code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
import math
def lowerCAmelCase__ ( lowerCamelCase_ : str ,lowerCamelCase_ : str):
'''simple docstring'''
lowerCAmelCase__ : int = len(_lowerCAmelCase)
lowerCAmelCase__ : List[str] = int(math.floor(math.sqrt(_lowerCAmelCase)))
lowerCAmelCase__ : Optional[Any] = 0
while arr[min(_lowerCAmelCase ,_lowerCAmelCase) - 1] < x:
lowerCAmelCase__ : List[str] = step
step += int(math.floor(math.sqrt(_lowerCAmelCase)))
if prev >= n:
return -1
while arr[prev] < x:
lowerCAmelCase__ : Optional[Any] = prev + 1
if prev == min(_lowerCAmelCase ,_lowerCAmelCase):
return -1
if arr[prev] == x:
return prev
return -1
if __name__ == "__main__":
__snake_case : int =input('Enter numbers separated by a comma:\n').strip()
__snake_case : Union[str, Any] =[int(item) for item in user_input.split(',')]
__snake_case : Dict =int(input('Enter the number to be searched:\n'))
__snake_case : Any =jump_search(arr, x)
if res == -1:
print('Number not found!')
else:
print(f"""Number {x} is at index {res}""")
| 129 |
"""simple docstring"""
import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field
import torch
from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
def lowercase ():
# Get the sagemaker specific mp parameters from smp_options variable.
__lowerCAmelCase = os.getenv("""SM_HP_MP_PARAMETERS""" , """{}""" )
try:
# Parse it and check the field "partitions" is included, it is required for model parallel.
__lowerCAmelCase = json.loads(_lowerCAmelCase )
if "partitions" not in smp_options:
return False
except json.JSONDecodeError:
return False
# Get the sagemaker specific framework parameters from mpi_options variable.
__lowerCAmelCase = os.getenv("""SM_FRAMEWORK_PARAMS""" , """{}""" )
try:
# Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
__lowerCAmelCase = json.loads(_lowerCAmelCase )
if not mpi_options.get("""sagemaker_mpi_enabled""" , _lowerCAmelCase ):
return False
except json.JSONDecodeError:
return False
# Lastly, check if the `smdistributed` module is present.
return importlib.util.find_spec("""smdistributed""" ) is not None
if is_sagemaker_model_parallel_available():
import smdistributed.modelparallel.torch as smp
smp.init()
@dataclass
class lowerCAmelCase_ ( A__ ):
'''simple docstring'''
_snake_case = field(
default='''''' , metadata={'''help''': '''Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer'''} , )
def A__ ( self ) -> Tuple:
super().__post_init__()
warnings.warn(
"""`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use """
"""`TrainingArguments` instead.""" , snake_case_ , )
@cached_property
def A__ ( self ) -> "torch.device":
logger.info("""PyTorch: setting up devices""" )
if torch.distributed.is_available() and torch.distributed.is_initialized() and self.local_rank == -1:
logger.warning(
"""torch.distributed process group is initialized, but local_rank == -1. """
"""In order to use Torch DDP, launch your script with `python -m torch.distributed.launch""" )
if self.no_cuda:
__lowerCAmelCase = torch.device("""cpu""" )
__lowerCAmelCase = 0
elif is_sagemaker_model_parallel_available():
__lowerCAmelCase = smp.local_rank()
__lowerCAmelCase = torch.device("""cuda""" , snake_case_ )
__lowerCAmelCase = 1
elif is_sagemaker_dp_enabled():
import smdistributed.dataparallel.torch.torch_smddp # noqa: F401
torch.distributed.init_process_group(backend="""smddp""" , timeout=self.ddp_timeout_delta )
__lowerCAmelCase = int(os.getenv("""SMDATAPARALLEL_LOCAL_RANK""" ) )
__lowerCAmelCase = torch.device("""cuda""" , self.local_rank )
__lowerCAmelCase = 1
elif self.local_rank == -1:
# if n_gpu is > 1 we'll use nn.DataParallel.
# If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
# Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will
# trigger an error that a device index is missing. Index 0 takes into account the
# GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`
# will use the first GPU in that env, i.e. GPU#1
__lowerCAmelCase = torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu""" )
# Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at
# the default value.
__lowerCAmelCase = torch.cuda.device_count()
else:
# Here, we'll use torch.distributed.
# Initializes the distributed backend which will take care of synchronizing nodes/GPUs
if not torch.distributed.is_initialized():
torch.distributed.init_process_group(backend="""nccl""" , timeout=self.ddp_timeout_delta )
__lowerCAmelCase = torch.device("""cuda""" , self.local_rank )
__lowerCAmelCase = 1
if device.type == "cuda":
torch.cuda.set_device(snake_case_ )
return device
@property
def A__ ( self ) -> Dict:
if is_sagemaker_model_parallel_available():
return smp.dp_size()
return super().world_size
@property
def A__ ( self ) -> Optional[int]:
return not is_sagemaker_model_parallel_available()
@property
def A__ ( self ) -> Tuple:
return False
| 301 | 0 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
UniSpeechConfig,
UniSpeechForCTC,
UniSpeechForPreTraining,
WavaVecaFeatureExtractor,
WavaVecaPhonemeCTCTokenizer,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
_UpperCamelCase : Optional[int] = logging.get_logger(__name__)
_UpperCamelCase : Dict = {
'post_extract_proj': 'feature_projection.projection',
'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
'fc2': 'encoder.layers.*.feed_forward.output_dense',
'final_layer_norm': 'encoder.layers.*.final_layer_norm',
'encoder.layer_norm': 'encoder.layer_norm',
'w2v_model.layer_norm': 'feature_projection.layer_norm',
'quantizer.weight_proj': 'quantizer.weight_proj',
'quantizer.vars': 'quantizer.codevectors',
'project_q': 'project_q',
'final_proj': 'project_hid',
'w2v_encoder.proj': 'ctc_proj',
'mask_emb': 'masked_spec_embed',
}
_UpperCamelCase : Dict = [
'ctc_proj',
'quantizer.weight_proj',
'quantizer.codevectors',
'project_q',
'project_hid',
]
def snake_case (A_ :str , A_ :Tuple , A_ :Optional[Any] , A_ :int , A_ :Optional[int] , A_ :Dict ):
'''simple docstring'''
for attribute in key.split('.' ):
if is_finetuned:
if attribute in ["quantizer", "project_q", "project_hid"]:
# those layers are only relevant for pretraining and should be dropped
return
if attribute == "ctc_proj":
# we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
a : int = 'lm_head'
a : Union[str, Any] = getattr(A_ , A_ )
if weight_type is not None:
a : Any = getattr(A_ , A_ ).shape
else:
a : str = hf_pointer.shape
assert hf_shape == value.shape, (
f'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''
f''' {value.shape} for {full_name}'''
)
if weight_type == "weight":
a : int = value
elif weight_type == "weight_g":
a : List[str] = value
elif weight_type == "weight_v":
a : List[Any] = value
elif weight_type == "bias":
a : List[str] = value
else:
a : Optional[int] = value
logger.info(f'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.''' )
def snake_case (A_ :str , A_ :List[str] , A_ :Optional[int] ):
'''simple docstring'''
a : Any = []
a : str = fairseq_model.state_dict()
a : Optional[int] = hf_model.unispeech.feature_extractor
for name, value in fairseq_dict.items():
a : Tuple = False
if "conv_layers" in name:
load_conv_layer(
A_ , A_ , A_ , A_ , hf_model.config.feat_extract_norm == 'group' , )
a : Optional[int] = True
else:
for key, mapped_key in MAPPING.items():
a : int = 'unispeech.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split('w2v_model.' )[-1] == name.split('.' )[0]:
a : Optional[Any] = True
if "*" in mapped_key:
a : Optional[int] = name.split(A_ )[0].split('.' )[-2]
a : List[str] = mapped_key.replace('*' , A_ )
if "weight_g" in name:
a : Any = 'weight_g'
elif "weight_v" in name:
a : List[Any] = 'weight_v'
elif "bias" in name:
a : str = 'bias'
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
a : Dict = 'weight'
else:
a : Optional[Any] = None
set_recursively(A_ , A_ , A_ , A_ , A_ , A_ )
continue
if not is_used:
unused_weights.append(A_ )
logger.warning(f'''Unused weights: {unused_weights}''' )
def snake_case (A_ :Union[str, Any] , A_ :Optional[Any] , A_ :List[str] , A_ :List[Any] , A_ :Any ):
'''simple docstring'''
a : Any = full_name.split('conv_layers.' )[-1]
a : Tuple = name.split('.' )
a : Optional[Any] = int(items[0] )
a : Optional[int] = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
f'''{full_name} has size {value.shape}, but'''
f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
)
a : Optional[int] = value
logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
f'''{full_name} has size {value.shape}, but'''
f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
)
a : Optional[int] = value
logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
f'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
" found."
)
a : Optional[Any] = value
logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
f'''{full_name} has size {value.shape}, but'''
f''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
)
a : Optional[int] = value
logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
else:
unused_weights.append(A_ )
@torch.no_grad()
def snake_case (A_ :List[Any] , A_ :Optional[Any] , A_ :str=None , A_ :Any=None , A_ :List[Any]=True ):
'''simple docstring'''
if config_path is not None:
a : int = UniSpeechConfig.from_pretrained(A_ )
else:
a : int = UniSpeechConfig()
if is_finetuned:
if dict_path:
a : Optional[int] = Dictionary.load_from_json(A_ )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
a : List[Any] = target_dict.pad_index
a : List[str] = target_dict.bos_index
a : Optional[int] = target_dict.eos_index
a : List[Any] = len(target_dict.symbols )
a : Union[str, Any] = os.path.join(A_ , 'vocab.json' )
if not os.path.isdir(A_ ):
logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(A_ ) )
return
os.makedirs(A_ , exist_ok=A_ )
a : Union[str, Any] = target_dict.indices
# fairseq has the <pad> and <s> switched
a : List[str] = 4_2
a : int = 4_3
with open(A_ , 'w' , encoding='utf-8' ) as vocab_handle:
json.dump(A_ , A_ )
a : List[str] = WavaVecaPhonemeCTCTokenizer(
A_ , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token='|' , do_lower_case=A_ , )
a : Optional[Any] = True if config.feat_extract_norm == 'layer' else False
a : List[Any] = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=1_6_0_0_0 , padding_value=0 , do_normalize=A_ , return_attention_mask=A_ , )
a : Dict = WavaVecaProcessor(feature_extractor=A_ , tokenizer=A_ )
processor.save_pretrained(A_ )
a : List[Any] = UniSpeechForCTC(A_ )
else:
a : int = UniSpeechForPreTraining(A_ )
if is_finetuned:
a, a, a : Union[str, Any] = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'data': '/'.join(dict_path.split('/' )[:-1] ), 'w2v_path': checkpoint_path} )
else:
a, a, a : Optional[int] = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] )
a : str = model[0].eval()
recursively_load_weights(A_ , A_ , A_ )
hf_unispeech.save_pretrained(A_ )
if __name__ == "__main__":
_UpperCamelCase : Dict = argparse.ArgumentParser()
parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
parser.add_argument(
'--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
)
_UpperCamelCase : Optional[int] = parser.parse_args()
convert_unispeech_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 186 |
"""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 : int = logging.get_logger(__name__)
class snake_case ( UpperCAmelCase ):
__magic_name__ = ['''input_features''', '''attention_mask''']
def __init__( self : Optional[int] , A : Optional[Any]=8_0 , A : str=1_6_0_0_0 , A : List[str]=0.0 , A : Any=1_0 , A : Union[str, Any]=2_5 , A : str="hamming_window" , A : str=3_27_68.0 , A : Union[str, Any]=0.97 , A : Dict=1.0 , A : Any=True , A : Union[str, Any]=True , A : List[Any]=False , **A : Tuple , ):
'''simple docstring'''
super().__init__(feature_size=A , sampling_rate=A , padding_value=A , **A )
a : Any = feature_size
a : List[Any] = sampling_rate
a : Any = padding_value
a : str = hop_length
a : Any = win_length
a : List[Any] = frame_signal_scale
a : Tuple = preemphasis_coeff
a : Dict = mel_floor
a : Optional[int] = normalize_means
a : List[str] = normalize_vars
a : Dict = win_function
a : Union[str, Any] = return_attention_mask
a : List[Any] = win_length * sampling_rate // 1_0_0_0
a : Tuple = hop_length * sampling_rate // 1_0_0_0
a : List[Any] = optimal_fft_length(self.sample_size )
a : Any = (self.n_fft // 2) + 1
def lowerCamelCase__ ( self : List[Any] , A : np.array ):
'''simple docstring'''
if self.win_function == "hamming_window":
a : List[str] = window_function(window_length=self.sample_size , name=self.win_function , periodic=A )
else:
a : Dict = window_function(window_length=self.sample_size , name=self.win_function )
a : str = 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 : List[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 lowerCamelCase__ ( self : int , A : Tuple , A : int , A : Optional[int] ):
'''simple docstring'''
if self.normalize_means:
a : Any = x[:input_length].mean(axis=0 )
a : Dict = np.subtract(A , A )
if self.normalize_vars:
a : Dict = x[:input_length].std(axis=0 )
a : Dict = np.divide(A , A )
if input_length < x.shape[0]:
a : Dict = padding_value
# make sure array is in float32
a : Optional[int] = x.astype(np.floataa )
return x
def lowerCamelCase__ ( self : str , A : List[np.ndarray] , A : Optional[np.ndarray] = None ):
'''simple docstring'''
a : str = 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 : Dict , 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 : Union[str, Any] , ):
'''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 : 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}''' )
a : Dict = is_batched_numpy or (
isinstance(A , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
a : str = [np.asarray(A , dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(A , np.ndarray ):
a : List[str] = np.asarray(A , dtype=np.floataa )
elif isinstance(A , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
a : Optional[int] = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
a : Any = [raw_speech]
# extract fbank features
a : str = [self._extract_mfsc_features(A ) for one_waveform in raw_speech]
# convert into correct format for padding
a : int = BatchFeature({'input_features': features} )
a : Union[str, 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
a : Optional[Any] = padded_inputs.get('input_features' )
if isinstance(input_features[0] , A ):
a : List[str] = [np.asarray(A , dtype=np.floataa ) for feature in input_features]
a : List[Any] = padded_inputs.get('attention_mask' )
if attention_mask is not None:
a : int = [np.asarray(A , dtype=np.intaa ) for array in attention_mask]
if self.normalize_means or self.normalize_vars:
a : 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
)
a : List[str] = self.normalize(
padded_inputs['input_features'] , attention_mask=A )
if return_tensors is not None:
a : Optional[int] = padded_inputs.convert_to_tensors(A )
return padded_inputs
| 186 | 1 |
'''simple docstring'''
import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel
from ...utils import logging
_A : Any =logging.get_logger(__name__)
class _lowercase ( _lowercase ):
a = CLIPConfig
a = ["""CLIPEncoderLayer"""]
def __init__( self: Union[str, Any] , UpperCamelCase__: CLIPConfig ):
super().__init__(UpperCamelCase__ )
lowerCamelCase__ : List[Any] = CLIPVisionModelWithProjection(config.vision_config )
lowerCamelCase__ : str = nn.Linear(config.vision_config.projection_dim , 1 )
lowerCamelCase__ : Any = nn.Linear(config.vision_config.projection_dim , 1 )
@torch.no_grad()
def lowerCamelCase_ ( self: Dict , UpperCamelCase__: List[str] , UpperCamelCase__: Optional[int] , UpperCamelCase__: List[Any]=0.5 , UpperCamelCase__: Tuple=0.5 ):
lowerCamelCase__ : List[str] = self.vision_model(UpperCamelCase__ )[0]
lowerCamelCase__ : Optional[Any] = self.p_head(UpperCamelCase__ )
lowerCamelCase__ : Any = nsfw_detected.flatten()
lowerCamelCase__ : List[str] = nsfw_detected > p_threshold
lowerCamelCase__ : int = nsfw_detected.tolist()
if any(UpperCamelCase__ ):
logger.warning(
"""Potential NSFW content was detected in one or more images. A black image will be returned instead."""
""" Try again with a different prompt and/or seed.""" )
for idx, nsfw_detected_ in enumerate(UpperCamelCase__ ):
if nsfw_detected_:
lowerCamelCase__ : Optional[Any] = np.zeros(images[idx].shape )
lowerCamelCase__ : List[Any] = self.w_head(UpperCamelCase__ )
lowerCamelCase__ : Any = watermark_detected.flatten()
lowerCamelCase__ : Optional[int] = watermark_detected > w_threshold
lowerCamelCase__ : int = watermark_detected.tolist()
if any(UpperCamelCase__ ):
logger.warning(
"""Potential watermarked content was detected in one or more images. A black image will be returned instead."""
""" Try again with a different prompt and/or seed.""" )
for idx, watermark_detected_ in enumerate(UpperCamelCase__ ):
if watermark_detected_:
lowerCamelCase__ : List[str] = np.zeros(images[idx].shape )
return images, nsfw_detected, watermark_detected
| 41 |
"""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 _lowerCamelCase ( _lowercase , unittest.TestCase ):
UpperCAmelCase_ = KandinskyVaaControlnetPipeline
UpperCAmelCase_ = ["image_embeds", "negative_image_embeds", "hint"]
UpperCAmelCase_ = ["image_embeds", "negative_image_embeds", "hint"]
UpperCAmelCase_ = [
"generator",
"height",
"width",
"latents",
"guidance_scale",
"num_inference_steps",
"return_dict",
"guidance_scale",
"num_images_per_prompt",
"output_type",
"return_dict",
]
UpperCAmelCase_ = False
@property
def snake_case_ (self ) -> Tuple:
return 32
@property
def snake_case_ (self ) -> Optional[int]:
return 32
@property
def snake_case_ (self ) -> int:
return self.time_input_dim
@property
def snake_case_ (self ) -> Dict:
return self.time_input_dim * 4
@property
def snake_case_ (self ) -> List[str]:
return 1_00
@property
def snake_case_ (self ) -> Union[str, Any]:
torch.manual_seed(0 )
UpperCamelCase = {
"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 = UNetaDConditionModel(**__a )
return model
@property
def snake_case_ (self ) -> Dict:
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 snake_case_ (self ) -> Optional[Any]:
torch.manual_seed(0 )
UpperCamelCase = VQModel(**self.dummy_movq_kwargs )
return model
def snake_case_ (self ) -> Optional[Any]:
UpperCamelCase = self.dummy_unet
UpperCamelCase = self.dummy_movq
UpperCamelCase = DDIMScheduler(
num_train_timesteps=10_00 , beta_schedule="linear" , beta_start=0.00085 , beta_end=0.012 , clip_sample=__a , set_alpha_to_one=__a , steps_offset=1 , prediction_type="epsilon" , thresholding=__a , )
UpperCamelCase = {
"unet": unet,
"scheduler": scheduler,
"movq": movq,
}
return components
def snake_case_ (self , __a , __a=0 ) -> Any:
UpperCamelCase = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(__a ) ).to(__a )
UpperCamelCase = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
__a )
# create hint
UpperCamelCase = floats_tensor((1, 3, 64, 64) , rng=random.Random(__a ) ).to(__a )
if str(__a ).startswith("mps" ):
UpperCamelCase = torch.manual_seed(__a )
else:
UpperCamelCase = torch.Generator(device=__a ).manual_seed(__a )
UpperCamelCase = {
"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 snake_case_ (self ) -> int:
UpperCamelCase = "cpu"
UpperCamelCase = self.get_dummy_components()
UpperCamelCase = self.pipeline_class(**__a )
UpperCamelCase = pipe.to(__a )
pipe.set_progress_bar_config(disable=__a )
UpperCamelCase = pipe(**self.get_dummy_inputs(__a ) )
UpperCamelCase = output.images
UpperCamelCase = pipe(
**self.get_dummy_inputs(__a ) , return_dict=__a , )[0]
UpperCamelCase = image[0, -3:, -3:, -1]
UpperCamelCase = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
UpperCamelCase = np.array(
[0.6959826, 0.868279, 0.7558092, 0.68769467, 0.85805804, 0.65977496, 0.44885302, 0.5959111, 0.4251595] )
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 _lowerCamelCase ( unittest.TestCase ):
def snake_case_ (self ) -> Optional[Any]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def snake_case_ (self ) -> Dict:
UpperCamelCase = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy" )
UpperCamelCase = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/kandinskyv22/hint_image_cat.png" )
UpperCamelCase = torch.from_numpy(np.array(__a ) ).float() / 255.0
UpperCamelCase = hint.permute(2 , 0 , 1 ).unsqueeze(0 )
UpperCamelCase = KandinskyVaaPriorPipeline.from_pretrained(
"kandinsky-community/kandinsky-2-2-prior" , torch_dtype=torch.floataa )
pipe_prior.to(__a )
UpperCamelCase = KandinskyVaaControlnetPipeline.from_pretrained(
"kandinsky-community/kandinsky-2-2-controlnet-depth" , torch_dtype=torch.floataa )
UpperCamelCase = pipeline.to(__a )
pipeline.set_progress_bar_config(disable=__a )
UpperCamelCase = "A robot, 4k photo"
UpperCamelCase = torch.Generator(device="cuda" ).manual_seed(0 )
UpperCamelCase , UpperCamelCase = pipe_prior(
__a , generator=__a , num_inference_steps=5 , negative_prompt="" , ).to_tuple()
UpperCamelCase = torch.Generator(device="cuda" ).manual_seed(0 )
UpperCamelCase = pipeline(
image_embeds=__a , negative_image_embeds=__a , hint=__a , generator=__a , num_inference_steps=1_00 , output_type="np" , )
UpperCamelCase = output.images[0]
assert image.shape == (5_12, 5_12, 3)
assert_mean_pixel_difference(__a , __a )
| 153 | 0 |
'''simple docstring'''
import pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize("""dataset_size""" , [None, 4_00 * 2**20, 6_00 * 2**20] )
@pytest.mark.parametrize("""input_in_memory_max_size""" , ["""default""", 0, 1_00 * 2**20, 9_00 * 2**20] )
def __lowerCamelCase ( __lowerCAmelCase : List[Any] , __lowerCAmelCase : Dict , __lowerCAmelCase : Union[str, Any] ) -> Any:
if input_in_memory_max_size != "default":
monkeypatch.setattr(datasets.config , """IN_MEMORY_MAX_SIZE""" , a__ )
snake_case = datasets.config.IN_MEMORY_MAX_SIZE
if input_in_memory_max_size == "default":
assert in_memory_max_size == 0
else:
assert in_memory_max_size == input_in_memory_max_size
if dataset_size and in_memory_max_size:
snake_case = dataset_size < in_memory_max_size
else:
snake_case = False
snake_case = is_small_dataset(a__ )
assert result == expected
| 368 |
'''simple docstring'''
_SCREAMING_SNAKE_CASE = {"a": ["c", "b"], "b": ["d", "e"], "c": [], "d": [], "e": []}
_SCREAMING_SNAKE_CASE = ["a", "b", "c", "d", "e"]
def __lowerCamelCase ( __lowerCAmelCase : List[Any] , __lowerCAmelCase : str , __lowerCAmelCase : Optional[Any] ) -> Optional[int]:
snake_case = start
# add current to visited
visited.append(__lowerCAmelCase )
snake_case = edges[current]
for neighbor in neighbors:
# if neighbor not in visited, visit
if neighbor not in visited:
snake_case = topological_sort(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
# if all neighbors visited add current to sort
sort.append(__lowerCAmelCase )
# if all vertices haven't been visited select a new one to visit
if len(__lowerCAmelCase ) != len(__lowerCAmelCase ):
for vertice in vertices:
if vertice not in visited:
snake_case = topological_sort(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
# return sort
return sort
if __name__ == "__main__":
_SCREAMING_SNAKE_CASE = topological_sort("a", [], [])
print(sort)
| 3 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase__ = {
"""configuration_mctct""": ["""MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MCTCTConfig"""],
"""feature_extraction_mctct""": ["""MCTCTFeatureExtractor"""],
"""processing_mctct""": ["""MCTCTProcessor"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase__ = [
"""MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""MCTCTForCTC""",
"""MCTCTModel""",
"""MCTCTPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
from .feature_extraction_mctct import MCTCTFeatureExtractor
from .processing_mctct import MCTCTProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel
else:
import sys
lowerCamelCase__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__) | 86 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow
if is_tf_available():
from transformers import (
AutoConfig,
BertConfig,
GPTaConfig,
TaConfig,
TFAutoModel,
TFAutoModelForCausalLM,
TFAutoModelForMaskedLM,
TFAutoModelForPreTraining,
TFAutoModelForQuestionAnswering,
TFAutoModelForSeqaSeqLM,
TFAutoModelForSequenceClassification,
TFAutoModelWithLMHead,
TFBertForMaskedLM,
TFBertForPreTraining,
TFBertForQuestionAnswering,
TFBertForSequenceClassification,
TFBertModel,
TFGPTaLMHeadModel,
TFRobertaForMaskedLM,
TFTaForConditionalGeneration,
)
from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
if is_torch_available():
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForPreTraining,
AutoModelForQuestionAnswering,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoModelWithLMHead,
BertForMaskedLM,
BertForPreTraining,
BertForQuestionAnswering,
BertForSequenceClassification,
BertModel,
GPTaLMHeadModel,
RobertaForMaskedLM,
TaForConditionalGeneration,
)
@is_pt_tf_cross_test
class A__ ( unittest.TestCase):
@slow
def __lowerCamelCase ( self ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
__lowerCAmelCase : Tuple = AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE )
self.assertIsNotNone(_SCREAMING_SNAKE_CASE )
self.assertIsInstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
__lowerCAmelCase : List[str] = TFAutoModel.from_pretrained(_SCREAMING_SNAKE_CASE , from_pt=_SCREAMING_SNAKE_CASE )
self.assertIsNotNone(_SCREAMING_SNAKE_CASE )
self.assertIsInstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Optional[int] = AutoModel.from_pretrained(_SCREAMING_SNAKE_CASE , from_tf=_SCREAMING_SNAKE_CASE )
self.assertIsNotNone(_SCREAMING_SNAKE_CASE )
self.assertIsInstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
@slow
def __lowerCamelCase ( self ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
__lowerCAmelCase : int = AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE )
self.assertIsNotNone(_SCREAMING_SNAKE_CASE )
self.assertIsInstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Tuple = TFAutoModelForPreTraining.from_pretrained(_SCREAMING_SNAKE_CASE , from_pt=_SCREAMING_SNAKE_CASE )
self.assertIsNotNone(_SCREAMING_SNAKE_CASE )
self.assertIsInstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
__lowerCAmelCase : str = AutoModelForPreTraining.from_pretrained(_SCREAMING_SNAKE_CASE , from_tf=_SCREAMING_SNAKE_CASE )
self.assertIsNotNone(_SCREAMING_SNAKE_CASE )
self.assertIsInstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
@slow
def __lowerCamelCase ( self ):
for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowerCAmelCase : Dict = AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE )
self.assertIsNotNone(_SCREAMING_SNAKE_CASE )
self.assertIsInstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
__lowerCAmelCase : List[str] = TFAutoModelForCausalLM.from_pretrained(_SCREAMING_SNAKE_CASE , from_pt=_SCREAMING_SNAKE_CASE )
__lowerCAmelCase , __lowerCAmelCase : List[str] = TFAutoModelForCausalLM.from_pretrained(
_SCREAMING_SNAKE_CASE , output_loading_info=_SCREAMING_SNAKE_CASE , from_pt=_SCREAMING_SNAKE_CASE )
self.assertIsNotNone(_SCREAMING_SNAKE_CASE )
self.assertIsInstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Any = AutoModelForCausalLM.from_pretrained(_SCREAMING_SNAKE_CASE , from_tf=_SCREAMING_SNAKE_CASE )
__lowerCAmelCase , __lowerCAmelCase : List[Any] = AutoModelForCausalLM.from_pretrained(
_SCREAMING_SNAKE_CASE , output_loading_info=_SCREAMING_SNAKE_CASE , from_tf=_SCREAMING_SNAKE_CASE )
self.assertIsNotNone(_SCREAMING_SNAKE_CASE )
self.assertIsInstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
@slow
def __lowerCamelCase ( self ):
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowerCAmelCase : Dict = AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE )
self.assertIsNotNone(_SCREAMING_SNAKE_CASE )
self.assertIsInstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
__lowerCAmelCase : int = TFAutoModelWithLMHead.from_pretrained(_SCREAMING_SNAKE_CASE , from_pt=_SCREAMING_SNAKE_CASE )
self.assertIsNotNone(_SCREAMING_SNAKE_CASE )
self.assertIsInstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Optional[int] = AutoModelWithLMHead.from_pretrained(_SCREAMING_SNAKE_CASE , from_tf=_SCREAMING_SNAKE_CASE )
self.assertIsNotNone(_SCREAMING_SNAKE_CASE )
self.assertIsInstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
@slow
def __lowerCamelCase ( self ):
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowerCAmelCase : Optional[Any] = AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE )
self.assertIsNotNone(_SCREAMING_SNAKE_CASE )
self.assertIsInstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
__lowerCAmelCase : List[Any] = TFAutoModelForMaskedLM.from_pretrained(_SCREAMING_SNAKE_CASE , from_pt=_SCREAMING_SNAKE_CASE )
__lowerCAmelCase , __lowerCAmelCase : int = TFAutoModelForMaskedLM.from_pretrained(
_SCREAMING_SNAKE_CASE , output_loading_info=_SCREAMING_SNAKE_CASE , from_pt=_SCREAMING_SNAKE_CASE )
self.assertIsNotNone(_SCREAMING_SNAKE_CASE )
self.assertIsInstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Union[str, Any] = AutoModelForMaskedLM.from_pretrained(_SCREAMING_SNAKE_CASE , from_tf=_SCREAMING_SNAKE_CASE )
__lowerCAmelCase , __lowerCAmelCase : str = AutoModelForMaskedLM.from_pretrained(
_SCREAMING_SNAKE_CASE , output_loading_info=_SCREAMING_SNAKE_CASE , from_tf=_SCREAMING_SNAKE_CASE )
self.assertIsNotNone(_SCREAMING_SNAKE_CASE )
self.assertIsInstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
@slow
def __lowerCamelCase ( self ):
for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowerCAmelCase : Optional[int] = AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE )
self.assertIsNotNone(_SCREAMING_SNAKE_CASE )
self.assertIsInstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Tuple = TFAutoModelForSeqaSeqLM.from_pretrained(_SCREAMING_SNAKE_CASE , from_pt=_SCREAMING_SNAKE_CASE )
__lowerCAmelCase , __lowerCAmelCase : Tuple = TFAutoModelForSeqaSeqLM.from_pretrained(
_SCREAMING_SNAKE_CASE , output_loading_info=_SCREAMING_SNAKE_CASE , from_pt=_SCREAMING_SNAKE_CASE )
self.assertIsNotNone(_SCREAMING_SNAKE_CASE )
self.assertIsInstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
__lowerCAmelCase : int = AutoModelForSeqaSeqLM.from_pretrained(_SCREAMING_SNAKE_CASE , from_tf=_SCREAMING_SNAKE_CASE )
__lowerCAmelCase , __lowerCAmelCase : Dict = AutoModelForSeqaSeqLM.from_pretrained(
_SCREAMING_SNAKE_CASE , output_loading_info=_SCREAMING_SNAKE_CASE , from_tf=_SCREAMING_SNAKE_CASE )
self.assertIsNotNone(_SCREAMING_SNAKE_CASE )
self.assertIsInstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
@slow
def __lowerCamelCase ( self ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
__lowerCAmelCase : Dict = AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE )
self.assertIsNotNone(_SCREAMING_SNAKE_CASE )
self.assertIsInstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
__lowerCAmelCase : List[str] = TFAutoModelForSequenceClassification.from_pretrained(_SCREAMING_SNAKE_CASE , from_pt=_SCREAMING_SNAKE_CASE )
self.assertIsNotNone(_SCREAMING_SNAKE_CASE )
self.assertIsInstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Union[str, Any] = AutoModelForSequenceClassification.from_pretrained(_SCREAMING_SNAKE_CASE , from_tf=_SCREAMING_SNAKE_CASE )
self.assertIsNotNone(_SCREAMING_SNAKE_CASE )
self.assertIsInstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
@slow
def __lowerCamelCase ( self ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
__lowerCAmelCase : Union[str, Any] = AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE )
self.assertIsNotNone(_SCREAMING_SNAKE_CASE )
self.assertIsInstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Optional[Any] = TFAutoModelForQuestionAnswering.from_pretrained(_SCREAMING_SNAKE_CASE , from_pt=_SCREAMING_SNAKE_CASE )
self.assertIsNotNone(_SCREAMING_SNAKE_CASE )
self.assertIsInstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Union[str, Any] = AutoModelForQuestionAnswering.from_pretrained(_SCREAMING_SNAKE_CASE , from_tf=_SCREAMING_SNAKE_CASE )
self.assertIsNotNone(_SCREAMING_SNAKE_CASE )
self.assertIsInstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def __lowerCamelCase ( self ):
__lowerCAmelCase : List[Any] = TFAutoModelWithLMHead.from_pretrained(_SCREAMING_SNAKE_CASE , from_pt=_SCREAMING_SNAKE_CASE )
self.assertIsInstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
self.assertEqual(model.num_parameters() , 1_44_10 )
self.assertEqual(model.num_parameters(only_trainable=_SCREAMING_SNAKE_CASE ) , 1_44_10 )
__lowerCAmelCase : Tuple = AutoModelWithLMHead.from_pretrained(_SCREAMING_SNAKE_CASE , from_tf=_SCREAMING_SNAKE_CASE )
self.assertIsInstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
self.assertEqual(model.num_parameters() , 1_44_10 )
self.assertEqual(model.num_parameters(only_trainable=_SCREAMING_SNAKE_CASE ) , 1_44_10 )
def __lowerCamelCase ( self ):
__lowerCAmelCase : int = TFAutoModelWithLMHead.from_pretrained(_SCREAMING_SNAKE_CASE , from_pt=_SCREAMING_SNAKE_CASE )
self.assertIsInstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
self.assertEqual(model.num_parameters() , 1_44_10 )
self.assertEqual(model.num_parameters(only_trainable=_SCREAMING_SNAKE_CASE ) , 1_44_10 )
