i + 1 )
return left if 0 < left < self.size else None
def __lowercase ( self : Any ,A : int ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = int(2 * i + 2 )
return right if 0 < right < self.size else None
def __lowercase ( self : List[Any] ,A : int ,A : int ):
'''simple docstring'''
UpperCAmelCase__ , UpperCAmelCase__ : int = (
self.pos_map[self.arr[j][0]],
self.pos_map[self.arr[i][0]],
)
# Then swap the items in the list.
UpperCAmelCase__ , UpperCAmelCase__ : Tuple = self.arr[j], self.arr[i]
def __lowercase ( self : Optional[int] ,A : int ,A : int ):
'''simple docstring'''
return self.arr[i][1] < self.arr[j][1]
def __lowercase ( self : Optional[int] ,A : int ):
'''simple docstring'''
UpperCAmelCase__ : int = self._left(A )
UpperCAmelCase__ : Dict = self._right(A )
UpperCAmelCase__ : Optional[int] = i
if left is not None and not self._cmp(A ,A ):
UpperCAmelCase__ : List[Any] = left
if right is not None and not self._cmp(A ,A ):
UpperCAmelCase__ : List[Any] = right
return valid_parent
def __lowercase ( self : int ,A : int ):
'''simple docstring'''
UpperCAmelCase__ : int = self._parent(A )
while parent is not None and not self._cmp(A ,A ):
self._swap(A ,A )
UpperCAmelCase__ , UpperCAmelCase__ : int = parent, self._parent(A )
def __lowercase ( self : str ,A : int ):
'''simple docstring'''
UpperCAmelCase__ : Any = self._get_valid_parent(A )
while valid_parent != index:
self._swap(A ,A )
UpperCAmelCase__ , UpperCAmelCase__ : Optional[int] = valid_parent, self._get_valid_parent(A )
def __lowercase ( self : Optional[Any] ,A : int ,A : int ):
'''simple docstring'''
if item not in self.pos_map:
return
UpperCAmelCase__ : Tuple = self.pos_map[item]
UpperCAmelCase__ : Dict = [item, self.key(A )]
# Make sure heap is right in both up and down direction.
# Ideally only one of them will make any change.
self._heapify_up(A )
self._heapify_down(A )
def __lowercase ( self : List[Any] ,A : int ):
'''simple docstring'''
if item not in self.pos_map:
return
UpperCAmelCase__ : Any = self.pos_map[item]
del self.pos_map[item]
UpperCAmelCase__ : Dict = self.arr[self.size - 1]
UpperCAmelCase__ : List[Any] = index
self.size -= 1
# Make sure heap is right in both up and down direction. Ideally only one
# of them will make any change- so no performance loss in calling both.
if self.size > index:
self._heapify_up(A )
self._heapify_down(A )
def __lowercase ( self : str ,A : int ,A : int ):
'''simple docstring'''
UpperCAmelCase__ : Dict = len(self.arr )
if arr_len == self.size:
self.arr.append([item, self.key(A )] )
else:
UpperCAmelCase__ : List[str] = [item, self.key(A )]
UpperCAmelCase__ : Union[str, Any] = self.size
self.size += 1
self._heapify_up(self.size - 1 )
def __lowercase ( self : str ):
'''simple docstring'''
return self.arr[0] if self.size else None
def __lowercase ( self : Dict ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = self.get_top()
if top_item_tuple:
self.delete_item(top_item_tuple[0] )
return top_item_tuple
def lowerCAmelCase ( ):
'''simple docstring'''
if __name__ == "__main__":
import doctest
doctest.testmod()
| 65 | 0 |
"""simple docstring"""
from __future__ import annotations
from cmath import sqrt
def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
if a == 0:
raise ValueError("Coefficient 'a' must not be zero." )
__lowerCAmelCase = b * b - 4 * a * c
__lowerCAmelCase = (-b + sqrt(__UpperCamelCase )) / (2 * a)
__lowerCAmelCase = (-b - sqrt(__UpperCamelCase )) / (2 * a)
return (
root_a.real if not root_a.imag else root_a,
root_a.real if not root_a.imag else root_a,
)
def _lowerCamelCase ( ):
'''simple docstring'''
__lowerCAmelCase = quadratic_roots(a=5 , b=6 , c=1 )
print(f"The solutions are: {solutiona} and {solutiona}" )
if __name__ == "__main__":
main()
| 636 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import PaddingStrategy, TensorType
from ....utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
class __lowercase ( __lowerCamelCase ):
snake_case_ = ["""input_features""", """attention_mask"""]
def __init__( self : Any ,A : str=80 ,A : Optional[int]=16_000 ,A : int=0.0 ,A : str=10 ,A : Any=25 ,A : str="hamming_window" ,A : int=3_2_7_6_8.0 ,A : List[str]=0.9_7 ,A : Optional[int]=1.0 ,A : Optional[Any]=True ,A : Tuple=True ,A : Any=False ,**A : int ,):
'''simple docstring'''
super().__init__(feature_size=A ,sampling_rate=A ,padding_value=A ,**A )
UpperCAmelCase__ : str = feature_size
UpperCAmelCase__ : int = sampling_rate
UpperCAmelCase__ : int = padding_value
UpperCAmelCase__ : Dict = hop_length
UpperCAmelCase__ : int = win_length
UpperCAmelCase__ : Dict = frame_signal_scale
UpperCAmelCase__ : Dict = preemphasis_coeff
UpperCAmelCase__ : str = mel_floor
UpperCAmelCase__ : Any = normalize_means
UpperCAmelCase__ : str = normalize_vars
UpperCAmelCase__ : int = win_function
UpperCAmelCase__ : List[Any] = return_attention_mask
UpperCAmelCase__ : str = win_length * sampling_rate // 1_000
UpperCAmelCase__ : List[Any] = hop_length * sampling_rate // 1_000
UpperCAmelCase__ : int = optimal_fft_length(self.sample_size )
UpperCAmelCase__ : List[Any] = (self.n_fft // 2) + 1
def __lowercase ( self : Union[str, Any] ,A : np.array ):
'''simple docstring'''
if self.win_function == "hamming_window":
UpperCAmelCase__ : Any = window_function(window_length=self.sample_size ,name=self.win_function ,periodic=A )
else:
UpperCAmelCase__ : Any = window_function(window_length=self.sample_size ,name=self.win_function )
UpperCAmelCase__ : Union[str, Any] = mel_filter_bank(
num_frequency_bins=self.n_freqs ,num_mel_filters=self.feature_size ,min_frequency=0.0 ,max_frequency=self.sampling_rate / 2.0 ,sampling_rate=self.sampling_rate ,)
UpperCAmelCase__ : Optional[Any] = spectrogram(
one_waveform * self.frame_signal_scale ,window=A ,frame_length=self.sample_size ,hop_length=self.sample_stride ,fft_length=self.n_fft ,center=A ,preemphasis=self.preemphasis_coeff ,mel_filters=A ,mel_floor=self.mel_floor ,log_mel="""log""" ,)
return msfc_features.T
def __lowercase ( self : str ,A : Any ,A : Optional[int] ,A : str ):
'''simple docstring'''
# make sure we normalize float32 arrays
if self.normalize_means:
UpperCAmelCase__ : Optional[Any] = x[:input_length].mean(axis=0 )
UpperCAmelCase__ : Any = np.subtract(A ,A )
if self.normalize_vars:
UpperCAmelCase__ : str = x[:input_length].std(axis=0 )
UpperCAmelCase__ : Optional[int] = np.divide(A ,A )
if input_length < x.shape[0]:
UpperCAmelCase__ : int = padding_value
# make sure array is in float32
UpperCAmelCase__ : str = x.astype(np.floataa )
return x
def __lowercase ( self : Union[str, Any] ,A : List[np.ndarray] ,A : Optional[np.ndarray] = None ):
'''simple docstring'''
UpperCAmelCase__ : Any = attention_mask.sum(-1 ) if attention_mask is not None else [x.shape[0] for x in input_features]
return [self._normalize_one(A ,A ,self.padding_value ) for x, n in zip(A ,A )]
def __call__( self : Union[str, Any] ,A : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] ,A : Union[bool, str, PaddingStrategy] = False ,A : Optional[int] = None ,A : bool = False ,A : Optional[int] = None ,A : Optional[bool] = None ,A : Optional[Union[str, TensorType]] = None ,A : Optional[int] = None ,**A : Tuple ,):
'''simple docstring'''
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 `raw_speech` 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.""" )
UpperCAmelCase__ : 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}" )
UpperCAmelCase__ : Any = is_batched_numpy or (
isinstance(A ,(list, tuple) ) and (isinstance(raw_speech[0] ,(np.ndarray, tuple, list) ))
)
if is_batched:
UpperCAmelCase__ : List[str] = [np.asarray(A ,dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(A ,np.ndarray ):
UpperCAmelCase__ : Union[str, Any] = np.asarray(A ,dtype=np.floataa )
elif isinstance(A ,np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
UpperCAmelCase__ : Optional[int] = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
UpperCAmelCase__ : Optional[Any] = [raw_speech]
# extract fbank features
UpperCAmelCase__ : Tuple = [self._extract_mfsc_features(A ) for one_waveform in raw_speech]
# convert into correct format for padding
UpperCAmelCase__ : str = BatchFeature({"""input_features""": features} )
UpperCAmelCase__ : Optional[Any] = self.pad(
A ,padding=A ,max_length=A ,truncation=A ,pad_to_multiple_of=A ,return_attention_mask=A ,**A ,)
# make sure list is in array format
UpperCAmelCase__ : Tuple = padded_inputs.get("""input_features""" )
if isinstance(input_features[0] ,A ):
UpperCAmelCase__ : Union[str, Any] = [np.asarray(A ,dtype=np.floataa ) for feature in input_features]
UpperCAmelCase__ : Dict = padded_inputs.get("""attention_mask""" )
if attention_mask is not None:
UpperCAmelCase__ : str = [np.asarray(A ,dtype=np.intaa ) for array in attention_mask]
if self.normalize_means or self.normalize_vars:
UpperCAmelCase__ : Union[str, Any] = (
np.array(A ,dtype=np.intaa )
if self._get_padding_strategies(A ,max_length=A ) is not PaddingStrategy.DO_NOT_PAD
and padding
else None
)
UpperCAmelCase__ : Any = self.normalize(
padded_inputs["""input_features"""] ,attention_mask=A )
if return_tensors is not None:
UpperCAmelCase__ : Union[str, Any] = padded_inputs.convert_to_tensors(A )
return padded_inputs
| 65 | 0 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer
__A = logging.get_logger(__name__)
__A = {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}
__A = {
"""vocab_file""": {
"""roberta-base""": """https://huggingface.co/roberta-base/resolve/main/vocab.json""",
"""roberta-large""": """https://huggingface.co/roberta-large/resolve/main/vocab.json""",
"""roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json""",
"""distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/vocab.json""",
"""roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json""",
"""roberta-large-openai-detector""": (
"""https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"""
),
},
"""merges_file""": {
"""roberta-base""": """https://huggingface.co/roberta-base/resolve/main/merges.txt""",
"""roberta-large""": """https://huggingface.co/roberta-large/resolve/main/merges.txt""",
"""roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt""",
"""distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/merges.txt""",
"""roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt""",
"""roberta-large-openai-detector""": (
"""https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"""
),
},
"""tokenizer_file""": {
"""roberta-base""": """https://huggingface.co/roberta-base/resolve/main/tokenizer.json""",
"""roberta-large""": """https://huggingface.co/roberta-large/resolve/main/tokenizer.json""",
"""roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json""",
"""distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json""",
"""roberta-base-openai-detector""": (
"""https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json"""
),
"""roberta-large-openai-detector""": (
"""https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json"""
),
},
}
__A = {
"""roberta-base""": 512,
"""roberta-large""": 512,
"""roberta-large-mnli""": 512,
"""distilroberta-base""": 512,
"""roberta-base-openai-detector""": 512,
"""roberta-large-openai-detector""": 512,
}
class _lowerCAmelCase ( __lowerCamelCase ):
"""simple docstring"""
__magic_name__ :str = VOCAB_FILES_NAMES
__magic_name__ :Optional[int] = PRETRAINED_VOCAB_FILES_MAP
__magic_name__ :Tuple = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__magic_name__ :Dict = ["""input_ids""", """attention_mask"""]
__magic_name__ :Dict = RobertaTokenizer
def __init__( self , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase="replace" , __UpperCAmelCase="<s>" , __UpperCAmelCase="</s>" , __UpperCAmelCase="</s>" , __UpperCAmelCase="<s>" , __UpperCAmelCase="<unk>" , __UpperCAmelCase="<pad>" , __UpperCAmelCase="<mask>" , __UpperCAmelCase=False , __UpperCAmelCase=True , **__UpperCAmelCase , ):
'''simple docstring'''
super().__init__(
__UpperCAmelCase , __UpperCAmelCase , tokenizer_file=__UpperCAmelCase , errors=__UpperCAmelCase , bos_token=__UpperCAmelCase , eos_token=__UpperCAmelCase , sep_token=__UpperCAmelCase , cls_token=__UpperCAmelCase , unk_token=__UpperCAmelCase , pad_token=__UpperCAmelCase , mask_token=__UpperCAmelCase , add_prefix_space=__UpperCAmelCase , trim_offsets=__UpperCAmelCase , **__UpperCAmelCase , )
lowerCAmelCase__ :Any = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get('add_prefix_space' , __UpperCAmelCase ) != add_prefix_space:
lowerCAmelCase__ :Union[str, Any] = getattr(__UpperCAmelCase , pre_tok_state.pop('type' ) )
lowerCAmelCase__ :Dict = add_prefix_space
lowerCAmelCase__ :Dict = pre_tok_class(**__UpperCAmelCase )
lowerCAmelCase__ :List[Any] = add_prefix_space
lowerCAmelCase__ :Dict = """post_processor"""
lowerCAmelCase__ :Any = getattr(self.backend_tokenizer , __UpperCAmelCase , __UpperCAmelCase )
if tokenizer_component_instance:
lowerCAmelCase__ :int = json.loads(tokenizer_component_instance.__getstate__() )
# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
if "sep" in state:
lowerCAmelCase__ :List[Any] = tuple(state['sep'] )
if "cls" in state:
lowerCAmelCase__ :List[str] = tuple(state['cls'] )
lowerCAmelCase__ :Dict = False
if state.get('add_prefix_space' , __UpperCAmelCase ) != add_prefix_space:
lowerCAmelCase__ :Tuple = add_prefix_space
lowerCAmelCase__ :str = True
if state.get('trim_offsets' , __UpperCAmelCase ) != trim_offsets:
lowerCAmelCase__ :Tuple = trim_offsets
lowerCAmelCase__ :str = True
if changes_to_apply:
lowerCAmelCase__ :List[str] = getattr(__UpperCAmelCase , state.pop('type' ) )
lowerCAmelCase__ :List[Any] = component_class(**__UpperCAmelCase )
setattr(self.backend_tokenizer , __UpperCAmelCase , __UpperCAmelCase )
@property
def snake_case ( self ):
'''simple docstring'''
if self._mask_token is None:
if self.verbose:
logger.error('Using mask_token, but it is not set yet.' )
return None
return str(self._mask_token )
@mask_token.setter
def snake_case ( self , __UpperCAmelCase ):
'''simple docstring'''
lowerCAmelCase__ :List[Any] = AddedToken(__UpperCAmelCase , lstrip=__UpperCAmelCase , rstrip=__UpperCAmelCase ) if isinstance(__UpperCAmelCase , __UpperCAmelCase ) else value
lowerCAmelCase__ :Union[str, Any] = value
def snake_case ( self , *__UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
lowerCAmelCase__ :Optional[int] = kwargs.get('is_split_into_words' , __UpperCAmelCase )
assert self.add_prefix_space or not is_split_into_words, (
F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
"to use it with pretokenized inputs."
)
return super()._batch_encode_plus(*__UpperCAmelCase , **__UpperCAmelCase )
def snake_case ( self , *__UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
lowerCAmelCase__ :str = kwargs.get('is_split_into_words' , __UpperCAmelCase )
assert self.add_prefix_space or not is_split_into_words, (
F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
"to use it with pretokenized inputs."
)
return super()._encode_plus(*__UpperCAmelCase , **__UpperCAmelCase )
def snake_case ( self , __UpperCAmelCase , __UpperCAmelCase = None ):
'''simple docstring'''
lowerCAmelCase__ :Optional[int] = self._tokenizer.model.save(__UpperCAmelCase , name=__UpperCAmelCase )
return tuple(__UpperCAmelCase )
def snake_case ( self , __UpperCAmelCase , __UpperCAmelCase=None ):
'''simple docstring'''
lowerCAmelCase__ :List[str] = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def snake_case ( self , __UpperCAmelCase , __UpperCAmelCase = None ):
'''simple docstring'''
lowerCAmelCase__ :Optional[Any] = [self.sep_token_id]
lowerCAmelCase__ :Dict = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
| 93 |
"""simple docstring"""
from math import factorial
def lowerCAmelCase ( __UpperCamelCase = 100 ):
'''simple docstring'''
return sum(int(__UpperCamelCase ) for x in str(factorial(__UpperCamelCase ) ) )
if __name__ == "__main__":
print(solution(int(input('Enter the Number: ').strip())))
| 65 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ = {"configuration_ibert": ["IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "IBertConfig", "IBertOnnxConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ = [
"IBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"IBertForMaskedLM",
"IBertForMultipleChoice",
"IBertForQuestionAnswering",
"IBertForSequenceClassification",
"IBertForTokenClassification",
"IBertModel",
"IBertPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_ibert import (
IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
IBertForMaskedLM,
IBertForMultipleChoice,
IBertForQuestionAnswering,
IBertForSequenceClassification,
IBertForTokenClassification,
IBertModel,
IBertPreTrainedModel,
)
else:
import sys
A_ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 42 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.distilbert.modeling_flax_distilbert import (
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertModel,
)
class __lowercase ( unittest.TestCase ):
def __init__( self : Union[str, Any] ,A : Optional[int] ,A : int=13 ,A : Tuple=7 ,A : Dict=True ,A : Optional[int]=True ,A : Tuple=True ,A : str=True ,A : Any=99 ,A : Tuple=32 ,A : Dict=5 ,A : Optional[int]=4 ,A : Dict=37 ,A : Any="gelu" ,A : Any=0.1 ,A : Optional[int]=0.1 ,A : Union[str, Any]=512 ,A : Any=16 ,A : List[str]=2 ,A : List[Any]=0.0_2 ,A : Optional[int]=4 ,):
'''simple docstring'''
UpperCAmelCase__ : Dict = parent
UpperCAmelCase__ : Any = batch_size
UpperCAmelCase__ : List[Any] = seq_length
UpperCAmelCase__ : Optional[int] = is_training
UpperCAmelCase__ : Optional[Any] = use_attention_mask
UpperCAmelCase__ : int = use_token_type_ids
UpperCAmelCase__ : int = use_labels
UpperCAmelCase__ : Any = vocab_size
UpperCAmelCase__ : Union[str, Any] = hidden_size
UpperCAmelCase__ : int = num_hidden_layers
UpperCAmelCase__ : int = num_attention_heads
UpperCAmelCase__ : Dict = intermediate_size
UpperCAmelCase__ : Any = hidden_act
UpperCAmelCase__ : Union[str, Any] = hidden_dropout_prob
UpperCAmelCase__ : Any = attention_probs_dropout_prob
UpperCAmelCase__ : str = max_position_embeddings
UpperCAmelCase__ : List[Any] = type_vocab_size
UpperCAmelCase__ : List[str] = type_sequence_label_size
UpperCAmelCase__ : List[Any] = initializer_range
UpperCAmelCase__ : List[Any] = num_choices
def __lowercase ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
UpperCAmelCase__ : List[str] = None
if self.use_attention_mask:
UpperCAmelCase__ : str = random_attention_mask([self.batch_size, self.seq_length] )
UpperCAmelCase__ : int = DistilBertConfig(
vocab_size=self.vocab_size ,dim=self.hidden_size ,n_layers=self.num_hidden_layers ,n_heads=self.num_attention_heads ,hidden_dim=self.intermediate_size ,hidden_act=self.hidden_act ,dropout=self.hidden_dropout_prob ,attention_dropout=self.attention_probs_dropout_prob ,max_position_embeddings=self.max_position_embeddings ,initializer_range=self.initializer_range ,tie_weights_=A ,)
return config, input_ids, attention_mask
def __lowercase ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = self.prepare_config_and_inputs()
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ : Any = config_and_inputs
UpperCAmelCase__ : str = {"""input_ids""": input_ids, """attention_mask""": attention_mask}
return config, inputs_dict
@require_flax
class __lowercase ( __lowerCamelCase , unittest.TestCase ):
snake_case_ = (
(
FlaxDistilBertModel,
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def __lowercase ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = FlaxDistilBertModelTester(self )
@slow
def __lowercase ( self : Optional[Any] ):
'''simple docstring'''
for model_class_name in self.all_model_classes:
UpperCAmelCase__ : Union[str, Any] = model_class_name.from_pretrained("""distilbert-base-uncased""" )
UpperCAmelCase__ : List[Any] = model(np.ones((1, 1) ) )
self.assertIsNotNone(A )
@require_flax
class __lowercase ( unittest.TestCase ):
@slow
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = FlaxDistilBertModel.from_pretrained("""distilbert-base-uncased""" )
UpperCAmelCase__ : List[Any] = np.array([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]] )
UpperCAmelCase__ : str = np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
UpperCAmelCase__ : Dict = model(A ,attention_mask=A )[0]
UpperCAmelCase__ : List[Any] = (1, 11, 768)
self.assertEqual(output.shape ,A )
UpperCAmelCase__ : Any = np.array([[[-0.1_6_3_9, 0.3_2_9_9, 0.1_6_4_8], [-0.1_7_4_6, 0.3_2_8_9, 0.1_7_1_0], [-0.1_8_8_4, 0.3_3_5_7, 0.1_8_1_0]]] )
self.assertTrue(jnp.allclose(output[:, 1:4, 1:4] ,A ,atol=1e-4 ) )
| 65 | 0 |
from typing import List, Optional, Union
import numpy as np
from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import PaddingStrategy, TensorType
from ....utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
class __lowerCAmelCase ( __lowerCamelCase ):
"""simple docstring"""
A__ : Dict = ["input_features", "attention_mask"]
def __init__( self : Any , _snake_case : str=80 , _snake_case : Optional[int]=1_60_00 , _snake_case : int=0.0 , _snake_case : str=10 , _snake_case : Any=25 , _snake_case : str="hamming_window" , _snake_case : int=3_27_68.0 , _snake_case : List[str]=0.97 , _snake_case : Optional[int]=1.0 , _snake_case : Optional[Any]=True , _snake_case : Tuple=True , _snake_case : Any=False , **_snake_case : int , ):
"""simple docstring"""
super().__init__(feature_size=_snake_case , sampling_rate=_snake_case , padding_value=_snake_case , **_snake_case )
A__ = feature_size
A__ = sampling_rate
A__ = padding_value
A__ = hop_length
A__ = win_length
A__ = frame_signal_scale
A__ = preemphasis_coeff
A__ = mel_floor
A__ = normalize_means
A__ = normalize_vars
A__ = win_function
A__ = return_attention_mask
A__ = win_length * sampling_rate // 10_00
A__ = hop_length * sampling_rate // 10_00
A__ = optimal_fft_length(self.sample_size )
A__ = (self.n_fft // 2) + 1
def _a ( self : Union[str, Any] , _snake_case : np.array ):
"""simple docstring"""
if self.win_function == "hamming_window":
A__ = window_function(window_length=self.sample_size , name=self.win_function , periodic=_snake_case )
else:
A__ = window_function(window_length=self.sample_size , name=self.win_function )
A__ = mel_filter_bank(
num_frequency_bins=self.n_freqs , num_mel_filters=self.feature_size , min_frequency=0.0 , max_frequency=self.sampling_rate / 2.0 , sampling_rate=self.sampling_rate , )
A__ = spectrogram(
one_waveform * self.frame_signal_scale , window=_snake_case , frame_length=self.sample_size , hop_length=self.sample_stride , fft_length=self.n_fft , center=_snake_case , preemphasis=self.preemphasis_coeff , mel_filters=_snake_case , mel_floor=self.mel_floor , log_mel='log' , )
return msfc_features.T
def _a ( self : str , _snake_case : Any , _snake_case : Optional[int] , _snake_case : str ):
"""simple docstring"""
if self.normalize_means:
A__ = x[:input_length].mean(axis=0 )
A__ = np.subtract(_snake_case , _snake_case )
if self.normalize_vars:
A__ = x[:input_length].std(axis=0 )
A__ = np.divide(_snake_case , _snake_case )
if input_length < x.shape[0]:
A__ = padding_value
# make sure array is in float32
A__ = x.astype(np.floataa )
return x
def _a ( self : Union[str, Any] , _snake_case : List[np.ndarray] , _snake_case : Optional[np.ndarray] = None ):
"""simple docstring"""
A__ = attention_mask.sum(-1 ) if attention_mask is not None else [x.shape[0] for x in input_features]
return [self._normalize_one(_snake_case , _snake_case , self.padding_value ) for x, n in zip(_snake_case , _snake_case )]
def __call__( self : Union[str, Any] , _snake_case : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , _snake_case : Union[bool, str, PaddingStrategy] = False , _snake_case : Optional[int] = None , _snake_case : bool = False , _snake_case : Optional[int] = None , _snake_case : Optional[bool] = None , _snake_case : Optional[Union[str, TensorType]] = None , _snake_case : Optional[int] = None , **_snake_case : Tuple , ):
"""simple docstring"""
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 `raw_speech` 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.' )
A__ = isinstance(_snake_case , 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}''' )
A__ = is_batched_numpy or (
isinstance(_snake_case , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
A__ = [np.asarray(_snake_case , dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(_snake_case , np.ndarray ):
A__ = np.asarray(_snake_case , dtype=np.floataa )
elif isinstance(_snake_case , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
A__ = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
A__ = [raw_speech]
# extract fbank features
A__ = [self._extract_mfsc_features(_snake_case ) for one_waveform in raw_speech]
# convert into correct format for padding
A__ = BatchFeature({'input_features': features} )
A__ = self.pad(
_snake_case , padding=_snake_case , max_length=_snake_case , truncation=_snake_case , pad_to_multiple_of=_snake_case , return_attention_mask=_snake_case , **_snake_case , )
# make sure list is in array format
A__ = padded_inputs.get('input_features' )
if isinstance(input_features[0] , _snake_case ):
A__ = [np.asarray(_snake_case , dtype=np.floataa ) for feature in input_features]
A__ = padded_inputs.get('attention_mask' )
if attention_mask is not None:
A__ = [np.asarray(_snake_case , dtype=np.intaa ) for array in attention_mask]
if self.normalize_means or self.normalize_vars:
A__ = (
np.array(_snake_case , dtype=np.intaa )
if self._get_padding_strategies(_snake_case , max_length=_snake_case ) is not PaddingStrategy.DO_NOT_PAD
and padding
else None
)
A__ = self.normalize(
padded_inputs['input_features'] , attention_mask=_snake_case )
if return_tensors is not None:
A__ = padded_inputs.convert_to_tensors(_snake_case )
return padded_inputs
| 9 |
"""simple docstring"""
__UpperCAmelCase = frozenset(
[
'prompt',
'height',
'width',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
'cross_attention_kwargs',
]
)
__UpperCAmelCase = frozenset(['prompt', 'negative_prompt'])
__UpperCAmelCase = frozenset([])
__UpperCAmelCase = frozenset(['image'])
__UpperCAmelCase = frozenset(
[
'image',
'height',
'width',
'guidance_scale',
]
)
__UpperCAmelCase = frozenset(['image'])
__UpperCAmelCase = frozenset(
[
'prompt',
'image',
'height',
'width',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
]
)
__UpperCAmelCase = frozenset(['prompt', 'image', 'negative_prompt'])
__UpperCAmelCase = frozenset(
[
# Text guided image variation with an image mask
'prompt',
'image',
'mask_image',
'height',
'width',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
]
)
__UpperCAmelCase = frozenset(['prompt', 'image', 'mask_image', 'negative_prompt'])
__UpperCAmelCase = frozenset(
[
# image variation with an image mask
'image',
'mask_image',
'height',
'width',
'guidance_scale',
]
)
__UpperCAmelCase = frozenset(['image', 'mask_image'])
__UpperCAmelCase = frozenset(
[
'example_image',
'image',
'mask_image',
'height',
'width',
'guidance_scale',
]
)
__UpperCAmelCase = frozenset(['example_image', 'image', 'mask_image'])
__UpperCAmelCase = frozenset(['class_labels'])
__UpperCAmelCase = frozenset(['class_labels'])
__UpperCAmelCase = frozenset(['batch_size'])
__UpperCAmelCase = frozenset([])
__UpperCAmelCase = frozenset(['batch_size'])
__UpperCAmelCase = frozenset([])
__UpperCAmelCase = frozenset(
[
'prompt',
'audio_length_in_s',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
'cross_attention_kwargs',
]
)
__UpperCAmelCase = frozenset(['prompt', 'negative_prompt'])
__UpperCAmelCase = frozenset(['input_tokens'])
__UpperCAmelCase = frozenset(['input_tokens'])
| 65 | 0 |
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ...test_tokenization_common import TokenizerTesterMixin
a_ :Any = get_tests_dir("fixtures/test_sentencepiece_bpe_char.model")
@require_sentencepiece
@require_tokenizers
class snake_case__ ( __lowerCamelCase , unittest.TestCase ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = SpeechTaTokenizer
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = True
def lowercase_ ( self : Union[str, Any] ) ->str:
super().setUp()
# We have a SentencePiece fixture for testing
snake_case__ : List[Any] = SpeechTaTokenizer(_snake_case )
snake_case__ : int = AddedToken('<mask>', lstrip=_snake_case, rstrip=_snake_case )
snake_case__ : int = mask_token
tokenizer.add_special_tokens({'mask_token': mask_token} )
tokenizer.add_tokens(['<ctc_blank>'] )
tokenizer.save_pretrained(self.tmpdirname )
def lowercase_ ( self : Optional[int], _snake_case : int ) ->Tuple:
snake_case__ : Optional[Any] = """this is a test"""
snake_case__ : Optional[int] = """this is a test"""
return input_text, output_text
def lowercase_ ( self : Optional[int], _snake_case : Dict, _snake_case : Dict=False, _snake_case : str=2_0, _snake_case : Dict=5 ) ->List[Any]:
snake_case__ : List[str] = self.get_input_output_texts(_snake_case )
snake_case__ : int = tokenizer.encode(_snake_case, add_special_tokens=_snake_case )
snake_case__ : Tuple = tokenizer.decode(_snake_case, clean_up_tokenization_spaces=_snake_case )
return text, ids
def lowercase_ ( self : int ) ->Any:
snake_case__ : Tuple = """<pad>"""
snake_case__ : str = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_snake_case ), _snake_case )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_snake_case ), _snake_case )
def lowercase_ ( self : int ) ->List[Any]:
snake_case__ : List[str] = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0], '<s>' )
self.assertEqual(vocab_keys[1], '<pad>' )
self.assertEqual(vocab_keys[-4], 'œ' )
self.assertEqual(vocab_keys[-2], '<mask>' )
self.assertEqual(vocab_keys[-1], '<ctc_blank>' )
self.assertEqual(len(_snake_case ), 8_1 )
def lowercase_ ( self : Any ) ->Tuple:
self.assertEqual(self.get_tokenizer().vocab_size, 7_9 )
def lowercase_ ( self : List[str] ) ->Tuple:
snake_case__ : List[Any] = self.get_tokenizers(do_lower_case=_snake_case )
for tokenizer in tokenizers:
with self.subTest(F'''{tokenizer.__class__.__name__}''' ):
snake_case__ : Optional[Any] = tokenizer.vocab_size
snake_case__ : Any = len(_snake_case )
self.assertNotEqual(_snake_case, 0 )
# We usually have added tokens from the start in tests because our vocab fixtures are
# smaller than the original vocabs - let's not assert this
# self.assertEqual(vocab_size, all_size)
snake_case__ : List[Any] = ["""aaaaa bbbbbb""", """cccccccccdddddddd"""]
snake_case__ : Optional[Any] = tokenizer.add_tokens(_snake_case )
snake_case__ : Dict = tokenizer.vocab_size
snake_case__ : Tuple = len(_snake_case )
self.assertNotEqual(_snake_case, 0 )
self.assertEqual(_snake_case, _snake_case )
self.assertEqual(_snake_case, len(_snake_case ) )
self.assertEqual(_snake_case, all_size + len(_snake_case ) )
snake_case__ : List[Any] = tokenizer.encode('aaaaa bbbbbb low cccccccccdddddddd l', add_special_tokens=_snake_case )
self.assertGreaterEqual(len(_snake_case ), 4 )
self.assertGreater(tokens[0], tokenizer.vocab_size - 1 )
self.assertGreater(tokens[-3], tokenizer.vocab_size - 1 )
snake_case__ : List[Any] = {"""eos_token""": """>>>>|||<||<<|<<""", """pad_token""": """<<<<<|||>|>>>>|>"""}
snake_case__ : Optional[Any] = tokenizer.add_special_tokens(_snake_case )
snake_case__ : Any = tokenizer.vocab_size
snake_case__ : Optional[Any] = len(_snake_case )
self.assertNotEqual(_snake_case, 0 )
self.assertEqual(_snake_case, _snake_case )
self.assertEqual(_snake_case, len(_snake_case ) )
self.assertEqual(_snake_case, all_size_a + len(_snake_case ) )
snake_case__ : str = tokenizer.encode(
'>>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l', add_special_tokens=_snake_case )
self.assertGreaterEqual(len(_snake_case ), 6 )
self.assertGreater(tokens[0], tokenizer.vocab_size - 1 )
self.assertGreater(tokens[0], tokens[1] )
self.assertGreater(tokens[-3], tokenizer.vocab_size - 1 )
self.assertGreater(tokens[-3], tokens[-4] )
self.assertEqual(tokens[0], tokenizer.eos_token_id )
self.assertEqual(tokens[-3], tokenizer.pad_token_id )
def lowercase_ ( self : str ) ->Dict:
pass
def lowercase_ ( self : Optional[Any] ) ->Any:
pass
def lowercase_ ( self : Optional[Any] ) ->str:
snake_case__ : List[Any] = self.get_tokenizer()
snake_case__ : Tuple = tokenizer.tokenize('This is a test' )
# fmt: off
self.assertListEqual(_snake_case, [SPIECE_UNDERLINE, 'T', 'h', 'i', 's', SPIECE_UNDERLINE, 'i', 's', SPIECE_UNDERLINE, 'a', SPIECE_UNDERLINE, 't', 'e', 's', 't'] )
# fmt: on
self.assertListEqual(
tokenizer.convert_tokens_to_ids(_snake_case ), [4, 3_2, 1_1, 1_0, 1_2, 4, 1_0, 1_2, 4, 7, 4, 6, 5, 1_2, 6], )
snake_case__ : Tuple = tokenizer.tokenize('I was born in 92000, and this is falsé.' )
self.assertListEqual(
_snake_case, [SPIECE_UNDERLINE, 'I', SPIECE_UNDERLINE, 'w', 'a', 's', SPIECE_UNDERLINE, 'b', 'o', 'r', 'n', SPIECE_UNDERLINE, 'i', 'n', SPIECE_UNDERLINE, '92000', ',', SPIECE_UNDERLINE, 'a', 'n', 'd', SPIECE_UNDERLINE, 't', 'h', 'i', 's', SPIECE_UNDERLINE, 'i', 's', SPIECE_UNDERLINE, 'f', 'a', 'l', 's', 'é', '.'] )
snake_case__ : Optional[int] = tokenizer.convert_tokens_to_ids(_snake_case )
# fmt: off
self.assertListEqual(_snake_case, [4, 3_0, 4, 2_0, 7, 1_2, 4, 2_5, 8, 1_3, 9, 4, 1_0, 9, 4, 3, 2_3, 4, 7, 9, 1_4, 4, 6, 1_1, 1_0, 1_2, 4, 1_0, 1_2, 4, 1_9, 7, 1_5, 1_2, 7_3, 2_6] )
# fmt: on
snake_case__ : Optional[int] = tokenizer.convert_ids_to_tokens(_snake_case )
self.assertListEqual(
_snake_case, [SPIECE_UNDERLINE, 'I', SPIECE_UNDERLINE, 'w', 'a', 's', SPIECE_UNDERLINE, 'b', 'o', 'r', 'n', SPIECE_UNDERLINE, 'i', 'n', SPIECE_UNDERLINE, '<unk>', ',', SPIECE_UNDERLINE, 'a', 'n', 'd', SPIECE_UNDERLINE, 't', 'h', 'i', 's', SPIECE_UNDERLINE, 'i', 's', SPIECE_UNDERLINE, 'f', 'a', 'l', 's', 'é', '.'] )
@slow
def lowercase_ ( self : str ) ->Union[str, Any]:
snake_case__ : Optional[Any] = [
"""Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides """
"""general-purpose architectures (BERT, GPT, RoBERTa, XLM, DistilBert, XLNet...) for Natural """
"""Language Understanding (NLU) and Natural Language Generation (NLG) with over thirty-two pretrained """
"""models in one hundred plus languages and deep interoperability between Jax, PyTorch and TensorFlow.""",
"""BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly """
"""conditioning on both left and right context in all layers.""",
"""The quick brown fox jumps over the lazy dog.""",
]
# fmt: off
snake_case__ : Optional[Any] = {
"""input_ids""": [
[4, 3_2, 1_3, 7, 9, 1_2, 1_9, 8, 1_3, 1_8, 5, 1_3, 1_2, 4, 6_4, 1_9, 8, 1_3, 1_8, 5, 1_3, 1_5, 2_2, 4, 2_8, 9, 8, 2_0, 9, 4, 7, 1_2, 4, 2_4, 2_2, 6, 8, 1_3, 1_7, 1_1, 3_9, 6, 1_3, 7, 9, 1_2, 1_9, 8, 1_3, 1_8, 5, 1_3, 1_2, 4, 7, 9, 1_4, 4, 2_4, 2_2, 6, 8, 1_3, 1_7, 1_1, 3_9, 2_4, 1_3, 5, 6, 1_3, 7, 1_0, 9, 5, 1_4, 3_9, 2_5, 5, 1_3, 6, 6_3, 4, 2_4, 1_3, 8, 2_7, 1_0, 1_4, 5, 1_2, 4, 2_1, 5, 9, 5, 1_3, 7, 1_5, 3_9, 2_4, 1_6, 1_3, 2_4, 8, 1_2, 5, 4, 7, 1_3, 1_7, 1_1, 1_0, 6, 5, 1_7, 6, 1_6, 1_3, 5, 1_2, 4, 6_4, 4_0, 4_7, 5_4, 3_2, 2_3, 4, 5_3, 4_9, 3_2, 2_3, 4, 5_4, 8, 4_0, 4_7, 5_4, 3_2, 7, 2_3, 4, 6_9, 5_2, 4_3, 2_3, 4, 5_1, 1_0, 1_2, 6, 1_0, 1_5, 4_0, 5, 1_3, 6, 2_3, 4, 6_9, 5_2, 4_8, 5, 6, 2_6, 2_6, 2_6, 6_3, 4, 1_9, 8, 1_3, 4, 4_8, 7, 6, 1_6, 1_3, 7, 1_5, 4, 5_2, 7, 9, 2_1, 1_6, 7, 2_1, 5, 4, 6_1, 9, 1_4, 5, 1_3, 1_2, 6, 7, 9, 1_4, 1_0, 9, 2_1, 4, 6_4, 4_8, 5_2, 6_1, 6_3, 4, 7, 9, 1_4, 4, 4_8, 7, 6, 1_6, 1_3, 7, 1_5, 4, 5_2, 7, 9, 2_1, 1_6, 7, 2_1, 5, 4, 5_3, 5, 9, 5, 1_3, 7, 6, 1_0, 8, 9, 4, 6_4, 4_8, 5_2, 5_3, 6_3, 4, 2_0, 1_0, 6, 1_1, 4, 8, 2_7, 5, 1_3, 4, 6, 1_1, 1_0, 1_3, 6, 2_2, 3_9, 6, 2_0, 8, 4, 2_4, 1_3, 5, 6, 1_3, 7, 1_0, 9, 5, 1_4, 4, 1_8, 8, 1_4, 5, 1_5, 1_2, 4, 1_0, 9, 4, 8, 9, 5, 4, 1_1, 1_6, 9, 1_4, 1_3, 5, 1_4, 4, 2_4, 1_5, 1_6, 1_2, 4, 1_5, 7, 9, 2_1, 1_6, 7, 2_1, 5, 1_2, 4, 7, 9, 1_4, 4, 1_4, 5, 5, 2_4, 4, 1_0, 9, 6, 5, 1_3, 8, 2_4, 5, 1_3, 7, 2_5, 1_0, 1_5, 1_0, 6, 2_2, 4, 2_5, 5, 6, 2_0, 5, 5, 9, 4, 5_8, 7, 3_7, 2_3, 4, 4_9, 2_2, 3_2, 8, 1_3, 1_7, 1_1, 4, 7, 9, 1_4, 4, 3_2, 5, 9, 1_2, 8, 1_3, 5_5, 1_5, 8, 2_0, 2_6, 2],
[4, 4_0, 4_7, 5_4, 3_2, 4, 1_0, 1_2, 4, 1_4, 5, 1_2, 1_0, 2_1, 9, 5, 1_4, 4, 6, 8, 4, 2_4, 1_3, 5, 3_9, 6, 1_3, 7, 1_0, 9, 4, 1_4, 5, 5, 2_4, 4, 2_5, 1_0, 1_4, 1_0, 1_3, 5, 1_7, 6, 1_0, 8, 9, 7, 1_5, 4, 1_3, 5, 2_4, 1_3, 5, 1_2, 5, 9, 6, 7, 6, 1_0, 8, 9, 1_2, 4, 1_9, 1_3, 8, 1_8, 4, 1_6, 9, 1_5, 7, 2_5, 5, 1_5, 5, 1_4, 4, 6, 5, 3_7, 6, 4, 2_5, 2_2, 4, 4_6, 8, 1_0, 9, 6, 1_5, 2_2, 4, 1_7, 8, 9, 1_4, 1_0, 6, 1_0, 8, 9, 1_0, 9, 2_1, 4, 8, 9, 4, 2_5, 8, 6, 1_1, 4, 1_5, 5, 1_9, 6, 4, 7, 9, 1_4, 4, 1_3, 1_0, 2_1, 1_1, 6, 4, 1_7, 8, 9, 6, 5, 3_7, 6, 4, 1_0, 9, 4, 7, 1_5, 1_5, 4, 1_5, 7, 2_2, 5, 1_3, 1_2, 2_6, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[4, 3_2, 1_1, 5, 4, 4_5, 1_6, 1_0, 1_7, 2_8, 4, 2_5, 1_3, 8, 2_0, 9, 4, 1_9, 8, 3_7, 4, 4_6, 1_6, 1_8, 2_4, 1_2, 4, 8, 2_7, 5, 1_3, 4, 6, 1_1, 5, 4, 1_5, 7, 5_7, 2_2, 4, 1_4, 8, 2_1, 2_6, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
]
}
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=_snake_case, model_name='microsoft/speecht5_asr', revision='c5ef64c71905caeccde0e4462ef3f9077224c524', sequences=_snake_case, )
| 478 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProcessor
from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.testing_utils import require_pyctcdecode, require_torch, require_torchaudio, slow
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_pyctcdecode_available, is_torch_available
from ..wavaveca.test_feature_extraction_wavaveca import floats_list
if is_pyctcdecode_available():
from huggingface_hub import snapshot_download
from pyctcdecode import BeamSearchDecoderCTC
from transformers.models.wavaveca_with_lm import WavaVecaProcessorWithLM
from transformers.models.wavaveca_with_lm.processing_wavaveca_with_lm import WavaVecaDecoderWithLMOutput
if is_torch_available():
from transformers import WavaVecaForCTC
@require_pyctcdecode
class __lowercase ( unittest.TestCase ):
def __lowercase ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase__ : Dict = """| <pad> <unk> <s> </s> a b c d e f g h i j k""".split()
UpperCAmelCase__ : Tuple = dict(zip(A ,range(len(A ) ) ) )
UpperCAmelCase__ : Optional[Any] = {
"""unk_token""": """<unk>""",
"""bos_token""": """<s>""",
"""eos_token""": """</s>""",
}
UpperCAmelCase__ : int = {
"""feature_size""": 1,
"""padding_value""": 0.0,
"""sampling_rate""": 16_000,
"""return_attention_mask""": False,
"""do_normalize""": True,
}
UpperCAmelCase__ : Optional[int] = tempfile.mkdtemp()
UpperCAmelCase__ : Optional[int] = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["""vocab_file"""] )
UpperCAmelCase__ : Tuple = os.path.join(self.tmpdirname ,A )
with open(self.vocab_file ,"""w""" ,encoding="""utf-8""" ) as fp:
fp.write(json.dumps(A ) + """\n""" )
with open(self.feature_extraction_file ,"""w""" ,encoding="""utf-8""" ) as fp:
fp.write(json.dumps(A ) + """\n""" )
# load decoder from hub
UpperCAmelCase__ : int = """hf-internal-testing/ngram-beam-search-decoder"""
def __lowercase ( self : str ,**A : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = self.add_kwargs_tokens_map.copy()
kwargs.update(A )
return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname ,**A )
def __lowercase ( self : List[str] ,**A : Dict ):
'''simple docstring'''
return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname ,**A )
def __lowercase ( self : Any ,**A : List[Any] ):
'''simple docstring'''
return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name ,**A )
def __lowercase ( self : Any ):
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def __lowercase ( self : str ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = self.get_tokenizer()
UpperCAmelCase__ : Dict = self.get_feature_extractor()
UpperCAmelCase__ : str = self.get_decoder()
UpperCAmelCase__ : Tuple = WavaVecaProcessorWithLM(tokenizer=A ,feature_extractor=A ,decoder=A )
processor.save_pretrained(self.tmpdirname )
UpperCAmelCase__ : str = WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname )
# tokenizer
self.assertEqual(processor.tokenizer.get_vocab() ,tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer ,A )
# feature extractor
self.assertEqual(processor.feature_extractor.to_json_string() ,feature_extractor.to_json_string() )
self.assertIsInstance(processor.feature_extractor ,A )
# decoder
self.assertEqual(processor.decoder._alphabet.labels ,decoder._alphabet.labels )
self.assertEqual(
processor.decoder.model_container[decoder._model_key]._unigram_set ,decoder.model_container[decoder._model_key]._unigram_set ,)
self.assertIsInstance(processor.decoder ,A )
def __lowercase ( self : int ):
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = WavaVecaProcessorWithLM(
tokenizer=self.get_tokenizer() ,feature_extractor=self.get_feature_extractor() ,decoder=self.get_decoder() )
processor.save_pretrained(self.tmpdirname )
# make sure that error is thrown when decoder alphabet doesn't match
UpperCAmelCase__ : Tuple = WavaVecaProcessorWithLM.from_pretrained(
self.tmpdirname ,alpha=5.0 ,beta=3.0 ,score_boundary=-7.0 ,unk_score_offset=3 )
# decoder
self.assertEqual(processor.language_model.alpha ,5.0 )
self.assertEqual(processor.language_model.beta ,3.0 )
self.assertEqual(processor.language_model.score_boundary ,-7.0 )
self.assertEqual(processor.language_model.unk_score_offset ,3 )
def __lowercase ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase__ : int = self.get_tokenizer()
# add token to trigger raise
tokenizer.add_tokens(["""xx"""] )
with self.assertRaisesRegex(A ,"""include""" ):
WavaVecaProcessorWithLM(
tokenizer=A ,feature_extractor=self.get_feature_extractor() ,decoder=self.get_decoder() )
def __lowercase ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = self.get_feature_extractor()
UpperCAmelCase__ : Optional[Any] = self.get_tokenizer()
UpperCAmelCase__ : Any = self.get_decoder()
UpperCAmelCase__ : Union[str, Any] = WavaVecaProcessorWithLM(tokenizer=A ,feature_extractor=A ,decoder=A )
UpperCAmelCase__ : str = floats_list((3, 1_000) )
UpperCAmelCase__ : Optional[Any] = feature_extractor(A ,return_tensors="""np""" )
UpperCAmelCase__ : List[Any] = processor(A ,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 __lowercase ( self : int ):
'''simple docstring'''
UpperCAmelCase__ : int = self.get_feature_extractor()
UpperCAmelCase__ : Union[str, Any] = self.get_tokenizer()
UpperCAmelCase__ : Optional[int] = self.get_decoder()
UpperCAmelCase__ : List[Any] = WavaVecaProcessorWithLM(tokenizer=A ,feature_extractor=A ,decoder=A )
UpperCAmelCase__ : List[Any] = """This is a test string"""
UpperCAmelCase__ : int = processor(text=A )
UpperCAmelCase__ : Dict = tokenizer(A )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] ,encoded_processor[key] )
def __lowercase ( self : Tuple ,A : List[Any]=(2, 10, 16) ,A : Dict=77 ):
'''simple docstring'''
np.random.seed(A )
return np.random.rand(*A )
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ : Dict = self.get_feature_extractor()
UpperCAmelCase__ : Optional[Any] = self.get_tokenizer()
UpperCAmelCase__ : int = self.get_decoder()
UpperCAmelCase__ : Optional[Any] = WavaVecaProcessorWithLM(tokenizer=A ,feature_extractor=A ,decoder=A )
UpperCAmelCase__ : Dict = self._get_dummy_logits(shape=(10, 16) ,seed=13 )
UpperCAmelCase__ : Tuple = processor.decode(A )
UpperCAmelCase__ : Union[str, Any] = decoder.decode_beams(A )[0]
self.assertEqual(decoded_decoder[0] ,decoded_processor.text )
self.assertEqual("""</s> <s> </s>""" ,decoded_processor.text )
self.assertEqual(decoded_decoder[-2] ,decoded_processor.logit_score )
self.assertEqual(decoded_decoder[-1] ,decoded_processor.lm_score )
@parameterized.expand([[None], ["""fork"""], ["""spawn"""]] )
def __lowercase ( self : List[str] ,A : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = self.get_feature_extractor()
UpperCAmelCase__ : int = self.get_tokenizer()
UpperCAmelCase__ : List[Any] = self.get_decoder()
UpperCAmelCase__ : Dict = WavaVecaProcessorWithLM(tokenizer=A ,feature_extractor=A ,decoder=A )
UpperCAmelCase__ : Optional[Any] = self._get_dummy_logits()