__lowerCAmelCase : Tuple = AutoModelWithLMHead.from_pretrained(_SCREAMING_SNAKE_CASE , from_tf=_SCREAMING_SNAKE_CASE )
self.assertIsInstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
self.assertEqual(model.num_parameters() , 1_44_10 )
self.assertEqual(model.num_parameters(only_trainable=_SCREAMING_SNAKE_CASE ) , 1_44_10 ) | 86 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : int = logging.get_logger(__name__)
lowercase : Optional[int] = {
"microsoft/cvt-13": "https://huggingface.co/microsoft/cvt-13/resolve/main/config.json",
# See all Cvt models at https://huggingface.co/models?filter=cvt
}
class SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ ):
"""simple docstring"""
lowercase : Tuple = 'cvt'
def __init__( self , __UpperCamelCase=3 , __UpperCamelCase=[7, 3, 3] , __UpperCamelCase=[4, 2, 2] , __UpperCamelCase=[2, 1, 1] , __UpperCamelCase=[64, 1_92, 3_84] , __UpperCamelCase=[1, 3, 6] , __UpperCamelCase=[1, 2, 10] , __UpperCamelCase=[4.0, 4.0, 4.0] , __UpperCamelCase=[0.0, 0.0, 0.0] , __UpperCamelCase=[0.0, 0.0, 0.0] , __UpperCamelCase=[0.0, 0.0, 0.1] , __UpperCamelCase=[True, True, True] , __UpperCamelCase=[False, False, True] , __UpperCamelCase=["dw_bn", "dw_bn", "dw_bn"] , __UpperCamelCase=[3, 3, 3] , __UpperCamelCase=[1, 1, 1] , __UpperCamelCase=[2, 2, 2] , __UpperCamelCase=[1, 1, 1] , __UpperCamelCase=[1, 1, 1] , __UpperCamelCase=0.02 , __UpperCamelCase=1E-12 , **__UpperCamelCase , ) -> Tuple:
'''simple docstring'''
super().__init__(**__UpperCamelCase )
__UpperCamelCase : Union[str, Any] = num_channels
__UpperCamelCase : int = patch_sizes
__UpperCamelCase : List[str] = patch_stride
__UpperCamelCase : Optional[int] = patch_padding
__UpperCamelCase : Union[str, Any] = embed_dim
__UpperCamelCase : Any = num_heads
__UpperCamelCase : Any = depth
__UpperCamelCase : str = mlp_ratio
__UpperCamelCase : Tuple = attention_drop_rate
__UpperCamelCase : Any = drop_rate
__UpperCamelCase : int = drop_path_rate
__UpperCamelCase : str = qkv_bias
__UpperCamelCase : Dict = cls_token
__UpperCamelCase : Optional[Any] = qkv_projection_method
__UpperCamelCase : Dict = kernel_qkv
__UpperCamelCase : List[Any] = padding_kv
__UpperCamelCase : Any = stride_kv
__UpperCamelCase : Any = padding_q
__UpperCamelCase : str = stride_q
__UpperCamelCase : List[Any] = initializer_range
__UpperCamelCase : Dict = layer_norm_eps | 171 |
def UpperCAmelCase_ (_lowerCAmelCase : list ):
if len(_lowerCAmelCase ) <= 1:
return lst
__UpperCamelCase : Dict = 1
while i < len(_lowerCAmelCase ):
if lst[i - 1] <= lst[i]:
i += 1
else:
__UpperCamelCase , __UpperCamelCase : Union[str, Any] = lst[i], lst[i - 1]
i -= 1
if i == 0:
__UpperCamelCase : Any = 1
return lst
if __name__ == "__main__":
lowercase : Dict = input("Enter numbers separated by a comma:\n").strip()
lowercase : Union[str, Any] = [int(item) for item in user_input.split(",")]
print(gnome_sort(unsorted)) | 171 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {'''ctrl''': '''https://huggingface.co/ctrl/resolve/main/config.json'''}
class SCREAMING_SNAKE_CASE__ ( lowercase ):
"""simple docstring"""
a : Optional[int] ="ctrl"
a : Dict =["past_key_values"]
a : Any ={
"max_position_embeddings": "n_positions",
"hidden_size": "n_embd",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__( self , snake_case__=246_534 , snake_case__=256 , snake_case__=1_280 , snake_case__=8_192 , snake_case__=48 , snake_case__=16 , snake_case__=0.1 , snake_case__=0.1 , snake_case__=1e-6 , snake_case__=0.02 , snake_case__=True , **snake_case__ , ):
"""simple docstring"""
lowerCAmelCase : str = vocab_size
lowerCAmelCase : str = n_positions
lowerCAmelCase : Dict = n_embd
lowerCAmelCase : List[Any] = n_layer
lowerCAmelCase : Tuple = n_head
lowerCAmelCase : Optional[Any] = dff
lowerCAmelCase : List[str] = resid_pdrop
lowerCAmelCase : List[str] = embd_pdrop
lowerCAmelCase : Union[str, Any] = layer_norm_epsilon
lowerCAmelCase : Dict = initializer_range
lowerCAmelCase : Union[str, Any] = use_cache
super().__init__(**snake_case__ )
| 108 |
"""simple docstring"""
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import require_elasticsearch, require_faiss
lowerCAmelCase__ = pytest.mark.integration
@require_faiss
class SCREAMING_SNAKE_CASE__ ( lowercase ):
"""simple docstring"""
def lowercase__ ( self ):
"""simple docstring"""
lowerCAmelCase : List[str] = Dataset.from_dict({"filename": ["my_name-train" + "_" + str(snake_case__ ) for x in np.arange(30 ).tolist()]} )
return dset
def lowercase__ ( self ):
"""simple docstring"""
import faiss
lowerCAmelCase : Dataset = self._create_dummy_dataset()
lowerCAmelCase : Union[str, Any] = dset.map(
lambda snake_case__ , snake_case__ : {"vecs": i * np.ones(5 , dtype=np.floataa )} , with_indices=snake_case__ , keep_in_memory=snake_case__ )
lowerCAmelCase : Union[str, Any] = dset.add_faiss_index("vecs" , batch_size=100 , metric_type=faiss.METRIC_INNER_PRODUCT )
lowerCAmelCase , lowerCAmelCase : Tuple = dset.get_nearest_examples("vecs" , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples["filename"][0] , "my_name-train_29" )
dset.drop_index("vecs" )
def lowercase__ ( self ):
"""simple docstring"""
import faiss
lowerCAmelCase : Dataset = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1 , 1 ) , index_name="vecs" , batch_size=100 , metric_type=faiss.METRIC_INNER_PRODUCT , )
lowerCAmelCase , lowerCAmelCase : Optional[Any] = dset.get_nearest_examples("vecs" , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples["filename"][0] , "my_name-train_29" )
def lowercase__ ( self ):
"""simple docstring"""
import faiss
lowerCAmelCase : Dataset = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1 , 1 ) , index_name="vecs" , metric_type=faiss.METRIC_INNER_PRODUCT , )
# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
# see https://bugs.python.org/issue14243 and
# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
with tempfile.NamedTemporaryFile(delete=snake_case__ ) as tmp_file:
dset.save_faiss_index("vecs" , tmp_file.name )
dset.load_faiss_index("vecs2" , tmp_file.name )
os.unlink(tmp_file.name )
lowerCAmelCase , lowerCAmelCase : int = dset.get_nearest_examples("vecs2" , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples["filename"][0] , "my_name-train_29" )
def lowercase__ ( self ):
"""simple docstring"""
lowerCAmelCase : Dataset = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1 , 1 ) , index_name="vecs" )
dset.drop_index("vecs" )
self.assertRaises(snake_case__ , partial(dset.get_nearest_examples , "vecs2" , np.ones(5 , dtype=np.floataa ) ) )
def lowercase__ ( self ):
"""simple docstring"""
from elasticsearch import Elasticsearch
lowerCAmelCase : Dataset = self._create_dummy_dataset()
with patch("elasticsearch.Elasticsearch.search" ) as mocked_search, patch(
"elasticsearch.client.IndicesClient.create" ) as mocked_index_create, patch("elasticsearch.helpers.streaming_bulk" ) as mocked_bulk:
lowerCAmelCase : List[str] = {"acknowledged": True}
mocked_bulk.return_value([(True, None)] * 30 )
lowerCAmelCase : List[str] = {"hits": {"hits": [{"_score": 1, "_id": 29}]}}
lowerCAmelCase : str = Elasticsearch()
dset.add_elasticsearch_index("filename" , es_client=snake_case__ )
lowerCAmelCase , lowerCAmelCase : int = dset.get_nearest_examples("filename" , "my_name-train_29" )
self.assertEqual(examples["filename"][0] , "my_name-train_29" )
@require_faiss
class SCREAMING_SNAKE_CASE__ ( lowercase ):
"""simple docstring"""
def lowercase__ ( self ):
"""simple docstring"""
import faiss
lowerCAmelCase : Optional[int] = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
# add vectors
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsNotNone(index.faiss_index )
self.assertEqual(index.faiss_index.ntotal , 5 )
index.add_vectors(np.zeros((5, 5) , dtype=np.floataa ) )
self.assertEqual(index.faiss_index.ntotal , 10 )
# single query
lowerCAmelCase : int = np.zeros(5 , dtype=np.floataa )
lowerCAmelCase : Optional[int] = 1
lowerCAmelCase , lowerCAmelCase : Optional[Any] = index.search(snake_case__ )
self.assertRaises(snake_case__ , index.search , query.reshape(-1 , 1 ) )
self.assertGreater(scores[0] , 0 )
self.assertEqual(indices[0] , 1 )
# batched queries
lowerCAmelCase : Union[str, Any] = np.eye(5 , dtype=np.floataa )[::-1]
lowerCAmelCase , lowerCAmelCase : str = index.search_batch(snake_case__ )
self.assertRaises(snake_case__ , index.search_batch , queries[0] )
lowerCAmelCase : Optional[int] = [scores[0] for scores in total_scores]
lowerCAmelCase : int = [indices[0] for indices in total_indices]
self.assertGreater(np.min(snake_case__ ) , 0 )
self.assertListEqual([4, 3, 2, 1, 0] , snake_case__ )
def lowercase__ ( self ):
"""simple docstring"""
import faiss
lowerCAmelCase : Dict = FaissIndex(string_factory="Flat" )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexFlat )
lowerCAmelCase : Union[str, Any] = FaissIndex(string_factory="LSH" )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexLSH )
with self.assertRaises(snake_case__ ):
lowerCAmelCase : List[Any] = FaissIndex(string_factory="Flat" , custom_index=faiss.IndexFlat(5 ) )
def lowercase__ ( self ):
"""simple docstring"""
import faiss
lowerCAmelCase : Any = faiss.IndexFlat(5 )
lowerCAmelCase : Union[str, Any] = FaissIndex(custom_index=snake_case__ )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexFlat )
def lowercase__ ( self ):
"""simple docstring"""
import faiss
lowerCAmelCase : str = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
# see https://bugs.python.org/issue14243 and
# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
with tempfile.NamedTemporaryFile(delete=snake_case__ ) as tmp_file:
index.save(tmp_file.name )
lowerCAmelCase : int = FaissIndex.load(tmp_file.name )
os.unlink(tmp_file.name )
lowerCAmelCase : Union[str, Any] = np.zeros(5 , dtype=np.floataa )
lowerCAmelCase : List[str] = 1
lowerCAmelCase , lowerCAmelCase : Tuple = index.search(snake_case__ )
self.assertGreater(scores[0] , 0 )
self.assertEqual(indices[0] , 1 )
@require_faiss
def a__ ( SCREAMING_SNAKE_CASE : Any ):
'''simple docstring'''
import faiss
lowerCAmelCase : List[Any] = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
lowerCAmelCase : Union[str, Any] = "index.faiss"
lowerCAmelCase : List[str] = f"""mock://{index_name}"""
index.save(SCREAMING_SNAKE_CASE , storage_options=mockfs.storage_options )
lowerCAmelCase : Optional[Any] = FaissIndex.load(SCREAMING_SNAKE_CASE , storage_options=mockfs.storage_options )
lowerCAmelCase : Optional[int] = np.zeros(5 , dtype=np.floataa )
lowerCAmelCase : Any = 1
lowerCAmelCase , lowerCAmelCase : Optional[int] = index.search(SCREAMING_SNAKE_CASE )
assert scores[0] > 0
assert indices[0] == 1
@require_elasticsearch
class SCREAMING_SNAKE_CASE__ ( lowercase ):
"""simple docstring"""
def lowercase__ ( self ):
"""simple docstring"""
from elasticsearch import Elasticsearch
with patch("elasticsearch.Elasticsearch.search" ) as mocked_search, patch(
"elasticsearch.client.IndicesClient.create" ) as mocked_index_create, patch("elasticsearch.helpers.streaming_bulk" ) as mocked_bulk:
lowerCAmelCase : List[str] = Elasticsearch()
lowerCAmelCase : Dict = {"acknowledged": True}
lowerCAmelCase : Optional[int] = ElasticSearchIndex(es_client=snake_case__ )
mocked_bulk.return_value([(True, None)] * 3 )
index.add_documents(["foo", "bar", "foobar"] )
# single query
lowerCAmelCase : List[str] = "foo"
lowerCAmelCase : List[str] = {"hits": {"hits": [{"_score": 1, "_id": 0}]}}
lowerCAmelCase , lowerCAmelCase : Optional[int] = index.search(snake_case__ )
self.assertEqual(scores[0] , 1 )
self.assertEqual(indices[0] , 0 )
# single query with timeout
lowerCAmelCase : int = "foo"
lowerCAmelCase : Any = {"hits": {"hits": [{"_score": 1, "_id": 0}]}}
lowerCAmelCase , lowerCAmelCase : str = index.search(snake_case__ , request_timeout=30 )
self.assertEqual(scores[0] , 1 )
self.assertEqual(indices[0] , 0 )
# batched queries
lowerCAmelCase : Any = ["foo", "bar", "foobar"]
lowerCAmelCase : Optional[int] = {"hits": {"hits": [{"_score": 1, "_id": 1}]}}
lowerCAmelCase , lowerCAmelCase : Any = index.search_batch(snake_case__ )
lowerCAmelCase : Tuple = [scores[0] for scores in total_scores]
lowerCAmelCase : Dict = [indices[0] for indices in total_indices]
self.assertGreater(np.min(snake_case__ ) , 0 )
self.assertListEqual([1, 1, 1] , snake_case__ )
# batched queries with timeout
lowerCAmelCase : Optional[Any] = ["foo", "bar", "foobar"]
lowerCAmelCase : List[Any] = {"hits": {"hits": [{"_score": 1, "_id": 1}]}}
lowerCAmelCase , lowerCAmelCase : Any = index.search_batch(snake_case__ , request_timeout=30 )
lowerCAmelCase : Dict = [scores[0] for scores in total_scores]
lowerCAmelCase : int = [indices[0] for indices in total_indices]
self.assertGreater(np.min(snake_case__ ) , 0 )
self.assertListEqual([1, 1, 1] , snake_case__ )
| 108 | 1 |
'''simple docstring'''
import argparse
import json
import re
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileNetVaConfig,
MobileNetVaForImageClassification,
MobileNetVaImageProcessor,
load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging
logging.set_verbosity_info()
lowercase__ = logging.get_logger(__name__)
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ ) -> Any:
'''simple docstring'''
snake_case : Optional[int] = MobileNetVaConfig(layer_norm_eps=0.001 )
if "_quant" in model_name:
raise ValueError('''Quantized models are not supported.''' )
snake_case : List[Any] = re.match(R'''^mobilenet_v1_([^_]*)_([^_]*)$''' , A__ )
if matches:
snake_case : List[str] = float(matches[1] )
snake_case : List[str] = int(matches[2] )
# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
# the usual 1000. The first class (index 0) is "background".
snake_case : int = 1001
snake_case : Optional[Any] = """imagenet-1k-id2label.json"""
snake_case : Optional[int] = """huggingface/label-files"""
snake_case : Tuple = json.load(open(hf_hub_download(A__ , A__ , repo_type='''dataset''' ) , '''r''' ) )
snake_case : int = {int(A__ ) + 1: v for k, v in idalabel.items()}
snake_case : str = """background"""
snake_case : Optional[int] = idalabel
snake_case : int = {v: k for k, v in idalabel.items()}
return config
def _UpperCamelCase ( ) -> Any:
'''simple docstring'''
snake_case : str = """http://images.cocodataset.org/val2017/000000039769.jpg"""
snake_case : str = Image.open(requests.get(A__ , stream=A__ ).raw )
return im
@torch.no_grad()
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=False ) -> List[Any]:
'''simple docstring'''
snake_case : Optional[int] = get_mobilenet_va_config(A__ )
# Load 🤗 model
snake_case : Optional[int] = MobileNetVaForImageClassification(A__ ).eval()
# Load weights from TensorFlow checkpoint
load_tf_weights_in_mobilenet_va(A__ , A__ , A__ )
# Check outputs on an image, prepared by MobileNetV1ImageProcessor
snake_case : Dict = MobileNetVaImageProcessor(
crop_size={'''width''': config.image_size, '''height''': config.image_size} , size={'''shortest_edge''': config.image_size + 32} , )
snake_case : List[str] = image_processor(images=prepare_img() , return_tensors='''pt''' )
snake_case : Tuple = model(**A__ )
snake_case : Union[str, Any] = outputs.logits
assert logits.shape == (1, 1001)
if model_name == "mobilenet_v1_1.0_224":
snake_case : Any = torch.tensor([-4.1739, -1.1233, 3.1205] )
elif model_name == "mobilenet_v1_0.75_192":
snake_case : str = torch.tensor([-3.9440, -2.3141, -0.3333] )
else:
snake_case : Optional[int] = None
if expected_logits is not None:
assert torch.allclose(logits[0, :3] , A__ , atol=1E-4 )
Path(A__ ).mkdir(exist_ok=A__ )
print(F'Saving model {model_name} to {pytorch_dump_folder_path}' )
model.save_pretrained(A__ )
print(F'Saving image processor to {pytorch_dump_folder_path}' )
image_processor.save_pretrained(A__ )
if push_to_hub:
print('''Pushing to the hub...''' )
snake_case : List[str] = """google/""" + model_name
image_processor.push_to_hub(A__ )
model.push_to_hub(A__ )
if __name__ == "__main__":
lowercase__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default="mobilenet_v1_1.0_224",
type=str,
help="Name of the MobileNetV1 model you'd like to convert. Should in the form 'mobilenet_v1_<depth>_<size>'.",
)
parser.add_argument(
"--checkpoint_path", required=True, type=str, help="Path to the original TensorFlow checkpoint (.ckpt file)."
)
parser.add_argument(
"--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory."
)
parser.add_argument(
"--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
)
lowercase__ = parser.parse_args()
convert_movilevit_checkpoint(
args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
)
| 350 |
'''simple docstring'''
import argparse
import torch
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
if __name__ == "__main__":
lowercase__ = argparse.ArgumentParser()
parser.add_argument(
"--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
)
# !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
parser.add_argument(
"--original_config_file",
default=None,
type=str,
help="The YAML config file corresponding to the original architecture.",
)
parser.add_argument(
"--num_in_channels",
default=None,
type=int,
help="The number of input channels. If `None` number of input channels will be automatically inferred.",
)
parser.add_argument(
"--scheduler_type",
default="pndm",
type=str,
help="Type of scheduler to use. Should be one of ['pndm', 'lms', 'ddim', 'euler', 'euler-ancestral', 'dpm']",
)
parser.add_argument(
"--pipeline_type",
default=None,
type=str,
help=(
"The pipeline type. One of 'FrozenOpenCLIPEmbedder', 'FrozenCLIPEmbedder', 'PaintByExample'"
". If `None` pipeline will be automatically inferred."
),
)
parser.add_argument(
"--image_size",
default=None,
type=int,
help=(
"The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2"
" Base. Use 768 for Stable Diffusion v2."
),
)
parser.add_argument(
"--prediction_type",
default=None,
type=str,
help=(
"The prediction type that the model was trained on. Use 'epsilon' for Stable Diffusion v1.X and Stable"
" Diffusion v2 Base. Use 'v_prediction' for Stable Diffusion v2."
),
)
parser.add_argument(
"--extract_ema",
action="store_true",
help=(
"Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights"
" or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield"
" higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning."
),
)
parser.add_argument(
"--upcast_attention",
action="store_true",
help=(
"Whether the attention computation should always be upcasted. This is necessary when running stable"
" diffusion 2.1."
),
)
parser.add_argument(
"--from_safetensors",
action="store_true",
help="If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.",
)
parser.add_argument(
"--to_safetensors",
action="store_true",
help="Whether to store pipeline in safetensors format or not.",
)
parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)")
parser.add_argument(
"--stable_unclip",
type=str,
default=None,
required=False,
help="Set if this is a stable unCLIP model. One of 'txt2img' or 'img2img'.",
)
parser.add_argument(
"--stable_unclip_prior",
type=str,
default=None,
required=False,
help="Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.",
)
parser.add_argument(
"--clip_stats_path",
type=str,
help="Path to the clip stats file. Only required if the stable unclip model's config specifies `model.params.noise_aug_config.params.clip_stats_path`.",
required=False,
)
parser.add_argument(
"--controlnet", action="store_true", default=None, help="Set flag if this is a controlnet checkpoint."
)
parser.add_argument("--half", action="store_true", help="Save weights in half precision.")
parser.add_argument(
"--vae_path",
type=str,
default=None,
required=False,
help="Set to a path, hub id to an already converted vae to not convert it again.",
)
lowercase__ = parser.parse_args()
lowercase__ = download_from_original_stable_diffusion_ckpt(
checkpoint_path=args.checkpoint_path,
original_config_file=args.original_config_file,
image_size=args.image_size,
prediction_type=args.prediction_type,
model_type=args.pipeline_type,
extract_ema=args.extract_ema,
scheduler_type=args.scheduler_type,
num_in_channels=args.num_in_channels,
upcast_attention=args.upcast_attention,
from_safetensors=args.from_safetensors,
device=args.device,
stable_unclip=args.stable_unclip,
stable_unclip_prior=args.stable_unclip_prior,
clip_stats_path=args.clip_stats_path,
controlnet=args.controlnet,
vae_path=args.vae_path,
)
if args.half:
pipe.to(torch_dtype=torch.floataa)
if args.controlnet:
# only save the controlnet model
pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
else:
pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
| 83 | 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 UpperCamelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase=True , UpperCAmelCase="pt" ) ->Tuple:
"""simple docstring"""
a_ = {"add_prefix_space": True} if isinstance(UpperCAmelCase , UpperCAmelCase ) and not line.startswith(" " ) else {}
a_ = 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 UpperCamelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase=None , ) ->Tuple:
"""simple docstring"""
a_ = 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 snake_case ( SCREAMING_SNAKE_CASE_ ):
def __init__( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase="train" , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase="" , ) ->Any:
super().__init__()
a_ = Path(__UpperCAmelCase).joinpath(type_path + ".source")
a_ = Path(__UpperCAmelCase).joinpath(type_path + ".target")
a_ = self.get_char_lens(self.src_file)
a_ = max_source_length
a_ = max_target_length
assert min(self.src_lens) > 0, F'''found empty line in {self.src_file}'''
a_ = tokenizer
a_ = prefix
if n_obs is not None:
a_ = self.src_lens[:n_obs]
a_ = src_lang
a_ = tgt_lang
def __len__( self) ->Optional[Any]:
return len(self.src_lens)
def __getitem__( self , __UpperCAmelCase) ->Dict[str, torch.Tensor]:
a_ = index + 1 # linecache starts at 1
a_ = self.prefix + linecache.getline(str(self.src_file) , __UpperCAmelCase).rstrip("\n")
a_ = linecache.getline(str(self.tgt_file) , __UpperCAmelCase).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 , __UpperCAmelCase):
source_line += self.tokenizer.eos_token
tgt_line += self.tokenizer.eos_token
# Pad source and target to the right
a_ = (
self.tokenizer.question_encoder if isinstance(self.tokenizer , __UpperCAmelCase) else self.tokenizer
)
a_ = self.tokenizer.generator if isinstance(self.tokenizer , __UpperCAmelCase) else self.tokenizer
a_ = encode_line(__UpperCAmelCase , __UpperCAmelCase , self.max_source_length , "right")
a_ = encode_line(__UpperCAmelCase , __UpperCAmelCase , self.max_target_length , "right")
a_ = source_inputs["input_ids"].squeeze()
a_ = target_inputs["input_ids"].squeeze()
a_ = source_inputs["attention_mask"].squeeze()
return {
"input_ids": source_ids,
"attention_mask": src_mask,
"decoder_input_ids": target_ids,
}
@staticmethod
def UpperCAmelCase__ ( __UpperCAmelCase) ->Dict:
return [len(__UpperCAmelCase) for x in Path(__UpperCAmelCase).open().readlines()]
def UpperCAmelCase__ ( self , __UpperCAmelCase) ->Dict[str, torch.Tensor]:
a_ = torch.stack([x["input_ids"] for x in batch])
a_ = torch.stack([x["attention_mask"] for x in batch])
a_ = torch.stack([x["decoder_input_ids"] for x in batch])
a_ = (
self.tokenizer.generator.pad_token_id
if isinstance(self.tokenizer , __UpperCAmelCase)
else self.tokenizer.pad_token_id
)
a_ = (
self.tokenizer.question_encoder.pad_token_id
if isinstance(self.tokenizer , __UpperCAmelCase)
else self.tokenizer.pad_token_id
)
a_ = trim_batch(__UpperCAmelCase , __UpperCAmelCase)
a_ , a_ = trim_batch(__UpperCAmelCase , __UpperCAmelCase , attention_mask=__UpperCAmelCase)
a_ = {
"input_ids": source_ids,
"attention_mask": source_mask,
"decoder_input_ids": y,
}
return batch
UpperCamelCase_ = getLogger(__name__)
def UpperCamelCase ( UpperCAmelCase ) ->Optional[int]:
"""simple docstring"""
return list(itertools.chain.from_iterable(UpperCAmelCase ) )
def UpperCamelCase ( UpperCAmelCase ) ->None:
"""simple docstring"""
a_ = get_git_info()
save_json(UpperCAmelCase , os.path.join(UpperCAmelCase , "git_log.json" ) )
def UpperCamelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase=4 , **UpperCAmelCase ) ->Tuple:
"""simple docstring"""
with open(UpperCAmelCase , "w" ) as f:
json.dump(UpperCAmelCase , UpperCAmelCase , indent=UpperCAmelCase , **UpperCAmelCase )
def UpperCamelCase ( UpperCAmelCase ) ->Tuple:
"""simple docstring"""
with open(UpperCAmelCase ) as f:
return json.load(UpperCAmelCase )
def UpperCamelCase ( ) ->Dict:
"""simple docstring"""
a_ = git.Repo(search_parent_directories=UpperCAmelCase )
a_ = {
"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 UpperCamelCase ( UpperCAmelCase , UpperCAmelCase ) ->List:
"""simple docstring"""
return list(map(UpperCAmelCase , UpperCAmelCase ) )
def UpperCamelCase ( UpperCAmelCase , UpperCAmelCase ) ->List[str]:
"""simple docstring"""
with open(UpperCAmelCase , "wb" ) as f:
return pickle.dump(UpperCAmelCase , UpperCAmelCase )
def UpperCamelCase ( UpperCAmelCase ) ->List[str]:
"""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 ):
a_ = 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 UpperCamelCase ( UpperCAmelCase , UpperCAmelCase ) ->int:
"""simple docstring"""
a_ = normalize_answer(UpperCAmelCase ).split()
a_ = normalize_answer(UpperCAmelCase ).split()
a_ = Counter(UpperCAmelCase ) & Counter(UpperCAmelCase )
a_ = sum(common.values() )
if num_same == 0:
return 0
a_ = 1.0 * num_same / len(UpperCAmelCase )
a_ = 1.0 * num_same / len(UpperCAmelCase )
a_ = (2 * precision * recall) / (precision + recall)
return fa
def UpperCamelCase ( UpperCAmelCase , UpperCAmelCase ) ->Optional[int]:
"""simple docstring"""
return normalize_answer(UpperCAmelCase ) == normalize_answer(UpperCAmelCase )
def UpperCamelCase ( UpperCAmelCase , UpperCAmelCase ) ->Dict:
"""simple docstring"""
assert len(UpperCAmelCase ) == len(UpperCAmelCase )
a_ = 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 UpperCamelCase ( UpperCAmelCase ) ->Optional[Any]:
"""simple docstring"""
return model_prefix.startswith("rag" )
def UpperCamelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) ->str:
"""simple docstring"""
a_ = {p: p for p in extra_params}
# T5 models don't have `dropout` param, they have `dropout_rate` instead
a_ = "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
a_ = p if hasattr(UpperCAmelCase , UpperCAmelCase ) else equivalent_param[p]
setattr(UpperCAmelCase , UpperCAmelCase , getattr(UpperCAmelCase , UpperCAmelCase ) )
delattr(UpperCAmelCase , UpperCAmelCase )
return hparams, config | 243 |
"""simple docstring"""
UpperCamelCase_ = {'a': ['c', 'b'], 'b': ['d', 'e'], 'c': [], 'd': [], 'e': []}
UpperCamelCase_ = ['a', 'b', 'c', 'd', 'e']
def UpperCamelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) ->Optional[int]:
"""simple docstring"""
a_ = start
# add current to visited
visited.append(UpperCAmelCase )
a_ = edges[current]
for neighbor in neighbors:
# if neighbor not in visited, visit
if neighbor not in visited:
a_ = topological_sort(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
# if all neighbors visited add current to sort
sort.append(UpperCAmelCase )
# if all vertices haven't been visited select a new one to visit
if len(UpperCAmelCase ) != len(UpperCAmelCase ):
for vertice in vertices:
if vertice not in visited:
a_ = topological_sort(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
# return sort
return sort
if __name__ == "__main__":
UpperCamelCase_ = topological_sort('a', [], [])
print(sort) | 243 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ..models.auto import AutoModelForVisionaSeq
from ..utils import requires_backends
from .base import PipelineTool
if TYPE_CHECKING:
from PIL import Image
class a_ ( lowerCamelCase ):
lowercase = """Salesforce/blip-image-captioning-base"""
lowercase = (
"""This is a tool that generates a description of an image. It takes an input named `image` which should be the """
"""image to caption, and returns a text that contains the description in English."""
)
lowercase = """image_captioner"""
lowercase = AutoModelForVisionaSeq
lowercase = ["""image"""]
lowercase = ["""text"""]
def __init__( self , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) -> Any:
"""simple docstring"""
requires_backends(self , ["""vision"""] )
super().__init__(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
def A__ ( self , _SCREAMING_SNAKE_CASE ) -> str:
"""simple docstring"""
return self.pre_processor(images=_SCREAMING_SNAKE_CASE , return_tensors="""pt""" )
def A__ ( self , _SCREAMING_SNAKE_CASE ) -> str:
"""simple docstring"""
return self.model.generate(**_SCREAMING_SNAKE_CASE )
def A__ ( self , _SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
"""simple docstring"""
return self.pre_processor.batch_decode(_SCREAMING_SNAKE_CASE , skip_special_tokens=_SCREAMING_SNAKE_CASE )[0].strip()
| 364 |
'''simple docstring'''
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {'vocab_file': 'vocab.txt'}
SCREAMING_SNAKE_CASE__ = {
'vocab_file': {
'facebook/esm2_t6_8M_UR50D': 'https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt',
'facebook/esm2_t12_35M_UR50D': 'https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt',
},
}
SCREAMING_SNAKE_CASE__ = {
'facebook/esm2_t6_8M_UR50D': 1_0_2_4,
'facebook/esm2_t12_35M_UR50D': 1_0_2_4,
}
def lowercase__ ( __UpperCamelCase )-> Any:
with open(__UpperCamelCase , """r""" ) as f:
UpperCamelCase = f.read().splitlines()
return [l.strip() for l in lines]
class a_ ( lowerCamelCase ):
lowercase = VOCAB_FILES_NAMES
lowercase = PRETRAINED_VOCAB_FILES_MAP
lowercase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase = ["""input_ids""", """attention_mask"""]
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE="<unk>" , _SCREAMING_SNAKE_CASE="<cls>" , _SCREAMING_SNAKE_CASE="<pad>" , _SCREAMING_SNAKE_CASE="<mask>" , _SCREAMING_SNAKE_CASE="<eos>" , **_SCREAMING_SNAKE_CASE , ) -> Any:
"""simple docstring"""
super().__init__(**_SCREAMING_SNAKE_CASE )
UpperCamelCase = load_vocab_file(_SCREAMING_SNAKE_CASE )
UpperCamelCase = dict(enumerate(self.all_tokens ) )
UpperCamelCase = {tok: ind for ind, tok in enumerate(self.all_tokens )}
UpperCamelCase = unk_token
UpperCamelCase = cls_token
UpperCamelCase = pad_token
UpperCamelCase = mask_token
UpperCamelCase = eos_token
UpperCamelCase = self.all_tokens
self._create_trie(self.unique_no_split_tokens )
def A__ ( self , _SCREAMING_SNAKE_CASE ) -> str:
"""simple docstring"""
return self._id_to_token.get(_SCREAMING_SNAKE_CASE , self.unk_token )
def A__ ( self , _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
return self._token_to_id.get(_SCREAMING_SNAKE_CASE , self._token_to_id.get(self.unk_token ) )
def A__ ( self , _SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) -> List[str]:
"""simple docstring"""
return text.split()
def A__ ( self , _SCREAMING_SNAKE_CASE=False ) -> Dict:
"""simple docstring"""
return len(self._id_to_token )
def A__ ( self ) -> Tuple:
"""simple docstring"""
return {token: i for i, token in enumerate(self.all_tokens )}
def A__ ( self , _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
return self._token_to_id.get(_SCREAMING_SNAKE_CASE , self._token_to_id.get(self.unk_token ) )
def A__ ( self , _SCREAMING_SNAKE_CASE ) -> str:
"""simple docstring"""
return self._id_to_token.get(_SCREAMING_SNAKE_CASE , self.unk_token )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None ) -> List[int]:
"""simple docstring"""
UpperCamelCase = [self.cls_token_id]
UpperCamelCase = [self.eos_token_id] # No sep token in ESM vocabulary
if token_ids_a is None:
if self.eos_token_id is None:
return cls + token_ids_a
else:
return cls + token_ids_a + sep
elif self.eos_token_id is None:
raise ValueError("""Cannot tokenize multiple sequences when EOS token is not set!""" )
return cls + token_ids_a + sep + token_ids_a + sep # Multiple inputs always have an EOS token
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = False ) -> List[int]:
"""simple docstring"""
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
"""You should not supply a second sequence if the provided sequence of """
"""ids is already formatted with special tokens for the model.""" )
return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
UpperCamelCase = [1] + ([0] * len(_SCREAMING_SNAKE_CASE )) + [1]
if token_ids_a is not None:
mask += [0] * len(_SCREAMING_SNAKE_CASE ) + [1]
return mask
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase = os.path.join(_SCREAMING_SNAKE_CASE , (filename_prefix + """-""" if filename_prefix else """""") + """vocab.txt""" )
with open(_SCREAMING_SNAKE_CASE , """w""" ) as f:
f.write("""\n""".join(self.all_tokens ) )
return (vocab_file,)
@property
def A__ ( self ) -> int:
"""simple docstring"""
return self.get_vocab_size(with_added_tokens=_SCREAMING_SNAKE_CASE )
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = False ) -> int:
"""simple docstring"""
return super()._add_tokens(_SCREAMING_SNAKE_CASE , special_tokens=_SCREAMING_SNAKE_CASE )
| 183 | 0 |
'''simple docstring'''
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
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 lowercase__ ( lowercase , unittest.TestCase ):
# TODO: is there an appropriate internal test set?
lowercase__ = """ssube/stable-diffusion-x4-upscaler-onnx"""
def UpperCamelCase_ ( self : List[Any] ,lowerCamelCase__ : Optional[int]=0 ):
'''simple docstring'''
_UpperCamelCase : Any = floats_tensor((1, 3, 128, 128) ,rng=random.Random(lowerCamelCase__ ) )
_UpperCamelCase : List[str] = torch.manual_seed(lowerCamelCase__ )
_UpperCamelCase : int = {
'prompt': 'A painting of a squirrel eating a burger',
'image': image,
'generator': generator,
'num_inference_steps': 3,
'guidance_scale': 7.5,
'output_type': 'numpy',
}
return inputs
def UpperCamelCase_ ( self : Dict ):
'''simple docstring'''
_UpperCamelCase : Tuple = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint ,provider='CPUExecutionProvider' )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
_UpperCamelCase : Optional[int] = self.get_dummy_inputs()
_UpperCamelCase : str = pipe(**lowerCamelCase__ ).images
_UpperCamelCase : Optional[Any] = image[0, -3:, -3:, -1].flatten()
# started as 128, should now be 512
assert image.shape == (1, 512, 512, 3)
_UpperCamelCase : Dict = np.array(
[0.6_9_7_4_7_8_2, 0.6_8_9_0_2_0_9_3, 0.7_0_1_3_5_8_8_5, 0.7_5_8_3_6_1_8, 0.7_8_0_4_5_4_5, 0.7_8_5_4_9_1_2, 0.7_8_6_6_7_4_2_6, 0.7_8_7_4_3_8_6_3, 0.7_8_0_7_0_2_2_3] )
assert np.abs(image_slice - expected_slice ).max() < 1E-1
def UpperCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
_UpperCamelCase : Tuple = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint ,provider='CPUExecutionProvider' )
_UpperCamelCase : Optional[int] = PNDMScheduler.from_config(pipe.scheduler.config ,skip_prk_steps=lowerCamelCase__ )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
_UpperCamelCase : int = self.get_dummy_inputs()
_UpperCamelCase : Tuple = pipe(**lowerCamelCase__ ).images
_UpperCamelCase : Dict = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
_UpperCamelCase : Optional[Any] = np.array(
[0.6_8_9_8_8_9_2, 0.5_9_2_4_0_5_5_6, 0.5_2_4_9_9_5_2_7, 0.5_8_8_6_6_2_1_5, 0.5_2_2_5_8_2_3_5, 0.5_2_5_7_2_7_1_5, 0.6_2_4_1_4_4_7_3, 0.6_1_7_4_3_8_7, 0.6_2_1_4_9_6_4] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
def UpperCamelCase_ ( self : Tuple ):
'''simple docstring'''
_UpperCamelCase : Dict = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint ,provider='CPUExecutionProvider' )
_UpperCamelCase : Any = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
_UpperCamelCase : Tuple = self.get_dummy_inputs()
_UpperCamelCase : Union[str, Any] = pipe(**lowerCamelCase__ ).images
_UpperCamelCase : Dict = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
_UpperCamelCase : Dict = np.array(
[0.7_6_5_9_2_7_8, 0.7_6_4_3_7_6_6_4, 0.7_5_5_7_9_1_0_7, 0.7_6_9_1_1_1_6, 0.7_7_6_6_6_9_8_6, 0.7_7_2_7_6_7_2, 0.7_7_5_8_6_6_4, 0.7_8_1_2_2_2_6, 0.7_6_9_4_2_5_1_5] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
def UpperCamelCase_ ( self : str ):
'''simple docstring'''
_UpperCamelCase : Tuple = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint ,provider='CPUExecutionProvider' )
_UpperCamelCase : Optional[Any] = EulerDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
_UpperCamelCase : Tuple = self.get_dummy_inputs()
_UpperCamelCase : Optional[int] = pipe(**lowerCamelCase__ ).images
_UpperCamelCase : Optional[int] = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
_UpperCamelCase : Union[str, Any] = np.array(
[0.6_9_7_4_7_8_2, 0.6_8_9_0_2_0_9_3, 0.7_0_1_3_5_8_8_5, 0.7_5_8_3_6_1_8, 0.7_8_0_4_5_4_5, 0.7_8_5_4_9_1_2, 0.7_8_6_6_7_4_2_6, 0.7_8_7_4_3_8_6_3, 0.7_8_0_7_0_2_2_3] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
def UpperCamelCase_ ( self : Any ):
'''simple docstring'''
_UpperCamelCase : str = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint ,provider='CPUExecutionProvider' )
_UpperCamelCase : Tuple = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
_UpperCamelCase : Dict = self.get_dummy_inputs()
_UpperCamelCase : int = pipe(**lowerCamelCase__ ).images
_UpperCamelCase : int = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
_UpperCamelCase : List[str] = np.array(
[0.7_7_4_2_4_4_9_6, 0.7_7_3_6_0_1, 0.7_6_4_5_2_8_8, 0.7_7_6_9_5_9_8, 0.7_7_7_2_7_3_9, 0.7_7_3_8_6_8_8, 0.7_8_1_8_7_2_3_3, 0.7_7_8_7_9_5_8_4, 0.7_6_7_0_4_3] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
@nightly
@require_onnxruntime
@require_torch_gpu
class lowercase__ ( unittest.TestCase ):
@property
def UpperCamelCase_ ( self : Any ):
'''simple docstring'''
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def UpperCamelCase_ ( self : Tuple ):
'''simple docstring'''
_UpperCamelCase : List[Any] = ort.SessionOptions()
_UpperCamelCase : Tuple = False
return options
def UpperCamelCase_ ( self : Tuple ):
'''simple docstring'''
_UpperCamelCase : Any = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/img2img/sketch-mountains-input.jpg' )
_UpperCamelCase : Union[str, Any] = init_image.resize((128, 128) )
# using the PNDM scheduler by default
_UpperCamelCase : Dict = OnnxStableDiffusionUpscalePipeline.from_pretrained(
'ssube/stable-diffusion-x4-upscaler-onnx' ,provider=self.gpu_provider ,sess_options=self.gpu_options ,)
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
_UpperCamelCase : Dict = 'A fantasy landscape, trending on artstation'
_UpperCamelCase : Optional[int] = torch.manual_seed(0 )
_UpperCamelCase : Optional[Any] = pipe(
prompt=lowerCamelCase__ ,image=lowerCamelCase__ ,guidance_scale=7.5 ,num_inference_steps=10 ,generator=lowerCamelCase__ ,output_type='np' ,)
_UpperCamelCase : Union[str, Any] = output.images
_UpperCamelCase : Any = images[0, 255:258, 383:386, -1]
assert images.shape == (1, 512, 512, 3)
_UpperCamelCase : List[Any] = np.array([0.4_8_8_3, 0.4_9_4_7, 0.4_9_8_0, 0.4_9_7_5, 0.4_9_8_2, 0.4_9_8_0, 0.5_0_0_0, 0.5_0_0_6, 0.4_9_7_2] )
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2E-2
def UpperCamelCase_ ( self : List[str] ):
'''simple docstring'''
_UpperCamelCase : Tuple = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/img2img/sketch-mountains-input.jpg' )
_UpperCamelCase : List[Any] = init_image.resize((128, 128) )
_UpperCamelCase : Optional[Any] = LMSDiscreteScheduler.from_pretrained(
'ssube/stable-diffusion-x4-upscaler-onnx' ,subfolder='scheduler' )
_UpperCamelCase : Any = OnnxStableDiffusionUpscalePipeline.from_pretrained(
'ssube/stable-diffusion-x4-upscaler-onnx' ,scheduler=lowerCamelCase__ ,provider=self.gpu_provider ,sess_options=self.gpu_options ,)
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
_UpperCamelCase : Optional[int] = 'A fantasy landscape, trending on artstation'
_UpperCamelCase : List[str] = torch.manual_seed(0 )
_UpperCamelCase : Union[str, Any] = pipe(
prompt=lowerCamelCase__ ,image=lowerCamelCase__ ,guidance_scale=7.5 ,num_inference_steps=20 ,generator=lowerCamelCase__ ,output_type='np' ,)
_UpperCamelCase : List[Any] = output.images
_UpperCamelCase : List[str] = images[0, 255:258, 383:386, -1]
assert images.shape == (1, 512, 512, 3)
_UpperCamelCase : List[str] = np.array(
[0.5_0_1_7_3_7_5_3, 0.5_0_2_2_3_3_5_6, 0.5_0_2_0_3_9, 0.5_0_2_3_3_0_3_6, 0.5_0_2_3_7_2_5, 0.5_0_2_2_6_0_1, 0.5_0_1_8_7_5_8, 0.5_0_2_3_4_0_8_5, 0.5_0_2_4_1_5_6_6] )
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2E-2
| 83 |
'''simple docstring'''
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ : int = logging.get_logger(__name__)
snake_case_ : Optional[Any] = {
'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json',
}
class lowercase__ ( lowercase ):
lowercase__ = """mvp"""
lowercase__ = ["""past_key_values"""]
lowercase__ = {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
def __init__( self : List[Any] ,lowerCamelCase__ : Any=50267 ,lowerCamelCase__ : Optional[int]=1024 ,lowerCamelCase__ : int=12 ,lowerCamelCase__ : Tuple=4096 ,lowerCamelCase__ : Union[str, Any]=16 ,lowerCamelCase__ : List[Any]=12 ,lowerCamelCase__ : Tuple=4096 ,lowerCamelCase__ : Any=16 ,lowerCamelCase__ : Optional[int]=0.0 ,lowerCamelCase__ : Optional[int]=0.0 ,lowerCamelCase__ : str="gelu" ,lowerCamelCase__ : Optional[int]=1024 ,lowerCamelCase__ : Tuple=0.1 ,lowerCamelCase__ : List[str]=0.0 ,lowerCamelCase__ : Union[str, Any]=0.0 ,lowerCamelCase__ : Union[str, Any]=0.0_2 ,lowerCamelCase__ : Union[str, Any]=0.0 ,lowerCamelCase__ : Tuple=False ,lowerCamelCase__ : Union[str, Any]=True ,lowerCamelCase__ : str=1 ,lowerCamelCase__ : Any=0 ,lowerCamelCase__ : Optional[int]=2 ,lowerCamelCase__ : Any=True ,lowerCamelCase__ : Dict=2 ,lowerCamelCase__ : Optional[int]=2 ,lowerCamelCase__ : Optional[int]=False ,lowerCamelCase__ : Tuple=100 ,lowerCamelCase__ : Optional[int]=800 ,**lowerCamelCase__ : int ,):
'''simple docstring'''
_UpperCamelCase : Optional[int] = vocab_size
_UpperCamelCase : Union[str, Any] = max_position_embeddings
_UpperCamelCase : Dict = d_model
_UpperCamelCase : Any = encoder_ffn_dim
_UpperCamelCase : Dict = encoder_layers
_UpperCamelCase : Optional[Any] = encoder_attention_heads
_UpperCamelCase : Optional[int] = decoder_ffn_dim
_UpperCamelCase : str = decoder_layers
_UpperCamelCase : int = decoder_attention_heads
_UpperCamelCase : str = dropout
_UpperCamelCase : str = attention_dropout
_UpperCamelCase : List[Any] = activation_dropout
_UpperCamelCase : Dict = activation_function
_UpperCamelCase : List[str] = init_std
_UpperCamelCase : Dict = encoder_layerdrop
_UpperCamelCase : Tuple = decoder_layerdrop
_UpperCamelCase : Optional[int] = classifier_dropout
_UpperCamelCase : str = use_cache
_UpperCamelCase : Union[str, Any] = encoder_layers
_UpperCamelCase : Any = scale_embedding # scale factor will be sqrt(d_model) if True
_UpperCamelCase : Any = use_prompt
_UpperCamelCase : Optional[int] = prompt_length
_UpperCamelCase : Any = prompt_mid_dim
super().__init__(
pad_token_id=lowerCamelCase__ ,bos_token_id=lowerCamelCase__ ,eos_token_id=lowerCamelCase__ ,is_encoder_decoder=lowerCamelCase__ ,decoder_start_token_id=lowerCamelCase__ ,forced_eos_token_id=lowerCamelCase__ ,**lowerCamelCase__ ,)
if self.forced_bos_token_id is None and kwargs.get('force_bos_token_to_be_generated' ,lowerCamelCase__ ):
_UpperCamelCase : Union[str, Any] = self.bos_token_id
warnings.warn(
F'Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '
'The config can simply be saved and uploaded again to be fixed.' )
| 83 | 1 |
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures
lowerCamelCase_ = logging.get_logger(__name__)
@dataclass
class a_ :
'''simple docstring'''
__a: str = field(metadata={'''help''': '''The name of the task to train on: ''' + ''', '''.join(glue_processors.keys() )} )
__a: str = field(
metadata={'''help''': '''The input data dir. Should contain the .tsv files (or other data files) for the task.'''} )
__a: int = field(
default=1_2_8 , metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
__a: bool = field(
default=a_ , metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )
def _lowercase ( self ) -> str:
'''simple docstring'''
lowerCAmelCase_ = self.task_name.lower()
class a_ ( a_ ):
'''simple docstring'''
__a: int = '''train'''
__a: Tuple = '''dev'''
__a: List[Any] = '''test'''
class a_ ( a_ ):
'''simple docstring'''
__a: GlueDataTrainingArguments
__a: str
__a: List[InputFeatures]
def __init__( self , lowercase_ , lowercase_ , lowercase_ = None , lowercase_ = Split.train , lowercase_ = None , ) -> List[Any]:
'''simple docstring'''
warnings.warn(
'This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets '
'library. You can have a look at this example script for pointers: '
'https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py' , lowercase_ , )
lowerCAmelCase_ = args
lowerCAmelCase_ = glue_processors[args.task_name]()
lowerCAmelCase_ = glue_output_modes[args.task_name]
if isinstance(lowercase_ , lowercase_ ):
try:
lowerCAmelCase_ = Split[mode]
except KeyError:
raise KeyError('mode is not a valid split name' )
# Load data features from cache or dataset file
lowerCAmelCase_ = 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}_{args.task_name}''' , )
lowerCAmelCase_ = self.processor.get_labels()
if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
"RobertaTokenizer",
"RobertaTokenizerFast",
"XLMRobertaTokenizer",
"BartTokenizer",
"BartTokenizerFast",
):
# HACK(label indices are swapped in RoBERTa pretrained model)
lowerCAmelCase_ , lowerCAmelCase_ = label_list[2], label_list[1]
lowerCAmelCase_ = label_list
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
lowerCAmelCase_ = cached_features_file + '.lock'
with FileLock(lowercase_ ):
if os.path.exists(lowercase_ ) and not args.overwrite_cache:
lowerCAmelCase_ = time.time()
lowerCAmelCase_ = torch.load(lowercase_ )
logger.info(
f'''Loading features from cached file {cached_features_file} [took %.3f s]''' , time.time() - start )
else:
logger.info(f'''Creating features from dataset file at {args.data_dir}''' )
if mode == Split.dev:
lowerCAmelCase_ = self.processor.get_dev_examples(args.data_dir )
elif mode == Split.test:
lowerCAmelCase_ = self.processor.get_test_examples(args.data_dir )
else:
lowerCAmelCase_ = self.processor.get_train_examples(args.data_dir )
if limit_length is not None:
lowerCAmelCase_ = examples[:limit_length]
lowerCAmelCase_ = glue_convert_examples_to_features(
lowercase_ , lowercase_ , max_length=args.max_seq_length , label_list=lowercase_ , output_mode=self.output_mode , )
lowerCAmelCase_ = time.time()
torch.save(self.features , lowercase_ )
# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
logger.info(
f'''Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]''' )
def __len__( self ) -> List[str]:
'''simple docstring'''
return len(self.features )
def __getitem__( self , lowercase_ ) -> InputFeatures:
'''simple docstring'''
return self.features[i]
def _lowercase ( self ) -> int:
'''simple docstring'''
return self.label_list
| 14 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow
from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class a_ ( a_ , a_ , a_ , unittest.TestCase ):
'''simple docstring'''
__a: int = StableDiffusionInpaintPipeline
__a: int = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
__a: Tuple = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
__a: int = frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
__a: List[str] = frozenset([] )
def _lowercase ( self ) -> Dict:
'''simple docstring'''
torch.manual_seed(0 )
lowerCAmelCase_ = UNetaDConditionModel(
block_out_channels=(3_2, 6_4) , layers_per_block=2 , sample_size=3_2 , in_channels=9 , out_channels=4 , down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') , up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D') , cross_attention_dim=3_2 , attention_head_dim=(2, 4) , use_linear_projection=lowercase_ , )
lowerCAmelCase_ = PNDMScheduler(skip_prk_steps=lowercase_ )
torch.manual_seed(0 )
lowerCAmelCase_ = 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 , sample_size=1_2_8 , )
torch.manual_seed(0 )
lowerCAmelCase_ = 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 , hidden_act='gelu' , projection_dim=5_1_2 , )
lowerCAmelCase_ = CLIPTextModel(lowercase_ )
lowerCAmelCase_ = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
lowerCAmelCase_ = {
'unet': unet,
'scheduler': scheduler,
'vae': vae,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'safety_checker': None,
'feature_extractor': None,
}
return components
def _lowercase ( self , lowercase_ , lowercase_=0 ) -> int:
'''simple docstring'''
lowerCAmelCase_ = floats_tensor((1, 3, 3_2, 3_2) , rng=random.Random(lowercase_ ) ).to(lowercase_ )
lowerCAmelCase_ = image.cpu().permute(0 , 2 , 3 , 1 )[0]
lowerCAmelCase_ = Image.fromarray(np.uinta(lowercase_ ) ).convert('RGB' ).resize((6_4, 6_4) )
lowerCAmelCase_ = Image.fromarray(np.uinta(image + 4 ) ).convert('RGB' ).resize((6_4, 6_4) )
if str(lowercase_ ).startswith('mps' ):
lowerCAmelCase_ = torch.manual_seed(lowercase_ )
else:
lowerCAmelCase_ = torch.Generator(device=lowercase_ ).manual_seed(lowercase_ )
lowerCAmelCase_ = {
'prompt': 'A painting of a squirrel eating a burger',
'image': init_image,
'mask_image': mask_image,
'generator': generator,
'num_inference_steps': 2,
'guidance_scale': 6.0,
'output_type': 'numpy',
}
return inputs
def _lowercase ( self ) -> str:
'''simple docstring'''
lowerCAmelCase_ = 'cpu' # ensure determinism for the device-dependent torch.Generator
lowerCAmelCase_ = self.get_dummy_components()
lowerCAmelCase_ = StableDiffusionInpaintPipeline(**lowercase_ )
lowerCAmelCase_ = sd_pipe.to(lowercase_ )
sd_pipe.set_progress_bar_config(disable=lowercase_ )
lowerCAmelCase_ = self.get_dummy_inputs(lowercase_ )
lowerCAmelCase_ = sd_pipe(**lowercase_ ).images
lowerCAmelCase_ = image[0, -3:, -3:, -1]
assert image.shape == (1, 6_4, 6_4, 3)
lowerCAmelCase_ = np.array([0.47_27, 0.57_35, 0.39_41, 0.54_46, 0.59_26, 0.43_94, 0.50_62, 0.46_54, 0.44_76] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def _lowercase ( self ) -> Any:
'''simple docstring'''
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
@slow
@require_torch_gpu
class a_ ( unittest.TestCase ):
'''simple docstring'''
def _lowercase ( self ) -> Tuple:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _lowercase ( self ) -> Optional[Any]:
'''simple docstring'''
lowerCAmelCase_ = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/sd2-inpaint/init_image.png' )
lowerCAmelCase_ = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png' )
lowerCAmelCase_ = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
'/yellow_cat_sitting_on_a_park_bench.npy' )
lowerCAmelCase_ = 'stabilityai/stable-diffusion-2-inpainting'
lowerCAmelCase_ = StableDiffusionInpaintPipeline.from_pretrained(lowercase_ , safety_checker=lowercase_ )
pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
pipe.enable_attention_slicing()
lowerCAmelCase_ = 'Face of a yellow cat, high resolution, sitting on a park bench'
lowerCAmelCase_ = torch.manual_seed(0 )
lowerCAmelCase_ = pipe(
prompt=lowercase_ , image=lowercase_ , mask_image=lowercase_ , generator=lowercase_ , output_type='np' , )
lowerCAmelCase_ = output.images[0]
assert image.shape == (5_1_2, 5_1_2, 3)
assert np.abs(expected_image - image ).max() < 9e-3
def _lowercase ( self ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase_ = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/sd2-inpaint/init_image.png' )
lowerCAmelCase_ = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png' )
lowerCAmelCase_ = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
'/yellow_cat_sitting_on_a_park_bench_fp16.npy' )
lowerCAmelCase_ = 'stabilityai/stable-diffusion-2-inpainting'
lowerCAmelCase_ = StableDiffusionInpaintPipeline.from_pretrained(
lowercase_ , torch_dtype=torch.floataa , safety_checker=lowercase_ , )
pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
pipe.enable_attention_slicing()
lowerCAmelCase_ = 'Face of a yellow cat, high resolution, sitting on a park bench'
lowerCAmelCase_ = torch.manual_seed(0 )
lowerCAmelCase_ = pipe(
prompt=lowercase_ , image=lowercase_ , mask_image=lowercase_ , generator=lowercase_ , output_type='np' , )
lowerCAmelCase_ = output.images[0]
assert image.shape == (5_1_2, 5_1_2, 3)
assert np.abs(expected_image - image ).max() < 5e-1
def _lowercase ( self ) -> List[str]:
'''simple docstring'''
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
lowerCAmelCase_ = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/sd2-inpaint/init_image.png' )
lowerCAmelCase_ = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png' )
lowerCAmelCase_ = 'stabilityai/stable-diffusion-2-inpainting'
lowerCAmelCase_ = PNDMScheduler.from_pretrained(lowercase_ , subfolder='scheduler' )
lowerCAmelCase_ = StableDiffusionInpaintPipeline.from_pretrained(
lowercase_ , safety_checker=lowercase_ , scheduler=lowercase_ , torch_dtype=torch.floataa , )
pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
pipe.enable_attention_slicing(1 )
pipe.enable_sequential_cpu_offload()
lowerCAmelCase_ = 'Face of a yellow cat, high resolution, sitting on a park bench'
lowerCAmelCase_ = torch.manual_seed(0 )
lowerCAmelCase_ = pipe(
prompt=lowercase_ , image=lowercase_ , mask_image=lowercase_ , generator=lowercase_ , num_inference_steps=2 , output_type='np' , )
lowerCAmelCase_ = torch.cuda.max_memory_allocated()
# make sure that less than 2.65 GB is allocated
assert mem_bytes < 2.65 * 1_0**9
| 14 | 1 |
"""simple docstring"""
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
EfficientNetConfig,
EfficientNetForImageClassification,
EfficientNetImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
A : Dict = logging.get_logger(__name__)
A : Dict = {
"b0": efficientnet.EfficientNetBa,
"b1": efficientnet.EfficientNetBa,
"b2": efficientnet.EfficientNetBa,
"b3": efficientnet.EfficientNetBa,
"b4": efficientnet.EfficientNetBa,
"b5": efficientnet.EfficientNetBa,
"b6": efficientnet.EfficientNetBa,
"b7": efficientnet.EfficientNetBa,
}
A : Optional[Any] = {
"b0": {
"hidden_dim": 1_2_8_0,
"width_coef": 1.0,
"depth_coef": 1.0,
"image_size": 2_2_4,
"dropout_rate": 0.2,
"dw_padding": [],
},
"b1": {
"hidden_dim": 1_2_8_0,
"width_coef": 1.0,
"depth_coef": 1.1,
"image_size": 2_4_0,
"dropout_rate": 0.2,
"dw_padding": [1_6],
},
"b2": {
"hidden_dim": 1_4_0_8,
"width_coef": 1.1,
"depth_coef": 1.2,
"image_size": 2_6_0,
"dropout_rate": 0.3,
"dw_padding": [5, 8, 1_6],
},
"b3": {
"hidden_dim": 1_5_3_6,
"width_coef": 1.2,
"depth_coef": 1.4,
"image_size": 3_0_0,
"dropout_rate": 0.3,
"dw_padding": [5, 1_8],
},
"b4": {
"hidden_dim": 1_7_9_2,
"width_coef": 1.4,
"depth_coef": 1.8,
"image_size": 3_8_0,
"dropout_rate": 0.4,
"dw_padding": [6],
},
"b5": {
"hidden_dim": 2_0_4_8,
"width_coef": 1.6,
"depth_coef": 2.2,
"image_size": 4_5_6,
"dropout_rate": 0.4,
"dw_padding": [1_3, 2_7],
},
"b6": {
"hidden_dim": 2_3_0_4,
"width_coef": 1.8,
"depth_coef": 2.6,
"image_size": 5_2_8,
"dropout_rate": 0.5,
"dw_padding": [3_1],
},
"b7": {
"hidden_dim": 2_5_6_0,
"width_coef": 2.0,
"depth_coef": 3.1,
"image_size": 6_0_0,
"dropout_rate": 0.5,
"dw_padding": [1_8],
},
}
def _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = EfficientNetConfig()
__lowerCAmelCase = CONFIG_MAP[model_name]["hidden_dim"]
__lowerCAmelCase = CONFIG_MAP[model_name]["width_coef"]
__lowerCAmelCase = CONFIG_MAP[model_name]["depth_coef"]
__lowerCAmelCase = CONFIG_MAP[model_name]["image_size"]
__lowerCAmelCase = CONFIG_MAP[model_name]["dropout_rate"]
__lowerCAmelCase = CONFIG_MAP[model_name]["dw_padding"]
__lowerCAmelCase = "huggingface/label-files"
__lowerCAmelCase = "imagenet-1k-id2label.json"
__lowerCAmelCase = 1000
__lowerCAmelCase = json.load(open(hf_hub_download(_UpperCamelCase , _UpperCamelCase , repo_type="dataset" ) , "r" ) )
__lowerCAmelCase = {int(_UpperCamelCase ): v for k, v in idalabel.items()}
__lowerCAmelCase = idalabel
__lowerCAmelCase = {v: k for k, v in idalabel.items()}
return config
def _lowerCamelCase ( ):
'''simple docstring'''
__lowerCAmelCase = "http://images.cocodataset.org/val2017/000000039769.jpg"
__lowerCAmelCase = Image.open(requests.get(_UpperCamelCase , stream=_UpperCamelCase ).raw )
return im
def _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = CONFIG_MAP[model_name]["image_size"]
__lowerCAmelCase = EfficientNetImageProcessor(
size={"height": size, "width": size} , image_mean=[0.4_85, 0.4_56, 0.4_06] , image_std=[0.47_85_39_44, 0.4_73_28_64, 0.47_43_41_63] , do_center_crop=_UpperCamelCase , )
return preprocessor
def _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = [v.split("_" )[0].split("block" )[1] for v in original_param_names if v.startswith("block" )]
__lowerCAmelCase = sorted(set(_UpperCamelCase ) )
__lowerCAmelCase = len(_UpperCamelCase )
__lowerCAmelCase = {b: str(_UpperCamelCase ) for b, i in zip(_UpperCamelCase , range(_UpperCamelCase ) )}
__lowerCAmelCase = []
rename_keys.append(("stem_conv/kernel:0", "embeddings.convolution.weight") )
rename_keys.append(("stem_bn/gamma:0", "embeddings.batchnorm.weight") )
rename_keys.append(("stem_bn/beta:0", "embeddings.batchnorm.bias") )
rename_keys.append(("stem_bn/moving_mean:0", "embeddings.batchnorm.running_mean") )
rename_keys.append(("stem_bn/moving_variance:0", "embeddings.batchnorm.running_var") )
for b in block_names:
__lowerCAmelCase = block_name_mapping[b]
rename_keys.append((f"block{b}_expand_conv/kernel:0", f"encoder.blocks.{hf_b}.expansion.expand_conv.weight") )
rename_keys.append((f"block{b}_expand_bn/gamma:0", f"encoder.blocks.{hf_b}.expansion.expand_bn.weight") )
rename_keys.append((f"block{b}_expand_bn/beta:0", f"encoder.blocks.{hf_b}.expansion.expand_bn.bias") )
rename_keys.append(
(f"block{b}_expand_bn/moving_mean:0", f"encoder.blocks.{hf_b}.expansion.expand_bn.running_mean") )
rename_keys.append(
(f"block{b}_expand_bn/moving_variance:0", f"encoder.blocks.{hf_b}.expansion.expand_bn.running_var") )
rename_keys.append(
(f"block{b}_dwconv/depthwise_kernel:0", f"encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight") )
rename_keys.append((f"block{b}_bn/gamma:0", f"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight") )
rename_keys.append((f"block{b}_bn/beta:0", f"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias") )
rename_keys.append(
(f"block{b}_bn/moving_mean:0", f"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean") )
rename_keys.append(
(f"block{b}_bn/moving_variance:0", f"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var") )
rename_keys.append((f"block{b}_se_reduce/kernel:0", f"encoder.blocks.{hf_b}.squeeze_excite.reduce.weight") )
rename_keys.append((f"block{b}_se_reduce/bias:0", f"encoder.blocks.{hf_b}.squeeze_excite.reduce.bias") )
rename_keys.append((f"block{b}_se_expand/kernel:0", f"encoder.blocks.{hf_b}.squeeze_excite.expand.weight") )
rename_keys.append((f"block{b}_se_expand/bias:0", f"encoder.blocks.{hf_b}.squeeze_excite.expand.bias") )
rename_keys.append(
(f"block{b}_project_conv/kernel:0", f"encoder.blocks.{hf_b}.projection.project_conv.weight") )
rename_keys.append((f"block{b}_project_bn/gamma:0", f"encoder.blocks.{hf_b}.projection.project_bn.weight") )
rename_keys.append((f"block{b}_project_bn/beta:0", f"encoder.blocks.{hf_b}.projection.project_bn.bias") )
rename_keys.append(
(f"block{b}_project_bn/moving_mean:0", f"encoder.blocks.{hf_b}.projection.project_bn.running_mean") )
rename_keys.append(
(f"block{b}_project_bn/moving_variance:0", f"encoder.blocks.{hf_b}.projection.project_bn.running_var") )
rename_keys.append(("top_conv/kernel:0", "encoder.top_conv.weight") )
rename_keys.append(("top_bn/gamma:0", "encoder.top_bn.weight") )
rename_keys.append(("top_bn/beta:0", "encoder.top_bn.bias") )
rename_keys.append(("top_bn/moving_mean:0", "encoder.top_bn.running_mean") )
rename_keys.append(("top_bn/moving_variance:0", "encoder.top_bn.running_var") )
__lowerCAmelCase = {}
for item in rename_keys:
if item[0] in original_param_names:
__lowerCAmelCase = "efficientnet." + item[1]
__lowerCAmelCase = "classifier.weight"
__lowerCAmelCase = "classifier.bias"
return key_mapping
def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
for key, value in tf_params.items():
if "normalization" in key:
continue
__lowerCAmelCase = key_mapping[key]
if "_conv" in key and "kernel" in key:
__lowerCAmelCase = torch.from_numpy(_UpperCamelCase ).permute(3 , 2 , 0 , 1 )
elif "depthwise_kernel" in key:
__lowerCAmelCase = torch.from_numpy(_UpperCamelCase ).permute(2 , 3 , 0 , 1 )
elif "kernel" in key:
__lowerCAmelCase = torch.from_numpy(np.transpose(_UpperCamelCase ) )
else:
__lowerCAmelCase = torch.from_numpy(_UpperCamelCase )
# Replace HF parameters with original TF model parameters
assert hf_params[hf_key].shape == new_hf_value.shape
hf_params[hf_key].copy_(_UpperCamelCase )
@torch.no_grad()
def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = model_classes[model_name](
include_top=_UpperCamelCase , weights="imagenet" , input_tensor=_UpperCamelCase , input_shape=_UpperCamelCase , pooling=_UpperCamelCase , classes=1000 , classifier_activation="softmax" , )
__lowerCAmelCase = original_model.trainable_variables
__lowerCAmelCase = original_model.non_trainable_variables
__lowerCAmelCase = {param.name: param.numpy() for param in tf_params}
for param in tf_non_train_params:
__lowerCAmelCase = param.numpy()
__lowerCAmelCase = list(tf_params.keys() )
# Load HuggingFace model
__lowerCAmelCase = get_efficientnet_config(_UpperCamelCase )
__lowerCAmelCase = EfficientNetForImageClassification(_UpperCamelCase ).eval()
__lowerCAmelCase = hf_model.state_dict()
# Create src-to-dst parameter name mapping dictionary
print("Converting parameters..." )
__lowerCAmelCase = rename_keys(_UpperCamelCase )
replace_params(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
# Initialize preprocessor and preprocess input image
__lowerCAmelCase = convert_image_processor(_UpperCamelCase )
__lowerCAmelCase = preprocessor(images=prepare_img() , return_tensors="pt" )
# HF model inference
hf_model.eval()
with torch.no_grad():
__lowerCAmelCase = hf_model(**_UpperCamelCase )
__lowerCAmelCase = outputs.logits.detach().numpy()
# Original model inference
__lowerCAmelCase = False
__lowerCAmelCase = CONFIG_MAP[model_name]["image_size"]
__lowerCAmelCase = prepare_img().resize((image_size, image_size) , resample=PIL.Image.NEAREST )
__lowerCAmelCase = image.img_to_array(_UpperCamelCase )
__lowerCAmelCase = np.expand_dims(_UpperCamelCase , axis=0 )
__lowerCAmelCase = original_model.predict(_UpperCamelCase )
# Check whether original and HF model outputs match -> np.allclose
assert np.allclose(_UpperCamelCase , _UpperCamelCase , atol=1e-3 ), "The predicted logits are not the same."
print("Model outputs match!" )
if save_model:
# Create folder to save model
if not os.path.isdir(_UpperCamelCase ):
os.mkdir(_UpperCamelCase )
# Save converted model and image processor
hf_model.save_pretrained(_UpperCamelCase )
preprocessor.save_pretrained(_UpperCamelCase )
if push_to_hub:
# Push model and image processor to hub
print(f"Pushing converted {model_name} to the hub..." )
__lowerCAmelCase = f"efficientnet-{model_name}"
preprocessor.push_to_hub(_UpperCamelCase )
hf_model.push_to_hub(_UpperCamelCase )
if __name__ == "__main__":
A : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default="b0",
type=str,
help="Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].",
)
parser.add_argument(
"--pytorch_dump_folder_path",
default="hf_model",
type=str,
help="Path to the output PyTorch model directory.",
)
parser.add_argument("--save_model", action="store_true", help="Save model to local")
parser.add_argument("--push_to_hub", action="store_true", help="Push model and image processor to the hub")
A : Any = parser.parse_args()
convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)
| 57 |
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available
from .test_pipelines_common import ANY
if is_torch_available():
import torch
@is_pipeline_test
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
__UpperCAmelCase : Union[str, Any] =MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
__UpperCAmelCase : Union[str, Any] =TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
def snake_case ( self , __a , __a , __a ):
__lowerCAmelCase = TextaTextGenerationPipeline(model=__a , tokenizer=__a )
return generator, ["Something to write", "Something else"]
def snake_case ( self , __a , __a ):
__lowerCAmelCase = generator("Something there" )
self.assertEqual(__a , [{"generated_text": ANY(__a )}] )
# These are encoder decoder, they don't just append to incoming string
self.assertFalse(outputs[0]["generated_text"].startswith("Something there" ) )
__lowerCAmelCase = generator(["This is great !", "Something else"] , num_return_sequences=2 , do_sample=__a )
self.assertEqual(
__a , [
[{"generated_text": ANY(__a )}, {"generated_text": ANY(__a )}],
[{"generated_text": ANY(__a )}, {"generated_text": ANY(__a )}],
] , )
__lowerCAmelCase = generator(
["This is great !", "Something else"] , num_return_sequences=2 , batch_size=2 , do_sample=__a )
self.assertEqual(
__a , [
[{"generated_text": ANY(__a )}, {"generated_text": ANY(__a )}],
[{"generated_text": ANY(__a )}, {"generated_text": ANY(__a )}],
] , )
with self.assertRaises(__a ):
generator(4 )
@require_torch
def snake_case ( self ):
__lowerCAmelCase = pipeline("text2text-generation" , model="patrickvonplaten/t5-tiny-random" , framework="pt" )
# do_sample=False necessary for reproducibility
__lowerCAmelCase = generator("Something there" , do_sample=__a )
self.assertEqual(__a , [{"generated_text": ""}] )
__lowerCAmelCase = 3
__lowerCAmelCase = generator(
"Something there" , num_return_sequences=__a , num_beams=__a , )
__lowerCAmelCase = [
{"generated_text": "Beide Beide Beide Beide Beide Beide Beide Beide Beide"},
{"generated_text": "Beide Beide Beide Beide Beide Beide Beide Beide"},
{"generated_text": ""},
]
self.assertEqual(__a , __a )
__lowerCAmelCase = generator("This is a test" , do_sample=__a , num_return_sequences=2 , return_tensors=__a )
self.assertEqual(
__a , [
{"generated_token_ids": ANY(torch.Tensor )},
{"generated_token_ids": ANY(torch.Tensor )},
] , )
__lowerCAmelCase = generator.model.config.eos_token_id
__lowerCAmelCase = "<pad>"
__lowerCAmelCase = generator(
["This is a test", "This is a second test"] , do_sample=__a , num_return_sequences=2 , batch_size=2 , return_tensors=__a , )
self.assertEqual(
__a , [
[
{"generated_token_ids": ANY(torch.Tensor )},
{"generated_token_ids": ANY(torch.Tensor )},
],
[
{"generated_token_ids": ANY(torch.Tensor )},
{"generated_token_ids": ANY(torch.Tensor )},
],
] , )
@require_tf
def snake_case ( self ):
__lowerCAmelCase = pipeline("text2text-generation" , model="patrickvonplaten/t5-tiny-random" , framework="tf" )
# do_sample=False necessary for reproducibility
__lowerCAmelCase = generator("Something there" , do_sample=__a )
self.assertEqual(__a , [{"generated_text": ""}] )
| 57 | 1 |
import argparse
import logging
import sys
from unittest.mock import patch
import run_glue_deebert
from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
logging.basicConfig(level=logging.DEBUG)
_A = logging.getLogger()
def lowercase_ ( ) -> Dict:
"""simple docstring"""
snake_case = argparse.ArgumentParser()
parser.add_argument("-f" )
snake_case = parser.parse_args()
return args.f
class lowerCamelCase ( A_ ):
def UpperCAmelCase(self : List[Any] ) -> None:
snake_case = logging.StreamHandler(sys.stdout )
logger.addHandler(_A )
def UpperCAmelCase(self : Optional[Any] , _A : int ) -> Optional[Any]:
snake_case = get_gpu_count()
if n_gpu > 1:
pass
# XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
# script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
# distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
# cmd = [sys.executable] + distributed_args + args
# execute_subprocess_async(cmd, env=self.get_env())
# XXX: test the results - need to save them first into .json file
else:
args.insert(0 , "run_glue_deebert.py" )
with patch.object(_A , "argv" , _A ):
snake_case = run_glue_deebert.main()
for value in result.values():
self.assertGreaterEqual(_A , 0.6_66 )
@slow
@require_torch_non_multi_gpu
def UpperCAmelCase(self : Any ) -> int:
snake_case = "\n --model_type roberta\n --model_name_or_path roberta-base\n --task_name MRPC\n --do_train\n --do_eval\n --do_lower_case\n --data_dir ./tests/fixtures/tests_samples/MRPC/\n --max_seq_length 128\n --per_gpu_eval_batch_size=1\n --per_gpu_train_batch_size=8\n --learning_rate 2e-4\n --num_train_epochs 3\n --overwrite_output_dir\n --seed 42\n --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n --plot_data_dir ./examples/deebert/results/\n --save_steps 0\n --overwrite_cache\n --eval_after_first_stage\n ".split()
self.run_and_check(_A )
snake_case = "\n --model_type roberta\n --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n --task_name MRPC\n --do_eval\n --do_lower_case\n --data_dir ./tests/fixtures/tests_samples/MRPC/\n --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n --plot_data_dir ./examples/deebert/results/\n --max_seq_length 128\n --eval_each_highway\n --eval_highway\n --overwrite_cache\n --per_gpu_eval_batch_size=1\n ".split()
self.run_and_check(_A )
snake_case = "\n --model_type roberta\n --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n --task_name MRPC\n --do_eval\n --do_lower_case\n --data_dir ./tests/fixtures/tests_samples/MRPC/\n --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n --plot_data_dir ./examples/deebert/results/\n --max_seq_length 128\n --early_exit_entropy 0.1\n --eval_highway\n --overwrite_cache\n --per_gpu_eval_batch_size=1\n ".split()
self.run_and_check(_A )
| 137 |
def lowercase_ ( A__ = 1000 ) -> int:
"""simple docstring"""
return sum(2 * a * ((a - 1) // 2) for a in range(3 , n + 1 ) )
if __name__ == "__main__":
print(solution())
| 137 | 1 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re
from ..models.auto import AutoProcessor
from ..models.vision_encoder_decoder import VisionEncoderDecoderModel
from ..utils import is_vision_available
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class _A ( _a ):
"""simple docstring"""
UpperCAmelCase : str = """naver-clova-ix/donut-base-finetuned-docvqa"""
UpperCAmelCase : Tuple = (
"""This is a tool that answers a question about an document (pdf). It takes an input named `document` which """
"""should be the document containing the information, as well as a `question` that is the question about the """
"""document. It returns a text that contains the answer to the question."""