# note: pool should be instantiated *after* Wav2Vec2ProcessorWithLM.
# otherwise, the LM won't be available to the pool's sub-processes.
# manual logic used to allow parameterized test for both pool=None and pool=Pool(...)
if pool_context is None:
UpperCAmelCase__ : List[str] = processor.batch_decode(A )
else:
with get_context(A ).Pool() as pool:
UpperCAmelCase__ : Union[str, Any] = processor.batch_decode(A ,A )
UpperCAmelCase__ : Optional[Any] = list(A )
with get_context("""fork""" ).Pool() as p:
UpperCAmelCase__ : Union[str, Any] = decoder.decode_beams_batch(A ,A )
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ : Optional[Any] = [], [], []
for beams in decoded_beams:
texts_decoder.append(beams[0][0] )
logit_scores_decoder.append(beams[0][-2] )
lm_scores_decoder.append(beams[0][-1] )
self.assertListEqual(A ,decoded_processor.text )
self.assertListEqual(["""<s> <s> </s>""", """<s> <s> <s>"""] ,decoded_processor.text )
self.assertListEqual(A ,decoded_processor.logit_score )
self.assertListEqual(A ,decoded_processor.lm_score )
def __lowercase ( self : int ):
'''simple docstring'''
UpperCAmelCase__ : Any = self.get_feature_extractor()
UpperCAmelCase__ : Tuple = self.get_tokenizer()
UpperCAmelCase__ : List[Any] = self.get_decoder()
UpperCAmelCase__ : Union[str, Any] = WavaVecaProcessorWithLM(tokenizer=A ,feature_extractor=A ,decoder=A )
UpperCAmelCase__ : Dict = self._get_dummy_logits()
UpperCAmelCase__ : Any = 15
UpperCAmelCase__ : Dict = -2_0.0
UpperCAmelCase__ : List[Any] = -4.0
UpperCAmelCase__ : Union[str, Any] = processor.batch_decode(
A ,beam_width=A ,beam_prune_logp=A ,token_min_logp=A ,)
UpperCAmelCase__ : List[str] = decoded_processor_out.text
UpperCAmelCase__ : List[str] = list(A )
with get_context("""fork""" ).Pool() as pool:
UpperCAmelCase__ : Tuple = decoder.decode_beams_batch(
A ,A ,beam_width=A ,beam_prune_logp=A ,token_min_logp=A ,)
UpperCAmelCase__ : List[Any] = [d[0][0] for d in decoded_decoder_out]
UpperCAmelCase__ : Any = [d[0][2] for d in decoded_decoder_out]
UpperCAmelCase__ : List[str] = [d[0][3] for d in decoded_decoder_out]
self.assertListEqual(A ,A )
self.assertListEqual(["""</s> <s> <s>""", """<s> <s> <s>"""] ,A )
self.assertTrue(np.array_equal(A ,decoded_processor_out.logit_score ) )
self.assertTrue(np.allclose([-2_0.0_5_4, -1_8.4_4_7] ,A ,atol=1e-3 ) )
self.assertTrue(np.array_equal(A ,decoded_processor_out.lm_score ) )
self.assertTrue(np.allclose([-1_5.5_5_4, -1_3.9_4_7_4] ,A ,atol=1e-3 ) )
def __lowercase ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = self.get_feature_extractor()
UpperCAmelCase__ : Optional[Any] = self.get_tokenizer()
UpperCAmelCase__ : int = self.get_decoder()
UpperCAmelCase__ : str = WavaVecaProcessorWithLM(tokenizer=A ,feature_extractor=A ,decoder=A )
UpperCAmelCase__ : Tuple = self._get_dummy_logits()
UpperCAmelCase__ : Tuple = 2.0
UpperCAmelCase__ : str = 5.0
UpperCAmelCase__ : Union[str, Any] = -2_0.0
UpperCAmelCase__ : Optional[Any] = True
UpperCAmelCase__ : str = processor.batch_decode(
A ,alpha=A ,beta=A ,unk_score_offset=A ,lm_score_boundary=A ,)
UpperCAmelCase__ : Any = decoded_processor_out.text
UpperCAmelCase__ : Union[str, Any] = list(A )
decoder.reset_params(
alpha=A ,beta=A ,unk_score_offset=A ,lm_score_boundary=A ,)
with get_context("""fork""" ).Pool() as pool:
UpperCAmelCase__ : List[Any] = decoder.decode_beams_batch(
A ,A ,)
UpperCAmelCase__ : Union[str, Any] = [d[0][0] for d in decoded_decoder_out]
self.assertListEqual(A ,A )
self.assertListEqual(["""<s> </s> <s> </s> </s>""", """</s> </s> <s> </s> </s>"""] ,A )
UpperCAmelCase__ : Union[str, Any] = processor.decoder.model_container[processor.decoder._model_key]
self.assertEqual(lm_model.alpha ,2.0 )
self.assertEqual(lm_model.beta ,5.0 )
self.assertEqual(lm_model.unk_score_offset ,-2_0.0 )
self.assertEqual(lm_model.score_boundary ,A )
def __lowercase ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Dict = WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm""" )
UpperCAmelCase__ : str = processor.decoder.model_container[processor.decoder._model_key]
UpperCAmelCase__ : Any = Path(language_model._kenlm_model.path.decode("""utf-8""" ) ).parent.parent.absolute()
UpperCAmelCase__ : Optional[int] = os.listdir(A )
UpperCAmelCase__ : List[Any] = ["""alphabet.json""", """language_model"""]
downloaded_decoder_files.sort()
expected_decoder_files.sort()
# test that only decoder relevant files from
# https://huggingface.co/hf-internal-testing/processor_with_lm/tree/main
# are downloaded and none of the rest (e.g. README.md, ...)
self.assertListEqual(A ,A )
def __lowercase ( self : int ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = snapshot_download("""hf-internal-testing/processor_with_lm""" )
UpperCAmelCase__ : Union[str, Any] = WavaVecaProcessorWithLM.from_pretrained(A )
UpperCAmelCase__ : Tuple = processor.decoder.model_container[processor.decoder._model_key]
UpperCAmelCase__ : Optional[int] = Path(language_model._kenlm_model.path.decode("""utf-8""" ) ).parent.parent.absolute()
UpperCAmelCase__ : Tuple = os.listdir(A )
UpperCAmelCase__ : Dict = os.listdir(A )
local_decoder_files.sort()
expected_decoder_files.sort()
# test that both decoder form hub and local files in cache are the same
self.assertListEqual(A ,A )
def __lowercase ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm""" )
UpperCAmelCase__ : Tuple = AutoProcessor.from_pretrained("""hf-internal-testing/processor_with_lm""" )
UpperCAmelCase__ : Dict = floats_list((3, 1_000) )
UpperCAmelCase__ : List[str] = processor_wavaveca(A ,return_tensors="""np""" )
UpperCAmelCase__ : Dict = processor_auto(A ,return_tensors="""np""" )
for key in input_wavaveca.keys():
self.assertAlmostEqual(input_wavaveca[key].sum() ,input_auto[key].sum() ,delta=1e-2 )
UpperCAmelCase__ : List[str] = self._get_dummy_logits()
UpperCAmelCase__ : Tuple = processor_wavaveca.batch_decode(A )
UpperCAmelCase__ : List[str] = processor_auto.batch_decode(A )
self.assertListEqual(decoded_wavaveca.text ,decoded_auto.text )
def __lowercase ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase__ : Dict = self.get_feature_extractor()
UpperCAmelCase__ : Tuple = self.get_tokenizer()
UpperCAmelCase__ : List[Any] = self.get_decoder()
UpperCAmelCase__ : int = WavaVecaProcessorWithLM(tokenizer=A ,feature_extractor=A ,decoder=A )
self.assertListEqual(
processor.model_input_names ,feature_extractor.model_input_names ,msg="""`processor` and `feature_extractor` model input names do not match""" ,)
@staticmethod
def __lowercase ( A : Optional[Any] ,A : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = [d[key] for d in offsets]
return retrieved_list
def __lowercase ( self : Dict ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm""" )
UpperCAmelCase__ : Dict = self._get_dummy_logits()[0]
UpperCAmelCase__ : List[str] = processor.decode(A ,output_word_offsets=A )
# check Wav2Vec2CTCTokenizerOutput keys for word
self.assertEqual(len(outputs.keys() ) ,4 )
self.assertTrue("""text""" in outputs )
self.assertTrue("""word_offsets""" in outputs )
self.assertTrue(isinstance(A ,A ) )
self.assertEqual(""" """.join(self.get_from_offsets(outputs["""word_offsets"""] ,"""word""" ) ) ,outputs.text )
self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""] ,"""word""" ) ,["""<s>""", """<s>""", """</s>"""] )
self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""] ,"""start_offset""" ) ,[0, 2, 4] )
self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""] ,"""end_offset""" ) ,[1, 3, 5] )
def __lowercase ( self : Dict ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm""" )
UpperCAmelCase__ : int = self._get_dummy_logits()
UpperCAmelCase__ : Any = processor.batch_decode(A ,output_word_offsets=A )
# check Wav2Vec2CTCTokenizerOutput keys for word
self.assertEqual(len(outputs.keys() ) ,4 )
self.assertTrue("""text""" in outputs )
self.assertTrue("""word_offsets""" in outputs )
self.assertTrue(isinstance(A ,A ) )
self.assertListEqual(
[""" """.join(self.get_from_offsets(A ,"""word""" ) ) for o in outputs["""word_offsets"""]] ,outputs.text )
self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""][0] ,"""word""" ) ,["""<s>""", """<s>""", """</s>"""] )
self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""][0] ,"""start_offset""" ) ,[0, 2, 4] )
self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""][0] ,"""end_offset""" ) ,[1, 3, 5] )
@slow
@require_torch
@require_torchaudio
def __lowercase ( self : Tuple ):
'''simple docstring'''
import torch
UpperCAmelCase__ : Any = load_dataset("""common_voice""" ,"""en""" ,split="""train""" ,streaming=A )
UpperCAmelCase__ : Tuple = ds.cast_column("""audio""" ,datasets.Audio(sampling_rate=16_000 ) )
UpperCAmelCase__ : Tuple = iter(A )
UpperCAmelCase__ : Optional[int] = next(A )
UpperCAmelCase__ : List[Any] = AutoProcessor.from_pretrained("""patrickvonplaten/wav2vec2-base-100h-with-lm""" )
UpperCAmelCase__ : Tuple = WavaVecaForCTC.from_pretrained("""patrickvonplaten/wav2vec2-base-100h-with-lm""" )
# compare to filename `common_voice_en_100038.mp3` of dataset viewer on https://huggingface.co/datasets/common_voice/viewer/en/train
UpperCAmelCase__ : Tuple = processor(sample["""audio"""]["""array"""] ,return_tensors="""pt""" ).input_values
with torch.no_grad():
UpperCAmelCase__ : Union[str, Any] = model(A ).logits.cpu().numpy()
UpperCAmelCase__ : Any = processor.decode(logits[0] ,output_word_offsets=A )
UpperCAmelCase__ : str = model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
UpperCAmelCase__ : Union[str, Any] = [
{
"""start_time""": d["""start_offset"""] * time_offset,
"""end_time""": d["""end_offset"""] * time_offset,
"""word""": d["""word"""],
}
for d in output["""word_offsets"""]
]
UpperCAmelCase__ : Dict = """WHY DOES MILISANDRA LOOK LIKE SHE WANTS TO CONSUME JOHN SNOW ON THE RIVER AT THE WALL"""
# output words
self.assertEqual(""" """.join(self.get_from_offsets(A ,"""word""" ) ) ,A )
self.assertEqual(""" """.join(self.get_from_offsets(A ,"""word""" ) ) ,output.text )
# output times
UpperCAmelCase__ : str = torch.tensor(self.get_from_offsets(A ,"""start_time""" ) )
UpperCAmelCase__ : List[Any] = torch.tensor(self.get_from_offsets(A ,"""end_time""" ) )
# fmt: off
UpperCAmelCase__ : Union[str, Any] = torch.tensor([1.4_1_9_9, 1.6_5_9_9, 2.2_5_9_9, 3.0, 3.2_4, 3.5_9_9_9, 3.7_9_9_9, 4.0_9_9_9, 4.2_6, 4.9_4, 5.2_8, 5.6_5_9_9, 5.7_8, 5.9_4, 6.3_2, 6.5_3_9_9, 6.6_5_9_9] )
UpperCAmelCase__ : List[Any] = torch.tensor([1.5_3_9_9, 1.8_9_9_9, 2.9, 3.1_6, 3.5_3_9_9, 3.7_2, 4.0_1_9_9, 4.1_7_9_9, 4.7_6, 5.1_5_9_9, 5.5_5_9_9, 5.6_9_9_9, 5.8_6, 6.1_9_9_9, 6.3_8, 6.6_1_9_9, 6.9_4] )
# fmt: on
self.assertTrue(torch.allclose(A ,A ,atol=0.0_1 ) )
self.assertTrue(torch.allclose(A ,A ,atol=0.0_1 ) )
| 65 | 0 |
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import ConvNextVaBackbone, ConvNextVaForImageClassification, ConvNextVaModel
from transformers.models.convnextva.modeling_convnextva import CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class __magic_name__ :
'''simple docstring'''
def __init__( self:List[str] , _a:List[Any] , _a:List[str]=13 , _a:Any=32 , _a:List[str]=3 , _a:Optional[int]=4 , _a:Optional[int]=[10, 20, 30, 40] , _a:str=[2, 2, 3, 2] , _a:Optional[Any]=True , _a:Dict=True , _a:Tuple=37 , _a:List[str]="gelu" , _a:Optional[int]=10 , _a:List[Any]=0.02 , _a:Optional[int]=["stage2", "stage3", "stage4"] , _a:List[Any]=[2, 3, 4] , _a:List[Any]=None , ):
snake_case__ = parent
snake_case__ = batch_size
snake_case__ = image_size
snake_case__ = num_channels
snake_case__ = num_stages
snake_case__ = hidden_sizes
snake_case__ = depths
snake_case__ = is_training
snake_case__ = use_labels
snake_case__ = intermediate_size
snake_case__ = hidden_act
snake_case__ = num_labels
snake_case__ = initializer_range
snake_case__ = out_features
snake_case__ = out_indices
snake_case__ = scope
def SCREAMING_SNAKE_CASE__ ( self:List[Any] ):
snake_case__ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
snake_case__ = None
if self.use_labels:
snake_case__ = ids_tensor([self.batch_size] , self.num_labels )
snake_case__ = self.get_config()
return config, pixel_values, labels
def SCREAMING_SNAKE_CASE__ ( self:int ):
return ConvNextVaConfig(
num_channels=self.num_channels , hidden_sizes=self.hidden_sizes , depths=self.depths , num_stages=self.num_stages , hidden_act=self.hidden_act , is_decoder=_a , initializer_range=self.initializer_range , out_features=self.out_features , out_indices=self.out_indices , num_labels=self.num_labels , )
def SCREAMING_SNAKE_CASE__ ( self:str , _a:List[Any] , _a:Union[str, Any] , _a:Optional[Any] ):
snake_case__ = ConvNextVaModel(config=_a )
model.to(_a )
model.eval()
snake_case__ = model(_a )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def SCREAMING_SNAKE_CASE__ ( self:Union[str, Any] , _a:Union[str, Any] , _a:Optional[Any] , _a:List[str] ):
snake_case__ = ConvNextVaForImageClassification(_a )
model.to(_a )
model.eval()
snake_case__ = model(_a , labels=_a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def SCREAMING_SNAKE_CASE__ ( self:int , _a:Optional[int] , _a:Optional[int] , _a:Union[str, Any] ):
snake_case__ = ConvNextVaBackbone(config=_a )
model.to(_a )
model.eval()
snake_case__ = model(_a )
# verify hidden states
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[1], 4, 4] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , config.hidden_sizes[1:] )
# verify backbone works with out_features=None
snake_case__ = None
snake_case__ = ConvNextVaBackbone(config=_a )
model.to(_a )
model.eval()
snake_case__ = model(_a )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , 1 )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[-1], 1, 1] )
# verify channels
self.parent.assertEqual(len(model.channels ) , 1 )
self.parent.assertListEqual(model.channels , [config.hidden_sizes[-1]] )
def SCREAMING_SNAKE_CASE__ ( self:str ):
snake_case__ = self.prepare_config_and_inputs()
snake_case__ = config_and_inputs
snake_case__ = {"""pixel_values""": pixel_values}
return config, inputs_dict
def SCREAMING_SNAKE_CASE__ ( self:str ):
snake_case__ = self.prepare_config_and_inputs()
snake_case__ = config_and_inputs
snake_case__ = {"""pixel_values""": pixel_values, """labels""": labels}
return config, inputs_dict
@require_torch
class __magic_name__ (__lowerCamelCase ,__lowerCamelCase ,unittest.TestCase ):
'''simple docstring'''
__lowercase : Union[str, Any] = (
(
ConvNextVaModel,
ConvNextVaForImageClassification,
ConvNextVaBackbone,
)
if is_torch_available()
else ()
)
__lowercase : Any = (
{'feature-extraction': ConvNextVaModel, 'image-classification': ConvNextVaForImageClassification}
if is_torch_available()
else {}
)
__lowercase : Tuple = False
__lowercase : Any = False
__lowercase : List[Any] = False
__lowercase : List[str] = False
__lowercase : Optional[int] = False
def SCREAMING_SNAKE_CASE__ ( self:Tuple ):
snake_case__ = ConvNextVaModelTester(self )
snake_case__ = ConfigTester(self , config_class=_a , has_text_modality=_a , hidden_size=37 )
def SCREAMING_SNAKE_CASE__ ( self:List[str] ):
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 SCREAMING_SNAKE_CASE__ ( self:List[str] ):
return
@unittest.skip(reason='''ConvNextV2 does not use inputs_embeds''' )
def SCREAMING_SNAKE_CASE__ ( self:Union[str, Any] ):
pass
@unittest.skip(reason='''ConvNextV2 does not support input and output embeddings''' )
def SCREAMING_SNAKE_CASE__ ( self:Optional[Any] ):
pass
@unittest.skip(reason='''ConvNextV2 does not use feedforward chunking''' )
def SCREAMING_SNAKE_CASE__ ( self:str ):
pass
def SCREAMING_SNAKE_CASE__ ( self:List[Any] ):
if not self.model_tester.is_training:
return
for model_class in self.all_model_classes:
snake_case__ = self.model_tester.prepare_config_and_inputs_with_labels()
snake_case__ = True
if model_class.__name__ in [
*get_values(_a ),
*get_values(_a ),
]:
continue
snake_case__ = model_class(_a )
model.to(_a )
model.train()
snake_case__ = self._prepare_for_class(_a , _a , return_labels=_a )
snake_case__ = model(**_a ).loss
loss.backward()
def SCREAMING_SNAKE_CASE__ ( self:Tuple ):
if not self.model_tester.is_training:
return
for model_class in self.all_model_classes:
snake_case__ = self.model_tester.prepare_config_and_inputs_with_labels()
snake_case__ = False
snake_case__ = True
if (
model_class.__name__
in [*get_values(_a ), *get_values(_a )]
or not model_class.supports_gradient_checkpointing
):
continue
snake_case__ = model_class(_a )
model.to(_a )
model.gradient_checkpointing_enable()
model.train()
snake_case__ = self._prepare_for_class(_a , _a , return_labels=_a )
snake_case__ = model(**_a ).loss
loss.backward()
def SCREAMING_SNAKE_CASE__ ( self:List[str] ):
snake_case__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case__ = model_class(_a )
snake_case__ = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
snake_case__ = [*signature.parameters.keys()]
snake_case__ = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , _a )
def SCREAMING_SNAKE_CASE__ ( self:Dict ):
snake_case__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_a )
def SCREAMING_SNAKE_CASE__ ( self:Any ):
def check_hidden_states_output(_a:Optional[Any] , _a:Union[str, Any] , _a:str ):
snake_case__ = model_class(_a )
model.to(_a )
model.eval()
with torch.no_grad():
snake_case__ = model(**self._prepare_for_class(_a , _a ) )
snake_case__ = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
snake_case__ = self.model_tester.num_stages
self.assertEqual(len(_a ) , expected_num_stages + 1 )
# ConvNextV2's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
snake_case__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
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"]
snake_case__ = True
check_hidden_states_output(_a , _a , _a )
def SCREAMING_SNAKE_CASE__ ( self:int ):
snake_case__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_a )
@slow
def SCREAMING_SNAKE_CASE__ ( self:Optional[Any] ):
for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case__ = ConvNextVaModel.from_pretrained(_a )
self.assertIsNotNone(_a )
def SCREAMING_SNAKE_CASE ( ) -> List[Any]:
snake_case__ = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class __magic_name__ (unittest.TestCase ):
'''simple docstring'''
@cached_property
def SCREAMING_SNAKE_CASE__ ( self:int ):
return AutoImageProcessor.from_pretrained('''facebook/convnextv2-tiny-1k-224''' ) if is_vision_available() else None
@slow
def SCREAMING_SNAKE_CASE__ ( self:Optional[Any] ):
snake_case__ = ConvNextVaForImageClassification.from_pretrained('''facebook/convnextv2-tiny-1k-224''' ).to(_a )
snake_case__ = self.default_image_processor
snake_case__ = prepare_img()
snake_case__ = preprocessor(images=_a , return_tensors='''pt''' ).to(_a )
# forward pass
with torch.no_grad():
snake_case__ = model(**_a )
# verify the logits
snake_case__ = torch.Size((1, 10_00) )
self.assertEqual(outputs.logits.shape , _a )
snake_case__ = torch.tensor([0.9996, 0.1966, -0.4386] ).to(_a )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _a , atol=1e-4 ) )
| 33 |
"""simple docstring"""
from sklearn.metrics import fa_score
import datasets
__UpperCAmelCase = '\nThe F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:\nF1 = 2 * (precision * recall) / (precision + recall)\n'
__UpperCAmelCase = '\nArgs:\n predictions (`list` of `int`): Predicted labels.\n references (`list` of `int`): Ground truth labels.\n labels (`list` of `int`): The set of labels to include when `average` is not set to `\'binary\'`, and the order of the labels if `average` is `None`. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class. Labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in `predictions` and `references` are used in sorted order. Defaults to None.\n pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.\n average (`string`): This parameter is required for multiclass/multilabel targets. If set to `None`, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `\'binary\'`.\n\n - \'binary\': Only report results for the class specified by `pos_label`. This is applicable only if the classes found in `predictions` and `references` are binary.\n - \'micro\': Calculate metrics globally by counting the total true positives, false negatives and false positives.\n - \'macro\': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.\n - \'weighted\': Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `\'macro\'` to account for label imbalance. This option can result in an F-score that is not between precision and recall.\n - \'samples\': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).\n sample_weight (`list` of `float`): Sample weights Defaults to None.\n\nReturns:\n f1 (`float` or `array` of `float`): F1 score or list of f1 scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher f1 scores are better.\n\nExamples:\n\n Example 1-A simple binary example\n >>> f1_metric = datasets.load_metric("f1")\n >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])\n >>> print(results)\n {\'f1\': 0.5}\n\n Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.\n >>> f1_metric = datasets.load_metric("f1")\n >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)\n >>> print(round(results[\'f1\'], 2))\n 0.67\n\n Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.\n >>> f1_metric = datasets.load_metric("f1")\n >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], sample_weight=[0.9, 0.5, 3.9, 1.2, 0.3])\n >>> print(round(results[\'f1\'], 2))\n 0.35\n\n Example 4-A multiclass example, with different values for the `average` input.\n >>> predictions = [0, 2, 1, 0, 0, 1]\n >>> references = [0, 1, 2, 0, 1, 2]\n >>> results = f1_metric.compute(predictions=predictions, references=references, average="macro")\n >>> print(round(results[\'f1\'], 2))\n 0.27\n >>> results = f1_metric.compute(predictions=predictions, references=references, average="micro")\n >>> print(round(results[\'f1\'], 2))\n 0.33\n >>> results = f1_metric.compute(predictions=predictions, references=references, average="weighted")\n >>> print(round(results[\'f1\'], 2))\n 0.27\n >>> results = f1_metric.compute(predictions=predictions, references=references, average=None)\n >>> print(results)\n {\'f1\': array([0.8, 0. , 0. ])}\n'
__UpperCAmelCase = '\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __lowercase ( datasets.Metric ):
def __lowercase ( self : List[Any] ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION ,citation=_CITATION ,inputs_description=_KWARGS_DESCRIPTION ,features=datasets.Features(
{
"""predictions""": datasets.Sequence(datasets.Value("""int32""" ) ),
"""references""": datasets.Sequence(datasets.Value("""int32""" ) ),
}
if self.config_name == """multilabel"""
else {
"""predictions""": datasets.Value("""int32""" ),
"""references""": datasets.Value("""int32""" ),
} ) ,reference_urls=["""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html"""] ,)
def __lowercase ( self : Union[str, Any] ,A : List[str] ,A : List[Any] ,A : Optional[Any]=None ,A : List[str]=1 ,A : Optional[Any]="binary" ,A : Any=None ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = fa_score(
A ,A ,labels=A ,pos_label=A ,average=A ,sample_weight=A )
return {"f1": float(A ) if score.size == 1 else score}
| 65 | 0 |
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class __lowercase ( __lowerCamelCase , unittest.TestCase ):
"""simple docstring"""
pass
@nightly
@require_onnxruntime
@require_torch_gpu
class __lowercase ( unittest.TestCase ):
"""simple docstring"""
@property
def __magic_name__ ( self )-> int:
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def __magic_name__ ( self )-> List[str]:
_SCREAMING_SNAKE_CASE = ort.SessionOptions()
_SCREAMING_SNAKE_CASE = False
return options
def __magic_name__ ( self )-> Optional[int]:
_SCREAMING_SNAKE_CASE = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/in_paint/overture-creations-5sI6fQgYIuo.png' )
_SCREAMING_SNAKE_CASE = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/in_paint/overture-creations-5sI6fQgYIuo_mask.png' )
_SCREAMING_SNAKE_CASE = OnnxStableDiffusionInpaintPipeline.from_pretrained(
'runwayml/stable-diffusion-inpainting' , revision='onnx' , safety_checker=A_ , feature_extractor=A_ , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=A_ )
_SCREAMING_SNAKE_CASE = """A red cat sitting on a park bench"""
_SCREAMING_SNAKE_CASE = np.random.RandomState(0 )
_SCREAMING_SNAKE_CASE = pipe(
prompt=A_ , image=A_ , mask_image=A_ , guidance_scale=7.5 , num_inference_steps=10 , generator=A_ , output_type='np' , )
_SCREAMING_SNAKE_CASE = output.images
_SCREAMING_SNAKE_CASE = images[0, 255:258, 255:258, -1]
assert images.shape == (1, 512, 512, 3)
_SCREAMING_SNAKE_CASE = np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def __magic_name__ ( self )-> Any:
_SCREAMING_SNAKE_CASE = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/in_paint/overture-creations-5sI6fQgYIuo.png' )
_SCREAMING_SNAKE_CASE = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/in_paint/overture-creations-5sI6fQgYIuo_mask.png' )
_SCREAMING_SNAKE_CASE = LMSDiscreteScheduler.from_pretrained(
'runwayml/stable-diffusion-inpainting' , subfolder='scheduler' , revision='onnx' )
_SCREAMING_SNAKE_CASE = OnnxStableDiffusionInpaintPipeline.from_pretrained(
'runwayml/stable-diffusion-inpainting' , revision='onnx' , scheduler=A_ , safety_checker=A_ , feature_extractor=A_ , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=A_ )
_SCREAMING_SNAKE_CASE = """A red cat sitting on a park bench"""
_SCREAMING_SNAKE_CASE = np.random.RandomState(0 )
_SCREAMING_SNAKE_CASE = pipe(
prompt=A_ , image=A_ , mask_image=A_ , guidance_scale=7.5 , num_inference_steps=20 , generator=A_ , output_type='np' , )
_SCREAMING_SNAKE_CASE = output.images
_SCREAMING_SNAKE_CASE = images[0, 255:258, 255:258, -1]
assert images.shape == (1, 512, 512, 3)
_SCREAMING_SNAKE_CASE = np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
| 605 |
"""simple docstring"""
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available
if is_sentencepiece_available():
from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json
from ...test_tokenization_common import TokenizerTesterMixin
__UpperCAmelCase = get_tests_dir('fixtures/test_sentencepiece.model')
__UpperCAmelCase = {'target_lang': 'fi', 'source_lang': 'en'}
__UpperCAmelCase = '>>zh<<'
__UpperCAmelCase = 'Helsinki-NLP/'
if is_torch_available():
__UpperCAmelCase = 'pt'
elif is_tf_available():
__UpperCAmelCase = 'tf'
else:
__UpperCAmelCase = 'jax'
@require_sentencepiece
class __lowercase ( __lowerCamelCase , unittest.TestCase ):
snake_case_ = MarianTokenizer
snake_case_ = False
snake_case_ = True
def __lowercase ( self : Optional[int] ):
'''simple docstring'''
super().setUp()
UpperCAmelCase__ : Optional[Any] = ["""</s>""", """<unk>""", """▁This""", """▁is""", """▁a""", """▁t""", """est""", """\u0120""", """<pad>"""]
UpperCAmelCase__ : int = dict(zip(A ,range(len(A ) ) ) )
UpperCAmelCase__ : Optional[int] = Path(self.tmpdirname )
save_json(A ,save_dir / VOCAB_FILES_NAMES["""vocab"""] )
save_json(A ,save_dir / VOCAB_FILES_NAMES["""tokenizer_config_file"""] )
if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
copyfile(A ,save_dir / VOCAB_FILES_NAMES["""source_spm"""] )
copyfile(A ,save_dir / VOCAB_FILES_NAMES["""target_spm"""] )
UpperCAmelCase__ : Dict = MarianTokenizer.from_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname )
def __lowercase ( self : List[Any] ,**A : List[Any] ):
'''simple docstring'''
return MarianTokenizer.from_pretrained(self.tmpdirname ,**A )
def __lowercase ( self : Union[str, Any] ,A : Tuple ):
'''simple docstring'''
return (
"This is a test",
"This is a test",
)
def __lowercase ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = """</s>"""
UpperCAmelCase__ : int = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(A ) ,A )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(A ) ,A )
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ : Dict = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] ,"""</s>""" )
self.assertEqual(vocab_keys[1] ,"""<unk>""" )
self.assertEqual(vocab_keys[-1] ,"""<pad>""" )
self.assertEqual(len(A ) ,9 )
def __lowercase ( self : Dict ):
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size ,9 )
def __lowercase ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = MarianTokenizer.from_pretrained(f"{ORG_NAME}opus-mt-en-de" )
UpperCAmelCase__ : List[str] = en_de_tokenizer(["""I am a small frog"""] ,return_tensors=A )
self.assertIsInstance(A ,A )
UpperCAmelCase__ : str = [38, 121, 14, 697, 38_848, 0]
self.assertListEqual(A ,batch.input_ids[0] )
UpperCAmelCase__ : Optional[Any] = tempfile.mkdtemp()
en_de_tokenizer.save_pretrained(A )
UpperCAmelCase__ : Tuple = [x.name for x in Path(A ).glob("""*""" )]
self.assertIn("""source.spm""" ,A )
MarianTokenizer.from_pretrained(A )
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = self.get_tokenizer()
UpperCAmelCase__ : Any = tok(
["""I am a small frog""" * 1_000, """I am a small frog"""] ,padding=A ,truncation=A ,return_tensors=A )
self.assertIsInstance(A ,A )
self.assertEqual(batch.input_ids.shape ,(2, 512) )
def __lowercase ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase__ : int = self.get_tokenizer()
UpperCAmelCase__ : Tuple = tok(["""I am a tiny frog""", """I am a small frog"""] ,padding=A ,return_tensors=A )
self.assertIsInstance(A ,A )
self.assertEqual(batch_smaller.input_ids.shape ,(2, 10) )
@slow
def __lowercase ( self : Dict ):
'''simple docstring'''
# fmt: off
UpperCAmelCase__ : Optional[int] = {"""input_ids""": [[43_495, 462, 20, 42_164, 1_369, 52, 464, 132, 1_703, 492, 13, 7_491, 38_999, 6, 8, 464, 132, 1_703, 492, 13, 4_669, 37_867, 13, 7_525, 27, 1_593, 988, 13, 33_972, 7_029, 6, 20, 8_251, 383, 2, 270, 5_866, 3_788, 2, 2_353, 8_251, 12_338, 2, 13_958, 387, 2, 3_629, 6_953, 188, 2_900, 2, 13_958, 8_011, 11_501, 23, 8_460, 4_073, 34_009, 20, 435, 11_439, 27, 8, 8_460, 4_073, 6_004, 20, 9_988, 375, 27, 33, 266, 1_945, 1_076, 1_350, 37_867, 3_288, 5, 577, 1_076, 4_374, 8, 5_082, 5, 26_453, 257, 556, 403, 2, 242, 132, 383, 316, 492, 8, 10_767, 6, 316, 304, 4_239, 3, 0], [148, 15_722, 19, 1_839, 12, 1_350, 13, 22_327, 5_082, 5_418, 47_567, 35_938, 59, 318, 19_552, 108, 2_183, 54, 14_976, 4_835, 32, 547, 1_114, 8, 315, 2_417, 5, 92, 19_088, 3, 0, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100], [36, 6_395, 12_570, 39_147, 11_597, 6, 266, 4, 45_405, 7_296, 3, 0, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100]], """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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=A ,model_name="""Helsinki-NLP/opus-mt-en-de""" ,revision="""1a8c2263da11e68e50938f97e10cd57820bd504c""" ,decode_kwargs={"""use_source_tokenizer""": True} ,)
def __lowercase ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = MarianTokenizer.from_pretrained("""hf-internal-testing/test-marian-two-vocabs""" )
UpperCAmelCase__ : Any = """Tämä on testi"""
UpperCAmelCase__ : int = """This is a test"""
UpperCAmelCase__ : List[str] = [76, 7, 2_047, 2]
UpperCAmelCase__ : Optional[Any] = [69, 12, 11, 940, 2]
UpperCAmelCase__ : List[str] = tokenizer(A ).input_ids
self.assertListEqual(A ,A )
UpperCAmelCase__ : Optional[int] = tokenizer(text_target=A ).input_ids
self.assertListEqual(A ,A )
UpperCAmelCase__ : int = tokenizer.decode(A ,skip_special_tokens=A )
self.assertEqual(A ,A )
| 65 | 0 |
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def _A ( ):
UpperCAmelCase__: Any = {
"""repo_name""": ["""test_repo1""", """test_repo2""", """test_repo3"""],
"""path""": ["""test_1.py""", """test_2.py""", """unit_test.py"""],
"""content""": ["""a """ * 2_0, """a """ * 3_0, """b """ * 7],
}
UpperCAmelCase__: Union[str, Any] = Dataset.from_dict(__UpperCamelCase )
return dataset
class __UpperCamelCase ( __lowerCamelCase ):
'''simple docstring'''
def _UpperCAmelCase ( self ):
UpperCAmelCase__: Any = get_dataset()
UpperCAmelCase__: int = make_duplicate_clusters(lowerCamelCase__ , 0.85 )
self.assertEqual(len(duplicate_clusters[0] ) , 2 )
def _UpperCAmelCase ( self ):
UpperCAmelCase__: Tuple = get_dataset()
UpperCAmelCase__: Dict = deduplicate_dataset(lowerCamelCase__ )
self.assertEqual(len(lowerCamelCase__ ) , 2 )
print(lowerCamelCase__ )
self.assertEqual(duplicate_clusters[0][0]["copies"] , 2 )
self.assertEqual(duplicate_clusters[0][0]["is_extreme"] , lowerCamelCase__ ) | 113 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class __lowercase ( metaclass=__lowerCamelCase ):
snake_case_ = ["""onnx"""]
def __init__( self : int ,*A : List[str] ,**A : int ):
'''simple docstring'''
requires_backends(self ,["""onnx"""] )
@classmethod
def __lowercase ( cls : Optional[Any] ,*A : List[str] ,**A : Dict ):
'''simple docstring'''
requires_backends(cls ,["""onnx"""] )
@classmethod
def __lowercase ( cls : List[Any] ,*A : Optional[int] ,**A : int ):
'''simple docstring'''
requires_backends(cls ,["""onnx"""] )
| 65 | 0 |
import unittest
from typing import Tuple
import torch
from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class _UpperCAmelCase :
'''simple docstring'''
@property
def __UpperCAmelCase ( self : Union[str, Any]) -> Union[str, Any]:
"""simple docstring"""
return self.get_dummy_input()
@property
def __UpperCAmelCase ( self : Dict) -> List[str]:
"""simple docstring"""
if self.block_type == "down":
return (4, 32, 16, 16)
elif self.block_type == "mid":
return (4, 32, 32, 32)
elif self.block_type == "up":
return (4, 32, 64, 64)
raise ValueError(f'\'{self.block_type}\' is not a supported block_type. Set it to \'up\', \'mid\', or \'down\'.')
def __UpperCAmelCase ( self : List[str] , lowercase_ : List[str]=True , lowercase_ : Optional[int]=False , lowercase_ : Any=False , lowercase_ : int=False , ) -> Union[str, Any]:
"""simple docstring"""
_UpperCamelCase = 4
_UpperCamelCase = 32
_UpperCamelCase = (32, 32)
_UpperCamelCase = torch.manual_seed(0)
_UpperCamelCase = torch.device(lowercase_)
_UpperCamelCase = (batch_size, num_channels) + sizes
_UpperCamelCase = randn_tensor(lowercase_ , generator=lowercase_ , device=lowercase_)
_UpperCamelCase = {"""hidden_states""": hidden_states}
if include_temb:
_UpperCamelCase = 128
_UpperCamelCase = randn_tensor((batch_size, temb_channels) , generator=lowercase_ , device=lowercase_)
if include_res_hidden_states_tuple:
_UpperCamelCase = torch.manual_seed(1)
_UpperCamelCase = (randn_tensor(lowercase_ , generator=lowercase_ , device=lowercase_),)
if include_encoder_hidden_states:
_UpperCamelCase = floats_tensor((batch_size, 32, 32)).to(lowercase_)
if include_skip_sample:
_UpperCamelCase = randn_tensor(((batch_size, 3) + sizes) , generator=lowercase_ , device=lowercase_)
return dummy_input
def __UpperCAmelCase ( self : Dict) -> List[Any]:
"""simple docstring"""
_UpperCamelCase = {
"""in_channels""": 32,
"""out_channels""": 32,
"""temb_channels""": 128,
}
if self.block_type == "up":
_UpperCamelCase = 32
if self.block_type == "mid":
init_dict.pop("out_channels")
_UpperCamelCase = self.dummy_input
return init_dict, inputs_dict
def __UpperCAmelCase ( self : List[Any] , lowercase_ : Optional[Any]) -> List[Any]:
"""simple docstring"""
_UpperCamelCase = self.prepare_init_args_and_inputs_for_common()
_UpperCamelCase = self.block_class(**lowercase_)
unet_block.to(lowercase_)
unet_block.eval()
with torch.no_grad():
_UpperCamelCase = unet_block(**lowercase_)
if isinstance(lowercase_ , lowercase_):
_UpperCamelCase = output[0]
self.assertEqual(output.shape , self.output_shape)
_UpperCamelCase = output[0, -1, -3:, -3:]
_UpperCamelCase = torch.tensor(lowercase_).to(lowercase_)
assert torch_all_close(output_slice.flatten() , lowercase_ , atol=5e-3)
@unittest.skipIf(torch_device == "mps" , "Training is not supported in mps")
def __UpperCAmelCase ( self : Optional[int]) -> str:
"""simple docstring"""
_UpperCamelCase = self.prepare_init_args_and_inputs_for_common()
_UpperCamelCase = self.block_class(**lowercase_)
model.to(lowercase_)
model.train()
_UpperCamelCase = model(**lowercase_)
if isinstance(lowercase_ , lowercase_):
_UpperCamelCase = output[0]
_UpperCamelCase = torch.device(lowercase_)
_UpperCamelCase = randn_tensor(output.shape , device=lowercase_)
_UpperCamelCase = torch.nn.functional.mse_loss(lowercase_ , lowercase_)
loss.backward()
| 547 |
"""simple docstring"""
from argparse import ArgumentParser
from .env import EnvironmentCommand
def lowerCAmelCase ( ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = ArgumentParser("""Diffusers CLI tool""" , usage="""diffusers-cli <command> [<args>]""" )
UpperCAmelCase__ : List[Any] = parser.add_subparsers(help="""diffusers-cli command helpers""" )
# Register commands
EnvironmentCommand.register_subcommand(__UpperCamelCase )
# Let's go
UpperCAmelCase__ : int = parser.parse_args()
if not hasattr(__UpperCamelCase , """func""" ):
parser.print_help()
exit(1 )
# Run
UpperCAmelCase__ : Union[str, Any] = args.func(__UpperCamelCase )
service.run()
if __name__ == "__main__":
main()
| 65 | 0 |
class __lowerCAmelCase :
"""simple docstring"""
def __init__( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
snake_case_ = {}
def lowerCAmelCase__ ( self : Any ) -> Tuple:
"""simple docstring"""
print(self.vertex )
for i in self.vertex:
print(_lowerCAmelCase , " -> " , " -> ".join([str(_lowerCAmelCase ) for j in self.vertex[i]] ) )
def lowerCAmelCase__ ( self : Tuple , _lowerCAmelCase : int , _lowerCAmelCase : int ) -> Tuple:
"""simple docstring"""
# check if vertex is already present,
if from_vertex in self.vertex:
self.vertex[from_vertex].append(_lowerCAmelCase )
else:
# else make a new vertex
snake_case_ = [to_vertex]
def lowerCAmelCase__ ( self : Optional[int] ) -> Any:
"""simple docstring"""
snake_case_ = [False] * len(self.vertex )
# call the recursive helper function
for i in range(len(self.vertex ) ):
if not visited[i]:
self.dfs_recursive(_lowerCAmelCase , _lowerCAmelCase )
def lowerCAmelCase__ ( self : List[Any] , _lowerCAmelCase : int , _lowerCAmelCase : list ) -> Dict:
"""simple docstring"""
snake_case_ = True
print(_lowerCAmelCase , end=" " )
# Recur for all the vertices that are adjacent to this node
for i in self.vertex:
if not visited[i]:
self.dfs_recursive(_lowerCAmelCase , _lowerCAmelCase )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE :Dict = Graph()
g.add_edge(0, 1)
g.add_edge(0, 2)
g.add_edge(1, 2)
g.add_edge(2, 0)
g.add_edge(2, 3)
g.add_edge(3, 3)
g.print_graph()
print('''DFS:''')
g.dfs()
# OUTPUT:
# 0 -> 1 -> 2
# 1 -> 2
# 2 -> 0 -> 3
# 3 -> 3
# DFS:
# 0 1 2 3
| 283 |
"""simple docstring"""
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
import os
# The slow tests are often failing with OOM error on GPU
# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
__UpperCAmelCase = 'platform'
import jax
import jax.numpy as jnp
import numpy as np
from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel
@require_flax
class __lowercase :
snake_case_ = PegasusConfig
snake_case_ = {}
snake_case_ = """gelu"""
def __init__( self : List[Any] ,A : int ,A : Optional[Any]=13 ,A : Dict=7 ,A : Dict=True ,A : Any=False ,A : Dict=99 ,A : int=32 ,A : Optional[int]=5 ,A : Union[str, Any]=4 ,A : Union[str, Any]=37 ,A : str=0.1 ,A : int=0.1 ,A : Optional[int]=20 ,A : Tuple=2 ,A : str=1 ,A : Optional[Any]=0 ,):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = parent
UpperCAmelCase__ : Union[str, Any] = batch_size
UpperCAmelCase__ : List[Any] = seq_length
UpperCAmelCase__ : int = is_training
UpperCAmelCase__ : Any = use_labels
UpperCAmelCase__ : int = vocab_size
UpperCAmelCase__ : Dict = hidden_size
UpperCAmelCase__ : Optional[Any] = num_hidden_layers
UpperCAmelCase__ : int = num_attention_heads
UpperCAmelCase__ : Any = intermediate_size
UpperCAmelCase__ : Optional[int] = hidden_dropout_prob
UpperCAmelCase__ : str = attention_probs_dropout_prob
UpperCAmelCase__ : str = max_position_embeddings
UpperCAmelCase__ : Union[str, Any] = eos_token_id
UpperCAmelCase__ : Union[str, Any] = pad_token_id
UpperCAmelCase__ : List[str] = bos_token_id
def __lowercase ( self : Dict ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = ids_tensor([self.batch_size, self.seq_length - 1] ,self.vocab_size ).clip(3 ,self.vocab_size )
UpperCAmelCase__ : List[str] = np.expand_dims(np.array([self.eos_token_id] * self.batch_size ) ,1 )
UpperCAmelCase__ : Any = np.concatenate([input_ids, eos_tensor] ,axis=1 )
UpperCAmelCase__ : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
UpperCAmelCase__ : str = 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 ,)
UpperCAmelCase__ : Optional[Any] = prepare_pegasus_inputs_dict(A ,A ,A )
return config, inputs_dict
def __lowercase ( self : Any ,A : Optional[int] ,A : str ,A : Optional[int] ):
'''simple docstring'''
UpperCAmelCase__ : Any = 20
UpperCAmelCase__ : Dict = model_class_name(A )
UpperCAmelCase__ : str = model.encode(inputs_dict["""input_ids"""] )
UpperCAmelCase__ , UpperCAmelCase__ : List[str] = (
inputs_dict["""decoder_input_ids"""],
inputs_dict["""decoder_attention_mask"""],
)
UpperCAmelCase__ : Union[str, Any] = model.init_cache(decoder_input_ids.shape[0] ,A ,A )
UpperCAmelCase__ : Union[str, Any] = jnp.ones((decoder_input_ids.shape[0], max_decoder_length) ,dtype="""i4""" )
UpperCAmelCase__ : str = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] ,(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) ,)
UpperCAmelCase__ : Optional[int] = model.decode(
decoder_input_ids[:, :-1] ,A ,decoder_attention_mask=A ,past_key_values=A ,decoder_position_ids=A ,)
UpperCAmelCase__ : Any = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] ,dtype="""i4""" )
UpperCAmelCase__ : int = model.decode(
decoder_input_ids[:, -1:] ,A ,decoder_attention_mask=A ,past_key_values=outputs_cache.past_key_values ,decoder_position_ids=A ,)
UpperCAmelCase__ : Dict = model.decode(A ,A )
UpperCAmelCase__ : str = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1e-3 ,msg=f"Max diff is {diff}" )
def __lowercase ( self : Optional[int] ,A : str ,A : Dict ,A : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ : Any = 20
UpperCAmelCase__ : str = model_class_name(A )
UpperCAmelCase__ : Any = model.encode(inputs_dict["""input_ids"""] )
UpperCAmelCase__ , UpperCAmelCase__ : Optional[int] = (
inputs_dict["""decoder_input_ids"""],
inputs_dict["""decoder_attention_mask"""],
)
UpperCAmelCase__ : Optional[int] = jnp.concatenate(
[
decoder_attention_mask,
jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1]) ),
] ,axis=-1 ,)
UpperCAmelCase__ : Union[str, Any] = model.init_cache(decoder_input_ids.shape[0] ,A ,A )
UpperCAmelCase__ : List[str] = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] ,(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) ,)
UpperCAmelCase__ : Union[str, Any] = model.decode(
decoder_input_ids[:, :-1] ,A ,decoder_attention_mask=A ,past_key_values=A ,decoder_position_ids=A ,)
UpperCAmelCase__ : int = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] ,dtype="""i4""" )
UpperCAmelCase__ : Dict = model.decode(
decoder_input_ids[:, -1:] ,A ,past_key_values=outputs_cache.past_key_values ,decoder_attention_mask=A ,decoder_position_ids=A ,)
UpperCAmelCase__ : Union[str, Any] = model.decode(A ,A ,decoder_attention_mask=A )
UpperCAmelCase__ : Union[str, 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}" )
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=None , __UpperCamelCase=None , ):
'''simple docstring'''
if attention_mask is None:
UpperCAmelCase__ : Union[str, Any] = np.not_equal(__UpperCamelCase , config.pad_token_id ).astype(np.inta )
if decoder_attention_mask is None:
UpperCAmelCase__ : Tuple = np.concatenate(
[
np.ones(decoder_input_ids[:, :1].shape , dtype=np.inta ),
np.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ).astype(np.inta ),
] , axis=-1 , )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
}
@require_flax
class __lowercase ( __lowerCamelCase , unittest.TestCase ):
snake_case_ = (
(
FlaxPegasusForConditionalGeneration,
FlaxPegasusModel,
)
if is_flax_available()
else ()
)
snake_case_ = (FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
snake_case_ = True
snake_case_ = False
snake_case_ = False
snake_case_ = False
def __lowercase ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase__ : int = FlaxPegasusModelTester(self )
UpperCAmelCase__ : Optional[Any] = ConfigTester(self ,config_class=A )
def __lowercase ( self : Tuple ):
'''simple docstring'''
self.config_tester.run_common_tests()
def __lowercase ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase__ , UpperCAmelCase__ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward(A ,A ,A )
def __lowercase ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase__ , UpperCAmelCase__ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward_with_attn_mask(A ,A ,A )
def __lowercase ( self : Any ):
'''simple docstring'''
UpperCAmelCase__ , UpperCAmelCase__ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
UpperCAmelCase__ : List[Any] = self._prepare_for_class(A ,A )
UpperCAmelCase__ : int = model_class(A )
@jax.jit
def encode_jitted(A : Optional[int] ,A : Union[str, Any]=None ,**A : Optional[Any] ):
return model.encode(input_ids=A ,attention_mask=A )
with self.subTest("""JIT Enabled""" ):
UpperCAmelCase__ : int = encode_jitted(**A ).to_tuple()
with self.subTest("""JIT Disabled""" ):
with jax.disable_jit():
UpperCAmelCase__ : Dict = encode_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 )
def __lowercase ( self : str ):
'''simple docstring'''
UpperCAmelCase__ , UpperCAmelCase__ : 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__ ):
UpperCAmelCase__ : Dict = model_class(A )
UpperCAmelCase__ : str = model.encode(inputs_dict["""input_ids"""] ,inputs_dict["""attention_mask"""] )
UpperCAmelCase__ : Dict = {
"""decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
"""decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
"""encoder_outputs""": encoder_outputs,
}
@jax.jit
def decode_jitted(A : List[Any] ,A : Any ,A : List[Any] ):
return model.decode(
decoder_input_ids=A ,decoder_attention_mask=A ,encoder_outputs=A ,)
with self.subTest("""JIT Enabled""" ):
UpperCAmelCase__ : Tuple = decode_jitted(**A ).to_tuple()
with self.subTest("""JIT Disabled""" ):
with jax.disable_jit():
UpperCAmelCase__ : str = decode_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 : List[Any] ):
'''simple docstring'''
for model_class_name in self.all_model_classes:
UpperCAmelCase__ : List[str] = model_class_name.from_pretrained("""google/pegasus-large""" ,from_pt=A )
UpperCAmelCase__ : Any = np.ones((1, 1) )
UpperCAmelCase__ : Optional[Any] = model(A )
self.assertIsNotNone(A )
@slow
def __lowercase ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase__ : Dict = FlaxPegasusForConditionalGeneration.from_pretrained("""google/pegasus-xsum""" )
UpperCAmelCase__ : Optional[Any] = PegasusTokenizer.from_pretrained("""google/pegasus-xsum""" )
UpperCAmelCase__ : Union[str, Any] = [
""" PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.""",
""" The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning 'Oh I think you're nominated'\", said Dappy.\"And I was like 'Oh yeah, which one?' And now we've got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it's best of us to come down and mingle with everyone and say hello to the cameras. And now we find we've got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn't be too disappointed if they didn't win this time around.\"At the end of the day we're grateful to be where we are in our careers.\"If it don't happen then it don't happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a re-working of The Killers' All These Things That I've Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year's Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" """,
]
UpperCAmelCase__ : str = [
"""California's largest electricity provider has turned off power to hundreds of thousands of customers.""",
"""Pop group N-Dubz have revealed they were surprised to get four nominations for this year's Mobo Awards.""",
]
UpperCAmelCase__ : str = tokenizer(A ,return_tensors="""np""" ,truncation=A ,max_length=512 ,padding=A )
UpperCAmelCase__ : Union[str, Any] = model.generate(**A ,num_beams=2 ).sequences
UpperCAmelCase__ : int = tokenizer.batch_decode(A ,skip_special_tokens=A )
assert tgt_text == decoded
| 65 | 0 |
import math
class lowercase :
'''simple docstring'''
def UpperCamelCase__ (self , __a , __a ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase__ = 0.0
UpperCAmelCase__ = 0.0
for i in range(len(__a ) ):
da += math.pow((sample[i] - weights[0][i]) , 2 )
da += math.pow((sample[i] - weights[1][i]) , 2 )
return 0 if da > da else 1
return 0
def UpperCamelCase__ (self , __a , __a , __a , __a ) -> List[str]:
"""simple docstring"""
for i in range(len(__a ) ):
weights[j][i] += alpha * (sample[i] - weights[j][i])
return weights
def UpperCamelCase_( ) -> Tuple:
UpperCAmelCase__ = [[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]
# weight initialization ( n, C )
UpperCAmelCase__ = [[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]
# training
UpperCAmelCase__ = SelfOrganizingMap()
UpperCAmelCase__ = 3
UpperCAmelCase__ = 0.5
for _ in range(__UpperCamelCase ):
for j in range(len(__UpperCamelCase ) ):
# training sample
UpperCAmelCase__ = training_samples[j]
# Compute the winning vector
UpperCAmelCase__ = self_organizing_map.get_winner(__UpperCamelCase , __UpperCamelCase )
# Update the winning vector
UpperCAmelCase__ = self_organizing_map.update(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
# classify test sample
UpperCAmelCase__ = [0, 0, 0, 1]
UpperCAmelCase__ = self_organizing_map.get_winner(__UpperCamelCase , __UpperCamelCase )
# results
print(f"Clusters that the test sample belongs to : {winner}" )
print(f"Weights that have been trained : {weights}" )
# running the main() function
if __name__ == "__main__":
main()
| 146 |
"""simple docstring"""
def lowerCAmelCase ( __UpperCamelCase ):
'''simple docstring'''
if not isinstance(__UpperCamelCase , __UpperCamelCase ) or number < 0:
raise ValueError("""Input must be a non-negative integer""" )
UpperCAmelCase__ : Union[str, Any] = 0
while number:
# This way we arrive at next set bit (next 1) instead of looping
# through each bit and checking for 1s hence the
# loop won't run 32 times it will only run the number of `1` times
number &= number - 1
count += 1
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 65 | 0 |
"""simple docstring"""
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationConfig,
ComputeEnvironment,
CustomDtype,
DeepSpeedPlugin,
DistributedDataParallelKwargs,
DistributedType,
DynamoBackend,
FPaRecipeKwargs,
FullyShardedDataParallelPlugin,
GradientAccumulationPlugin,
GradScalerKwargs,
InitProcessGroupKwargs,
KwargsHandler,
LoggerType,
MegatronLMPlugin,
PrecisionType,
ProjectConfiguration,
RNGType,
SageMakerDistributedType,
TensorInformation,
TorchDynamoPlugin,
)
from .environment import get_int_from_env, parse_choice_from_env, parse_flag_from_env
from .imports import (
get_ccl_version,
is_abit_bnb_available,
is_abit_bnb_available,
is_aim_available,
is_bfaa_available,
is_bnb_available,
is_botoa_available,
is_ccl_available,
is_comet_ml_available,
is_datasets_available,
is_deepspeed_available,
is_fpa_available,
is_ipex_available,
is_megatron_lm_available,
is_mlflow_available,
is_mps_available,
is_npu_available,
is_rich_available,
is_safetensors_available,
is_sagemaker_available,
is_tensorboard_available,
is_tpu_available,
is_transformers_available,
is_wandb_available,
is_xpu_available,
)
from .modeling import (
check_device_map,
check_tied_parameters_in_config,
check_tied_parameters_on_same_device,
compute_module_sizes,
convert_file_size_to_int,
dtype_byte_size,
find_tied_parameters,
get_balanced_memory,
get_max_layer_size,
get_max_memory,
get_mixed_precision_context_manager,
id_tensor_storage,
infer_auto_device_map,
load_checkpoint_in_model,
load_offloaded_weights,
load_state_dict,
named_module_tensors,
retie_parameters,
set_module_tensor_to_device,
shard_checkpoint,
)
from .offload import (
OffloadedWeightsLoader,
PrefixedDataset,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
save_offload_index,
)
from .operations import (
broadcast,
broadcast_object_list,
concatenate,
convert_outputs_to_fpaa,
convert_to_fpaa,
find_batch_size,
find_device,
gather,
gather_object,
get_data_structure,
honor_type,
initialize_tensors,
is_namedtuple,
is_tensor_information,
is_torch_tensor,
listify,
pad_across_processes,
recursively_apply,
reduce,
send_to_device,
slice_tensors,
)
from .versions import compare_versions, is_torch_version
if is_deepspeed_available():
from .deepspeed import (
DeepSpeedEngineWrapper,
DeepSpeedOptimizerWrapper,
DeepSpeedSchedulerWrapper,
DummyOptim,
DummyScheduler,
HfDeepSpeedConfig,
)
from .bnb import has_abit_bnb_layers, load_and_quantize_model
from .fsdp_utils import load_fsdp_model, load_fsdp_optimizer, save_fsdp_model, save_fsdp_optimizer
from .launch import (
PrepareForLaunch,
_filter_args,
prepare_deepspeed_cmd_env,
prepare_multi_gpu_env,
prepare_sagemager_args_inputs,
prepare_simple_launcher_cmd_env,
prepare_tpu,
)
from .megatron_lm import (
AbstractTrainStep,
BertTrainStep,
GPTTrainStep,
MegatronEngine,
MegatronLMDummyDataLoader,
MegatronLMDummyScheduler,
MegatronLMOptimizerWrapper,
MegatronLMSchedulerWrapper,
TaTrainStep,
avg_losses_across_data_parallel_group,
gather_across_data_parallel_groups,
)
from .megatron_lm import initialize as megatron_lm_initialize
from .megatron_lm import prepare_data_loader as megatron_lm_prepare_data_loader
from .megatron_lm import prepare_model as megatron_lm_prepare_model
from .megatron_lm import prepare_optimizer as megatron_lm_prepare_optimizer
from .megatron_lm import prepare_scheduler as megatron_lm_prepare_scheduler
from .memory import find_executable_batch_size, release_memory
from .other import (
extract_model_from_parallel,
get_pretty_name,
is_port_in_use,
merge_dicts,
patch_environment,
save,
wait_for_everyone,
write_basic_config,
)
from .random import set_seed, synchronize_rng_state, synchronize_rng_states
from .torch_xla import install_xla
from .tqdm import tqdm
from .transformer_engine import convert_model, has_transformer_engine_layers
| 636 |
"""simple docstring"""
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def lowerCAmelCase ( __UpperCamelCase = "isbn/0140328726" ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = olid.strip().strip("""/""" ) # Remove leading/trailing whitespace & slashes
if new_olid.count("""/""" ) != 1:
UpperCAmelCase__ : Dict = F"{olid} is not a valid Open Library olid"
raise ValueError(__UpperCamelCase )
return requests.get(F"https://openlibrary.org/{new_olid}.json" ).json()
def lowerCAmelCase ( __UpperCamelCase ):
'''simple docstring'''
UpperCAmelCase__ : Any = {
"""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__ : Dict = {better_key: ol_book_data[key] for key, better_key in desired_keys.items()}
UpperCAmelCase__ : str = [
get_openlibrary_data(author["""key"""] )["""name"""] for author in data["""Authors"""]
]
UpperCAmelCase__ : Dict = data["""First sentence"""]["""value"""]
for key, value in data.items():
if isinstance(__UpperCamelCase , __UpperCamelCase ):
UpperCAmelCase__ : Dict = """, """.join(__UpperCamelCase )
return data
if __name__ == "__main__":
import doctest
doctest.testmod()
while True:
__UpperCAmelCase = 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:
__UpperCAmelCase = 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}.")
| 65 | 0 |
"""simple docstring"""
__A = [
(1000, """M"""),
(900, """CM"""),
(500, """D"""),
(400, """CD"""),
(100, """C"""),
(90, """XC"""),
(50, """L"""),
(40, """XL"""),
(10, """X"""),
(9, """IX"""),
(5, """V"""),
(4, """IV"""),
(1, """I"""),
]
def __A (_SCREAMING_SNAKE_CASE ) ->Tuple:
"""simple docstring"""
lowerCAmelCase__ :Optional[Any] = {"""I""": 1, """V""": 5, """X""": 10, """L""": 50, """C""": 100, """D""": 500, """M""": 1000}
lowerCAmelCase__ :List[Any] = 0
lowerCAmelCase__ :Optional[Any] = 0
while place < len(__UpperCamelCase ):
if (place + 1 < len(__UpperCamelCase )) and (vals[roman[place]] < vals[roman[place + 1]]):
total += vals[roman[place + 1]] - vals[roman[place]]
place += 2
else:
total += vals[roman[place]]
place += 1
return total
def __A (_SCREAMING_SNAKE_CASE ) ->str:
"""simple docstring"""
lowerCAmelCase__ :Optional[int] = []
for arabic, roman in ROMAN:
(lowerCAmelCase__) :Optional[int] = divmod(__UpperCamelCase , __UpperCamelCase )
result.append(roman * factor )
if number == 0:
break
return "".join(__UpperCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 93 |
"""simple docstring"""
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from transformers import BartTokenizer, RagTokenizer, TaTokenizer
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=True , __UpperCamelCase="pt" ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = {"""add_prefix_space""": True} if isinstance(__UpperCamelCase , __UpperCamelCase ) and not line.startswith(""" """ ) else {}
UpperCAmelCase__ : List[str] = padding_side
return tokenizer(
[line] , max_length=__UpperCamelCase , padding="""max_length""" if pad_to_max_length else None , truncation=__UpperCamelCase , return_tensors=__UpperCamelCase , add_special_tokens=__UpperCamelCase , **__UpperCamelCase , )
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=None , ):
'''simple docstring'''
UpperCAmelCase__ : str = input_ids.ne(__UpperCamelCase ).any(dim=0 )
if attention_mask is None:
return input_ids[:, keep_column_mask]
else:
return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
class __lowercase ( __lowerCamelCase ):
def __init__( self : Tuple ,A : List[Any] ,A : Union[str, Any] ,A : Any ,A : Optional[int] ,A : Union[str, Any]="train" ,A : Tuple=None ,A : Union[str, Any]=None ,A : Tuple=None ,A : int="" ,):
'''simple docstring'''
super().__init__()
UpperCAmelCase__ : Optional[Any] = Path(A ).joinpath(type_path + """.source""" )
UpperCAmelCase__ : List[str] = Path(A ).joinpath(type_path + """.target""" )
UpperCAmelCase__ : Dict = self.get_char_lens(self.src_file )
UpperCAmelCase__ : int = max_source_length
UpperCAmelCase__ : List[str] = max_target_length
assert min(self.src_lens ) > 0, f"found empty line in {self.src_file}"
UpperCAmelCase__ : Dict = tokenizer
UpperCAmelCase__ : str = prefix
if n_obs is not None:
UpperCAmelCase__ : int = self.src_lens[:n_obs]
UpperCAmelCase__ : Any = src_lang
UpperCAmelCase__ : Any = tgt_lang
def __len__( self : Optional[Any] ):
'''simple docstring'''
return len(self.src_lens )
def __getitem__( self : Union[str, Any] ,A : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = index + 1 # linecache starts at 1
UpperCAmelCase__ : Tuple = self.prefix + linecache.getline(str(self.src_file ) ,A ).rstrip("""\n""" )
UpperCAmelCase__ : Dict = linecache.getline(str(self.tgt_file ) ,A ).rstrip("""\n""" )
assert source_line, f"empty source line for index {index}"
assert tgt_line, f"empty tgt line for index {index}"
# Need to add eos token manually for T5
if isinstance(self.tokenizer ,A ):
source_line += self.tokenizer.eos_token
tgt_line += self.tokenizer.eos_token
# Pad source and target to the right
UpperCAmelCase__ : str = (
self.tokenizer.question_encoder if isinstance(self.tokenizer ,A ) else self.tokenizer
)
UpperCAmelCase__ : Tuple = self.tokenizer.generator if isinstance(self.tokenizer ,A ) else self.tokenizer
UpperCAmelCase__ : Tuple = encode_line(A ,A ,self.max_source_length ,"""right""" )
UpperCAmelCase__ : Dict = encode_line(A ,A ,self.max_target_length ,"""right""" )
UpperCAmelCase__ : Optional[Any] = source_inputs["""input_ids"""].squeeze()
UpperCAmelCase__ : List[str] = target_inputs["""input_ids"""].squeeze()
UpperCAmelCase__ : Union[str, Any] = source_inputs["""attention_mask"""].squeeze()
return {
"input_ids": source_ids,
"attention_mask": src_mask,
"decoder_input_ids": target_ids,
}
@staticmethod
def __lowercase ( A : int ):
'''simple docstring'''
return [len(A ) for x in Path(A ).open().readlines()]
def __lowercase ( self : List[Any] ,A : Any ):
'''simple docstring'''
UpperCAmelCase__ : int = torch.stack([x["""input_ids"""] for x in batch] )
UpperCAmelCase__ : Union[str, Any] = torch.stack([x["""attention_mask"""] for x in batch] )
UpperCAmelCase__ : Any = torch.stack([x["""decoder_input_ids"""] for x in batch] )
UpperCAmelCase__ : List[Any] = (
self.tokenizer.generator.pad_token_id
if isinstance(self.tokenizer ,A )
else self.tokenizer.pad_token_id
)
UpperCAmelCase__ : Any = (
self.tokenizer.question_encoder.pad_token_id
if isinstance(self.tokenizer ,A )
else self.tokenizer.pad_token_id
)
UpperCAmelCase__ : str = trim_batch(A ,A )
UpperCAmelCase__ , UpperCAmelCase__ : Union[str, Any] = trim_batch(A ,A ,attention_mask=A )
UpperCAmelCase__ : List[str] = {
"""input_ids""": source_ids,
"""attention_mask""": source_mask,
"""decoder_input_ids""": y,
}
return batch
__UpperCAmelCase = getLogger(__name__)
def lowerCAmelCase ( __UpperCamelCase ):
'''simple docstring'''
return list(itertools.chain.from_iterable(__UpperCamelCase ) )
def lowerCAmelCase ( __UpperCamelCase ):
'''simple docstring'''
UpperCAmelCase__ : Dict = get_git_info()
save_json(__UpperCamelCase , os.path.join(__UpperCamelCase , """git_log.json""" ) )
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=4 , **__UpperCamelCase ):
'''simple docstring'''
with open(__UpperCamelCase , """w""" ) as f:
json.dump(__UpperCamelCase , __UpperCamelCase , indent=__UpperCamelCase , **__UpperCamelCase )
def lowerCAmelCase ( __UpperCamelCase ):
'''simple docstring'''
with open(__UpperCamelCase ) as f:
return json.load(__UpperCamelCase )
def lowerCAmelCase ( ):
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = git.Repo(search_parent_directories=__UpperCamelCase )
UpperCAmelCase__ : List[str] = {
"""repo_id""": str(__UpperCamelCase ),
"""repo_sha""": str(repo.head.object.hexsha ),
"""repo_branch""": str(repo.active_branch ),
"""hostname""": str(socket.gethostname() ),
}
return repo_infos
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase ):
'''simple docstring'''
return list(map(__UpperCamelCase , __UpperCamelCase ) )
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase ):
'''simple docstring'''
with open(__UpperCamelCase , """wb""" ) as f:
return pickle.dump(__UpperCamelCase , __UpperCamelCase )
def lowerCAmelCase ( __UpperCamelCase ):
'''simple docstring'''
def remove_articles(__UpperCamelCase ):
return re.sub(r"""\b(a|an|the)\b""" , """ """ , __UpperCamelCase )
def white_space_fix(__UpperCamelCase ):
return " ".join(text.split() )
def remove_punc(__UpperCamelCase ):
UpperCAmelCase__ : List[Any] = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(__UpperCamelCase ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(__UpperCamelCase ) ) ) )
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase ):
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = normalize_answer(__UpperCamelCase ).split()
UpperCAmelCase__ : Dict = normalize_answer(__UpperCamelCase ).split()
UpperCAmelCase__ : int = Counter(__UpperCamelCase ) & Counter(__UpperCamelCase )
UpperCAmelCase__ : List[str] = sum(common.values() )
if num_same == 0:
return 0
UpperCAmelCase__ : str = 1.0 * num_same / len(__UpperCamelCase )
UpperCAmelCase__ : Optional[int] = 1.0 * num_same / len(__UpperCamelCase )
UpperCAmelCase__ : Tuple = (2 * precision * recall) / (precision + recall)
return fa
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase ):
'''simple docstring'''
return normalize_answer(__UpperCamelCase ) == normalize_answer(__UpperCamelCase )
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase ):
'''simple docstring'''
assert len(__UpperCamelCase ) == len(__UpperCamelCase )
UpperCAmelCase__ : Union[str, Any] = 0
for hypo, pred in zip(__UpperCamelCase , __UpperCamelCase ):
em += exact_match_score(__UpperCamelCase , __UpperCamelCase )
if len(__UpperCamelCase ) > 0:
em /= len(__UpperCamelCase )
return {"em": em}
def lowerCAmelCase ( __UpperCamelCase ):
'''simple docstring'''
return model_prefix.startswith("""rag""" )
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = {p: p for p in extra_params}
# T5 models don't have `dropout` param, they have `dropout_rate` instead
UpperCAmelCase__ : str = """dropout_rate"""
for p in extra_params:
if getattr(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
if not hasattr(__UpperCamelCase , __UpperCamelCase ) and not hasattr(__UpperCamelCase , equivalent_param[p] ):
logger.info("""config doesn't have a `{}` attribute""".format(__UpperCamelCase ) )
delattr(__UpperCamelCase , __UpperCamelCase )
continue
UpperCAmelCase__ : Tuple = p if hasattr(__UpperCamelCase , __UpperCamelCase ) else equivalent_param[p]
setattr(__UpperCamelCase , __UpperCamelCase , getattr(__UpperCamelCase , __UpperCamelCase ) )
delattr(__UpperCamelCase , __UpperCamelCase )
return hparams, config
| 65 | 0 |
'''simple docstring'''
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available
if is_sentencepiece_available():
from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json
from ...test_tokenization_common import TokenizerTesterMixin
A_ = get_tests_dir("fixtures/test_sentencepiece.model")
A_ = {"target_lang": "fi", "source_lang": "en"}
A_ = ">>zh<<"
A_ = "Helsinki-NLP/"
if is_torch_available():
A_ = "pt"
elif is_tf_available():
A_ = "tf"
else:
A_ = "jax"
@require_sentencepiece
class UpperCAmelCase ( __lowerCamelCase , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = MarianTokenizer
SCREAMING_SNAKE_CASE_ = False
SCREAMING_SNAKE_CASE_ = True
def UpperCamelCase( self ) -> List[str]:
'''simple docstring'''
super().setUp()
lowerCamelCase_ = ["""</s>""", """<unk>""", """▁This""", """▁is""", """▁a""", """▁t""", """est""", """\u0120""", """<pad>"""]
lowerCamelCase_ = dict(zip(SCREAMING_SNAKE_CASE_ , range(len(SCREAMING_SNAKE_CASE_ ) ) ) )
lowerCamelCase_ = Path(self.tmpdirname )
save_json(SCREAMING_SNAKE_CASE_ , save_dir / VOCAB_FILES_NAMES['vocab'] )
save_json(SCREAMING_SNAKE_CASE_ , save_dir / VOCAB_FILES_NAMES['tokenizer_config_file'] )
if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
copyfile(SCREAMING_SNAKE_CASE_ , save_dir / VOCAB_FILES_NAMES['source_spm'] )
copyfile(SCREAMING_SNAKE_CASE_ , save_dir / VOCAB_FILES_NAMES['target_spm'] )
lowerCamelCase_ = MarianTokenizer.from_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname )
def UpperCamelCase( self , **SCREAMING_SNAKE_CASE_ ) -> Optional[Any]:
'''simple docstring'''
return MarianTokenizer.from_pretrained(self.tmpdirname , **SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> Any:
'''simple docstring'''
return (
"This is a test",
"This is a test",
)
def UpperCamelCase( self ) -> Optional[Any]:
'''simple docstring'''
lowerCamelCase_ = """</s>"""
lowerCamelCase_ = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> Optional[int]:
'''simple docstring'''
lowerCamelCase_ = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '</s>' )
self.assertEqual(vocab_keys[1] , '<unk>' )
self.assertEqual(vocab_keys[-1] , '<pad>' )
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , 9 )
def UpperCamelCase( self ) -> Tuple:
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 9 )
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
lowerCamelCase_ = MarianTokenizer.from_pretrained(f'''{ORG_NAME}opus-mt-en-de''' )
lowerCamelCase_ = en_de_tokenizer(['I am a small frog'] , return_tensors=SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = [38, 121, 14, 697, 38848, 0]
self.assertListEqual(SCREAMING_SNAKE_CASE_ , batch.input_ids[0] )
lowerCamelCase_ = tempfile.mkdtemp()
en_de_tokenizer.save_pretrained(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = [x.name for x in Path(SCREAMING_SNAKE_CASE_ ).glob('*' )]
self.assertIn('source.spm' , SCREAMING_SNAKE_CASE_ )
MarianTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> Union[str, Any]:
'''simple docstring'''
lowerCamelCase_ = self.get_tokenizer()
lowerCamelCase_ = tok(
['I am a small frog' * 1000, 'I am a small frog'] , padding=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ , return_tensors=SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertEqual(batch.input_ids.shape , (2, 512) )
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = self.get_tokenizer()
lowerCamelCase_ = tok(['I am a tiny frog', 'I am a small frog'] , padding=SCREAMING_SNAKE_CASE_ , return_tensors=SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertEqual(batch_smaller.input_ids.shape , (2, 10) )
@slow
def UpperCamelCase( self ) -> int:
'''simple docstring'''
lowerCamelCase_ = {"""input_ids""": [[43495, 462, 20, 42164, 1369, 52, 464, 132, 1703, 492, 13, 7491, 38999, 6, 8, 464, 132, 1703, 492, 13, 4669, 37867, 13, 7525, 27, 1593, 988, 13, 33972, 7029, 6, 20, 8251, 383, 2, 270, 5866, 3788, 2, 2353, 8251, 12338, 2, 13958, 387, 2, 3629, 6953, 188, 2900, 2, 13958, 8011, 11501, 23, 8460, 4073, 34009, 20, 435, 11439, 27, 8, 8460, 4073, 6004, 20, 9988, 375, 27, 33, 266, 1945, 1076, 1350, 37867, 3288, 5, 577, 1076, 4374, 8, 5082, 5, 26453, 257, 556, 403, 2, 242, 132, 383, 316, 492, 8, 10767, 6, 316, 304, 4239, 3, 0], [148, 15722, 19, 1839, 12, 1350, 13, 22327, 5082, 5418, 47567, 35938, 59, 318, 19552, 108, 2183, 54, 14976, 4835, 32, 547, 1114, 8, 315, 2417, 5, 92, 19088, 3, 0, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100], [36, 6395, 12570, 39147, 11597, 6, 266, 4, 45405, 7296, 3, 0, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100]], """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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=SCREAMING_SNAKE_CASE_ , model_name='Helsinki-NLP/opus-mt-en-de' , revision='1a8c2263da11e68e50938f97e10cd57820bd504c' , decode_kwargs={'use_source_tokenizer': True} , )
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = MarianTokenizer.from_pretrained('hf-internal-testing/test-marian-two-vocabs' )
lowerCamelCase_ = """Tämä on testi"""
lowerCamelCase_ = """This is a test"""
lowerCamelCase_ = [76, 7, 2047, 2]
lowerCamelCase_ = [69, 12, 11, 940, 2]
lowerCamelCase_ = tokenizer(SCREAMING_SNAKE_CASE_ ).input_ids
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer(text_target=SCREAMING_SNAKE_CASE_ ).input_ids
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer.decode(SCREAMING_SNAKE_CASE_ , skip_special_tokens=SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
| 42 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetPipeline,
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 __lowercase ( __lowerCamelCase , unittest.TestCase ):
snake_case_ = KandinskyVaaControlnetPipeline
snake_case_ = ["""image_embeds""", """negative_image_embeds""", """hint"""]
snake_case_ = ["""image_embeds""", """negative_image_embeds""", """hint"""]
snake_case_ = [
"""generator""",
"""height""",
"""width""",
"""latents""",
"""guidance_scale""",
"""num_inference_steps""",
"""return_dict""",
"""guidance_scale""",
"""num_images_per_prompt""",
"""output_type""",
"""return_dict""",
]
snake_case_ = False
@property
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
return 32
@property
def __lowercase ( self : int ):
'''simple docstring'''
return 32
@property
def __lowercase ( self : Dict ):
'''simple docstring'''
return self.time_input_dim
@property
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
return self.time_input_dim * 4
@property
def __lowercase ( self : Any ):
'''simple docstring'''
return 100
@property
def __lowercase ( self : Any ):
'''simple docstring'''
torch.manual_seed(0 )
UpperCAmelCase__ : Tuple = {
"""in_channels""": 8,
# Out channels is double in channels because predicts mean and variance
"""out_channels""": 8,
"""addition_embed_type""": """image_hint""",
"""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,
}
UpperCAmelCase__ : int = UNetaDConditionModel(**A )
return model
@property
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
return {
"block_out_channels": [32, 32, 64, 64],
"down_block_types": [
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"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", "UpDecoderBlock2D", "UpDecoderBlock2D"],
"vq_embed_dim": 4,
}
@property
def __lowercase ( self : Dict ):
'''simple docstring'''
torch.manual_seed(0 )
UpperCAmelCase__ : str = VQModel(**self.dummy_movq_kwargs )
return model
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ : str = self.dummy_unet
UpperCAmelCase__ : List[Any] = self.dummy_movq
UpperCAmelCase__ : List[Any] = DDIMScheduler(
num_train_timesteps=1_000 ,beta_schedule="""linear""" ,beta_start=0.0_0_0_8_5 ,beta_end=0.0_1_2 ,clip_sample=A ,set_alpha_to_one=A ,steps_offset=1 ,prediction_type="""epsilon""" ,thresholding=A ,)
UpperCAmelCase__ : Optional[Any] = {
"""unet""": unet,
"""scheduler""": scheduler,
"""movq""": movq,
}
return components
def __lowercase ( self : str ,A : Optional[Any] ,A : Any=0 ):
'''simple docstring'''
UpperCAmelCase__ : str = floats_tensor((1, self.text_embedder_hidden_size) ,rng=random.Random(A ) ).to(A )
UpperCAmelCase__ : Union[str, Any] = floats_tensor((1, self.text_embedder_hidden_size) ,rng=random.Random(seed + 1 ) ).to(
A )
# create hint
UpperCAmelCase__ : int = floats_tensor((1, 3, 64, 64) ,rng=random.Random(A ) ).to(A )
if str(A ).startswith("""mps""" ):
UpperCAmelCase__ : Optional[int] = torch.manual_seed(A )
else:
UpperCAmelCase__ : Dict = torch.Generator(device=A ).manual_seed(A )
UpperCAmelCase__ : Dict = {
"""image_embeds""": image_embeds,
"""negative_image_embeds""": negative_image_embeds,
"""hint""": hint,
"""generator""": generator,
"""height""": 64,
"""width""": 64,
"""guidance_scale""": 4.0,
"""num_inference_steps""": 2,
"""output_type""": """np""",
}
return inputs
def __lowercase ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase__ : Dict = """cpu"""
UpperCAmelCase__ : List[Any] = self.get_dummy_components()
UpperCAmelCase__ : Union[str, Any] = self.pipeline_class(**A )
UpperCAmelCase__ : Optional[int] = pipe.to(A )
pipe.set_progress_bar_config(disable=A )
UpperCAmelCase__ : Optional[int] = pipe(**self.get_dummy_inputs(A ) )
UpperCAmelCase__ : Tuple = output.images
UpperCAmelCase__ : Dict = pipe(
**self.get_dummy_inputs(A ) ,return_dict=A ,)[0]
UpperCAmelCase__ : Tuple = image[0, -3:, -3:, -1]
UpperCAmelCase__ : Dict = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
UpperCAmelCase__ : Optional[int] = np.array(
[0.6_9_5_9_8_2_6, 0.8_6_8_2_7_9, 0.7_5_5_8_0_9_2, 0.6_8_7_6_9_4_6_7, 0.8_5_8_0_5_8_0_4, 0.6_5_9_7_7_4_9_6, 0.4_4_8_8_5_3_0_2, 0.5_9_5_9_1_1_1, 0.4_2_5_1_5_9_5] )
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 __lowercase ( unittest.TestCase ):
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __lowercase ( self : int ):
'''simple docstring'''
UpperCAmelCase__ : int = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy""" )
UpperCAmelCase__ : int = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/kandinskyv22/hint_image_cat.png""" )
UpperCAmelCase__ : int = torch.from_numpy(np.array(A ) ).float() / 2_5_5.0
UpperCAmelCase__ : Union[str, Any] = hint.permute(2 ,0 ,1 ).unsqueeze(0 )
UpperCAmelCase__ : List[str] = KandinskyVaaPriorPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-2-prior""" ,torch_dtype=torch.floataa )
pipe_prior.to(A )
UpperCAmelCase__ : List[Any] = KandinskyVaaControlnetPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-2-controlnet-depth""" ,torch_dtype=torch.floataa )
UpperCAmelCase__ : int = pipeline.to(A )
pipeline.set_progress_bar_config(disable=A )
UpperCAmelCase__ : Optional[Any] = """A robot, 4k photo"""
UpperCAmelCase__ : List[Any] = torch.Generator(device="""cuda""" ).manual_seed(0 )
UpperCAmelCase__ , UpperCAmelCase__ : Tuple = pipe_prior(
A ,generator=A ,num_inference_steps=5 ,negative_prompt="""""" ,).to_tuple()
UpperCAmelCase__ : List[str] = torch.Generator(device="""cuda""" ).manual_seed(0 )
UpperCAmelCase__ : int = pipeline(
image_embeds=A ,negative_image_embeds=A ,hint=A ,generator=A ,num_inference_steps=100 ,output_type="""np""" ,)
UpperCAmelCase__ : Any = output.images[0]
assert image.shape == (512, 512, 3)
assert_mean_pixel_difference(A ,A )
| 65 | 0 |
from __future__ import annotations
from collections.abc import Callable
def A ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = 100 , ) -> Optional[Any]:
A__ = x_start
A__ = fnc(__UpperCamelCase )
A__ = 0.0
for _ in range(__UpperCamelCase ):
# Approximates small segments of curve as linear and solve
# for trapezoidal area
A__ = (x_end - x_start) / steps + xa
A__ = fnc(__UpperCamelCase )
area += abs(fxa + fxa ) * (xa - xa) / 2
# Increment step
A__ = xa
A__ = fxa
return area
if __name__ == "__main__":
def A ( __UpperCamelCase ) -> Union[str, Any]:
return x**3 + x**2
print('''f(x) = x^3 + x^2''')
print('''The area between the curve, x = -5, x = 5 and the x axis is:''')
SCREAMING_SNAKE_CASE__ = 1_0
while i <= 1_0_0_0_0_0:
print(f'with {i} steps: {trapezoidal_area(f, -5, 5, i)}')
i *= 1_0
| 9 |
"""simple docstring"""
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHECKING:
from ... import PreTrainedTokenizerBase, TensorType
__UpperCAmelCase = logging.get_logger(__name__)
class __lowercase ( __lowerCamelCase ):
snake_case_ = """vision-encoder-decoder"""
snake_case_ = True
def __init__( self : List[Any] ,**A : Union[str, Any] ):
'''simple docstring'''
super().__init__(**A )
if "encoder" not in kwargs or "decoder" not in kwargs:
raise ValueError(
f"A configuraton of type {self.model_type} cannot be instantiated because "
f"not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}" )
UpperCAmelCase__ : int = kwargs.pop("""encoder""" )
UpperCAmelCase__ : int = encoder_config.pop("""model_type""" )
UpperCAmelCase__ : str = kwargs.pop("""decoder""" )
UpperCAmelCase__ : Dict = decoder_config.pop("""model_type""" )
UpperCAmelCase__ : List[Any] = AutoConfig.for_model(A ,**A )
UpperCAmelCase__ : Any = AutoConfig.for_model(A ,**A )
UpperCAmelCase__ : Union[str, Any] = True
@classmethod
def __lowercase ( cls : List[Any] ,A : PretrainedConfig ,A : PretrainedConfig ,**A : Tuple ):
'''simple docstring'''
logger.info("""Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config""" )
UpperCAmelCase__ : Union[str, Any] = True
UpperCAmelCase__ : List[Any] = True
return cls(encoder=encoder_config.to_dict() ,decoder=decoder_config.to_dict() ,**A )
def __lowercase ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = copy.deepcopy(self.__dict__ )
UpperCAmelCase__ : Dict = self.encoder.to_dict()
UpperCAmelCase__ : Any = self.decoder.to_dict()
UpperCAmelCase__ : Dict = self.__class__.model_type
return output
class __lowercase ( __lowerCamelCase ):
snake_case_ = version.parse("""1.11""" )
@property
def __lowercase ( self : Optional[int] ):
'''simple docstring'''
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def __lowercase ( self : List[Any] ):
'''simple docstring'''
return 1e-4
@property
def __lowercase ( self : List[Any] ):
'''simple docstring'''
return OrderedDict({"""last_hidden_state""": {0: """batch""", 1: """encoder_sequence"""}} )
class __lowercase ( __lowerCamelCase ):
@property
def __lowercase ( self : Any ):
'''simple docstring'''
UpperCAmelCase__ : int = OrderedDict()
UpperCAmelCase__ : Dict = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
UpperCAmelCase__ : Optional[Any] = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
UpperCAmelCase__ : List[str] = {0: """batch""", 1: """encoder_sequence"""}
return common_inputs
def __lowercase ( self : Dict ,A : "PreTrainedTokenizerBase" ,A : int = -1 ,A : int = -1 ,A : bool = False ,A : Optional["TensorType"] = None ,):
'''simple docstring'''
import torch
UpperCAmelCase__ : int = OrderedDict()
UpperCAmelCase__ : List[Any] = super().generate_dummy_inputs(
A ,batch_size=A ,seq_length=A ,is_pair=A ,framework=A )
UpperCAmelCase__ , UpperCAmelCase__ : int = dummy_input["""input_ids"""].shape
UpperCAmelCase__ : int = (batch, encoder_sequence, self._config.encoder_hidden_size)
UpperCAmelCase__ : Tuple = dummy_input.pop("""input_ids""" )
UpperCAmelCase__ : Optional[int] = dummy_input.pop("""attention_mask""" )
UpperCAmelCase__ : Dict = torch.zeros(A )
return common_inputs
class __lowercase ( __lowerCamelCase ):
@property
def __lowercase ( self : str ):
'''simple docstring'''
pass
def __lowercase ( self : Any ,A : PretrainedConfig ):
'''simple docstring'''
return VisionEncoderDecoderEncoderOnnxConfig(A )
def __lowercase ( self : Dict ,A : PretrainedConfig ,A : PretrainedConfig ,A : str = "default" ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = encoder_config.hidden_size
return VisionEncoderDecoderDecoderOnnxConfig(A ,A )
| 65 | 0 |
from __future__ import annotations
def lowercase_ (A : List[str] ):
return [ord(__UpperCamelCase ) - 9_6 for elem in plain]
def lowercase_ (A : Dict ):
return "".join(chr(elem + 9_6 ) for elem in encoded )
def lowercase_ ():
snake_case__ : Optional[Any] = encode(input('-> ' ).strip().lower() )
print('Encoded: ' , __UpperCamelCase )
print('Decoded:' , decode(__UpperCamelCase ) )
if __name__ == "__main__":
main()
| 478 |
"""simple docstring"""
import requests
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = {"""Content-Type""": """application/json"""}
UpperCAmelCase__ : Optional[Any] = requests.post(__UpperCamelCase , json={"""text""": message_body} , headers=__UpperCamelCase )
if response.status_code != 200:
UpperCAmelCase__ : Any = (
"""Request to slack returned an error """
F"{response.status_code}, the response is:\n{response.text}"
)
raise ValueError(__UpperCamelCase )
if __name__ == "__main__":
# Set the slack url to the one provided by Slack when you create the webhook at
# https://my.slack.com/services/new/incoming-webhook/
send_slack_message('<YOUR MESSAGE BODY>', '<SLACK CHANNEL URL>')
| 65 | 0 |
from math import isqrt, loga
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase ) -> Any:
snake_case__ = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , __UpperCamelCase , __UpperCamelCase ):
snake_case__ = False
return [i for i in range(2 , __UpperCamelCase ) if is_prime[i]]
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase = 80_0800 , __lowerCAmelCase = 80_0800 ) -> int:
snake_case__ = degree * loga(__UpperCamelCase )
snake_case__ = int(__UpperCamelCase )
snake_case__ = calculate_prime_numbers(__UpperCamelCase )
snake_case__ = 0
snake_case__ = 0
snake_case__ = len(__UpperCamelCase ) - 1
while left < right:
while (
prime_numbers[right] * loga(prime_numbers[left] )
+ prime_numbers[left] * loga(prime_numbers[right] )
> upper_bound
):
right -= 1
hybrid_integers_count += right - left
left += 1
return hybrid_integers_count
if __name__ == "__main__":
print(F"""{solution() = }""")
| 33 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class __lowercase ( __lowerCamelCase , unittest.TestCase ):
snake_case_ = CTRLTokenizer
snake_case_ = False
snake_case_ = False
def __lowercase ( self : List[str] ):
'''simple docstring'''
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
UpperCAmelCase__ : List[Any] = ["""adapt""", """re@@""", """a@@""", """apt""", """c@@""", """t""", """<unk>"""]
UpperCAmelCase__ : Optional[int] = dict(zip(A ,range(len(A ) ) ) )
UpperCAmelCase__ : List[Any] = ["""#version: 0.2""", """a p""", """ap t</w>""", """r e""", """a d""", """ad apt</w>""", """"""]
UpperCAmelCase__ : int = {"""unk_token""": """<unk>"""}
UpperCAmelCase__ : Optional[Any] = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["""vocab_file"""] )
UpperCAmelCase__ : str = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["""merges_file"""] )
with open(self.vocab_file ,"""w""" ,encoding="""utf-8""" ) as fp:
fp.write(json.dumps(A ) + """\n""" )
with open(self.merges_file ,"""w""" ,encoding="""utf-8""" ) as fp:
fp.write("""\n""".join(A ) )
def __lowercase ( self : int ,**A : Dict ):
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return CTRLTokenizer.from_pretrained(self.tmpdirname ,**A )
def __lowercase ( self : List[Any] ,A : Any ):
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = """adapt react readapt apt"""
UpperCAmelCase__ : Any = """adapt react readapt apt"""
return input_text, output_text
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = CTRLTokenizer(self.vocab_file ,self.merges_file ,**self.special_tokens_map )
UpperCAmelCase__ : Tuple = """adapt react readapt apt"""
UpperCAmelCase__ : Optional[int] = """adapt re@@ a@@ c@@ t re@@ adapt apt""".split()
UpperCAmelCase__ : Dict = tokenizer.tokenize(A )
self.assertListEqual(A ,A )
UpperCAmelCase__ : Any = tokens + [tokenizer.unk_token]
UpperCAmelCase__ : Dict = [0, 1, 2, 4, 5, 1, 0, 3, 6]
self.assertListEqual(tokenizer.convert_tokens_to_ids(A ) ,A )
| 65 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
snake_case : Any = logging.get_logger(__name__)
snake_case : Tuple = {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}
snake_case : Union[str, Any] = {
'vocab_file': {
'distilbert-base-uncased': 'https://huggingface.co/distilbert-base-uncased/resolve/main/vocab.txt',
'distilbert-base-uncased-distilled-squad': (
'https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/vocab.txt'
),
'distilbert-base-cased': 'https://huggingface.co/distilbert-base-cased/resolve/main/vocab.txt',
'distilbert-base-cased-distilled-squad': (
'https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/vocab.txt'
),
'distilbert-base-german-cased': 'https://huggingface.co/distilbert-base-german-cased/resolve/main/vocab.txt',
'distilbert-base-multilingual-cased': (
'https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/vocab.txt'
),
},
'tokenizer_file': {
'distilbert-base-uncased': 'https://huggingface.co/distilbert-base-uncased/resolve/main/tokenizer.json',
'distilbert-base-uncased-distilled-squad': (
'https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/tokenizer.json'
),
'distilbert-base-cased': 'https://huggingface.co/distilbert-base-cased/resolve/main/tokenizer.json',
'distilbert-base-cased-distilled-squad': (
'https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/tokenizer.json'
),
'distilbert-base-german-cased': (
'https://huggingface.co/distilbert-base-german-cased/resolve/main/tokenizer.json'
),
'distilbert-base-multilingual-cased': (
'https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/tokenizer.json'
),
},
}
snake_case : int = {
'distilbert-base-uncased': 5_12,
'distilbert-base-uncased-distilled-squad': 5_12,
'distilbert-base-cased': 5_12,
'distilbert-base-cased-distilled-squad': 5_12,
'distilbert-base-german-cased': 5_12,
'distilbert-base-multilingual-cased': 5_12,
}
snake_case : Optional[int] = {
'distilbert-base-uncased': {'do_lower_case': True},
'distilbert-base-uncased-distilled-squad': {'do_lower_case': True},
'distilbert-base-cased': {'do_lower_case': False},
'distilbert-base-cased-distilled-squad': {'do_lower_case': False},
'distilbert-base-german-cased': {'do_lower_case': False},
'distilbert-base-multilingual-cased': {'do_lower_case': False},
}
class __lowercase ( __lowerCamelCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE : Dict = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE : Tuple = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE : List[Any] = PRETRAINED_INIT_CONFIGURATION
SCREAMING_SNAKE_CASE : int = ["input_ids", "attention_mask"]
SCREAMING_SNAKE_CASE : Optional[Any] = DistilBertTokenizer
def __init__( self , A_=None , A_=None , A_=True , A_="[UNK]" , A_="[SEP]" , A_="[PAD]" , A_="[CLS]" , A_="[MASK]" , A_=True , A_=None , **A_ , )-> Optional[int]:
super().__init__(
A_ , tokenizer_file=A_ , do_lower_case=A_ , unk_token=A_ , sep_token=A_ , pad_token=A_ , cls_token=A_ , mask_token=A_ , tokenize_chinese_chars=A_ , strip_accents=A_ , **A_ , )
_SCREAMING_SNAKE_CASE = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('lowercase' , A_ ) != do_lower_case
or normalizer_state.get('strip_accents' , A_ ) != strip_accents
or normalizer_state.get('handle_chinese_chars' , A_ ) != tokenize_chinese_chars
):
_SCREAMING_SNAKE_CASE = getattr(A_ , normalizer_state.pop('type' ) )
_SCREAMING_SNAKE_CASE = do_lower_case
_SCREAMING_SNAKE_CASE = strip_accents
_SCREAMING_SNAKE_CASE = tokenize_chinese_chars
_SCREAMING_SNAKE_CASE = normalizer_class(**A_ )
_SCREAMING_SNAKE_CASE = do_lower_case
def __magic_name__ ( self , A_ , A_=None )-> Tuple:
_SCREAMING_SNAKE_CASE = [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 __magic_name__ ( self , A_ , A_ = None )-> Optional[Any]:
_SCREAMING_SNAKE_CASE = [self.sep_token_id]
_SCREAMING_SNAKE_CASE = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def __magic_name__ ( self , A_ , A_ = None )-> int:
_SCREAMING_SNAKE_CASE = self._tokenizer.model.save(A_ , name=A_ )
return tuple(A_ )
| 605 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__UpperCAmelCase = {
'configuration_bridgetower': [
'BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'BridgeTowerConfig',
'BridgeTowerTextConfig',
'BridgeTowerVisionConfig',
],
'processing_bridgetower': ['BridgeTowerProcessor'],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = ['BridgeTowerImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
'BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST',
'BridgeTowerForContrastiveLearning',
'BridgeTowerForImageAndTextRetrieval',
'BridgeTowerForMaskedLM',
'BridgeTowerModel',
'BridgeTowerPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_bridgetower import (
BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
BridgeTowerConfig,
BridgeTowerTextConfig,
BridgeTowerVisionConfig,
)
from .processing_bridgetower import BridgeTowerProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_bridgetower import BridgeTowerImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_bridgetower import (
BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
BridgeTowerForContrastiveLearning,
BridgeTowerForImageAndTextRetrieval,
BridgeTowerForMaskedLM,
BridgeTowerModel,
BridgeTowerPreTrainedModel,
)
else:
import sys
__UpperCAmelCase = _LazyModule(__name__, globals()['__file__'], _import_structure)
| 65 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase : Tuple =logging.get_logger(__name__)
_lowerCAmelCase : Union[str, Any] ={
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class __UpperCamelCase ( __lowerCamelCase ):
'''simple docstring'''
__magic_name__ = "megatron-bert"
def __init__( self , lowerCamelCase__=2_9_0_5_6 , lowerCamelCase__=1_0_2_4 , lowerCamelCase__=2_4 , lowerCamelCase__=1_6 , lowerCamelCase__=4_0_9_6 , lowerCamelCase__="gelu" , lowerCamelCase__=0.1 , lowerCamelCase__=0.1 , lowerCamelCase__=5_1_2 , lowerCamelCase__=2 , lowerCamelCase__=0.02 , lowerCamelCase__=1e-12 , lowerCamelCase__=0 , lowerCamelCase__="absolute" , lowerCamelCase__=True , **lowerCamelCase__ , ):
super().__init__(pad_token_id=lowerCamelCase__ , **lowerCamelCase__ )
UpperCAmelCase__: Any = vocab_size
UpperCAmelCase__: Union[str, Any] = hidden_size
UpperCAmelCase__: List[Any] = num_hidden_layers
UpperCAmelCase__: Optional[Any] = num_attention_heads
UpperCAmelCase__: List[Any] = hidden_act
UpperCAmelCase__: int = intermediate_size
UpperCAmelCase__: Tuple = hidden_dropout_prob
UpperCAmelCase__: Any = attention_probs_dropout_prob
UpperCAmelCase__: int = max_position_embeddings
UpperCAmelCase__: Optional[Any] = type_vocab_size
UpperCAmelCase__: Dict = initializer_range
UpperCAmelCase__: Optional[Any] = layer_norm_eps
UpperCAmelCase__: List[Any] = position_embedding_type
UpperCAmelCase__: Any = use_cache | 113 |
"""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 __lowercase ( __lowerCamelCase ):
snake_case_ = ["""input_features""", """is_longer"""]
def __init__( self : str ,A : Union[str, Any]=64 ,A : Tuple=48_000 ,A : Dict=480 ,A : List[str]=10 ,A : str=1_024 ,A : Any=0.0 ,A : Optional[int]=False ,A : float = 0 ,A : float = 14_000 ,A : int = None ,A : str = "fusion" ,A : str = "repeatpad" ,**A : List[Any] ,):
'''simple docstring'''
super().__init__(
feature_size=A ,sampling_rate=A ,padding_value=A ,return_attention_mask=A ,**A ,)
UpperCAmelCase__ : List[Any] = top_db
UpperCAmelCase__ : Union[str, Any] = truncation
UpperCAmelCase__ : Optional[int] = padding
UpperCAmelCase__ : List[Any] = fft_window_size
UpperCAmelCase__ : Optional[Any] = (fft_window_size >> 1) + 1
UpperCAmelCase__ : Any = hop_length
UpperCAmelCase__ : List[str] = max_length_s
UpperCAmelCase__ : List[Any] = max_length_s * sampling_rate
UpperCAmelCase__ : List[Any] = sampling_rate
UpperCAmelCase__ : Optional[int] = frequency_min
UpperCAmelCase__ : Tuple = frequency_max
UpperCAmelCase__ : 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=A ,mel_scale="""htk""" ,)
UpperCAmelCase__ : 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 __lowercase ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = copy.deepcopy(self.__dict__ )
UpperCAmelCase__ : 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 __lowercase ( self : List[str] ,A : np.array ,A : Optional[np.array] = None ):
'''simple docstring'''
UpperCAmelCase__ : Dict = 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 : Optional[Any] ,A : Union[str, Any] ,A : int ,A : Optional[int] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[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
UpperCAmelCase__ : List[str] = [0]
if len(ranges[2] ) == 0:
# if the audio is too short, we just use the first chunk
UpperCAmelCase__ : int = [0]
# randomly choose index for each part
UpperCAmelCase__ : Tuple = np.random.choice(ranges[0] )
UpperCAmelCase__ : Tuple = np.random.choice(ranges[1] )
UpperCAmelCase__ : str = np.random.choice(ranges[2] )
UpperCAmelCase__ : List[str] = mel[idx_front : idx_front + chunk_frames, :]
UpperCAmelCase__ : List[str] = mel[idx_middle : idx_middle + chunk_frames, :]
UpperCAmelCase__ : Dict = mel[idx_back : idx_back + chunk_frames, :]
UpperCAmelCase__ : Optional[Any] = torch.tensor(mel[None, None, :] )
UpperCAmelCase__ : int = torch.nn.functional.interpolate(
A ,size=[chunk_frames, 64] ,mode="""bilinear""" ,align_corners=A )
UpperCAmelCase__ : Dict = mel_shrink[0][0].numpy()
UpperCAmelCase__ : Dict = np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back] ,axis=0 )
return mel_fusion
def __lowercase ( self : Any ,A : np.array ,A : Optional[int] ,A : Any ,A : Tuple ):
'''simple docstring'''
if waveform.shape[0] > max_length:
if truncation == "rand_trunc":
UpperCAmelCase__ : int = True
# random crop to max_length (for compatibility) -> this should be handled by self.pad
UpperCAmelCase__ : str = len(A ) - max_length
UpperCAmelCase__ : Optional[Any] = np.random.randint(0 ,overflow + 1 )
UpperCAmelCase__ : Optional[int] = waveform[idx : idx + max_length]
UpperCAmelCase__ : Any = self._np_extract_fbank_features(A ,self.mel_filters_slaney )[None, :]
elif truncation == "fusion":
UpperCAmelCase__ : Tuple = self._np_extract_fbank_features(A ,self.mel_filters )
UpperCAmelCase__ : Optional[int] = max_length // self.hop_length + 1 # the +1 related to how the spectrogram is computed
UpperCAmelCase__ : 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.
UpperCAmelCase__ : List[Any] = np.stack([mel, mel, mel, mel] ,axis=0 )
UpperCAmelCase__ : Any = False
else:
UpperCAmelCase__ : Union[str, Any] = self._random_mel_fusion(A ,A ,A )
UpperCAmelCase__ : List[str] = True
else:
raise NotImplementedError(f"data_truncating {truncation} not implemented" )
else:
UpperCAmelCase__ : Optional[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":
UpperCAmelCase__ : str = int(max_length / len(A ) )
UpperCAmelCase__ : int = np.stack(np.tile(A ,n_repeat + 1 ) )[:max_length]
if padding == "repeatpad":
UpperCAmelCase__ : List[Any] = int(max_length / len(A ) )
UpperCAmelCase__ : str = np.stack(np.tile(A ,A ) )
UpperCAmelCase__ : Optional[Any] = np.pad(A ,(0, max_length - waveform.shape[0]) ,mode="""constant""" ,constant_values=0 )
if truncation == "fusion":
UpperCAmelCase__ : int = self._np_extract_fbank_features(A ,self.mel_filters )
UpperCAmelCase__ : List[Any] = np.stack([input_mel, input_mel, input_mel, input_mel] ,axis=0 )
else:
UpperCAmelCase__ : Any = self._np_extract_fbank_features(A ,self.mel_filters_slaney )[None, :]
return input_mel, longer
def __call__( self : str ,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 : List[str] ,):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = truncation if truncation is not None else self.truncation
UpperCAmelCase__ : Dict = 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__ : Optional[int] = 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__ : List[str] = is_batched_numpy or (
isinstance(A ,(list, tuple) ) and (isinstance(raw_speech[0] ,(np.ndarray, tuple, list) ))
)
if is_batched:
UpperCAmelCase__ : str = [np.asarray(A ,dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(A ,np.ndarray ):
UpperCAmelCase__ : Any = np.asarray(A ,dtype=np.floataa )
elif isinstance(A ,np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
UpperCAmelCase__ : str = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
UpperCAmelCase__ : Optional[Any] = [np.asarray(A )]
# convert to mel spectrogram, truncate and pad if needed.
UpperCAmelCase__ : Tuple = [
self._get_input_mel(A ,max_length if max_length else self.nb_max_samples ,A ,A )
for waveform in raw_speech
]
UpperCAmelCase__ : Optional[int] = []
UpperCAmelCase__ : 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
UpperCAmelCase__ : List[str] = np.random.randint(0 ,len(A ) )
UpperCAmelCase__ : int = True
if isinstance(input_mel[0] ,A ):
UpperCAmelCase__ : Tuple = [np.asarray(A ,dtype=np.floataa ) for feature in input_mel]
# is_longer is a list of bool
UpperCAmelCase__ : List[str] = [[longer] for longer in is_longer]
UpperCAmelCase__ : List[Any] = {"""input_features""": input_mel, """is_longer""": is_longer}
UpperCAmelCase__ : str = BatchFeature(A )
if return_tensors is not None:
UpperCAmelCase__ : int = input_features.convert_to_tensors(A )
return input_features
| 65 | 0 |
import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer
class _UpperCAmelCase ( __lowerCamelCase ):
'''simple docstring'''
__A = ['''image_processor''', '''tokenizer''']
__A = '''BlipImageProcessor'''
__A = '''AutoTokenizer'''
def __init__( self : List[str] , lowercase_ : List[Any] , lowercase_ : Optional[int] , lowercase_ : Union[str, Any]) -> List[str]:
"""simple docstring"""
super().__init__(lowercase_ , lowercase_)
# add QFormer tokenizer
_UpperCamelCase = qformer_tokenizer
def __call__( self : List[str] , lowercase_ : ImageInput = None , lowercase_ : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None , lowercase_ : bool = True , lowercase_ : Union[bool, str, PaddingStrategy] = False , lowercase_ : Union[bool, str, TruncationStrategy] = None , lowercase_ : Optional[int] = None , lowercase_ : int = 0 , lowercase_ : Optional[int] = None , lowercase_ : Optional[bool] = None , lowercase_ : bool = False , lowercase_ : bool = False , lowercase_ : bool = False , lowercase_ : bool = False , lowercase_ : bool = False , lowercase_ : bool = True , lowercase_ : Optional[Union[str, TensorType]] = None , **lowercase_ : Optional[Any] , ) -> str:
"""simple docstring"""
if images is None and text is None:
raise ValueError("You have to specify at least images or text.")
_UpperCamelCase = BatchFeature()
if text is not None:
_UpperCamelCase = self.tokenizer(
text=lowercase_ , add_special_tokens=lowercase_ , padding=lowercase_ , truncation=lowercase_ , max_length=lowercase_ , stride=lowercase_ , pad_to_multiple_of=lowercase_ , return_attention_mask=lowercase_ , return_overflowing_tokens=lowercase_ , return_special_tokens_mask=lowercase_ , return_offsets_mapping=lowercase_ , return_token_type_ids=lowercase_ , return_length=lowercase_ , verbose=lowercase_ , return_tensors=lowercase_ , **lowercase_ , )
encoding.update(lowercase_)
_UpperCamelCase = self.qformer_tokenizer(
text=lowercase_ , add_special_tokens=lowercase_ , padding=lowercase_ , truncation=lowercase_ , max_length=lowercase_ , stride=lowercase_ , pad_to_multiple_of=lowercase_ , return_attention_mask=lowercase_ , return_overflowing_tokens=lowercase_ , return_special_tokens_mask=lowercase_ , return_offsets_mapping=lowercase_ , return_token_type_ids=lowercase_ , return_length=lowercase_ , verbose=lowercase_ , return_tensors=lowercase_ , **lowercase_ , )
_UpperCamelCase = qformer_text_encoding.pop("input_ids")
_UpperCamelCase = qformer_text_encoding.pop("attention_mask")
if images is not None:
_UpperCamelCase = self.image_processor(lowercase_ , return_tensors=lowercase_)
encoding.update(lowercase_)
return encoding
def __UpperCAmelCase ( self : Tuple , *lowercase_ : Union[str, Any] , **lowercase_ : List[str]) -> Union[str, Any]:
"""simple docstring"""
return self.tokenizer.batch_decode(*lowercase_ , **lowercase_)
def __UpperCAmelCase ( self : Dict , *lowercase_ : Optional[Any] , **lowercase_ : int) -> Optional[Any]:
"""simple docstring"""
return self.tokenizer.decode(*lowercase_ , **lowercase_)
@property
# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
def __UpperCAmelCase ( self : Dict) -> Any:
"""simple docstring"""
_UpperCamelCase = self.tokenizer.model_input_names
_UpperCamelCase = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
def __UpperCAmelCase ( self : str , lowercase_ : Optional[Any] , **lowercase_ : str) -> Union[str, Any]:
"""simple docstring"""
if os.path.isfile(lowercase_):
raise ValueError(f'Provided path ({save_directory}) should be a directory, not a file')
os.makedirs(lowercase_ , exist_ok=lowercase_)
_UpperCamelCase = os.path.join(lowercase_ , "qformer_tokenizer")
self.qformer_tokenizer.save_pretrained(lowercase_)
return super().save_pretrained(lowercase_ , **lowercase_)
@classmethod
def __UpperCAmelCase ( cls : List[str] , lowercase_ : Any , **lowercase_ : int) -> Any:
"""simple docstring"""
_UpperCamelCase = AutoTokenizer.from_pretrained(lowercase_ , subfolder="qformer_tokenizer")
_UpperCamelCase = cls._get_arguments_from_pretrained(lowercase_ , **lowercase_)
args.append(lowercase_)
return cls(*lowercase_)
| 547 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, 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 DonutImageProcessor
class __lowercase ( unittest.TestCase ):
def __init__( self : Union[str, Any] ,A : Union[str, Any] ,A : Dict=7 ,A : Optional[int]=3 ,A : List[str]=18 ,A : Union[str, Any]=30 ,A : Tuple=400 ,A : Dict=True ,A : List[str]=None ,A : str=True ,A : Optional[Any]=False ,A : Optional[Any]=True ,A : List[str]=True ,A : Optional[int]=[0.5, 0.5, 0.5] ,A : List[str]=[0.5, 0.5, 0.5] ,):
'''simple docstring'''
UpperCAmelCase__ : str = parent
UpperCAmelCase__ : List[str] = batch_size
UpperCAmelCase__ : List[str] = num_channels
UpperCAmelCase__ : Union[str, Any] = image_size
UpperCAmelCase__ : List[Any] = min_resolution
UpperCAmelCase__ : Optional[int] = max_resolution
UpperCAmelCase__ : str = do_resize
UpperCAmelCase__ : Tuple = size if size is not None else {"""height""": 18, """width""": 20}
UpperCAmelCase__ : List[str] = do_thumbnail
UpperCAmelCase__ : Optional[int] = do_align_axis
UpperCAmelCase__ : Union[str, Any] = do_pad
UpperCAmelCase__ : Tuple = do_normalize
UpperCAmelCase__ : Optional[Any] = image_mean
UpperCAmelCase__ : List[Any] = image_std
def __lowercase ( self : Optional[int] ):
'''simple docstring'''
return {
"do_resize": self.do_resize,
"size": self.size,
"do_thumbnail": self.do_thumbnail,
"do_align_long_axis": self.do_align_axis,
"do_pad": self.do_pad,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
}
@require_torch
@require_vision
class __lowercase ( __lowerCamelCase , unittest.TestCase ):
snake_case_ = DonutImageProcessor if is_vision_available() else None
def __lowercase ( self : str ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = DonutImageProcessingTester(self )
@property
def __lowercase ( self : Dict ):
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def __lowercase ( self : Any ):
'''simple docstring'''
UpperCAmelCase__ : Any = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(A ,"""do_resize""" ) )
self.assertTrue(hasattr(A ,"""size""" ) )
self.assertTrue(hasattr(A ,"""do_thumbnail""" ) )
self.assertTrue(hasattr(A ,"""do_align_long_axis""" ) )
self.assertTrue(hasattr(A ,"""do_pad""" ) )
self.assertTrue(hasattr(A ,"""do_normalize""" ) )
self.assertTrue(hasattr(A ,"""image_mean""" ) )
self.assertTrue(hasattr(A ,"""image_std""" ) )
def __lowercase ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size ,{"""height""": 18, """width""": 20} )
UpperCAmelCase__ : str = self.image_processing_class.from_dict(self.image_processor_dict ,size=42 )
self.assertEqual(image_processor.size ,{"""height""": 42, """width""": 42} )
# Previous config had dimensions in (width, height) order
UpperCAmelCase__ : str = self.image_processing_class.from_dict(self.image_processor_dict ,size=(42, 84) )
self.assertEqual(image_processor.size ,{"""height""": 84, """width""": 42} )
def __lowercase ( self : Dict ):
'''simple docstring'''
pass
@is_flaky()
def __lowercase ( self : int ):
'''simple docstring'''
# Initialize image_processing
UpperCAmelCase__ : Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
UpperCAmelCase__ : Dict = prepare_image_inputs(self.image_processor_tester ,equal_resolution=A )
for image in image_inputs:
self.assertIsInstance(A ,Image.Image )
# Test not batched input
UpperCAmelCase__ : int = image_processing(image_inputs[0] ,return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) ,)
# Test batched
UpperCAmelCase__ : Tuple = image_processing(A ,return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) ,)
@is_flaky()
def __lowercase ( self : List[str] ):
'''simple docstring'''
# Initialize image_processing
UpperCAmelCase__ : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
UpperCAmelCase__ : Dict = prepare_image_inputs(self.image_processor_tester ,equal_resolution=A ,numpify=A )
for image in image_inputs:
self.assertIsInstance(A ,np.ndarray )
# Test not batched input
UpperCAmelCase__ : List[str] = image_processing(image_inputs[0] ,return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) ,)
# Test batched
UpperCAmelCase__ : Optional[int] = image_processing(A ,return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) ,)
@is_flaky()
def __lowercase ( self : Any ):
'''simple docstring'''
# Initialize image_processing
UpperCAmelCase__ : int = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
UpperCAmelCase__ : int = prepare_image_inputs(self.image_processor_tester ,equal_resolution=A ,torchify=A )
for image in image_inputs:
self.assertIsInstance(A ,torch.Tensor )
# Test not batched input
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.size["""height"""],
self.image_processor_tester.size["""width"""],
) ,)
# Test batched
UpperCAmelCase__ : List[Any] = image_processing(A ,return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) ,)
| 65 | 0 |
from ..utils import DummyObject, requires_backends
class __lowerCAmelCase ( metaclass=__lowerCamelCase ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = ['onnx']
def __init__( self : int , *_lowerCAmelCase : List[str] , **_lowerCAmelCase : int ) -> int:
"""simple docstring"""
requires_backends(self , ["onnx"] )
@classmethod
def lowerCAmelCase__ ( cls : Optional[Any] , *_lowerCAmelCase : List[str] , **_lowerCAmelCase : Dict ) -> Optional[int]:
"""simple docstring"""
requires_backends(cls , ["onnx"] )
@classmethod
def lowerCAmelCase__ ( cls : List[Any] , *_lowerCAmelCase : Optional[int] , **_lowerCAmelCase : int ) -> Optional[Any]:
"""simple docstring"""
requires_backends(cls , ["onnx"] )
| 283 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
's-JoL/Open-Llama-V1': 'https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json',
}
class __lowercase ( __lowerCamelCase ):
snake_case_ = """open-llama"""
def __init__( self : Dict ,A : str=100_000 ,A : str=4_096 ,A : Optional[Any]=11_008 ,A : Tuple=32 ,A : str=32 ,A : Optional[int]="silu" ,A : List[Any]=2_048 ,A : str=0.0_2 ,A : Optional[int]=1e-6 ,A : int=True ,A : Tuple=0 ,A : str=1 ,A : Any=2 ,A : Optional[Any]=False ,A : int=True ,A : Any=0.1 ,A : Optional[Any]=0.1 ,A : Optional[Any]=True ,A : Union[str, Any]=True ,A : Tuple=None ,**A : Optional[int] ,):
'''simple docstring'''
UpperCAmelCase__ : str = vocab_size
UpperCAmelCase__ : List[str] = max_position_embeddings
UpperCAmelCase__ : Union[str, Any] = hidden_size
UpperCAmelCase__ : Tuple = intermediate_size
UpperCAmelCase__ : Optional[int] = num_hidden_layers
UpperCAmelCase__ : Any = num_attention_heads
UpperCAmelCase__ : str = hidden_act
UpperCAmelCase__ : Optional[Any] = initializer_range
UpperCAmelCase__ : Optional[int] = rms_norm_eps
UpperCAmelCase__ : Any = use_cache
UpperCAmelCase__ : Optional[Any] = kwargs.pop(
"""use_memorry_efficient_attention""" ,A )
UpperCAmelCase__ : Any = hidden_dropout_prob
UpperCAmelCase__ : str = attention_dropout_prob
UpperCAmelCase__ : Optional[int] = use_stable_embedding
UpperCAmelCase__ : Tuple = shared_input_output_embedding
UpperCAmelCase__ : Tuple = rope_scaling
self._rope_scaling_validation()
super().__init__(
pad_token_id=A ,bos_token_id=A ,eos_token_id=A ,tie_word_embeddings=A ,**A ,)
def __lowercase ( self : Optional[Any] ):
'''simple docstring'''
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling ,A ) or len(self.rope_scaling ) != 2:
raise ValueError(
"""`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, """
f"got {self.rope_scaling}" )
UpperCAmelCase__ : List[Any] = self.rope_scaling.get("""type""" ,A )
UpperCAmelCase__ : int = self.rope_scaling.get("""factor""" ,A )
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
raise ValueError(
f"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}" )
if rope_scaling_factor is None or not isinstance(A ,A ) or rope_scaling_factor <= 1.0:
raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}" )
| 65 | 0 |
from itertools import permutations
def UpperCamelCase_( snake_case__: List[str] ) -> Union[str, Any]:
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
UpperCAmelCase__ = [7, 11, 13, 17]
for i, test in enumerate(__UpperCamelCase ):
if (num[i + 4] * 1_00 + num[i + 5] * 10 + num[i + 6]) % test != 0:
return False
return True
def UpperCamelCase_( snake_case__: Union[str, Any] = 10 ) -> List[str]:
return sum(
int(''.join(map(__UpperCamelCase , __UpperCamelCase ) ) )
for num in permutations(range(__UpperCamelCase ) )
if is_substring_divisible(__UpperCamelCase ) )
if __name__ == "__main__":
print(F"""{solution() = }""")
| 146 |
"""simple docstring"""
from collections.abc import Callable
class __lowercase :
def __init__( self : Tuple ,A : Callable | None = None ):
'''simple docstring'''