)
UpperCAmelCase : List[str] = """document_qa"""
UpperCAmelCase : str = AutoProcessor
UpperCAmelCase : Optional[int] = VisionEncoderDecoderModel
UpperCAmelCase : int = ["""image""", """text"""]
UpperCAmelCase : int = ["""text"""]
def __init__( self : Tuple , *__UpperCAmelCase : Union[str, Any] , **__UpperCAmelCase : Any):
if not is_vision_available():
raise ValueError("Pillow must be installed to use the DocumentQuestionAnsweringTool.")
super().__init__(*__UpperCAmelCase , **__UpperCAmelCase)
def __snake_case ( self : Tuple , __UpperCAmelCase : "Image" , __UpperCAmelCase : str):
a : Any = "<s_docvqa><s_question>{user_input}</s_question><s_answer>"
a : Union[str, Any] = task_prompt.replace("{user_input}" , __UpperCAmelCase)
a : Optional[Any] = self.pre_processor.tokenizer(
__UpperCAmelCase , add_special_tokens=__UpperCAmelCase , return_tensors="pt").input_ids
a : Any = self.pre_processor(__UpperCAmelCase , return_tensors="pt").pixel_values
return {"decoder_input_ids": decoder_input_ids, "pixel_values": pixel_values}
def __snake_case ( self : int , __UpperCAmelCase : int):
return self.model.generate(
inputs["pixel_values"].to(self.device) , decoder_input_ids=inputs["decoder_input_ids"].to(self.device) , max_length=self.model.decoder.config.max_position_embeddings , early_stopping=__UpperCAmelCase , pad_token_id=self.pre_processor.tokenizer.pad_token_id , eos_token_id=self.pre_processor.tokenizer.eos_token_id , use_cache=__UpperCAmelCase , num_beams=1 , bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]] , return_dict_in_generate=__UpperCAmelCase , ).sequences
def __snake_case ( self : str , __UpperCAmelCase : List[Any]):
a : Union[str, Any] = self.pre_processor.batch_decode(__UpperCAmelCase)[0]
a : Optional[Any] = sequence.replace(self.pre_processor.tokenizer.eos_token , "")
a : Any = sequence.replace(self.pre_processor.tokenizer.pad_token , "")
a : Optional[Any] = re.sub(r"<.*?>" , "" , __UpperCAmelCase , count=1).strip() # remove first task start token
a : List[str] = self.pre_processor.tokenajson(__UpperCAmelCase)
return sequence["answer"]
| 40 |
"""simple docstring"""
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# this script dumps information about the environment
import os
import platform
import sys
lowercase__ = """3"""
print("""Python version:""", sys.version)
print("""OS platform:""", platform.platform())
print("""OS architecture:""", platform.machine())
try:
import torch
print("""Torch version:""", torch.__version__)
print("""Cuda available:""", torch.cuda.is_available())
print("""Cuda version:""", torch.version.cuda)
print("""CuDNN version:""", torch.backends.cudnn.version())
print("""Number of GPUs available:""", torch.cuda.device_count())
except ImportError:
print("""Torch version:""", None)
try:
import transformers
print("""transformers version:""", transformers.__version__)
except ImportError:
print("""transformers version:""", None)
| 241 | 0 |
'''simple docstring'''
import unittest
from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow
if is_flax_available():
import jax
from transformers.models.auto.modeling_flax_auto import FlaxAutoModel
from transformers.models.bert.modeling_flax_bert import FlaxBertModel
from transformers.models.roberta.modeling_flax_roberta import FlaxRobertaModel
@require_flax
class a__ ( unittest.TestCase ):
"""simple docstring"""
@slow
def _snake_case (self ):
for model_name in ["bert-base-cased", "bert-large-uncased"]:
with self.subTest(__lowercase ):
__lowerCAmelCase = AutoConfig.from_pretrained(__lowercase )
self.assertIsNotNone(__lowercase )
self.assertIsInstance(__lowercase , __lowercase )
__lowerCAmelCase = FlaxAutoModel.from_pretrained(__lowercase )
self.assertIsNotNone(__lowercase )
self.assertIsInstance(__lowercase , __lowercase )
@slow
def _snake_case (self ):
for model_name in ["roberta-base", "roberta-large"]:
with self.subTest(__lowercase ):
__lowerCAmelCase = AutoConfig.from_pretrained(__lowercase )
self.assertIsNotNone(__lowercase )
self.assertIsInstance(__lowercase , __lowercase )
__lowerCAmelCase = FlaxAutoModel.from_pretrained(__lowercase )
self.assertIsNotNone(__lowercase )
self.assertIsInstance(__lowercase , __lowercase )
@slow
def _snake_case (self ):
for model_name in ["bert-base-cased", "bert-large-uncased"]:
__lowerCAmelCase = AutoTokenizer.from_pretrained(__lowercase )
__lowerCAmelCase = FlaxBertModel.from_pretrained(__lowercase )
__lowerCAmelCase = tokenizer('''Do you support jax jitted function?''' , return_tensors=TensorType.JAX )
@jax.jit
def eval(**__lowercase ):
return model(**__lowercase )
eval(**__lowercase ).block_until_ready()
@slow
def _snake_case (self ):
for model_name in ["roberta-base", "roberta-large"]:
__lowerCAmelCase = AutoTokenizer.from_pretrained(__lowercase )
__lowerCAmelCase = FlaxRobertaModel.from_pretrained(__lowercase )
__lowerCAmelCase = tokenizer('''Do you support jax jitted function?''' , return_tensors=TensorType.JAX )
@jax.jit
def eval(**__lowercase ):
return model(**__lowercase )
eval(**__lowercase ).block_until_ready()
def _snake_case (self ):
with self.assertRaisesRegex(
__lowercase , '''bert-base is not a local folder and is not a valid model identifier''' ):
__lowerCAmelCase = FlaxAutoModel.from_pretrained('''bert-base''' )
def _snake_case (self ):
with self.assertRaisesRegex(
__lowercase , R'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)''' ):
__lowerCAmelCase = FlaxAutoModel.from_pretrained(__lowercase , revision='''aaaaaa''' )
def _snake_case (self ):
with self.assertRaisesRegex(
__lowercase , '''hf-internal-testing/config-no-model does not appear to have a file named flax_model.msgpack''' , ):
__lowerCAmelCase = FlaxAutoModel.from_pretrained('''hf-internal-testing/config-no-model''' )
def _snake_case (self ):
with self.assertRaisesRegex(__lowercase , '''Use `from_pt=True` to load this model''' ):
__lowerCAmelCase = FlaxAutoModel.from_pretrained('''hf-internal-testing/tiny-bert-pt-only''' )
| 9 |
'''simple docstring'''
from __future__ import annotations
import math
def __magic_name__( lowerCamelCase, lowerCamelCase):
if len(lowerCamelCase) != 2 or len(a[0]) != 2 or len(lowerCamelCase) != 2 or len(b[0]) != 2:
raise Exception('''Matrices are not 2x2''')
__lowerCAmelCase = [
[a[0][0] * b[0][0] + a[0][1] * b[1][0], a[0][0] * b[0][1] + a[0][1] * b[1][1]],
[a[1][0] * b[0][0] + a[1][1] * b[1][0], a[1][0] * b[0][1] + a[1][1] * b[1][1]],
]
return new_matrix
def __magic_name__( lowerCamelCase, lowerCamelCase):
return [
[matrix_a[row][col] + matrix_b[row][col] for col in range(len(matrix_a[row]))]
for row in range(len(lowerCamelCase))
]
def __magic_name__( lowerCamelCase, lowerCamelCase):
return [
[matrix_a[row][col] - matrix_b[row][col] for col in range(len(matrix_a[row]))]
for row in range(len(lowerCamelCase))
]
def __magic_name__( lowerCamelCase):
if len(lowerCamelCase) % 2 != 0 or len(a[0]) % 2 != 0:
raise Exception('''Odd matrices are not supported!''')
__lowerCAmelCase = len(lowerCamelCase)
__lowerCAmelCase = matrix_length // 2
__lowerCAmelCase = [[a[i][j] for j in range(lowerCamelCase, lowerCamelCase)] for i in range(lowerCamelCase)]
__lowerCAmelCase = [
[a[i][j] for j in range(lowerCamelCase, lowerCamelCase)] for i in range(lowerCamelCase, lowerCamelCase)
]
__lowerCAmelCase = [[a[i][j] for j in range(lowerCamelCase)] for i in range(lowerCamelCase)]
__lowerCAmelCase = [[a[i][j] for j in range(lowerCamelCase)] for i in range(lowerCamelCase, lowerCamelCase)]
return top_left, top_right, bot_left, bot_right
def __magic_name__( lowerCamelCase):
return len(lowerCamelCase), len(matrix[0])
def __magic_name__( lowerCamelCase):
print('''\n'''.join(str(lowerCamelCase) for line in matrix))
def __magic_name__( lowerCamelCase, lowerCamelCase):
if matrix_dimensions(lowerCamelCase) == (2, 2):
return default_matrix_multiplication(lowerCamelCase, lowerCamelCase)
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = split_matrix(lowerCamelCase)
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = split_matrix(lowerCamelCase)
__lowerCAmelCase = actual_strassen(lowerCamelCase, matrix_subtraction(lowerCamelCase, lowerCamelCase))
__lowerCAmelCase = actual_strassen(matrix_addition(lowerCamelCase, lowerCamelCase), lowerCamelCase)
__lowerCAmelCase = actual_strassen(matrix_addition(lowerCamelCase, lowerCamelCase), lowerCamelCase)
__lowerCAmelCase = actual_strassen(lowerCamelCase, matrix_subtraction(lowerCamelCase, lowerCamelCase))
__lowerCAmelCase = actual_strassen(matrix_addition(lowerCamelCase, lowerCamelCase), matrix_addition(lowerCamelCase, lowerCamelCase))
__lowerCAmelCase = actual_strassen(matrix_subtraction(lowerCamelCase, lowerCamelCase), matrix_addition(lowerCamelCase, lowerCamelCase))
__lowerCAmelCase = actual_strassen(matrix_subtraction(lowerCamelCase, lowerCamelCase), matrix_addition(lowerCamelCase, lowerCamelCase))
__lowerCAmelCase = matrix_addition(matrix_subtraction(matrix_addition(lowerCamelCase, lowerCamelCase), lowerCamelCase), lowerCamelCase)
__lowerCAmelCase = matrix_addition(lowerCamelCase, lowerCamelCase)
__lowerCAmelCase = matrix_addition(lowerCamelCase, lowerCamelCase)
__lowerCAmelCase = matrix_subtraction(matrix_subtraction(matrix_addition(lowerCamelCase, lowerCamelCase), lowerCamelCase), lowerCamelCase)
# construct the new matrix from our 4 quadrants
__lowerCAmelCase = []
for i in range(len(lowerCamelCase)):
new_matrix.append(top_left[i] + top_right[i])
for i in range(len(lowerCamelCase)):
new_matrix.append(bot_left[i] + bot_right[i])
return new_matrix
def __magic_name__( lowerCamelCase, lowerCamelCase):
if matrix_dimensions(lowerCamelCase)[1] != matrix_dimensions(lowerCamelCase)[0]:
__lowerCAmelCase = (
'''Unable to multiply these matrices, please check the dimensions.\n'''
F"""Matrix A: {matrixa}\n"""
F"""Matrix B: {matrixa}"""
)
raise Exception(lowerCamelCase)
__lowerCAmelCase = matrix_dimensions(lowerCamelCase)
__lowerCAmelCase = matrix_dimensions(lowerCamelCase)
if dimensiona[0] == dimensiona[1] and dimensiona[0] == dimensiona[1]:
return [matrixa, matrixa]
__lowerCAmelCase = max(*lowerCamelCase, *lowerCamelCase)
__lowerCAmelCase = int(math.pow(2, math.ceil(math.loga(lowerCamelCase))))
__lowerCAmelCase = matrixa
__lowerCAmelCase = matrixa
# Adding zeros to the matrices so that the arrays dimensions are the same and also
# power of 2
for i in range(0, lowerCamelCase):
if i < dimensiona[0]:
for _ in range(dimensiona[1], lowerCamelCase):
new_matrixa[i].append(0)
else:
new_matrixa.append([0] * maxim)
if i < dimensiona[0]:
for _ in range(dimensiona[1], lowerCamelCase):
new_matrixa[i].append(0)
else:
new_matrixa.append([0] * maxim)
__lowerCAmelCase = actual_strassen(lowerCamelCase, lowerCamelCase)
# Removing the additional zeros
for i in range(0, lowerCamelCase):
if i < dimensiona[0]:
for _ in range(dimensiona[1], lowerCamelCase):
final_matrix[i].pop()
else:
final_matrix.pop()
return final_matrix
if __name__ == "__main__":
_UpperCAmelCase : List[str] = [
[2, 3, 4, 5],
[6, 4, 3, 1],
[2, 3, 6, 7],
[3, 1, 2, 4],
[2, 3, 4, 5],
[6, 4, 3, 1],
[2, 3, 6, 7],
[3, 1, 2, 4],
[2, 3, 4, 5],
[6, 2, 3, 1],
]
_UpperCAmelCase : Optional[Any] = [[0, 2, 1, 1], [1_6, 2, 3, 3], [2, 2, 7, 7], [1_3, 1_1, 2_2, 4]]
print(strassen(matrixa, matrixa))
| 9 | 1 |
"""simple docstring"""
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common import TokenizerTesterMixin
UpperCAmelCase__ : Dict = get_tests_dir('fixtures/test_sentencepiece.model')
if is_torch_available():
from transformers.models.plbart.modeling_plbart import shift_tokens_right
UpperCAmelCase__ : Any = 5_0_0_0_3
UpperCAmelCase__ : Tuple = 5_0_0_0_2
@require_sentencepiece
@require_tokenizers
class lowerCAmelCase_ (a__ , unittest.TestCase ):
"""simple docstring"""
__UpperCamelCase : Optional[Any] = PLBartTokenizer
__UpperCamelCase : Optional[Any] = None
__UpperCamelCase : Dict = False
def __magic_name__ (self ) -> Optional[int]:
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
SCREAMING_SNAKE_CASE__ : List[Any] = PLBartTokenizer(SCREAMING_SNAKE_CASE__ , language_codes="""base""" , keep_accents=SCREAMING_SNAKE_CASE__ )
tokenizer.save_pretrained(self.tmpdirname )
def __magic_name__ (self ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = PLBartTokenizer(SCREAMING_SNAKE_CASE__ , language_codes="""base""" , keep_accents=SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Tuple = tokenizer.tokenize("""This is a test""" )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__ ) , [value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]] , )
SCREAMING_SNAKE_CASE__ : int = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
self.assertListEqual(
SCREAMING_SNAKE_CASE__ , [
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""9""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""é""",
""".""",
] , )
SCREAMING_SNAKE_CASE__ : int = tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__ )
self.assertListEqual(
SCREAMING_SNAKE_CASE__ , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
] , )
SCREAMING_SNAKE_CASE__ : Any = tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__ )
self.assertListEqual(
SCREAMING_SNAKE_CASE__ , [
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""<unk>""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""<unk>""",
""".""",
] , )
SCREAMING_SNAKE_CASE__ : Dict = tokenizer.vocab_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = [tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__ ) for x in range(end - 4 , SCREAMING_SNAKE_CASE__ )]
self.assertListEqual(SCREAMING_SNAKE_CASE__ , ["""__java__""", """__python__""", """__en_XX__""", """<mask>"""] )
SCREAMING_SNAKE_CASE__ : str = """java.lang.Exception, python.lang.Exception, javascript, php, ruby, go"""
SCREAMING_SNAKE_CASE__ : Tuple = tokenizer(SCREAMING_SNAKE_CASE__ ).input_ids
self.assertEqual(
tokenizer.decode(SCREAMING_SNAKE_CASE__ , skip_special_tokens=SCREAMING_SNAKE_CASE__ , clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__ ) , SCREAMING_SNAKE_CASE__ , )
def __magic_name__ (self ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = PLBartTokenizer(SCREAMING_SNAKE_CASE__ , language_codes="""multi""" , keep_accents=SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Tuple = tokenizer.tokenize("""This is a test""" )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__ ) , [value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]] , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
self.assertListEqual(
SCREAMING_SNAKE_CASE__ , [
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""9""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""é""",
""".""",
] , )
SCREAMING_SNAKE_CASE__ : Tuple = tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__ )
self.assertListEqual(
SCREAMING_SNAKE_CASE__ , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
] , )
SCREAMING_SNAKE_CASE__ : Dict = tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__ )
self.assertListEqual(
SCREAMING_SNAKE_CASE__ , [
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""<unk>""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""<unk>""",
""".""",
] , )
SCREAMING_SNAKE_CASE__ : List[Any] = tokenizer.vocab_size
SCREAMING_SNAKE_CASE__ : Dict = [tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__ ) for x in range(end - 7 , SCREAMING_SNAKE_CASE__ )]
self.assertListEqual(
SCREAMING_SNAKE_CASE__ , ["""__java__""", """__python__""", """__en_XX__""", """__javascript__""", """__php__""", """__ruby__""", """__go__"""] )
SCREAMING_SNAKE_CASE__ : str = """java.lang.Exception, python.lang.Exception, javascript, php, ruby, go"""
SCREAMING_SNAKE_CASE__ : str = tokenizer(SCREAMING_SNAKE_CASE__ ).input_ids
self.assertEqual(
tokenizer.decode(SCREAMING_SNAKE_CASE__ , skip_special_tokens=SCREAMING_SNAKE_CASE__ , clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__ ) , SCREAMING_SNAKE_CASE__ , )
@require_torch
@require_sentencepiece
@require_tokenizers
class lowerCAmelCase_ (unittest.TestCase ):
"""simple docstring"""
__UpperCamelCase : Optional[int] = '''uclanlp/plbart-python-en_XX'''
__UpperCamelCase : Tuple = [
'''def maximum(a,b,c):NEW_LINE_INDENTreturn max([a,b,c])''',
'''def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])''',
]
__UpperCamelCase : Optional[int] = [
'''Returns the maximum value of a b c.''',
'''Sums the values of a b c.''',
]
__UpperCamelCase : Union[str, Any] = [
134,
5452,
33460,
33441,
33463,
33465,
33463,
33449,
988,
20,
33456,
19,
33456,
771,
39,
4258,
889,
3318,
33441,
33463,
33465,
33463,
33449,
2471,
2,
PYTHON_CODE,
]
@classmethod
def __magic_name__ (cls ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : PLBartTokenizer = PLBartTokenizer.from_pretrained(
cls.checkpoint_name , language_codes="""base""" , src_lang="""python""" , tgt_lang="""en_XX""" )
SCREAMING_SNAKE_CASE__ : Dict = 1
return cls
def __magic_name__ (self ) -> List[Any]:
"""simple docstring"""
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""__java__"""] , 5_00_01 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""__python__"""] , 5_00_02 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""__en_XX__"""] , 5_00_03 )
def __magic_name__ (self ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , SCREAMING_SNAKE_CASE__ )
def __magic_name__ (self ) -> Tuple:
"""simple docstring"""
self.assertIn(SCREAMING_SNAKE_CASE__ , self.tokenizer.all_special_ids )
SCREAMING_SNAKE_CASE__ : int = [EN_CODE, 90_37, 3_34_42, 57, 7_52, 1_53, 14, 56, 18, 9, 2]
SCREAMING_SNAKE_CASE__ : Tuple = self.tokenizer.decode(SCREAMING_SNAKE_CASE__ , skip_special_tokens=SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : int = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=SCREAMING_SNAKE_CASE__ )
self.assertEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
self.assertNotIn(self.tokenizer.eos_token , SCREAMING_SNAKE_CASE__ )
def __magic_name__ (self ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = ["""def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])""" * 20]
self.assertIsInstance(src_text[0] , SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Tuple = 10
SCREAMING_SNAKE_CASE__ : Dict = self.tokenizer(SCREAMING_SNAKE_CASE__ , max_length=SCREAMING_SNAKE_CASE__ , truncation=SCREAMING_SNAKE_CASE__ ).input_ids[0]
self.assertEqual(ids[-2] , 2 )
self.assertEqual(ids[-1] , SCREAMING_SNAKE_CASE__ )
self.assertEqual(len(SCREAMING_SNAKE_CASE__ ) , SCREAMING_SNAKE_CASE__ )
def __magic_name__ (self ) -> List[str]:
"""simple docstring"""
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """__java__"""] ) , [5_00_04, 5_00_01] )
def __magic_name__ (self ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = tempfile.mkdtemp()
SCREAMING_SNAKE_CASE__ : Any = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : List[Any] = PLBartTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__ )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , SCREAMING_SNAKE_CASE__ )
@require_torch
def __magic_name__ (self ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = self.tokenizer(self.src_text , text_target=self.tgt_text , padding=SCREAMING_SNAKE_CASE__ , return_tensors="""pt""" )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = shift_tokens_right(batch["""labels"""] , self.tokenizer.pad_token_id )
# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
self.assertEqual(batch.input_ids[1][-2:].tolist() , [2, PYTHON_CODE] )
self.assertEqual(batch.decoder_input_ids[1][0] , SCREAMING_SNAKE_CASE__ )
self.assertEqual(batch.decoder_input_ids[1][-1] , 2 )
self.assertEqual(batch.labels[1][-2:].tolist() , [2, EN_CODE] )
@require_torch
def __magic_name__ (self ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=SCREAMING_SNAKE_CASE__ , truncation=SCREAMING_SNAKE_CASE__ , max_length=len(self.expected_src_tokens ) , return_tensors="""pt""" , )
SCREAMING_SNAKE_CASE__ : Optional[Any] = shift_tokens_right(batch["""labels"""] , self.tokenizer.pad_token_id )
self.assertIsInstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
self.assertEqual((2, 26) , batch.input_ids.shape )
self.assertEqual((2, 26) , batch.attention_mask.shape )
SCREAMING_SNAKE_CASE__ : Optional[Any] = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , SCREAMING_SNAKE_CASE__ )
self.assertEqual(2 , batch.decoder_input_ids[0, -1] ) # EOS
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens , [] )
self.assertEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id, PYTHON_CODE] )
def __magic_name__ (self ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = self.tokenizer(self.src_text , padding=SCREAMING_SNAKE_CASE__ , truncation=SCREAMING_SNAKE_CASE__ , max_length=3 , return_tensors="""pt""" )
SCREAMING_SNAKE_CASE__ : int = self.tokenizer(
text_target=self.tgt_text , padding=SCREAMING_SNAKE_CASE__ , truncation=SCREAMING_SNAKE_CASE__ , max_length=10 , return_tensors="""pt""" )
SCREAMING_SNAKE_CASE__ : Optional[int] = targets["""input_ids"""]
SCREAMING_SNAKE_CASE__ : Dict = shift_tokens_right(SCREAMING_SNAKE_CASE__ , self.tokenizer.pad_token_id )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.decoder_input_ids.shape[1] , 10 )
@require_torch
def __magic_name__ (self ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.tokenizer._build_translation_inputs(
"""A test""" , return_tensors="""pt""" , src_lang="""en_XX""" , tgt_lang="""java""" )
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE__ ) , {
# A, test, EOS, en_XX
"""input_ids""": [[1_50, 2_42, 2, 5_00_03]],
"""attention_mask""": [[1, 1, 1, 1]],
# java
"""forced_bos_token_id""": 5_00_01,
} , )
| 25 |
"""simple docstring"""
UpperCAmelCase__ : List[str] = [
9_9_9,
8_0_0,
7_9_9,
6_0_0,
5_9_9,
5_0_0,
4_0_0,
3_9_9,
3_7_7,
3_5_5,
3_3_3,
3_1_1,
2_8_8,
2_6_6,
2_4_4,
2_2_2,
2_0_0,
1_9_9,
1_7_7,
1_5_5,
1_3_3,
1_1_1,
8_8,
6_6,
4_4,
2_2,
0,
]
UpperCAmelCase__ : int = [
9_9_9,
9_7_6,
9_5_2,
9_2_8,
9_0_5,
8_8_2,
8_5_8,
8_5_7,
8_1_0,
7_6_2,
7_1_5,
7_1_4,
5_7_2,
4_2_9,
4_2_8,
2_8_6,
2_8_5,
2_3_8,
1_9_0,
1_4_3,
1_4_2,
1_1_8,
9_5,
7_1,
4_7,
2_4,
0,
]
UpperCAmelCase__ : int = [
9_9_9,
9_8_8,
9_7_7,
9_6_6,
9_5_5,
9_4_4,
9_3_3,
9_2_2,
9_1_1,
9_0_0,
8_9_9,
8_7_9,
8_5_9,
8_4_0,
8_2_0,
8_0_0,
7_9_9,
7_6_6,
7_3_3,
7_0_0,
6_9_9,
6_5_0,
6_0_0,
5_9_9,
5_0_0,
4_9_9,
4_0_0,
3_9_9,
3_5_0,
3_0_0,
2_9_9,
2_6_6,
2_3_3,
2_0_0,
1_9_9,
1_7_9,
1_5_9,
1_4_0,
1_2_0,
1_0_0,
9_9,
8_8,
7_7,
6_6,
5_5,
4_4,
3_3,
2_2,
1_1,
0,
]
UpperCAmelCase__ : int = [
9_9_9,
9_9_5,
9_9_2,
9_8_9,
9_8_5,
9_8_1,
9_7_8,
9_7_5,
9_7_1,
9_6_7,
9_6_4,
9_6_1,
9_5_7,
9_5_6,
9_5_1,
9_4_7,
9_4_2,
9_3_7,
9_3_3,
9_2_8,
9_2_3,
9_1_9,
9_1_4,
9_1_3,
9_0_8,
9_0_3,
8_9_7,
8_9_2,
8_8_7,
8_8_1,
8_7_6,
8_7_1,
8_7_0,
8_6_4,
8_5_8,
8_5_2,
8_4_6,
8_4_0,
8_3_4,
8_2_8,
8_2_7,
8_2_0,
8_1_3,
8_0_6,
7_9_9,
7_9_2,
7_8_5,
7_8_4,
7_7_7,
7_7_0,
7_6_3,
7_5_6,
7_4_9,
7_4_2,
7_4_1,
7_3_3,
7_2_4,
7_1_6,
7_0_7,
6_9_9,
6_9_8,
6_8_8,
6_7_7,
6_6_6,
6_5_6,
6_5_5,
6_4_5,
6_3_4,
6_2_3,
6_1_3,
6_1_2,
5_9_8,
5_8_4,
5_7_0,
5_6_9,
5_5_5,
5_4_1,
5_2_7,
5_2_6,
5_0_5,
4_8_4,
4_8_3,
4_6_2,
4_4_0,
4_3_9,
3_9_6,
3_9_5,
3_5_2,
3_5_1,
3_0_8,
3_0_7,
2_6_4,
2_6_3,
2_2_0,
2_1_9,
1_7_6,
1_3_2,
8_8,
4_4,
0,
]
UpperCAmelCase__ : Tuple = [
9_9_9,
9_9_7,
9_9_5,
9_9_2,
9_9_0,
9_8_8,
9_8_6,
9_8_4,
9_8_1,
9_7_9,
9_7_7,
9_7_5,
9_7_2,
9_7_0,
9_6_8,
9_6_6,
9_6_4,
9_6_1,
9_5_9,
9_5_7,
9_5_6,
9_5_4,
9_5_1,
9_4_9,
9_4_6,
9_4_4,
9_4_1,
9_3_9,
9_3_6,
9_3_4,
9_3_1,
9_2_9,
9_2_6,
9_2_4,
9_2_1,
9_1_9,
9_1_6,
9_1_4,
9_1_3,
9_1_0,
9_0_7,
9_0_5,
9_0_2,
8_9_9,
8_9_6,
8_9_3,
8_9_1,
8_8_8,
8_8_5,
8_8_2,
8_7_9,
8_7_7,
8_7_4,
8_7_1,
8_7_0,
8_6_7,
8_6_4,
8_6_1,
8_5_8,
8_5_5,
8_5_2,
8_4_9,
8_4_6,
8_4_3,
8_4_0,
8_3_7,
8_3_4,
8_3_1,
8_2_8,
8_2_7,
8_2_4,
8_2_1,
8_1_7,
8_1_4,
8_1_1,
8_0_8,
8_0_4,
8_0_1,
7_9_8,
7_9_5,
7_9_1,
7_8_8,
7_8_5,
7_8_4,
7_8_0,
7_7_7,
7_7_4,
7_7_0,
7_6_6,
7_6_3,
7_6_0,
7_5_6,
7_5_2,
7_4_9,
7_4_6,
7_4_2,
7_4_1,
7_3_7,
7_3_3,
7_3_0,
7_2_6,
7_2_2,
7_1_8,
7_1_4,
7_1_0,
7_0_7,
7_0_3,
6_9_9,
6_9_8,
6_9_4,
6_9_0,
6_8_5,
6_8_1,
6_7_7,
6_7_3,
6_6_9,
6_6_4,
6_6_0,
6_5_6,
6_5_5,
6_5_0,
6_4_6,
6_4_1,
6_3_6,
6_3_2,
6_2_7,
6_2_2,
6_1_8,
6_1_3,
6_1_2,
6_0_7,
6_0_2,
5_9_6,
5_9_1,
5_8_6,
5_8_0,
5_7_5,
5_7_0,
5_6_9,
5_6_3,
5_5_7,
5_5_1,
5_4_5,
5_3_9,
5_3_3,
5_2_7,
5_2_6,
5_1_9,
5_1_2,
5_0_5,
4_9_8,
4_9_1,
4_8_4,
4_8_3,
4_7_4,
4_6_6,
4_5_7,
4_4_9,
4_4_0,
4_3_9,
4_2_8,
4_1_8,
4_0_7,
3_9_6,
3_9_5,
3_8_1,
3_6_6,
3_5_2,
3_5_1,
3_3_0,
3_0_8,
3_0_7,
2_8_6,
2_6_4,
2_6_3,
2_4_2,
2_2_0,
2_1_9,
1_7_6,
1_7_5,
1_3_2,
1_3_1,
8_8,
4_4,
0,
]
UpperCAmelCase__ : Union[str, Any] = [
9_9_9,
9_9_1,
9_8_2,
9_7_4,
9_6_6,
9_5_8,
9_5_0,
9_4_1,
9_3_3,
9_2_5,
9_1_6,
9_0_8,
9_0_0,
8_9_9,
8_7_4,
8_5_0,
8_2_5,
8_0_0,
7_9_9,
7_0_0,
6_0_0,
5_0_0,
4_0_0,
3_0_0,
2_0_0,
1_0_0,
0,
]
UpperCAmelCase__ : str = [
9_9_9,
9_9_2,
9_8_5,
9_7_8,
9_7_1,
9_6_4,
9_5_7,
9_4_9,
9_4_2,
9_3_5,
9_2_8,
9_2_1,
9_1_4,
9_0_7,
9_0_0,
8_9_9,
8_7_9,
8_5_9,
8_4_0,
8_2_0,
8_0_0,
7_9_9,
7_6_6,
7_3_3,
7_0_0,
6_9_9,
6_5_0,
6_0_0,
5_9_9,
5_0_0,
4_9_9,
4_0_0,
3_9_9,
3_0_0,
2_9_9,
2_0_0,
1_9_9,
1_0_0,
9_9,
0,
]
UpperCAmelCase__ : str = [
9_9_9,
9_9_6,
9_9_2,
9_8_9,
9_8_5,
9_8_2,
9_7_9,
9_7_5,
9_7_2,
9_6_8,
9_6_5,
9_6_1,
9_5_8,
9_5_5,
9_5_1,
9_4_8,
9_4_4,
9_4_1,
9_3_8,
9_3_4,
9_3_1,
9_2_7,
9_2_4,
9_2_0,
9_1_7,
9_1_4,
9_1_0,
9_0_7,
9_0_3,
9_0_0,
8_9_9,
8_9_1,
8_8_4,
8_7_6,
8_6_9,
8_6_1,
8_5_3,
8_4_6,
8_3_8,
8_3_0,
8_2_3,
8_1_5,
8_0_8,
8_0_0,
7_9_9,
7_8_8,
7_7_7,
7_6_6,
7_5_5,
7_4_4,
7_3_3,
7_2_2,
7_1_1,
7_0_0,
6_9_9,
6_8_8,
6_7_7,
6_6_6,
6_5_5,
6_4_4,
6_3_3,
6_2_2,
6_1_1,
6_0_0,
5_9_9,
5_8_5,
5_7_1,
5_5_7,
5_4_2,
5_2_8,
5_1_4,
5_0_0,
4_9_9,
4_8_5,
4_7_1,
4_5_7,
4_4_2,
4_2_8,
4_1_4,
4_0_0,
3_9_9,
3_7_9,
3_5_9,
3_4_0,
3_2_0,
3_0_0,
2_9_9,
2_7_9,
2_5_9,
2_4_0,
2_2_0,
2_0_0,
1_9_9,
1_6_6,
1_3_3,
1_0_0,
9_9,
6_6,
3_3,
0,
]
| 25 | 1 |
"""simple docstring"""
def __lowercase ( snake_case_ : str ,snake_case_ : bool = False ) ->str:
'''simple docstring'''
if not isinstance(snake_case_ ,snake_case_ ):
__A : Any = F"""Expected string as input, found {type(snake_case_ )}"""
raise ValueError(snake_case_ )
if not isinstance(snake_case_ ,snake_case_ ):
__A : Any = F"""Expected boolean as use_pascal parameter, found {type(snake_case_ )}"""
raise ValueError(snake_case_ )
__A : int = input_str.split('''_''' )
__A : List[Any] = 0 if use_pascal else 1
__A : str = words[start_index:]
__A : List[Any] = [word[0].upper() + word[1:] for word in words_to_capitalize]
__A : Tuple = '''''' if use_pascal else words[0]
return "".join([initial_word, *capitalized_words] )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 291 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a_ = {
"""configuration_vivit""": ["""VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VivitConfig"""],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ["""VivitImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
"""VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""VivitModel""",
"""VivitPreTrainedModel""",
"""VivitForVideoClassification""",
]
if TYPE_CHECKING:
from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_vivit import VivitImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vivit import (
VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
VivitForVideoClassification,
VivitModel,
VivitPreTrainedModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 291 | 1 |
"""simple docstring"""
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class __A ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
@staticmethod
@abstractmethod
def SCREAMING_SNAKE_CASE ( __A ) -> int:
raise NotImplementedError()
@abstractmethod
def SCREAMING_SNAKE_CASE ( self ) -> str:
raise NotImplementedError() | 81 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
a = TypeVar('''T''')
class lowercase_ ( Generic[T] ):
'''simple docstring'''
def __init__( self : Any , _UpperCAmelCase : T ):
_A = data
_A = None
def __str__( self : str ):
return F'''{self.data}'''
class lowercase_ ( Generic[T] ):
'''simple docstring'''
def __init__( self : Tuple ):
_A = None
def __iter__( self : List[Any] ):
_A = self.top
while node:
yield node.data
_A = node.next
def __str__( self : Union[str, Any] ):
return "->".join([str(_UpperCAmelCase ) for item in self] )
def __len__( self : List[Any] ):
return len(tuple(iter(self ) ) )
def lowerCAmelCase_ ( self : str ):
return self.top is None
def lowerCAmelCase_ ( self : Union[str, Any] , _UpperCAmelCase : T ):
_A = Node(_UpperCAmelCase )
if not self.is_empty():
_A = self.top
_A = node
def lowerCAmelCase_ ( self : Dict ):
if self.is_empty():
raise IndexError('pop from empty stack' )
assert isinstance(self.top , _UpperCAmelCase )
_A = self.top
_A = self.top.next
return pop_node.data
def lowerCAmelCase_ ( self : Tuple ):
if self.is_empty():
raise IndexError('peek from empty stack' )
assert self.top is not None
return self.top.data
def lowerCAmelCase_ ( self : Optional[Any] ):
_A = None
if __name__ == "__main__":
from doctest import testmod
testmod()
| 315 | 0 |
def __lowerCamelCase ( lowerCamelCase__ : Optional[Any] ):
'''simple docstring'''
assert isinstance(__lowerCamelCase , __lowerCamelCase ), f'The input value of [n={number}] is not an integer'
if number == 1:
return 2
elif number < 1:
lowerCamelCase = f'The input value of [n={number}] has to be > 0'
raise ValueError(__lowerCamelCase )
else:
lowerCamelCase = sylvester(number - 1 )
lowerCamelCase = num - 1
lowerCamelCase = num
return lower * upper + 1
if __name__ == "__main__":
print(f"""The 8th number in Sylvester's sequence: {sylvester(8)}""")
| 351 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase : Union[str, Any] = {
"configuration_mobilebert": [
"MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"MobileBertConfig",
"MobileBertOnnxConfig",
],
"tokenization_mobilebert": ["MobileBertTokenizer"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase : Dict = ["MobileBertTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase : str = [
"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:
UpperCAmelCase : 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
UpperCAmelCase : int = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 66 | 0 |
from torch import nn
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE ):
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise ValueError(f'''Unsupported activation function: {act_fn}''' )
| 186 |
import copy
import tempfile
import unittest
from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError
from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
class _lowerCamelCase ( unittest.TestCase ):
"""simple docstring"""
@parameterized.expand([(None,), ('''foo.json''',)] )
def _snake_case ( self , _SCREAMING_SNAKE_CASE )->Tuple:
'''simple docstring'''
A_ : Dict = GenerationConfig(
do_sample=_SCREAMING_SNAKE_CASE , temperature=0.7 , length_penalty=1.0 , bad_words_ids=[[1, 2, 3], [4, 5]] , )
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(_SCREAMING_SNAKE_CASE , config_name=_SCREAMING_SNAKE_CASE )
A_ : Tuple = GenerationConfig.from_pretrained(_SCREAMING_SNAKE_CASE , config_name=_SCREAMING_SNAKE_CASE )
# Checks parameters that were specified
self.assertEqual(loaded_config.do_sample , _SCREAMING_SNAKE_CASE )
self.assertEqual(loaded_config.temperature , 0.7 )
self.assertEqual(loaded_config.length_penalty , 1.0 )
self.assertEqual(loaded_config.bad_words_ids , [[1, 2, 3], [4, 5]] )
# Checks parameters that were not specified (defaults)
self.assertEqual(loaded_config.top_k , 50 )
self.assertEqual(loaded_config.max_length , 20 )
self.assertEqual(loaded_config.max_time , _SCREAMING_SNAKE_CASE )
def _snake_case ( self )->Dict:
'''simple docstring'''
A_ : str = AutoConfig.from_pretrained('''gpt2''' )
A_ : int = GenerationConfig.from_model_config(_SCREAMING_SNAKE_CASE )
A_ : Dict = GenerationConfig()
# The generation config has loaded a few non-default parameters from the model config
self.assertNotEqual(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# One of those parameters is eos_token_id -- check if it matches
self.assertNotEqual(generation_config_from_model.eos_token_id , default_generation_config.eos_token_id )
self.assertEqual(generation_config_from_model.eos_token_id , model_config.eos_token_id )
def _snake_case ( self )->Optional[int]:
'''simple docstring'''
A_ : List[Any] = GenerationConfig()
A_ : Optional[Any] = {
'''max_new_tokens''': 1024,
'''foo''': '''bar''',
}
A_ : Dict = copy.deepcopy(_SCREAMING_SNAKE_CASE )
A_ : Optional[int] = generation_config.update(**_SCREAMING_SNAKE_CASE )
# update_kwargs was not modified (no side effects)
self.assertEqual(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# update_kwargs was used to update the config on valid attributes
self.assertEqual(generation_config.max_new_tokens , 1024 )
# `.update()` returns a dictionary of unused kwargs
self.assertEqual(_SCREAMING_SNAKE_CASE , {'''foo''': '''bar'''} )
def _snake_case ( self )->str:
'''simple docstring'''
A_ : List[str] = GenerationConfig()
A_ : int = '''bar'''
with tempfile.TemporaryDirectory('''test-generation-config''' ) as tmp_dir:
generation_config.save_pretrained(_SCREAMING_SNAKE_CASE )
A_ : Optional[Any] = GenerationConfig.from_pretrained(_SCREAMING_SNAKE_CASE )
# update_kwargs was used to update the config on valid attributes
self.assertEqual(new_config.foo , '''bar''' )
A_ : Optional[int] = GenerationConfig.from_model_config(_SCREAMING_SNAKE_CASE )
assert not hasattr(_SCREAMING_SNAKE_CASE , '''foo''' ) # no new kwargs should be initialized if from config
def _snake_case ( self )->List[Any]:
'''simple docstring'''
A_ : str = GenerationConfig()
self.assertEqual(default_config.temperature , 1.0 )
self.assertEqual(default_config.do_sample , _SCREAMING_SNAKE_CASE )
self.assertEqual(default_config.num_beams , 1 )
A_ : str = GenerationConfig(
do_sample=_SCREAMING_SNAKE_CASE , temperature=0.7 , length_penalty=1.0 , bad_words_ids=[[1, 2, 3], [4, 5]] , )
self.assertEqual(config.temperature , 0.7 )
self.assertEqual(config.do_sample , _SCREAMING_SNAKE_CASE )
self.assertEqual(config.num_beams , 1 )
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(_SCREAMING_SNAKE_CASE )
A_ : Dict = GenerationConfig.from_pretrained(_SCREAMING_SNAKE_CASE , temperature=1.0 )
self.assertEqual(loaded_config.temperature , 1.0 )
self.assertEqual(loaded_config.do_sample , _SCREAMING_SNAKE_CASE )
self.assertEqual(loaded_config.num_beams , 1 ) # default value
@is_staging_test
class _lowerCamelCase ( unittest.TestCase ):
"""simple docstring"""
@classmethod
def _snake_case ( cls )->Optional[int]:
'''simple docstring'''
A_ : str = TOKEN
HfFolder.save_token(_SCREAMING_SNAKE_CASE )
@classmethod
def _snake_case ( cls )->List[Any]:
'''simple docstring'''
try:
delete_repo(token=cls._token , repo_id='''test-generation-config''' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='''valid_org/test-generation-config-org''' )
except HTTPError:
pass
def _snake_case ( self )->List[Any]:
'''simple docstring'''
A_ : List[Any] = GenerationConfig(
do_sample=_SCREAMING_SNAKE_CASE , temperature=0.7 , length_penalty=1.0 , )
config.push_to_hub('''test-generation-config''' , use_auth_token=self._token )
A_ : Optional[int] = GenerationConfig.from_pretrained(F'''{USER}/test-generation-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(_SCREAMING_SNAKE_CASE , getattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
# Reset repo
delete_repo(token=self._token , repo_id='''test-generation-config''' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
_SCREAMING_SNAKE_CASE , repo_id='''test-generation-config''' , push_to_hub=_SCREAMING_SNAKE_CASE , use_auth_token=self._token )
A_ : Optional[Any] = GenerationConfig.from_pretrained(F'''{USER}/test-generation-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(_SCREAMING_SNAKE_CASE , getattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
def _snake_case ( self )->List[str]:
'''simple docstring'''
A_ : Tuple = GenerationConfig(
do_sample=_SCREAMING_SNAKE_CASE , temperature=0.7 , length_penalty=1.0 , )
config.push_to_hub('''valid_org/test-generation-config-org''' , use_auth_token=self._token )
A_ : Union[str, Any] = GenerationConfig.from_pretrained('''valid_org/test-generation-config-org''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(_SCREAMING_SNAKE_CASE , getattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
# Reset repo
delete_repo(token=self._token , repo_id='''valid_org/test-generation-config-org''' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
_SCREAMING_SNAKE_CASE , repo_id='''valid_org/test-generation-config-org''' , push_to_hub=_SCREAMING_SNAKE_CASE , use_auth_token=self._token )
A_ : Dict = GenerationConfig.from_pretrained('''valid_org/test-generation-config-org''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(_SCREAMING_SNAKE_CASE , getattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
| 186 | 1 |
import gc
import unittest
from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
CTRLForSequenceClassification,
CTRLLMHeadModel,
CTRLModel,
)
class lowercase :
def __init__( self , A_ , A_=14 , A_=7 , A_=True , A_=True , A_=True , A_=True , A_=True , A_=99 , A_=32 , A_=5 , A_=4 , A_=37 , A_="gelu" , A_=0.1 , A_=0.1 , A_=512 , A_=16 , A_=2 , A_=0.02 , A_=3 , A_=4 , A_=None , ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase = parent
UpperCamelCase = batch_size
UpperCamelCase = seq_length
UpperCamelCase = is_training
UpperCamelCase = use_token_type_ids
UpperCamelCase = use_input_mask
UpperCamelCase = use_labels
UpperCamelCase = use_mc_token_ids
UpperCamelCase = vocab_size
UpperCamelCase = hidden_size
UpperCamelCase = num_hidden_layers
UpperCamelCase = num_attention_heads
UpperCamelCase = intermediate_size
UpperCamelCase = hidden_act
UpperCamelCase = hidden_dropout_prob
UpperCamelCase = attention_probs_dropout_prob
UpperCamelCase = max_position_embeddings
UpperCamelCase = type_vocab_size
UpperCamelCase = type_sequence_label_size
UpperCamelCase = initializer_range
UpperCamelCase = num_labels
UpperCamelCase = num_choices
UpperCamelCase = scope
UpperCamelCase = self.vocab_size - 1
def __UpperCamelCase ( self ) -> int:
"""simple docstring"""
UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase = None
if self.use_input_mask:
UpperCamelCase = random_attention_mask([self.batch_size, self.seq_length] )
UpperCamelCase = None
if self.use_token_type_ids:
UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
UpperCamelCase = None
if self.use_mc_token_ids:
UpperCamelCase = ids_tensor([self.batch_size, self.num_choices] , self.seq_length )
UpperCamelCase = None
UpperCamelCase = None
UpperCamelCase = None
if self.use_labels:
UpperCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCamelCase = ids_tensor([self.batch_size] , self.num_choices )
UpperCamelCase = self.get_config()
UpperCamelCase = ids_tensor([self.num_hidden_layers, self.num_attention_heads] , 2 )
return (
config,
input_ids,
input_mask,
head_mask,
token_type_ids,
mc_token_ids,
sequence_labels,
token_labels,
choice_labels,
)
def __UpperCamelCase ( self ) -> int:
"""simple docstring"""
return CTRLConfig(
vocab_size=self.vocab_size , n_embd=self.hidden_size , n_layer=self.num_hidden_layers , n_head=self.num_attention_heads , n_positions=self.max_position_embeddings , pad_token_id=self.pad_token_id , )
def __UpperCamelCase ( self , A_ , A_ , A_ , A_ , A_ , *A_ ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase = CTRLModel(config=A_ )
model.to(A_ )
model.eval()
model(A_ , token_type_ids=A_ , head_mask=A_ )
model(A_ , token_type_ids=A_ )
UpperCamelCase = model(A_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(len(result.past_key_values ) , config.n_layer )
def __UpperCamelCase ( self , A_ , A_ , A_ , A_ , A_ , *A_ ) -> Tuple:
"""simple docstring"""
UpperCamelCase = CTRLLMHeadModel(A_ )
model.to(A_ )
model.eval()
UpperCamelCase = model(A_ , token_type_ids=A_ , labels=A_ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __UpperCamelCase ( self ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase = self.prepare_config_and_inputs()
(
(
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) ,
) = config_and_inputs
UpperCamelCase = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'head_mask': head_mask}
return config, inputs_dict
def __UpperCamelCase ( self , A_ , A_ , A_ , A_ , *A_ ) -> List[Any]:
"""simple docstring"""
UpperCamelCase = self.num_labels
UpperCamelCase = CTRLForSequenceClassification(A_ )
model.to(A_ )
model.eval()
UpperCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCamelCase = model(A_ , token_type_ids=A_ , labels=A_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
@require_torch
class lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , unittest.TestCase ):
__lowercase : int = (CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
__lowercase : Optional[Any] = (CTRLLMHeadModel,) if is_torch_available() else ()
__lowercase : Optional[int] = (
{
"feature-extraction": CTRLModel,
"text-classification": CTRLForSequenceClassification,
"text-generation": CTRLLMHeadModel,
"zero-shot": CTRLForSequenceClassification,
}
if is_torch_available()
else {}
)
__lowercase : int = True
__lowercase : Dict = False
__lowercase : int = False
def __UpperCamelCase ( self , A_ , A_ , A_ , A_ , A_ ) -> Dict:
"""simple docstring"""
if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
# `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
# config could not be created.
return True
return False
def __UpperCamelCase ( self ) -> Dict:
"""simple docstring"""
UpperCamelCase = CTRLModelTester(self )
UpperCamelCase = ConfigTester(self , config_class=A_ , n_embd=37 )
def __UpperCamelCase ( self ) -> Union[str, Any]:
"""simple docstring"""
super().tearDown()
# clean-up as much as possible GPU memory occupied by PyTorch
gc.collect()
torch.cuda.empty_cache()
def __UpperCamelCase ( self ) -> Optional[Any]:
"""simple docstring"""
self.config_tester.run_common_tests()
def __UpperCamelCase ( self ) -> Any:
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_ctrl_model(*A_ )
def __UpperCamelCase ( self ) -> str:
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*A_ )
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def __UpperCamelCase ( self ) -> Optional[Any]:
"""simple docstring"""
pass
@slow
def __UpperCamelCase ( self ) -> str:
"""simple docstring"""
for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCamelCase = CTRLModel.from_pretrained(A_ )
self.assertIsNotNone(A_ )
@unittest.skip('The model doesn\'t support left padding' ) # and it's not used enough to be worth fixing :)
def __UpperCamelCase ( self ) -> Union[str, Any]:
"""simple docstring"""
pass
@require_torch
class lowercase ( unittest.TestCase ):
def __UpperCamelCase ( self ) -> int:
"""simple docstring"""
super().tearDown()
# clean-up as much as possible GPU memory occupied by PyTorch
gc.collect()
torch.cuda.empty_cache()
@slow
def __UpperCamelCase ( self ) -> Dict:
"""simple docstring"""
UpperCamelCase = CTRLLMHeadModel.from_pretrained('ctrl' )
model.to(A_ )
UpperCamelCase = torch.tensor(
[[11_859, 0, 1_611, 8]] , dtype=torch.long , device=A_ ) # Legal the president is
UpperCamelCase = [
11_859,
0,
1_611,
8,
5,
150,
26_449,
2,
19,
348,
469,
3,
2_595,
48,
20_740,
246_533,
246_533,
19,
30,
5,
] # Legal the president is a good guy and I don't want to lose my job. \n \n I have a
UpperCamelCase = model.generate(A_ , do_sample=A_ )
self.assertListEqual(output_ids[0].tolist() , A_ )
| 110 |
from __future__ import annotations
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFViTMAEForPreTraining, TFViTMAEModel
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class lowercase :
def __init__( self , A_ , A_=13 , A_=30 , A_=2 , A_=3 , A_=True , A_=True , A_=32 , A_=2 , A_=4 , A_=37 , A_="gelu" , A_=0.1 , A_=0.1 , A_=10 , A_=0.02 , A_=3 , A_=0.6 , A_=None , ) -> str:
"""simple docstring"""
UpperCamelCase = parent
UpperCamelCase = batch_size
UpperCamelCase = image_size
UpperCamelCase = patch_size
UpperCamelCase = num_channels
UpperCamelCase = is_training
UpperCamelCase = use_labels
UpperCamelCase = hidden_size
UpperCamelCase = num_hidden_layers
UpperCamelCase = num_attention_heads
UpperCamelCase = intermediate_size
UpperCamelCase = hidden_act
UpperCamelCase = hidden_dropout_prob
UpperCamelCase = attention_probs_dropout_prob
UpperCamelCase = type_sequence_label_size
UpperCamelCase = initializer_range
UpperCamelCase = mask_ratio
UpperCamelCase = scope
# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
# (we add 1 for the [CLS] token)
UpperCamelCase = (image_size // patch_size) ** 2
UpperCamelCase = int(math.ceil((1 - mask_ratio) * (num_patches + 1) ) )
def __UpperCamelCase ( self ) -> int:
"""simple docstring"""
UpperCamelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCamelCase = None
if self.use_labels:
UpperCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCamelCase = self.get_config()
return config, pixel_values, labels
def __UpperCamelCase ( self ) -> Any:
"""simple docstring"""
return ViTMAEConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , decoder_hidden_size=self.hidden_size , decoder_num_hidden_layers=self.num_hidden_layers , decoder_num_attention_heads=self.num_attention_heads , decoder_intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=A_ , initializer_range=self.initializer_range , mask_ratio=self.mask_ratio , )
def __UpperCamelCase ( self , A_ , A_ , A_ ) -> Dict:
"""simple docstring"""
UpperCamelCase = TFViTMAEModel(config=A_ )
UpperCamelCase = model(A_ , training=A_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __UpperCamelCase ( self , A_ , A_ , A_ ) -> Tuple:
"""simple docstring"""
UpperCamelCase = TFViTMAEForPreTraining(A_ )
UpperCamelCase = model(A_ , training=A_ )
# expected sequence length = num_patches
UpperCamelCase = (self.image_size // self.patch_size) ** 2
UpperCamelCase = self.patch_size**2 * self.num_channels
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_patches, expected_num_channels) )
# test greyscale images
UpperCamelCase = 1
UpperCamelCase = TFViTMAEForPreTraining(A_ )
UpperCamelCase = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
UpperCamelCase = model(A_ , training=A_ )
UpperCamelCase = self.patch_size**2
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_patches, expected_num_channels) )
def __UpperCamelCase ( self ) -> Any:
"""simple docstring"""
UpperCamelCase = self.prepare_config_and_inputs()
((UpperCamelCase) , (UpperCamelCase) , (UpperCamelCase)) = config_and_inputs
UpperCamelCase = {'pixel_values': pixel_values}
return config, inputs_dict
@require_tf
class lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , unittest.TestCase ):
__lowercase : Union[str, Any] = (TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
__lowercase : Optional[int] = {"feature-extraction": TFViTMAEModel} if is_tf_available() else {}
__lowercase : Optional[Any] = False
__lowercase : Optional[Any] = False
__lowercase : Dict = False
__lowercase : List[str] = False
def __UpperCamelCase ( self ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase = TFViTMAEModelTester(self )
UpperCamelCase = ConfigTester(self , config_class=A_ , has_text_modality=A_ , hidden_size=37 )
def __UpperCamelCase ( self ) -> Dict:
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason='ViTMAE does not use inputs_embeds' )
def __UpperCamelCase ( self ) -> List[Any]:
"""simple docstring"""
pass
def __UpperCamelCase ( self ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase , UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCamelCase = model_class(A_ )
self.assertIsInstance(model.get_input_embeddings() , (tf.keras.layers.Layer) )
UpperCamelCase = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(A_ , tf.keras.layers.Layer ) )
def __UpperCamelCase ( self ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase , UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCamelCase = model_class(A_ )
UpperCamelCase = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCamelCase = [*signature.parameters.keys()]
UpperCamelCase = ['pixel_values']
self.assertListEqual(arg_names[:1] , A_ )
def __UpperCamelCase ( self ) -> str:
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A_ )
def __UpperCamelCase ( self ) -> str:
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*A_ )
def __UpperCamelCase ( self ) -> List[str]:
"""simple docstring"""
# make the mask reproducible
np.random.seed(2 )
UpperCamelCase , UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
UpperCamelCase = int((config.image_size // config.patch_size) ** 2 )
UpperCamelCase = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
for model_class in self.all_model_classes:
UpperCamelCase = model_class(A_ )
UpperCamelCase = self._prepare_for_class(A_ , A_ )
UpperCamelCase = model(A_ , noise=A_ )
UpperCamelCase = copy.deepcopy(self._prepare_for_class(A_ , A_ ) )
UpperCamelCase = model(**A_ , noise=A_ )
UpperCamelCase = outputs_dict[0].numpy()
UpperCamelCase = outputs_keywords[0].numpy()
self.assertLess(np.sum(np.abs(output_dict - output_keywords ) ) , 1e-6 )
def __UpperCamelCase ( self ) -> int:
"""simple docstring"""
# make the mask reproducible
np.random.seed(2 )
UpperCamelCase , UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
UpperCamelCase = int((config.image_size // config.patch_size) ** 2 )
UpperCamelCase = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
def prepare_numpy_arrays(A_ ):
UpperCamelCase = {}
for k, v in inputs_dict.items():
if tf.is_tensor(A_ ):
UpperCamelCase = v.numpy()
else:
UpperCamelCase = np.array(A_ )
return inputs_np_dict
for model_class in self.all_model_classes:
UpperCamelCase = model_class(A_ )
UpperCamelCase = self._prepare_for_class(A_ , A_ )
UpperCamelCase = prepare_numpy_arrays(A_ )
UpperCamelCase = model(A_ , noise=A_ )
UpperCamelCase = model(**A_ , noise=A_ )
self.assert_outputs_same(A_ , A_ )
def __UpperCamelCase ( self , A_ , A_ , A_ ) -> List[Any]:
"""simple docstring"""
# make masks reproducible
np.random.seed(2 )
UpperCamelCase = int((tf_model.config.image_size // tf_model.config.patch_size) ** 2 )
UpperCamelCase = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
UpperCamelCase = tf.constant(A_ )
# Add `noise` argument.
# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
UpperCamelCase = tf_noise
super().check_pt_tf_models(A_ , A_ , A_ )
def __UpperCamelCase ( self ) -> Dict:
"""simple docstring"""
# make mask reproducible
np.random.seed(2 )
UpperCamelCase , UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
UpperCamelCase = {
module_member
for model_class in self.all_model_classes
for module in (import_module(model_class.__module__ ),)
for module_member_name in dir(A_ )
if module_member_name.endswith('MainLayer' )
# This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
and module_member_name[: -len('MainLayer' )] == model_class.__name__[: -len('Model' )]
for module_member in (getattr(A_ , A_ ),)
if isinstance(A_ , A_ )
and tf.keras.layers.Layer in module_member.__bases__
and getattr(A_ , '_keras_serializable' , A_ )
}
UpperCamelCase = int((config.image_size // config.patch_size) ** 2 )
UpperCamelCase = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
UpperCamelCase = tf.convert_to_tensor(A_ )
inputs_dict.update({'noise': noise} )
for main_layer_class in tf_main_layer_classes:
UpperCamelCase = main_layer_class(A_ )
UpperCamelCase = {
name: tf.keras.Input(tensor.shape[1:] , dtype=tensor.dtype ) for name, tensor in inputs_dict.items()
}
UpperCamelCase = tf.keras.Model(A_ , outputs=main_layer(A_ ) )
UpperCamelCase = model(A_ )
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCamelCase = os.path.join(A_ , 'keras_model.h5' )
model.save(A_ )
UpperCamelCase = tf.keras.models.load_model(
A_ , custom_objects={main_layer_class.__name__: main_layer_class} )
assert isinstance(A_ , tf.keras.Model )
UpperCamelCase = model(A_ )
self.assert_outputs_same(A_ , A_ )
@slow
def __UpperCamelCase ( self ) -> int:
"""simple docstring"""
# make mask reproducible
np.random.seed(2 )
UpperCamelCase , UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
UpperCamelCase = int((config.image_size // config.patch_size) ** 2 )
UpperCamelCase = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
for model_class in self.all_model_classes:
UpperCamelCase = model_class(A_ )
UpperCamelCase = self._prepare_for_class(A_ , A_ )
UpperCamelCase = model(A_ , noise=A_ )
if model_class.__name__ == "TFViTMAEModel":
UpperCamelCase = outputs.last_hidden_state.numpy()
UpperCamelCase = 0
else:
UpperCamelCase = outputs.logits.numpy()
UpperCamelCase = 0
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(A_ , saved_model=A_ )
UpperCamelCase = model_class.from_pretrained(A_ )
UpperCamelCase = model(A_ , noise=A_ )
if model_class.__name__ == "TFViTMAEModel":
UpperCamelCase = after_outputs['last_hidden_state'].numpy()
UpperCamelCase = 0
else:
UpperCamelCase = after_outputs['logits'].numpy()
UpperCamelCase = 0
UpperCamelCase = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(A_ , 1e-5 )
def __UpperCamelCase ( self ) -> Optional[Any]:
"""simple docstring"""
# make mask reproducible
np.random.seed(2 )
UpperCamelCase , UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
UpperCamelCase = int((config.image_size // config.patch_size) ** 2 )
UpperCamelCase = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
for model_class in self.all_model_classes:
UpperCamelCase = model_class(A_ )
UpperCamelCase = self._prepare_for_class(A_ , A_ )
UpperCamelCase = model(A_ , noise=A_ )
UpperCamelCase = model.get_config()
# make sure that returned config is jsonifiable, which is required by keras
json.dumps(A_ )
UpperCamelCase = model_class.from_config(model.get_config() )
# make sure it also accepts a normal config
UpperCamelCase = model_class.from_config(model.config )
UpperCamelCase = new_model(A_ ) # Build model
new_model.set_weights(model.get_weights() )
UpperCamelCase = new_model(A_ , noise=A_ )
self.assert_outputs_same(A_ , A_ )
@unittest.skip(
reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n to get deterministic results.' )
def __UpperCamelCase ( self ) -> List[str]:
"""simple docstring"""
pass
@unittest.skip(reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load' )
def __UpperCamelCase ( self ) -> Optional[Any]:
"""simple docstring"""
pass
@slow
def __UpperCamelCase ( self ) -> List[Any]:
"""simple docstring"""
UpperCamelCase = TFViTMAEModel.from_pretrained('google/vit-base-patch16-224' )
self.assertIsNotNone(A_ )
def A ( ) -> Optional[int]:
'''simple docstring'''
UpperCamelCase = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_tf
@require_vision
class lowercase ( unittest.TestCase ):
@cached_property
def __UpperCamelCase ( self ) -> Tuple:
"""simple docstring"""
return ViTImageProcessor.from_pretrained('facebook/vit-mae-base' ) if is_vision_available() else None
@slow
def __UpperCamelCase ( self ) -> List[str]:
"""simple docstring"""
# make random mask reproducible across the PT and TF model
np.random.seed(2 )
UpperCamelCase = TFViTMAEForPreTraining.from_pretrained('facebook/vit-mae-base' )
UpperCamelCase = self.default_image_processor
UpperCamelCase = prepare_img()
UpperCamelCase = image_processor(images=A_ , return_tensors='tf' )
# prepare a noise vector that will be also used for testing the TF model
# (this way we can ensure that the PT and TF models operate on the same inputs)
UpperCamelCase = ViTMAEConfig()
UpperCamelCase = int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2 )
UpperCamelCase = np.random.uniform(size=(1, num_patches) )
# forward pass
UpperCamelCase = model(**A_ , noise=A_ )
# verify the logits
UpperCamelCase = tf.convert_to_tensor([1, 196, 768] )
self.assertEqual(outputs.logits.shape , A_ )
UpperCamelCase = tf.convert_to_tensor(
[[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]] )
tf.debugging.assert_near(outputs.logits[0, :3, :3] , A_ , atol=1e-4 )
| 110 | 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__ ):
lowerCamelCase_ = 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}}" )
lowerCamelCase_ = 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__ ):
lowerCamelCase_ = str(lowerCamelCase__ )
dataset_info.write_to_directory(lowerCamelCase__ )
lowerCamelCase_ = DatasetInfo.from_directory(lowerCamelCase__ )
assert dataset_info == reloaded
assert os.path.exists(os.path.join(lowerCamelCase__ , "dataset_info.json" ) )
def lowerCamelCase_ ( ):
lowerCamelCase_ = 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 , )
lowerCamelCase_ = 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) )
lowerCamelCase_ = yaml.safe_dump(lowerCamelCase__ )
lowerCamelCase_ = yaml.safe_load(lowerCamelCase__ )
assert dataset_info_yaml_dict == reloaded
def lowerCamelCase_ ( ):
lowerCamelCase_ = DatasetInfo()
lowerCamelCase_ = 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__ ):
lowerCamelCase_ = str(lowerCamelCase__ )
dataset_infos_dict.write_to_directory(lowerCamelCase__ )
lowerCamelCase_ = 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():
lowerCamelCase_ = 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
lowerCamelCase_ = 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" ) )
| 19 |
'''simple docstring'''
import argparse
import gc
import json
import os
import re
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpoint
lowercase : Optional[int] = {
'169M': 12,
'430M': 24,
'1B5': 24,
'3B': 32,
'7B': 32,
'14B': 40,
}
lowercase : Optional[Any] = {
'169M': 7_68,
'430M': 10_24,
'1B5': 20_48,
'3B': 25_60,
'7B': 40_96,
'14B': 51_20,
}
def lowerCAmelCase_ ( snake_case__ ):
'''simple docstring'''
A : Optional[Any] = list(state_dict.keys() )
for name in state_dict_keys:
A : str = state_dict.pop(snake_case__ )
# emb -> embedding
if name.startswith('''emb.''' ):
A : Optional[Any] = name.replace('''emb.''' , '''embeddings.''' )
# ln_0 -> pre_ln (only present at block 0)
if name.startswith('''blocks.0.ln0''' ):
A : Union[str, Any] = name.replace('''blocks.0.ln0''' , '''blocks.0.pre_ln''' )
# att -> attention
A : int = re.sub(R'''blocks\.(\d+)\.att''' , R'''blocks.\1.attention''' , snake_case__ )
# ffn -> feed_forward
A : List[Any] = re.sub(R'''blocks\.(\d+)\.ffn''' , R'''blocks.\1.feed_forward''' , snake_case__ )
# time_mix_k -> time_mix_key and reshape
if name.endswith('''.time_mix_k''' ):
A : List[str] = name.replace('''.time_mix_k''' , '''.time_mix_key''' )
# time_mix_v -> time_mix_value and reshape
if name.endswith('''.time_mix_v''' ):
A : Union[str, Any] = name.replace('''.time_mix_v''' , '''.time_mix_value''' )
# time_mix_r -> time_mix_key and reshape
if name.endswith('''.time_mix_r''' ):
A : Union[str, Any] = name.replace('''.time_mix_r''' , '''.time_mix_receptance''' )
if name != "head.weight":
A : List[Any] = '''rwkv.''' + name
A : Dict = weight
return state_dict
def lowerCAmelCase_ ( snake_case__ , snake_case__ , snake_case__ , snake_case__=None , snake_case__=None , snake_case__=False , snake_case__=None ):
'''simple docstring'''
if tokenizer_file is None:
print('''No `--tokenizer_file` provided, we will use the default tokenizer.''' )
A : int = 5_0277
A : Optional[int] = AutoTokenizer.from_pretrained('''EleutherAI/gpt-neox-20b''' )
else:
A : str = PreTrainedTokenizerFast(tokenizer_file=snake_case__ )
A : Any = len(snake_case__ )
tokenizer.save_pretrained(snake_case__ )
# 2. Build the config
A : List[str] = list(NUM_HIDDEN_LAYERS_MAPPING.keys() )
if size is None:
# Try to infer size from the checkpoint name
for candidate in possible_sizes:
if candidate in checkpoint_file:
A : List[str] = candidate
break
if size is None:
raise ValueError('''Could not infer the size, please provide it with the `--size` argument.''' )
if size not in possible_sizes:
raise ValueError(F'`size` should be one of {possible_sizes}, got {size}.' )
A : Any = RwkvConfig(
vocab_size=snake_case__ , num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size] , hidden_size=HIDEN_SIZE_MAPPING[size] , )
config.save_pretrained(snake_case__ )
# 3. Download model file then convert state_dict
A : Union[str, Any] = hf_hub_download(snake_case__ , snake_case__ )
A : Tuple = torch.load(snake_case__ , map_location='''cpu''' )
A : List[Any] = convert_state_dict(snake_case__ )
# 4. Split in shards and save
A, A : List[str] = shard_checkpoint(snake_case__ )
for shard_file, shard in shards.items():
torch.save(snake_case__ , os.path.join(snake_case__ , snake_case__ ) )
if index is not None:
A : Dict = os.path.join(snake_case__ , snake_case__ )
# Save the index as well
with open(snake_case__ , '''w''' , encoding='''utf-8''' ) as f:
A : List[Any] = json.dumps(snake_case__ , indent=2 , sort_keys=snake_case__ ) + '''\n'''
f.write(snake_case__ )
# 5. Clean up shards (for some reason the file PyTorch saves take the same space as the whole state_dict
print(
'''Cleaning up shards. This may error with an OOM error, it this is the case don\'t worry you still have converted the model.''' )
A : List[Any] = list(shards.keys() )
del state_dict
del shards
gc.collect()
for shard_file in shard_files:
A : Union[str, Any] = torch.load(os.path.join(snake_case__ , snake_case__ ) )
torch.save({k: v.cpu().clone() for k, v in state_dict.items()} , os.path.join(snake_case__ , snake_case__ ) )
del state_dict
gc.collect()
if push_to_hub:
if model_name is None:
raise ValueError('''Please provide a `model_name` to push the model to the Hub.''' )
A : int = AutoModelForCausalLM.from_pretrained(snake_case__ )
model.push_to_hub(snake_case__ , max_shard_size='''2GB''' )
tokenizer.push_to_hub(snake_case__ )
if __name__ == "__main__":
lowercase : List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--repo_id', default=None, type=str, required=True, help='Repo ID from which to pull the checkpoint.'
)
parser.add_argument(
'--checkpoint_file', default=None, type=str, required=True, help='Name of the checkpoint file in the repo.'
)
parser.add_argument(
'--output_dir', default=None, type=str, required=True, help='Where to save the converted model.'
)
parser.add_argument(
'--tokenizer_file',
default=None,
type=str,
help='Path to the tokenizer file to use (if not provided, only the model is converted).',
)
parser.add_argument(
'--size',
default=None,
type=str,
help='Size of the model. Will be inferred from the `checkpoint_file` if not passed.',
)
parser.add_argument(
'--push_to_hub',
action='store_true',
help='Push to the Hub the converted model.',
)
parser.add_argument(
'--model_name',
default=None,
type=str,
help='Name of the pushed model on the Hub, including the username / organization.',
)
lowercase : Union[str, Any] = parser.parse_args()
convert_rmkv_checkpoint_to_hf_format(
args.repo_id,
args.checkpoint_file,
args.output_dir,
size=args.size,
tokenizer_file=args.tokenizer_file,
push_to_hub=args.push_to_hub,
model_name=args.model_name,
)
| 3 | 0 |
"""simple docstring"""
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__)
| 230 |
"""simple docstring"""
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
import platform
import numpy as np
import psutil
import torch
from accelerate import __version__ as version
from accelerate.commands.config import default_config_file, load_config_from_file
from ..utils import is_npu_available, is_xpu_available
def _SCREAMING_SNAKE_CASE ( lowercase_=None ) -> Any:
if subparsers is not None:
A__ = subparsers.add_parser("env" )
else:
A__ = argparse.ArgumentParser("Accelerate env command" )
parser.add_argument(
"--config_file" , default=lowercase_ , help="The config file to use for the default values in the launching script." )
if subparsers is not None:
parser.set_defaults(func=lowercase_ )
return parser
def _SCREAMING_SNAKE_CASE ( lowercase_ ) -> Dict:
A__ = torch.__version__
A__ = torch.cuda.is_available()
A__ = is_xpu_available()
A__ = is_npu_available()
A__ = "Not found"
# Get the default from the config file.
if args.config_file is not None or os.path.isfile(lowercase_ ):
A__ = load_config_from_file(args.config_file ).to_dict()
A__ = {
"`Accelerate` version": version,
"Platform": platform.platform(),
"Python version": platform.python_version(),
"Numpy version": np.__version__,
"PyTorch version (GPU?)": f"""{pt_version} ({pt_cuda_available})""",
"PyTorch XPU available": str(lowercase_ ),
"PyTorch NPU available": str(lowercase_ ),
"System RAM": f"""{psutil.virtual_memory().total / 10_24 ** 3:.2f} GB""",
}
if pt_cuda_available:
A__ = torch.cuda.get_device_name()
print("\nCopy-and-paste the text below in your GitHub issue\n" )
print("\n".join([f"""- {prop}: {val}""" for prop, val in info.items()] ) )
print("- `Accelerate` default config:" if args.config_file is None else "- `Accelerate` config passed:" )
A__ = (
"\n".join([f"""\t- {prop}: {val}""" for prop, val in accelerate_config.items()] )
if isinstance(lowercase_ , lowercase_ )
else f"""\t{accelerate_config}"""
)
print(lowercase_ )
A__ = accelerate_config
return info
def _SCREAMING_SNAKE_CASE ( ) -> int:
A__ = env_command_parser()
A__ = parser.parse_args()
env_command(lowercase_ )
return 0
if __name__ == "__main__":
raise SystemExit(main())
| 230 | 1 |
"""simple docstring"""
from __future__ import annotations
import numpy as np
def a__ ( lowerCAmelCase ) -> Any:
return np.maximum(0 , lowerCAmelCase )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| 171 |
"""simple docstring"""
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
_A = 10
def a__ ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ) -> int:
for i in range(lowerCAmelCase , lowerCAmelCase ):
if array[i] == target:
return i
return -1
def a__ ( lowerCAmelCase , lowerCAmelCase ) -> int:
UpperCAmelCase__ : str = 0
UpperCAmelCase__ : Dict = len(lowerCAmelCase )
while left <= right:
if right - left < precision:
return lin_search(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
UpperCAmelCase__ : Union[str, Any] = (left + right) // 3 + 1
UpperCAmelCase__ : Optional[Any] = 2 * (left + right) // 3 + 1
if array[one_third] == target:
return one_third
elif array[two_third] == target:
return two_third
elif target < array[one_third]:
UpperCAmelCase__ : str = one_third - 1
elif array[two_third] < target:
UpperCAmelCase__ : Tuple = two_third + 1
else:
UpperCAmelCase__ : Any = one_third + 1
UpperCAmelCase__ : str = two_third - 1
else:
return -1
def a__ ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ) -> int:
if left < right:
if right - left < precision:
return lin_search(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
UpperCAmelCase__ : List[str] = (left + right) // 3 + 1
UpperCAmelCase__ : Tuple = 2 * (left + right) // 3 + 1
if array[one_third] == target:
return one_third
elif array[two_third] == target:
return two_third
elif target < array[one_third]:
return rec_ternary_search(lowerCAmelCase , one_third - 1 , lowerCAmelCase , lowerCAmelCase )
elif array[two_third] < target:
return rec_ternary_search(two_third + 1 , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
else:
return rec_ternary_search(one_third + 1 , two_third - 1 , lowerCAmelCase , lowerCAmelCase )
else:
return -1
if __name__ == "__main__":
import doctest
doctest.testmod()
_A = input("""Enter numbers separated by comma:\n""").strip()
_A = [int(item.strip()) for item in user_input.split(""",""")]
assert collection == sorted(collection), f"List must be ordered.\n{collection}."