# Stores actual heap items.
UpperCAmelCase__ : list = []
# Stores indexes of each item for supporting updates and deletion.
UpperCAmelCase__ : dict = {}
# Stores current size of heap.
UpperCAmelCase__ : Any = 0
# Stores function used to evaluate the score of an item on which basis ordering
# will be done.
UpperCAmelCase__ : int = key or (lambda A : x)
def __lowercase ( self : Union[str, Any] ,A : int ):
'''simple docstring'''
return int((i - 1) / 2 ) if i > 0 else None
def __lowercase ( self : Tuple ,A : int ):
'''simple docstring'''
UpperCAmelCase__ : Any = int(2 * i + 1 )
return left if 0 < left < self.size else None
def __lowercase ( self : Any ,A : int ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = int(2 * i + 2 )
return right if 0 < right < self.size else None
def __lowercase ( self : List[Any] ,A : int ,A : int ):
'''simple docstring'''
UpperCAmelCase__ , UpperCAmelCase__ : int = (
self.pos_map[self.arr[j][0]],
self.pos_map[self.arr[i][0]],
)
# Then swap the items in the list.
UpperCAmelCase__ , UpperCAmelCase__ : Tuple = self.arr[j], self.arr[i]
def __lowercase ( self : Optional[int] ,A : int ,A : int ):
'''simple docstring'''
return self.arr[i][1] < self.arr[j][1]
def __lowercase ( self : Optional[int] ,A : int ):
'''simple docstring'''
UpperCAmelCase__ : int = self._left(A )
UpperCAmelCase__ : Dict = self._right(A )
UpperCAmelCase__ : Optional[int] = i
if left is not None and not self._cmp(A ,A ):
UpperCAmelCase__ : List[Any] = left
if right is not None and not self._cmp(A ,A ):
UpperCAmelCase__ : List[Any] = right
return valid_parent
def __lowercase ( self : int ,A : int ):
'''simple docstring'''
UpperCAmelCase__ : int = self._parent(A )
while parent is not None and not self._cmp(A ,A ):
self._swap(A ,A )
UpperCAmelCase__ , UpperCAmelCase__ : int = parent, self._parent(A )
def __lowercase ( self : str ,A : int ):
'''simple docstring'''
UpperCAmelCase__ : Any = self._get_valid_parent(A )
while valid_parent != index:
self._swap(A ,A )
UpperCAmelCase__ , UpperCAmelCase__ : Optional[int] = valid_parent, self._get_valid_parent(A )
def __lowercase ( self : Optional[Any] ,A : int ,A : int ):
'''simple docstring'''
if item not in self.pos_map:
return
UpperCAmelCase__ : Tuple = self.pos_map[item]
UpperCAmelCase__ : Dict = [item, self.key(A )]
# Make sure heap is right in both up and down direction.
# Ideally only one of them will make any change.
self._heapify_up(A )
self._heapify_down(A )
def __lowercase ( self : List[Any] ,A : int ):
'''simple docstring'''
if item not in self.pos_map:
return
UpperCAmelCase__ : Any = self.pos_map[item]
del self.pos_map[item]
UpperCAmelCase__ : Dict = self.arr[self.size - 1]
UpperCAmelCase__ : List[Any] = index
self.size -= 1
# Make sure heap is right in both up and down direction. Ideally only one
# of them will make any change- so no performance loss in calling both.
if self.size > index:
self._heapify_up(A )
self._heapify_down(A )
def __lowercase ( self : str ,A : int ,A : int ):
'''simple docstring'''
UpperCAmelCase__ : Dict = len(self.arr )
if arr_len == self.size:
self.arr.append([item, self.key(A )] )
else:
UpperCAmelCase__ : List[str] = [item, self.key(A )]
UpperCAmelCase__ : Union[str, Any] = self.size
self.size += 1
self._heapify_up(self.size - 1 )
def __lowercase ( self : str ):
'''simple docstring'''
return self.arr[0] if self.size else None
def __lowercase ( self : Dict ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = self.get_top()
if top_item_tuple:
self.delete_item(top_item_tuple[0] )
return top_item_tuple
def lowerCAmelCase ( ):
'''simple docstring'''
if __name__ == "__main__":
import doctest
doctest.testmod()
| 65 | 0 |
"""simple docstring"""
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class _UpperCamelCase ( __lowerCamelCase ):
'''simple docstring'''
__UpperCAmelCase : List[Any] =(UnCLIPScheduler,)
def snake_case ( self , **__a ):
__lowerCAmelCase = {
"""num_train_timesteps""": 10_00,
"""variance_type""": """fixed_small_log""",
"""clip_sample""": True,
"""clip_sample_range""": 1.0,
"""prediction_type""": """epsilon""",
}
config.update(**__a )
return config
def snake_case ( self ):
for timesteps in [1, 5, 1_00, 10_00]:
self.check_over_configs(num_train_timesteps=__a )
def snake_case ( self ):
for variance in ["fixed_small_log", "learned_range"]:
self.check_over_configs(variance_type=__a )
def snake_case ( self ):
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=__a )
def snake_case ( self ):
for clip_sample_range in [1, 5, 10, 20]:
self.check_over_configs(clip_sample_range=__a )
def snake_case ( self ):
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(prediction_type=__a )
def snake_case ( self ):
for time_step in [0, 5_00, 9_99]:
for prev_timestep in [None, 5, 1_00, 2_50, 5_00, 7_50]:
if prev_timestep is not None and prev_timestep >= time_step:
continue
self.check_over_forward(time_step=__a , prev_timestep=__a )
def snake_case ( self ):
__lowerCAmelCase = self.scheduler_classes[0]
__lowerCAmelCase = self.get_scheduler_config(variance_type="fixed_small_log" )
__lowerCAmelCase = scheduler_class(**__a )
assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 1.0_0_0_0e-1_0 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(4_87 ) - 0.0_5_4_9_6_2_5 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(9_99 ) - 0.9_9_9_4_9_8_7 ) ) < 1e-5
def snake_case ( self ):
__lowerCAmelCase = self.scheduler_classes[0]
__lowerCAmelCase = self.get_scheduler_config(variance_type="learned_range" )
__lowerCAmelCase = scheduler_class(**__a )
__lowerCAmelCase = 0.5
assert scheduler._get_variance(1 , predicted_variance=__a ) - -10.1_71_27_90 < 1e-5
assert scheduler._get_variance(4_87 , predicted_variance=__a ) - -5.7_9_9_8_0_5_2 < 1e-5
assert scheduler._get_variance(9_99 , predicted_variance=__a ) - -0.0_0_1_0_0_1_1 < 1e-5
def snake_case ( self ):
__lowerCAmelCase = self.scheduler_classes[0]
__lowerCAmelCase = self.get_scheduler_config()
__lowerCAmelCase = scheduler_class(**__a )
__lowerCAmelCase = scheduler.timesteps
__lowerCAmelCase = self.dummy_model()
__lowerCAmelCase = self.dummy_sample_deter
__lowerCAmelCase = torch.manual_seed(0 )
for i, t in enumerate(__a ):
# 1. predict noise residual
__lowerCAmelCase = model(__a , __a )
# 2. predict previous mean of sample x_t-1
__lowerCAmelCase = scheduler.step(__a , __a , __a , generator=__a ).prev_sample
__lowerCAmelCase = pred_prev_sample
__lowerCAmelCase = torch.sum(torch.abs(__a ) )
__lowerCAmelCase = torch.mean(torch.abs(__a ) )
assert abs(result_sum.item() - 2_52.2_68_24_95 ) < 1e-2
assert abs(result_mean.item() - 0.3_2_8_4_7_4_3 ) < 1e-3
def snake_case ( self ):
__lowerCAmelCase = self.scheduler_classes[0]
__lowerCAmelCase = self.get_scheduler_config()
__lowerCAmelCase = scheduler_class(**__a )
scheduler.set_timesteps(25 )
__lowerCAmelCase = scheduler.timesteps
__lowerCAmelCase = self.dummy_model()
__lowerCAmelCase = self.dummy_sample_deter
__lowerCAmelCase = torch.manual_seed(0 )
for i, t in enumerate(__a ):
# 1. predict noise residual
__lowerCAmelCase = model(__a , __a )
if i + 1 == timesteps.shape[0]:
__lowerCAmelCase = None
else:
__lowerCAmelCase = timesteps[i + 1]
# 2. predict previous mean of sample x_t-1
__lowerCAmelCase = scheduler.step(
__a , __a , __a , prev_timestep=__a , generator=__a ).prev_sample
__lowerCAmelCase = pred_prev_sample
__lowerCAmelCase = torch.sum(torch.abs(__a ) )
__lowerCAmelCase = torch.mean(torch.abs(__a ) )
assert abs(result_sum.item() - 2_58.2_04_49_83 ) < 1e-2
assert abs(result_mean.item() - 0.3_3_6_2_0_3_8 ) < 1e-3
def snake_case ( self ):
pass
def snake_case ( self ):
pass
| 636 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import PaddingStrategy, TensorType
from ....utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
class __lowercase ( __lowerCamelCase ):
snake_case_ = ["""input_features""", """attention_mask"""]
def __init__( self : Any ,A : str=80 ,A : Optional[int]=16_000 ,A : int=0.0 ,A : str=10 ,A : Any=25 ,A : str="hamming_window" ,A : int=3_2_7_6_8.0 ,A : List[str]=0.9_7 ,A : Optional[int]=1.0 ,A : Optional[Any]=True ,A : Tuple=True ,A : Any=False ,**A : int ,):
'''simple docstring'''
super().__init__(feature_size=A ,sampling_rate=A ,padding_value=A ,**A )
UpperCAmelCase__ : str = feature_size
UpperCAmelCase__ : int = sampling_rate
UpperCAmelCase__ : int = padding_value
UpperCAmelCase__ : Dict = hop_length
UpperCAmelCase__ : int = win_length
UpperCAmelCase__ : Dict = frame_signal_scale
UpperCAmelCase__ : Dict = preemphasis_coeff
UpperCAmelCase__ : str = mel_floor
UpperCAmelCase__ : Any = normalize_means
UpperCAmelCase__ : str = normalize_vars
UpperCAmelCase__ : int = win_function
UpperCAmelCase__ : List[Any] = return_attention_mask
UpperCAmelCase__ : str = win_length * sampling_rate // 1_000
UpperCAmelCase__ : List[Any] = hop_length * sampling_rate // 1_000
UpperCAmelCase__ : int = optimal_fft_length(self.sample_size )
UpperCAmelCase__ : List[Any] = (self.n_fft // 2) + 1
def __lowercase ( self : Union[str, Any] ,A : np.array ):
'''simple docstring'''
if self.win_function == "hamming_window":
UpperCAmelCase__ : Any = window_function(window_length=self.sample_size ,name=self.win_function ,periodic=A )
else:
UpperCAmelCase__ : Any = window_function(window_length=self.sample_size ,name=self.win_function )
UpperCAmelCase__ : Union[str, Any] = mel_filter_bank(
num_frequency_bins=self.n_freqs ,num_mel_filters=self.feature_size ,min_frequency=0.0 ,max_frequency=self.sampling_rate / 2.0 ,sampling_rate=self.sampling_rate ,)
UpperCAmelCase__ : Optional[Any] = spectrogram(
one_waveform * self.frame_signal_scale ,window=A ,frame_length=self.sample_size ,hop_length=self.sample_stride ,fft_length=self.n_fft ,center=A ,preemphasis=self.preemphasis_coeff ,mel_filters=A ,mel_floor=self.mel_floor ,log_mel="""log""" ,)
return msfc_features.T
def __lowercase ( self : str ,A : Any ,A : Optional[int] ,A : str ):
'''simple docstring'''
# make sure we normalize float32 arrays
if self.normalize_means:
UpperCAmelCase__ : Optional[Any] = x[:input_length].mean(axis=0 )
UpperCAmelCase__ : Any = np.subtract(A ,A )
if self.normalize_vars:
UpperCAmelCase__ : str = x[:input_length].std(axis=0 )
UpperCAmelCase__ : Optional[int] = np.divide(A ,A )
if input_length < x.shape[0]:
UpperCAmelCase__ : int = padding_value
# make sure array is in float32
UpperCAmelCase__ : str = x.astype(np.floataa )
return x
def __lowercase ( self : Union[str, Any] ,A : List[np.ndarray] ,A : Optional[np.ndarray] = None ):
'''simple docstring'''
UpperCAmelCase__ : Any = attention_mask.sum(-1 ) if attention_mask is not None else [x.shape[0] for x in input_features]
return [self._normalize_one(A ,A ,self.padding_value ) for x, n in zip(A ,A )]
def __call__( self : Union[str, Any] ,A : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] ,A : Union[bool, str, PaddingStrategy] = False ,A : Optional[int] = None ,A : bool = False ,A : Optional[int] = None ,A : Optional[bool] = None ,A : Optional[Union[str, TensorType]] = None ,A : Optional[int] = None ,**A : Tuple ,):
'''simple docstring'''
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 `raw_speech` 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.""" )
UpperCAmelCase__ : 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}" )
UpperCAmelCase__ : Any = is_batched_numpy or (
isinstance(A ,(list, tuple) ) and (isinstance(raw_speech[0] ,(np.ndarray, tuple, list) ))
)
if is_batched:
UpperCAmelCase__ : List[str] = [np.asarray(A ,dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(A ,np.ndarray ):
UpperCAmelCase__ : Union[str, Any] = np.asarray(A ,dtype=np.floataa )
elif isinstance(A ,np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
UpperCAmelCase__ : Optional[int] = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
UpperCAmelCase__ : Optional[Any] = [raw_speech]
# extract fbank features
UpperCAmelCase__ : Tuple = [self._extract_mfsc_features(A ) for one_waveform in raw_speech]
# convert into correct format for padding
UpperCAmelCase__ : str = BatchFeature({"""input_features""": features} )
UpperCAmelCase__ : Optional[Any] = self.pad(
A ,padding=A ,max_length=A ,truncation=A ,pad_to_multiple_of=A ,return_attention_mask=A ,**A ,)
# make sure list is in array format
UpperCAmelCase__ : Tuple = padded_inputs.get("""input_features""" )
if isinstance(input_features[0] ,A ):
UpperCAmelCase__ : Union[str, Any] = [np.asarray(A ,dtype=np.floataa ) for feature in input_features]
UpperCAmelCase__ : Dict = padded_inputs.get("""attention_mask""" )
if attention_mask is not None:
UpperCAmelCase__ : str = [np.asarray(A ,dtype=np.intaa ) for array in attention_mask]
if self.normalize_means or self.normalize_vars:
UpperCAmelCase__ : Union[str, Any] = (
np.array(A ,dtype=np.intaa )
if self._get_padding_strategies(A ,max_length=A ) is not PaddingStrategy.DO_NOT_PAD
and padding
else None
)
UpperCAmelCase__ : Any = self.normalize(
padded_inputs["""input_features"""] ,attention_mask=A )
if return_tensors is not None:
UpperCAmelCase__ : Union[str, Any] = padded_inputs.convert_to_tensors(A )
return padded_inputs
| 65 | 0 |
"""simple docstring"""
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
__A = 1e-4
if is_torch_available():
import torch
from transformers import AutoformerConfig, AutoformerForPrediction, AutoformerModel
from transformers.models.autoformer.modeling_autoformer import AutoformerDecoder, AutoformerEncoder
@require_torch
class _lowerCAmelCase :
"""simple docstring"""
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=1_6 , __UpperCAmelCase=1_3 , __UpperCAmelCase=7 , __UpperCAmelCase=1_4 , __UpperCAmelCase=1_0 , __UpperCAmelCase=1_9 , __UpperCAmelCase=5 , __UpperCAmelCase=4 , __UpperCAmelCase=True , __UpperCAmelCase=1_6 , __UpperCAmelCase=2 , __UpperCAmelCase=4 , __UpperCAmelCase=4 , __UpperCAmelCase="gelu" , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.1 , __UpperCAmelCase=[1, 2, 3, 4, 5] , __UpperCAmelCase=2_5 , __UpperCAmelCase=5 , ):
'''simple docstring'''
lowerCAmelCase__ :List[Any] = d_model
lowerCAmelCase__ :str = parent
lowerCAmelCase__ :int = batch_size
lowerCAmelCase__ :Any = prediction_length
lowerCAmelCase__ :List[str] = context_length
lowerCAmelCase__ :Optional[int] = cardinality
lowerCAmelCase__ :Dict = num_time_features
lowerCAmelCase__ :Tuple = lags_sequence
lowerCAmelCase__ :str = embedding_dimension
lowerCAmelCase__ :int = is_training
lowerCAmelCase__ :List[Any] = hidden_size
lowerCAmelCase__ :Union[str, Any] = num_hidden_layers
lowerCAmelCase__ :Optional[int] = num_attention_heads
lowerCAmelCase__ :List[Any] = intermediate_size
lowerCAmelCase__ :Dict = hidden_act
lowerCAmelCase__ :Tuple = hidden_dropout_prob
lowerCAmelCase__ :Tuple = attention_probs_dropout_prob
lowerCAmelCase__ :Any = context_length
lowerCAmelCase__ :Optional[Any] = prediction_length + label_length
lowerCAmelCase__ :List[Any] = label_length
lowerCAmelCase__ :str = moving_average
lowerCAmelCase__ :Dict = autocorrelation_factor
def snake_case ( self ):
'''simple docstring'''
return AutoformerConfig(
d_model=self.d_model , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , prediction_length=self.prediction_length , context_length=self.context_length , label_length=self.label_length , lags_sequence=self.lags_sequence , num_time_features=self.num_time_features , num_static_categorical_features=1 , cardinality=[self.cardinality] , embedding_dimension=[self.embedding_dimension] , moving_average=self.moving_average , )
def snake_case ( self , __UpperCAmelCase ):
'''simple docstring'''
lowerCAmelCase__ :List[Any] = config.context_length + max(config.lags_sequence )
lowerCAmelCase__ :List[str] = ids_tensor([self.batch_size, 1] , config.cardinality[0] )
lowerCAmelCase__ :Union[str, Any] = floats_tensor([self.batch_size, _past_length, config.num_time_features] )
lowerCAmelCase__ :List[Any] = floats_tensor([self.batch_size, _past_length] )
lowerCAmelCase__ :Any = floats_tensor([self.batch_size, _past_length] ) > 0.5
# decoder inputs
lowerCAmelCase__ :str = floats_tensor([self.batch_size, config.prediction_length, config.num_time_features] )
lowerCAmelCase__ :int = floats_tensor([self.batch_size, config.prediction_length] )
lowerCAmelCase__ :List[Any] = {
"""past_values""": past_values,
"""static_categorical_features""": static_categorical_features,
"""past_time_features""": past_time_features,
"""past_observed_mask""": past_observed_mask,
"""future_time_features""": future_time_features,
"""future_values""": future_values,
}
return inputs_dict
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :Optional[int] = self.get_config()
lowerCAmelCase__ :List[str] = self.prepare_autoformer_inputs_dict(__UpperCAmelCase )
return config, inputs_dict
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :int = self.prepare_config_and_inputs()
return config, inputs_dict
def snake_case ( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
lowerCAmelCase__ :str = AutoformerModel(config=__UpperCAmelCase ).to(__UpperCAmelCase ).eval()
lowerCAmelCase__ :List[Any] = model(**__UpperCAmelCase )
lowerCAmelCase__ :List[str] = outputs.encoder_last_hidden_state
lowerCAmelCase__ :int = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCAmelCase__ :Any = model.get_encoder()
encoder.save_pretrained(__UpperCAmelCase )
lowerCAmelCase__ :Optional[int] = AutoformerEncoder.from_pretrained(__UpperCAmelCase ).to(__UpperCAmelCase )
lowerCAmelCase__ :List[str] = model.create_network_inputs(**__UpperCAmelCase )
lowerCAmelCase__ :Tuple = model.decomposition_layer(transformer_inputs[:, : config.context_length, ...] )
lowerCAmelCase__ :List[str] = torch.cat(
(transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...]) , dim=-1 , )
lowerCAmelCase__ :Union[str, Any] = encoder(inputs_embeds=__UpperCAmelCase )[0]
self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3 )
lowerCAmelCase__ :Dict = (
torch.mean(transformer_inputs[:, : config.context_length, ...] , dim=1 )
.unsqueeze(1 )
.repeat(1 , config.prediction_length , 1 )
)
lowerCAmelCase__ :Dict = torch.zeros(
[transformer_inputs.shape[0], config.prediction_length, transformer_inputs.shape[2]] , device=enc_input.device , )
lowerCAmelCase__ :str = torch.cat(
(
torch.cat((seasonal_input[:, -config.label_length :, ...], zeros) , dim=1 ),
feature[:, config.context_length - config.label_length :, ...],
) , dim=-1 , )
lowerCAmelCase__ :List[str] = torch.cat(
(
torch.cat((trend_input[:, -config.label_length :, ...], mean) , dim=1 ),
feature[:, config.context_length - config.label_length :, ...],
) , dim=-1 , )
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCAmelCase__ :List[Any] = model.get_decoder()
decoder.save_pretrained(__UpperCAmelCase )
lowerCAmelCase__ :Dict = AutoformerDecoder.from_pretrained(__UpperCAmelCase ).to(__UpperCAmelCase )
lowerCAmelCase__ :Any = decoder(
trend=__UpperCAmelCase , inputs_embeds=__UpperCAmelCase , encoder_hidden_states=__UpperCAmelCase , )[0]
self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3 )
@require_torch
class _lowerCAmelCase ( __lowerCamelCase , __lowerCamelCase , unittest.TestCase ):
"""simple docstring"""
__magic_name__ :int = (AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
__magic_name__ :Dict = (AutoformerForPrediction,) if is_torch_available() else ()
__magic_name__ :Dict = {"""feature-extraction""": AutoformerModel} if is_torch_available() else {}
__magic_name__ :int = False
__magic_name__ :Optional[Any] = False
__magic_name__ :Any = False
__magic_name__ :Dict = False
__magic_name__ :Any = False
__magic_name__ :Tuple = False
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :Tuple = AutoformerModelTester(self )
lowerCAmelCase__ :int = ConfigTester(self , config_class=__UpperCAmelCase , has_text_modality=__UpperCAmelCase )
def snake_case ( self ):
'''simple docstring'''
self.config_tester.run_common_tests()
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :Optional[int] = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
lowerCAmelCase__ :Optional[int] = model_class(__UpperCAmelCase )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(__UpperCAmelCase )
lowerCAmelCase__ :Dict = model_class.from_pretrained(__UpperCAmelCase , output_loading_info=__UpperCAmelCase )
self.assertEqual(info['missing_keys'] , [] )
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :int = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*__UpperCAmelCase )
@unittest.skip(reason='Model has no tokens embeddings' )
def snake_case ( self ):
'''simple docstring'''
pass
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :List[str] = inspect.signature(getattr(__UpperCAmelCase , 'forward' ) )
# The main input is the name of the argument after `self`
lowerCAmelCase__ :Optional[int] = list(model_signature.parameters.keys() )[1]
self.assertEqual(AutoformerModel.main_input_name , __UpperCAmelCase )
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase__ :Optional[Any] = model_class(__UpperCAmelCase )
lowerCAmelCase__ :List[Any] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCAmelCase__ :List[Any] = [*signature.parameters.keys()]
lowerCAmelCase__ :List[Any] = [
"""past_values""",
"""past_time_features""",
"""past_observed_mask""",
"""static_categorical_features""",
"""static_real_features""",
"""future_values""",
"""future_time_features""",
]
if model.__class__.__name__ in ["AutoformerForPrediction"]:
expected_arg_names.append('future_observed_mask' )
expected_arg_names.extend(
[
'decoder_attention_mask',
'head_mask',
'decoder_head_mask',
'cross_attn_head_mask',
'encoder_outputs',
'past_key_values',
'output_hidden_states',
'output_attentions',
'use_cache',
'return_dict',
] )
self.assertListEqual(arg_names[: len(__UpperCAmelCase )] , __UpperCAmelCase )
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :Tuple = self.model_tester.prepare_config_and_inputs_for_common()
lowerCAmelCase__ :str = True
lowerCAmelCase__ :str = getattr(self.model_tester , 'seq_length' , __UpperCAmelCase )
lowerCAmelCase__ :int = getattr(self.model_tester , 'decoder_seq_length' , __UpperCAmelCase )
lowerCAmelCase__ :str = getattr(self.model_tester , 'encoder_seq_length' , __UpperCAmelCase )
lowerCAmelCase__ :int = getattr(self.model_tester , 'd_model' , __UpperCAmelCase )
lowerCAmelCase__ :Union[str, Any] = getattr(self.model_tester , 'num_attention_heads' , __UpperCAmelCase )
lowerCAmelCase__ :List[Any] = d_model // num_attention_heads
for model_class in self.all_model_classes:
lowerCAmelCase__ :Union[str, Any] = True
lowerCAmelCase__ :Optional[int] = False
lowerCAmelCase__ :str = True
lowerCAmelCase__ :Optional[int] = model_class(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
with torch.no_grad():
lowerCAmelCase__ :Union[str, Any] = model(**self._prepare_for_class(__UpperCAmelCase , __UpperCAmelCase ) )
lowerCAmelCase__ :Any = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(__UpperCAmelCase ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
lowerCAmelCase__ :int = True
lowerCAmelCase__ :List[Any] = model_class(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
with torch.no_grad():
lowerCAmelCase__ :Union[str, Any] = model(**self._prepare_for_class(__UpperCAmelCase , __UpperCAmelCase ) )
lowerCAmelCase__ :List[str] = outputs.encoder_attentions
self.assertEqual(len(__UpperCAmelCase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, dim] , )
lowerCAmelCase__ :List[str] = len(__UpperCAmelCase )
lowerCAmelCase__ :Dict = 7
if "last_hidden_state" in outputs:
correct_outlen += 1
if "trend" in outputs:
correct_outlen += 1
if "past_key_values" in outputs:
correct_outlen += 1 # past_key_values have been returned
if "loss" in outputs:
correct_outlen += 1
if "params" in outputs:
correct_outlen += 1
self.assertEqual(__UpperCAmelCase , __UpperCAmelCase )
# decoder attentions
lowerCAmelCase__ :int = outputs.decoder_attentions
self.assertIsInstance(__UpperCAmelCase , (list, tuple) )
self.assertEqual(len(__UpperCAmelCase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, decoder_seq_length, dim] , )
# cross attentions
lowerCAmelCase__ :int = outputs.cross_attentions
self.assertIsInstance(__UpperCAmelCase , (list, tuple) )
self.assertEqual(len(__UpperCAmelCase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(cross_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, decoder_seq_length, dim] , )
# Check attention is always last and order is fine
lowerCAmelCase__ :List[Any] = True
lowerCAmelCase__ :Union[str, Any] = True
lowerCAmelCase__ :Any = model_class(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
with torch.no_grad():
lowerCAmelCase__ :List[str] = model(**self._prepare_for_class(__UpperCAmelCase , __UpperCAmelCase ) )
self.assertEqual(out_len + 2 , len(__UpperCAmelCase ) )
lowerCAmelCase__ :Tuple = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(__UpperCAmelCase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, dim] , )
@is_flaky()
def snake_case ( self ):
'''simple docstring'''
super().test_retain_grad_hidden_states_attentions()
def __A (_SCREAMING_SNAKE_CASE="train-batch.pt" ) ->Optional[Any]:
"""simple docstring"""
lowerCAmelCase__ :Any = hf_hub_download(repo_id='hf-internal-testing/tourism-monthly-batch' , filename=__UpperCamelCase , repo_type='dataset' )
lowerCAmelCase__ :Dict = torch.load(__UpperCamelCase , map_location=__UpperCamelCase )
return batch
@require_torch
@slow
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :Any = AutoformerModel.from_pretrained('huggingface/autoformer-tourism-monthly' ).to(__UpperCAmelCase )
lowerCAmelCase__ :Dict = prepare_batch()
with torch.no_grad():
lowerCAmelCase__ :Any = model(
past_values=batch['past_values'] , past_time_features=batch['past_time_features'] , past_observed_mask=batch['past_observed_mask'] , static_categorical_features=batch['static_categorical_features'] , future_values=batch['future_values'] , future_time_features=batch['future_time_features'] , )[0]
lowerCAmelCase__ :Dict = torch.Size(
(6_4, model.config.prediction_length + model.config.label_length, model.config.feature_size) )
self.assertEqual(output.shape , __UpperCAmelCase )
lowerCAmelCase__ :str = torch.tensor(
[[0.35_93, -1.33_98, 0.63_30], [0.22_79, 1.53_96, -0.17_92], [0.04_50, 1.32_25, -0.23_35]] , device=__UpperCAmelCase )
self.assertTrue(torch.allclose(output[0, :3, :3] , __UpperCAmelCase , atol=__UpperCAmelCase ) )
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :Union[str, Any] = AutoformerForPrediction.from_pretrained('huggingface/autoformer-tourism-monthly' ).to(__UpperCAmelCase )
lowerCAmelCase__ :Tuple = prepare_batch('val-batch.pt' )
with torch.no_grad():
lowerCAmelCase__ :List[Any] = model(
past_values=batch['past_values'] , past_time_features=batch['past_time_features'] , past_observed_mask=batch['past_observed_mask'] , static_categorical_features=batch['static_categorical_features'] , ).encoder_last_hidden_state
lowerCAmelCase__ :Tuple = torch.Size((6_4, model.config.context_length, model.config.d_model) )
self.assertEqual(output.shape , __UpperCAmelCase )
lowerCAmelCase__ :Optional[int] = torch.tensor(
[[-0.07_34, -0.90_36, 0.83_58], [4.71_86, 2.41_13, 1.95_81], [1.79_53, 2.35_58, 1.29_70]] , device=__UpperCAmelCase )
self.assertTrue(torch.allclose(output[0, :3, :3] , __UpperCAmelCase , atol=__UpperCAmelCase ) )
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :Union[str, Any] = AutoformerForPrediction.from_pretrained('huggingface/autoformer-tourism-monthly' ).to(__UpperCAmelCase )
lowerCAmelCase__ :Any = prepare_batch('val-batch.pt' )
with torch.no_grad():
lowerCAmelCase__ :List[Any] = model.generate(
static_categorical_features=batch['static_categorical_features'] , past_time_features=batch['past_time_features'] , past_values=batch['past_values'] , future_time_features=batch['future_time_features'] , past_observed_mask=batch['past_observed_mask'] , )
lowerCAmelCase__ :List[Any] = torch.Size((6_4, model.config.num_parallel_samples, model.config.prediction_length) )
self.assertEqual(outputs.sequences.shape , __UpperCAmelCase )
lowerCAmelCase__ :Optional[int] = torch.tensor([3_1_3_0.6_7_6_3, 4_0_5_6.5_2_9_3, 7_0_5_3.0_7_8_6] , device=__UpperCAmelCase )
lowerCAmelCase__ :Any = outputs.sequences.mean(dim=1 )
self.assertTrue(torch.allclose(mean_prediction[0, -3:] , __UpperCAmelCase , rtol=1E-1 ) )
| 93 |
"""simple docstring"""
from math import factorial
def lowerCAmelCase ( __UpperCamelCase = 100 ):
'''simple docstring'''
return sum(int(__UpperCamelCase ) for x in str(factorial(__UpperCamelCase ) ) )
if __name__ == "__main__":
print(solution(int(input('Enter the Number: ').strip())))
| 65 | 0 |
'''simple docstring'''
import math
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ) -> Optional[int]:
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")
| 42 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.distilbert.modeling_flax_distilbert import (
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertModel,
)
class __lowercase ( unittest.TestCase ):
def __init__( self : Union[str, Any] ,A : Optional[int] ,A : int=13 ,A : Tuple=7 ,A : Dict=True ,A : Optional[int]=True ,A : Tuple=True ,A : str=True ,A : Any=99 ,A : Tuple=32 ,A : Dict=5 ,A : Optional[int]=4 ,A : Dict=37 ,A : Any="gelu" ,A : Any=0.1 ,A : Optional[int]=0.1 ,A : Union[str, Any]=512 ,A : Any=16 ,A : List[str]=2 ,A : List[Any]=0.0_2 ,A : Optional[int]=4 ,):
'''simple docstring'''
UpperCAmelCase__ : Dict = parent
UpperCAmelCase__ : Any = batch_size
UpperCAmelCase__ : List[Any] = seq_length
UpperCAmelCase__ : Optional[int] = is_training
UpperCAmelCase__ : Optional[Any] = use_attention_mask
UpperCAmelCase__ : int = use_token_type_ids
UpperCAmelCase__ : int = use_labels
UpperCAmelCase__ : Any = vocab_size
UpperCAmelCase__ : Union[str, Any] = hidden_size
UpperCAmelCase__ : int = num_hidden_layers
UpperCAmelCase__ : int = num_attention_heads
UpperCAmelCase__ : Dict = intermediate_size
UpperCAmelCase__ : Any = hidden_act
UpperCAmelCase__ : Union[str, Any] = hidden_dropout_prob
UpperCAmelCase__ : Any = attention_probs_dropout_prob
UpperCAmelCase__ : str = max_position_embeddings
UpperCAmelCase__ : List[Any] = type_vocab_size
UpperCAmelCase__ : List[str] = type_sequence_label_size
UpperCAmelCase__ : List[Any] = initializer_range
UpperCAmelCase__ : List[Any] = num_choices
def __lowercase ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
UpperCAmelCase__ : List[str] = None
if self.use_attention_mask:
UpperCAmelCase__ : str = random_attention_mask([self.batch_size, self.seq_length] )
UpperCAmelCase__ : int = DistilBertConfig(
vocab_size=self.vocab_size ,dim=self.hidden_size ,n_layers=self.num_hidden_layers ,n_heads=self.num_attention_heads ,hidden_dim=self.intermediate_size ,hidden_act=self.hidden_act ,dropout=self.hidden_dropout_prob ,attention_dropout=self.attention_probs_dropout_prob ,max_position_embeddings=self.max_position_embeddings ,initializer_range=self.initializer_range ,tie_weights_=A ,)
return config, input_ids, attention_mask
def __lowercase ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = self.prepare_config_and_inputs()
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ : Any = config_and_inputs
UpperCAmelCase__ : str = {"""input_ids""": input_ids, """attention_mask""": attention_mask}
return config, inputs_dict
@require_flax
class __lowercase ( __lowerCamelCase , unittest.TestCase ):
snake_case_ = (
(
FlaxDistilBertModel,
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def __lowercase ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = FlaxDistilBertModelTester(self )
@slow
def __lowercase ( self : Optional[Any] ):
'''simple docstring'''
for model_class_name in self.all_model_classes:
UpperCAmelCase__ : Union[str, Any] = model_class_name.from_pretrained("""distilbert-base-uncased""" )
UpperCAmelCase__ : List[Any] = model(np.ones((1, 1) ) )
self.assertIsNotNone(A )
@require_flax
class __lowercase ( unittest.TestCase ):
@slow
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = FlaxDistilBertModel.from_pretrained("""distilbert-base-uncased""" )
UpperCAmelCase__ : List[Any] = np.array([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]] )
UpperCAmelCase__ : str = np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
UpperCAmelCase__ : Dict = model(A ,attention_mask=A )[0]
UpperCAmelCase__ : List[Any] = (1, 11, 768)
self.assertEqual(output.shape ,A )
UpperCAmelCase__ : Any = np.array([[[-0.1_6_3_9, 0.3_2_9_9, 0.1_6_4_8], [-0.1_7_4_6, 0.3_2_8_9, 0.1_7_1_0], [-0.1_8_8_4, 0.3_3_5_7, 0.1_8_1_0]]] )
self.assertTrue(jnp.allclose(output[:, 1:4, 1:4] ,A ,atol=1e-4 ) )
| 65 | 0 |
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHECKING:
from ... import PreTrainedTokenizerBase, TensorType
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
class __lowerCAmelCase ( __lowerCamelCase ):
"""simple docstring"""
A__ : List[Any] = "vision-encoder-decoder"
A__ : Dict = True
def __init__( self : List[Any] , **_snake_case : Union[str, Any] ):
"""simple docstring"""
super().__init__(**_snake_case )
if "encoder" not in kwargs or "decoder" not in kwargs:
raise ValueError(
F'''A configuraton of type {self.model_type} cannot be instantiated because '''
F'''not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}''' )
A__ = kwargs.pop('encoder' )
A__ = encoder_config.pop('model_type' )
A__ = kwargs.pop('decoder' )
A__ = decoder_config.pop('model_type' )
A__ = AutoConfig.for_model(_snake_case , **_snake_case )
A__ = AutoConfig.for_model(_snake_case , **_snake_case )
A__ = True
@classmethod
def _a ( cls : List[Any] , _snake_case : PretrainedConfig , _snake_case : PretrainedConfig , **_snake_case : Tuple ):
"""simple docstring"""
logger.info('Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config' )
A__ = True
A__ = True
return cls(encoder=encoder_config.to_dict() , decoder=decoder_config.to_dict() , **_snake_case )
def _a ( self : Optional[int] ):
"""simple docstring"""
A__ = copy.deepcopy(self.__dict__ )
A__ = self.encoder.to_dict()
A__ = self.decoder.to_dict()
A__ = self.__class__.model_type
return output
class __lowerCAmelCase ( __lowerCamelCase ):
"""simple docstring"""
A__ : Optional[int] = version.parse("1.11" )
@property
def _a ( self : Optional[int] ):
"""simple docstring"""
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
] )
@property
def _a ( self : List[Any] ):
"""simple docstring"""
return 1E-4
@property
def _a ( self : List[Any] ):
"""simple docstring"""
return OrderedDict({'last_hidden_state': {0: 'batch', 1: 'encoder_sequence'}} )
class __lowerCAmelCase ( __lowerCamelCase ):
"""simple docstring"""
@property
def _a ( self : Any ):
"""simple docstring"""
A__ = OrderedDict()
A__ = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
A__ = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
A__ = {0: """batch""", 1: """encoder_sequence"""}
return common_inputs
def _a ( self : Dict , _snake_case : "PreTrainedTokenizerBase" , _snake_case : int = -1 , _snake_case : int = -1 , _snake_case : bool = False , _snake_case : Optional["TensorType"] = None , ):
"""simple docstring"""
import torch
A__ = OrderedDict()
A__ = super().generate_dummy_inputs(
_snake_case , batch_size=_snake_case , seq_length=_snake_case , is_pair=_snake_case , framework=_snake_case )
A__ = dummy_input["""input_ids"""].shape
A__ = (batch, encoder_sequence, self._config.encoder_hidden_size)
A__ = dummy_input.pop('input_ids' )
A__ = dummy_input.pop('attention_mask' )
A__ = torch.zeros(_snake_case )
return common_inputs
class __lowerCAmelCase ( __lowerCamelCase ):
"""simple docstring"""
@property
def _a ( self : str ):
"""simple docstring"""
pass
def _a ( self : Any , _snake_case : PretrainedConfig ):
"""simple docstring"""
return VisionEncoderDecoderEncoderOnnxConfig(_snake_case )
def _a ( self : Dict , _snake_case : PretrainedConfig , _snake_case : PretrainedConfig , _snake_case : str = "default" ):
"""simple docstring"""
A__ = encoder_config.hidden_size
return VisionEncoderDecoderDecoderOnnxConfig(_snake_case , _snake_case )
| 9 |
"""simple docstring"""
__UpperCAmelCase = frozenset(
[
'prompt',
'height',
'width',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
'cross_attention_kwargs',
]
)
__UpperCAmelCase = frozenset(['prompt', 'negative_prompt'])
__UpperCAmelCase = frozenset([])
__UpperCAmelCase = frozenset(['image'])
__UpperCAmelCase = frozenset(
[
'image',
'height',
'width',
'guidance_scale',
]
)
__UpperCAmelCase = frozenset(['image'])
__UpperCAmelCase = frozenset(
[
'prompt',
'image',
'height',
'width',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
]
)
__UpperCAmelCase = frozenset(['prompt', 'image', 'negative_prompt'])
__UpperCAmelCase = frozenset(
[
# Text guided image variation with an image mask
'prompt',
'image',
'mask_image',
'height',
'width',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
]
)
__UpperCAmelCase = frozenset(['prompt', 'image', 'mask_image', 'negative_prompt'])
__UpperCAmelCase = frozenset(
[
# image variation with an image mask
'image',
'mask_image',
'height',
'width',
'guidance_scale',
]
)
__UpperCAmelCase = frozenset(['image', 'mask_image'])
__UpperCAmelCase = frozenset(
[
'example_image',
'image',
'mask_image',
'height',
'width',
'guidance_scale',
]
)
__UpperCAmelCase = frozenset(['example_image', 'image', 'mask_image'])
__UpperCAmelCase = frozenset(['class_labels'])
__UpperCAmelCase = frozenset(['class_labels'])
__UpperCAmelCase = frozenset(['batch_size'])
__UpperCAmelCase = frozenset([])
__UpperCAmelCase = frozenset(['batch_size'])
__UpperCAmelCase = frozenset([])
__UpperCAmelCase = frozenset(
[
'prompt',
'audio_length_in_s',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
'cross_attention_kwargs',
]
)
__UpperCAmelCase = frozenset(['prompt', 'negative_prompt'])
__UpperCAmelCase = frozenset(['input_tokens'])
__UpperCAmelCase = frozenset(['input_tokens'])
| 65 | 0 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPanoramaPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
@skip_mps
class snake_case__ ( __lowerCamelCase , __lowerCamelCase , unittest.TestCase ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = StableDiffusionPanoramaPipeline
_SCREAMING_SNAKE_CASE = TEXT_TO_IMAGE_PARAMS
_SCREAMING_SNAKE_CASE = TEXT_TO_IMAGE_BATCH_PARAMS
_SCREAMING_SNAKE_CASE = TEXT_TO_IMAGE_IMAGE_PARAMS
_SCREAMING_SNAKE_CASE = TEXT_TO_IMAGE_IMAGE_PARAMS
def lowercase_ ( self : Union[str, Any] ) ->List[Any]:
torch.manual_seed(0 )
snake_case__ : Tuple = UNetaDConditionModel(
block_out_channels=(3_2, 6_4), layers_per_block=1, sample_size=3_2, in_channels=4, out_channels=4, down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D'), up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D'), cross_attention_dim=3_2, )
snake_case__ : int = DDIMScheduler()
torch.manual_seed(0 )
snake_case__ : 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 )
snake_case__ : Optional[int] = 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, )
snake_case__ : Optional[Any] = CLIPTextModel(_snake_case )
snake_case__ : List[Any] = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
snake_case__ : 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 : Union[str, Any], _snake_case : Dict, _snake_case : Tuple=0 ) ->Tuple:
snake_case__ : List[str] = torch.manual_seed(_snake_case )