_A = int(input("""Enter the number to be found in the list:\n""").strip())
_A = ite_ternary_search(collection, target)
_A = rec_ternary_search(0, len(collection) - 1, collection, target)
if resulta != -1:
print(f'''Iterative search: {target} found at positions: {resulta}''')
print(f'''Recursive search: {target} found at positions: {resulta}''')
else:
print("""Not found""")
| 171 | 1 |
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
__magic_name__ : Optional[int] = pd.read_csv("""sample_data.csv""", header=None)
__magic_name__ : List[Any] = df.shape[:1][0]
# If you're using some other dataset input the target column
__magic_name__ : Tuple = df.iloc[:, 1:2]
__magic_name__ : Tuple = actual_data.values.reshape(len_data, 1)
__magic_name__ : List[str] = MinMaxScaler().fit_transform(actual_data)
__magic_name__ : List[str] = 10
__magic_name__ : List[str] = 5
__magic_name__ : Optional[Any] = 20
__magic_name__ : int = len_data - periods * look_back
__magic_name__ : Any = actual_data[:division]
__magic_name__ : str = actual_data[division - look_back :]
__magic_name__ , __magic_name__ : str = [], []
__magic_name__ , __magic_name__ : Optional[int] = [], []
for i in range(0, len(train_data) - forward_days - look_back + 1):
train_x.append(train_data[i : i + look_back])
train_y.append(train_data[i + look_back : i + look_back + forward_days])
for i in range(0, len(test_data) - forward_days - look_back + 1):
test_x.append(test_data[i : i + look_back])
test_y.append(test_data[i + look_back : i + look_back + forward_days])
__magic_name__ : Tuple = np.array(train_x)
__magic_name__ : Dict = np.array(test_x)
__magic_name__ : List[str] = np.array([list(i.ravel()) for i in train_y])
__magic_name__ : str = np.array([list(i.ravel()) for i in test_y])
__magic_name__ : Optional[Any] = Sequential()
model.add(LSTM(128, input_shape=(look_back, 1), return_sequences=True))
model.add(LSTM(64, input_shape=(128, 1)))
model.add(Dense(forward_days))
model.compile(loss="""mean_squared_error""", optimizer="""adam""")
__magic_name__ : str = model.fit(
x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
)
__magic_name__ : Union[str, Any] = model.predict(x_test)
| 367 |
import unittest
from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMWithLMHeadModel,
)
from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST
class lowercase :
def __init__( self , snake_case , snake_case=13 , snake_case=7 , snake_case=True , snake_case=True , snake_case=True , snake_case=True , snake_case=True , snake_case=False , snake_case=False , snake_case=False , snake_case=2 , snake_case=99 , snake_case=0 , snake_case=32 , snake_case=5 , snake_case=4 , snake_case=0.1 , snake_case=0.1 , snake_case=512 , snake_case=2 , snake_case=0.02 , snake_case=2 , snake_case=4 , snake_case="last" , snake_case=True , snake_case=None , snake_case=0 , ):
snake_case_ = parent
snake_case_ = batch_size
snake_case_ = seq_length
snake_case_ = is_training
snake_case_ = use_input_lengths
snake_case_ = use_token_type_ids
snake_case_ = use_labels
snake_case_ = gelu_activation
snake_case_ = sinusoidal_embeddings
snake_case_ = causal
snake_case_ = asm
snake_case_ = n_langs
snake_case_ = vocab_size
snake_case_ = n_special
snake_case_ = hidden_size
snake_case_ = num_hidden_layers
snake_case_ = num_attention_heads
snake_case_ = hidden_dropout_prob
snake_case_ = attention_probs_dropout_prob
snake_case_ = max_position_embeddings
snake_case_ = type_sequence_label_size
snake_case_ = initializer_range
snake_case_ = num_labels
snake_case_ = num_choices
snake_case_ = summary_type
snake_case_ = use_proj
snake_case_ = scope
snake_case_ = bos_token_id
def a ( self ):
snake_case_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case_ = random_attention_mask([self.batch_size, self.seq_length] )
snake_case_ = None
if self.use_input_lengths:
snake_case_ = (
ids_tensor([self.batch_size] , vocab_size=2 ) + self.seq_length - 2
) # small variation of seq_length
snake_case_ = None
if self.use_token_type_ids:
snake_case_ = ids_tensor([self.batch_size, self.seq_length] , self.n_langs )
snake_case_ = None
snake_case_ = None
snake_case_ = None
if self.use_labels:
snake_case_ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case_ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
snake_case_ = ids_tensor([self.batch_size] , 2 ).float()
snake_case_ = ids_tensor([self.batch_size] , self.num_choices )
snake_case_ = self.get_config()
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def a ( self ):
return XLMConfig(
vocab_size=self.vocab_size , n_special=self.n_special , emb_dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , gelu_activation=self.gelu_activation , sinusoidal_embeddings=self.sinusoidal_embeddings , asm=self.asm , causal=self.causal , n_langs=self.n_langs , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , summary_type=self.summary_type , use_proj=self.use_proj , num_labels=self.num_labels , bos_token_id=self.bos_token_id , )
def a ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , ):
snake_case_ = XLMModel(config=snake_case )
model.to(snake_case )
model.eval()
snake_case_ = model(snake_case , lengths=snake_case , langs=snake_case )
snake_case_ = model(snake_case , langs=snake_case )
snake_case_ = model(snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def a ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , ):
snake_case_ = XLMWithLMHeadModel(snake_case )
model.to(snake_case )
model.eval()
snake_case_ = model(snake_case , token_type_ids=snake_case , labels=snake_case )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def a ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , ):
snake_case_ = XLMForQuestionAnsweringSimple(snake_case )
model.to(snake_case )
model.eval()
snake_case_ = model(snake_case )
snake_case_ = model(snake_case , start_positions=snake_case , end_positions=snake_case )
snake_case_ = outputs
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def a ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , ):
snake_case_ = XLMForQuestionAnswering(snake_case )
model.to(snake_case )
model.eval()
snake_case_ = model(snake_case )
snake_case_ = model(
snake_case , start_positions=snake_case , end_positions=snake_case , cls_index=snake_case , is_impossible=snake_case , p_mask=snake_case , )
snake_case_ = model(
snake_case , start_positions=snake_case , end_positions=snake_case , cls_index=snake_case , is_impossible=snake_case , )
((snake_case_) , ) = result_with_labels.to_tuple()
snake_case_ = model(snake_case , start_positions=snake_case , end_positions=snake_case )
((snake_case_) , ) = result_with_labels.to_tuple()
self.parent.assertEqual(result_with_labels.loss.shape , () )
self.parent.assertEqual(result.start_top_log_probs.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(result.start_top_index.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(
result.end_top_log_probs.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(
result.end_top_index.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(result.cls_logits.shape , (self.batch_size,) )
def a ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , ):
snake_case_ = XLMForSequenceClassification(snake_case )
model.to(snake_case )
model.eval()
snake_case_ = model(snake_case )
snake_case_ = model(snake_case , labels=snake_case )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def a ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , ):
snake_case_ = self.num_labels
snake_case_ = XLMForTokenClassification(snake_case )
model.to(snake_case )
model.eval()
snake_case_ = model(snake_case , attention_mask=snake_case , labels=snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def a ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , ):
snake_case_ = self.num_choices
snake_case_ = XLMForMultipleChoice(config=snake_case )
model.to(snake_case )
model.eval()
snake_case_ = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
snake_case_ = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
snake_case_ = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
snake_case_ = model(
snake_case , attention_mask=snake_case , token_type_ids=snake_case , labels=snake_case , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def a ( self ):
snake_case_ = self.prepare_config_and_inputs()
(
(
snake_case_
) , (
snake_case_
) , (
snake_case_
) , (
snake_case_
) , (
snake_case_
) , (
snake_case_
) , (
snake_case_
) , (
snake_case_
) , (
snake_case_
) ,
) = config_and_inputs
snake_case_ = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'lengths': input_lengths}
return config, inputs_dict
@require_torch
class lowercase ( lowercase_ , lowercase_ , lowercase_ , unittest.TestCase ):
__SCREAMING_SNAKE_CASE : List[Any] = (
(
XLMModel,
XLMWithLMHeadModel,
XLMForQuestionAnswering,
XLMForSequenceClassification,
XLMForQuestionAnsweringSimple,
XLMForTokenClassification,
XLMForMultipleChoice,
)
if is_torch_available()
else ()
)
__SCREAMING_SNAKE_CASE : Tuple = (
(XLMWithLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Check other models whether language generation is also applicable
__SCREAMING_SNAKE_CASE : int = (
{
'''feature-extraction''': XLMModel,
'''fill-mask''': XLMWithLMHeadModel,
'''question-answering''': XLMForQuestionAnsweringSimple,
'''text-classification''': XLMForSequenceClassification,
'''text-generation''': XLMWithLMHeadModel,
'''token-classification''': XLMForTokenClassification,
'''zero-shot''': XLMForSequenceClassification,
}
if is_torch_available()
else {}
)
def a ( self , snake_case , snake_case , snake_case , snake_case , snake_case ):
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith('Fast' )
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def a ( self , snake_case , snake_case , snake_case=False ):
snake_case_ = super()._prepare_for_class(snake_case , snake_case , return_labels=snake_case )
if return_labels:
if model_class.__name__ == "XLMForQuestionAnswering":
snake_case_ = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=snake_case )
snake_case_ = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=snake_case )
return inputs_dict
def a ( self ):
snake_case_ = XLMModelTester(self )
snake_case_ = ConfigTester(self , config_class=snake_case , emb_dim=37 )
def a ( self ):
self.config_tester.run_common_tests()
def a ( self ):
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_model(*snake_case )
def a ( self ):
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_lm_head(*snake_case )
def a ( self ):
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_simple_qa(*snake_case )
def a ( self ):
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_qa(*snake_case )
def a ( self ):
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_sequence_classif(*snake_case )
def a ( self ):
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_token_classif(*snake_case )
def a ( self ):
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_for_multiple_choice(*snake_case )
def a ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case=False , snake_case=1 ):
self.assertIsInstance(snake_case , snake_case )
self.assertListEqual(
[isinstance(snake_case , snake_case ) for iter_attentions in attentions] , [True] * len(snake_case ) )
self.assertEqual(len(snake_case ) , (max_length - min_length) * num_beam_groups )
for idx, iter_attentions in enumerate(snake_case ):
# adds PAD dummy token
snake_case_ = min_length + idx + 1
snake_case_ = min_length + idx + 1
snake_case_ = (
batch_size * num_beam_groups,
config.num_attention_heads,
tgt_len,
src_len,
)
# check attn size
self.assertListEqual(
[layer_attention.shape for layer_attention in iter_attentions] , [expected_shape] * len(snake_case ) )
def a ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case=False , snake_case=1 ):
self.assertIsInstance(snake_case , snake_case )
self.assertListEqual(
[isinstance(snake_case , snake_case ) for iter_hidden_states in hidden_states] , [True] * len(snake_case ) , )
self.assertEqual(len(snake_case ) , (max_length - min_length) * num_beam_groups )
for idx, iter_hidden_states in enumerate(snake_case ):
# adds PAD dummy token
snake_case_ = min_length + idx + 1
snake_case_ = (batch_size * num_beam_groups, seq_len, config.hidden_size)
# check hidden size
self.assertListEqual(
[layer_hidden_states.shape for layer_hidden_states in iter_hidden_states] , [expected_shape] * len(snake_case ) , )
pass
@slow
def a ( self ):
for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case_ = XLMModel.from_pretrained(snake_case )
self.assertIsNotNone(snake_case )
@require_torch
class lowercase ( unittest.TestCase ):
@slow
def a ( self ):
snake_case_ = XLMWithLMHeadModel.from_pretrained('xlm-mlm-en-2048' )
model.to(snake_case )
snake_case_ = torch.tensor([[14, 447]] , dtype=torch.long , device=snake_case ) # the president
snake_case_ = [
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
] # the president the president the president the president the president the president the president the president the president the president
# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
snake_case_ = model.generate(snake_case , do_sample=snake_case )
self.assertListEqual(output_ids[0].cpu().numpy().tolist() , snake_case )
| 200 | 0 |
import inspect
import os
import sys
import unittest
import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu
class snake_case_ ( unittest.TestCase ):
def __UpperCamelCase ( self : Union[str, Any] ) -> int:
lowercase__ : List[Any] = inspect.getfile(accelerate.test_utils )
lowercase__ : Any = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ["scripts", "test_script.py"] )
lowercase__ : str = os.path.sep.join(inspect.getfile(self.__class__ ).split(os.path.sep )[:-1] )
@require_tpu
def __UpperCamelCase ( self : Optional[Any] ) -> Tuple:
lowercase__ : List[str] = F'''
{self.test_dir}/xla_spawn.py
--num_cores 8
{self.test_file_path}
'''.split()
lowercase__ : Optional[Any] = [sys.executable] + distributed_args
execute_subprocess_async(lowercase_ , env=os.environ.copy() )
| 87 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
snake_case_ : Dict = logging.get_logger(__name__)
class lowercase__ ( lowercase ):
def __init__( self : List[Any] ,lowerCamelCase__ : int ,lowerCamelCase__ : int ,lowerCamelCase__ : float ,**lowerCamelCase__ : int ):
'''simple docstring'''
_UpperCamelCase : List[Any] = feature_size
_UpperCamelCase : Any = sampling_rate
_UpperCamelCase : Optional[Any] = padding_value
_UpperCamelCase : Union[str, Any] = kwargs.pop('padding_side' ,'right' )
_UpperCamelCase : Dict = kwargs.pop('return_attention_mask' ,lowerCamelCase__ )
super().__init__(**lowerCamelCase__ )
def UpperCamelCase_ ( self : Optional[Any] ,lowerCamelCase__ : Union[
BatchFeature,
List[BatchFeature],
Dict[str, BatchFeature],
Dict[str, List[BatchFeature]],
List[Dict[str, BatchFeature]],
] ,lowerCamelCase__ : Union[bool, str, PaddingStrategy] = True ,lowerCamelCase__ : Optional[int] = None ,lowerCamelCase__ : bool = False ,lowerCamelCase__ : Optional[int] = None ,lowerCamelCase__ : Optional[bool] = None ,lowerCamelCase__ : Optional[Union[str, TensorType]] = None ,):
'''simple docstring'''
# If we have a list of dicts, let's convert it in a dict of lists
# We do this to allow using this method as a collate_fn function in PyTorch Dataloader
if isinstance(lowerCamelCase__ ,(list, tuple) ) and isinstance(processed_features[0] ,(dict, BatchFeature) ):
_UpperCamelCase : int = {
key: [example[key] for example in processed_features] for key in processed_features[0].keys()
}
# The model's main input name, usually `input_values`, has be passed for padding
if self.model_input_names[0] not in processed_features:
raise ValueError(
'You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`'
F' to this method that includes {self.model_input_names[0]}, but you provided'
F' {list(processed_features.keys() )}' )
_UpperCamelCase : List[Any] = processed_features[self.model_input_names[0]]
_UpperCamelCase : Dict = (
return_attention_mask if return_attention_mask is not None else self.return_attention_mask
)
if len(lowerCamelCase__ ) == 0:
if return_attention_mask:
_UpperCamelCase : Union[str, Any] = []
return processed_features
# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
# and rebuild them afterwards if no return_tensors is specified
# Note that we lose the specific device the tensor may be on for PyTorch
_UpperCamelCase : List[str] = required_input[0]
if isinstance(lowerCamelCase__ ,(list, tuple) ):
# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
_UpperCamelCase : List[str] = 0
while len(required_input[index] ) == 0:
index += 1
if index < len(lowerCamelCase__ ):
_UpperCamelCase : Dict = required_input[index][0]
if return_tensors is None:
if is_tf_tensor(lowerCamelCase__ ):
_UpperCamelCase : Any = 'tf'
elif is_torch_tensor(lowerCamelCase__ ):
_UpperCamelCase : Optional[int] = 'pt'
elif isinstance(lowerCamelCase__ ,(int, float, list, tuple, np.ndarray) ):
_UpperCamelCase : int = 'np'
else:
raise ValueError(
F'type of {first_element} unknown: {type(lowerCamelCase__ )}. '
'Should be one of a python, numpy, pytorch or tensorflow object.' )
for key, value in processed_features.items():
if isinstance(value[0] ,(int, float) ):
_UpperCamelCase : Any = to_numpy(lowerCamelCase__ )
else:
_UpperCamelCase : Any = [to_numpy(lowerCamelCase__ ) for v in value]
# Convert padding_strategy in PaddingStrategy
_UpperCamelCase : Optional[int] = self._get_padding_strategies(padding=lowerCamelCase__ ,max_length=lowerCamelCase__ )
_UpperCamelCase : str = processed_features[self.model_input_names[0]]
_UpperCamelCase : List[str] = len(lowerCamelCase__ )
if not all(len(lowerCamelCase__ ) == batch_size for v in processed_features.values() ):
raise ValueError('Some items in the output dictionary have a different batch size than others.' )
_UpperCamelCase : List[str] = []
for i in range(lowerCamelCase__ ):
_UpperCamelCase : List[str] = {k: v[i] for k, v in processed_features.items()}
# truncation
_UpperCamelCase : List[str] = self._truncate(
lowerCamelCase__ ,max_length=lowerCamelCase__ ,pad_to_multiple_of=lowerCamelCase__ ,truncation=lowerCamelCase__ ,)
truncated_inputs.append(lowerCamelCase__ )
if padding_strategy == PaddingStrategy.LONGEST:
# make sure that `max_length` cannot be longer than the longest truncated length
_UpperCamelCase : Union[str, Any] = max(len(input_slice[self.model_input_names[0]] ) for input_slice in truncated_inputs )
_UpperCamelCase : Any = PaddingStrategy.MAX_LENGTH
_UpperCamelCase : Optional[Any] = {}
for i in range(lowerCamelCase__ ):
# padding
_UpperCamelCase : Any = self._pad(
truncated_inputs[i] ,max_length=lowerCamelCase__ ,padding_strategy=lowerCamelCase__ ,pad_to_multiple_of=lowerCamelCase__ ,return_attention_mask=lowerCamelCase__ ,)
for key, value in outputs.items():
if key not in batch_outputs:
_UpperCamelCase : Dict = []
if value.dtype is np.dtype(np.floataa ):
_UpperCamelCase : Any = value.astype(np.floataa )
batch_outputs[key].append(lowerCamelCase__ )
return BatchFeature(lowerCamelCase__ ,tensor_type=lowerCamelCase__ )
def UpperCamelCase_ ( self : Any ,lowerCamelCase__ : Union[Dict[str, np.ndarray], BatchFeature] ,lowerCamelCase__ : Optional[int] = None ,lowerCamelCase__ : PaddingStrategy = PaddingStrategy.DO_NOT_PAD ,lowerCamelCase__ : Optional[int] = None ,lowerCamelCase__ : Optional[bool] = None ,):
'''simple docstring'''
_UpperCamelCase : Union[str, Any] = processed_features[self.model_input_names[0]]
if padding_strategy == PaddingStrategy.LONGEST:
_UpperCamelCase : Optional[Any] = len(lowerCamelCase__ )
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
_UpperCamelCase : str = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
_UpperCamelCase : str = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(lowerCamelCase__ ) < max_length
if return_attention_mask and "attention_mask" not in processed_features:
_UpperCamelCase : Tuple = np.ones(len(lowerCamelCase__ ) ,dtype=np.intaa )
if needs_to_be_padded:
_UpperCamelCase : Dict = max_length - len(lowerCamelCase__ )
if self.padding_side == "right":
if return_attention_mask:
_UpperCamelCase : Optional[int] = np.pad(
processed_features['attention_mask'] ,(0, difference) )
_UpperCamelCase : Union[str, Any] = ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
_UpperCamelCase : List[Any] = np.pad(
lowerCamelCase__ ,lowerCamelCase__ ,'constant' ,constant_values=self.padding_value )
elif self.padding_side == "left":
if return_attention_mask:
_UpperCamelCase : List[Any] = np.pad(
processed_features['attention_mask'] ,(difference, 0) )
_UpperCamelCase : List[Any] = ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
_UpperCamelCase : List[str] = np.pad(
lowerCamelCase__ ,lowerCamelCase__ ,'constant' ,constant_values=self.padding_value )
else:
raise ValueError('Invalid padding strategy:' + str(self.padding_side ) )
return processed_features
def UpperCamelCase_ ( self : Tuple ,lowerCamelCase__ : Union[Dict[str, np.ndarray], BatchFeature] ,lowerCamelCase__ : Optional[int] = None ,lowerCamelCase__ : Optional[int] = None ,lowerCamelCase__ : Optional[bool] = None ,):
'''simple docstring'''
if not truncation:
return processed_features
elif truncation and max_length is None:
raise ValueError('When setting ``truncation=True``, make sure that ``max_length`` is defined.' )
_UpperCamelCase : int = processed_features[self.model_input_names[0]]
# find `max_length` that fits `pad_to_multiple_of`
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
_UpperCamelCase : Optional[Any] = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
_UpperCamelCase : Optional[int] = len(lowerCamelCase__ ) > max_length
if needs_to_be_truncated:
_UpperCamelCase : Dict = processed_features[self.model_input_names[0]][:max_length]
if "attention_mask" in processed_features:
_UpperCamelCase : Optional[Any] = processed_features['attention_mask'][:max_length]
return processed_features
def UpperCamelCase_ ( self : Tuple ,lowerCamelCase__ : int=False ,lowerCamelCase__ : Optional[Any]=None ):
'''simple docstring'''
# Get padding strategy
if padding is not False:
if padding is True:
_UpperCamelCase : Optional[Any] = PaddingStrategy.LONGEST # Default to pad to the longest sequence in the batch
elif not isinstance(lowerCamelCase__ ,lowerCamelCase__ ):
_UpperCamelCase : Tuple = PaddingStrategy(lowerCamelCase__ )
elif isinstance(lowerCamelCase__ ,lowerCamelCase__ ):
_UpperCamelCase : Union[str, Any] = padding
else:
_UpperCamelCase : List[Any] = PaddingStrategy.DO_NOT_PAD
# Set max length if needed
if max_length is None:
if padding_strategy == PaddingStrategy.MAX_LENGTH:
raise ValueError(
F'When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined' )
# Test if we have a padding value
if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
raise ValueError(
'Asking to pad but the feature_extractor does not have a padding value. Please select a value to use'
' as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.' )
return padding_strategy
| 83 | 0 |
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
'''simple docstring'''
__UpperCamelCase : Optional[int] = []
__UpperCamelCase : str = []
__UpperCamelCase : Dict = []
for rt in rc.restypes:
__UpperCamelCase : Any = rc.restype_name_to_atomaa_names[rc.restype_atoa[rt]]
restype_atomaa_to_atomaa_list.append([(rc.atom_order[name] if name else 0) for name in atom_names])
__UpperCamelCase : Any = {name: i for i, name in enumerate(_lowerCamelCase)}
restype_atomaa_to_atomaa_list.append(
[(atom_name_to_idxaa[name] if name in atom_name_to_idxaa else 0) for name in rc.atom_types])
restype_atomaa_mask_list.append([(1.0 if name else 0.0) for name in atom_names])
# Add dummy mapping for restype 'UNK'
restype_atomaa_to_atomaa_list.append([0] * 14)
restype_atomaa_to_atomaa_list.append([0] * 37)
restype_atomaa_mask_list.append([0.0] * 14)
__UpperCamelCase : List[Any] = torch.tensor(
_lowerCamelCase , dtype=torch.intaa , device=protein["aatype"].device , )
__UpperCamelCase : int = torch.tensor(
_lowerCamelCase , dtype=torch.intaa , device=protein["aatype"].device , )
__UpperCamelCase : Optional[int] = torch.tensor(
_lowerCamelCase , dtype=torch.floataa , device=protein["aatype"].device , )
__UpperCamelCase : Any = protein["aatype"].to(torch.long)
# create the mapping for (residx, atom14) --> atom37, i.e. an array
# with shape (num_res, 14) containing the atom37 indices for this protein
__UpperCamelCase : int = restype_atomaa_to_atomaa[protein_aatype]
__UpperCamelCase : str = restype_atomaa_mask[protein_aatype]
__UpperCamelCase : Dict = residx_atomaa_mask
__UpperCamelCase : Optional[Any] = residx_atomaa_to_atomaa.long()
# create the gather indices for mapping back
__UpperCamelCase : Tuple = restype_atomaa_to_atomaa[protein_aatype]
__UpperCamelCase : Tuple = residx_atomaa_to_atomaa.long()
# create the corresponding mask
__UpperCamelCase : Optional[Any] = torch.zeros([21, 37] , dtype=torch.floataa , device=protein["aatype"].device)
for restype, restype_letter in enumerate(rc.restypes):
__UpperCamelCase : Any = rc.restype_atoa[restype_letter]
__UpperCamelCase : List[str] = rc.residue_atoms[restype_name]
for atom_name in atom_names:
__UpperCamelCase : Any = rc.atom_order[atom_name]
__UpperCamelCase : List[Any] = 1
__UpperCamelCase : List[Any] = restype_atomaa_mask[protein_aatype]
__UpperCamelCase : Any = residx_atomaa_mask
return protein
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : Dict[str, torch.Tensor]) -> Dict[str, np.ndarray]:
'''simple docstring'''
__UpperCamelCase : int = tree_map(lambda _lowerCamelCase: torch.tensor(_lowerCamelCase , device=batch["aatype"].device) , _lowerCamelCase , np.ndarray)
__UpperCamelCase : List[str] = tensor_tree_map(lambda _lowerCamelCase: np.array(_lowerCamelCase) , make_atomaa_masks(_lowerCamelCase))
return out | 151 |
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : List[Any] , _lowerCamelCase : str) -> Any:
'''simple docstring'''
__UpperCamelCase : Dict = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return res
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : Optional[Any] , _lowerCamelCase : Optional[int] , _lowerCamelCase : Tuple) -> Any:
'''simple docstring'''
__UpperCamelCase : Union[str, Any] = 0
while b > 0:
if b & 1:
__UpperCamelCase : str = ((res % c) + (a % c)) % c
a += a
b >>= 1
return res | 151 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
def _a( UpperCamelCase__ : int, UpperCamelCase__ : int, UpperCamelCase__ : bool, UpperCamelCase__ : list[int], UpperCamelCase__ : float ):
'''simple docstring'''
if depth < 0:
raise ValueError('''Depth cannot be less than 0''' )
if not scores:
raise ValueError('''Scores cannot be empty''' )
if depth == height:
return scores[node_index]
return (
max(
minimax(depth + 1, node_index * 2, _lowerCamelCase, _lowerCamelCase, _lowerCamelCase ), minimax(depth + 1, node_index * 2 + 1, _lowerCamelCase, _lowerCamelCase, _lowerCamelCase ), )
if is_max
else min(
minimax(depth + 1, node_index * 2, _lowerCamelCase, _lowerCamelCase, _lowerCamelCase ), minimax(depth + 1, node_index * 2 + 1, _lowerCamelCase, _lowerCamelCase, _lowerCamelCase ), )
)
def _a( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : str =[9_0, 2_3, 6, 3_3, 2_1, 6_5, 1_2_3, 3_4_4_2_3]
SCREAMING_SNAKE_CASE__ : Tuple =math.log(len(_lowerCamelCase ), 2 )
print(f"Optimal value : {minimax(0, 0, _lowerCamelCase, _lowerCamelCase, _lowerCamelCase )}" )
if __name__ == "__main__":
import doctest
doctest.testmod()
main() | 152 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_pytesseract_available():
from PIL import Image
from transformers import LayoutLMvaImageProcessor
class a ( unittest.TestCase ):
def __init__( self : Any , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : List[str]=7 , __SCREAMING_SNAKE_CASE : Dict=3 , __SCREAMING_SNAKE_CASE : Dict=18 , __SCREAMING_SNAKE_CASE : Union[str, Any]=30 , __SCREAMING_SNAKE_CASE : Optional[Any]=400 , __SCREAMING_SNAKE_CASE : Dict=True , __SCREAMING_SNAKE_CASE : Dict=None , __SCREAMING_SNAKE_CASE : Any=True , ) -> str:
lowerCamelCase_ = size if size is not None else {'height': 18, 'width': 18}
lowerCamelCase_ = parent
lowerCamelCase_ = batch_size
lowerCamelCase_ = num_channels
lowerCamelCase_ = image_size
lowerCamelCase_ = min_resolution
lowerCamelCase_ = max_resolution
lowerCamelCase_ = do_resize
lowerCamelCase_ = size
lowerCamelCase_ = apply_ocr
def UpperCamelCase ( self : int ) -> Tuple:
return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}
@require_torch
@require_pytesseract
class a ( __snake_case , unittest.TestCase ):
SCREAMING_SNAKE_CASE : str = LayoutLMvaImageProcessor if is_pytesseract_available() else None
def UpperCamelCase ( self : List[str] ) -> int:
lowerCamelCase_ = LayoutLMvaImageProcessingTester(self )
@property
def UpperCamelCase ( self : Optional[Any] ) -> Any:
return self.image_processor_tester.prepare_image_processor_dict()
def UpperCamelCase ( self : Tuple ) -> Optional[Any]:
lowerCamelCase_ = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE , 'do_resize' ) )
self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE , 'size' ) )
self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE , 'apply_ocr' ) )
def UpperCamelCase ( self : Any ) -> Any:
lowerCamelCase_ = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'height': 18, 'width': 18} )
lowerCamelCase_ = self.image_processing_class.from_dict(self.image_processor_dict , size=42 )
self.assertEqual(image_processor.size , {'height': 42, 'width': 42} )
def UpperCamelCase ( self : Dict ) -> Any:
pass
def UpperCamelCase ( self : int ) -> Dict:
# Initialize image_processing
lowerCamelCase_ = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
lowerCamelCase_ = prepare_image_inputs(self.image_processor_tester , equal_resolution=__SCREAMING_SNAKE_CASE )
for image in image_inputs:
self.assertIsInstance(__SCREAMING_SNAKE_CASE , Image.Image )
# Test not batched input
lowerCamelCase_ = image_processing(image_inputs[0] , return_tensors='pt' )
self.assertEqual(
encoding.pixel_values.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
self.assertIsInstance(encoding.words , __SCREAMING_SNAKE_CASE )
self.assertIsInstance(encoding.boxes , __SCREAMING_SNAKE_CASE )
# Test batched
lowerCamelCase_ = image_processing(__SCREAMING_SNAKE_CASE , 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'],
) , )
def UpperCamelCase ( self : Optional[int] ) -> Optional[Any]:
# Initialize image_processing
lowerCamelCase_ = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
lowerCamelCase_ = prepare_image_inputs(self.image_processor_tester , equal_resolution=__SCREAMING_SNAKE_CASE , numpify=__SCREAMING_SNAKE_CASE )
for image in image_inputs:
self.assertIsInstance(__SCREAMING_SNAKE_CASE , np.ndarray )
# Test not batched input
lowerCamelCase_ = 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
lowerCamelCase_ = image_processing(__SCREAMING_SNAKE_CASE , 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'],
) , )
def UpperCamelCase ( self : Dict ) -> int:
# Initialize image_processing
lowerCamelCase_ = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
lowerCamelCase_ = prepare_image_inputs(self.image_processor_tester , equal_resolution=__SCREAMING_SNAKE_CASE , torchify=__SCREAMING_SNAKE_CASE )
for image in image_inputs:
self.assertIsInstance(__SCREAMING_SNAKE_CASE , torch.Tensor )
# Test not batched input
lowerCamelCase_ = 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
lowerCamelCase_ = image_processing(__SCREAMING_SNAKE_CASE , 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'],
) , )
def UpperCamelCase ( self : Dict ) -> Any:
# with apply_OCR = True
lowerCamelCase_ = LayoutLMvaImageProcessor()
from datasets import load_dataset
lowerCamelCase_ = load_dataset('hf-internal-testing/fixtures_docvqa' , split='test' )
lowerCamelCase_ = Image.open(ds[0]['file'] ).convert('RGB' )
lowerCamelCase_ = image_processing(__SCREAMING_SNAKE_CASE , return_tensors='pt' )
self.assertEqual(encoding.pixel_values.shape , (1, 3, 224, 224) )
self.assertEqual(len(encoding.words ) , len(encoding.boxes ) )
# fmt: off
# the words and boxes were obtained with Tesseract 4.1.1
lowerCamelCase_ = [['11:14', 'to', '11:39', 'a.m', '11:39', 'to', '11:44', 'a.m.', '11:44', 'a.m.', 'to', '12:25', 'p.m.', '12:25', 'to', '12:58', 'p.m.', '12:58', 'to', '4:00', 'p.m.', '2:00', 'to', '5:00', 'p.m.', 'Coffee', 'Break', 'Coffee', 'will', 'be', 'served', 'for', 'men', 'and', 'women', 'in', 'the', 'lobby', 'adjacent', 'to', 'exhibit', 'area.', 'Please', 'move', 'into', 'exhibit', 'area.', '(Exhibits', 'Open)', 'TRRF', 'GENERAL', 'SESSION', '(PART', '|)', 'Presiding:', 'Lee', 'A.', 'Waller', 'TRRF', 'Vice', 'President', '“Introductory', 'Remarks”', 'Lee', 'A.', 'Waller,', 'TRRF', 'Vice', 'Presi-', 'dent', 'Individual', 'Interviews', 'with', 'TRRF', 'Public', 'Board', 'Members', 'and', 'Sci-', 'entific', 'Advisory', 'Council', 'Mem-', 'bers', 'Conducted', 'by', 'TRRF', 'Treasurer', 'Philip', 'G.', 'Kuehn', 'to', 'get', 'answers', 'which', 'the', 'public', 'refrigerated', 'warehousing', 'industry', 'is', 'looking', 'for.', 'Plus', 'questions', 'from', 'the', 'floor.', 'Dr.', 'Emil', 'M.', 'Mrak,', 'University', 'of', 'Cal-', 'ifornia,', 'Chairman,', 'TRRF', 'Board;', 'Sam', 'R.', 'Cecil,', 'University', 'of', 'Georgia', 'College', 'of', 'Agriculture;', 'Dr.', 'Stanley', 'Charm,', 'Tufts', 'University', 'School', 'of', 'Medicine;', 'Dr.', 'Robert', 'H.', 'Cotton,', 'ITT', 'Continental', 'Baking', 'Company;', 'Dr.', 'Owen', 'Fennema,', 'University', 'of', 'Wis-', 'consin;', 'Dr.', 'Robert', 'E.', 'Hardenburg,', 'USDA.', 'Questions', 'and', 'Answers', 'Exhibits', 'Open', 'Capt.', 'Jack', 'Stoney', 'Room', 'TRRF', 'Scientific', 'Advisory', 'Council', 'Meeting', 'Ballroom', 'Foyer']] # noqa: E231
lowerCamelCase_ = [[[141, 57, 214, 69], [228, 58, 252, 69], [141, 75, 216, 88], [230, 79, 280, 88], [142, 260, 218, 273], [230, 261, 255, 273], [143, 279, 218, 290], [231, 282, 290, 291], [143, 342, 218, 354], [231, 345, 289, 355], [202, 362, 227, 373], [143, 379, 220, 392], [231, 382, 291, 394], [144, 714, 220, 726], [231, 715, 256, 726], [144, 732, 220, 745], [232, 736, 291, 747], [144, 769, 218, 782], [231, 770, 256, 782], [141, 788, 202, 801], [215, 791, 274, 804], [143, 826, 204, 838], [215, 826, 240, 838], [142, 844, 202, 857], [215, 847, 274, 859], [334, 57, 427, 69], [440, 57, 522, 69], [369, 75, 461, 88], [469, 75, 516, 88], [528, 76, 562, 88], [570, 76, 667, 88], [675, 75, 711, 87], [721, 79, 778, 88], [789, 75, 840, 88], [369, 97, 470, 107], [484, 94, 507, 106], [518, 94, 562, 107], [576, 94, 655, 110], [668, 94, 792, 109], [804, 95, 829, 107], [369, 113, 465, 125], [477, 116, 547, 125], [562, 113, 658, 125], [671, 116, 748, 125], [761, 113, 811, 125], [369, 131, 465, 143], [477, 133, 548, 143], [563, 130, 698, 145], [710, 130, 802, 146], [336, 171, 412, 183], [423, 171, 572, 183], [582, 170, 716, 184], [728, 171, 817, 187], [829, 171, 844, 186], [338, 197, 482, 212], [507, 196, 557, 209], [569, 196, 595, 208], [610, 196, 702, 209], [505, 214, 583, 226], [595, 214, 656, 227], [670, 215, 807, 227], [335, 259, 543, 274], [556, 259, 708, 272], [372, 279, 422, 291], [435, 279, 460, 291], [474, 279, 574, 292], [587, 278, 664, 291], [676, 278, 738, 291], [751, 279, 834, 291], [372, 298, 434, 310], [335, 341, 483, 354], [497, 341, 655, 354], [667, 341, 728, 354], [740, 341, 825, 354], [335, 360, 430, 372], [442, 360, 534, 372], [545, 359, 687, 372], [697, 360, 754, 372], [765, 360, 823, 373], [334, 378, 428, 391], [440, 378, 577, 394], [590, 378, 705, 391], [720, 378, 801, 391], [334, 397, 400, 409], [370, 416, 529, 429], [544, 416, 576, 432], [587, 416, 665, 428], [677, 416, 814, 429], [372, 435, 452, 450], [465, 434, 495, 447], [511, 434, 600, 447], [611, 436, 637, 447], [649, 436, 694, 451], [705, 438, 824, 447], [369, 453, 452, 466], [464, 454, 509, 466], [522, 453, 611, 469], [625, 453, 792, 469], [370, 472, 556, 488], [570, 472, 684, 487], [697, 472, 718, 485], [732, 472, 835, 488], [369, 490, 411, 503], [425, 490, 484, 503], [496, 490, 635, 506], [645, 490, 707, 503], [718, 491, 761, 503], [771, 490, 840, 503], [336, 510, 374, 521], [388, 510, 447, 522], [460, 510, 489, 521], [503, 510, 580, 522], [592, 509, 736, 525], [745, 509, 770, 522], [781, 509, 840, 522], [338, 528, 434, 541], [448, 528, 596, 541], [609, 527, 687, 540], [700, 528, 792, 541], [336, 546, 397, 559], [407, 546, 431, 559], [443, 546, 525, 560], [537, 546, 680, 562], [688, 546, 714, 559], [722, 546, 837, 562], [336, 565, 449, 581], [461, 565, 485, 577], [497, 565, 665, 581], [681, 565, 718, 577], [732, 565, 837, 580], [337, 584, 438, 597], [452, 583, 521, 596], [535, 584, 677, 599], [690, 583, 787, 596], [801, 583, 825, 596], [338, 602, 478, 615], [492, 602, 530, 614], [543, 602, 638, 615], [650, 602, 676, 614], [688, 602, 788, 615], [802, 602, 843, 614], [337, 621, 502, 633], [516, 621, 615, 637], [629, 621, 774, 636], [789, 621, 827, 633], [337, 639, 418, 652], [432, 640, 571, 653], [587, 639, 731, 655], [743, 639, 769, 652], [780, 639, 841, 652], [338, 658, 440, 673], [455, 658, 491, 670], [508, 658, 602, 671], [616, 658, 638, 670], [654, 658, 835, 674], [337, 677, 429, 689], [337, 714, 482, 726], [495, 714, 548, 726], [561, 714, 683, 726], [338, 770, 461, 782], [474, 769, 554, 785], [489, 788, 562, 803], [576, 788, 643, 801], [656, 787, 751, 804], [764, 788, 844, 801], [334, 825, 421, 838], [430, 824, 574, 838], [584, 824, 723, 841], [335, 844, 450, 857], [464, 843, 583, 860], [628, 862, 755, 875], [769, 861, 848, 878]]] # noqa: E231
# fmt: on
self.assertListEqual(encoding.words , __SCREAMING_SNAKE_CASE )
self.assertListEqual(encoding.boxes , __SCREAMING_SNAKE_CASE )
# with apply_OCR = False
lowerCamelCase_ = LayoutLMvaImageProcessor(apply_ocr=__SCREAMING_SNAKE_CASE )
lowerCamelCase_ = image_processing(__SCREAMING_SNAKE_CASE , return_tensors='pt' )
self.assertEqual(encoding.pixel_values.shape , (1, 3, 224, 224) )
| 183 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_A : str ={
'''configuration_roformer''': ['''ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoFormerConfig''', '''RoFormerOnnxConfig'''],
'''tokenization_roformer''': ['''RoFormerTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A : List[str] =['''RoFormerTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A : Tuple =[
'''ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''RoFormerForCausalLM''',
'''RoFormerForMaskedLM''',
'''RoFormerForMultipleChoice''',
'''RoFormerForQuestionAnswering''',
'''RoFormerForSequenceClassification''',
'''RoFormerForTokenClassification''',
'''RoFormerLayer''',
'''RoFormerModel''',
'''RoFormerPreTrainedModel''',
'''load_tf_weights_in_roformer''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A : Any =[
'''TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFRoFormerForCausalLM''',
'''TFRoFormerForMaskedLM''',
'''TFRoFormerForMultipleChoice''',
'''TFRoFormerForQuestionAnswering''',
'''TFRoFormerForSequenceClassification''',
'''TFRoFormerForTokenClassification''',
'''TFRoFormerLayer''',
'''TFRoFormerModel''',
'''TFRoFormerPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A : str =[
'''FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''FlaxRoFormerForMaskedLM''',
'''FlaxRoFormerForMultipleChoice''',
'''FlaxRoFormerForQuestionAnswering''',
'''FlaxRoFormerForSequenceClassification''',
'''FlaxRoFormerForTokenClassification''',
'''FlaxRoFormerModel''',
'''FlaxRoFormerPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
from .tokenization_roformer import RoFormerTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_roformer_fast import RoFormerTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roformer import (
ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
RoFormerForCausalLM,
RoFormerForMaskedLM,
RoFormerForMultipleChoice,
RoFormerForQuestionAnswering,
RoFormerForSequenceClassification,
RoFormerForTokenClassification,
RoFormerLayer,
RoFormerModel,
RoFormerPreTrainedModel,
load_tf_weights_in_roformer,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roformer import (
TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForMultipleChoice,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerLayer,
TFRoFormerModel,
TFRoFormerPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roformer import (
FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
FlaxRoFormerForMaskedLM,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerModel,
FlaxRoFormerPreTrainedModel,
)
else:
import sys
_A : Optional[int] =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 368 |
'''simple docstring'''
from torch import nn
def SCREAMING_SNAKE_CASE_ (UpperCamelCase ) -> Dict:
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise ValueError(f'''Unsupported activation function: {act_fn}''' )
| 129 | 0 |
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures
_lowerCamelCase : List[str] = logging.get_logger(__name__)
@dataclass
class UpperCamelCase_ :
'''simple docstring'''
UpperCAmelCase__ = field(metadata={'''help''': '''The name of the task to train on: ''' + ''', '''.join(glue_processors.keys() )} )
UpperCAmelCase__ = field(
metadata={'''help''': '''The input data dir. Should contain the .tsv files (or other data files) for the task.'''} )
UpperCAmelCase__ = 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.'''