snake_case__ : Union[str, Any] = {
"""prompt""": """a photo of the dolomites""",
"""generator""": generator,
# Setting height and width to None to prevent OOMs on CPU.
"""height""": None,
"""width""": None,
"""num_inference_steps""": 1,
"""guidance_scale""": 6.0,
"""output_type""": """numpy""",
}
return inputs
def lowercase_ ( self : Optional[Any] ) ->Tuple:
snake_case__ : List[Any] = """cpu""" # ensure determinism for the device-dependent torch.Generator
snake_case__ : str = self.get_dummy_components()
snake_case__ : Any = StableDiffusionPanoramaPipeline(**_snake_case )
snake_case__ : Dict = sd_pipe.to(_snake_case )
sd_pipe.set_progress_bar_config(disable=_snake_case )
snake_case__ : Union[str, Any] = self.get_dummy_inputs(_snake_case )
snake_case__ : Dict = sd_pipe(**_snake_case ).images
snake_case__ : str = image[0, -3:, -3:, -1]
assert image.shape == (1, 6_4, 6_4, 3)
snake_case__ : Union[str, Any] = np.array([0.6_1_8_6, 0.5_3_7_4, 0.4_9_1_5, 0.4_1_3_5, 0.4_1_1_4, 0.4_5_6_3, 0.5_1_2_8, 0.4_9_7_7, 0.4_7_5_7] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def lowercase_ ( self : List[str] ) ->Optional[Any]:
super().test_inference_batch_consistent(batch_sizes=[1, 2] )
def lowercase_ ( self : List[Any] ) ->Union[str, Any]:
super().test_inference_batch_single_identical(batch_size=2, expected_max_diff=3.25e-3 )
def lowercase_ ( self : List[str] ) ->Union[str, Any]:
snake_case__ : Dict = """cpu""" # ensure determinism for the device-dependent torch.Generator
snake_case__ : str = self.get_dummy_components()
snake_case__ : Union[str, Any] = StableDiffusionPanoramaPipeline(**_snake_case )
snake_case__ : Optional[Any] = sd_pipe.to(_snake_case )
sd_pipe.set_progress_bar_config(disable=_snake_case )
snake_case__ : List[Any] = self.get_dummy_inputs(_snake_case )
snake_case__ : Optional[int] = """french fries"""
snake_case__ : List[str] = sd_pipe(**_snake_case, negative_prompt=_snake_case )
snake_case__ : Optional[Any] = output.images
snake_case__ : Any = image[0, -3:, -3:, -1]
assert image.shape == (1, 6_4, 6_4, 3)
snake_case__ : str = np.array([0.6_1_8_7, 0.5_3_7_5, 0.4_9_1_5, 0.4_1_3_6, 0.4_1_1_4, 0.4_5_6_3, 0.5_1_2_8, 0.4_9_7_6, 0.4_7_5_7] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def lowercase_ ( self : Dict ) ->Tuple:
snake_case__ : List[Any] = """cpu""" # ensure determinism for the device-dependent torch.Generator
snake_case__ : str = self.get_dummy_components()
snake_case__ : Dict = StableDiffusionPanoramaPipeline(**_snake_case )
snake_case__ : List[str] = sd_pipe.to(_snake_case )
sd_pipe.set_progress_bar_config(disable=_snake_case )
snake_case__ : Any = self.get_dummy_inputs(_snake_case )
snake_case__ : List[str] = sd_pipe(**_snake_case, view_batch_size=2 )
snake_case__ : Dict = output.images
snake_case__ : int = image[0, -3:, -3:, -1]
assert image.shape == (1, 6_4, 6_4, 3)
snake_case__ : Optional[Any] = np.array([0.6_1_8_7, 0.5_3_7_5, 0.4_9_1_5, 0.4_1_3_6, 0.4_1_1_4, 0.4_5_6_3, 0.5_1_2_8, 0.4_9_7_6, 0.4_7_5_7] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def lowercase_ ( self : List[Any] ) ->Optional[Any]:
snake_case__ : str = """cpu""" # ensure determinism for the device-dependent torch.Generator
snake_case__ : Optional[int] = self.get_dummy_components()
snake_case__ : List[str] = EulerAncestralDiscreteScheduler(
beta_start=0.0_0_0_8_5, beta_end=0.0_1_2, beta_schedule='scaled_linear' )
snake_case__ : Optional[Any] = StableDiffusionPanoramaPipeline(**_snake_case )
snake_case__ : List[str] = sd_pipe.to(_snake_case )
sd_pipe.set_progress_bar_config(disable=_snake_case )
snake_case__ : Optional[int] = self.get_dummy_inputs(_snake_case )
snake_case__ : Tuple = sd_pipe(**_snake_case ).images
snake_case__ : Dict = image[0, -3:, -3:, -1]
assert image.shape == (1, 6_4, 6_4, 3)
snake_case__ : Any = np.array([0.4_0_2_4, 0.6_5_1_0, 0.4_9_0_1, 0.5_3_7_8, 0.5_8_1_3, 0.5_6_2_2, 0.4_7_9_5, 0.4_4_6_7, 0.4_9_5_2] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def lowercase_ ( self : Union[str, Any] ) ->str:
snake_case__ : str = """cpu""" # ensure determinism for the device-dependent torch.Generator
snake_case__ : List[Any] = self.get_dummy_components()
snake_case__ : Union[str, Any] = PNDMScheduler(
beta_start=0.0_0_0_8_5, beta_end=0.0_1_2, beta_schedule='scaled_linear', skip_prk_steps=_snake_case )
snake_case__ : List[str] = StableDiffusionPanoramaPipeline(**_snake_case )
snake_case__ : int = sd_pipe.to(_snake_case )
sd_pipe.set_progress_bar_config(disable=_snake_case )
snake_case__ : List[str] = self.get_dummy_inputs(_snake_case )
snake_case__ : int = sd_pipe(**_snake_case ).images
snake_case__ : Tuple = image[0, -3:, -3:, -1]
assert image.shape == (1, 6_4, 6_4, 3)
snake_case__ : str = np.array([0.6_3_9_1, 0.6_2_9_1, 0.4_8_6_1, 0.5_1_3_4, 0.5_5_5_2, 0.4_5_7_8, 0.5_0_3_2, 0.5_0_2_3, 0.4_5_3_9] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
@slow
@require_torch_gpu
class snake_case__ ( unittest.TestCase ):
"""simple docstring"""
def lowercase_ ( self : List[str] ) ->int:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowercase_ ( self : int, _snake_case : Dict=0 ) ->Optional[Any]:
snake_case__ : Dict = torch.manual_seed(_snake_case )
snake_case__ : Tuple = {
"""prompt""": """a photo of the dolomites""",
"""generator""": generator,
"""num_inference_steps""": 3,
"""guidance_scale""": 7.5,
"""output_type""": """numpy""",
}
return inputs
def lowercase_ ( self : List[str] ) ->Union[str, Any]:
snake_case__ : Union[str, Any] = """stabilityai/stable-diffusion-2-base"""
snake_case__ : Union[str, Any] = DDIMScheduler.from_pretrained(_snake_case, subfolder='scheduler' )
snake_case__ : List[str] = StableDiffusionPanoramaPipeline.from_pretrained(_snake_case, scheduler=_snake_case, safety_checker=_snake_case )
pipe.to(_snake_case )
pipe.set_progress_bar_config(disable=_snake_case )
pipe.enable_attention_slicing()
snake_case__ : Union[str, Any] = self.get_inputs()
snake_case__ : Tuple = pipe(**_snake_case ).images
snake_case__ : Tuple = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 5_1_2, 2_0_4_8, 3)
snake_case__ : List[Any] = np.array(
[
0.3_6_9_6_8_3_9_2,
0.2_7_0_2_5_3_7_2,
0.3_2_4_4_6_7_6_6,
0.2_8_3_7_9_3_8_7,
0.3_6_3_6_3_2_7_4,
0.3_0_7_3_3_3_4_7,
0.2_7_1_0_0_0_2_7,
0.2_7_0_5_4_1_2_5,
0.2_5_5_3_6_0_9_6,
] )
assert np.abs(expected_slice - image_slice ).max() < 1e-2
def lowercase_ ( self : int ) ->Optional[Any]:
snake_case__ : str = StableDiffusionPanoramaPipeline.from_pretrained(
'stabilityai/stable-diffusion-2-base', safety_checker=_snake_case )
snake_case__ : Optional[int] = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.to(_snake_case )
pipe.set_progress_bar_config(disable=_snake_case )
pipe.enable_attention_slicing()
snake_case__ : List[str] = self.get_inputs()
snake_case__ : Union[str, Any] = pipe(**_snake_case ).images
snake_case__ : List[str] = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 5_1_2, 2_0_4_8, 3)
snake_case__ : Any = np.array(
[
[
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
]
] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def lowercase_ ( self : Optional[int] ) ->str:
snake_case__ : Dict = 0
def callback_fn(_snake_case : int, _snake_case : int, _snake_case : torch.FloatTensor ) -> None:
snake_case__ : Union[str, Any] = True
nonlocal number_of_steps
number_of_steps += 1
if step == 1:
snake_case__ : Tuple = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 6_4, 2_5_6)
snake_case__ : Union[str, Any] = latents[0, -3:, -3:, -1]
snake_case__ : Any = np.array(
[
0.1_8_6_8_1_8_6_9,
0.3_3_9_0_7_8_1_6,
0.5_3_6_1_2_7_6,
0.1_4_4_3_2_8_6_5,
-0.0_2_8_5_6_6_1_1,
-0.7_3_9_4_1_1_2_3,
0.2_3_3_9_7_9_8_7,
0.4_7_3_2_2_6_8_2,
-0.3_7_8_2_3_1_6_4,
] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2
elif step == 2:
snake_case__ : Any = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 6_4, 2_5_6)
snake_case__ : Union[str, Any] = latents[0, -3:, -3:, -1]
snake_case__ : int = np.array(
[
0.1_8_5_3_9_6_4_5,
0.3_3_9_8_7_2_4_8,
0.5_3_7_8_5_5_9,
0.1_4_4_3_7_1_4_2,
-0.0_2_4_5_5_2_6_1,
-0.7_3_3_8_3_1_7,
0.2_3_9_9_0_7_5_5,
0.4_7_3_5_6_2_7_2,
-0.3_7_8_6_5_0_5,
] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2
snake_case__ : str = False
snake_case__ : Union[str, Any] = """stabilityai/stable-diffusion-2-base"""
snake_case__ : Optional[int] = DDIMScheduler.from_pretrained(_snake_case, subfolder='scheduler' )
snake_case__ : int = StableDiffusionPanoramaPipeline.from_pretrained(_snake_case, scheduler=_snake_case, safety_checker=_snake_case )
snake_case__ : List[Any] = pipe.to(_snake_case )
pipe.set_progress_bar_config(disable=_snake_case )
pipe.enable_attention_slicing()
snake_case__ : List[str] = 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[Any]:
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
snake_case__ : int = """stabilityai/stable-diffusion-2-base"""
snake_case__ : List[str] = DDIMScheduler.from_pretrained(_snake_case, subfolder='scheduler' )
snake_case__ : Dict = StableDiffusionPanoramaPipeline.from_pretrained(_snake_case, scheduler=_snake_case, safety_checker=_snake_case )
snake_case__ : List[str] = pipe.to(_snake_case )
pipe.set_progress_bar_config(disable=_snake_case )
pipe.enable_attention_slicing(1 )
pipe.enable_sequential_cpu_offload()
snake_case__ : str = self.get_inputs()
snake_case__ : Union[str, Any] = pipe(**_snake_case )
snake_case__ : int = torch.cuda.max_memory_allocated()
# make sure that less than 5.2 GB is allocated
assert mem_bytes < 5.5 * 1_0**9
| 478 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProcessor
from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.testing_utils import require_pyctcdecode, require_torch, require_torchaudio, slow
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_pyctcdecode_available, is_torch_available
from ..wavaveca.test_feature_extraction_wavaveca import floats_list
if is_pyctcdecode_available():
from huggingface_hub import snapshot_download
from pyctcdecode import BeamSearchDecoderCTC
from transformers.models.wavaveca_with_lm import WavaVecaProcessorWithLM
from transformers.models.wavaveca_with_lm.processing_wavaveca_with_lm import WavaVecaDecoderWithLMOutput
if is_torch_available():
from transformers import WavaVecaForCTC
@require_pyctcdecode
class __lowercase ( unittest.TestCase ):
def __lowercase ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase__ : Dict = """| <pad> <unk> <s> </s> a b c d e f g h i j k""".split()
UpperCAmelCase__ : Tuple = dict(zip(A ,range(len(A ) ) ) )
UpperCAmelCase__ : Optional[Any] = {
"""unk_token""": """<unk>""",
"""bos_token""": """<s>""",
"""eos_token""": """</s>""",
}
UpperCAmelCase__ : int = {
"""feature_size""": 1,
"""padding_value""": 0.0,
"""sampling_rate""": 16_000,
"""return_attention_mask""": False,
"""do_normalize""": True,
}
UpperCAmelCase__ : Optional[int] = tempfile.mkdtemp()
UpperCAmelCase__ : Optional[int] = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["""vocab_file"""] )
UpperCAmelCase__ : Tuple = os.path.join(self.tmpdirname ,A )
with open(self.vocab_file ,"""w""" ,encoding="""utf-8""" ) as fp:
fp.write(json.dumps(A ) + """\n""" )
with open(self.feature_extraction_file ,"""w""" ,encoding="""utf-8""" ) as fp:
fp.write(json.dumps(A ) + """\n""" )
# load decoder from hub
UpperCAmelCase__ : int = """hf-internal-testing/ngram-beam-search-decoder"""
def __lowercase ( self : str ,**A : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = self.add_kwargs_tokens_map.copy()
kwargs.update(A )
return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname ,**A )
def __lowercase ( self : List[str] ,**A : Dict ):
'''simple docstring'''
return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname ,**A )
def __lowercase ( self : Any ,**A : List[Any] ):
'''simple docstring'''
return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name ,**A )
def __lowercase ( self : Any ):
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def __lowercase ( self : str ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = self.get_tokenizer()
UpperCAmelCase__ : Dict = self.get_feature_extractor()
UpperCAmelCase__ : str = self.get_decoder()
UpperCAmelCase__ : Tuple = WavaVecaProcessorWithLM(tokenizer=A ,feature_extractor=A ,decoder=A )
processor.save_pretrained(self.tmpdirname )
UpperCAmelCase__ : str = WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname )
# tokenizer
self.assertEqual(processor.tokenizer.get_vocab() ,tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer ,A )
# feature extractor
self.assertEqual(processor.feature_extractor.to_json_string() ,feature_extractor.to_json_string() )
self.assertIsInstance(processor.feature_extractor ,A )
# decoder
self.assertEqual(processor.decoder._alphabet.labels ,decoder._alphabet.labels )
self.assertEqual(
processor.decoder.model_container[decoder._model_key]._unigram_set ,decoder.model_container[decoder._model_key]._unigram_set ,)
self.assertIsInstance(processor.decoder ,A )
def __lowercase ( self : int ):
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = WavaVecaProcessorWithLM(
tokenizer=self.get_tokenizer() ,feature_extractor=self.get_feature_extractor() ,decoder=self.get_decoder() )
processor.save_pretrained(self.tmpdirname )
# make sure that error is thrown when decoder alphabet doesn't match
UpperCAmelCase__ : Tuple = WavaVecaProcessorWithLM.from_pretrained(
self.tmpdirname ,alpha=5.0 ,beta=3.0 ,score_boundary=-7.0 ,unk_score_offset=3 )
# decoder
self.assertEqual(processor.language_model.alpha ,5.0 )
self.assertEqual(processor.language_model.beta ,3.0 )
self.assertEqual(processor.language_model.score_boundary ,-7.0 )
self.assertEqual(processor.language_model.unk_score_offset ,3 )
def __lowercase ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase__ : int = self.get_tokenizer()
# add token to trigger raise
tokenizer.add_tokens(["""xx"""] )
with self.assertRaisesRegex(A ,"""include""" ):
WavaVecaProcessorWithLM(
tokenizer=A ,feature_extractor=self.get_feature_extractor() ,decoder=self.get_decoder() )
def __lowercase ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = self.get_feature_extractor()
UpperCAmelCase__ : Optional[Any] = self.get_tokenizer()
UpperCAmelCase__ : Any = self.get_decoder()
UpperCAmelCase__ : Union[str, Any] = WavaVecaProcessorWithLM(tokenizer=A ,feature_extractor=A ,decoder=A )
UpperCAmelCase__ : str = floats_list((3, 1_000) )
UpperCAmelCase__ : Optional[Any] = feature_extractor(A ,return_tensors="""np""" )
UpperCAmelCase__ : List[Any] = processor(A ,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 __lowercase ( self : int ):
'''simple docstring'''
UpperCAmelCase__ : int = self.get_feature_extractor()
UpperCAmelCase__ : Union[str, Any] = self.get_tokenizer()
UpperCAmelCase__ : Optional[int] = self.get_decoder()
UpperCAmelCase__ : List[Any] = WavaVecaProcessorWithLM(tokenizer=A ,feature_extractor=A ,decoder=A )
UpperCAmelCase__ : List[Any] = """This is a test string"""
UpperCAmelCase__ : int = processor(text=A )
UpperCAmelCase__ : Dict = tokenizer(A )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] ,encoded_processor[key] )
def __lowercase ( self : Tuple ,A : List[Any]=(2, 10, 16) ,A : Dict=77 ):
'''simple docstring'''
np.random.seed(A )
return np.random.rand(*A )
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ : Dict = self.get_feature_extractor()
UpperCAmelCase__ : Optional[Any] = self.get_tokenizer()
UpperCAmelCase__ : int = self.get_decoder()
UpperCAmelCase__ : Optional[Any] = WavaVecaProcessorWithLM(tokenizer=A ,feature_extractor=A ,decoder=A )
UpperCAmelCase__ : Dict = self._get_dummy_logits(shape=(10, 16) ,seed=13 )
UpperCAmelCase__ : Tuple = processor.decode(A )
UpperCAmelCase__ : Union[str, Any] = decoder.decode_beams(A )[0]
self.assertEqual(decoded_decoder[0] ,decoded_processor.text )
self.assertEqual("""</s> <s> </s>""" ,decoded_processor.text )
self.assertEqual(decoded_decoder[-2] ,decoded_processor.logit_score )
self.assertEqual(decoded_decoder[-1] ,decoded_processor.lm_score )
@parameterized.expand([[None], ["""fork"""], ["""spawn"""]] )
def __lowercase ( self : List[str] ,A : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = self.get_feature_extractor()
UpperCAmelCase__ : int = self.get_tokenizer()
UpperCAmelCase__ : List[Any] = self.get_decoder()
UpperCAmelCase__ : Dict = WavaVecaProcessorWithLM(tokenizer=A ,feature_extractor=A ,decoder=A )
UpperCAmelCase__ : Optional[Any] = self._get_dummy_logits()
# note: pool should be instantiated *after* Wav2Vec2ProcessorWithLM.
# otherwise, the LM won't be available to the pool's sub-processes.
# manual logic used to allow parameterized test for both pool=None and pool=Pool(...)
if pool_context is None:
UpperCAmelCase__ : List[str] = processor.batch_decode(A )
else:
with get_context(A ).Pool() as pool:
UpperCAmelCase__ : Union[str, Any] = processor.batch_decode(A ,A )
UpperCAmelCase__ : Optional[Any] = list(A )
with get_context("""fork""" ).Pool() as p:
UpperCAmelCase__ : Union[str, Any] = decoder.decode_beams_batch(A ,A )
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ : Optional[Any] = [], [], []
for beams in decoded_beams:
texts_decoder.append(beams[0][0] )
logit_scores_decoder.append(beams[0][-2] )
lm_scores_decoder.append(beams[0][-1] )
self.assertListEqual(A ,decoded_processor.text )
self.assertListEqual(["""<s> <s> </s>""", """<s> <s> <s>"""] ,decoded_processor.text )
self.assertListEqual(A ,decoded_processor.logit_score )
self.assertListEqual(A ,decoded_processor.lm_score )
def __lowercase ( self : int ):
'''simple docstring'''
UpperCAmelCase__ : Any = self.get_feature_extractor()
UpperCAmelCase__ : Tuple = self.get_tokenizer()
UpperCAmelCase__ : List[Any] = self.get_decoder()
UpperCAmelCase__ : Union[str, Any] = WavaVecaProcessorWithLM(tokenizer=A ,feature_extractor=A ,decoder=A )
UpperCAmelCase__ : Dict = self._get_dummy_logits()
UpperCAmelCase__ : Any = 15
UpperCAmelCase__ : Dict = -2_0.0
UpperCAmelCase__ : List[Any] = -4.0
UpperCAmelCase__ : Union[str, Any] = processor.batch_decode(
A ,beam_width=A ,beam_prune_logp=A ,token_min_logp=A ,)
UpperCAmelCase__ : List[str] = decoded_processor_out.text
UpperCAmelCase__ : List[str] = list(A )
with get_context("""fork""" ).Pool() as pool:
UpperCAmelCase__ : Tuple = decoder.decode_beams_batch(
A ,A ,beam_width=A ,beam_prune_logp=A ,token_min_logp=A ,)
UpperCAmelCase__ : List[Any] = [d[0][0] for d in decoded_decoder_out]
UpperCAmelCase__ : Any = [d[0][2] for d in decoded_decoder_out]
UpperCAmelCase__ : List[str] = [d[0][3] for d in decoded_decoder_out]
self.assertListEqual(A ,A )
self.assertListEqual(["""</s> <s> <s>""", """<s> <s> <s>"""] ,A )
self.assertTrue(np.array_equal(A ,decoded_processor_out.logit_score ) )
self.assertTrue(np.allclose([-2_0.0_5_4, -1_8.4_4_7] ,A ,atol=1e-3 ) )
self.assertTrue(np.array_equal(A ,decoded_processor_out.lm_score ) )
self.assertTrue(np.allclose([-1_5.5_5_4, -1_3.9_4_7_4] ,A ,atol=1e-3 ) )
def __lowercase ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = self.get_feature_extractor()
UpperCAmelCase__ : Optional[Any] = self.get_tokenizer()
UpperCAmelCase__ : int = self.get_decoder()
UpperCAmelCase__ : str = WavaVecaProcessorWithLM(tokenizer=A ,feature_extractor=A ,decoder=A )
UpperCAmelCase__ : Tuple = self._get_dummy_logits()
UpperCAmelCase__ : Tuple = 2.0
UpperCAmelCase__ : str = 5.0
UpperCAmelCase__ : Union[str, Any] = -2_0.0
UpperCAmelCase__ : Optional[Any] = True
UpperCAmelCase__ : str = processor.batch_decode(
A ,alpha=A ,beta=A ,unk_score_offset=A ,lm_score_boundary=A ,)
UpperCAmelCase__ : Any = decoded_processor_out.text
UpperCAmelCase__ : Union[str, Any] = list(A )
decoder.reset_params(
alpha=A ,beta=A ,unk_score_offset=A ,lm_score_boundary=A ,)
with get_context("""fork""" ).Pool() as pool:
UpperCAmelCase__ : List[Any] = decoder.decode_beams_batch(
A ,A ,)
UpperCAmelCase__ : Union[str, Any] = [d[0][0] for d in decoded_decoder_out]
self.assertListEqual(A ,A )
self.assertListEqual(["""<s> </s> <s> </s> </s>""", """</s> </s> <s> </s> </s>"""] ,A )
UpperCAmelCase__ : Union[str, Any] = processor.decoder.model_container[processor.decoder._model_key]
self.assertEqual(lm_model.alpha ,2.0 )
self.assertEqual(lm_model.beta ,5.0 )
self.assertEqual(lm_model.unk_score_offset ,-2_0.0 )
self.assertEqual(lm_model.score_boundary ,A )
def __lowercase ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Dict = WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm""" )
UpperCAmelCase__ : str = processor.decoder.model_container[processor.decoder._model_key]
UpperCAmelCase__ : Any = Path(language_model._kenlm_model.path.decode("""utf-8""" ) ).parent.parent.absolute()
UpperCAmelCase__ : Optional[int] = os.listdir(A )
UpperCAmelCase__ : List[Any] = ["""alphabet.json""", """language_model"""]
downloaded_decoder_files.sort()
expected_decoder_files.sort()
# test that only decoder relevant files from
# https://huggingface.co/hf-internal-testing/processor_with_lm/tree/main
# are downloaded and none of the rest (e.g. README.md, ...)
self.assertListEqual(A ,A )
def __lowercase ( self : int ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = snapshot_download("""hf-internal-testing/processor_with_lm""" )
UpperCAmelCase__ : Union[str, Any] = WavaVecaProcessorWithLM.from_pretrained(A )
UpperCAmelCase__ : Tuple = processor.decoder.model_container[processor.decoder._model_key]
UpperCAmelCase__ : Optional[int] = Path(language_model._kenlm_model.path.decode("""utf-8""" ) ).parent.parent.absolute()
UpperCAmelCase__ : Tuple = os.listdir(A )
UpperCAmelCase__ : Dict = os.listdir(A )
local_decoder_files.sort()
expected_decoder_files.sort()
# test that both decoder form hub and local files in cache are the same
self.assertListEqual(A ,A )
def __lowercase ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm""" )
UpperCAmelCase__ : Tuple = AutoProcessor.from_pretrained("""hf-internal-testing/processor_with_lm""" )
UpperCAmelCase__ : Dict = floats_list((3, 1_000) )
UpperCAmelCase__ : List[str] = processor_wavaveca(A ,return_tensors="""np""" )
UpperCAmelCase__ : Dict = processor_auto(A ,return_tensors="""np""" )
for key in input_wavaveca.keys():
self.assertAlmostEqual(input_wavaveca[key].sum() ,input_auto[key].sum() ,delta=1e-2 )
UpperCAmelCase__ : List[str] = self._get_dummy_logits()
UpperCAmelCase__ : Tuple = processor_wavaveca.batch_decode(A )
UpperCAmelCase__ : List[str] = processor_auto.batch_decode(A )
self.assertListEqual(decoded_wavaveca.text ,decoded_auto.text )
def __lowercase ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase__ : Dict = self.get_feature_extractor()
UpperCAmelCase__ : Tuple = self.get_tokenizer()
UpperCAmelCase__ : List[Any] = self.get_decoder()
UpperCAmelCase__ : int = WavaVecaProcessorWithLM(tokenizer=A ,feature_extractor=A ,decoder=A )
self.assertListEqual(
processor.model_input_names ,feature_extractor.model_input_names ,msg="""`processor` and `feature_extractor` model input names do not match""" ,)
@staticmethod
def __lowercase ( A : Optional[Any] ,A : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = [d[key] for d in offsets]
return retrieved_list
def __lowercase ( self : Dict ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm""" )
UpperCAmelCase__ : Dict = self._get_dummy_logits()[0]
UpperCAmelCase__ : List[str] = processor.decode(A ,output_word_offsets=A )
# check Wav2Vec2CTCTokenizerOutput keys for word
self.assertEqual(len(outputs.keys() ) ,4 )
self.assertTrue("""text""" in outputs )
self.assertTrue("""word_offsets""" in outputs )
self.assertTrue(isinstance(A ,A ) )
self.assertEqual(""" """.join(self.get_from_offsets(outputs["""word_offsets"""] ,"""word""" ) ) ,outputs.text )
self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""] ,"""word""" ) ,["""<s>""", """<s>""", """</s>"""] )
self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""] ,"""start_offset""" ) ,[0, 2, 4] )
self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""] ,"""end_offset""" ) ,[1, 3, 5] )
def __lowercase ( self : Dict ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm""" )
UpperCAmelCase__ : int = self._get_dummy_logits()
UpperCAmelCase__ : Any = processor.batch_decode(A ,output_word_offsets=A )
# check Wav2Vec2CTCTokenizerOutput keys for word
self.assertEqual(len(outputs.keys() ) ,4 )
self.assertTrue("""text""" in outputs )
self.assertTrue("""word_offsets""" in outputs )
self.assertTrue(isinstance(A ,A ) )
self.assertListEqual(
[""" """.join(self.get_from_offsets(A ,"""word""" ) ) for o in outputs["""word_offsets"""]] ,outputs.text )
self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""][0] ,"""word""" ) ,["""<s>""", """<s>""", """</s>"""] )
self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""][0] ,"""start_offset""" ) ,[0, 2, 4] )
self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""][0] ,"""end_offset""" ) ,[1, 3, 5] )
@slow
@require_torch
@require_torchaudio
def __lowercase ( self : Tuple ):
'''simple docstring'''
import torch
UpperCAmelCase__ : Any = load_dataset("""common_voice""" ,"""en""" ,split="""train""" ,streaming=A )
UpperCAmelCase__ : Tuple = ds.cast_column("""audio""" ,datasets.Audio(sampling_rate=16_000 ) )
UpperCAmelCase__ : Tuple = iter(A )
UpperCAmelCase__ : Optional[int] = next(A )
UpperCAmelCase__ : List[Any] = AutoProcessor.from_pretrained("""patrickvonplaten/wav2vec2-base-100h-with-lm""" )
UpperCAmelCase__ : Tuple = WavaVecaForCTC.from_pretrained("""patrickvonplaten/wav2vec2-base-100h-with-lm""" )
# compare to filename `common_voice_en_100038.mp3` of dataset viewer on https://huggingface.co/datasets/common_voice/viewer/en/train
UpperCAmelCase__ : Tuple = processor(sample["""audio"""]["""array"""] ,return_tensors="""pt""" ).input_values
with torch.no_grad():
UpperCAmelCase__ : Union[str, Any] = model(A ).logits.cpu().numpy()
UpperCAmelCase__ : Any = processor.decode(logits[0] ,output_word_offsets=A )
UpperCAmelCase__ : str = model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
UpperCAmelCase__ : Union[str, Any] = [
{
"""start_time""": d["""start_offset"""] * time_offset,
"""end_time""": d["""end_offset"""] * time_offset,
"""word""": d["""word"""],
}
for d in output["""word_offsets"""]
]
UpperCAmelCase__ : Dict = """WHY DOES MILISANDRA LOOK LIKE SHE WANTS TO CONSUME JOHN SNOW ON THE RIVER AT THE WALL"""
# output words
self.assertEqual(""" """.join(self.get_from_offsets(A ,"""word""" ) ) ,A )
self.assertEqual(""" """.join(self.get_from_offsets(A ,"""word""" ) ) ,output.text )
# output times
UpperCAmelCase__ : str = torch.tensor(self.get_from_offsets(A ,"""start_time""" ) )
UpperCAmelCase__ : List[Any] = torch.tensor(self.get_from_offsets(A ,"""end_time""" ) )
# fmt: off
UpperCAmelCase__ : Union[str, Any] = torch.tensor([1.4_1_9_9, 1.6_5_9_9, 2.2_5_9_9, 3.0, 3.2_4, 3.5_9_9_9, 3.7_9_9_9, 4.0_9_9_9, 4.2_6, 4.9_4, 5.2_8, 5.6_5_9_9, 5.7_8, 5.9_4, 6.3_2, 6.5_3_9_9, 6.6_5_9_9] )
UpperCAmelCase__ : List[Any] = torch.tensor([1.5_3_9_9, 1.8_9_9_9, 2.9, 3.1_6, 3.5_3_9_9, 3.7_2, 4.0_1_9_9, 4.1_7_9_9, 4.7_6, 5.1_5_9_9, 5.5_5_9_9, 5.6_9_9_9, 5.8_6, 6.1_9_9_9, 6.3_8, 6.6_1_9_9, 6.9_4] )
# fmt: on
self.assertTrue(torch.allclose(A ,A ,atol=0.0_1 ) )
self.assertTrue(torch.allclose(A ,A ,atol=0.0_1 ) )
| 65 | 0 |
import operator as op
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase ) -> Union[str, Any]:
snake_case__ = []
snake_case__ = lambda __lowerCAmelCase , __lowerCAmelCase : int(x / y ) # noqa: E731 integer division operation
snake_case__ = {
"""^""": op.pow,
"""*""": op.mul,
"""/""": div,
"""+""": op.add,
"""-""": op.sub,
} # operators & their respective operation
# print table header
print('''Symbol'''.center(8 ) , '''Action'''.center(12 ) , '''Stack''' , sep=''' | ''' )
print('''-''' * (30 + len(__UpperCamelCase )) )
for x in post_fix:
if x.isdigit(): # if x in digit
stack.append(__UpperCamelCase ) # append x to stack
# output in tabular format
print(x.rjust(8 ) , ('''push(''' + x + ''')''').ljust(12 ) , ''','''.join(__UpperCamelCase ) , sep=''' | ''' )
else:
snake_case__ = stack.pop() # pop stack
# output in tabular format
print(''''''.rjust(8 ) , ('''pop(''' + b + ''')''').ljust(12 ) , ''','''.join(__UpperCamelCase ) , sep=''' | ''' )
snake_case__ = stack.pop() # pop stack
# output in tabular format
print(''''''.rjust(8 ) , ('''pop(''' + a + ''')''').ljust(12 ) , ''','''.join(__UpperCamelCase ) , sep=''' | ''' )
stack.append(
str(opr[x](int(__UpperCamelCase ) , int(__UpperCamelCase ) ) ) ) # evaluate the 2 values popped from stack & push result to stack
# output in tabular format
print(
x.rjust(8 ) , ('''push(''' + a + x + b + ''')''').ljust(12 ) , ''','''.join(__UpperCamelCase ) , sep=''' | ''' , )
return int(stack[0] )
if __name__ == "__main__":
lowerCamelCase__ : Optional[Any] = input("""\n\nEnter a Postfix Equation (space separated) = """).split(""" """)
print("""\n\tResult = """, solve(Postfix))
| 33 |
"""simple docstring"""
from sklearn.metrics import fa_score
import datasets
__UpperCAmelCase = '\nThe F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:\nF1 = 2 * (precision * recall) / (precision + recall)\n'
__UpperCAmelCase = '\nArgs:\n predictions (`list` of `int`): Predicted labels.\n references (`list` of `int`): Ground truth labels.\n labels (`list` of `int`): The set of labels to include when `average` is not set to `\'binary\'`, and the order of the labels if `average` is `None`. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class. Labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in `predictions` and `references` are used in sorted order. Defaults to None.\n pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.\n average (`string`): This parameter is required for multiclass/multilabel targets. If set to `None`, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `\'binary\'`.\n\n - \'binary\': Only report results for the class specified by `pos_label`. This is applicable only if the classes found in `predictions` and `references` are binary.\n - \'micro\': Calculate metrics globally by counting the total true positives, false negatives and false positives.\n - \'macro\': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.\n - \'weighted\': Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `\'macro\'` to account for label imbalance. This option can result in an F-score that is not between precision and recall.\n - \'samples\': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).\n sample_weight (`list` of `float`): Sample weights Defaults to None.\n\nReturns:\n f1 (`float` or `array` of `float`): F1 score or list of f1 scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher f1 scores are better.\n\nExamples:\n\n Example 1-A simple binary example\n >>> f1_metric = datasets.load_metric("f1")\n >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])\n >>> print(results)\n {\'f1\': 0.5}\n\n Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.\n >>> f1_metric = datasets.load_metric("f1")\n >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)\n >>> print(round(results[\'f1\'], 2))\n 0.67\n\n Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.\n >>> f1_metric = datasets.load_metric("f1")\n >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], sample_weight=[0.9, 0.5, 3.9, 1.2, 0.3])\n >>> print(round(results[\'f1\'], 2))\n 0.35\n\n Example 4-A multiclass example, with different values for the `average` input.\n >>> predictions = [0, 2, 1, 0, 0, 1]\n >>> references = [0, 1, 2, 0, 1, 2]\n >>> results = f1_metric.compute(predictions=predictions, references=references, average="macro")\n >>> print(round(results[\'f1\'], 2))\n 0.27\n >>> results = f1_metric.compute(predictions=predictions, references=references, average="micro")\n >>> print(round(results[\'f1\'], 2))\n 0.33\n >>> results = f1_metric.compute(predictions=predictions, references=references, average="weighted")\n >>> print(round(results[\'f1\'], 2))\n 0.27\n >>> results = f1_metric.compute(predictions=predictions, references=references, average=None)\n >>> print(results)\n {\'f1\': array([0.8, 0. , 0. ])}\n'
__UpperCAmelCase = '\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __lowercase ( datasets.Metric ):
def __lowercase ( self : List[Any] ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION ,citation=_CITATION ,inputs_description=_KWARGS_DESCRIPTION ,features=datasets.Features(
{
"""predictions""": datasets.Sequence(datasets.Value("""int32""" ) ),
"""references""": datasets.Sequence(datasets.Value("""int32""" ) ),
}
if self.config_name == """multilabel"""
else {
"""predictions""": datasets.Value("""int32""" ),
"""references""": datasets.Value("""int32""" ),
} ) ,reference_urls=["""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html"""] ,)
def __lowercase ( self : Union[str, Any] ,A : List[str] ,A : List[Any] ,A : Optional[Any]=None ,A : List[str]=1 ,A : Optional[Any]="binary" ,A : Any=None ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = fa_score(
A ,A ,labels=A ,pos_label=A ,average=A ,sample_weight=A )
return {"f1": float(A ) if score.size == 1 else score}
| 65 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
snake_case : str = {
'configuration_mobilevit': ['MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MobileViTConfig', 'MobileViTOnnxConfig'],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case : Union[str, Any] = ['MobileViTFeatureExtractor']
snake_case : Optional[Any] = ['MobileViTImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case : List[Any] = [
'MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
'MobileViTForImageClassification',
'MobileViTForSemanticSegmentation',
'MobileViTModel',
'MobileViTPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case : List[str] = [
'TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFMobileViTForImageClassification',
'TFMobileViTForSemanticSegmentation',
'TFMobileViTModel',
'TFMobileViTPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_mobilevit import MobileViTFeatureExtractor
from .image_processing_mobilevit import MobileViTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mobilevit import (
MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTModel,
MobileViTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mobilevit import (
TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFMobileViTForImageClassification,
TFMobileViTForSemanticSegmentation,
TFMobileViTModel,
TFMobileViTPreTrainedModel,
)
else:
import sys
snake_case : Optional[Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 605 |
"""simple docstring"""
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available
if is_sentencepiece_available():
from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json
from ...test_tokenization_common import TokenizerTesterMixin
__UpperCAmelCase = get_tests_dir('fixtures/test_sentencepiece.model')
__UpperCAmelCase = {'target_lang': 'fi', 'source_lang': 'en'}
__UpperCAmelCase = '>>zh<<'
__UpperCAmelCase = 'Helsinki-NLP/'
if is_torch_available():
__UpperCAmelCase = 'pt'
elif is_tf_available():
__UpperCAmelCase = 'tf'
else:
__UpperCAmelCase = 'jax'
@require_sentencepiece
class __lowercase ( __lowerCamelCase , unittest.TestCase ):
snake_case_ = MarianTokenizer
snake_case_ = False
snake_case_ = True
def __lowercase ( self : Optional[int] ):
'''simple docstring'''
super().setUp()
UpperCAmelCase__ : Optional[Any] = ["""</s>""", """<unk>""", """▁This""", """▁is""", """▁a""", """▁t""", """est""", """\u0120""", """<pad>"""]
UpperCAmelCase__ : int = dict(zip(A ,range(len(A ) ) ) )
UpperCAmelCase__ : Optional[int] = Path(self.tmpdirname )
save_json(A ,save_dir / VOCAB_FILES_NAMES["""vocab"""] )
save_json(A ,save_dir / VOCAB_FILES_NAMES["""tokenizer_config_file"""] )
if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
copyfile(A ,save_dir / VOCAB_FILES_NAMES["""source_spm"""] )
copyfile(A ,save_dir / VOCAB_FILES_NAMES["""target_spm"""] )
UpperCAmelCase__ : Dict = MarianTokenizer.from_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname )
def __lowercase ( self : List[Any] ,**A : List[Any] ):
'''simple docstring'''
return MarianTokenizer.from_pretrained(self.tmpdirname ,**A )
def __lowercase ( self : Union[str, Any] ,A : Tuple ):
'''simple docstring'''
return (
"This is a test",
"This is a test",
)
def __lowercase ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = """</s>"""
UpperCAmelCase__ : int = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(A ) ,A )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(A ) ,A )
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ : Dict = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] ,"""</s>""" )
self.assertEqual(vocab_keys[1] ,"""<unk>""" )
self.assertEqual(vocab_keys[-1] ,"""<pad>""" )
self.assertEqual(len(A ) ,9 )
def __lowercase ( self : Dict ):
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size ,9 )
def __lowercase ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = MarianTokenizer.from_pretrained(f"{ORG_NAME}opus-mt-en-de" )
UpperCAmelCase__ : List[str] = en_de_tokenizer(["""I am a small frog"""] ,return_tensors=A )
self.assertIsInstance(A ,A )
UpperCAmelCase__ : str = [38, 121, 14, 697, 38_848, 0]
self.assertListEqual(A ,batch.input_ids[0] )
UpperCAmelCase__ : Optional[Any] = tempfile.mkdtemp()
en_de_tokenizer.save_pretrained(A )
UpperCAmelCase__ : Tuple = [x.name for x in Path(A ).glob("""*""" )]
self.assertIn("""source.spm""" ,A )
MarianTokenizer.from_pretrained(A )
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = self.get_tokenizer()
UpperCAmelCase__ : Any = tok(
["""I am a small frog""" * 1_000, """I am a small frog"""] ,padding=A ,truncation=A ,return_tensors=A )
self.assertIsInstance(A ,A )
self.assertEqual(batch.input_ids.shape ,(2, 512) )
def __lowercase ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase__ : int = self.get_tokenizer()
UpperCAmelCase__ : Tuple = tok(["""I am a tiny frog""", """I am a small frog"""] ,padding=A ,return_tensors=A )
self.assertIsInstance(A ,A )
self.assertEqual(batch_smaller.input_ids.shape ,(2, 10) )
@slow
def __lowercase ( self : Dict ):
'''simple docstring'''
# fmt: off
UpperCAmelCase__ : Optional[int] = {"""input_ids""": [[43_495, 462, 20, 42_164, 1_369, 52, 464, 132, 1_703, 492, 13, 7_491, 38_999, 6, 8, 464, 132, 1_703, 492, 13, 4_669, 37_867, 13, 7_525, 27, 1_593, 988, 13, 33_972, 7_029, 6, 20, 8_251, 383, 2, 270, 5_866, 3_788, 2, 2_353, 8_251, 12_338, 2, 13_958, 387, 2, 3_629, 6_953, 188, 2_900, 2, 13_958, 8_011, 11_501, 23, 8_460, 4_073, 34_009, 20, 435, 11_439, 27, 8, 8_460, 4_073, 6_004, 20, 9_988, 375, 27, 33, 266, 1_945, 1_076, 1_350, 37_867, 3_288, 5, 577, 1_076, 4_374, 8, 5_082, 5, 26_453, 257, 556, 403, 2, 242, 132, 383, 316, 492, 8, 10_767, 6, 316, 304, 4_239, 3, 0], [148, 15_722, 19, 1_839, 12, 1_350, 13, 22_327, 5_082, 5_418, 47_567, 35_938, 59, 318, 19_552, 108, 2_183, 54, 14_976, 4_835, 32, 547, 1_114, 8, 315, 2_417, 5, 92, 19_088, 3, 0, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100], [36, 6_395, 12_570, 39_147, 11_597, 6, 266, 4, 45_405, 7_296, 3, 0, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100]], """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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=A ,model_name="""Helsinki-NLP/opus-mt-en-de""" ,revision="""1a8c2263da11e68e50938f97e10cd57820bd504c""" ,decode_kwargs={"""use_source_tokenizer""": True} ,)
def __lowercase ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = MarianTokenizer.from_pretrained("""hf-internal-testing/test-marian-two-vocabs""" )
UpperCAmelCase__ : Any = """Tämä on testi"""
UpperCAmelCase__ : int = """This is a test"""
UpperCAmelCase__ : List[str] = [76, 7, 2_047, 2]
UpperCAmelCase__ : Optional[Any] = [69, 12, 11, 940, 2]
UpperCAmelCase__ : List[str] = tokenizer(A ).input_ids
self.assertListEqual(A ,A )
UpperCAmelCase__ : Optional[int] = tokenizer(text_target=A ).input_ids
self.assertListEqual(A ,A )
UpperCAmelCase__ : int = tokenizer.decode(A ,skip_special_tokens=A )
self.assertEqual(A ,A )
| 65 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCAmelCase : Dict ={
"""configuration_mobilebert""": [
"""MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""MobileBertConfig""",
"""MobileBertOnnxConfig""",
],
"""tokenization_mobilebert""": ["""MobileBertTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase : List[Any] =["""MobileBertTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase : Tuple =[
"""MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""MobileBertForMaskedLM""",
"""MobileBertForMultipleChoice""",
"""MobileBertForNextSentencePrediction""",
"""MobileBertForPreTraining""",
"""MobileBertForQuestionAnswering""",
"""MobileBertForSequenceClassification""",
"""MobileBertForTokenClassification""",
"""MobileBertLayer""",
"""MobileBertModel""",
"""MobileBertPreTrainedModel""",
"""load_tf_weights_in_mobilebert""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase : List[str] =[
"""TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFMobileBertForMaskedLM""",
"""TFMobileBertForMultipleChoice""",
"""TFMobileBertForNextSentencePrediction""",
"""TFMobileBertForPreTraining""",
"""TFMobileBertForQuestionAnswering""",
"""TFMobileBertForSequenceClassification""",
"""TFMobileBertForTokenClassification""",
"""TFMobileBertMainLayer""",
"""TFMobileBertModel""",
"""TFMobileBertPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_mobilebert import (
MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
MobileBertConfig,
MobileBertOnnxConfig,
)
from .tokenization_mobilebert import MobileBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mobilebert_fast import MobileBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mobilebert import (
MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
MobileBertForMaskedLM,
MobileBertForMultipleChoice,
MobileBertForNextSentencePrediction,
MobileBertForPreTraining,
MobileBertForQuestionAnswering,
MobileBertForSequenceClassification,
MobileBertForTokenClassification,
MobileBertLayer,
MobileBertModel,
MobileBertPreTrainedModel,
load_tf_weights_in_mobilebert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mobilebert import (
TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFMobileBertForMaskedLM,
TFMobileBertForMultipleChoice,
TFMobileBertForNextSentencePrediction,
TFMobileBertForPreTraining,
TFMobileBertForQuestionAnswering,
TFMobileBertForSequenceClassification,
TFMobileBertForTokenClassification,
TFMobileBertMainLayer,
TFMobileBertModel,
TFMobileBertPreTrainedModel,
)
else:
import sys
_lowerCAmelCase : int =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__) | 113 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class __lowercase ( metaclass=__lowerCamelCase ):
snake_case_ = ["""onnx"""]
def __init__( self : int ,*A : List[str] ,**A : int ):
'''simple docstring'''
requires_backends(self ,["""onnx"""] )
@classmethod
def __lowercase ( cls : Optional[Any] ,*A : List[str] ,**A : Dict ):
'''simple docstring'''
requires_backends(cls ,["""onnx"""] )
@classmethod
def __lowercase ( cls : List[Any] ,*A : Optional[int] ,**A : int ):
'''simple docstring'''
requires_backends(cls ,["""onnx"""] )
| 65 | 0 |
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def lowerCAmelCase__ ( a__ ) ->Union[str, Any]:
'''simple docstring'''
_UpperCamelCase = {}
_UpperCamelCase = tokenizer(example["content"] , truncation=__UpperCamelCase )["""input_ids"""]
_UpperCamelCase = len(example["content"] ) / len(output["input_ids"] )
return output
lowerCamelCase__ = HfArgumentParser(PretokenizationArguments)
lowerCamelCase__ = parser.parse_args()
if args.num_workers is None:
lowerCamelCase__ = multiprocessing.cpu_count()
lowerCamelCase__ = AutoTokenizer.from_pretrained(args.tokenizer_dir)
lowerCamelCase__ = time.time()
lowerCamelCase__ = load_dataset(args.dataset_name, split='''train''')
print(F"Dataset loaded in {time.time()-t_start:.2f}s")
lowerCamelCase__ = time.time()
lowerCamelCase__ = ds.map(
tokenize,
num_proc=args.num_workers,
remove_columns=[
'''repo_name''',
'''path''',
'''copies''',
'''size''',
'''content''',
'''license''',
'''hash''',
'''line_mean''',
'''line_max''',
'''alpha_frac''',
'''autogenerated''',
],
)
print(F"Dataset tokenized in {time.time()-t_start:.2f}s")
lowerCamelCase__ = time.time()
ds.push_to_hub(args.tokenized_data_repo)
print(F"Data pushed to the hub in {time.time()-t_start:.2f}s")
| 547 |
"""simple docstring"""
from argparse import ArgumentParser
from .env import EnvironmentCommand
def lowerCAmelCase ( ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = ArgumentParser("""Diffusers CLI tool""" , usage="""diffusers-cli <command> [<args>]""" )
UpperCAmelCase__ : List[Any] = parser.add_subparsers(help="""diffusers-cli command helpers""" )
# Register commands
EnvironmentCommand.register_subcommand(__UpperCamelCase )
# Let's go
UpperCAmelCase__ : int = parser.parse_args()
if not hasattr(__UpperCamelCase , """func""" ):
parser.print_help()
exit(1 )
# Run
UpperCAmelCase__ : Union[str, Any] = args.func(__UpperCamelCase )
service.run()
if __name__ == "__main__":
main()
| 65 | 0 |
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE :Union[str, Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE :Optional[int] = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}
SCREAMING_SNAKE_CASE :Dict = {
'''vocab_file''': {
'''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json''',
'''allenai/longformer-large-4096''': (
'''https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'''
),
'''allenai/longformer-large-4096-finetuned-triviaqa''': (
'''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'''
),
'''allenai/longformer-base-4096-extra.pos.embd.only''': (
'''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'''
),
'''allenai/longformer-large-4096-extra.pos.embd.only''': (
'''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'''
),
},
'''merges_file''': {
'''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt''',
'''allenai/longformer-large-4096''': (
'''https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'''
),
'''allenai/longformer-large-4096-finetuned-triviaqa''': (
'''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'''
),
'''allenai/longformer-base-4096-extra.pos.embd.only''': (
'''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'''
),
'''allenai/longformer-large-4096-extra.pos.embd.only''': (
'''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'''
),
},
}
SCREAMING_SNAKE_CASE :int = {
'''allenai/longformer-base-4096''': 40_96,
'''allenai/longformer-large-4096''': 40_96,
'''allenai/longformer-large-4096-finetuned-triviaqa''': 40_96,
'''allenai/longformer-base-4096-extra.pos.embd.only''': 40_96,
'''allenai/longformer-large-4096-extra.pos.embd.only''': 40_96,
}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def _lowerCAmelCase ( )->Tuple:
'''simple docstring'''
snake_case_ = (
list(range(ord("!" ) , ord("~" ) + 1 ) ) + list(range(ord("¡" ) , ord("¬" ) + 1 ) ) + list(range(ord("®" ) , ord("ÿ" ) + 1 ) )
)
snake_case_ = bs[:]
snake_case_ = 0
for b in range(2**8 ):
if b not in bs:
bs.append(__UpperCamelCase )
cs.append(2**8 + n )
n += 1
snake_case_ = [chr(__UpperCamelCase ) for n in cs]
return dict(zip(__UpperCamelCase , __UpperCamelCase ) )
def _lowerCAmelCase ( lowerCAmelCase_ :Dict )->Tuple:
'''simple docstring'''
snake_case_ = set()
snake_case_ = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
snake_case_ = char
return pairs
class __lowerCAmelCase ( __lowerCamelCase ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = VOCAB_FILES_NAMES
_SCREAMING_SNAKE_CASE = PRETRAINED_VOCAB_FILES_MAP
_SCREAMING_SNAKE_CASE = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_SCREAMING_SNAKE_CASE = ['input_ids', 'attention_mask']
def __init__( self : Optional[int] , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Union[str, Any]="replace" , _lowerCAmelCase : Any="<s>" , _lowerCAmelCase : List[Any]="</s>" , _lowerCAmelCase : Any="</s>" , _lowerCAmelCase : str="<s>" , _lowerCAmelCase : Optional[Any]="<unk>" , _lowerCAmelCase : int="<pad>" , _lowerCAmelCase : Dict="<mask>" , _lowerCAmelCase : Optional[Any]=False , **_lowerCAmelCase : str , ) -> Dict:
"""simple docstring"""
snake_case_ = AddedToken(_lowerCAmelCase , lstrip=_lowerCAmelCase , rstrip=_lowerCAmelCase ) if isinstance(_lowerCAmelCase , _lowerCAmelCase ) else bos_token
snake_case_ = AddedToken(_lowerCAmelCase , lstrip=_lowerCAmelCase , rstrip=_lowerCAmelCase ) if isinstance(_lowerCAmelCase , _lowerCAmelCase ) else eos_token
snake_case_ = AddedToken(_lowerCAmelCase , lstrip=_lowerCAmelCase , rstrip=_lowerCAmelCase ) if isinstance(_lowerCAmelCase , _lowerCAmelCase ) else sep_token
snake_case_ = AddedToken(_lowerCAmelCase , lstrip=_lowerCAmelCase , rstrip=_lowerCAmelCase ) if isinstance(_lowerCAmelCase , _lowerCAmelCase ) else cls_token
snake_case_ = AddedToken(_lowerCAmelCase , lstrip=_lowerCAmelCase , rstrip=_lowerCAmelCase ) if isinstance(_lowerCAmelCase , _lowerCAmelCase ) else unk_token
snake_case_ = AddedToken(_lowerCAmelCase , lstrip=_lowerCAmelCase , rstrip=_lowerCAmelCase ) if isinstance(_lowerCAmelCase , _lowerCAmelCase ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
snake_case_ = AddedToken(_lowerCAmelCase , lstrip=_lowerCAmelCase , rstrip=_lowerCAmelCase ) if isinstance(_lowerCAmelCase , _lowerCAmelCase ) else mask_token
super().__init__(
errors=_lowerCAmelCase , bos_token=_lowerCAmelCase , eos_token=_lowerCAmelCase , unk_token=_lowerCAmelCase , sep_token=_lowerCAmelCase , cls_token=_lowerCAmelCase , pad_token=_lowerCAmelCase , mask_token=_lowerCAmelCase , add_prefix_space=_lowerCAmelCase , **_lowerCAmelCase , )
with open(_lowerCAmelCase , encoding="utf-8" ) as vocab_handle:
snake_case_ = json.load(_lowerCAmelCase )
snake_case_ = {v: k for k, v in self.encoder.items()}
snake_case_ = errors # how to handle errors in decoding
snake_case_ = bytes_to_unicode()
snake_case_ = {v: k for k, v in self.byte_encoder.items()}
with open(_lowerCAmelCase , encoding="utf-8" ) as merges_handle:
snake_case_ = merges_handle.read().split("\n" )[1:-1]
snake_case_ = [tuple(merge.split() ) for merge in bpe_merges]
snake_case_ = dict(zip(_lowerCAmelCase , range(len(_lowerCAmelCase ) ) ) )
snake_case_ = {}
snake_case_ = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
snake_case_ = re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+" )
@property
def lowerCAmelCase__ ( self : List[Any] ) -> Dict:
"""simple docstring"""
return len(self.encoder )
def lowerCAmelCase__ ( self : Optional[Any] ) -> List[Any]:
"""simple docstring"""
return dict(self.encoder , **self.added_tokens_encoder )
def lowerCAmelCase__ ( self : Optional[int] , _lowerCAmelCase : Union[str, Any] ) -> str:
"""simple docstring"""
if token in self.cache:
return self.cache[token]
snake_case_ = tuple(_lowerCAmelCase )
snake_case_ = get_pairs(_lowerCAmelCase )
if not pairs:
return token
while True:
snake_case_ = min(_lowerCAmelCase , key=lambda _lowerCAmelCase : self.bpe_ranks.get(_lowerCAmelCase , float("inf" ) ) )
if bigram not in self.bpe_ranks:
break
snake_case_ = bigram
snake_case_ = []
snake_case_ = 0
while i < len(_lowerCAmelCase ):
try:
snake_case_ = word.index(_lowerCAmelCase , _lowerCAmelCase )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
snake_case_ = j
if word[i] == first and i < len(_lowerCAmelCase ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
snake_case_ = tuple(_lowerCAmelCase )
snake_case_ = new_word
if len(_lowerCAmelCase ) == 1:
break
else:
snake_case_ = get_pairs(_lowerCAmelCase )
snake_case_ = """ """.join(_lowerCAmelCase )
snake_case_ = word
return word
def lowerCAmelCase__ ( self : List[Any] , _lowerCAmelCase : List[str] ) -> Tuple:
"""simple docstring"""
snake_case_ = []
for token in re.findall(self.pat , _lowerCAmelCase ):
snake_case_ = """""".join(
self.byte_encoder[b] for b in token.encode("utf-8" ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_lowerCAmelCase ).split(" " ) )
return bpe_tokens
def lowerCAmelCase__ ( self : Dict , _lowerCAmelCase : List[Any] ) -> List[str]:
"""simple docstring"""
return self.encoder.get(_lowerCAmelCase , self.encoder.get(self.unk_token ) )
def lowerCAmelCase__ ( self : Optional[int] , _lowerCAmelCase : Any ) -> Any:
"""simple docstring"""
return self.decoder.get(_lowerCAmelCase )
def lowerCAmelCase__ ( self : Dict , _lowerCAmelCase : List[str] ) -> int:
"""simple docstring"""
snake_case_ = """""".join(_lowerCAmelCase )
snake_case_ = bytearray([self.byte_decoder[c] for c in text] ).decode("utf-8" , errors=self.errors )
return text
def lowerCAmelCase__ ( self : List[str] , _lowerCAmelCase : str , _lowerCAmelCase : Optional[str] = None ) -> List[Any]:
"""simple docstring"""
if not os.path.isdir(_lowerCAmelCase ):
logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
return
snake_case_ = os.path.join(
_lowerCAmelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
snake_case_ = os.path.join(
_lowerCAmelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] )
with open(_lowerCAmelCase , "w" , encoding="utf-8" ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=_lowerCAmelCase , ensure_ascii=_lowerCAmelCase ) + "\n" )
snake_case_ = 0
with open(_lowerCAmelCase , "w" , encoding="utf-8" ) as writer:
writer.write("#version: 0.2\n" )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda _lowerCAmelCase : kv[1] ):
if index != token_index:
logger.warning(
F'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
" Please check that the tokenizer is not corrupted!" )
snake_case_ = token_index
writer.write(" ".join(_lowerCAmelCase ) + "\n" )
index += 1
return vocab_file, merge_file
def lowerCAmelCase__ ( self : str , _lowerCAmelCase : List[int] , _lowerCAmelCase : Optional[List[int]] = None ) -> List[Any]:
"""simple docstring"""
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
snake_case_ = [self.cls_token_id]
snake_case_ = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def lowerCAmelCase__ ( self : List[Any] , _lowerCAmelCase : List[int] , _lowerCAmelCase : Optional[List[int]] = None , _lowerCAmelCase : bool = False ) -> Union[str, Any]:
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_lowerCAmelCase , token_ids_a=_lowerCAmelCase , already_has_special_tokens=_lowerCAmelCase )
if token_ids_a is None:
return [1] + ([0] * len(_lowerCAmelCase )) + [1]
return [1] + ([0] * len(_lowerCAmelCase )) + [1, 1] + ([0] * len(_lowerCAmelCase )) + [1]
def lowerCAmelCase__ ( self : List[str] , _lowerCAmelCase : List[int] , _lowerCAmelCase : Optional[List[int]] = None ) -> List[Any]:
"""simple docstring"""
snake_case_ = [self.sep_token_id]
snake_case_ = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def lowerCAmelCase__ ( self : str , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : int=False , **_lowerCAmelCase : List[Any] ) -> Any:
"""simple docstring"""
snake_case_ = kwargs.pop("add_prefix_space" , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(_lowerCAmelCase ) > 0 and not text[0].isspace()):
snake_case_ = """ """ + text
return (text, kwargs)
| 283 |
"""simple docstring"""
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
import os
# The slow tests are often failing with OOM error on GPU
# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
__UpperCAmelCase = 'platform'
import jax
import jax.numpy as jnp
import numpy as np
from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel
@require_flax
class __lowercase :
snake_case_ = PegasusConfig
snake_case_ = {}
snake_case_ = """gelu"""
def __init__( self : List[Any] ,A : int ,A : Optional[Any]=13 ,A : Dict=7 ,A : Dict=True ,A : Any=False ,A : Dict=99 ,A : int=32 ,A : Optional[int]=5 ,A : Union[str, Any]=4 ,A : Union[str, Any]=37 ,A : str=0.1 ,A : int=0.1 ,A : Optional[int]=20 ,A : Tuple=2 ,A : str=1 ,A : Optional[Any]=0 ,):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = parent
UpperCAmelCase__ : Union[str, Any] = batch_size
UpperCAmelCase__ : List[Any] = seq_length
UpperCAmelCase__ : int = is_training
UpperCAmelCase__ : Any = use_labels
UpperCAmelCase__ : int = vocab_size
UpperCAmelCase__ : Dict = hidden_size
UpperCAmelCase__ : Optional[Any] = num_hidden_layers
UpperCAmelCase__ : int = num_attention_heads
UpperCAmelCase__ : Any = intermediate_size
UpperCAmelCase__ : Optional[int] = hidden_dropout_prob
UpperCAmelCase__ : str = attention_probs_dropout_prob
UpperCAmelCase__ : str = max_position_embeddings
UpperCAmelCase__ : Union[str, Any] = eos_token_id
UpperCAmelCase__ : Union[str, Any] = pad_token_id
UpperCAmelCase__ : List[str] = bos_token_id
def __lowercase ( self : Dict ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = ids_tensor([self.batch_size, self.seq_length - 1] ,self.vocab_size ).clip(3 ,self.vocab_size )
UpperCAmelCase__ : List[str] = np.expand_dims(np.array([self.eos_token_id] * self.batch_size ) ,1 )
UpperCAmelCase__ : Any = np.concatenate([input_ids, eos_tensor] ,axis=1 )
UpperCAmelCase__ : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
UpperCAmelCase__ : str = 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 ,)
UpperCAmelCase__ : Optional[Any] = prepare_pegasus_inputs_dict(A ,A ,A )
return config, inputs_dict
def __lowercase ( self : Any ,A : Optional[int] ,A : str ,A : Optional[int] ):
'''simple docstring'''
UpperCAmelCase__ : Any = 20
UpperCAmelCase__ : Dict = model_class_name(A )
UpperCAmelCase__ : str = model.encode(inputs_dict["""input_ids"""] )
UpperCAmelCase__ , UpperCAmelCase__ : List[str] = (
inputs_dict["""decoder_input_ids"""],
inputs_dict["""decoder_attention_mask"""],
)
UpperCAmelCase__ : Union[str, Any] = model.init_cache(decoder_input_ids.shape[0] ,A ,A )
UpperCAmelCase__ : Union[str, Any] = jnp.ones((decoder_input_ids.shape[0], max_decoder_length) ,dtype="""i4""" )
UpperCAmelCase__ : str = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] ,(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) ,)
UpperCAmelCase__ : Optional[int] = model.decode(
decoder_input_ids[:, :-1] ,A ,decoder_attention_mask=A ,past_key_values=A ,decoder_position_ids=A ,)
UpperCAmelCase__ : Any = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] ,dtype="""i4""" )
UpperCAmelCase__ : int = model.decode(
decoder_input_ids[:, -1:] ,A ,decoder_attention_mask=A ,past_key_values=outputs_cache.past_key_values ,decoder_position_ids=A ,)
UpperCAmelCase__ : Dict = model.decode(A ,A )
UpperCAmelCase__ : str = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1e-3 ,msg=f"Max diff is {diff}" )
def __lowercase ( self : Optional[int] ,A : str ,A : Dict ,A : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ : Any = 20
UpperCAmelCase__ : str = model_class_name(A )
UpperCAmelCase__ : Any = model.encode(inputs_dict["""input_ids"""] )
UpperCAmelCase__ , UpperCAmelCase__ : Optional[int] = (
inputs_dict["""decoder_input_ids"""],
inputs_dict["""decoder_attention_mask"""],
)
UpperCAmelCase__ : Optional[int] = jnp.concatenate(
[
decoder_attention_mask,
jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1]) ),
] ,axis=-1 ,)
UpperCAmelCase__ : Union[str, Any] = model.init_cache(decoder_input_ids.shape[0] ,A ,A )
UpperCAmelCase__ : List[str] = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] ,(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) ,)
UpperCAmelCase__ : Union[str, Any] = model.decode(
decoder_input_ids[:, :-1] ,A ,decoder_attention_mask=A ,past_key_values=A ,decoder_position_ids=A ,)
UpperCAmelCase__ : int = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] ,dtype="""i4""" )
UpperCAmelCase__ : Dict = model.decode(
decoder_input_ids[:, -1:] ,A ,past_key_values=outputs_cache.past_key_values ,decoder_attention_mask=A ,decoder_position_ids=A ,)
UpperCAmelCase__ : Union[str, Any] = model.decode(A ,A ,decoder_attention_mask=A )
UpperCAmelCase__ : Union[str, 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}" )
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=None , __UpperCamelCase=None , ):
'''simple docstring'''
if attention_mask is None:
UpperCAmelCase__ : Union[str, Any] = np.not_equal(__UpperCamelCase , config.pad_token_id ).astype(np.inta )
if decoder_attention_mask is None:
UpperCAmelCase__ : Tuple = np.concatenate(
[
np.ones(decoder_input_ids[:, :1].shape , dtype=np.inta ),
np.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ).astype(np.inta ),
] , axis=-1 , )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
}
@require_flax
class __lowercase ( __lowerCamelCase , unittest.TestCase ):
snake_case_ = (
(
FlaxPegasusForConditionalGeneration,
FlaxPegasusModel,
)
if is_flax_available()
else ()
)
snake_case_ = (FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
snake_case_ = True
snake_case_ = False
snake_case_ = False
snake_case_ = False
def __lowercase ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase__ : int = FlaxPegasusModelTester(self )
UpperCAmelCase__ : Optional[Any] = ConfigTester(self ,config_class=A )
def __lowercase ( self : Tuple ):
'''simple docstring'''
self.config_tester.run_common_tests()
def __lowercase ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase__ , UpperCAmelCase__ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward(A ,A ,A )
def __lowercase ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase__ , UpperCAmelCase__ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward_with_attn_mask(A ,A ,A )
def __lowercase ( self : Any ):
'''simple docstring'''
UpperCAmelCase__ , UpperCAmelCase__ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
UpperCAmelCase__ : List[Any] = self._prepare_for_class(A ,A )
UpperCAmelCase__ : int = model_class(A )
@jax.jit
def encode_jitted(A : Optional[int] ,A : Union[str, Any]=None ,**A : Optional[Any] ):
return model.encode(input_ids=A ,attention_mask=A )
with self.subTest("""JIT Enabled""" ):
UpperCAmelCase__ : int = encode_jitted(**A ).to_tuple()
with self.subTest("""JIT Disabled""" ):
with jax.disable_jit():
UpperCAmelCase__ : Dict = encode_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 )
def __lowercase ( self : str ):
'''simple docstring'''
UpperCAmelCase__ , UpperCAmelCase__ : 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__ ):
UpperCAmelCase__ : Dict = model_class(A )
UpperCAmelCase__ : str = model.encode(inputs_dict["""input_ids"""] ,inputs_dict["""attention_mask"""] )
UpperCAmelCase__ : Dict = {
"""decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
"""decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
"""encoder_outputs""": encoder_outputs,
}
@jax.jit
def decode_jitted(A : List[Any] ,A : Any ,A : List[Any] ):
return model.decode(
decoder_input_ids=A ,decoder_attention_mask=A ,encoder_outputs=A ,)
with self.subTest("""JIT Enabled""" ):
UpperCAmelCase__ : Tuple = decode_jitted(**A ).to_tuple()
with self.subTest("""JIT Disabled""" ):
with jax.disable_jit():
UpperCAmelCase__ : str = decode_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 : List[Any] ):
'''simple docstring'''
for model_class_name in self.all_model_classes:
UpperCAmelCase__ : List[str] = model_class_name.from_pretrained("""google/pegasus-large""" ,from_pt=A )
UpperCAmelCase__ : Any = np.ones((1, 1) )
UpperCAmelCase__ : Optional[Any] = model(A )
self.assertIsNotNone(A )
@slow
def __lowercase ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase__ : Dict = FlaxPegasusForConditionalGeneration.from_pretrained("""google/pegasus-xsum""" )
UpperCAmelCase__ : Optional[Any] = PegasusTokenizer.from_pretrained("""google/pegasus-xsum""" )
UpperCAmelCase__ : Union[str, Any] = [
""" PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.""",
""" The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning 'Oh I think you're nominated'\", said Dappy.\"And I was like 'Oh yeah, which one?' And now we've got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it's best of us to come down and mingle with everyone and say hello to the cameras. And now we find we've got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn't be too disappointed if they didn't win this time around.\"At the end of the day we're grateful to be where we are in our careers.\"If it don't happen then it don't happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a re-working of The Killers' All These Things That I've Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year's Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" """,
]
UpperCAmelCase__ : str = [
"""California's largest electricity provider has turned off power to hundreds of thousands of customers.""",
"""Pop group N-Dubz have revealed they were surprised to get four nominations for this year's Mobo Awards.""",
]
UpperCAmelCase__ : str = tokenizer(A ,return_tensors="""np""" ,truncation=A ,max_length=512 ,padding=A )
UpperCAmelCase__ : Union[str, Any] = model.generate(**A ,num_beams=2 ).sequences
UpperCAmelCase__ : int = tokenizer.batch_decode(A ,skip_special_tokens=A )
assert tgt_text == decoded
| 65 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_UpperCamelCase = logging.get_logger(__name__)
_UpperCamelCase = {
'''facebook/data2vec-text-base''': '''https://huggingface.co/data2vec/resolve/main/config.json''',
}
class lowercase ( __lowerCamelCase ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = """data2vec-text"""
def __init__(self , __a=30522 , __a=768 , __a=12 , __a=12 , __a=3072 , __a="gelu" , __a=0.1 , __a=0.1 , __a=512 , __a=2 , __a=0.02 , __a=1E-1_2 , __a=1 , __a=0 , __a=2 , __a="absolute" , __a=True , __a=None , **__a , ) -> Any:
"""simple docstring"""
super().__init__(pad_token_id=__a , bos_token_id=__a , eos_token_id=__a , **__a )
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__ = position_embedding_type
UpperCAmelCase__ = use_cache
UpperCAmelCase__ = classifier_dropout
class lowercase ( __lowerCamelCase ):
'''simple docstring'''
@property
def UpperCamelCase__ (self ) -> int:
"""simple docstring"""
if self.task == "multiple-choice":
UpperCAmelCase__ = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
UpperCAmelCase__ = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
] )
| 146 |
"""simple docstring"""
def lowerCAmelCase ( __UpperCamelCase ):
'''simple docstring'''
if not isinstance(__UpperCamelCase , __UpperCamelCase ) or number < 0:
raise ValueError("""Input must be a non-negative integer""" )
UpperCAmelCase__ : Union[str, Any] = 0
while number:
# This way we arrive at next set bit (next 1) instead of looping
# through each bit and checking for 1s hence the
# loop won't run 32 times it will only run the number of `1` times
number &= number - 1
count += 1
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 65 | 0 |
"""simple docstring"""
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from transformers import BartTokenizer, RagTokenizer, TaTokenizer
def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase=True , _UpperCamelCase="pt" ):
'''simple docstring'''
__lowerCAmelCase = {"""add_prefix_space""": True} if isinstance(__UpperCamelCase , __UpperCamelCase ) and not line.startswith(" " ) else {}
__lowerCAmelCase = padding_side
return tokenizer(
[line] , max_length=__UpperCamelCase , padding="max_length" if pad_to_max_length else None , truncation=__UpperCamelCase , return_tensors=__UpperCamelCase , add_special_tokens=__UpperCamelCase , **__UpperCamelCase , )
def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase=None , ):
'''simple docstring'''
__lowerCAmelCase = input_ids.ne(__UpperCamelCase ).any(dim=0 )
if attention_mask is None:
return input_ids[:, keep_column_mask]
else:
return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
class _UpperCamelCase ( __lowerCamelCase ):
'''simple docstring'''
def __init__( self , __a , __a , __a , __a , __a="train" , __a=None , __a=None , __a=None , __a="" , ):
super().__init__()
__lowerCAmelCase = Path(__a ).joinpath(type_path + ".source" )
__lowerCAmelCase = Path(__a ).joinpath(type_path + ".target" )
__lowerCAmelCase = self.get_char_lens(self.src_file )
__lowerCAmelCase = max_source_length
__lowerCAmelCase = max_target_length
assert min(self.src_lens ) > 0, f"found empty line in {self.src_file}"
__lowerCAmelCase = tokenizer
__lowerCAmelCase = prefix
if n_obs is not None:
__lowerCAmelCase = self.src_lens[:n_obs]
__lowerCAmelCase = src_lang
__lowerCAmelCase = tgt_lang
def __len__( self ):
return len(self.src_lens )
def __getitem__( self , __a ):
__lowerCAmelCase = index + 1 # linecache starts at 1
__lowerCAmelCase = self.prefix + linecache.getline(str(self.src_file ) , __a ).rstrip("\n" )
__lowerCAmelCase = linecache.getline(str(self.tgt_file ) , __a ).rstrip("\n" )
assert source_line, f"empty source line for index {index}"
assert tgt_line, f"empty tgt line for index {index}"
# Need to add eos token manually for T5
if isinstance(self.tokenizer , __a ):
source_line += self.tokenizer.eos_token
tgt_line += self.tokenizer.eos_token
# Pad source and target to the right
__lowerCAmelCase = (
self.tokenizer.question_encoder if isinstance(self.tokenizer , __a ) else self.tokenizer
)
__lowerCAmelCase = self.tokenizer.generator if isinstance(self.tokenizer , __a ) else self.tokenizer
__lowerCAmelCase = encode_line(__a , __a , self.max_source_length , "right" )
__lowerCAmelCase = encode_line(__a , __a , self.max_target_length , "right" )
__lowerCAmelCase = source_inputs["""input_ids"""].squeeze()
__lowerCAmelCase = target_inputs["""input_ids"""].squeeze()
__lowerCAmelCase = source_inputs["""attention_mask"""].squeeze()
return {
"input_ids": source_ids,
"attention_mask": src_mask,
"decoder_input_ids": target_ids,
}
@staticmethod
def snake_case ( __a ):
return [len(__a ) for x in Path(__a ).open().readlines()]
def snake_case ( self , __a ):
__lowerCAmelCase = torch.stack([x["input_ids"] for x in batch] )
__lowerCAmelCase = torch.stack([x["attention_mask"] for x in batch] )
__lowerCAmelCase = torch.stack([x["decoder_input_ids"] for x in batch] )
__lowerCAmelCase = (
self.tokenizer.generator.pad_token_id
if isinstance(self.tokenizer , __a )
else self.tokenizer.pad_token_id
)
__lowerCAmelCase = (
self.tokenizer.question_encoder.pad_token_id
if isinstance(self.tokenizer , __a )
else self.tokenizer.pad_token_id
)
__lowerCAmelCase = trim_batch(__a , __a )
__lowerCAmelCase = trim_batch(__a , __a , attention_mask=__a )
__lowerCAmelCase = {
"""input_ids""": source_ids,
"""attention_mask""": source_mask,
"""decoder_input_ids""": y,
}
return batch
A : Any = getLogger(__name__)
def _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
return list(itertools.chain.from_iterable(__UpperCamelCase ) )
def _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = get_git_info()
save_json(__UpperCamelCase , os.path.join(__UpperCamelCase , "git_log.json" ) )
def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase=4 , **_UpperCamelCase ):
'''simple docstring'''
with open(__UpperCamelCase , "w" ) as f:
json.dump(__UpperCamelCase , __UpperCamelCase , indent=__UpperCamelCase , **__UpperCamelCase )
def _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
with open(__UpperCamelCase ) as f:
return json.load(__UpperCamelCase )
def _lowerCamelCase ( ):
'''simple docstring'''
__lowerCAmelCase = git.Repo(search_parent_directories=__UpperCamelCase )
__lowerCAmelCase = {
"""repo_id""": str(__UpperCamelCase ),
"""repo_sha""": str(repo.head.object.hexsha ),
"""repo_branch""": str(repo.active_branch ),
"""hostname""": str(socket.gethostname() ),
}
return repo_infos
def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
return list(map(__UpperCamelCase , __UpperCamelCase ) )
def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
with open(__UpperCamelCase , "wb" ) as f:
return pickle.dump(__UpperCamelCase , __UpperCamelCase )
def _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
def remove_articles(_UpperCamelCase ):
return re.sub(R"\b(a|an|the)\b" , " " , __UpperCamelCase )
def white_space_fix(_UpperCamelCase ):
return " ".join(text.split() )
def remove_punc(_UpperCamelCase ):
__lowerCAmelCase = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(_UpperCamelCase ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(__UpperCamelCase ) ) ) )
def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = normalize_answer(__UpperCamelCase ).split()
__lowerCAmelCase = normalize_answer(__UpperCamelCase ).split()
__lowerCAmelCase = Counter(__UpperCamelCase ) & Counter(__UpperCamelCase )
__lowerCAmelCase = sum(common.values() )
if num_same == 0:
return 0
__lowerCAmelCase = 1.0 * num_same / len(__UpperCamelCase )
__lowerCAmelCase = 1.0 * num_same / len(__UpperCamelCase )
__lowerCAmelCase = (2 * precision * recall) / (precision + recall)
return fa
def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
return normalize_answer(__UpperCamelCase ) == normalize_answer(__UpperCamelCase )
def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
assert len(__UpperCamelCase ) == len(__UpperCamelCase )
__lowerCAmelCase = 0
for hypo, pred in zip(__UpperCamelCase , __UpperCamelCase ):
em += exact_match_score(__UpperCamelCase , __UpperCamelCase )
if len(__UpperCamelCase ) > 0:
em /= len(__UpperCamelCase )
return {"em": em}
def _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
return model_prefix.startswith("rag" )
def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = {p: p for p in extra_params}
# T5 models don't have `dropout` param, they have `dropout_rate` instead
__lowerCAmelCase = """dropout_rate"""
for p in extra_params:
if getattr(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
if not hasattr(__UpperCamelCase , __UpperCamelCase ) and not hasattr(__UpperCamelCase , equivalent_param[p] ):
logger.info("config doesn't have a `{}` attribute".format(__UpperCamelCase ) )
delattr(__UpperCamelCase , __UpperCamelCase )
continue
__lowerCAmelCase = p if hasattr(__UpperCamelCase , __UpperCamelCase ) else equivalent_param[p]
setattr(__UpperCamelCase , __UpperCamelCase , getattr(__UpperCamelCase , __UpperCamelCase ) )
delattr(__UpperCamelCase , __UpperCamelCase )
return hparams, config
| 636 |
"""simple docstring"""
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def lowerCAmelCase ( __UpperCamelCase = "isbn/0140328726" ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = olid.strip().strip("""/""" ) # Remove leading/trailing whitespace & slashes
if new_olid.count("""/""" ) != 1:
UpperCAmelCase__ : Dict = F"{olid} is not a valid Open Library olid"
raise ValueError(__UpperCamelCase )
return requests.get(F"https://openlibrary.org/{new_olid}.json" ).json()
def lowerCAmelCase ( __UpperCamelCase ):
'''simple docstring'''
UpperCAmelCase__ : Any = {
"""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__ : Dict = {better_key: ol_book_data[key] for key, better_key in desired_keys.items()}
UpperCAmelCase__ : str = [
get_openlibrary_data(author["""key"""] )["""name"""] for author in data["""Authors"""]
]
UpperCAmelCase__ : Dict = data["""First sentence"""]["""value"""]
for key, value in data.items():
if isinstance(__UpperCamelCase , __UpperCamelCase ):
UpperCAmelCase__ : Dict = """, """.join(__UpperCamelCase )
return data
if __name__ == "__main__":
import doctest
doctest.testmod()
while True:
__UpperCAmelCase = 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:
__UpperCAmelCase = 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}.")
| 65 | 0 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
from .pipeline_unclip import UnCLIPPipeline
from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
from .text_proj import UnCLIPTextProjModel
| 93 |
"""simple docstring"""
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from transformers import BartTokenizer, RagTokenizer, TaTokenizer
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=True , __UpperCamelCase="pt" ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = {"""add_prefix_space""": True} if isinstance(__UpperCamelCase , __UpperCamelCase ) and not line.startswith(""" """ ) else {}
UpperCAmelCase__ : List[str] = padding_side
return tokenizer(
[line] , max_length=__UpperCamelCase , padding="""max_length""" if pad_to_max_length else None , truncation=__UpperCamelCase , return_tensors=__UpperCamelCase , add_special_tokens=__UpperCamelCase , **__UpperCamelCase , )
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=None , ):
'''simple docstring'''
UpperCAmelCase__ : str = input_ids.ne(__UpperCamelCase ).any(dim=0 )
if attention_mask is None:
return input_ids[:, keep_column_mask]
else:
return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
class __lowercase ( __lowerCamelCase ):
def __init__( self : Tuple ,A : List[Any] ,A : Union[str, Any] ,A : Any ,A : Optional[int] ,A : Union[str, Any]="train" ,A : Tuple=None ,A : Union[str, Any]=None ,A : Tuple=None ,A : int="" ,):
'''simple docstring'''
super().__init__()
UpperCAmelCase__ : Optional[Any] = Path(A ).joinpath(type_path + """.source""" )
UpperCAmelCase__ : List[str] = Path(A ).joinpath(type_path + """.target""" )
UpperCAmelCase__ : Dict = self.get_char_lens(self.src_file )
UpperCAmelCase__ : int = max_source_length
UpperCAmelCase__ : List[str] = max_target_length
assert min(self.src_lens ) > 0, f"found empty line in {self.src_file}"
UpperCAmelCase__ : Dict = tokenizer
UpperCAmelCase__ : str = prefix
if n_obs is not None:
UpperCAmelCase__ : int = self.src_lens[:n_obs]
UpperCAmelCase__ : Any = src_lang
UpperCAmelCase__ : Any = tgt_lang
def __len__( self : Optional[Any] ):
'''simple docstring'''
return len(self.src_lens )
def __getitem__( self : Union[str, Any] ,A : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = index + 1 # linecache starts at 1
UpperCAmelCase__ : Tuple = self.prefix + linecache.getline(str(self.src_file ) ,A ).rstrip("""\n""" )
UpperCAmelCase__ : Dict = linecache.getline(str(self.tgt_file ) ,A ).rstrip("""\n""" )
assert source_line, f"empty source line for index {index}"
assert tgt_line, f"empty tgt line for index {index}"
# Need to add eos token manually for T5
if isinstance(self.tokenizer ,A ):
source_line += self.tokenizer.eos_token
tgt_line += self.tokenizer.eos_token
# Pad source and target to the right
UpperCAmelCase__ : str = (
self.tokenizer.question_encoder if isinstance(self.tokenizer ,A ) else self.tokenizer
)
UpperCAmelCase__ : Tuple = self.tokenizer.generator if isinstance(self.tokenizer ,A ) else self.tokenizer
UpperCAmelCase__ : Tuple = encode_line(A ,A ,self.max_source_length ,"""right""" )
UpperCAmelCase__ : Dict = encode_line(A ,A ,self.max_target_length ,"""right""" )
UpperCAmelCase__ : Optional[Any] = source_inputs["""input_ids"""].squeeze()
UpperCAmelCase__ : List[str] = target_inputs["""input_ids"""].squeeze()
UpperCAmelCase__ : Union[str, Any] = source_inputs["""attention_mask"""].squeeze()
return {
"input_ids": source_ids,
"attention_mask": src_mask,
"decoder_input_ids": target_ids,
}
@staticmethod
def __lowercase ( A : int ):
'''simple docstring'''
return [len(A ) for x in Path(A ).open().readlines()]
def __lowercase ( self : List[Any] ,A : Any ):
'''simple docstring'''
UpperCAmelCase__ : int = torch.stack([x["""input_ids"""] for x in batch] )
UpperCAmelCase__ : Union[str, Any] = torch.stack([x["""attention_mask"""] for x in batch] )
UpperCAmelCase__ : Any = torch.stack([x["""decoder_input_ids"""] for x in batch] )
UpperCAmelCase__ : List[Any] = (
self.tokenizer.generator.pad_token_id
if isinstance(self.tokenizer ,A )
else self.tokenizer.pad_token_id
)
UpperCAmelCase__ : Any = (
self.tokenizer.question_encoder.pad_token_id
if isinstance(self.tokenizer ,A )
else self.tokenizer.pad_token_id
)
UpperCAmelCase__ : str = trim_batch(A ,A )
UpperCAmelCase__ , UpperCAmelCase__ : Union[str, Any] = trim_batch(A ,A ,attention_mask=A )
UpperCAmelCase__ : List[str] = {
"""input_ids""": source_ids,
"""attention_mask""": source_mask,
"""decoder_input_ids""": y,
}
return batch
__UpperCAmelCase = getLogger(__name__)
def lowerCAmelCase ( __UpperCamelCase ):
'''simple docstring'''
return list(itertools.chain.from_iterable(__UpperCamelCase ) )
def lowerCAmelCase ( __UpperCamelCase ):
'''simple docstring'''
UpperCAmelCase__ : Dict = get_git_info()
save_json(__UpperCamelCase , os.path.join(__UpperCamelCase , """git_log.json""" ) )
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=4 , **__UpperCamelCase ):
'''simple docstring'''
with open(__UpperCamelCase , """w""" ) as f:
json.dump(__UpperCamelCase , __UpperCamelCase , indent=__UpperCamelCase , **__UpperCamelCase )
def lowerCAmelCase ( __UpperCamelCase ):
'''simple docstring'''
with open(__UpperCamelCase ) as f:
return json.load(__UpperCamelCase )
def lowerCAmelCase ( ):
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = git.Repo(search_parent_directories=__UpperCamelCase )
UpperCAmelCase__ : List[str] = {
"""repo_id""": str(__UpperCamelCase ),
"""repo_sha""": str(repo.head.object.hexsha ),
"""repo_branch""": str(repo.active_branch ),
"""hostname""": str(socket.gethostname() ),
}
return repo_infos
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase ):
'''simple docstring'''
return list(map(__UpperCamelCase , __UpperCamelCase ) )
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase ):
'''simple docstring'''
with open(__UpperCamelCase , """wb""" ) as f:
return pickle.dump(__UpperCamelCase , __UpperCamelCase )
def lowerCAmelCase ( __UpperCamelCase ):
'''simple docstring'''
def remove_articles(__UpperCamelCase ):
return re.sub(r"""\b(a|an|the)\b""" , """ """ , __UpperCamelCase )
def white_space_fix(__UpperCamelCase ):
return " ".join(text.split() )
def remove_punc(__UpperCamelCase ):
UpperCAmelCase__ : List[Any] = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(__UpperCamelCase ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(__UpperCamelCase ) ) ) )
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase ):
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = normalize_answer(__UpperCamelCase ).split()
UpperCAmelCase__ : Dict = normalize_answer(__UpperCamelCase ).split()
UpperCAmelCase__ : int = Counter(__UpperCamelCase ) & Counter(__UpperCamelCase )
UpperCAmelCase__ : List[str] = sum(common.values() )
if num_same == 0:
return 0
UpperCAmelCase__ : str = 1.0 * num_same / len(__UpperCamelCase )
UpperCAmelCase__ : Optional[int] = 1.0 * num_same / len(__UpperCamelCase )
UpperCAmelCase__ : Tuple = (2 * precision * recall) / (precision + recall)
return fa
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase ):
'''simple docstring'''
return normalize_answer(__UpperCamelCase ) == normalize_answer(__UpperCamelCase )
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase ):
'''simple docstring'''
assert len(__UpperCamelCase ) == len(__UpperCamelCase )
UpperCAmelCase__ : Union[str, Any] = 0
for hypo, pred in zip(__UpperCamelCase , __UpperCamelCase ):
em += exact_match_score(__UpperCamelCase , __UpperCamelCase )
if len(__UpperCamelCase ) > 0:
em /= len(__UpperCamelCase )
return {"em": em}
def lowerCAmelCase ( __UpperCamelCase ):
'''simple docstring'''
return model_prefix.startswith("""rag""" )
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = {p: p for p in extra_params}
# T5 models don't have `dropout` param, they have `dropout_rate` instead
UpperCAmelCase__ : str = """dropout_rate"""
for p in extra_params:
if getattr(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
if not hasattr(__UpperCamelCase , __UpperCamelCase ) and not hasattr(__UpperCamelCase , equivalent_param[p] ):
logger.info("""config doesn't have a `{}` attribute""".format(__UpperCamelCase ) )
delattr(__UpperCamelCase , __UpperCamelCase )
continue
UpperCAmelCase__ : Tuple = p if hasattr(__UpperCamelCase , __UpperCamelCase ) else equivalent_param[p]
setattr(__UpperCamelCase , __UpperCamelCase , getattr(__UpperCamelCase , __UpperCamelCase ) )
delattr(__UpperCamelCase , __UpperCamelCase )
return hparams, config
| 65 | 0 |
'''simple docstring'''
import os
import unittest
from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class UpperCAmelCase ( __lowerCamelCase , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = LxmertTokenizer
SCREAMING_SNAKE_CASE_ = LxmertTokenizerFast
SCREAMING_SNAKE_CASE_ = True
SCREAMING_SNAKE_CASE_ = True
def UpperCamelCase( self ) -> List[str]:
'''simple docstring'''
super().setUp()
lowerCamelCase_ = [
"""[UNK]""",
"""[CLS]""",
"""[SEP]""",
"""want""",
"""##want""",
"""##ed""",
"""wa""",
"""un""",
"""runn""",
"""##ing""",
""",""",
"""low""",
"""lowest""",
]
lowerCamelCase_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in vocab_tokens] ) )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
'''simple docstring'''
lowerCamelCase_ = """UNwant\u00E9d,running"""
lowerCamelCase_ = """unwanted, running"""
return input_text, output_text
def UpperCamelCase( self ) -> Union[str, Any]:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer_class(self.vocab_file )
lowerCamelCase_ = tokenizer.tokenize('UNwant\u00E9d,running' )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , ['un', '##want', '##ed', ',', 'runn', '##ing'] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ ) , [7, 4, 5, 10, 8, 9] )
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
if not self.test_rust_tokenizer:
return
lowerCamelCase_ = self.get_tokenizer()
lowerCamelCase_ = self.get_rust_tokenizer()
lowerCamelCase_ = """I was born in 92000, and this is falsé."""
lowerCamelCase_ = tokenizer.tokenize(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer.encode(SCREAMING_SNAKE_CASE_ , add_special_tokens=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = rust_tokenizer.encode(SCREAMING_SNAKE_CASE_ , add_special_tokens=SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = self.get_rust_tokenizer()
lowerCamelCase_ = tokenizer.encode(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = rust_tokenizer.encode(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
| 42 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetPipeline,
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 __lowercase ( __lowerCamelCase , unittest.TestCase ):
snake_case_ = KandinskyVaaControlnetPipeline
snake_case_ = ["""image_embeds""", """negative_image_embeds""", """hint"""]
snake_case_ = ["""image_embeds""", """negative_image_embeds""", """hint"""]
snake_case_ = [
"""generator""",
"""height""",
"""width""",
"""latents""",
"""guidance_scale""",
"""num_inference_steps""",
"""return_dict""",
"""guidance_scale""",
"""num_images_per_prompt""",
"""output_type""",
"""return_dict""",
]
snake_case_ = False
@property
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
return 32
@property
def __lowercase ( self : int ):
'''simple docstring'''
return 32
@property
def __lowercase ( self : Dict ):
'''simple docstring'''
return self.time_input_dim
@property
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
return self.time_input_dim * 4
@property
def __lowercase ( self : Any ):
'''simple docstring'''
return 100
@property
def __lowercase ( self : Any ):
'''simple docstring'''
torch.manual_seed(0 )
UpperCAmelCase__ : Tuple = {
"""in_channels""": 8,
# Out channels is double in channels because predicts mean and variance
"""out_channels""": 8,
"""addition_embed_type""": """image_hint""",
"""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,
}
UpperCAmelCase__ : int = UNetaDConditionModel(**A )
return model
@property
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
return {
"block_out_channels": [32, 32, 64, 64],
"down_block_types": [
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"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", "UpDecoderBlock2D", "UpDecoderBlock2D"],
"vq_embed_dim": 4,
}
@property
def __lowercase ( self : Dict ):
'''simple docstring'''
torch.manual_seed(0 )
UpperCAmelCase__ : str = VQModel(**self.dummy_movq_kwargs )
return model
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ : str = self.dummy_unet
UpperCAmelCase__ : List[Any] = self.dummy_movq
UpperCAmelCase__ : List[Any] = DDIMScheduler(
num_train_timesteps=1_000 ,beta_schedule="""linear""" ,beta_start=0.0_0_0_8_5 ,beta_end=0.0_1_2 ,clip_sample=A ,set_alpha_to_one=A ,steps_offset=1 ,prediction_type="""epsilon""" ,thresholding=A ,)
UpperCAmelCase__ : Optional[Any] = {
"""unet""": unet,
"""scheduler""": scheduler,
"""movq""": movq,
}
return components
def __lowercase ( self : str ,A : Optional[Any] ,A : Any=0 ):
'''simple docstring'''
UpperCAmelCase__ : str = floats_tensor((1, self.text_embedder_hidden_size) ,rng=random.Random(A ) ).to(A )
UpperCAmelCase__ : Union[str, Any] = floats_tensor((1, self.text_embedder_hidden_size) ,rng=random.Random(seed + 1 ) ).to(
A )
# create hint
UpperCAmelCase__ : int = floats_tensor((1, 3, 64, 64) ,rng=random.Random(A ) ).to(A )
if str(A ).startswith("""mps""" ):
UpperCAmelCase__ : Optional[int] = torch.manual_seed(A )
else:
UpperCAmelCase__ : Dict = torch.Generator(device=A ).manual_seed(A )
UpperCAmelCase__ : Dict = {
"""image_embeds""": image_embeds,
"""negative_image_embeds""": negative_image_embeds,
"""hint""": hint,
"""generator""": generator,
"""height""": 64,
"""width""": 64,
"""guidance_scale""": 4.0,
"""num_inference_steps""": 2,
"""output_type""": """np""",
}
return inputs
def __lowercase ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase__ : Dict = """cpu"""
UpperCAmelCase__ : List[Any] = self.get_dummy_components()
UpperCAmelCase__ : Union[str, Any] = self.pipeline_class(**A )
UpperCAmelCase__ : Optional[int] = pipe.to(A )
pipe.set_progress_bar_config(disable=A )
UpperCAmelCase__ : Optional[int] = pipe(**self.get_dummy_inputs(A ) )
UpperCAmelCase__ : Tuple = output.images
UpperCAmelCase__ : Dict = pipe(
**self.get_dummy_inputs(A ) ,return_dict=A ,)[0]
UpperCAmelCase__ : Tuple = image[0, -3:, -3:, -1]
UpperCAmelCase__ : Dict = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
UpperCAmelCase__ : Optional[int] = np.array(
[0.6_9_5_9_8_2_6, 0.8_6_8_2_7_9, 0.7_5_5_8_0_9_2, 0.6_8_7_6_9_4_6_7, 0.8_5_8_0_5_8_0_4, 0.6_5_9_7_7_4_9_6, 0.4_4_8_8_5_3_0_2, 0.5_9_5_9_1_1_1, 0.4_2_5_1_5_9_5] )
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 __lowercase ( unittest.TestCase ):
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __lowercase ( self : int ):
'''simple docstring'''
UpperCAmelCase__ : int = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy""" )
UpperCAmelCase__ : int = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/kandinskyv22/hint_image_cat.png""" )
UpperCAmelCase__ : int = torch.from_numpy(np.array(A ) ).float() / 2_5_5.0
UpperCAmelCase__ : Union[str, Any] = hint.permute(2 ,0 ,1 ).unsqueeze(0 )
UpperCAmelCase__ : List[str] = KandinskyVaaPriorPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-2-prior""" ,torch_dtype=torch.floataa )
pipe_prior.to(A )
UpperCAmelCase__ : List[Any] = KandinskyVaaControlnetPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-2-controlnet-depth""" ,torch_dtype=torch.floataa )
UpperCAmelCase__ : int = pipeline.to(A )
pipeline.set_progress_bar_config(disable=A )
UpperCAmelCase__ : Optional[Any] = """A robot, 4k photo"""
UpperCAmelCase__ : List[Any] = torch.Generator(device="""cuda""" ).manual_seed(0 )
UpperCAmelCase__ , UpperCAmelCase__ : Tuple = pipe_prior(
A ,generator=A ,num_inference_steps=5 ,negative_prompt="""""" ,).to_tuple()
UpperCAmelCase__ : List[str] = torch.Generator(device="""cuda""" ).manual_seed(0 )
UpperCAmelCase__ : int = pipeline(
image_embeds=A ,negative_image_embeds=A ,hint=A ,generator=A ,num_inference_steps=100 ,output_type="""np""" ,)
UpperCAmelCase__ : Any = output.images[0]
assert image.shape == (512, 512, 3)
assert_mean_pixel_difference(A ,A )
| 65 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {
'''google/fnet-base''': '''https://huggingface.co/google/fnet-base/resolve/main/config.json''',
'''google/fnet-large''': '''https://huggingface.co/google/fnet-large/resolve/main/config.json'''
# See all FNet models at https://huggingface.co/models?filter=fnet
}
class __lowerCAmelCase ( __lowerCamelCase ):
"""simple docstring"""
A__ : Optional[Any] = "fnet"
def __init__( self : Optional[Any] , _snake_case : Tuple=3_20_00 , _snake_case : str=7_68 , _snake_case : List[str]=12 , _snake_case : List[Any]=30_72 , _snake_case : Optional[Any]="gelu_new" , _snake_case : Tuple=0.1 , _snake_case : List[str]=5_12 , _snake_case : Any=4 , _snake_case : Optional[int]=0.02 , _snake_case : Optional[int]=1E-12 , _snake_case : List[Any]=False , _snake_case : str=5_12 , _snake_case : List[Any]=3 , _snake_case : int=1 , _snake_case : List[str]=2 , **_snake_case : Optional[Any] , ):
"""simple docstring"""
super().__init__(pad_token_id=_snake_case , bos_token_id=_snake_case , eos_token_id=_snake_case , **_snake_case )
A__ = vocab_size
A__ = max_position_embeddings
A__ = hidden_size
A__ = num_hidden_layers
A__ = intermediate_size
A__ = hidden_act
A__ = hidden_dropout_prob
A__ = initializer_range
A__ = type_vocab_size
A__ = layer_norm_eps
A__ = use_tpu_fourier_optimizations
A__ = tpu_short_seq_length
| 9 |
"""simple docstring"""
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHECKING:
from ... import PreTrainedTokenizerBase, TensorType
__UpperCAmelCase = logging.get_logger(__name__)
class __lowercase ( __lowerCamelCase ):
snake_case_ = """vision-encoder-decoder"""
snake_case_ = True
def __init__( self : List[Any] ,**A : Union[str, Any] ):
'''simple docstring'''
super().__init__(**A )
if "encoder" not in kwargs or "decoder" not in kwargs:
raise ValueError(
f"A configuraton of type {self.model_type} cannot be instantiated because "
f"not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}" )
UpperCAmelCase__ : int = kwargs.pop("""encoder""" )
UpperCAmelCase__ : int = encoder_config.pop("""model_type""" )
UpperCAmelCase__ : str = kwargs.pop("""decoder""" )
UpperCAmelCase__ : Dict = decoder_config.pop("""model_type""" )
UpperCAmelCase__ : List[Any] = AutoConfig.for_model(A ,**A )
UpperCAmelCase__ : Any = AutoConfig.for_model(A ,**A )
UpperCAmelCase__ : Union[str, Any] = True
@classmethod
def __lowercase ( cls : List[Any] ,A : PretrainedConfig ,A : PretrainedConfig ,**A : Tuple ):
'''simple docstring'''
logger.info("""Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config""" )
UpperCAmelCase__ : Union[str, Any] = True
UpperCAmelCase__ : List[Any] = True
return cls(encoder=encoder_config.to_dict() ,decoder=decoder_config.to_dict() ,**A )
def __lowercase ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = copy.deepcopy(self.__dict__ )
UpperCAmelCase__ : Dict = self.encoder.to_dict()
UpperCAmelCase__ : Any = self.decoder.to_dict()
UpperCAmelCase__ : Dict = self.__class__.model_type
return output
class __lowercase ( __lowerCamelCase ):
snake_case_ = version.parse("""1.11""" )
@property
def __lowercase ( self : Optional[int] ):
'''simple docstring'''
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def __lowercase ( self : List[Any] ):
'''simple docstring'''
return 1e-4
@property
def __lowercase ( self : List[Any] ):
'''simple docstring'''
return OrderedDict({"""last_hidden_state""": {0: """batch""", 1: """encoder_sequence"""}} )
class __lowercase ( __lowerCamelCase ):
@property
def __lowercase ( self : Any ):
'''simple docstring'''
UpperCAmelCase__ : int = OrderedDict()
UpperCAmelCase__ : Dict = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
UpperCAmelCase__ : Optional[Any] = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
UpperCAmelCase__ : List[str] = {0: """batch""", 1: """encoder_sequence"""}
return common_inputs
def __lowercase ( self : Dict ,A : "PreTrainedTokenizerBase" ,A : int = -1 ,A : int = -1 ,A : bool = False ,A : Optional["TensorType"] = None ,):
'''simple docstring'''
import torch
UpperCAmelCase__ : int = OrderedDict()
UpperCAmelCase__ : List[Any] = super().generate_dummy_inputs(
A ,batch_size=A ,seq_length=A ,is_pair=A ,framework=A )
UpperCAmelCase__ , UpperCAmelCase__ : int = dummy_input["""input_ids"""].shape
UpperCAmelCase__ : int = (batch, encoder_sequence, self._config.encoder_hidden_size)
UpperCAmelCase__ : Tuple = dummy_input.pop("""input_ids""" )
UpperCAmelCase__ : Optional[int] = dummy_input.pop("""attention_mask""" )
UpperCAmelCase__ : Dict = torch.zeros(A )
return common_inputs
class __lowercase ( __lowerCamelCase ):
@property
def __lowercase ( self : str ):
'''simple docstring'''
pass
def __lowercase ( self : Any ,A : PretrainedConfig ):
'''simple docstring'''
return VisionEncoderDecoderEncoderOnnxConfig(A )
def __lowercase ( self : Dict ,A : PretrainedConfig ,A : PretrainedConfig ,A : str = "default" ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = encoder_config.hidden_size
return VisionEncoderDecoderDecoderOnnxConfig(A ,A )
| 65 | 0 |
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
a_ :Union[str, Any] = logging.get_logger(__name__)
a_ :Union[str, Any] = list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
a_ :Dict = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class snake_case__ :
"""simple docstring"""
_SCREAMING_SNAKE_CASE = field(
default=__lowerCamelCase , metadata={"""help""": """Model type selected in the list: """ + """, """.join(__lowerCamelCase )} )
_SCREAMING_SNAKE_CASE = field(
default=__lowerCamelCase , metadata={"""help""": """The input data dir. Should contain the .json files for the SQuAD task."""} )
_SCREAMING_SNAKE_CASE = field(
default=128 , metadata={
"""help""": (
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
)
} , )
_SCREAMING_SNAKE_CASE = field(
default=128 , metadata={"""help""": """When splitting up a long document into chunks, how much stride to take between chunks."""} , )
_SCREAMING_SNAKE_CASE = field(
default=64 , metadata={
"""help""": (
"""The maximum number of tokens for the question. Questions longer than this will """
"""be truncated to this length."""
)
} , )
_SCREAMING_SNAKE_CASE = field(
default=30 , metadata={
"""help""": (
"""The maximum length of an answer that can be generated. This is needed because the start """
"""and end predictions are not conditioned on one another."""
)
} , )
_SCREAMING_SNAKE_CASE = field(
default=__lowerCamelCase , metadata={"""help""": """Overwrite the cached training and evaluation sets"""} )
_SCREAMING_SNAKE_CASE = field(
default=__lowerCamelCase , metadata={"""help""": """If true, the SQuAD examples contain some that do not have an answer."""} )
_SCREAMING_SNAKE_CASE = field(
default=0.0 , metadata={"""help""": """If null_score - best_non_null is greater than the threshold predict null."""} )
_SCREAMING_SNAKE_CASE = field(
default=20 , metadata={"""help""": """If null_score - best_non_null is greater than the threshold predict null."""} )
_SCREAMING_SNAKE_CASE = field(
default=0 , metadata={
"""help""": (
"""language id of input for language-specific xlm models (see"""
""" tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)"""
)
} , )
_SCREAMING_SNAKE_CASE = field(default=1 , metadata={"""help""": """multiple threads for converting example to features"""} )
class snake_case__ ( __lowerCamelCase ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = """train"""
_SCREAMING_SNAKE_CASE = """dev"""
class snake_case__ ( __lowerCamelCase ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = 42
_SCREAMING_SNAKE_CASE = 42
_SCREAMING_SNAKE_CASE = 42
_SCREAMING_SNAKE_CASE = 42
def __init__( self : List[str], _snake_case : SquadDataTrainingArguments, _snake_case : PreTrainedTokenizer, _snake_case : Optional[int] = None, _snake_case : Union[str, Split] = Split.train, _snake_case : Optional[bool] = False, _snake_case : Optional[str] = None, _snake_case : Optional[str] = "pt", ) ->Dict:
snake_case__ : Dict = args
snake_case__ : Optional[Any] = is_language_sensitive
snake_case__ : str = SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
if isinstance(_snake_case, _snake_case ):
try:
snake_case__ : int = Split[mode]
except KeyError:
raise KeyError('mode is not a valid split name' )
snake_case__ : List[Any] = mode
# Load data features from cache or dataset file
snake_case__ : str = """v2""" if args.version_2_with_negative else """v1"""
snake_case__ : List[str] = os.path.join(
cache_dir if cache_dir is not None else args.data_dir, F'''cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}''', )
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
snake_case__ : int = cached_features_file + """.lock"""
with FileLock(_snake_case ):
if os.path.exists(_snake_case ) and not args.overwrite_cache:
snake_case__ : Optional[Any] = time.time()
snake_case__ : List[Any] = torch.load(_snake_case )
# Legacy cache files have only features, while new cache files
# will have dataset and examples also.
snake_case__ : Any = self.old_features["""features"""]
snake_case__ : Tuple = self.old_features.get('dataset', _snake_case )
snake_case__ : Tuple = self.old_features.get('examples', _snake_case )
logger.info(
F'''Loading features from cached file {cached_features_file} [took %.3f s]''', time.time() - start )
if self.dataset is None or self.examples is None:
logger.warning(
F'''Deleting cached file {cached_features_file} will allow dataset and examples to be cached in'''
' future run' )
else:
if mode == Split.dev:
snake_case__ : Tuple = self.processor.get_dev_examples(args.data_dir )
else:
snake_case__ : Dict = self.processor.get_train_examples(args.data_dir )
snake_case__ : List[str] = squad_convert_examples_to_features(
examples=self.examples, tokenizer=_snake_case, max_seq_length=args.max_seq_length, doc_stride=args.doc_stride, max_query_length=args.max_query_length, is_training=mode == Split.train, threads=args.threads, return_dataset=_snake_case, )
snake_case__ : str = time.time()
torch.save(
{'features': self.features, 'dataset': self.dataset, 'examples': self.examples}, _snake_case, )
# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
logger.info(
F'''Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]''' )
def __len__( self : List[Any] ) ->Optional[Any]:
return len(self.features )
def __getitem__( self : str, _snake_case : List[Any] ) ->int:
snake_case__ : Optional[Any] = self.features[i]
snake_case__ : Any = torch.tensor(feature.input_ids, dtype=torch.long )
snake_case__ : int = torch.tensor(feature.attention_mask, dtype=torch.long )
snake_case__ : Tuple = torch.tensor(feature.token_type_ids, dtype=torch.long )
snake_case__ : List[Any] = torch.tensor(feature.cls_index, dtype=torch.long )
snake_case__ : Optional[int] = torch.tensor(feature.p_mask, dtype=torch.float )
snake_case__ : Optional[int] = torch.tensor(feature.is_impossible, dtype=torch.float )
snake_case__ : List[str] = {
"""input_ids""": input_ids,
"""attention_mask""": attention_mask,
"""token_type_ids""": token_type_ids,
}
if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
del inputs["token_type_ids"]
if self.args.model_type in ["xlnet", "xlm"]:
inputs.update({'cls_index': cls_index, 'p_mask': p_mask} )
if self.args.version_2_with_negative:
inputs.update({'is_impossible': is_impossible} )
if self.is_language_sensitive:
inputs.update({'langs': (torch.ones(input_ids.shape, dtype=torch.intaa ) * self.args.lang_id)} )
if self.mode == Split.train:
snake_case__ : Union[str, Any] = torch.tensor(feature.start_position, dtype=torch.long )
snake_case__ : Dict = torch.tensor(feature.end_position, dtype=torch.long )
inputs.update({'start_positions': start_positions, 'end_positions': end_positions} )
return inputs
| 478 |
"""simple docstring"""
import requests
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = {"""Content-Type""": """application/json"""}
UpperCAmelCase__ : Optional[Any] = requests.post(__UpperCamelCase , json={"""text""": message_body} , headers=__UpperCamelCase )
if response.status_code != 200:
UpperCAmelCase__ : Any = (
"""Request to slack returned an error """
F"{response.status_code}, the response is:\n{response.text}"
)
raise ValueError(__UpperCamelCase )
if __name__ == "__main__":
# Set the slack url to the one provided by Slack when you create the webhook at
# https://my.slack.com/services/new/incoming-webhook/
send_slack_message('<YOUR MESSAGE BODY>', '<SLACK CHANNEL URL>')
| 65 | 0 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import DeformableDetrImageProcessor
class __magic_name__ (unittest.TestCase ):
'''simple docstring'''
def __init__( self:List[Any] , _a:Optional[Any] , _a:List[Any]=7 , _a:List[Any]=3 , _a:Optional[Any]=30 , _a:List[str]=4_00 , _a:Any=True , _a:str=None , _a:str=True , _a:int=[0.5, 0.5, 0.5] , _a:List[Any]=[0.5, 0.5, 0.5] , _a:Any=True , _a:Dict=1 / 2_55 , _a:str=True , ):
snake_case__ = size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 13_33}
snake_case__ = parent
snake_case__ = batch_size
snake_case__ = num_channels
snake_case__ = min_resolution
snake_case__ = max_resolution
snake_case__ = do_resize
snake_case__ = size
snake_case__ = do_normalize
snake_case__ = image_mean
snake_case__ = image_std
snake_case__ = do_rescale
snake_case__ = rescale_factor
snake_case__ = do_pad
def SCREAMING_SNAKE_CASE__ ( self:Union[str, Any] ):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_pad": self.do_pad,
}
def SCREAMING_SNAKE_CASE__ ( self:str , _a:int , _a:Optional[int]=False ):
if not batched:
snake_case__ = image_inputs[0]
if isinstance(_a , Image.Image ):
snake_case__ = image.size
else:
snake_case__ = image.shape[1], image.shape[2]
if w < h:
snake_case__ = int(self.size['''shortest_edge'''] * h / w )
snake_case__ = self.size["""shortest_edge"""]
elif w > h:
snake_case__ = self.size["""shortest_edge"""]
snake_case__ = int(self.size['''shortest_edge'''] * w / h )
else:
snake_case__ = self.size["""shortest_edge"""]
snake_case__ = self.size["""shortest_edge"""]
else:
snake_case__ = []
for image in image_inputs:
snake_case__ = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
snake_case__ = max(_a , key=lambda _a : item[0] )[0]
snake_case__ = max(_a , key=lambda _a : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class __magic_name__ (__lowerCamelCase ,unittest.TestCase ):
'''simple docstring'''
__lowercase : Dict = DeformableDetrImageProcessor if is_vision_available() else None
def SCREAMING_SNAKE_CASE__ ( self:Any ):
snake_case__ = DeformableDetrImageProcessingTester(self )
@property
def SCREAMING_SNAKE_CASE__ ( self:str ):
return self.image_processor_tester.prepare_image_processor_dict()
def SCREAMING_SNAKE_CASE__ ( self:Optional[Any] ):
snake_case__ = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_a , '''image_mean''' ) )
self.assertTrue(hasattr(_a , '''image_std''' ) )
self.assertTrue(hasattr(_a , '''do_normalize''' ) )
self.assertTrue(hasattr(_a , '''do_resize''' ) )
self.assertTrue(hasattr(_a , '''do_rescale''' ) )
self.assertTrue(hasattr(_a , '''do_pad''' ) )
self.assertTrue(hasattr(_a , '''size''' ) )
def SCREAMING_SNAKE_CASE__ ( self:Optional[int] ):
snake_case__ = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''shortest_edge''': 18, '''longest_edge''': 13_33} )
self.assertEqual(image_processor.do_pad , _a )
snake_case__ = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=_a )
self.assertEqual(image_processor.size , {'''shortest_edge''': 42, '''longest_edge''': 84} )
self.assertEqual(image_processor.do_pad , _a )
def SCREAMING_SNAKE_CASE__ ( self:List[Any] ):
pass
def SCREAMING_SNAKE_CASE__ ( self:Dict ):
snake_case__ = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
snake_case__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=_a )
for image in image_inputs:
self.assertIsInstance(_a , Image.Image )
# Test not batched input
snake_case__ = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
snake_case__ = self.image_processor_tester.get_expected_values(_a )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
snake_case__ = self.image_processor_tester.get_expected_values(_a , batched=_a )
snake_case__ = image_processing(_a , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def SCREAMING_SNAKE_CASE__ ( self:Dict ):
snake_case__ = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
snake_case__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=_a , numpify=_a )
for image in image_inputs:
self.assertIsInstance(_a , np.ndarray )
# Test not batched input
snake_case__ = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
snake_case__ = self.image_processor_tester.get_expected_values(_a )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
snake_case__ = image_processing(_a , return_tensors='''pt''' ).pixel_values
snake_case__ = self.image_processor_tester.get_expected_values(_a , batched=_a )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def SCREAMING_SNAKE_CASE__ ( self:int ):
snake_case__ = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
snake_case__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=_a , torchify=_a )
for image in image_inputs:
self.assertIsInstance(_a , torch.Tensor )
# Test not batched input
snake_case__ = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
snake_case__ = self.image_processor_tester.get_expected_values(_a )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
snake_case__ = image_processing(_a , return_tensors='''pt''' ).pixel_values
snake_case__ = self.image_processor_tester.get_expected_values(_a , batched=_a )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
@slow
def SCREAMING_SNAKE_CASE__ ( self:Dict ):
snake_case__ = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt''' , '''r''' ) as f:
snake_case__ = json.loads(f.read() )
snake_case__ = {"""image_id""": 3_97_69, """annotations""": target}
# encode them
snake_case__ = DeformableDetrImageProcessor()
snake_case__ = image_processing(images=_a , annotations=_a , return_tensors='''pt''' )
# verify pixel values
snake_case__ = torch.Size([1, 3, 8_00, 10_66] )
self.assertEqual(encoding['''pixel_values'''].shape , _a )
snake_case__ = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , _a , atol=1e-4 ) )
# verify area
snake_case__ = torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , _a ) )
# verify boxes
snake_case__ = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , _a )
snake_case__ = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , _a , atol=1e-3 ) )
# verify image_id
snake_case__ = torch.tensor([3_97_69] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , _a ) )
# verify is_crowd
snake_case__ = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , _a ) )
# verify class_labels
snake_case__ = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , _a ) )
# verify orig_size
snake_case__ = torch.tensor([4_80, 6_40] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , _a ) )
# verify size
snake_case__ = torch.tensor([8_00, 10_66] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , _a ) )
@slow
def SCREAMING_SNAKE_CASE__ ( self:str ):
snake_case__ = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt''' , '''r''' ) as f:
snake_case__ = json.loads(f.read() )
snake_case__ = {"""file_name""": """000000039769.png""", """image_id""": 3_97_69, """segments_info""": target}
snake_case__ = pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic''' )
# encode them
snake_case__ = DeformableDetrImageProcessor(format='''coco_panoptic''' )
snake_case__ = image_processing(images=_a , annotations=_a , masks_path=_a , return_tensors='''pt''' )
# verify pixel values
snake_case__ = torch.Size([1, 3, 8_00, 10_66] )
self.assertEqual(encoding['''pixel_values'''].shape , _a )
snake_case__ = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , _a , atol=1e-4 ) )
# verify area
snake_case__ = torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , _a ) )
# verify boxes
snake_case__ = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , _a )
snake_case__ = torch.tensor([0.2625, 0.5437, 0.4688, 0.8625] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , _a , atol=1e-3 ) )
# verify image_id
snake_case__ = torch.tensor([3_97_69] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , _a ) )
# verify is_crowd
snake_case__ = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , _a ) )
# verify class_labels
snake_case__ = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , _a ) )
# verify masks
snake_case__ = 82_28_73
self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item() , _a )
# verify orig_size
snake_case__ = torch.tensor([4_80, 6_40] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , _a ) )
# verify size
snake_case__ = torch.tensor([8_00, 10_66] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , _a ) )
| 33 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class __lowercase ( __lowerCamelCase , unittest.TestCase ):
snake_case_ = CTRLTokenizer
snake_case_ = False
snake_case_ = False
def __lowercase ( self : List[str] ):
'''simple docstring'''
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
UpperCAmelCase__ : List[Any] = ["""adapt""", """re@@""", """a@@""", """apt""", """c@@""", """t""", """<unk>"""]
UpperCAmelCase__ : Optional[int] = dict(zip(A ,range(len(A ) ) ) )
UpperCAmelCase__ : List[Any] = ["""#version: 0.2""", """a p""", """ap t</w>""", """r e""", """a d""", """ad apt</w>""", """"""]
UpperCAmelCase__ : int = {"""unk_token""": """<unk>"""}
UpperCAmelCase__ : Optional[Any] = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["""vocab_file"""] )
UpperCAmelCase__ : str = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["""merges_file"""] )
with open(self.vocab_file ,"""w""" ,encoding="""utf-8""" ) as fp:
fp.write(json.dumps(A ) + """\n""" )
with open(self.merges_file ,"""w""" ,encoding="""utf-8""" ) as fp:
fp.write("""\n""".join(A ) )
def __lowercase ( self : int ,**A : Dict ):
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return CTRLTokenizer.from_pretrained(self.tmpdirname ,**A )
def __lowercase ( self : List[Any] ,A : Any ):
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = """adapt react readapt apt"""
UpperCAmelCase__ : Any = """adapt react readapt apt"""
return input_text, output_text
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = CTRLTokenizer(self.vocab_file ,self.merges_file ,**self.special_tokens_map )
UpperCAmelCase__ : Tuple = """adapt react readapt apt"""
UpperCAmelCase__ : Optional[int] = """adapt re@@ a@@ c@@ t re@@ adapt apt""".split()
UpperCAmelCase__ : Dict = tokenizer.tokenize(A )
self.assertListEqual(A ,A )
UpperCAmelCase__ : Any = tokens + [tokenizer.unk_token]
UpperCAmelCase__ : Dict = [0, 1, 2, 4, 5, 1, 0, 3, 6]
self.assertListEqual(tokenizer.convert_tokens_to_ids(A ) ,A )
| 65 | 0 |
import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
monkeypatch.setattr('datasets.utils.deprecation_utils._emitted_deprecation_warnings' ,set() )
@pytest.fixture
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
class __lowercase :
"""simple docstring"""
def __init__( self , A_ )-> List[Any]:
_SCREAMING_SNAKE_CASE = metric_id
class __lowercase :
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = [MetricMock(__lowerCamelCase ) for metric_id in ["accuracy", "mse", "precision", "codeparrot/apps_metric"]]
def __magic_name__ ( self )-> Union[str, Any]:
return self._metrics
monkeypatch.setattr('datasets.inspect.huggingface_hub' ,HfhMock() )
@pytest.mark.parametrize(
'func, args' ,[(load_metric, ('metrics/mse',)), (list_metrics, ()), (inspect_metric, ('metrics/mse', 'tmp_path'))] )
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ):
"""simple docstring"""
if "tmp_path" in args:
_SCREAMING_SNAKE_CASE = tuple(arg if arg != 'tmp_path' else tmp_path for arg in args )
with pytest.warns(__UpperCamelCase ,match='https://huggingface.co/docs/evaluate' ):
func(*__UpperCamelCase )
| 605 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__UpperCAmelCase = {
'configuration_bridgetower': [
'BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'BridgeTowerConfig',
'BridgeTowerTextConfig',
'BridgeTowerVisionConfig',
],
'processing_bridgetower': ['BridgeTowerProcessor'],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = ['BridgeTowerImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
'BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST',
'BridgeTowerForContrastiveLearning',
'BridgeTowerForImageAndTextRetrieval',
'BridgeTowerForMaskedLM',
'BridgeTowerModel',
'BridgeTowerPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_bridgetower import (
BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
BridgeTowerConfig,
BridgeTowerTextConfig,
BridgeTowerVisionConfig,
)
from .processing_bridgetower import BridgeTowerProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_bridgetower import BridgeTowerImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_bridgetower import (
BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
BridgeTowerForContrastiveLearning,
BridgeTowerForImageAndTextRetrieval,
BridgeTowerForMaskedLM,
BridgeTowerModel,
BridgeTowerPreTrainedModel,
)
else:
import sys
__UpperCAmelCase = _LazyModule(__name__, globals()['__file__'], _import_structure)
| 65 | 0 |
from __future__ import annotations
def _A ( SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ):
if len(__UpperCamelCase ) <= 1 or n <= 1:
return
insert_next(__UpperCamelCase ,n - 1 )
rec_insertion_sort(__UpperCamelCase ,n - 1 )
def _A ( SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ):
if index >= len(__UpperCamelCase ) or collection[index - 1] <= collection[index]:
return
# Swaps adjacent elements since they are not in ascending order
UpperCAmelCase__: Any = (
collection[index],
collection[index - 1],
)
insert_next(__UpperCamelCase ,index + 1 )
if __name__ == "__main__":
_lowerCAmelCase : Union[str, Any] =input("""Enter integers separated by spaces: """)
_lowerCAmelCase : List[str] =[int(num) for num in numbers.split()]
rec_insertion_sort(number_list, len(number_list))
print(number_list) | 113 |
"""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 __lowercase ( __lowerCamelCase ):
snake_case_ = ["""input_features""", """is_longer"""]
def __init__( self : str ,A : Union[str, Any]=64 ,A : Tuple=48_000 ,A : Dict=480 ,A : List[str]=10 ,A : str=1_024 ,A : Any=0.0 ,A : Optional[int]=False ,A : float = 0 ,A : float = 14_000 ,A : int = None ,A : str = "fusion" ,A : str = "repeatpad" ,**A : List[Any] ,):
'''simple docstring'''
super().__init__(
feature_size=A ,sampling_rate=A ,padding_value=A ,return_attention_mask=A ,**A ,)
UpperCAmelCase__ : List[Any] = top_db
UpperCAmelCase__ : Union[str, Any] = truncation
UpperCAmelCase__ : Optional[int] = padding
UpperCAmelCase__ : List[Any] = fft_window_size
UpperCAmelCase__ : Optional[Any] = (fft_window_size >> 1) + 1
UpperCAmelCase__ : Any = hop_length
UpperCAmelCase__ : List[str] = max_length_s
UpperCAmelCase__ : List[Any] = max_length_s * sampling_rate
UpperCAmelCase__ : List[Any] = sampling_rate
UpperCAmelCase__ : Optional[int] = frequency_min
UpperCAmelCase__ : Tuple = frequency_max
UpperCAmelCase__ : 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=A ,mel_scale="""htk""" ,)
UpperCAmelCase__ : 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 __lowercase ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = copy.deepcopy(self.__dict__ )
UpperCAmelCase__ : 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 __lowercase ( self : List[str] ,A : np.array ,A : Optional[np.array] = None ):
'''simple docstring'''
UpperCAmelCase__ : Dict = 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 : Optional[Any] ,A : Union[str, Any] ,A : int ,A : Optional[int] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[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
UpperCAmelCase__ : List[str] = [0]
if len(ranges[2] ) == 0:
# if the audio is too short, we just use the first chunk
UpperCAmelCase__ : int = [0]
# randomly choose index for each part
UpperCAmelCase__ : Tuple = np.random.choice(ranges[0] )
UpperCAmelCase__ : Tuple = np.random.choice(ranges[1] )
UpperCAmelCase__ : str = np.random.choice(ranges[2] )
UpperCAmelCase__ : List[str] = mel[idx_front : idx_front + chunk_frames, :]
UpperCAmelCase__ : List[str] = mel[idx_middle : idx_middle + chunk_frames, :]
UpperCAmelCase__ : Dict = mel[idx_back : idx_back + chunk_frames, :]
UpperCAmelCase__ : Optional[Any] = torch.tensor(mel[None, None, :] )
UpperCAmelCase__ : int = torch.nn.functional.interpolate(
A ,size=[chunk_frames, 64] ,mode="""bilinear""" ,align_corners=A )
UpperCAmelCase__ : Dict = mel_shrink[0][0].numpy()
UpperCAmelCase__ : Dict = np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back] ,axis=0 )
return mel_fusion
def __lowercase ( self : Any ,A : np.array ,A : Optional[int] ,A : Any ,A : Tuple ):
'''simple docstring'''
if waveform.shape[0] > max_length:
if truncation == "rand_trunc":
UpperCAmelCase__ : int = True
# random crop to max_length (for compatibility) -> this should be handled by self.pad
UpperCAmelCase__ : str = len(A ) - max_length
UpperCAmelCase__ : Optional[Any] = np.random.randint(0 ,overflow + 1 )
UpperCAmelCase__ : Optional[int] = waveform[idx : idx + max_length]
UpperCAmelCase__ : Any = self._np_extract_fbank_features(A ,self.mel_filters_slaney )[None, :]
elif truncation == "fusion":
UpperCAmelCase__ : Tuple = self._np_extract_fbank_features(A ,self.mel_filters )
UpperCAmelCase__ : Optional[int] = max_length // self.hop_length + 1 # the +1 related to how the spectrogram is computed
UpperCAmelCase__ : 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.
UpperCAmelCase__ : List[Any] = np.stack([mel, mel, mel, mel] ,axis=0 )
UpperCAmelCase__ : Any = False
else:
UpperCAmelCase__ : Union[str, Any] = self._random_mel_fusion(A ,A ,A )
UpperCAmelCase__ : List[str] = True
else:
raise NotImplementedError(f"data_truncating {truncation} not implemented" )
else:
UpperCAmelCase__ : Optional[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":
UpperCAmelCase__ : str = int(max_length / len(A ) )
UpperCAmelCase__ : int = np.stack(np.tile(A ,n_repeat + 1 ) )[:max_length]
if padding == "repeatpad":
UpperCAmelCase__ : List[Any] = int(max_length / len(A ) )
UpperCAmelCase__ : str = np.stack(np.tile(A ,A ) )
UpperCAmelCase__ : Optional[Any] = np.pad(A ,(0, max_length - waveform.shape[0]) ,mode="""constant""" ,constant_values=0 )
if truncation == "fusion":
UpperCAmelCase__ : int = self._np_extract_fbank_features(A ,self.mel_filters )
UpperCAmelCase__ : List[Any] = np.stack([input_mel, input_mel, input_mel, input_mel] ,axis=0 )
else:
UpperCAmelCase__ : Any = self._np_extract_fbank_features(A ,self.mel_filters_slaney )[None, :]
return input_mel, longer
def __call__( self : str ,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 : List[str] ,):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = truncation if truncation is not None else self.truncation
UpperCAmelCase__ : Dict = 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__ : Optional[int] = 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__ : List[str] = is_batched_numpy or (
isinstance(A ,(list, tuple) ) and (isinstance(raw_speech[0] ,(np.ndarray, tuple, list) ))
)
if is_batched:
UpperCAmelCase__ : str = [np.asarray(A ,dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(A ,np.ndarray ):
UpperCAmelCase__ : Any = np.asarray(A ,dtype=np.floataa )
elif isinstance(A ,np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
UpperCAmelCase__ : str = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
UpperCAmelCase__ : Optional[Any] = [np.asarray(A )]
# convert to mel spectrogram, truncate and pad if needed.
UpperCAmelCase__ : Tuple = [
self._get_input_mel(A ,max_length if max_length else self.nb_max_samples ,A ,A )
for waveform in raw_speech
]
UpperCAmelCase__ : Optional[int] = []
UpperCAmelCase__ : 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
UpperCAmelCase__ : List[str] = np.random.randint(0 ,len(A ) )
UpperCAmelCase__ : int = True
if isinstance(input_mel[0] ,A ):
UpperCAmelCase__ : Tuple = [np.asarray(A ,dtype=np.floataa ) for feature in input_mel]
# is_longer is a list of bool
UpperCAmelCase__ : List[str] = [[longer] for longer in is_longer]
UpperCAmelCase__ : List[Any] = {"""input_features""": input_mel, """is_longer""": is_longer}
UpperCAmelCase__ : str = BatchFeature(A )
if return_tensors is not None:
UpperCAmelCase__ : int = input_features.convert_to_tensors(A )
return input_features
| 65 | 0 |
from collections.abc import Callable
class _UpperCAmelCase :
'''simple docstring'''
def __init__( self : Tuple , lowercase_ : Callable | None = None) -> List[str]:
"""simple docstring"""
_UpperCamelCase = []
# Stores indexes of each item for supporting updates and deletion.
_UpperCamelCase = {}
# Stores current size of heap.
_UpperCamelCase = 0
# Stores function used to evaluate the score of an item on which basis ordering
# will be done.
_UpperCamelCase = key or (lambda lowercase_: x)
def __UpperCAmelCase ( self : Union[str, Any] , lowercase_ : int) -> Dict:
"""simple docstring"""
return int((i - 1) / 2) if i > 0 else None
def __UpperCAmelCase ( self : Tuple , lowercase_ : int) -> Union[str, Any]:
"""simple docstring"""
_UpperCamelCase = int(2 * i + 1)
return left if 0 < left < self.size else None
def __UpperCAmelCase ( self : Any , lowercase_ : int) -> int:
"""simple docstring"""
_UpperCamelCase = int(2 * i + 2)
return right if 0 < right < self.size else None
def __UpperCAmelCase ( self : List[Any] , lowercase_ : int , lowercase_ : int) -> Dict:
"""simple docstring"""
_UpperCamelCase = (
self.pos_map[self.arr[j][0]],
self.pos_map[self.arr[i][0]],
)
# Then swap the items in the list.
_UpperCamelCase = self.arr[j], self.arr[i]
def __UpperCAmelCase ( self : Optional[int] , lowercase_ : int , lowercase_ : int) -> Tuple:
"""simple docstring"""
return self.arr[i][1] < self.arr[j][1]
def __UpperCAmelCase ( self : Optional[int] , lowercase_ : int) -> Tuple:
"""simple docstring"""
_UpperCamelCase = self._left(lowercase_)
_UpperCamelCase = self._right(lowercase_)
_UpperCamelCase = i
if left is not None and not self._cmp(lowercase_ , lowercase_):
_UpperCamelCase = left
if right is not None and not self._cmp(lowercase_ , lowercase_):
_UpperCamelCase = right
return valid_parent
def __UpperCAmelCase ( self : int , lowercase_ : int) -> Dict:
"""simple docstring"""
_UpperCamelCase = self._parent(lowercase_)
while parent is not None and not self._cmp(lowercase_ , lowercase_):
self._swap(lowercase_ , lowercase_)
_UpperCamelCase = parent, self._parent(lowercase_)
def __UpperCAmelCase ( self : str , lowercase_ : int) -> Union[str, Any]:
"""simple docstring"""
_UpperCamelCase = self._get_valid_parent(lowercase_)
while valid_parent != index:
self._swap(lowercase_ , lowercase_)
_UpperCamelCase = valid_parent, self._get_valid_parent(lowercase_)
def __UpperCAmelCase ( self : Optional[Any] , lowercase_ : int , lowercase_ : int) -> List[Any]:
"""simple docstring"""
if item not in self.pos_map:
return
_UpperCamelCase = self.pos_map[item]
_UpperCamelCase = [item, self.key(lowercase_)]
# Make sure heap is right in both up and down direction.
# Ideally only one of them will make any change.
self._heapify_up(lowercase_)
self._heapify_down(lowercase_)
def __UpperCAmelCase ( self : List[Any] , lowercase_ : int) -> Any:
"""simple docstring"""
if item not in self.pos_map:
return
_UpperCamelCase = self.pos_map[item]
del self.pos_map[item]
_UpperCamelCase = self.arr[self.size - 1]
_UpperCamelCase = index
self.size -= 1
# Make sure heap is right in both up and down direction. Ideally only one
# of them will make any change- so no performance loss in calling both.
if self.size > index:
self._heapify_up(lowercase_)
self._heapify_down(lowercase_)
def __UpperCAmelCase ( self : str , lowercase_ : int , lowercase_ : int) -> str:
"""simple docstring"""
_UpperCamelCase = len(self.arr)
if arr_len == self.size:
self.arr.append([item, self.key(lowercase_)])
else:
_UpperCamelCase = [item, self.key(lowercase_)]
_UpperCamelCase = self.size
self.size += 1
self._heapify_up(self.size - 1)
def __UpperCAmelCase ( self : str) -> Tuple:
"""simple docstring"""
return self.arr[0] if self.size else None
def __UpperCAmelCase ( self : Dict) -> Tuple:
"""simple docstring"""
_UpperCamelCase = self.get_top()
if top_item_tuple:
self.delete_item(top_item_tuple[0])
return top_item_tuple
def lowerCAmelCase__ ( ) ->Optional[Any]:
'''simple docstring'''
if __name__ == "__main__":
import doctest
doctest.testmod()
| 547 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, 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 DonutImageProcessor
class __lowercase ( unittest.TestCase ):
def __init__( self : Union[str, Any] ,A : Union[str, Any] ,A : Dict=7 ,A : Optional[int]=3 ,A : List[str]=18 ,A : Union[str, Any]=30 ,A : Tuple=400 ,A : Dict=True ,A : List[str]=None ,A : str=True ,A : Optional[Any]=False ,A : Optional[Any]=True ,A : List[str]=True ,A : Optional[int]=[0.5, 0.5, 0.5] ,A : List[str]=[0.5, 0.5, 0.5] ,):
'''simple docstring'''
UpperCAmelCase__ : str = parent
UpperCAmelCase__ : List[str] = batch_size
UpperCAmelCase__ : List[str] = num_channels
UpperCAmelCase__ : Union[str, Any] = image_size
UpperCAmelCase__ : List[Any] = min_resolution
UpperCAmelCase__ : Optional[int] = max_resolution
UpperCAmelCase__ : str = do_resize
UpperCAmelCase__ : Tuple = size if size is not None else {"""height""": 18, """width""": 20}
UpperCAmelCase__ : List[str] = do_thumbnail
UpperCAmelCase__ : Optional[int] = do_align_axis
UpperCAmelCase__ : Union[str, Any] = do_pad
UpperCAmelCase__ : Tuple = do_normalize
UpperCAmelCase__ : Optional[Any] = image_mean
UpperCAmelCase__ : List[Any] = image_std
def __lowercase ( self : Optional[int] ):
'''simple docstring'''
return {
"do_resize": self.do_resize,
"size": self.size,
"do_thumbnail": self.do_thumbnail,
"do_align_long_axis": self.do_align_axis,
"do_pad": self.do_pad,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
}
@require_torch
@require_vision
class __lowercase ( __lowerCamelCase , unittest.TestCase ):
snake_case_ = DonutImageProcessor if is_vision_available() else None
def __lowercase ( self : str ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = DonutImageProcessingTester(self )
@property
def __lowercase ( self : Dict ):
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def __lowercase ( self : Any ):
'''simple docstring'''
UpperCAmelCase__ : Any = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(A ,"""do_resize""" ) )
self.assertTrue(hasattr(A ,"""size""" ) )
self.assertTrue(hasattr(A ,"""do_thumbnail""" ) )
self.assertTrue(hasattr(A ,"""do_align_long_axis""" ) )
self.assertTrue(hasattr(A ,"""do_pad""" ) )
self.assertTrue(hasattr(A ,"""do_normalize""" ) )
self.assertTrue(hasattr(A ,"""image_mean""" ) )
self.assertTrue(hasattr(A ,"""image_std""" ) )
def __lowercase ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size ,{"""height""": 18, """width""": 20} )
UpperCAmelCase__ : str = self.image_processing_class.from_dict(self.image_processor_dict ,size=42 )
self.assertEqual(image_processor.size ,{"""height""": 42, """width""": 42} )
# Previous config had dimensions in (width, height) order
UpperCAmelCase__ : str = self.image_processing_class.from_dict(self.image_processor_dict ,size=(42, 84) )
self.assertEqual(image_processor.size ,{"""height""": 84, """width""": 42} )
def __lowercase ( self : Dict ):
'''simple docstring'''
pass
@is_flaky()
def __lowercase ( self : int ):
'''simple docstring'''
# Initialize image_processing
UpperCAmelCase__ : Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
UpperCAmelCase__ : Dict = prepare_image_inputs(self.image_processor_tester ,equal_resolution=A )
for image in image_inputs:
self.assertIsInstance(A ,Image.Image )
# Test not batched input
UpperCAmelCase__ : int = image_processing(image_inputs[0] ,return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) ,)
# Test batched
UpperCAmelCase__ : Tuple = image_processing(A ,return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) ,)
@is_flaky()
def __lowercase ( self : List[str] ):
'''simple docstring'''
# Initialize image_processing
UpperCAmelCase__ : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
UpperCAmelCase__ : Dict = prepare_image_inputs(self.image_processor_tester ,equal_resolution=A ,numpify=A )
for image in image_inputs:
self.assertIsInstance(A ,np.ndarray )
# Test not batched input
UpperCAmelCase__ : List[str] = image_processing(image_inputs[0] ,return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) ,)
# Test batched
UpperCAmelCase__ : Optional[int] = image_processing(A ,return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) ,)
@is_flaky()
def __lowercase ( self : Any ):
'''simple docstring'''
# Initialize image_processing
UpperCAmelCase__ : int = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
UpperCAmelCase__ : int = prepare_image_inputs(self.image_processor_tester ,equal_resolution=A ,torchify=A )
for image in image_inputs:
self.assertIsInstance(A ,torch.Tensor )
# Test not batched input
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.size["""height"""],
self.image_processor_tester.size["""width"""],
) ,)
# Test batched
UpperCAmelCase__ : List[Any] = image_processing(A ,return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) ,)
| 65 | 0 |
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def _lowerCAmelCase ( lowerCAmelCase_ :Optional[Any] , lowerCAmelCase_ :Union[str, Any] )->Optional[Any]:
'''simple docstring'''
assert isinstance(__UpperCamelCase , __UpperCamelCase )
assert dataset.num_rows == 4
assert dataset.num_columns == 1
assert dataset.column_names == ["text"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("keep_in_memory" , [False, True] )
def _lowerCAmelCase ( lowerCAmelCase_ :int , lowerCAmelCase_ :Union[str, Any] , lowerCAmelCase_ :Any )->Optional[Any]:
'''simple docstring'''
snake_case_ = tmp_path / """cache"""
snake_case_ = {"""text""": """string"""}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
snake_case_ = TextDatasetReader(__UpperCamelCase , cache_dir=__UpperCamelCase , keep_in_memory=__UpperCamelCase ).read()
_check_text_dataset(__UpperCamelCase , __UpperCamelCase )
@pytest.mark.parametrize(
"features" , [
None,
{"text": "string"},
{"text": "int32"},
{"text": "float32"},
] , )
def _lowerCAmelCase ( lowerCAmelCase_ :Any , lowerCAmelCase_ :Optional[int] , lowerCAmelCase_ :List[str] )->Optional[int]:
'''simple docstring'''
snake_case_ = tmp_path / """cache"""
snake_case_ = {"""text""": """string"""}
snake_case_ = features.copy() if features else default_expected_features
snake_case_ = (
Features({feature: Value(__UpperCamelCase ) for feature, dtype in features.items()} ) if features is not None else None
)
snake_case_ = TextDatasetReader(__UpperCamelCase , features=__UpperCamelCase , cache_dir=__UpperCamelCase ).read()
_check_text_dataset(__UpperCamelCase , __UpperCamelCase )
@pytest.mark.parametrize("split" , [None, NamedSplit("train" ), "train", "test"] )
def _lowerCAmelCase ( lowerCAmelCase_ :Any , lowerCAmelCase_ :Any , lowerCAmelCase_ :List[str] )->Optional[Any]:
'''simple docstring'''
snake_case_ = tmp_path / """cache"""
snake_case_ = {"""text""": """string"""}
snake_case_ = TextDatasetReader(__UpperCamelCase , cache_dir=__UpperCamelCase , split=__UpperCamelCase ).read()
_check_text_dataset(__UpperCamelCase , __UpperCamelCase )
assert dataset.split == split if split else "train"
@pytest.mark.parametrize("path_type" , [str, list] )
def _lowerCAmelCase ( lowerCAmelCase_ :str , lowerCAmelCase_ :int , lowerCAmelCase_ :str )->Optional[Any]:
'''simple docstring'''
if issubclass(__UpperCamelCase , __UpperCamelCase ):
snake_case_ = text_path
elif issubclass(__UpperCamelCase , __UpperCamelCase ):
snake_case_ = [text_path]
snake_case_ = tmp_path / """cache"""
snake_case_ = {"""text""": """string"""}
snake_case_ = TextDatasetReader(__UpperCamelCase , cache_dir=__UpperCamelCase ).read()
_check_text_dataset(__UpperCamelCase , __UpperCamelCase )
def _lowerCAmelCase ( lowerCAmelCase_ :int , lowerCAmelCase_ :Union[str, Any] , lowerCAmelCase_ :Any=("train",) )->Union[str, Any]:
'''simple docstring'''
assert isinstance(__UpperCamelCase , __UpperCamelCase )
for split in splits:
snake_case_ = dataset_dict[split]
assert dataset.num_rows == 4
assert dataset.num_columns == 1
assert dataset.column_names == ["text"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("keep_in_memory" , [False, True] )
def _lowerCAmelCase ( lowerCAmelCase_ :str , lowerCAmelCase_ :Any , lowerCAmelCase_ :Any )->List[Any]:
'''simple docstring'''
snake_case_ = tmp_path / """cache"""
snake_case_ = {"""text""": """string"""}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
snake_case_ = TextDatasetReader({"train": text_path} , cache_dir=__UpperCamelCase , keep_in_memory=__UpperCamelCase ).read()
_check_text_datasetdict(__UpperCamelCase , __UpperCamelCase )
@pytest.mark.parametrize(
"features" , [
None,
{"text": "string"},
{"text": "int32"},
{"text": "float32"},
] , )
def _lowerCAmelCase ( lowerCAmelCase_ :int , lowerCAmelCase_ :Optional[Any] , lowerCAmelCase_ :Dict )->Any:
'''simple docstring'''
snake_case_ = tmp_path / """cache"""
# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
snake_case_ = {"""text""": """string"""}
snake_case_ = features.copy() if features else default_expected_features
snake_case_ = (
Features({feature: Value(__UpperCamelCase ) for feature, dtype in features.items()} ) if features is not None else None
)
snake_case_ = TextDatasetReader({"train": text_path} , features=__UpperCamelCase , cache_dir=__UpperCamelCase ).read()
_check_text_datasetdict(__UpperCamelCase , __UpperCamelCase )
@pytest.mark.parametrize("split" , [None, NamedSplit("train" ), "train", "test"] )
def _lowerCAmelCase ( lowerCAmelCase_ :Dict , lowerCAmelCase_ :Union[str, Any] , lowerCAmelCase_ :Any )->Union[str, Any]:
'''simple docstring'''
if split:
snake_case_ = {split: text_path}
else:
snake_case_ = """train"""
snake_case_ = {"""train""": text_path, """test""": text_path}
snake_case_ = tmp_path / """cache"""
snake_case_ = {"""text""": """string"""}
snake_case_ = TextDatasetReader(__UpperCamelCase , cache_dir=__UpperCamelCase ).read()
_check_text_datasetdict(__UpperCamelCase , __UpperCamelCase , splits=list(path.keys() ) )
assert all(dataset[split].split == split for split in path.keys() )
| 283 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
's-JoL/Open-Llama-V1': 'https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json',
}
class __lowercase ( __lowerCamelCase ):
snake_case_ = """open-llama"""
def __init__( self : Dict ,A : str=100_000 ,A : str=4_096 ,A : Optional[Any]=11_008 ,A : Tuple=32 ,A : str=32 ,A : Optional[int]="silu" ,A : List[Any]=2_048 ,A : str=0.0_2 ,A : Optional[int]=1e-6 ,A : int=True ,A : Tuple=0 ,A : str=1 ,A : Any=2 ,A : Optional[Any]=False ,A : int=True ,A : Any=0.1 ,A : Optional[Any]=0.1 ,A : Optional[Any]=True ,A : Union[str, Any]=True ,A : Tuple=None ,**A : Optional[int] ,):
'''simple docstring'''
UpperCAmelCase__ : str = vocab_size
UpperCAmelCase__ : List[str] = max_position_embeddings
UpperCAmelCase__ : Union[str, Any] = hidden_size
UpperCAmelCase__ : Tuple = intermediate_size
UpperCAmelCase__ : Optional[int] = num_hidden_layers
UpperCAmelCase__ : Any = num_attention_heads
UpperCAmelCase__ : str = hidden_act
UpperCAmelCase__ : Optional[Any] = initializer_range
UpperCAmelCase__ : Optional[int] = rms_norm_eps
UpperCAmelCase__ : Any = use_cache
UpperCAmelCase__ : Optional[Any] = kwargs.pop(
"""use_memorry_efficient_attention""" ,A )
UpperCAmelCase__ : Any = hidden_dropout_prob
UpperCAmelCase__ : str = attention_dropout_prob
UpperCAmelCase__ : Optional[int] = use_stable_embedding
UpperCAmelCase__ : Tuple = shared_input_output_embedding
UpperCAmelCase__ : Tuple = rope_scaling
self._rope_scaling_validation()
super().__init__(
pad_token_id=A ,bos_token_id=A ,eos_token_id=A ,tie_word_embeddings=A ,**A ,)
def __lowercase ( self : Optional[Any] ):
'''simple docstring'''
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling ,A ) or len(self.rope_scaling ) != 2:
raise ValueError(
"""`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, """
f"got {self.rope_scaling}" )
UpperCAmelCase__ : List[Any] = self.rope_scaling.get("""type""" ,A )
UpperCAmelCase__ : int = self.rope_scaling.get("""factor""" ,A )
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
raise ValueError(
f"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}" )
if rope_scaling_factor is None or not isinstance(A ,A ) or rope_scaling_factor <= 1.0:
raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}" )
| 65 | 0 |
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProcessor
from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.testing_utils import require_pyctcdecode, require_torch, require_torchaudio, slow
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_pyctcdecode_available, is_torch_available
from ..wavaveca.test_feature_extraction_wavaveca import floats_list
if is_pyctcdecode_available():
from huggingface_hub import snapshot_download
from pyctcdecode import BeamSearchDecoderCTC
from transformers.models.wavaveca_with_lm import WavaVecaProcessorWithLM
from transformers.models.wavaveca_with_lm.processing_wavaveca_with_lm import WavaVecaDecoderWithLMOutput
if is_torch_available():
from transformers import WavaVecaForCTC
@require_pyctcdecode
class lowercase ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase__ (self ) -> Any:
"""simple docstring"""
UpperCAmelCase__ = """| <pad> <unk> <s> </s> a b c d e f g h i j k""".split()
UpperCAmelCase__ = dict(zip(__a , range(len(__a ) ) ) )
UpperCAmelCase__ = {
"""unk_token""": """<unk>""",
"""bos_token""": """<s>""",
"""eos_token""": """</s>""",
}
UpperCAmelCase__ = {
"""feature_size""": 1,
"""padding_value""": 0.0,
"""sampling_rate""": 16000,
"""return_attention_mask""": False,
"""do_normalize""": True,
}
UpperCAmelCase__ = tempfile.mkdtemp()
UpperCAmelCase__ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
UpperCAmelCase__ = os.path.join(self.tmpdirname , __a )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as fp:
fp.write(json.dumps(__a ) + '\n' )
with open(self.feature_extraction_file , 'w' , encoding='utf-8' ) as fp:
fp.write(json.dumps(__a ) + '\n' )
# load decoder from hub
UpperCAmelCase__ = """hf-internal-testing/ngram-beam-search-decoder"""
def UpperCamelCase__ (self , **__a ) -> Optional[int]:
"""simple docstring"""
UpperCAmelCase__ = self.add_kwargs_tokens_map.copy()
kwargs.update(__a )
return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname , **__a )
def UpperCamelCase__ (self , **__a ) -> Union[str, Any]:
"""simple docstring"""
return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname , **__a )
def UpperCamelCase__ (self , **__a ) -> Union[str, Any]:
"""simple docstring"""
return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name , **__a )
def UpperCamelCase__ (self ) -> Tuple:
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def UpperCamelCase__ (self ) -> str:
"""simple docstring"""
UpperCAmelCase__ = self.get_tokenizer()
UpperCAmelCase__ = self.get_feature_extractor()
UpperCAmelCase__ = self.get_decoder()
UpperCAmelCase__ = WavaVecaProcessorWithLM(tokenizer=__a , feature_extractor=__a , decoder=__a )
processor.save_pretrained(self.tmpdirname )
UpperCAmelCase__ = WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname )
# tokenizer
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , __a )
# feature extractor
self.assertEqual(processor.feature_extractor.to_json_string() , feature_extractor.to_json_string() )
self.assertIsInstance(processor.feature_extractor , __a )
# decoder
self.assertEqual(processor.decoder._alphabet.labels , decoder._alphabet.labels )
self.assertEqual(
processor.decoder.model_container[decoder._model_key]._unigram_set , decoder.model_container[decoder._model_key]._unigram_set , )
self.assertIsInstance(processor.decoder , __a )
def UpperCamelCase__ (self ) -> Any:
"""simple docstring"""
UpperCAmelCase__ = WavaVecaProcessorWithLM(
tokenizer=self.get_tokenizer() , feature_extractor=self.get_feature_extractor() , decoder=self.get_decoder() )
processor.save_pretrained(self.tmpdirname )
# make sure that error is thrown when decoder alphabet doesn't match
UpperCAmelCase__ = WavaVecaProcessorWithLM.from_pretrained(
self.tmpdirname , alpha=5.0 , beta=3.0 , score_boundary=-7.0 , unk_score_offset=3 )
# decoder
self.assertEqual(processor.language_model.alpha , 5.0 )
self.assertEqual(processor.language_model.beta , 3.0 )
self.assertEqual(processor.language_model.score_boundary , -7.0 )
self.assertEqual(processor.language_model.unk_score_offset , 3 )
def UpperCamelCase__ (self ) -> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase__ = self.get_tokenizer()
# add token to trigger raise
tokenizer.add_tokens(['xx'] )
with self.assertRaisesRegex(__a , 'include' ):
WavaVecaProcessorWithLM(
tokenizer=__a , feature_extractor=self.get_feature_extractor() , decoder=self.get_decoder() )
def UpperCamelCase__ (self ) -> Dict:
"""simple docstring"""
UpperCAmelCase__ = self.get_feature_extractor()
UpperCAmelCase__ = self.get_tokenizer()
UpperCAmelCase__ = self.get_decoder()
UpperCAmelCase__ = WavaVecaProcessorWithLM(tokenizer=__a , feature_extractor=__a , decoder=__a )
UpperCAmelCase__ = floats_list((3, 1000) )
UpperCAmelCase__ = feature_extractor(__a , return_tensors='np' )
UpperCAmelCase__ = processor(__a , 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 UpperCamelCase__ (self ) -> int:
"""simple docstring"""
UpperCAmelCase__ = self.get_feature_extractor()
UpperCAmelCase__ = self.get_tokenizer()
UpperCAmelCase__ = self.get_decoder()
UpperCAmelCase__ = WavaVecaProcessorWithLM(tokenizer=__a , feature_extractor=__a , decoder=__a )
UpperCAmelCase__ = """This is a test string"""
UpperCAmelCase__ = processor(text=__a )
UpperCAmelCase__ = tokenizer(__a )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def UpperCamelCase__ (self , __a=(2, 10, 16) , __a=77 ) -> List[Any]:
"""simple docstring"""
np.random.seed(__a )
return np.random.rand(*__a )
def UpperCamelCase__ (self ) -> Any:
"""simple docstring"""
UpperCAmelCase__ = self.get_feature_extractor()
UpperCAmelCase__ = self.get_tokenizer()
UpperCAmelCase__ = self.get_decoder()
UpperCAmelCase__ = WavaVecaProcessorWithLM(tokenizer=__a , feature_extractor=__a , decoder=__a )
UpperCAmelCase__ = self._get_dummy_logits(shape=(10, 16) , seed=13 )
UpperCAmelCase__ = processor.decode(__a )
UpperCAmelCase__ = decoder.decode_beams(__a )[0]
self.assertEqual(decoded_decoder[0] , decoded_processor.text )
self.assertEqual('</s> <s> </s>' , decoded_processor.text )
self.assertEqual(decoded_decoder[-2] , decoded_processor.logit_score )
self.assertEqual(decoded_decoder[-1] , decoded_processor.lm_score )
@parameterized.expand([[None], ['fork'], ['spawn']] )
def UpperCamelCase__ (self , __a ) -> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase__ = self.get_feature_extractor()
UpperCAmelCase__ = self.get_tokenizer()
UpperCAmelCase__ = self.get_decoder()
UpperCAmelCase__ = WavaVecaProcessorWithLM(tokenizer=__a , feature_extractor=__a , decoder=__a )
UpperCAmelCase__ = self._get_dummy_logits()
# note: pool should be instantiated *after* Wav2Vec2ProcessorWithLM.
# otherwise, the LM won't be available to the pool's sub-processes.
# manual logic used to allow parameterized test for both pool=None and pool=Pool(...)
if pool_context is None:
UpperCAmelCase__ = processor.batch_decode(__a )
else:
with get_context(__a ).Pool() as pool:
UpperCAmelCase__ = processor.batch_decode(__a , __a )
UpperCAmelCase__ = list(__a )
with get_context('fork' ).Pool() as p:
UpperCAmelCase__ = decoder.decode_beams_batch(__a , __a )
UpperCAmelCase__ = [], [], []
for beams in decoded_beams:
texts_decoder.append(beams[0][0] )
logit_scores_decoder.append(beams[0][-2] )
lm_scores_decoder.append(beams[0][-1] )
self.assertListEqual(__a , decoded_processor.text )
self.assertListEqual(['<s> <s> </s>', '<s> <s> <s>'] , decoded_processor.text )
self.assertListEqual(__a , decoded_processor.logit_score )
self.assertListEqual(__a , decoded_processor.lm_score )
def UpperCamelCase__ (self ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase__ = self.get_feature_extractor()
UpperCAmelCase__ = self.get_tokenizer()
UpperCAmelCase__ = self.get_decoder()
UpperCAmelCase__ = WavaVecaProcessorWithLM(tokenizer=__a , feature_extractor=__a , decoder=__a )
UpperCAmelCase__ = self._get_dummy_logits()
UpperCAmelCase__ = 15
UpperCAmelCase__ = -20.0
UpperCAmelCase__ = -4.0
UpperCAmelCase__ = processor.batch_decode(
__a , beam_width=__a , beam_prune_logp=__a , token_min_logp=__a , )
UpperCAmelCase__ = decoded_processor_out.text
UpperCAmelCase__ = list(__a )
with get_context('fork' ).Pool() as pool:
UpperCAmelCase__ = decoder.decode_beams_batch(
__a , __a , beam_width=__a , beam_prune_logp=__a , token_min_logp=__a , )
UpperCAmelCase__ = [d[0][0] for d in decoded_decoder_out]
UpperCAmelCase__ = [d[0][2] for d in decoded_decoder_out]
UpperCAmelCase__ = [d[0][3] for d in decoded_decoder_out]
self.assertListEqual(__a , __a )
self.assertListEqual(['</s> <s> <s>', '<s> <s> <s>'] , __a )
self.assertTrue(np.array_equal(__a , decoded_processor_out.logit_score ) )
self.assertTrue(np.allclose([-20.054, -18.447] , __a , atol=1E-3 ) )
self.assertTrue(np.array_equal(__a , decoded_processor_out.lm_score ) )
self.assertTrue(np.allclose([-15.554, -13.9474] , __a , atol=1E-3 ) )
def UpperCamelCase__ (self ) -> str:
"""simple docstring"""
UpperCAmelCase__ = self.get_feature_extractor()
UpperCAmelCase__ = self.get_tokenizer()
UpperCAmelCase__ = self.get_decoder()
UpperCAmelCase__ = WavaVecaProcessorWithLM(tokenizer=__a , feature_extractor=__a , decoder=__a )
UpperCAmelCase__ = self._get_dummy_logits()
UpperCAmelCase__ = 2.0
UpperCAmelCase__ = 5.0
UpperCAmelCase__ = -20.0
UpperCAmelCase__ = True
UpperCAmelCase__ = processor.batch_decode(
__a , alpha=__a , beta=__a , unk_score_offset=__a , lm_score_boundary=__a , )
UpperCAmelCase__ = decoded_processor_out.text
UpperCAmelCase__ = list(__a )
decoder.reset_params(
alpha=__a , beta=__a , unk_score_offset=__a , lm_score_boundary=__a , )
with get_context('fork' ).Pool() as pool:
UpperCAmelCase__ = decoder.decode_beams_batch(
__a , __a , )
UpperCAmelCase__ = [d[0][0] for d in decoded_decoder_out]
self.assertListEqual(__a , __a )
self.assertListEqual(['<s> </s> <s> </s> </s>', '</s> </s> <s> </s> </s>'] , __a )
UpperCAmelCase__ = processor.decoder.model_container[processor.decoder._model_key]
self.assertEqual(lm_model.alpha , 2.0 )
self.assertEqual(lm_model.beta , 5.0 )
self.assertEqual(lm_model.unk_score_offset , -20.0 )
self.assertEqual(lm_model.score_boundary , __a )
def UpperCamelCase__ (self ) -> Tuple:
"""simple docstring"""
UpperCAmelCase__ = WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm' )
UpperCAmelCase__ = processor.decoder.model_container[processor.decoder._model_key]
UpperCAmelCase__ = Path(language_model._kenlm_model.path.decode('utf-8' ) ).parent.parent.absolute()
UpperCAmelCase__ = os.listdir(__a )
UpperCAmelCase__ = ["""alphabet.json""", """language_model"""]
downloaded_decoder_files.sort()
expected_decoder_files.sort()
# test that only decoder relevant files from
# https://huggingface.co/hf-internal-testing/processor_with_lm/tree/main
# are downloaded and none of the rest (e.g. README.md, ...)
self.assertListEqual(__a , __a )
def UpperCamelCase__ (self ) -> Dict:
"""simple docstring"""
UpperCAmelCase__ = snapshot_download('hf-internal-testing/processor_with_lm' )
UpperCAmelCase__ = WavaVecaProcessorWithLM.from_pretrained(__a )
UpperCAmelCase__ = processor.decoder.model_container[processor.decoder._model_key]
UpperCAmelCase__ = Path(language_model._kenlm_model.path.decode('utf-8' ) ).parent.parent.absolute()
UpperCAmelCase__ = os.listdir(__a )
UpperCAmelCase__ = os.listdir(__a )
local_decoder_files.sort()
expected_decoder_files.sort()
# test that both decoder form hub and local files in cache are the same
self.assertListEqual(__a , __a )
def UpperCamelCase__ (self ) -> int:
"""simple docstring"""
UpperCAmelCase__ = WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm' )
UpperCAmelCase__ = AutoProcessor.from_pretrained('hf-internal-testing/processor_with_lm' )
UpperCAmelCase__ = floats_list((3, 1000) )
UpperCAmelCase__ = processor_wavaveca(__a , return_tensors='np' )
UpperCAmelCase__ = processor_auto(__a , return_tensors='np' )
for key in input_wavaveca.keys():
self.assertAlmostEqual(input_wavaveca[key].sum() , input_auto[key].sum() , delta=1E-2 )
UpperCAmelCase__ = self._get_dummy_logits()
UpperCAmelCase__ = processor_wavaveca.batch_decode(__a )
UpperCAmelCase__ = processor_auto.batch_decode(__a )
self.assertListEqual(decoded_wavaveca.text , decoded_auto.text )
def UpperCamelCase__ (self ) -> str:
"""simple docstring"""
UpperCAmelCase__ = self.get_feature_extractor()
UpperCAmelCase__ = self.get_tokenizer()
UpperCAmelCase__ = self.get_decoder()
UpperCAmelCase__ = WavaVecaProcessorWithLM(tokenizer=__a , feature_extractor=__a , decoder=__a )
self.assertListEqual(
processor.model_input_names , feature_extractor.model_input_names , msg='`processor` and `feature_extractor` model input names do not match' , )
@staticmethod
def UpperCamelCase__ (__a , __a ) -> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase__ = [d[key] for d in offsets]
return retrieved_list
def UpperCamelCase__ (self ) -> Any:
"""simple docstring"""
UpperCAmelCase__ = WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm' )
UpperCAmelCase__ = self._get_dummy_logits()[0]
UpperCAmelCase__ = processor.decode(__a , output_word_offsets=__a )
# check Wav2Vec2CTCTokenizerOutput keys for word
self.assertEqual(len(outputs.keys() ) , 4 )
self.assertTrue('text' in outputs )
self.assertTrue('word_offsets' in outputs )
self.assertTrue(isinstance(__a , __a ) )
self.assertEqual(' '.join(self.get_from_offsets(outputs['word_offsets'] , 'word' ) ) , outputs.text )
self.assertListEqual(self.get_from_offsets(outputs['word_offsets'] , 'word' ) , ['<s>', '<s>', '</s>'] )
self.assertListEqual(self.get_from_offsets(outputs['word_offsets'] , 'start_offset' ) , [0, 2, 4] )
self.assertListEqual(self.get_from_offsets(outputs['word_offsets'] , 'end_offset' ) , [1, 3, 5] )
def UpperCamelCase__ (self ) -> Any:
"""simple docstring"""
UpperCAmelCase__ = WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm' )
UpperCAmelCase__ = self._get_dummy_logits()
UpperCAmelCase__ = processor.batch_decode(__a , output_word_offsets=__a )
# check Wav2Vec2CTCTokenizerOutput keys for word
self.assertEqual(len(outputs.keys() ) , 4 )
self.assertTrue('text' in outputs )
self.assertTrue('word_offsets' in outputs )
self.assertTrue(isinstance(__a , __a ) )
self.assertListEqual(
[' '.join(self.get_from_offsets(__a , 'word' ) ) for o in outputs['word_offsets']] , outputs.text )
self.assertListEqual(self.get_from_offsets(outputs['word_offsets'][0] , 'word' ) , ['<s>', '<s>', '</s>'] )
self.assertListEqual(self.get_from_offsets(outputs['word_offsets'][0] , 'start_offset' ) , [0, 2, 4] )
self.assertListEqual(self.get_from_offsets(outputs['word_offsets'][0] , 'end_offset' ) , [1, 3, 5] )
@slow
@require_torch
@require_torchaudio
def UpperCamelCase__ (self ) -> List[str]:
"""simple docstring"""
import torch
UpperCAmelCase__ = load_dataset('common_voice' , 'en' , split='train' , streaming=__a )
UpperCAmelCase__ = ds.cast_column('audio' , datasets.Audio(sampling_rate=16000 ) )
UpperCAmelCase__ = iter(__a )
UpperCAmelCase__ = next(__a )
UpperCAmelCase__ = AutoProcessor.from_pretrained('patrickvonplaten/wav2vec2-base-100h-with-lm' )
UpperCAmelCase__ = WavaVecaForCTC.from_pretrained('patrickvonplaten/wav2vec2-base-100h-with-lm' )
# compare to filename `common_voice_en_100038.mp3` of dataset viewer on https://huggingface.co/datasets/common_voice/viewer/en/train
UpperCAmelCase__ = processor(sample['audio']['array'] , return_tensors='pt' ).input_values
with torch.no_grad():
UpperCAmelCase__ = model(__a ).logits.cpu().numpy()
UpperCAmelCase__ = processor.decode(logits[0] , output_word_offsets=__a )
UpperCAmelCase__ = model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
UpperCAmelCase__ = [
{
"""start_time""": d["""start_offset"""] * time_offset,
"""end_time""": d["""end_offset"""] * time_offset,
"""word""": d["""word"""],
}
for d in output["""word_offsets"""]
]
UpperCAmelCase__ = """WHY DOES MILISANDRA LOOK LIKE SHE WANTS TO CONSUME JOHN SNOW ON THE RIVER AT THE WALL"""
# output words
self.assertEqual(' '.join(self.get_from_offsets(__a , 'word' ) ) , __a )
self.assertEqual(' '.join(self.get_from_offsets(__a , 'word' ) ) , output.text )
# output times
UpperCAmelCase__ = torch.tensor(self.get_from_offsets(__a , 'start_time' ) )
UpperCAmelCase__ = torch.tensor(self.get_from_offsets(__a , 'end_time' ) )
# fmt: off
UpperCAmelCase__ = torch.tensor([1.41_99, 1.65_99, 2.25_99, 3.0, 3.24, 3.59_99, 3.79_99, 4.09_99, 4.26, 4.94, 5.28, 5.65_99, 5.78, 5.94, 6.32, 6.53_99, 6.65_99] )
UpperCAmelCase__ = torch.tensor([1.53_99, 1.89_99, 2.9, 3.16, 3.53_99, 3.72, 4.01_99, 4.17_99, 4.76, 5.15_99, 5.55_99, 5.69_99, 5.86, 6.19_99, 6.38, 6.61_99, 6.94] )
# fmt: on
self.assertTrue(torch.allclose(__a , __a , atol=0.01 ) )
self.assertTrue(torch.allclose(__a , __a , atol=0.01 ) )
| 146 |
"""simple docstring"""
from collections.abc import Callable
class __lowercase :
def __init__( self : Tuple ,A : Callable | None = None ):
'''simple docstring'''
# Stores actual heap items.
UpperCAmelCase__ : list = []
# Stores indexes of each item for supporting updates and deletion.
UpperCAmelCase__ : dict = {}
# Stores current size of heap.
UpperCAmelCase__ : Any = 0
# Stores function used to evaluate the score of an item on which basis ordering
# will be done.
UpperCAmelCase__ : int = key or (lambda A : x)
def __lowercase ( self : Union[str, Any] ,A : int ):
'''simple docstring'''
return int((i - 1) / 2 ) if i > 0 else None
def __lowercase ( self : Tuple ,A : int ):
'''simple docstring'''
UpperCAmelCase__ : Any = int(2 * i + 1 )
return left if 0 < left < self.size else None
def __lowercase ( self : Any ,A : int ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = int(2 * i + 2 )
return right if 0 < right < self.size else None
def __lowercase ( self : List[Any] ,A : int ,A : int ):
'''simple docstring'''
UpperCAmelCase__ , UpperCAmelCase__ : int = (
self.pos_map[self.arr[j][0]],
self.pos_map[self.arr[i][0]],
)
# Then swap the items in the list.
UpperCAmelCase__ , UpperCAmelCase__ : Tuple = self.arr[j], self.arr[i]
def __lowercase ( self : Optional[int] ,A : int ,A : int ):
'''simple docstring'''
return self.arr[i][1] < self.arr[j][1]
def __lowercase ( self : Optional[int] ,A : int ):
'''simple docstring'''
UpperCAmelCase__ : int = self._left(A )
UpperCAmelCase__ : Dict = self._right(A )
UpperCAmelCase__ : Optional[int] = i
if left is not None and not self._cmp(A ,A ):
UpperCAmelCase__ : List[Any] = left
if right is not None and not self._cmp(A ,A ):
UpperCAmelCase__ : List[Any] = right
return valid_parent
def __lowercase ( self : int ,A : int ):
'''simple docstring'''
UpperCAmelCase__ : int = self._parent(A )
while parent is not None and not self._cmp(A ,A ):
self._swap(A ,A )
UpperCAmelCase__ , UpperCAmelCase__ : int = parent, self._parent(A )
def __lowercase ( self : str ,A : int ):
'''simple docstring'''
UpperCAmelCase__ : Any = self._get_valid_parent(A )
while valid_parent != index:
self._swap(A ,A )
UpperCAmelCase__ , UpperCAmelCase__ : Optional[int] = valid_parent, self._get_valid_parent(A )
def __lowercase ( self : Optional[Any] ,A : int ,A : int ):
'''simple docstring'''
if item not in self.pos_map:
return
UpperCAmelCase__ : Tuple = self.pos_map[item]
UpperCAmelCase__ : Dict = [item, self.key(A )]
# Make sure heap is right in both up and down direction.
# Ideally only one of them will make any change.
self._heapify_up(A )
self._heapify_down(A )
def __lowercase ( self : List[Any] ,A : int ):
'''simple docstring'''
if item not in self.pos_map:
return
UpperCAmelCase__ : Any = self.pos_map[item]
del self.pos_map[item]
UpperCAmelCase__ : Dict = self.arr[self.size - 1]
UpperCAmelCase__ : List[Any] = index
self.size -= 1
# Make sure heap is right in both up and down direction. Ideally only one
# of them will make any change- so no performance loss in calling both.
if self.size > index:
self._heapify_up(A )
self._heapify_down(A )
def __lowercase ( self : str ,A : int ,A : int ):
'''simple docstring'''
UpperCAmelCase__ : Dict = len(self.arr )
if arr_len == self.size:
self.arr.append([item, self.key(A )] )
else:
UpperCAmelCase__ : List[str] = [item, self.key(A )]
UpperCAmelCase__ : Union[str, Any] = self.size
self.size += 1
self._heapify_up(self.size - 1 )
def __lowercase ( self : str ):
'''simple docstring'''
return self.arr[0] if self.size else None
def __lowercase ( self : Dict ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = self.get_top()
if top_item_tuple:
self.delete_item(top_item_tuple[0] )
return top_item_tuple
def lowerCAmelCase ( ):
'''simple docstring'''
if __name__ == "__main__":
import doctest
doctest.testmod()
| 65 | 0 |
"""simple docstring"""
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 : List[Any] = logging.get_logger(__name__)
@add_end_docstrings(__lowerCamelCase )
class _UpperCamelCase ( __lowerCamelCase ):
'''simple docstring'''
def __init__( self , *__a , **__a ):
super().__init__(*__a , **__a )
requires_backends(self , "vision" )
self.check_model_type(__a )
def __call__( self , __a , **__a ):
return super().__call__(__a , **__a )
def snake_case ( self , **__a ):
return {}, {}, {}
def snake_case ( self , __a ):
__lowerCAmelCase = load_image(__a )
__lowerCAmelCase = image.size
__lowerCAmelCase = self.image_processor(images=__a , return_tensors=self.framework )
return model_inputs
def snake_case ( self , __a ):
__lowerCAmelCase = self.model(**__a )
return model_outputs
def snake_case ( self , __a ):
__lowerCAmelCase = model_outputs.predicted_depth
__lowerCAmelCase = torch.nn.functional.interpolate(
predicted_depth.unsqueeze(1 ) , size=self.image_size[::-1] , mode="bicubic" , align_corners=__a )
__lowerCAmelCase = prediction.squeeze().cpu().numpy()
__lowerCAmelCase = (output * 2_55 / np.max(__a )).astype("uint8" )
__lowerCAmelCase = Image.fromarray(__a )
__lowerCAmelCase = {}
__lowerCAmelCase = predicted_depth
__lowerCAmelCase = depth
return output_dict
| 636 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import PaddingStrategy, TensorType
from ....utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
class __lowercase ( __lowerCamelCase ):
snake_case_ = ["""input_features""", """attention_mask"""]
def __init__( self : Any ,A : str=80 ,A : Optional[int]=16_000 ,A : int=0.0 ,A : str=10 ,A : Any=25 ,A : str="hamming_window" ,A : int=3_2_7_6_8.0 ,A : List[str]=0.9_7 ,A : Optional[int]=1.0 ,A : Optional[Any]=True ,A : Tuple=True ,A : Any=False ,**A : int ,):
'''simple docstring'''
super().__init__(feature_size=A ,sampling_rate=A ,padding_value=A ,**A )
UpperCAmelCase__ : str = feature_size
UpperCAmelCase__ : int = sampling_rate
UpperCAmelCase__ : int = padding_value
UpperCAmelCase__ : Dict = hop_length
UpperCAmelCase__ : int = win_length
UpperCAmelCase__ : Dict = frame_signal_scale
UpperCAmelCase__ : Dict = preemphasis_coeff
UpperCAmelCase__ : str = mel_floor
UpperCAmelCase__ : Any = normalize_means
UpperCAmelCase__ : str = normalize_vars
UpperCAmelCase__ : int = win_function
UpperCAmelCase__ : List[Any] = return_attention_mask
UpperCAmelCase__ : str = win_length * sampling_rate // 1_000
UpperCAmelCase__ : List[Any] = hop_length * sampling_rate // 1_000
UpperCAmelCase__ : int = optimal_fft_length(self.sample_size )
UpperCAmelCase__ : List[Any] = (self.n_fft // 2) + 1
def __lowercase ( self : Union[str, Any] ,A : np.array ):
'''simple docstring'''
if self.win_function == "hamming_window":
UpperCAmelCase__ : Any = window_function(window_length=self.sample_size ,name=self.win_function ,periodic=A )
else:
UpperCAmelCase__ : Any = window_function(window_length=self.sample_size ,name=self.win_function )
UpperCAmelCase__ : Union[str, Any] = mel_filter_bank(
num_frequency_bins=self.n_freqs ,num_mel_filters=self.feature_size ,min_frequency=0.0 ,max_frequency=self.sampling_rate / 2.0 ,sampling_rate=self.sampling_rate ,)
UpperCAmelCase__ : Optional[Any] = spectrogram(
one_waveform * self.frame_signal_scale ,window=A ,frame_length=self.sample_size ,hop_length=self.sample_stride ,fft_length=self.n_fft ,center=A ,preemphasis=self.preemphasis_coeff ,mel_filters=A ,mel_floor=self.mel_floor ,log_mel="""log""" ,)
return msfc_features.T
def __lowercase ( self : str ,A : Any ,A : Optional[int] ,A : str ):
'''simple docstring'''
# make sure we normalize float32 arrays
if self.normalize_means:
UpperCAmelCase__ : Optional[Any] = x[:input_length].mean(axis=0 )
UpperCAmelCase__ : Any = np.subtract(A ,A )
if self.normalize_vars:
UpperCAmelCase__ : str = x[:input_length].std(axis=0 )
UpperCAmelCase__ : Optional[int] = np.divide(A ,A )
if input_length < x.shape[0]:
UpperCAmelCase__ : int = padding_value
# make sure array is in float32
UpperCAmelCase__ : str = x.astype(np.floataa )
return x
def __lowercase ( self : Union[str, Any] ,A : List[np.ndarray] ,A : Optional[np.ndarray] = None ):
'''simple docstring'''
UpperCAmelCase__ : Any = attention_mask.sum(-1 ) if attention_mask is not None else [x.shape[0] for x in input_features]
return [self._normalize_one(A ,A ,self.padding_value ) for x, n in zip(A ,A )]
def __call__( self : Union[str, Any] ,A : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] ,A : Union[bool, str, PaddingStrategy] = False ,A : Optional[int] = None ,A : bool = False ,A : Optional[int] = None ,A : Optional[bool] = None ,A : Optional[Union[str, TensorType]] = None ,A : Optional[int] = None ,**A : Tuple ,):
'''simple docstring'''
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 `raw_speech` 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.""" )
UpperCAmelCase__ : 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}" )
UpperCAmelCase__ : Any = is_batched_numpy or (
isinstance(A ,(list, tuple) ) and (isinstance(raw_speech[0] ,(np.ndarray, tuple, list) ))
)
if is_batched:
UpperCAmelCase__ : List[str] = [np.asarray(A ,dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(A ,np.ndarray ):
UpperCAmelCase__ : Union[str, Any] = np.asarray(A ,dtype=np.floataa )
elif isinstance(A ,np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
UpperCAmelCase__ : Optional[int] = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
UpperCAmelCase__ : Optional[Any] = [raw_speech]
# extract fbank features
UpperCAmelCase__ : Tuple = [self._extract_mfsc_features(A ) for one_waveform in raw_speech]
# convert into correct format for padding
UpperCAmelCase__ : str = BatchFeature({"""input_features""": features} )
UpperCAmelCase__ : Optional[Any] = self.pad(
A ,padding=A ,max_length=A ,truncation=A ,pad_to_multiple_of=A ,return_attention_mask=A ,**A ,)
# make sure list is in array format
UpperCAmelCase__ : Tuple = padded_inputs.get("""input_features""" )
if isinstance(input_features[0] ,A ):
UpperCAmelCase__ : Union[str, Any] = [np.asarray(A ,dtype=np.floataa ) for feature in input_features]
UpperCAmelCase__ : Dict = padded_inputs.get("""attention_mask""" )
if attention_mask is not None:
UpperCAmelCase__ : str = [np.asarray(A ,dtype=np.intaa ) for array in attention_mask]
if self.normalize_means or self.normalize_vars:
UpperCAmelCase__ : Union[str, Any] = (
np.array(A ,dtype=np.intaa )
if self._get_padding_strategies(A ,max_length=A ) is not PaddingStrategy.DO_NOT_PAD
and padding
else None
)
UpperCAmelCase__ : Any = self.normalize(
padded_inputs["""input_features"""] ,attention_mask=A )
if return_tensors is not None:
UpperCAmelCase__ : Union[str, Any] = padded_inputs.convert_to_tensors(A )
return padded_inputs
| 65 | 0 |
"""simple docstring"""
import re
def __A (_SCREAMING_SNAKE_CASE ) ->str:
"""simple docstring"""
lowerCAmelCase__ :Any = re.compile(r'^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$' )
if match := re.search(__UpperCamelCase , __UpperCamelCase ):
return match.string == phone
return False
if __name__ == "__main__":
print(indian_phone_validator("""+918827897895"""))
| 93 |
"""simple docstring"""
from math import factorial
def lowerCAmelCase ( __UpperCamelCase = 100 ):
'''simple docstring'''
return sum(int(__UpperCamelCase ) for x in str(factorial(__UpperCamelCase ) ) )
if __name__ == "__main__":
print(solution(int(input('Enter the Number: ').strip())))
| 65 | 0 |
'''simple docstring'''
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
A_ = logging.get_logger(__name__)
def _UpperCamelCase ( __UpperCamelCase=None ,__UpperCamelCase=None ) -> Tuple:
return field(default_factory=lambda: default ,metadata=__UpperCamelCase )
@dataclass
class UpperCAmelCase :
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = 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'
)
} , )
SCREAMING_SNAKE_CASE_ = list_field(
default=[8] , metadata={'help': 'List of batch sizes for which memory and time performance will be evaluated'} )
SCREAMING_SNAKE_CASE_ = list_field(
default=[8, 32, 1_28, 5_12] , metadata={'help': 'List of sequence lengths for which memory and time performance will be evaluated'} , )
SCREAMING_SNAKE_CASE_ = field(
default=__lowerCamelCase , metadata={'help': 'Whether to benchmark inference of model. Inference can be disabled via --no-inference.'} , )
SCREAMING_SNAKE_CASE_ = field(
default=__lowerCamelCase , metadata={'help': 'Whether to run on available cuda devices. Cuda can be disabled via --no-cuda.'} , )
SCREAMING_SNAKE_CASE_ = field(
default=__lowerCamelCase , metadata={'help': 'Whether to run on available tpu devices. TPU can be disabled via --no-tpu.'} )
SCREAMING_SNAKE_CASE_ = field(default=__lowerCamelCase , metadata={'help': 'Use FP16 to accelerate inference.'} )
SCREAMING_SNAKE_CASE_ = field(default=__lowerCamelCase , metadata={'help': 'Benchmark training of model'} )
SCREAMING_SNAKE_CASE_ = field(default=__lowerCamelCase , metadata={'help': 'Verbose memory tracing'} )
SCREAMING_SNAKE_CASE_ = field(
default=__lowerCamelCase , metadata={'help': 'Whether to perform speed measurements. Speed measurements can be disabled via --no-speed.'} , )
SCREAMING_SNAKE_CASE_ = field(
default=__lowerCamelCase , metadata={
'help': 'Whether to perform memory measurements. Memory measurements can be disabled via --no-memory'
} , )
SCREAMING_SNAKE_CASE_ = field(default=__lowerCamelCase , metadata={'help': 'Trace memory line by line'} )
SCREAMING_SNAKE_CASE_ = field(default=__lowerCamelCase , metadata={'help': 'Save result to a CSV file'} )
SCREAMING_SNAKE_CASE_ = field(default=__lowerCamelCase , metadata={'help': 'Save all print statements in a log file'} )
SCREAMING_SNAKE_CASE_ = field(default=__lowerCamelCase , metadata={'help': 'Whether to print environment information'} )
SCREAMING_SNAKE_CASE_ = field(
default=__lowerCamelCase , 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.'