)
} , )
UpperCAmelCase__ = field(
default=UpperCAmelCase__ , metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )
def SCREAMING_SNAKE_CASE ( self : str) ->str:
'''simple docstring'''
A__ = self.task_name.lower()
class UpperCamelCase_ ( UpperCAmelCase__ ):
'''simple docstring'''
UpperCAmelCase__ = '''train'''
UpperCAmelCase__ = '''dev'''
UpperCAmelCase__ = '''test'''
class UpperCamelCase_ ( UpperCAmelCase__ ):
'''simple docstring'''
UpperCAmelCase__ = 42
UpperCAmelCase__ = 42
UpperCAmelCase__ = 42
def __init__( self : int , UpperCAmelCase__ : GlueDataTrainingArguments , UpperCAmelCase__ : PreTrainedTokenizerBase , UpperCAmelCase__ : Optional[int] = None , UpperCAmelCase__ : Union[str, Split] = Split.train , UpperCAmelCase__ : Optional[str] = None , ) ->str:
'''simple docstring'''
warnings.warn(
'''This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets '''
'''library. You can have a look at this example script for pointers: '''
'''https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py''' , UpperCAmelCase__ , )
A__ = args
A__ = glue_processors[args.task_name]()
A__ = glue_output_modes[args.task_name]
if isinstance(UpperCAmelCase__ , UpperCAmelCase__):
try:
A__ = Split[mode]
except KeyError:
raise KeyError('''mode is not a valid split name''')
# Load data features from cache or dataset file
A__ = 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}_{args.task_name}""" , )
A__ = self.processor.get_labels()
if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
"RobertaTokenizer",
"RobertaTokenizerFast",
"XLMRobertaTokenizer",
"BartTokenizer",
"BartTokenizerFast",
):
# HACK(label indices are swapped in RoBERTa pretrained model)
A__ , A__ = label_list[2], label_list[1]
A__ = label_list
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
A__ = cached_features_file + '''.lock'''
with FileLock(UpperCAmelCase__):
if os.path.exists(UpperCAmelCase__) and not args.overwrite_cache:
A__ = time.time()
A__ = torch.load(UpperCAmelCase__)
logger.info(
f"""Loading features from cached file {cached_features_file} [took %.3f s]""" , time.time() - start)
else:
logger.info(f"""Creating features from dataset file at {args.data_dir}""")
if mode == Split.dev:
A__ = self.processor.get_dev_examples(args.data_dir)
elif mode == Split.test:
A__ = self.processor.get_test_examples(args.data_dir)
else:
A__ = self.processor.get_train_examples(args.data_dir)
if limit_length is not None:
A__ = examples[:limit_length]
A__ = glue_convert_examples_to_features(
UpperCAmelCase__ , UpperCAmelCase__ , max_length=args.max_seq_length , label_list=UpperCAmelCase__ , output_mode=self.output_mode , )
A__ = time.time()
torch.save(self.features , UpperCAmelCase__)
# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
logger.info(
f"""Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]""")
def __len__( self : Optional[Any]) ->Dict:
'''simple docstring'''
return len(self.features)
def __getitem__( self : int , UpperCAmelCase__ : int) ->InputFeatures:
'''simple docstring'''
return self.features[i]
def SCREAMING_SNAKE_CASE ( self : List[Any]) ->Any:
'''simple docstring'''
return self.label_list
| 14 |
import os
import pytest
from transformers.dynamic_module_utils import get_imports
_lowerCamelCase : Any = """
import os
"""
_lowerCamelCase : Optional[int] = """
def foo():
import os
return False
"""
_lowerCamelCase : List[Any] = """
def foo():
def bar():
if True:
import os
return False
return bar()
"""
_lowerCamelCase : List[Any] = """
import os
try:
import bar
except ImportError:
raise ValueError()
"""
_lowerCamelCase : Union[str, Any] = """
import os
def foo():
try:
import bar
except ImportError:
raise ValueError()
"""
_lowerCamelCase : List[Any] = """
import os
try:
import bar
except (ImportError, AttributeError):
raise ValueError()
"""
_lowerCamelCase : List[Any] = """
import os
try:
import bar
except ImportError as e:
raise ValueError()
"""
_lowerCamelCase : str = """
import os
try:
import bar
except:
raise ValueError()
"""
_lowerCamelCase : Optional[Any] = """
import os
try:
import bar
import baz
except ImportError:
raise ValueError()
"""
_lowerCamelCase : Any = """
import os
try:
import bar
import baz
except ImportError:
x = 1
raise ValueError()
"""
_lowerCamelCase : Dict = [
TOP_LEVEL_IMPORT,
IMPORT_IN_FUNCTION,
DEEPLY_NESTED_IMPORT,
TOP_LEVEL_TRY_IMPORT,
GENERIC_EXCEPT_IMPORT,
MULTILINE_TRY_IMPORT,
MULTILINE_BOTH_IMPORT,
MULTIPLE_EXCEPTS_IMPORT,
EXCEPT_AS_IMPORT,
TRY_IMPORT_IN_FUNCTION,
]
@pytest.mark.parametrize('''case''' , lowercase_ )
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> int:
"""simple docstring"""
A__ = os.path.join(lowercase_ , '''test_file.py''' )
with open(lowercase_ , '''w''' ) as _tmp_file:
_tmp_file.write(lowercase_ )
A__ = get_imports(lowercase_ )
assert parsed_imports == ["os"]
| 14 | 1 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowercase__ : int = logging.get_logger(__name__)
lowercase__ : Tuple = {'''vocab_file''': '''spiece.model'''}
lowercase__ : str = {
'''vocab_file''': {
'''bert_for_seq_generation''': (
'''https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model'''
),
}
}
lowercase__ : Optional[Any] = {'''bert_for_seq_generation''': 5_12}
class _UpperCAmelCase ( lowerCAmelCase__):
_lowerCAmelCase : str = VOCAB_FILES_NAMES
_lowerCAmelCase : int = PRETRAINED_VOCAB_FILES_MAP
_lowerCAmelCase : int = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_lowerCAmelCase : List[int] = []
_lowerCAmelCase : List[Any] = ["""input_ids""", """attention_mask"""]
def __init__( self : Optional[int] , lowercase_ : Any , lowercase_ : Dict="<s>" , lowercase_ : List[Any]="</s>" , lowercase_ : List[Any]="<unk>" , lowercase_ : str="<pad>" , lowercase_ : Dict="<::::>" , lowercase_ : Optional[Dict[str, Any]] = None , **lowercase_ : List[str] , ):
snake_case_ : Dict = {} if sp_model_kwargs is None else sp_model_kwargs
# Add extra_ids to the special token list
super().__init__(
bos_token=lowercase_ , eos_token=lowercase_ , unk_token=lowercase_ , pad_token=lowercase_ , sep_token=lowercase_ , sp_model_kwargs=self.sp_model_kwargs , **lowercase_ , )
snake_case_ : Tuple = vocab_file
snake_case_ : Dict = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(lowercase_ )
@property
def _snake_case ( self : Union[str, Any] ):
return self.sp_model.get_piece_size()
def _snake_case ( self : Union[str, Any] ):
snake_case_ : List[Any] = {self.convert_ids_to_tokens(lowercase_ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self : Union[str, Any] ):
snake_case_ : str = self.__dict__.copy()
snake_case_ : List[Any] = None
return state
def __setstate__( self : Dict , lowercase_ : List[str] ):
snake_case_ : Any = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
snake_case_ : List[str] = {}
snake_case_ : str = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def _snake_case ( self : List[str] , lowercase_ : str ):
return self.sp_model.encode(lowercase_ , out_type=lowercase_ )
def _snake_case ( self : List[Any] , lowercase_ : Tuple ):
return self.sp_model.piece_to_id(lowercase_ )
def _snake_case ( self : Optional[int] , lowercase_ : Optional[Any] ):
snake_case_ : Any = self.sp_model.IdToPiece(lowercase_ )
return token
def _snake_case ( self : int , lowercase_ : Any ):
snake_case_ : int = []
snake_case_ : Dict = ''''''
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(lowercase_ ) + token
snake_case_ : Dict = []
else:
current_sub_tokens.append(lowercase_ )
out_string += self.sp_model.decode(lowercase_ )
return out_string.strip()
def _snake_case ( self : str , lowercase_ : str , lowercase_ : Optional[str] = None ):
if not os.path.isdir(lowercase_ ):
logger.error(f"Vocabulary path ({save_directory}) should be a directory" )
return
snake_case_ : Any = os.path.join(
lowercase_ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(lowercase_ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , lowercase_ )
elif not os.path.isfile(self.vocab_file ):
with open(lowercase_ , '''wb''' ) as fi:
snake_case_ : Dict = self.sp_model.serialized_model_proto()
fi.write(lowercase_ )
return (out_vocab_file,)
| 155 |
"""simple docstring"""
from collections.abc import Generator
def __lowercase ( ):
snake_case_, snake_case_ : List[str] = 0, 1
while True:
snake_case_, snake_case_ : List[str] = b, a + b
yield b
def __lowercase ( _a = 1_000 ):
snake_case_ : Tuple = 1
snake_case_ : List[str] = fibonacci_generator()
while len(str(next(_a ) ) ) < n:
answer += 1
return answer + 1
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 155 | 1 |
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase):
SCREAMING_SNAKE_CASE = [0 for i in range(r + 1)]
# nc0 = 1
SCREAMING_SNAKE_CASE = 1
for i in range(1 , n + 1):
# to compute current row from previous row.
SCREAMING_SNAKE_CASE = min(_UpperCAmelCase , _UpperCAmelCase)
while j > 0:
c[j] += c[j - 1]
j -= 1
return c[r]
print(binomial_coefficient(n=10, r=5))
| 137 |
from math import factorial
def lowerCamelCase__ (_UpperCAmelCase = 100):
return sum(int(_UpperCAmelCase) for x in str(factorial(_UpperCAmelCase)))
if __name__ == "__main__":
print(solution(int(input('Enter the Number: ').strip())))
| 137 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A : Dict = {'''configuration_opt''': ['''OPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''OPTConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Optional[Any] = [
'''OPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''OPTForCausalLM''',
'''OPTModel''',
'''OPTPreTrainedModel''',
'''OPTForSequenceClassification''',
'''OPTForQuestionAnswering''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : List[Any] = ['''TFOPTForCausalLM''', '''TFOPTModel''', '''TFOPTPreTrainedModel''']
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : int = [
'''FlaxOPTForCausalLM''',
'''FlaxOPTModel''',
'''FlaxOPTPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_opt import (
OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
OPTForCausalLM,
OPTForQuestionAnswering,
OPTForSequenceClassification,
OPTModel,
OPTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel
else:
import sys
__A : Union[str, Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 323 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A : List[Any] = {
'''configuration_autoformer''': [
'''AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''AutoformerConfig''',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : List[str] = [
'''AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''AutoformerForPrediction''',
'''AutoformerModel''',
'''AutoformerPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_autoformer import (
AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoformerConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_autoformer import (
AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
AutoformerForPrediction,
AutoformerModel,
AutoformerPreTrainedModel,
)
else:
import sys
__A : List[str] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 323 | 1 |
import unittest
from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow
if is_flax_available():
import jax
from transformers.models.auto.modeling_flax_auto import FlaxAutoModel
from transformers.models.bert.modeling_flax_bert import FlaxBertModel
from transformers.models.roberta.modeling_flax_roberta import FlaxRobertaModel
@require_flax
class _lowercase ( unittest.TestCase ):
'''simple docstring'''
@slow
def __magic_name__( self :str ) -> int:
for model_name in ["bert-base-cased", "bert-large-uncased"]:
with self.subTest(lowerCAmelCase__ ):
__SCREAMING_SNAKE_CASE : Dict = AutoConfig.from_pretrained(lowerCAmelCase__ )
self.assertIsNotNone(lowerCAmelCase__ )
self.assertIsInstance(lowerCAmelCase__ , lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : int = FlaxAutoModel.from_pretrained(lowerCAmelCase__ )
self.assertIsNotNone(lowerCAmelCase__ )
self.assertIsInstance(lowerCAmelCase__ , lowerCAmelCase__ )
@slow
def __magic_name__( self :Dict ) -> Dict:
for model_name in ["roberta-base", "roberta-large"]:
with self.subTest(lowerCAmelCase__ ):
__SCREAMING_SNAKE_CASE : List[Any] = AutoConfig.from_pretrained(lowerCAmelCase__ )
self.assertIsNotNone(lowerCAmelCase__ )
self.assertIsInstance(lowerCAmelCase__ , lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Optional[int] = FlaxAutoModel.from_pretrained(lowerCAmelCase__ )
self.assertIsNotNone(lowerCAmelCase__ )
self.assertIsInstance(lowerCAmelCase__ , lowerCAmelCase__ )
@slow
def __magic_name__( self :Optional[int] ) -> List[Any]:
for model_name in ["bert-base-cased", "bert-large-uncased"]:
__SCREAMING_SNAKE_CASE : Optional[int] = AutoTokenizer.from_pretrained(lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Optional[int] = FlaxBertModel.from_pretrained(lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Optional[Any] = tokenizer('''Do you support jax jitted function?''' , return_tensors=TensorType.JAX )
@jax.jit
def eval(**lowerCAmelCase__ :List[Any] ):
return model(**lowerCAmelCase__ )
eval(**lowerCAmelCase__ ).block_until_ready()
@slow
def __magic_name__( self :List[str] ) -> Optional[int]:
for model_name in ["roberta-base", "roberta-large"]:
__SCREAMING_SNAKE_CASE : str = AutoTokenizer.from_pretrained(lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Optional[Any] = FlaxRobertaModel.from_pretrained(lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Optional[int] = tokenizer('''Do you support jax jitted function?''' , return_tensors=TensorType.JAX )
@jax.jit
def eval(**lowerCAmelCase__ :List[Any] ):
return model(**lowerCAmelCase__ )
eval(**lowerCAmelCase__ ).block_until_ready()
def __magic_name__( self :Dict ) -> Union[str, Any]:
with self.assertRaisesRegex(
lowerCAmelCase__ , '''bert-base is not a local folder and is not a valid model identifier''' ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = FlaxAutoModel.from_pretrained('''bert-base''' )
def __magic_name__( self :int ) -> Optional[int]:
with self.assertRaisesRegex(
lowerCAmelCase__ , r'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)''' ):
__SCREAMING_SNAKE_CASE : Optional[int] = FlaxAutoModel.from_pretrained(lowerCAmelCase__ , revision='''aaaaaa''' )
def __magic_name__( self :str ) -> Tuple:
with self.assertRaisesRegex(
lowerCAmelCase__ , '''hf-internal-testing/config-no-model does not appear to have a file named flax_model.msgpack''' , ):
__SCREAMING_SNAKE_CASE : str = FlaxAutoModel.from_pretrained('''hf-internal-testing/config-no-model''' )
def __magic_name__( self :Dict ) -> int:
with self.assertRaisesRegex(lowerCAmelCase__ , '''Use `from_pt=True` to load this model''' ):
__SCREAMING_SNAKE_CASE : Dict = FlaxAutoModel.from_pretrained('''hf-internal-testing/tiny-bert-pt-only''' )
| 9 |
import unittest
from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available
from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
if is_bsa_available():
from transformers import MarkupLMFeatureExtractor
class _lowercase ( unittest.TestCase ):
'''simple docstring'''
def __init__( self :Optional[Any] , lowerCAmelCase__ :Optional[Any] ) -> str:
__SCREAMING_SNAKE_CASE : Optional[Any] = parent
def __magic_name__( self :List[Any] ) -> Tuple:
return {}
def _UpperCamelCase ( ):
__SCREAMING_SNAKE_CASE : Optional[Any] = '''<HTML>
<HEAD>
<TITLE>sample document</TITLE>
</HEAD>
<BODY BGCOLOR="FFFFFF">
<HR>
<a href="http://google.com">Goog</a>
<H1>This is one header</H1>
<H2>This is a another Header</H2>
<P>Travel from
<P>
<B>SFO to JFK</B>
<BR>
<B><I>on May 2, 2015 at 2:00 pm. For details go to confirm.com </I></B>
<HR>
<div style="color:#0000FF">
<h3>Traveler <b> name </b> is
<p> John Doe </p>
</div>'''
__SCREAMING_SNAKE_CASE : str = '''
<!DOCTYPE html>
<html>
<body>
<h1>My First Heading</h1>
<p>My first paragraph.</p>
</body>
</html>
'''
return [html_string_a, html_string_a]
@require_bsa
class _lowercase ( A__ , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] = MarkupLMFeatureExtractor if is_bsa_available() else None
def __magic_name__( self :int ) -> Optional[Any]:
__SCREAMING_SNAKE_CASE : Optional[Any] = MarkupLMFeatureExtractionTester(self )
@property
def __magic_name__( self :Any ) -> Optional[Any]:
return self.feature_extract_tester.prepare_feat_extract_dict()
def __magic_name__( self :Optional[int] ) -> Any:
# Initialize feature_extractor
__SCREAMING_SNAKE_CASE : int = self.feature_extraction_class()
# Test not batched input
__SCREAMING_SNAKE_CASE : Tuple = get_html_strings()[0]
__SCREAMING_SNAKE_CASE : Dict = feature_extractor(lowerCAmelCase__ )
# fmt: off
__SCREAMING_SNAKE_CASE : str = [['''sample document''', '''Goog''', '''This is one header''', '''This is a another Header''', '''Travel from''', '''SFO to JFK''', '''on May 2, 2015 at 2:00 pm. For details go to confirm.com''', '''Traveler''', '''name''', '''is''', '''John Doe''']]
__SCREAMING_SNAKE_CASE : List[str] = [['''/html/head/title''', '''/html/body/a''', '''/html/body/h1''', '''/html/body/h2''', '''/html/body/p''', '''/html/body/p/p/b[1]''', '''/html/body/p/p/b[2]/i''', '''/html/body/p/p/div/h3''', '''/html/body/p/p/div/h3/b''', '''/html/body/p/p/div/h3''', '''/html/body/p/p/div/h3/p''']]
# fmt: on
self.assertEqual(encoding.nodes , lowerCAmelCase__ )
self.assertEqual(encoding.xpaths , lowerCAmelCase__ )
# Test batched
__SCREAMING_SNAKE_CASE : Tuple = get_html_strings()
__SCREAMING_SNAKE_CASE : Dict = feature_extractor(lowerCAmelCase__ )
# fmt: off
__SCREAMING_SNAKE_CASE : int = expected_nodes + [['''My First Heading''', '''My first paragraph.''']]
__SCREAMING_SNAKE_CASE : str = expected_xpaths + [['''/html/body/h1''', '''/html/body/p''']]
self.assertEqual(len(encoding.nodes ) , 2 )
self.assertEqual(len(encoding.xpaths ) , 2 )
self.assertEqual(encoding.nodes , lowerCAmelCase__ )
self.assertEqual(encoding.xpaths , lowerCAmelCase__ )
| 9 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
"facebook/data2vec-vision-base-ft": (
"https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json"
),
}
class snake_case ( lowercase__):
__UpperCamelCase = '''data2vec-vision'''
def __init__( self : List[Any] , a__ : Optional[Any]=7_68 , a__ : Optional[int]=12 , a__ : Any=12 , a__ : Dict=30_72 , a__ : Tuple="gelu" , a__ : Union[str, Any]=0.0 , a__ : List[str]=0.0 , a__ : Any=0.0_2 , a__ : int=1E-1_2 , a__ : Union[str, Any]=2_24 , a__ : Optional[Any]=16 , a__ : int=3 , a__ : Dict=False , a__ : Tuple=False , a__ : List[str]=False , a__ : Tuple=False , a__ : List[str]=0.1 , a__ : int=0.1 , a__ : Union[str, Any]=True , a__ : str=[3, 5, 7, 11] , a__ : Any=[1, 2, 3, 6] , a__ : List[Any]=True , a__ : Dict=0.4 , a__ : Optional[Any]=2_56 , a__ : List[str]=1 , a__ : Dict=False , a__ : int=2_55 , **a__ : List[str] , ) -> int:
'''simple docstring'''
super().__init__(**_a )
_A = hidden_size
_A = num_hidden_layers
_A = num_attention_heads
_A = intermediate_size
_A = hidden_act
_A = hidden_dropout_prob
_A = attention_probs_dropout_prob
_A = initializer_range
_A = layer_norm_eps
_A = image_size
_A = patch_size
_A = num_channels
_A = use_mask_token
_A = use_absolute_position_embeddings
_A = use_relative_position_bias
_A = use_shared_relative_position_bias
_A = layer_scale_init_value
_A = drop_path_rate
_A = use_mean_pooling
# decode head attributes (semantic segmentation)
_A = out_indices
_A = pool_scales
# auxiliary head attributes (semantic segmentation)
_A = use_auxiliary_head
_A = auxiliary_loss_weight
_A = auxiliary_channels
_A = auxiliary_num_convs
_A = auxiliary_concat_input
_A = semantic_loss_ignore_index
class snake_case ( lowercase__):
__UpperCamelCase = version.parse('1.11')
@property
def a_ ( self : Optional[int] ) -> Optional[int]:
'''simple docstring'''
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
] )
@property
def a_ ( self : Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
return 1E-4 | 355 |
"""simple docstring"""
def a__ ( __lowercase ) -> int:
assert (
isinstance(__lowercase , __lowercase ) and number_of_steps > 0
), f"""number_of_steps needs to be positive integer, your input {number_of_steps}"""
if number_of_steps == 1:
return 1
_A , _A = 1, 1
for _ in range(number_of_steps - 1 ):
_A , _A = current + previous, current
return current
if __name__ == "__main__":
import doctest
doctest.testmod() | 163 | 0 |
"""simple docstring"""
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
lowerCAmelCase : Tuple = abspath(join(dirname(dirname(dirname(__file__))), """src"""))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action="""ignore""", category=FutureWarning)
def a__ ( snake_case__ ) -> Optional[Any]:
from transformers.testing_utils import pytest_addoption_shared
pytest_addoption_shared(snake_case__ )
def a__ ( snake_case__ ) -> Union[str, Any]:
from transformers.testing_utils import pytest_terminal_summary_main
lowerCamelCase = terminalreporter.config.getoption("""--make-reports""" )
if make_reports:
pytest_terminal_summary_main(snake_case__ , id=snake_case__ )
| 291 |
"""simple docstring"""
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def a__ ( ) -> Union[str, Any]:
lowerCamelCase = ArgumentParser(
description=(
"""PyTorch TPU distributed training launch """
"""helper utility that will spawn up """
"""multiple distributed processes"""
) )
# Optional arguments for the launch helper
parser.add_argument("""--num_cores""" , type=snake_case__ , default=1 , help="""Number of TPU cores to use (1 or 8).""" )
# positional
parser.add_argument(
"""training_script""" , type=snake_case__ , help=(
"""The full path to the single TPU training """
"""program/script to be launched in parallel, """
"""followed by all the arguments for the """
"""training script"""
) , )
# rest from the training program
parser.add_argument("""training_script_args""" , nargs=snake_case__ )
return parser.parse_args()
def a__ ( ) -> List[str]:
lowerCamelCase = parse_args()
# Import training_script as a module.
lowerCamelCase = Path(args.training_script )
sys.path.append(str(script_fpath.parent.resolve() ) )
lowerCamelCase = script_fpath.stem
lowerCamelCase = importlib.import_module(snake_case__ )
# Patch sys.argv
lowerCamelCase = [args.training_script] + args.training_script_args + ["""--tpu_num_cores""", str(args.num_cores )]
xmp.spawn(mod._mp_fn , args=() , nprocs=args.num_cores )
if __name__ == "__main__":
main()
| 291 | 1 |
import argparse
import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor
from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor
def lowerCAmelCase_ ( _lowercase : int) -> Tuple:
"""simple docstring"""
a__ : str = SwinaSRConfig()
if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
a__ : Optional[int] = 4
elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
a__ : Tuple = 4
a__ : str = 48
a__ : int = """pixelshuffle_aux"""
elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
a__ : Optional[Any] = [6, 6, 6, 6]
a__ : List[str] = 60
a__ : List[Any] = [6, 6, 6, 6]
a__ : Any = """pixelshuffledirect"""
elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
a__ : Dict = 4
a__ : Tuple = """nearest+conv"""
elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
a__ : Dict = 1
a__ : str = 1
a__ : List[str] = 126
a__ : Optional[Any] = 7
a__ : Tuple = 255.0
a__ : Dict = """"""
return config
def lowerCAmelCase_ ( _lowercase : str , _lowercase : Optional[int]) -> List[Any]:
"""simple docstring"""
if "patch_embed.proj" in name and "layers" not in name:
a__ : str = name.replace("""patch_embed.proj""" , """embeddings.patch_embeddings.projection""")
if "patch_embed.norm" in name:
a__ : str = name.replace("""patch_embed.norm""" , """embeddings.patch_embeddings.layernorm""")
if "layers" in name:
a__ : Optional[Any] = name.replace("""layers""" , """encoder.stages""")
if "residual_group.blocks" in name:
a__ : Union[str, Any] = name.replace("""residual_group.blocks""" , """layers""")
if "attn.proj" in name:
a__ : int = name.replace("""attn.proj""" , """attention.output.dense""")
if "attn" in name:
a__ : Any = name.replace("""attn""" , """attention.self""")
if "norm1" in name:
a__ : int = name.replace("""norm1""" , """layernorm_before""")
if "norm2" in name:
a__ : Dict = name.replace("""norm2""" , """layernorm_after""")
if "mlp.fc1" in name:
a__ : Any = name.replace("""mlp.fc1""" , """intermediate.dense""")
if "mlp.fc2" in name:
a__ : Dict = name.replace("""mlp.fc2""" , """output.dense""")
if "q_bias" in name:
a__ : str = name.replace("""q_bias""" , """query.bias""")
if "k_bias" in name:
a__ : Optional[int] = name.replace("""k_bias""" , """key.bias""")
if "v_bias" in name:
a__ : str = name.replace("""v_bias""" , """value.bias""")
if "cpb_mlp" in name:
a__ : Optional[int] = name.replace("""cpb_mlp""" , """continuous_position_bias_mlp""")
if "patch_embed.proj" in name:
a__ : Any = name.replace("""patch_embed.proj""" , """patch_embed.projection""")
if name == "norm.weight":
a__ : Optional[int] = """layernorm.weight"""
if name == "norm.bias":
a__ : List[str] = """layernorm.bias"""
if "conv_first" in name:
a__ : List[Any] = name.replace("""conv_first""" , """first_convolution""")
if (
"upsample" in name
or "conv_before_upsample" in name
or "conv_bicubic" in name
or "conv_up" in name
or "conv_hr" in name
or "conv_last" in name
or "aux" in name
):
# heads
if "conv_last" in name:
a__ : Dict = name.replace("""conv_last""" , """final_convolution""")
if config.upsampler in ["pixelshuffle", "pixelshuffle_aux", "nearest+conv"]:
if "conv_before_upsample.0" in name:
a__ : Any = name.replace("""conv_before_upsample.0""" , """conv_before_upsample""")
if "upsample.0" in name:
a__ : Any = name.replace("""upsample.0""" , """upsample.convolution_0""")
if "upsample.2" in name:
a__ : Optional[Any] = name.replace("""upsample.2""" , """upsample.convolution_1""")
a__ : List[str] = """upsample.""" + name
elif config.upsampler == "pixelshuffledirect":
a__ : List[Any] = name.replace("""upsample.0.weight""" , """upsample.conv.weight""")
a__ : List[Any] = name.replace("""upsample.0.bias""" , """upsample.conv.bias""")
else:
pass
else:
a__ : Tuple = """swin2sr.""" + name
return name
def lowerCAmelCase_ ( _lowercase : List[Any] , _lowercase : List[Any]) -> Tuple:
"""simple docstring"""
for key in orig_state_dict.copy().keys():
a__ : int = orig_state_dict.pop(_lowercase)
if "qkv" in key:
a__ : Tuple = key.split(""".""")
a__ : Any = int(key_split[1])
a__ : Tuple = int(key_split[4])
a__ : List[Any] = config.embed_dim
if "weight" in key:
a__ : int = val[:dim, :]
a__ : str = val[dim : dim * 2, :]
a__ : Tuple = val[-dim:, :]
else:
a__ : Dict = val[:dim]
a__ : Optional[Any] = val[dim : dim * 2]
a__ : Optional[Any] = val[-dim:]
pass
else:
a__ : Optional[Any] = val
return orig_state_dict
def lowerCAmelCase_ ( _lowercase : Any , _lowercase : List[Any] , _lowercase : Union[str, Any]) -> Union[str, Any]:
"""simple docstring"""
a__ : List[Any] = get_config(_lowercase)
a__ : Optional[Any] = SwinaSRForImageSuperResolution(_lowercase)
model.eval()
a__ : List[Any] = torch.hub.load_state_dict_from_url(_lowercase , map_location="""cpu""")
a__ : Any = convert_state_dict(_lowercase , _lowercase)
a__ : Tuple = model.load_state_dict(_lowercase , strict=_lowercase)
if len(_lowercase) > 0:
raise ValueError("""Missing keys when converting: {}""".format(_lowercase))
for key in unexpected_keys:
if not ("relative_position_index" in key or "relative_coords_table" in key or "self_mask" in key):
raise ValueError(F'''Unexpected key {key} in state_dict''')
# verify values
a__ : int = """https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true"""
a__ : str = Image.open(requests.get(_lowercase , stream=_lowercase).raw).convert("""RGB""")
a__ : Any = SwinaSRImageProcessor()
# pixel_values = processor(image, return_tensors="pt").pixel_values
a__ : str = 126 if """Jpeg""" in checkpoint_url else 256
a__ : int = Compose(
[
Resize((image_size, image_size)),
ToTensor(),
Normalize(mean=[0.485, 0.456, 0.406] , std=[0.229, 0.224, 0.225]),
])
a__ : Optional[Any] = transforms(_lowercase).unsqueeze(0)
if config.num_channels == 1:
a__ : Tuple = pixel_values[:, 0, :, :].unsqueeze(1)
a__ : List[str] = model(_lowercase)
# assert values
if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
a__ : Optional[Any] = torch.Size([1, 3, 512, 512])
a__ : str = torch.tensor(
[[-0.7087, -0.7138, -0.6721], [-0.8340, -0.8095, -0.7298], [-0.9149, -0.8414, -0.7940]])
elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
a__ : Tuple = torch.Size([1, 3, 1024, 1024])
a__ : Any = torch.tensor(
[[-0.7775, -0.8105, -0.8933], [-0.7764, -0.8356, -0.9225], [-0.7976, -0.8686, -0.9579]])
elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
# TODO values didn't match exactly here
a__ : str = torch.Size([1, 3, 1024, 1024])
a__ : str = torch.tensor(
[[-0.8035, -0.7504, -0.7491], [-0.8538, -0.8124, -0.7782], [-0.8804, -0.8651, -0.8493]])
elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
a__ : List[Any] = torch.Size([1, 3, 512, 512])
a__ : Any = torch.tensor(
[[-0.7669, -0.8662, -0.8767], [-0.8810, -0.9962, -0.9820], [-0.9340, -1.0322, -1.1149]])
elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
a__ : Any = torch.Size([1, 3, 1024, 1024])
a__ : Tuple = torch.tensor(
[[-0.5238, -0.5557, -0.6321], [-0.6016, -0.5903, -0.6391], [-0.6244, -0.6334, -0.6889]])
assert (
outputs.reconstruction.shape == expected_shape
), F'''Shape of reconstruction should be {expected_shape}, but is {outputs.reconstruction.shape}'''
assert torch.allclose(outputs.reconstruction[0, 0, :3, :3] , _lowercase , atol=1e-3)
print("""Looks ok!""")
a__ : List[Any] = {
"""https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth""": (
"""swin2SR-classical-sr-x2-64"""
),
"""https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X4_64.pth""": (
"""swin2SR-classical-sr-x4-64"""
),
"""https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_CompressedSR_X4_48.pth""": (
"""swin2SR-compressed-sr-x4-48"""
),
"""https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_Lightweight_X2_64.pth""": (
"""swin2SR-lightweight-x2-64"""
),
"""https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR.pth""": (
"""swin2SR-realworld-sr-x4-64-bsrgan-psnr"""
),
}
a__ : Dict = url_to_name[checkpoint_url]
if pytorch_dump_folder_path is not None:
print(F'''Saving model {model_name} to {pytorch_dump_folder_path}''')
model.save_pretrained(_lowercase)
print(F'''Saving image processor to {pytorch_dump_folder_path}''')
processor.save_pretrained(_lowercase)
if push_to_hub:
model.push_to_hub(F'''caidas/{model_name}''')
processor.push_to_hub(F'''caidas/{model_name}''')
if __name__ == "__main__":
_lowercase : Any =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--checkpoint_url",
default="https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth",
type=str,
help="URL of the original Swin2SR checkpoint you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
parser.add_argument("--push_to_hub", action="store_true", help="Whether to push the converted model to the hub.")