)
} , )
SCREAMING_SNAKE_CASE_ = field(
default=f"inference_time_{round(time() )}.csv" , metadata={'help': 'CSV filename used if saving time results to csv.'} , )
SCREAMING_SNAKE_CASE_ = field(
default=f"inference_memory_{round(time() )}.csv" , metadata={'help': 'CSV filename used if saving memory results to csv.'} , )
SCREAMING_SNAKE_CASE_ = field(
default=f"train_time_{round(time() )}.csv" , metadata={'help': 'CSV filename used if saving time results to csv for training.'} , )
SCREAMING_SNAKE_CASE_ = field(
default=f"train_memory_{round(time() )}.csv" , metadata={'help': 'CSV filename used if saving memory results to csv for training.'} , )
SCREAMING_SNAKE_CASE_ = field(
default=f"env_info_{round(time() )}.csv" , metadata={'help': 'CSV filename used if saving environment information.'} , )
SCREAMING_SNAKE_CASE_ = field(
default=f"log_{round(time() )}.csv" , metadata={'help': 'Log filename used if print statements are saved in log.'} , )
SCREAMING_SNAKE_CASE_ = field(default=3 , metadata={'help': 'Times an experiment will be run.'} )
SCREAMING_SNAKE_CASE_ = field(
default=__lowerCamelCase , metadata={
'help': (
'Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain'
' model weights.'
)
} , )
def UpperCamelCase( self ) -> Dict:
'''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.' , SCREAMING_SNAKE_CASE_ , )
def UpperCamelCase( self ) -> Tuple:
'''simple docstring'''
return json.dumps(dataclasses.asdict(self ) , indent=2 )
@property
def UpperCamelCase( self ) -> Union[str, Any]:
'''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 UpperCamelCase( self ) -> 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
| 42 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.distilbert.modeling_flax_distilbert import (
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertModel,
)
class __lowercase ( unittest.TestCase ):
def __init__( self : Union[str, Any] ,A : Optional[int] ,A : int=13 ,A : Tuple=7 ,A : Dict=True ,A : Optional[int]=True ,A : Tuple=True ,A : str=True ,A : Any=99 ,A : Tuple=32 ,A : Dict=5 ,A : Optional[int]=4 ,A : Dict=37 ,A : Any="gelu" ,A : Any=0.1 ,A : Optional[int]=0.1 ,A : Union[str, Any]=512 ,A : Any=16 ,A : List[str]=2 ,A : List[Any]=0.0_2 ,A : Optional[int]=4 ,):
'''simple docstring'''
UpperCAmelCase__ : Dict = parent
UpperCAmelCase__ : Any = batch_size
UpperCAmelCase__ : List[Any] = seq_length
UpperCAmelCase__ : Optional[int] = is_training
UpperCAmelCase__ : Optional[Any] = use_attention_mask
UpperCAmelCase__ : int = use_token_type_ids
UpperCAmelCase__ : int = use_labels
UpperCAmelCase__ : Any = vocab_size
UpperCAmelCase__ : Union[str, Any] = hidden_size
UpperCAmelCase__ : int = num_hidden_layers
UpperCAmelCase__ : int = num_attention_heads
UpperCAmelCase__ : Dict = intermediate_size
UpperCAmelCase__ : Any = hidden_act
UpperCAmelCase__ : Union[str, Any] = hidden_dropout_prob
UpperCAmelCase__ : Any = attention_probs_dropout_prob
UpperCAmelCase__ : str = max_position_embeddings
UpperCAmelCase__ : List[Any] = type_vocab_size
UpperCAmelCase__ : List[str] = type_sequence_label_size
UpperCAmelCase__ : List[Any] = initializer_range
UpperCAmelCase__ : List[Any] = num_choices
def __lowercase ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
UpperCAmelCase__ : List[str] = None
if self.use_attention_mask:
UpperCAmelCase__ : str = random_attention_mask([self.batch_size, self.seq_length] )
UpperCAmelCase__ : int = DistilBertConfig(
vocab_size=self.vocab_size ,dim=self.hidden_size ,n_layers=self.num_hidden_layers ,n_heads=self.num_attention_heads ,hidden_dim=self.intermediate_size ,hidden_act=self.hidden_act ,dropout=self.hidden_dropout_prob ,attention_dropout=self.attention_probs_dropout_prob ,max_position_embeddings=self.max_position_embeddings ,initializer_range=self.initializer_range ,tie_weights_=A ,)
return config, input_ids, attention_mask
def __lowercase ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = self.prepare_config_and_inputs()
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ : Any = config_and_inputs
UpperCAmelCase__ : str = {"""input_ids""": input_ids, """attention_mask""": attention_mask}
return config, inputs_dict
@require_flax
class __lowercase ( __lowerCamelCase , unittest.TestCase ):
snake_case_ = (
(
FlaxDistilBertModel,
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def __lowercase ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = FlaxDistilBertModelTester(self )
@slow
def __lowercase ( self : Optional[Any] ):
'''simple docstring'''
for model_class_name in self.all_model_classes:
UpperCAmelCase__ : Union[str, Any] = model_class_name.from_pretrained("""distilbert-base-uncased""" )
UpperCAmelCase__ : List[Any] = model(np.ones((1, 1) ) )
self.assertIsNotNone(A )
@require_flax
class __lowercase ( unittest.TestCase ):
@slow
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = FlaxDistilBertModel.from_pretrained("""distilbert-base-uncased""" )
UpperCAmelCase__ : List[Any] = np.array([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]] )
UpperCAmelCase__ : str = np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
UpperCAmelCase__ : Dict = model(A ,attention_mask=A )[0]
UpperCAmelCase__ : List[Any] = (1, 11, 768)
self.assertEqual(output.shape ,A )
UpperCAmelCase__ : Any = np.array([[[-0.1_6_3_9, 0.3_2_9_9, 0.1_6_4_8], [-0.1_7_4_6, 0.3_2_8_9, 0.1_7_1_0], [-0.1_8_8_4, 0.3_3_5_7, 0.1_8_1_0]]] )
self.assertTrue(jnp.allclose(output[:, 1:4, 1:4] ,A ,atol=1e-4 ) )
| 65 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
SCREAMING_SNAKE_CASE__ = {
'''configuration_longt5''': ['''LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LongT5Config''', '''LongT5OnnxConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = [
'''LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''LongT5EncoderModel''',
'''LongT5ForConditionalGeneration''',
'''LongT5Model''',
'''LongT5PreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = [
'''FlaxLongT5ForConditionalGeneration''',
'''FlaxLongT5Model''',
'''FlaxLongT5PreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_longta import LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP, LongTaConfig, LongTaOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_longta import (
LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST,
LongTaEncoderModel,
LongTaForConditionalGeneration,
LongTaModel,
LongTaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_longta import (
FlaxLongTaForConditionalGeneration,
FlaxLongTaModel,
FlaxLongTaPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE__ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 9 |
"""simple docstring"""
__UpperCAmelCase = frozenset(
[
'prompt',
'height',
'width',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
'cross_attention_kwargs',
]
)
__UpperCAmelCase = frozenset(['prompt', 'negative_prompt'])
__UpperCAmelCase = frozenset([])
__UpperCAmelCase = frozenset(['image'])
__UpperCAmelCase = frozenset(
[
'image',
'height',
'width',
'guidance_scale',
]
)
__UpperCAmelCase = frozenset(['image'])
__UpperCAmelCase = frozenset(
[
'prompt',
'image',
'height',
'width',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
]
)
__UpperCAmelCase = frozenset(['prompt', 'image', 'negative_prompt'])
__UpperCAmelCase = frozenset(
[
# Text guided image variation with an image mask
'prompt',
'image',
'mask_image',
'height',
'width',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
]
)
__UpperCAmelCase = frozenset(['prompt', 'image', 'mask_image', 'negative_prompt'])
__UpperCAmelCase = frozenset(
[
# image variation with an image mask
'image',
'mask_image',
'height',
'width',
'guidance_scale',
]
)
__UpperCAmelCase = frozenset(['image', 'mask_image'])
__UpperCAmelCase = frozenset(
[
'example_image',
'image',
'mask_image',
'height',
'width',
'guidance_scale',
]
)
__UpperCAmelCase = frozenset(['example_image', 'image', 'mask_image'])
__UpperCAmelCase = frozenset(['class_labels'])
__UpperCAmelCase = frozenset(['class_labels'])
__UpperCAmelCase = frozenset(['batch_size'])
__UpperCAmelCase = frozenset([])
__UpperCAmelCase = frozenset(['batch_size'])
__UpperCAmelCase = frozenset([])
__UpperCAmelCase = frozenset(
[
'prompt',
'audio_length_in_s',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
'cross_attention_kwargs',
]
)
__UpperCAmelCase = frozenset(['prompt', 'negative_prompt'])
__UpperCAmelCase = frozenset(['input_tokens'])
__UpperCAmelCase = frozenset(['input_tokens'])
| 65 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
a_ :List[str] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ :List[str] = ["NllbTokenizer"]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ :Dict = ["NllbTokenizerFast"]
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_nllb import NllbTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_nllb_fast import NllbTokenizerFast
else:
import sys
a_ :Any = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 478 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProcessor
from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.testing_utils import require_pyctcdecode, require_torch, require_torchaudio, slow
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_pyctcdecode_available, is_torch_available
from ..wavaveca.test_feature_extraction_wavaveca import floats_list
if is_pyctcdecode_available():
from huggingface_hub import snapshot_download
from pyctcdecode import BeamSearchDecoderCTC
from transformers.models.wavaveca_with_lm import WavaVecaProcessorWithLM
from transformers.models.wavaveca_with_lm.processing_wavaveca_with_lm import WavaVecaDecoderWithLMOutput
if is_torch_available():
from transformers import WavaVecaForCTC
@require_pyctcdecode
class __lowercase ( unittest.TestCase ):
def __lowercase ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase__ : Dict = """| <pad> <unk> <s> </s> a b c d e f g h i j k""".split()
UpperCAmelCase__ : Tuple = dict(zip(A ,range(len(A ) ) ) )
UpperCAmelCase__ : Optional[Any] = {
"""unk_token""": """<unk>""",
"""bos_token""": """<s>""",
"""eos_token""": """</s>""",
}
UpperCAmelCase__ : int = {
"""feature_size""": 1,
"""padding_value""": 0.0,
"""sampling_rate""": 16_000,
"""return_attention_mask""": False,
"""do_normalize""": True,
}
UpperCAmelCase__ : Optional[int] = tempfile.mkdtemp()
UpperCAmelCase__ : Optional[int] = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["""vocab_file"""] )
UpperCAmelCase__ : Tuple = os.path.join(self.tmpdirname ,A )
with open(self.vocab_file ,"""w""" ,encoding="""utf-8""" ) as fp:
fp.write(json.dumps(A ) + """\n""" )
with open(self.feature_extraction_file ,"""w""" ,encoding="""utf-8""" ) as fp:
fp.write(json.dumps(A ) + """\n""" )
# load decoder from hub
UpperCAmelCase__ : int = """hf-internal-testing/ngram-beam-search-decoder"""
def __lowercase ( self : str ,**A : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = self.add_kwargs_tokens_map.copy()
kwargs.update(A )
return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname ,**A )
def __lowercase ( self : List[str] ,**A : Dict ):
'''simple docstring'''
return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname ,**A )
def __lowercase ( self : Any ,**A : List[Any] ):
'''simple docstring'''
return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name ,**A )
def __lowercase ( self : Any ):
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def __lowercase ( self : str ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = self.get_tokenizer()
UpperCAmelCase__ : Dict = self.get_feature_extractor()
UpperCAmelCase__ : str = self.get_decoder()
UpperCAmelCase__ : Tuple = WavaVecaProcessorWithLM(tokenizer=A ,feature_extractor=A ,decoder=A )
processor.save_pretrained(self.tmpdirname )
UpperCAmelCase__ : str = WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname )
# tokenizer
self.assertEqual(processor.tokenizer.get_vocab() ,tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer ,A )
# feature extractor
self.assertEqual(processor.feature_extractor.to_json_string() ,feature_extractor.to_json_string() )
self.assertIsInstance(processor.feature_extractor ,A )
# decoder
self.assertEqual(processor.decoder._alphabet.labels ,decoder._alphabet.labels )
self.assertEqual(
processor.decoder.model_container[decoder._model_key]._unigram_set ,decoder.model_container[decoder._model_key]._unigram_set ,)
self.assertIsInstance(processor.decoder ,A )
def __lowercase ( self : int ):
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = WavaVecaProcessorWithLM(
tokenizer=self.get_tokenizer() ,feature_extractor=self.get_feature_extractor() ,decoder=self.get_decoder() )
processor.save_pretrained(self.tmpdirname )
# make sure that error is thrown when decoder alphabet doesn't match
UpperCAmelCase__ : Tuple = WavaVecaProcessorWithLM.from_pretrained(
self.tmpdirname ,alpha=5.0 ,beta=3.0 ,score_boundary=-7.0 ,unk_score_offset=3 )
# decoder
self.assertEqual(processor.language_model.alpha ,5.0 )
self.assertEqual(processor.language_model.beta ,3.0 )
self.assertEqual(processor.language_model.score_boundary ,-7.0 )
self.assertEqual(processor.language_model.unk_score_offset ,3 )
def __lowercase ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase__ : int = self.get_tokenizer()
# add token to trigger raise
tokenizer.add_tokens(["""xx"""] )
with self.assertRaisesRegex(A ,"""include""" ):
WavaVecaProcessorWithLM(
tokenizer=A ,feature_extractor=self.get_feature_extractor() ,decoder=self.get_decoder() )
def __lowercase ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = self.get_feature_extractor()
UpperCAmelCase__ : Optional[Any] = self.get_tokenizer()
UpperCAmelCase__ : Any = self.get_decoder()
UpperCAmelCase__ : Union[str, Any] = WavaVecaProcessorWithLM(tokenizer=A ,feature_extractor=A ,decoder=A )
UpperCAmelCase__ : str = floats_list((3, 1_000) )
UpperCAmelCase__ : Optional[Any] = feature_extractor(A ,return_tensors="""np""" )
UpperCAmelCase__ : List[Any] = processor(A ,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 __lowercase ( self : int ):
'''simple docstring'''
UpperCAmelCase__ : int = self.get_feature_extractor()
UpperCAmelCase__ : Union[str, Any] = self.get_tokenizer()
UpperCAmelCase__ : Optional[int] = self.get_decoder()
UpperCAmelCase__ : List[Any] = WavaVecaProcessorWithLM(tokenizer=A ,feature_extractor=A ,decoder=A )
UpperCAmelCase__ : List[Any] = """This is a test string"""
UpperCAmelCase__ : int = processor(text=A )
UpperCAmelCase__ : Dict = tokenizer(A )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] ,encoded_processor[key] )
def __lowercase ( self : Tuple ,A : List[Any]=(2, 10, 16) ,A : Dict=77 ):
'''simple docstring'''
np.random.seed(A )
return np.random.rand(*A )
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ : Dict = self.get_feature_extractor()
UpperCAmelCase__ : Optional[Any] = self.get_tokenizer()
UpperCAmelCase__ : int = self.get_decoder()
UpperCAmelCase__ : Optional[Any] = WavaVecaProcessorWithLM(tokenizer=A ,feature_extractor=A ,decoder=A )
UpperCAmelCase__ : Dict = self._get_dummy_logits(shape=(10, 16) ,seed=13 )
UpperCAmelCase__ : Tuple = processor.decode(A )
UpperCAmelCase__ : Union[str, Any] = decoder.decode_beams(A )[0]
self.assertEqual(decoded_decoder[0] ,decoded_processor.text )
self.assertEqual("""</s> <s> </s>""" ,decoded_processor.text )
self.assertEqual(decoded_decoder[-2] ,decoded_processor.logit_score )
self.assertEqual(decoded_decoder[-1] ,decoded_processor.lm_score )
@parameterized.expand([[None], ["""fork"""], ["""spawn"""]] )
def __lowercase ( self : List[str] ,A : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = self.get_feature_extractor()
UpperCAmelCase__ : int = self.get_tokenizer()
UpperCAmelCase__ : List[Any] = self.get_decoder()
UpperCAmelCase__ : Dict = WavaVecaProcessorWithLM(tokenizer=A ,feature_extractor=A ,decoder=A )
UpperCAmelCase__ : Optional[Any] = self._get_dummy_logits()
# note: pool should be instantiated *after* Wav2Vec2ProcessorWithLM.
# otherwise, the LM won't be available to the pool's sub-processes.
# manual logic used to allow parameterized test for both pool=None and pool=Pool(...)
if pool_context is None:
UpperCAmelCase__ : List[str] = processor.batch_decode(A )
else:
with get_context(A ).Pool() as pool:
UpperCAmelCase__ : Union[str, Any] = processor.batch_decode(A ,A )
UpperCAmelCase__ : Optional[Any] = list(A )
with get_context("""fork""" ).Pool() as p:
UpperCAmelCase__ : Union[str, Any] = decoder.decode_beams_batch(A ,A )
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ : Optional[Any] = [], [], []
for beams in decoded_beams:
texts_decoder.append(beams[0][0] )
logit_scores_decoder.append(beams[0][-2] )
lm_scores_decoder.append(beams[0][-1] )
self.assertListEqual(A ,decoded_processor.text )
self.assertListEqual(["""<s> <s> </s>""", """<s> <s> <s>"""] ,decoded_processor.text )
self.assertListEqual(A ,decoded_processor.logit_score )
self.assertListEqual(A ,decoded_processor.lm_score )
def __lowercase ( self : int ):
'''simple docstring'''
UpperCAmelCase__ : Any = self.get_feature_extractor()
UpperCAmelCase__ : Tuple = self.get_tokenizer()
UpperCAmelCase__ : List[Any] = self.get_decoder()
UpperCAmelCase__ : Union[str, Any] = WavaVecaProcessorWithLM(tokenizer=A ,feature_extractor=A ,decoder=A )
UpperCAmelCase__ : Dict = self._get_dummy_logits()
UpperCAmelCase__ : Any = 15
UpperCAmelCase__ : Dict = -2_0.0
UpperCAmelCase__ : List[Any] = -4.0
UpperCAmelCase__ : Union[str, Any] = processor.batch_decode(
A ,beam_width=A ,beam_prune_logp=A ,token_min_logp=A ,)
UpperCAmelCase__ : List[str] = decoded_processor_out.text
UpperCAmelCase__ : List[str] = list(A )
with get_context("""fork""" ).Pool() as pool:
UpperCAmelCase__ : Tuple = decoder.decode_beams_batch(
A ,A ,beam_width=A ,beam_prune_logp=A ,token_min_logp=A ,)
UpperCAmelCase__ : List[Any] = [d[0][0] for d in decoded_decoder_out]
UpperCAmelCase__ : Any = [d[0][2] for d in decoded_decoder_out]
UpperCAmelCase__ : List[str] = [d[0][3] for d in decoded_decoder_out]
self.assertListEqual(A ,A )
self.assertListEqual(["""</s> <s> <s>""", """<s> <s> <s>"""] ,A )
self.assertTrue(np.array_equal(A ,decoded_processor_out.logit_score ) )
self.assertTrue(np.allclose([-2_0.0_5_4, -1_8.4_4_7] ,A ,atol=1e-3 ) )
self.assertTrue(np.array_equal(A ,decoded_processor_out.lm_score ) )
self.assertTrue(np.allclose([-1_5.5_5_4, -1_3.9_4_7_4] ,A ,atol=1e-3 ) )
def __lowercase ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = self.get_feature_extractor()
UpperCAmelCase__ : Optional[Any] = self.get_tokenizer()
UpperCAmelCase__ : int = self.get_decoder()
UpperCAmelCase__ : str = WavaVecaProcessorWithLM(tokenizer=A ,feature_extractor=A ,decoder=A )
UpperCAmelCase__ : Tuple = self._get_dummy_logits()
UpperCAmelCase__ : Tuple = 2.0
UpperCAmelCase__ : str = 5.0
UpperCAmelCase__ : Union[str, Any] = -2_0.0
UpperCAmelCase__ : Optional[Any] = True
UpperCAmelCase__ : str = processor.batch_decode(
A ,alpha=A ,beta=A ,unk_score_offset=A ,lm_score_boundary=A ,)
UpperCAmelCase__ : Any = decoded_processor_out.text
UpperCAmelCase__ : Union[str, Any] = list(A )
decoder.reset_params(
alpha=A ,beta=A ,unk_score_offset=A ,lm_score_boundary=A ,)
with get_context("""fork""" ).Pool() as pool:
UpperCAmelCase__ : List[Any] = decoder.decode_beams_batch(
A ,A ,)
UpperCAmelCase__ : Union[str, Any] = [d[0][0] for d in decoded_decoder_out]
self.assertListEqual(A ,A )
self.assertListEqual(["""<s> </s> <s> </s> </s>""", """</s> </s> <s> </s> </s>"""] ,A )
UpperCAmelCase__ : Union[str, Any] = processor.decoder.model_container[processor.decoder._model_key]
self.assertEqual(lm_model.alpha ,2.0 )
self.assertEqual(lm_model.beta ,5.0 )
self.assertEqual(lm_model.unk_score_offset ,-2_0.0 )
self.assertEqual(lm_model.score_boundary ,A )
def __lowercase ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Dict = WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm""" )
UpperCAmelCase__ : str = processor.decoder.model_container[processor.decoder._model_key]
UpperCAmelCase__ : Any = Path(language_model._kenlm_model.path.decode("""utf-8""" ) ).parent.parent.absolute()
UpperCAmelCase__ : Optional[int] = os.listdir(A )
UpperCAmelCase__ : List[Any] = ["""alphabet.json""", """language_model"""]
downloaded_decoder_files.sort()
expected_decoder_files.sort()
# test that only decoder relevant files from
# https://huggingface.co/hf-internal-testing/processor_with_lm/tree/main
# are downloaded and none of the rest (e.g. README.md, ...)
self.assertListEqual(A ,A )
def __lowercase ( self : int ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = snapshot_download("""hf-internal-testing/processor_with_lm""" )
UpperCAmelCase__ : Union[str, Any] = WavaVecaProcessorWithLM.from_pretrained(A )
UpperCAmelCase__ : Tuple = processor.decoder.model_container[processor.decoder._model_key]
UpperCAmelCase__ : Optional[int] = Path(language_model._kenlm_model.path.decode("""utf-8""" ) ).parent.parent.absolute()
UpperCAmelCase__ : Tuple = os.listdir(A )
UpperCAmelCase__ : Dict = os.listdir(A )
local_decoder_files.sort()
expected_decoder_files.sort()
# test that both decoder form hub and local files in cache are the same
self.assertListEqual(A ,A )
def __lowercase ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm""" )
UpperCAmelCase__ : Tuple = AutoProcessor.from_pretrained("""hf-internal-testing/processor_with_lm""" )
UpperCAmelCase__ : Dict = floats_list((3, 1_000) )
UpperCAmelCase__ : List[str] = processor_wavaveca(A ,return_tensors="""np""" )
UpperCAmelCase__ : Dict = processor_auto(A ,return_tensors="""np""" )
for key in input_wavaveca.keys():
self.assertAlmostEqual(input_wavaveca[key].sum() ,input_auto[key].sum() ,delta=1e-2 )
UpperCAmelCase__ : List[str] = self._get_dummy_logits()
UpperCAmelCase__ : Tuple = processor_wavaveca.batch_decode(A )
UpperCAmelCase__ : List[str] = processor_auto.batch_decode(A )
self.assertListEqual(decoded_wavaveca.text ,decoded_auto.text )
def __lowercase ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase__ : Dict = self.get_feature_extractor()
UpperCAmelCase__ : Tuple = self.get_tokenizer()
UpperCAmelCase__ : List[Any] = self.get_decoder()
UpperCAmelCase__ : int = WavaVecaProcessorWithLM(tokenizer=A ,feature_extractor=A ,decoder=A )
self.assertListEqual(
processor.model_input_names ,feature_extractor.model_input_names ,msg="""`processor` and `feature_extractor` model input names do not match""" ,)
@staticmethod
def __lowercase ( A : Optional[Any] ,A : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = [d[key] for d in offsets]
return retrieved_list
def __lowercase ( self : Dict ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm""" )
UpperCAmelCase__ : Dict = self._get_dummy_logits()[0]
UpperCAmelCase__ : List[str] = processor.decode(A ,output_word_offsets=A )
# check Wav2Vec2CTCTokenizerOutput keys for word
self.assertEqual(len(outputs.keys() ) ,4 )
self.assertTrue("""text""" in outputs )
self.assertTrue("""word_offsets""" in outputs )
self.assertTrue(isinstance(A ,A ) )
self.assertEqual(""" """.join(self.get_from_offsets(outputs["""word_offsets"""] ,"""word""" ) ) ,outputs.text )
self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""] ,"""word""" ) ,["""<s>""", """<s>""", """</s>"""] )
self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""] ,"""start_offset""" ) ,[0, 2, 4] )
self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""] ,"""end_offset""" ) ,[1, 3, 5] )
def __lowercase ( self : Dict ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm""" )
UpperCAmelCase__ : int = self._get_dummy_logits()
UpperCAmelCase__ : Any = processor.batch_decode(A ,output_word_offsets=A )
# check Wav2Vec2CTCTokenizerOutput keys for word
self.assertEqual(len(outputs.keys() ) ,4 )
self.assertTrue("""text""" in outputs )
self.assertTrue("""word_offsets""" in outputs )
self.assertTrue(isinstance(A ,A ) )
self.assertListEqual(
[""" """.join(self.get_from_offsets(A ,"""word""" ) ) for o in outputs["""word_offsets"""]] ,outputs.text )
self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""][0] ,"""word""" ) ,["""<s>""", """<s>""", """</s>"""] )
self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""][0] ,"""start_offset""" ) ,[0, 2, 4] )
self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""][0] ,"""end_offset""" ) ,[1, 3, 5] )
@slow
@require_torch
@require_torchaudio
def __lowercase ( self : Tuple ):
'''simple docstring'''
import torch
UpperCAmelCase__ : Any = load_dataset("""common_voice""" ,"""en""" ,split="""train""" ,streaming=A )
UpperCAmelCase__ : Tuple = ds.cast_column("""audio""" ,datasets.Audio(sampling_rate=16_000 ) )
UpperCAmelCase__ : Tuple = iter(A )
UpperCAmelCase__ : Optional[int] = next(A )
UpperCAmelCase__ : List[Any] = AutoProcessor.from_pretrained("""patrickvonplaten/wav2vec2-base-100h-with-lm""" )
UpperCAmelCase__ : Tuple = WavaVecaForCTC.from_pretrained("""patrickvonplaten/wav2vec2-base-100h-with-lm""" )
# compare to filename `common_voice_en_100038.mp3` of dataset viewer on https://huggingface.co/datasets/common_voice/viewer/en/train
UpperCAmelCase__ : Tuple = processor(sample["""audio"""]["""array"""] ,return_tensors="""pt""" ).input_values
with torch.no_grad():
UpperCAmelCase__ : Union[str, Any] = model(A ).logits.cpu().numpy()
UpperCAmelCase__ : Any = processor.decode(logits[0] ,output_word_offsets=A )
UpperCAmelCase__ : str = model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
UpperCAmelCase__ : Union[str, Any] = [
{
"""start_time""": d["""start_offset"""] * time_offset,
"""end_time""": d["""end_offset"""] * time_offset,
"""word""": d["""word"""],
}
for d in output["""word_offsets"""]
]
UpperCAmelCase__ : Dict = """WHY DOES MILISANDRA LOOK LIKE SHE WANTS TO CONSUME JOHN SNOW ON THE RIVER AT THE WALL"""
# output words
self.assertEqual(""" """.join(self.get_from_offsets(A ,"""word""" ) ) ,A )
self.assertEqual(""" """.join(self.get_from_offsets(A ,"""word""" ) ) ,output.text )
# output times
UpperCAmelCase__ : str = torch.tensor(self.get_from_offsets(A ,"""start_time""" ) )
UpperCAmelCase__ : List[Any] = torch.tensor(self.get_from_offsets(A ,"""end_time""" ) )
# fmt: off
UpperCAmelCase__ : Union[str, Any] = torch.tensor([1.4_1_9_9, 1.6_5_9_9, 2.2_5_9_9, 3.0, 3.2_4, 3.5_9_9_9, 3.7_9_9_9, 4.0_9_9_9, 4.2_6, 4.9_4, 5.2_8, 5.6_5_9_9, 5.7_8, 5.9_4, 6.3_2, 6.5_3_9_9, 6.6_5_9_9] )
UpperCAmelCase__ : List[Any] = torch.tensor([1.5_3_9_9, 1.8_9_9_9, 2.9, 3.1_6, 3.5_3_9_9, 3.7_2, 4.0_1_9_9, 4.1_7_9_9, 4.7_6, 5.1_5_9_9, 5.5_5_9_9, 5.6_9_9_9, 5.8_6, 6.1_9_9_9, 6.3_8, 6.6_1_9_9, 6.9_4] )
# fmt: on
self.assertTrue(torch.allclose(A ,A ,atol=0.0_1 ) )
self.assertTrue(torch.allclose(A ,A ,atol=0.0_1 ) )
| 65 | 0 |
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase ) -> Dict:
return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase=0 ) -> Union[str, Any]:
return sorted(__UpperCamelCase , key=lambda __lowerCAmelCase : x[column] )
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=float('''inf''' ) ) -> Any:
for i in range(points_counts - 1 ):
for j in range(i + 1 , __UpperCamelCase ):
snake_case__ = euclidean_distance_sqr(points[i] , points[j] )
if current_dis < min_dis:
snake_case__ = current_dis
return min_dis
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=float('''inf''' ) ) -> str:
for i in range(min(6 , points_counts - 1 ) , __UpperCamelCase ):
for j in range(max(0 , i - 6 ) , __UpperCamelCase ):
snake_case__ = euclidean_distance_sqr(points[i] , points[j] )
if current_dis < min_dis:
snake_case__ = current_dis
return min_dis
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Union[str, Any]:
if points_counts <= 3:
return dis_between_closest_pair(__UpperCamelCase , __UpperCamelCase )
# recursion
snake_case__ = points_counts // 2
snake_case__ = closest_pair_of_points_sqr(
__UpperCamelCase , points_sorted_on_y[:mid] , __UpperCamelCase )
snake_case__ = closest_pair_of_points_sqr(
__UpperCamelCase , points_sorted_on_y[mid:] , points_counts - mid )
snake_case__ = min(__UpperCamelCase , __UpperCamelCase )
snake_case__ = []
for point in points_sorted_on_x:
if abs(point[0] - points_sorted_on_x[mid][0] ) < closest_pair_dis:
cross_strip.append(__UpperCamelCase )
snake_case__ = dis_between_closest_in_strip(
__UpperCamelCase , len(__UpperCamelCase ) , __UpperCamelCase )
return min(__UpperCamelCase , __UpperCamelCase )
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase ) -> int:
snake_case__ = column_based_sort(__UpperCamelCase , column=0 )
snake_case__ = column_based_sort(__UpperCamelCase , column=1 )
return (
closest_pair_of_points_sqr(
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
) ** 0.5
if __name__ == "__main__":
lowerCamelCase__ : Dict = [(2, 3), (1_2, 3_0), (4_0, 5_0), (5, 1), (1_2, 1_0), (3, 4)]
print("""Distance:""", closest_pair_of_points(points, len(points)))
| 33 |
"""simple docstring"""
from sklearn.metrics import fa_score
import datasets
__UpperCAmelCase = '\nThe F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:\nF1 = 2 * (precision * recall) / (precision + recall)\n'
__UpperCAmelCase = '\nArgs:\n predictions (`list` of `int`): Predicted labels.\n references (`list` of `int`): Ground truth labels.\n labels (`list` of `int`): The set of labels to include when `average` is not set to `\'binary\'`, and the order of the labels if `average` is `None`. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class. Labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in `predictions` and `references` are used in sorted order. Defaults to None.\n pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.\n average (`string`): This parameter is required for multiclass/multilabel targets. If set to `None`, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `\'binary\'`.\n\n - \'binary\': Only report results for the class specified by `pos_label`. This is applicable only if the classes found in `predictions` and `references` are binary.\n - \'micro\': Calculate metrics globally by counting the total true positives, false negatives and false positives.\n - \'macro\': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.\n - \'weighted\': Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `\'macro\'` to account for label imbalance. This option can result in an F-score that is not between precision and recall.\n - \'samples\': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).\n sample_weight (`list` of `float`): Sample weights Defaults to None.\n\nReturns:\n f1 (`float` or `array` of `float`): F1 score or list of f1 scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher f1 scores are better.\n\nExamples:\n\n Example 1-A simple binary example\n >>> f1_metric = datasets.load_metric("f1")\n >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])\n >>> print(results)\n {\'f1\': 0.5}\n\n Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.\n >>> f1_metric = datasets.load_metric("f1")\n >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)\n >>> print(round(results[\'f1\'], 2))\n 0.67\n\n Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.\n >>> f1_metric = datasets.load_metric("f1")\n >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], sample_weight=[0.9, 0.5, 3.9, 1.2, 0.3])\n >>> print(round(results[\'f1\'], 2))\n 0.35\n\n Example 4-A multiclass example, with different values for the `average` input.\n >>> predictions = [0, 2, 1, 0, 0, 1]\n >>> references = [0, 1, 2, 0, 1, 2]\n >>> results = f1_metric.compute(predictions=predictions, references=references, average="macro")\n >>> print(round(results[\'f1\'], 2))\n 0.27\n >>> results = f1_metric.compute(predictions=predictions, references=references, average="micro")\n >>> print(round(results[\'f1\'], 2))\n 0.33\n >>> results = f1_metric.compute(predictions=predictions, references=references, average="weighted")\n >>> print(round(results[\'f1\'], 2))\n 0.27\n >>> results = f1_metric.compute(predictions=predictions, references=references, average=None)\n >>> print(results)\n {\'f1\': array([0.8, 0. , 0. ])}\n'
__UpperCAmelCase = '\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __lowercase ( datasets.Metric ):
def __lowercase ( self : List[Any] ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION ,citation=_CITATION ,inputs_description=_KWARGS_DESCRIPTION ,features=datasets.Features(
{
"""predictions""": datasets.Sequence(datasets.Value("""int32""" ) ),
"""references""": datasets.Sequence(datasets.Value("""int32""" ) ),
}
if self.config_name == """multilabel"""
else {
"""predictions""": datasets.Value("""int32""" ),
"""references""": datasets.Value("""int32""" ),
} ) ,reference_urls=["""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html"""] ,)
def __lowercase ( self : Union[str, Any] ,A : List[str] ,A : List[Any] ,A : Optional[Any]=None ,A : List[str]=1 ,A : Optional[Any]="binary" ,A : Any=None ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = fa_score(
A ,A ,labels=A ,pos_label=A ,average=A ,sample_weight=A )
return {"f1": float(A ) if score.size == 1 else score}
| 65 | 0 |
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..utils.py_utils import no_op_if_value_is_null, string_to_dict
if TYPE_CHECKING:
from .features import FeatureType
snake_case , snake_case , snake_case : int = False, False, False
@dataclass
class __lowercase :
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = None
SCREAMING_SNAKE_CASE : Optional[Any] = True
SCREAMING_SNAKE_CASE : str = True
SCREAMING_SNAKE_CASE : Tuple = None
# Automatically constructed
SCREAMING_SNAKE_CASE : Optional[int] = "dict"
SCREAMING_SNAKE_CASE : Tuple = pa.struct({"bytes": pa.binary(), "path": pa.string()} )
SCREAMING_SNAKE_CASE : int = field(default="Audio" , init=__lowerCamelCase , repr=__lowerCamelCase )
def __call__( self )-> Any:
return self.pa_type
def __magic_name__ ( self , A_ )-> Optional[int]:
try:
import soundfile as sf # soundfile is a dependency of librosa, needed to decode audio files.
except ImportError as err:
raise ImportError('To support encoding audio data, please install \'soundfile\'.' ) from err
if isinstance(A_ , A_ ):
return {"bytes": None, "path": value}
elif isinstance(A_ , A_ ):
return {"bytes": value, "path": None}
elif "array" in value:
# convert the audio array to wav bytes
_SCREAMING_SNAKE_CASE = BytesIO()
sf.write(A_ , value['array'] , value['sampling_rate'] , format='wav' )
return {"bytes": buffer.getvalue(), "path": None}
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
if value["path"].endswith('pcm' ):
# "PCM" only has raw audio bytes
if value.get('sampling_rate' ) is None:
# At least, If you want to convert "PCM-byte" to "WAV-byte", you have to know sampling rate
raise KeyError('To use PCM files, please specify a \'sampling_rate\' in Audio object' )
if value.get('bytes' ):
# If we already had PCM-byte, we don`t have to make "read file, make bytes" (just use it!)
_SCREAMING_SNAKE_CASE = np.frombuffer(value['bytes'] , dtype=np.intaa ).astype(np.floataa ) / 32767
else:
_SCREAMING_SNAKE_CASE = np.memmap(value['path'] , dtype='h' , mode='r' ).astype(np.floataa ) / 32767
_SCREAMING_SNAKE_CASE = BytesIO(bytes() )
sf.write(A_ , A_ , value['sampling_rate'] , format='wav' )
return {"bytes": buffer.getvalue(), "path": None}
else:
return {"bytes": None, "path": value.get('path' )}
elif value.get('bytes' ) is not None or value.get('path' ) is not None:
# store the audio bytes, and path is used to infer the audio format using the file extension
return {"bytes": value.get('bytes' ), "path": value.get('path' )}
else:
raise ValueError(
F'''An audio sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.''' )
def __magic_name__ ( self , A_ , A_ = None )-> Dict:
if not self.decode:
raise RuntimeError('Decoding is disabled for this feature. Please use Audio(decode=True) instead.' )
_SCREAMING_SNAKE_CASE = (value["""path"""], BytesIO(value['bytes'] )) if value["""bytes"""] is not None else (value["""path"""], None)
if path is None and file is None:
raise ValueError(F'''An audio sample should have one of \'path\' or \'bytes\' but both are None in {value}.''' )
try:
import librosa
import soundfile as sf
except ImportError as err:
raise ImportError('To support decoding audio files, please install \'librosa\' and \'soundfile\'.' ) from err
_SCREAMING_SNAKE_CASE = xsplitext(A_ )[1][1:].lower() if path is not None else None
if not config.IS_OPUS_SUPPORTED and audio_format == "opus":
raise RuntimeError(
'Decoding \'opus\' files requires system library \'libsndfile\'>=1.0.31, '
'You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. ' )
elif not config.IS_MP3_SUPPORTED and audio_format == "mp3":
raise RuntimeError(
'Decoding \'mp3\' files requires system library \'libsndfile\'>=1.1.0, '
'You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. ' )
if file is None:
_SCREAMING_SNAKE_CASE = token_per_repo_id or {}
_SCREAMING_SNAKE_CASE = path.split('::' )[-1]
try:
_SCREAMING_SNAKE_CASE = string_to_dict(A_ , config.HUB_DATASETS_URL )["""repo_id"""]
_SCREAMING_SNAKE_CASE = token_per_repo_id[repo_id]
except (ValueError, KeyError):
_SCREAMING_SNAKE_CASE = None
with xopen(A_ , 'rb' , use_auth_token=A_ ) as f:
_SCREAMING_SNAKE_CASE = sf.read(A_ )
else:
_SCREAMING_SNAKE_CASE = sf.read(A_ )
_SCREAMING_SNAKE_CASE = array.T
if self.mono:
_SCREAMING_SNAKE_CASE = librosa.to_mono(A_ )
if self.sampling_rate and self.sampling_rate != sampling_rate:
_SCREAMING_SNAKE_CASE = librosa.resample(A_ , orig_sr=A_ , target_sr=self.sampling_rate )
_SCREAMING_SNAKE_CASE = self.sampling_rate
return {"path": path, "array": array, "sampling_rate": sampling_rate}
def __magic_name__ ( self )-> Optional[int]:
from .features import Value
if self.decode:
raise ValueError('Cannot flatten a decoded Audio feature.' )
return {
"bytes": Value('binary' ),
"path": Value('string' ),
}
def __magic_name__ ( self , A_ )-> List[Any]:
if pa.types.is_string(storage.type ):
_SCREAMING_SNAKE_CASE = pa.array([None] * len(A_ ) , type=pa.binary() )
_SCREAMING_SNAKE_CASE = pa.StructArray.from_arrays([bytes_array, storage] , ['bytes', 'path'] , mask=storage.is_null() )
elif pa.types.is_binary(storage.type ):
_SCREAMING_SNAKE_CASE = pa.array([None] * len(A_ ) , type=pa.string() )
_SCREAMING_SNAKE_CASE = pa.StructArray.from_arrays([storage, path_array] , ['bytes', 'path'] , mask=storage.is_null() )
elif pa.types.is_struct(storage.type ) and storage.type.get_all_field_indices('array' ):
_SCREAMING_SNAKE_CASE = pa.array([Audio().encode_example(A_ ) if x is not None else None for x in storage.to_pylist()] )
elif pa.types.is_struct(storage.type ):
if storage.type.get_field_index('bytes' ) >= 0:
_SCREAMING_SNAKE_CASE = storage.field('bytes' )
else:
_SCREAMING_SNAKE_CASE = pa.array([None] * len(A_ ) , type=pa.binary() )
if storage.type.get_field_index('path' ) >= 0:
_SCREAMING_SNAKE_CASE = storage.field('path' )
else:
_SCREAMING_SNAKE_CASE = pa.array([None] * len(A_ ) , type=pa.string() )
_SCREAMING_SNAKE_CASE = pa.StructArray.from_arrays([bytes_array, path_array] , ['bytes', 'path'] , mask=storage.is_null() )
return array_cast(A_ , self.pa_type )
def __magic_name__ ( self , A_ )-> str:
@no_op_if_value_is_null
def path_to_bytes(A_ ):
with xopen(A_ , 'rb' ) as f:
_SCREAMING_SNAKE_CASE = f.read()
return bytes_
_SCREAMING_SNAKE_CASE = 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() , )
_SCREAMING_SNAKE_CASE = pa.array(
[os.path.basename(A_ ) if path is not None else None for path in storage.field('path' ).to_pylist()] , type=pa.string() , )
_SCREAMING_SNAKE_CASE = pa.StructArray.from_arrays([bytes_array, path_array] , ['bytes', 'path'] , mask=bytes_array.is_null() )
return array_cast(A_ , self.pa_type )
| 605 |
"""simple docstring"""
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available
if is_sentencepiece_available():
from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json
from ...test_tokenization_common import TokenizerTesterMixin
__UpperCAmelCase = get_tests_dir('fixtures/test_sentencepiece.model')
__UpperCAmelCase = {'target_lang': 'fi', 'source_lang': 'en'}
__UpperCAmelCase = '>>zh<<'
__UpperCAmelCase = 'Helsinki-NLP/'
if is_torch_available():
__UpperCAmelCase = 'pt'
elif is_tf_available():
__UpperCAmelCase = 'tf'
else:
__UpperCAmelCase = 'jax'
@require_sentencepiece
class __lowercase ( __lowerCamelCase , unittest.TestCase ):
snake_case_ = MarianTokenizer
snake_case_ = False
snake_case_ = True
def __lowercase ( self : Optional[int] ):
'''simple docstring'''
super().setUp()
UpperCAmelCase__ : Optional[Any] = ["""</s>""", """<unk>""", """▁This""", """▁is""", """▁a""", """▁t""", """est""", """\u0120""", """<pad>"""]
UpperCAmelCase__ : int = dict(zip(A ,range(len(A ) ) ) )
UpperCAmelCase__ : Optional[int] = Path(self.tmpdirname )
save_json(A ,save_dir / VOCAB_FILES_NAMES["""vocab"""] )
save_json(A ,save_dir / VOCAB_FILES_NAMES["""tokenizer_config_file"""] )
if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
copyfile(A ,save_dir / VOCAB_FILES_NAMES["""source_spm"""] )
copyfile(A ,save_dir / VOCAB_FILES_NAMES["""target_spm"""] )
UpperCAmelCase__ : Dict = MarianTokenizer.from_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname )
def __lowercase ( self : List[Any] ,**A : List[Any] ):
'''simple docstring'''
return MarianTokenizer.from_pretrained(self.tmpdirname ,**A )
def __lowercase ( self : Union[str, Any] ,A : Tuple ):
'''simple docstring'''
return (
"This is a test",
"This is a test",
)
def __lowercase ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = """</s>"""
UpperCAmelCase__ : int = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(A ) ,A )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(A ) ,A )
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ : Dict = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] ,"""</s>""" )
self.assertEqual(vocab_keys[1] ,"""<unk>""" )
self.assertEqual(vocab_keys[-1] ,"""<pad>""" )
self.assertEqual(len(A ) ,9 )
def __lowercase ( self : Dict ):
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size ,9 )
def __lowercase ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = MarianTokenizer.from_pretrained(f"{ORG_NAME}opus-mt-en-de" )
UpperCAmelCase__ : List[str] = en_de_tokenizer(["""I am a small frog"""] ,return_tensors=A )
self.assertIsInstance(A ,A )
UpperCAmelCase__ : str = [38, 121, 14, 697, 38_848, 0]
self.assertListEqual(A ,batch.input_ids[0] )
UpperCAmelCase__ : Optional[Any] = tempfile.mkdtemp()
en_de_tokenizer.save_pretrained(A )
UpperCAmelCase__ : Tuple = [x.name for x in Path(A ).glob("""*""" )]
self.assertIn("""source.spm""" ,A )
MarianTokenizer.from_pretrained(A )
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = self.get_tokenizer()
UpperCAmelCase__ : Any = tok(
["""I am a small frog""" * 1_000, """I am a small frog"""] ,padding=A ,truncation=A ,return_tensors=A )
self.assertIsInstance(A ,A )
self.assertEqual(batch.input_ids.shape ,(2, 512) )
def __lowercase ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase__ : int = self.get_tokenizer()
UpperCAmelCase__ : Tuple = tok(["""I am a tiny frog""", """I am a small frog"""] ,padding=A ,return_tensors=A )
self.assertIsInstance(A ,A )
self.assertEqual(batch_smaller.input_ids.shape ,(2, 10) )
@slow
def __lowercase ( self : Dict ):
'''simple docstring'''
# fmt: off
UpperCAmelCase__ : Optional[int] = {"""input_ids""": [[43_495, 462, 20, 42_164, 1_369, 52, 464, 132, 1_703, 492, 13, 7_491, 38_999, 6, 8, 464, 132, 1_703, 492, 13, 4_669, 37_867, 13, 7_525, 27, 1_593, 988, 13, 33_972, 7_029, 6, 20, 8_251, 383, 2, 270, 5_866, 3_788, 2, 2_353, 8_251, 12_338, 2, 13_958, 387, 2, 3_629, 6_953, 188, 2_900, 2, 13_958, 8_011, 11_501, 23, 8_460, 4_073, 34_009, 20, 435, 11_439, 27, 8, 8_460, 4_073, 6_004, 20, 9_988, 375, 27, 33, 266, 1_945, 1_076, 1_350, 37_867, 3_288, 5, 577, 1_076, 4_374, 8, 5_082, 5, 26_453, 257, 556, 403, 2, 242, 132, 383, 316, 492, 8, 10_767, 6, 316, 304, 4_239, 3, 0], [148, 15_722, 19, 1_839, 12, 1_350, 13, 22_327, 5_082, 5_418, 47_567, 35_938, 59, 318, 19_552, 108, 2_183, 54, 14_976, 4_835, 32, 547, 1_114, 8, 315, 2_417, 5, 92, 19_088, 3, 0, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100], [36, 6_395, 12_570, 39_147, 11_597, 6, 266, 4, 45_405, 7_296, 3, 0, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100]], """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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=A ,model_name="""Helsinki-NLP/opus-mt-en-de""" ,revision="""1a8c2263da11e68e50938f97e10cd57820bd504c""" ,decode_kwargs={"""use_source_tokenizer""": True} ,)
def __lowercase ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = MarianTokenizer.from_pretrained("""hf-internal-testing/test-marian-two-vocabs""" )
UpperCAmelCase__ : Any = """Tämä on testi"""
UpperCAmelCase__ : int = """This is a test"""
UpperCAmelCase__ : List[str] = [76, 7, 2_047, 2]
UpperCAmelCase__ : Optional[Any] = [69, 12, 11, 940, 2]
UpperCAmelCase__ : List[str] = tokenizer(A ).input_ids
self.assertListEqual(A ,A )
UpperCAmelCase__ : Optional[int] = tokenizer(text_target=A ).input_ids
self.assertListEqual(A ,A )
UpperCAmelCase__ : int = tokenizer.decode(A ,skip_special_tokens=A )
self.assertEqual(A ,A )
| 65 | 0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.