_lowercase : Dict =parser.parse_args()
convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
| 358 |
import os
import unittest
from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOCAB_FILES_NAMES, ProphetNetTokenizer
from transformers.testing_utils import require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
class snake_case__ (A__ , unittest.TestCase ):
"""simple docstring"""
__lowerCAmelCase :Union[str, Any] = ProphetNetTokenizer
__lowerCAmelCase :Any = False
def SCREAMING_SNAKE_CASE__( self ) -> Union[str, Any]:
"""simple docstring"""
super().setUp()
a__ : Optional[Any] = [
"""[UNK]""",
"""[CLS]""",
"""[SEP]""",
"""[PAD]""",
"""[MASK]""",
"""want""",
"""##want""",
"""##ed""",
"""wa""",
"""un""",
"""runn""",
"""##ing""",
""",""",
"""low""",
"""lowest""",
]
a__ : List[str] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) )
def SCREAMING_SNAKE_CASE__( self , __lowercase ) -> str:
"""simple docstring"""
a__ : Any = """UNwant\u00E9d,running"""
a__ : Dict = """unwanted, running"""
return input_text, output_text
def SCREAMING_SNAKE_CASE__( self ) -> Dict:
"""simple docstring"""
a__ : Tuple = self.tokenizer_class(self.vocab_file )
a__ : int = tokenizer.tokenize("""UNwant\u00E9d,running""" )
self.assertListEqual(__lowercase , ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowercase ) , [9, 6, 7, 1_2, 1_0, 1_1] )
def SCREAMING_SNAKE_CASE__( self ) -> Union[str, Any]:
"""simple docstring"""
a__ : str = BasicTokenizer()
self.assertListEqual(tokenizer.tokenize("""ah\u535A\u63A8zz""" ) , ["""ah""", """\u535A""", """\u63A8""", """zz"""] )
def SCREAMING_SNAKE_CASE__( self ) -> int:
"""simple docstring"""
a__ : int = BasicTokenizer(do_lower_case=__lowercase )
self.assertListEqual(
tokenizer.tokenize(""" \tHeLLo!how \n Are yoU? """ ) , ["""hello""", """!""", """how""", """are""", """you""", """?"""] )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""hello"""] )
def SCREAMING_SNAKE_CASE__( self ) -> List[Any]:
"""simple docstring"""
a__ : str = BasicTokenizer(do_lower_case=__lowercase , strip_accents=__lowercase )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""hällo""", """!""", """how""", """are""", """you""", """?"""] )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""h\u00E9llo"""] )
def SCREAMING_SNAKE_CASE__( self ) -> str:
"""simple docstring"""
a__ : List[str] = BasicTokenizer(do_lower_case=__lowercase , strip_accents=__lowercase )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""hallo""", """!""", """how""", """are""", """you""", """?"""] )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""hello"""] )
def SCREAMING_SNAKE_CASE__( self ) -> List[Any]:
"""simple docstring"""
a__ : Optional[Any] = BasicTokenizer(do_lower_case=__lowercase )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""hallo""", """!""", """how""", """are""", """you""", """?"""] )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""hello"""] )
def SCREAMING_SNAKE_CASE__( self ) -> Optional[int]:
"""simple docstring"""
a__ : List[str] = BasicTokenizer(do_lower_case=__lowercase )
self.assertListEqual(
tokenizer.tokenize(""" \tHeLLo!how \n Are yoU? """ ) , ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?"""] )
def SCREAMING_SNAKE_CASE__( self ) -> List[str]:
"""simple docstring"""
a__ : str = BasicTokenizer(do_lower_case=__lowercase , strip_accents=__lowercase )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""HäLLo""", """!""", """how""", """Are""", """yoU""", """?"""] )
def SCREAMING_SNAKE_CASE__( self ) -> Optional[Any]:
"""simple docstring"""
a__ : List[str] = BasicTokenizer(do_lower_case=__lowercase , strip_accents=__lowercase )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""HaLLo""", """!""", """how""", """Are""", """yoU""", """?"""] )
def SCREAMING_SNAKE_CASE__( self ) -> List[Any]:
"""simple docstring"""
a__ : Union[str, Any] = BasicTokenizer(do_lower_case=__lowercase , never_split=["""[UNK]"""] )
self.assertListEqual(
tokenizer.tokenize(""" \tHeLLo!how \n Are yoU? [UNK]""" ) , ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?""", """[UNK]"""] )
def SCREAMING_SNAKE_CASE__( self ) -> Optional[Any]:
"""simple docstring"""
a__ : Optional[Any] = ["""[UNK]""", """[CLS]""", """[SEP]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing"""]
a__ : Dict = {}
for i, token in enumerate(__lowercase ):
a__ : Optional[Any] = i
a__ : str = WordpieceTokenizer(vocab=__lowercase , unk_token="""[UNK]""" )
self.assertListEqual(tokenizer.tokenize("""""" ) , [] )
self.assertListEqual(tokenizer.tokenize("""unwanted running""" ) , ["""un""", """##want""", """##ed""", """runn""", """##ing"""] )
self.assertListEqual(tokenizer.tokenize("""unwantedX running""" ) , ["""[UNK]""", """runn""", """##ing"""] )
@require_torch
def SCREAMING_SNAKE_CASE__( self ) -> List[Any]:
"""simple docstring"""
a__ : List[Any] = self.tokenizer_class.from_pretrained("""microsoft/prophetnet-large-uncased""" )
a__ : List[Any] = ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
a__ : Optional[Any] = [1_0_3_7, 2_1_4_6, 2_0_4_2_3, 2_0_0_5, 7_6_8_0, 7_8_4_9, 3_9_8_9, 1_0_1_2, 1_0_2]
a__ : List[Any] = tokenizer(__lowercase , padding=__lowercase , return_tensors="""pt""" )
self.assertIsInstance(__lowercase , __lowercase )
a__ : Optional[int] = list(batch.input_ids.numpy()[0] )
self.assertListEqual(__lowercase , __lowercase )
self.assertEqual((2, 9) , batch.input_ids.shape )
self.assertEqual((2, 9) , batch.attention_mask.shape )
def SCREAMING_SNAKE_CASE__( self ) -> List[str]:
"""simple docstring"""
self.assertTrue(_is_whitespace(""" """ ) )
self.assertTrue(_is_whitespace("""\t""" ) )
self.assertTrue(_is_whitespace("""\r""" ) )
self.assertTrue(_is_whitespace("""\n""" ) )
self.assertTrue(_is_whitespace("""\u00A0""" ) )
self.assertFalse(_is_whitespace("""A""" ) )
self.assertFalse(_is_whitespace("""-""" ) )
def SCREAMING_SNAKE_CASE__( self ) -> Optional[Any]:
"""simple docstring"""
self.assertTrue(_is_control("""\u0005""" ) )
self.assertFalse(_is_control("""A""" ) )
self.assertFalse(_is_control(""" """ ) )
self.assertFalse(_is_control("""\t""" ) )
self.assertFalse(_is_control("""\r""" ) )
def SCREAMING_SNAKE_CASE__( self ) -> Optional[Any]:
"""simple docstring"""
self.assertTrue(_is_punctuation("""-""" ) )
self.assertTrue(_is_punctuation("""$""" ) )
self.assertTrue(_is_punctuation("""`""" ) )
self.assertTrue(_is_punctuation(""".""" ) )
self.assertFalse(_is_punctuation("""A""" ) )
self.assertFalse(_is_punctuation(""" """ ) )
@slow
def SCREAMING_SNAKE_CASE__( self ) -> Optional[int]:
"""simple docstring"""
a__ : Optional[Any] = self.tokenizer_class.from_pretrained("""microsoft/prophetnet-large-uncased""" )
a__ : Dict = tokenizer.encode("""sequence builders""" , add_special_tokens=__lowercase )
a__ : str = tokenizer.encode("""multi-sequence build""" , add_special_tokens=__lowercase )
a__ : Optional[Any] = tokenizer.build_inputs_with_special_tokens(__lowercase )
a__ : Optional[int] = tokenizer.build_inputs_with_special_tokens(__lowercase , __lowercase )
assert encoded_sentence == text + [1_0_2]
assert encoded_pair == text + [1_0_2] + text_a + [1_0_2]
| 266 | 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 _lowerCamelCase( _a ):
lowercase_ : Optional[int] = ["""image_processor""", """tokenizer"""]
lowercase_ : Dict = """BlipImageProcessor"""
lowercase_ : List[str] = """AutoTokenizer"""
def __init__( self, lowerCamelCase, lowerCamelCase, lowerCamelCase) -> List[Any]:
"""simple docstring"""
super().__init__(lowerCamelCase, lowerCamelCase)
# add QFormer tokenizer
_lowercase : int = qformer_tokenizer
def __call__( self, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = True, lowerCamelCase = False, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = 0, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = False, lowerCamelCase = False, lowerCamelCase = False, lowerCamelCase = False, lowerCamelCase = False, lowerCamelCase = True, lowerCamelCase = None, **lowerCamelCase, ) -> BatchFeature:
"""simple docstring"""
if images is None and text is None:
raise ValueError('You have to specify at least images or text.')
_lowercase : int = BatchFeature()
if text is not None:
_lowercase : Union[str, Any] = 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, )
encoding.update(lowerCamelCase)
_lowercase : str = self.qformer_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, )
_lowercase : Tuple = qformer_text_encoding.pop('input_ids')
_lowercase : Tuple = qformer_text_encoding.pop('attention_mask')
if images is not None:
_lowercase : Any = self.image_processor(lowerCamelCase, return_tensors=lowerCamelCase)
encoding.update(lowerCamelCase)
return encoding
def UpperCamelCase ( self, *lowerCamelCase, **lowerCamelCase) -> int:
"""simple docstring"""
return self.tokenizer.batch_decode(*lowerCamelCase, **lowerCamelCase)
def UpperCamelCase ( self, *lowerCamelCase, **lowerCamelCase) -> int:
"""simple docstring"""
return self.tokenizer.decode(*lowerCamelCase, **lowerCamelCase)
@property
# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
def UpperCamelCase ( self) -> Union[str, Any]:
"""simple docstring"""
_lowercase : Optional[int] = self.tokenizer.model_input_names
_lowercase : Union[str, Any] = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
def UpperCamelCase ( self, lowerCamelCase, **lowerCamelCase) -> Optional[Any]:
"""simple docstring"""
if os.path.isfile(lowerCamelCase):
raise ValueError(F'''Provided path ({save_directory}) should be a directory, not a file''')
os.makedirs(lowerCamelCase, exist_ok=lowerCamelCase)
_lowercase : Tuple = os.path.join(lowerCamelCase, 'qformer_tokenizer')
self.qformer_tokenizer.save_pretrained(lowerCamelCase)
return super().save_pretrained(lowerCamelCase, **lowerCamelCase)
@classmethod
def UpperCamelCase ( cls, lowerCamelCase, **lowerCamelCase) -> str:
"""simple docstring"""
_lowercase : Optional[Any] = AutoTokenizer.from_pretrained(lowerCamelCase, subfolder='qformer_tokenizer')
_lowercase : Any = cls._get_arguments_from_pretrained(lowerCamelCase, **lowerCamelCase)
args.append(lowerCamelCase)
return cls(*lowerCamelCase)
| 21 |
"""simple docstring"""
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def A_ ( _lowercase ):
'''simple docstring'''
snake_case_ :Union[str, Any] = os.path.join(args.tf_model_dir, """parameters.json""" )
snake_case_ :Any = json.loads(open(_lowercase ).read() )
if not params:
raise ValueError(
f"""It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file.""" )
if not args.output.endswith(""".pt""" ):
snake_case_ :Optional[int] = args.output + """.pt"""
snake_case_ :List[str] = OrderedDict()
with tf.device("""/CPU:0""" ):
snake_case_ :Dict = tf.train.load_checkpoint(args.tf_model_dir )
snake_case_ :str = reader.get_variable_to_shape_map()
for key_name in shapes.keys():
snake_case_ :List[Any] = reader.get_tensor(_lowercase ).astype(np.floataa )
if key_name.endswith("""/adam_m""" ) or key_name.endswith("""/adam_v""" ):
continue
if key_name.startswith("""pasts/""" ):
if key_name.startswith("""pasts/mlp""" ):
snake_case_ :Any = int(key_name[9] )
elif key_name.startswith("""pasts/out""" ):
snake_case_ :Optional[int] = 8
snake_case_ :List[str] = """model.sqout.%d.weight""" % (player * 2) # enter to nn.Sequencial with Tanh, so 2 at a time
snake_case_ :Optional[Any] = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
snake_case_ :List[str] = torch.tensor(_lowercase )
elif key_name.startswith("""model/moe""" ):
snake_case_ :Tuple = int(key_name[9:].split("""/""" )[0] )
if key_name.endswith("""/switch_gating/kernel""" ):
snake_case_ :Union[str, Any] = """model.blocks.%d.feed_forward.mlp.router.classifier.weight""" % player
snake_case_ :Optional[Any] = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
snake_case_ :Optional[Any] = torch.tensor(_lowercase )
elif key_name.endswith("""/softmlp/kernel""" ):
snake_case_ :List[Any] = """model.blocks.%d.feed_forward.soft_bypass_mlp.weight""" % player
snake_case_ :Optional[int] = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
snake_case_ :Optional[Any] = torch.tensor(_lowercase )
elif key_name.endswith("""/wo/kernel""" ) or key_name.endswith("""/wi/kernel""" ):
snake_case_ :Dict = key_name[-9:-7]
for i in range(16 ):
snake_case_ :str = """model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight""" % (player, i, nlayer)
snake_case_ :Tuple = (
vnp[i].transpose([1, 0] ).copy()
) # In Mesh-Tensorflow, it is one array, so it is divided
snake_case_ :Optional[int] = torch.tensor(_lowercase )
elif key_name.startswith("""model/mlp""" ):
snake_case_ :Optional[int] = int(key_name[9:].split("""/""" )[0] )
if key_name.endswith("""/p1/kernel""" ):
snake_case_ :Union[str, Any] = """model.blocks.%d.feed_forward.mlp.wi.weight""" % player
snake_case_ :Dict = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
snake_case_ :Optional[Any] = torch.tensor(_lowercase )
elif key_name.endswith("""/p1/bias""" ):
snake_case_ :List[Any] = """model.blocks.%d.feed_forward.mlp.wi.bias""" % player
snake_case_ :str = vnp.copy() # same because it is one dimensional
snake_case_ :Optional[Any] = torch.tensor(_lowercase )
elif key_name.endswith("""/p2/kernel""" ):
snake_case_ :Union[str, Any] = """model.blocks.%d.feed_forward.mlp.wo.weight""" % player
snake_case_ :Dict = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
snake_case_ :Tuple = torch.tensor(_lowercase )
elif key_name.endswith("""/p2/bias""" ):
snake_case_ :Dict = """model.blocks.%d.feed_forward.mlp.wo.bias""" % player
snake_case_ :Any = vnp.copy() # same because it is one dimensional
snake_case_ :Optional[int] = torch.tensor(_lowercase )
elif key_name.startswith("""model/ln""" ):
snake_case_ :Union[str, Any] = int(key_name[8:].split("""/""" )[0] )
if key_name.endswith("""/b""" ):
snake_case_ :str = """model.blocks.%d.feed_forward.norm.bias""" % player
snake_case_ :Dict = vnp.copy() # same because it is one dimensional
snake_case_ :int = torch.tensor(_lowercase )
elif key_name.endswith("""/g""" ):
snake_case_ :Dict = """model.blocks.%d.feed_forward.norm.weight""" % player
snake_case_ :Dict = vnp.copy() # same because it is one dimensional
snake_case_ :Tuple = torch.tensor(_lowercase )
elif key_name.startswith("""model/att""" ):
snake_case_ :List[str] = int(key_name[9:].split("""/""" )[0] )
if key_name.endswith("""/qkv/kernel""" ):
snake_case_ :Optional[int] = vnp.copy() # Compute same dimension as Mesh-tensorflow using einsum
snake_case_ :Dict = state[:, 0, :, :]
snake_case_ :int = state[:, 1, :, :]
snake_case_ :List[str] = state[:, 2, :, :]
snake_case_ :str = (
state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]] )
.transpose([1, 0] )
.copy()
) # Mesh-Tensorflow is a diagonal matrix
snake_case_ :Any = (
state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]] )
.transpose([1, 0] )
.copy()
) # Mesh-Tensorflow is a diagonal matrix
snake_case_ :Optional[int] = (
state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]] )
.transpose([1, 0] )
.copy()
) # Mesh-Tensorflow is a diagonal matrix
snake_case_ :int = """model.blocks.%d.self_attn.self_attn.q_proj.weight""" % player
snake_case_ :int = torch.tensor(_lowercase )
snake_case_ :Optional[Any] = """model.blocks.%d.self_attn.self_attn.k_proj.weight""" % player
snake_case_ :Dict = torch.tensor(_lowercase )
snake_case_ :Dict = """model.blocks.%d.self_attn.self_attn.v_proj.weight""" % player
snake_case_ :Optional[Any] = torch.tensor(_lowercase )
elif key_name.endswith("""/o/kernel""" ):
snake_case_ :str = """model.blocks.%d.self_attn.self_attn.out_proj.weight""" % player
snake_case_ :str = (
vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]] ).transpose([1, 0] ).copy()
) # Mesh-Tensorflow is a diagonal matrix
snake_case_ :Any = torch.tensor(_lowercase )
elif key_name.startswith("""model/an""" ):
snake_case_ :Optional[int] = int(key_name[8:].split("""/""" )[0] )
if key_name.endswith("""/b""" ):
snake_case_ :Any = """model.blocks.%d.self_attn.norm.bias""" % player
snake_case_ :Optional[int] = vnp.copy() # same because it is one dimensional
snake_case_ :Tuple = torch.tensor(_lowercase )
elif key_name.endswith("""/g""" ):
snake_case_ :Union[str, Any] = """model.blocks.%d.self_attn.norm.weight""" % player
snake_case_ :Dict = vnp.copy() # same because it is one dimensional
snake_case_ :Optional[int] = torch.tensor(_lowercase )
elif (
key_name.startswith("""model/wte""" )
or key_name.startswith("""model/wpe""" )
or key_name.startswith("""model/ete""" )
):
snake_case_ :List[Any] = {"""wte""": """embed_tokens""", """wpe""": """position_embeddings""", """ete""": """extra_position_embeddings"""}[
key_name[-3:]
]
snake_case_ :Optional[Any] = """model.%s.weight""" % nlayer
snake_case_ :Any = vnp.copy() # same in embedded
snake_case_ :List[Any] = torch.tensor(_lowercase )
if key_name.startswith("""model/wte""" ):
snake_case_ :Tuple = """lm_head.weight"""
snake_case_ :List[str] = vnp.copy() # same in embedded
snake_case_ :List[Any] = torch.tensor(_lowercase )
elif key_name.startswith("""model/wob""" ):
snake_case_ :str = """final_logits_bias"""
snake_case_ :Any = vnp.copy() # same in embedded
snake_case_ :List[Any] = state.reshape((1, -1) )
snake_case_ :Union[str, Any] = torch.tensor(_lowercase )
elif key_name == "model/dense/kernel":
snake_case_ :str = """model.last_project.weight"""
snake_case_ :Dict = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
snake_case_ :int = torch.tensor(_lowercase )
elif key_name == "model/dense_1/bias":
snake_case_ :Optional[int] = """model.last_project.bias"""
snake_case_ :Tuple = vnp.copy() # same because it is one dimensional
snake_case_ :Any = torch.tensor(_lowercase )
torch.save(_lowercase, args.output )
if __name__ == "__main__":
__a = argparse.ArgumentParser(
description="model converter.", formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
parser.add_argument("--tf_model_dir", metavar="PATH", type=str, required=True, help="import model")
parser.add_argument("--output", metavar="PATH", type=str, required=True, help="output model")
__a = parser.parse_args()
convert_tf_gptsan_to_pt(args)
| 66 | 0 |
def __magic_name__ ( __lowerCAmelCase : int , __lowerCAmelCase : int ) -> int:
return abs(__lowerCAmelCase ) if a == 0 else greatest_common_divisor(b % a , __lowerCAmelCase )
def __magic_name__ ( __lowerCAmelCase : int , __lowerCAmelCase : int ) -> int:
while y: # --> when y=0 then loop will terminate and return x as final GCD.
__lowerCamelCase , __lowerCamelCase = y, x % y
return abs(__lowerCAmelCase )
def __magic_name__ ( ) -> Tuple:
try:
__lowerCamelCase = input('''Enter two integers separated by comma (,): ''' ).split(''',''' )
__lowerCamelCase = int(nums[0] )
__lowerCamelCase = int(nums[1] )
print(
f'''greatest_common_divisor({num_a}, {num_a}) = '''
f'''{greatest_common_divisor(__lowerCAmelCase , __lowerCAmelCase )}''' )
print(f'''By iterative gcd({num_a}, {num_a}) = {gcd_by_iterative(__lowerCAmelCase , __lowerCAmelCase )}''' )
except (IndexError, UnboundLocalError, ValueError):
print('''Wrong input''' )
if __name__ == "__main__":
main()
| 368 |
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
SCREAMING_SNAKE_CASE__ : Tuple = collections.namedtuple("_Datasets", ["train", "validation", "test"])
# CVDF mirror of http://yann.lecun.com/exdb/mnist/
SCREAMING_SNAKE_CASE__ : List[str] = "https://storage.googleapis.com/cvdf-datasets/mnist/"
def __magic_name__ ( __lowerCAmelCase : Any ) -> int:
__lowerCamelCase = numpy.dtype(numpy.uintaa ).newbyteorder('''>''' )
return numpy.frombuffer(bytestream.read(4 ) , dtype=__lowerCAmelCase )[0]
@deprecated(__lowerCAmelCase , '''Please use tf.data to implement this functionality.''' )
def __magic_name__ ( __lowerCAmelCase : List[Any] ) -> str:
print('''Extracting''' , f.name )
with gzip.GzipFile(fileobj=__lowerCAmelCase ) as bytestream:
__lowerCamelCase = _readaa(__lowerCAmelCase )
if magic != 2051:
raise ValueError(
'''Invalid magic number %d in MNIST image file: %s''' % (magic, f.name) )
__lowerCamelCase = _readaa(__lowerCAmelCase )
__lowerCamelCase = _readaa(__lowerCAmelCase )
__lowerCamelCase = _readaa(__lowerCAmelCase )
__lowerCamelCase = bytestream.read(rows * cols * num_images )
__lowerCamelCase = numpy.frombuffer(__lowerCAmelCase , dtype=numpy.uinta )
__lowerCamelCase = data.reshape(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , 1 )
return data
@deprecated(__lowerCAmelCase , '''Please use tf.one_hot on tensors.''' )
def __magic_name__ ( __lowerCAmelCase : int , __lowerCAmelCase : List[Any] ) -> Dict:
__lowerCamelCase = labels_dense.shape[0]
__lowerCamelCase = numpy.arange(__lowerCAmelCase ) * num_classes
__lowerCamelCase = numpy.zeros((num_labels, num_classes) )
__lowerCamelCase = 1
return labels_one_hot
@deprecated(__lowerCAmelCase , '''Please use tf.data to implement this functionality.''' )
def __magic_name__ ( __lowerCAmelCase : Any , __lowerCAmelCase : str=False , __lowerCAmelCase : List[str]=10 ) -> List[str]:
print('''Extracting''' , f.name )
with gzip.GzipFile(fileobj=__lowerCAmelCase ) as bytestream:
__lowerCamelCase = _readaa(__lowerCAmelCase )
if magic != 2049:
raise ValueError(
'''Invalid magic number %d in MNIST label file: %s''' % (magic, f.name) )
__lowerCamelCase = _readaa(__lowerCAmelCase )
__lowerCamelCase = bytestream.read(__lowerCAmelCase )
__lowerCamelCase = numpy.frombuffer(__lowerCAmelCase , dtype=numpy.uinta )
if one_hot:
return _dense_to_one_hot(__lowerCAmelCase , __lowerCAmelCase )
return labels
class lowerCAmelCase__ :
@deprecated(
SCREAMING_SNAKE_CASE__ , '''Please use alternatives such as official/mnist/_DataSet.py'''
''' from tensorflow/models.''' , )
def __init__( self : str , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : Optional[Any]=False , SCREAMING_SNAKE_CASE__ : List[str]=False , SCREAMING_SNAKE_CASE__ : str=dtypes.floataa , SCREAMING_SNAKE_CASE__ : Tuple=True , SCREAMING_SNAKE_CASE__ : str=None , ) -> Optional[int]:
__lowerCamelCase , __lowerCamelCase = random_seed.get_seed(SCREAMING_SNAKE_CASE__ )
# If op level seed is not set, use whatever graph level seed is returned
numpy.random.seed(seeda if seed is None else seeda )
__lowerCamelCase = dtypes.as_dtype(SCREAMING_SNAKE_CASE__ ).base_dtype
if dtype not in (dtypes.uinta, dtypes.floataa):
raise TypeError('''Invalid image dtype %r, expected uint8 or float32''' % dtype )
if fake_data:
__lowerCamelCase = 1_00_00
__lowerCamelCase = one_hot
else:
assert (
images.shape[0] == labels.shape[0]
), f'''images.shape: {images.shape} labels.shape: {labels.shape}'''
__lowerCamelCase = images.shape[0]
# Convert shape from [num examples, rows, columns, depth]
# to [num examples, rows*columns] (assuming depth == 1)
if reshape:
assert images.shape[3] == 1
__lowerCamelCase = images.reshape(
images.shape[0] , images.shape[1] * images.shape[2] )
if dtype == dtypes.floataa:
# Convert from [0, 255] -> [0.0, 1.0].
__lowerCamelCase = images.astype(numpy.floataa )
__lowerCamelCase = numpy.multiply(SCREAMING_SNAKE_CASE__ , 1.0 / 255.0 )
__lowerCamelCase = images
__lowerCamelCase = labels
__lowerCamelCase = 0
__lowerCamelCase = 0
@property
def __A ( self : str ) -> Optional[int]:
return self._images
@property
def __A ( self : Any ) -> Dict:
return self._labels
@property
def __A ( self : List[Any] ) -> int:
return self._num_examples
@property
def __A ( self : str ) -> Any:
return self._epochs_completed
def __A ( self : int , SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Optional[Any]=False , SCREAMING_SNAKE_CASE__ : str=True ) -> str:
if fake_data:
__lowerCamelCase = [1] * 7_84
__lowerCamelCase = [1] + [0] * 9 if self.one_hot else 0
return (
[fake_image for _ in range(SCREAMING_SNAKE_CASE__ )],
[fake_label for _ in range(SCREAMING_SNAKE_CASE__ )],
)
__lowerCamelCase = self._index_in_epoch
# Shuffle for the first epoch
if self._epochs_completed == 0 and start == 0 and shuffle:
__lowerCamelCase = numpy.arange(self._num_examples )
numpy.random.shuffle(SCREAMING_SNAKE_CASE__ )
__lowerCamelCase = self.images[perma]
__lowerCamelCase = self.labels[perma]
# Go to the next epoch
if start + batch_size > self._num_examples:
# Finished epoch
self._epochs_completed += 1
# Get the rest examples in this epoch
__lowerCamelCase = self._num_examples - start
__lowerCamelCase = self._images[start : self._num_examples]
__lowerCamelCase = self._labels[start : self._num_examples]
# Shuffle the data
if shuffle:
__lowerCamelCase = numpy.arange(self._num_examples )
numpy.random.shuffle(SCREAMING_SNAKE_CASE__ )
__lowerCamelCase = self.images[perm]
__lowerCamelCase = self.labels[perm]
# Start next epoch
__lowerCamelCase = 0
__lowerCamelCase = batch_size - rest_num_examples
__lowerCamelCase = self._index_in_epoch
__lowerCamelCase = self._images[start:end]
__lowerCamelCase = self._labels[start:end]
return (
numpy.concatenate((images_rest_part, images_new_part) , axis=0 ),
numpy.concatenate((labels_rest_part, labels_new_part) , axis=0 ),
)
else:
self._index_in_epoch += batch_size
__lowerCamelCase = self._index_in_epoch
return self._images[start:end], self._labels[start:end]
@deprecated(__lowerCAmelCase , '''Please write your own downloading logic.''' )
def __magic_name__ ( __lowerCAmelCase : int , __lowerCAmelCase : int , __lowerCAmelCase : Union[str, Any] ) -> List[Any]:
if not gfile.Exists(__lowerCAmelCase ):
gfile.MakeDirs(__lowerCAmelCase )
__lowerCamelCase = os.path.join(__lowerCAmelCase , __lowerCAmelCase )
if not gfile.Exists(__lowerCAmelCase ):
urllib.request.urlretrieve(__lowerCAmelCase , __lowerCAmelCase ) # noqa: S310
with gfile.GFile(__lowerCAmelCase ) as f:
__lowerCamelCase = f.size()
print('''Successfully downloaded''' , __lowerCAmelCase , __lowerCAmelCase , '''bytes.''' )
return filepath
@deprecated(
__lowerCAmelCase , '''Please use alternatives such as:''' ''' tensorflow_datasets.load(\'mnist\')''' )
def __magic_name__ ( __lowerCAmelCase : Tuple , __lowerCAmelCase : Optional[Any]=False , __lowerCAmelCase : Dict=False , __lowerCAmelCase : List[str]=dtypes.floataa , __lowerCAmelCase : Union[str, Any]=True , __lowerCAmelCase : int=5000 , __lowerCAmelCase : Any=None , __lowerCAmelCase : List[str]=DEFAULT_SOURCE_URL , ) -> Optional[Any]:
if fake_data:
def fake():
return _DataSet(
[] , [] , fake_data=__lowerCAmelCase , one_hot=__lowerCAmelCase , dtype=__lowerCAmelCase , seed=__lowerCAmelCase )
__lowerCamelCase = fake()
__lowerCamelCase = fake()
__lowerCamelCase = fake()
return _Datasets(train=__lowerCAmelCase , validation=__lowerCAmelCase , test=__lowerCAmelCase )
if not source_url: # empty string check
__lowerCamelCase = DEFAULT_SOURCE_URL
__lowerCamelCase = '''train-images-idx3-ubyte.gz'''
__lowerCamelCase = '''train-labels-idx1-ubyte.gz'''
__lowerCamelCase = '''t10k-images-idx3-ubyte.gz'''
__lowerCamelCase = '''t10k-labels-idx1-ubyte.gz'''
__lowerCamelCase = _maybe_download(
__lowerCAmelCase , __lowerCAmelCase , source_url + train_images_file )
with gfile.Open(__lowerCAmelCase , '''rb''' ) as f:
__lowerCamelCase = _extract_images(__lowerCAmelCase )
__lowerCamelCase = _maybe_download(
__lowerCAmelCase , __lowerCAmelCase , source_url + train_labels_file )
with gfile.Open(__lowerCAmelCase , '''rb''' ) as f:
__lowerCamelCase = _extract_labels(__lowerCAmelCase , one_hot=__lowerCAmelCase )
__lowerCamelCase = _maybe_download(
__lowerCAmelCase , __lowerCAmelCase , source_url + test_images_file )
with gfile.Open(__lowerCAmelCase , '''rb''' ) as f:
__lowerCamelCase = _extract_images(__lowerCAmelCase )
__lowerCamelCase = _maybe_download(
__lowerCAmelCase , __lowerCAmelCase , source_url + test_labels_file )
with gfile.Open(__lowerCAmelCase , '''rb''' ) as f:
__lowerCamelCase = _extract_labels(__lowerCAmelCase , one_hot=__lowerCAmelCase )
if not 0 <= validation_size <= len(__lowerCAmelCase ):
__lowerCamelCase = (
'''Validation size should be between 0 and '''
f'''{len(__lowerCAmelCase )}. Received: {validation_size}.'''
)
raise ValueError(__lowerCAmelCase )
__lowerCamelCase = train_images[:validation_size]
__lowerCamelCase = train_labels[:validation_size]
__lowerCamelCase = train_images[validation_size:]
__lowerCamelCase = train_labels[validation_size:]
__lowerCamelCase = {'''dtype''': dtype, '''reshape''': reshape, '''seed''': seed}
__lowerCamelCase = _DataSet(__lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase )
__lowerCamelCase = _DataSet(__lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase )
__lowerCamelCase = _DataSet(__lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase )
return _Datasets(train=__lowerCAmelCase , validation=__lowerCAmelCase , test=__lowerCAmelCase )
| 339 | 0 |
import json
import os
import unittest
from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class _a ( UpperCamelCase__ , unittest.TestCase ):
_lowercase : str = DebertaTokenizer
_lowercase : Any = True
_lowercase : Union[str, Any] = DebertaTokenizerFast
def lowerCamelCase_ ( self: Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
lowercase__ = [
'''l''',
'''o''',
'''w''',
'''e''',
'''r''',
'''s''',
'''t''',
'''i''',
'''d''',
'''n''',
'''\u0120''',
'''\u0120l''',
'''\u0120n''',
'''\u0120lo''',
'''\u0120low''',
'''er''',
'''\u0120lowest''',
'''\u0120newer''',
'''\u0120wider''',
'''[UNK]''',
]
lowercase__ = dict(zip(UpperCamelCase_ , range(len(UpperCamelCase_ ) ) ) )
lowercase__ = ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
lowercase__ = {'''unk_token''': '''[UNK]'''}
lowercase__ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
lowercase__ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''merges_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(UpperCamelCase_ ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(UpperCamelCase_ ) )
def lowerCamelCase_ ( self: Union[str, Any] , **UpperCamelCase_: Union[str, Any] ) -> Dict:
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname , **UpperCamelCase_ )
def lowerCamelCase_ ( self: List[str] , UpperCamelCase_: int ) -> Tuple:
"""simple docstring"""
lowercase__ = '''lower newer'''
lowercase__ = '''lower newer'''
return input_text, output_text
def lowerCamelCase_ ( self: Optional[int] ) -> Any:
"""simple docstring"""
lowercase__ = self.get_tokenizer()
lowercase__ = '''lower newer'''
lowercase__ = ['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
lowercase__ = tokenizer.tokenize(UpperCamelCase_ )
self.assertListEqual(UpperCamelCase_ , UpperCamelCase_ )
lowercase__ = tokens + [tokenizer.unk_token]
lowercase__ = [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase_ ) , UpperCamelCase_ )
def lowerCamelCase_ ( self: List[Any] ) -> Tuple:
"""simple docstring"""
lowercase__ = self.get_tokenizer()
lowercase__ = tokenizer('''Hello''' , '''World''' )
lowercase__ = [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
self.assertListEqual(tokd['''token_type_ids'''] , UpperCamelCase_ )
@slow
def lowerCamelCase_ ( self: int ) -> List[str]:
"""simple docstring"""
lowercase__ = self.tokenizer_class.from_pretrained('''microsoft/deberta-base''' )
lowercase__ = tokenizer.encode('''sequence builders''' , add_special_tokens=UpperCamelCase_ )
lowercase__ = tokenizer.encode('''multi-sequence build''' , add_special_tokens=UpperCamelCase_ )
lowercase__ = tokenizer.encode(
'''sequence builders''' , add_special_tokens=UpperCamelCase_ , add_prefix_space=UpperCamelCase_ )
lowercase__ = tokenizer.encode(
'''sequence builders''' , '''multi-sequence build''' , add_special_tokens=UpperCamelCase_ , add_prefix_space=UpperCamelCase_ )
lowercase__ = tokenizer.build_inputs_with_special_tokens(UpperCamelCase_ )
lowercase__ = tokenizer.build_inputs_with_special_tokens(UpperCamelCase_ , UpperCamelCase_ )
assert encoded_sentence == encoded_text_from_decode
assert encoded_pair == encoded_pair_from_decode
@slow
def lowerCamelCase_ ( self: str ) -> Dict:
"""simple docstring"""
lowercase__ = [self.tokenizer_class]
if self.test_rust_tokenizer:
tokenizer_classes.append(self.rust_tokenizer_class )
for tokenizer_class in tokenizer_classes:
lowercase__ = tokenizer_class.from_pretrained('''microsoft/deberta-base''' )
lowercase__ = [
'''ALBERT: A Lite BERT for Self-supervised Learning of Language Representations''',
'''ALBERT incorporates two parameter reduction techniques''',
'''The first one is a factorized embedding parameterization. By decomposing the large vocabulary'''
''' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'''
''' vocabulary embedding.''',
]
lowercase__ = tokenizer(UpperCamelCase_ , padding=UpperCamelCase_ )
lowercase__ = [tokenizer.decode(UpperCamelCase_ , skip_special_tokens=UpperCamelCase_ ) for seq in encoding['''input_ids''']]
# fmt: off
lowercase__ = {
'''input_ids''': [
[1, 2_118, 11_126, 565, 35, 83, 25_191, 163, 18_854, 13, 12_156, 12, 16_101, 25_376, 13_807, 9, 22_205, 27_893, 1_635, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 2_118, 11_126, 565, 24_536, 80, 43_797, 4_878, 7_373, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 133, 78, 65, 16, 10, 3_724, 1_538, 33_183, 11_303, 43_797, 1_938, 4, 870, 24_165, 29_105, 5, 739, 32_644, 33_183, 11_303, 36_173, 88, 80, 650, 7_821, 45_940, 6, 52, 2_559, 5, 1_836, 9, 5, 7_397, 13_171, 31, 5, 1_836, 9, 32_644, 33_183, 11_303, 4, 2]
],
'''token_type_ids''': [
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
],
'''attention_mask''': [
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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]
]
}
# fmt: on
lowercase__ = [
'''ALBERT: A Lite BERT for Self-supervised Learning of Language Representations''',
'''ALBERT incorporates two parameter reduction techniques''',
'''The first one is a factorized embedding parameterization. By decomposing the large vocabulary'''
''' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'''
''' vocabulary embedding.''',
]
self.assertDictEqual(encoding.data , UpperCamelCase_ )
for expected, decoded in zip(UpperCamelCase_ , UpperCamelCase_ ):
self.assertEqual(UpperCamelCase_ , UpperCamelCase_ )
| 110 |
from __future__ import annotations
class _a :
def __init__( self: Union[str, Any] , UpperCamelCase_: str , UpperCamelCase_: str ) -> List[Any]:
"""simple docstring"""
lowercase__ , lowercase__ = text, pattern
lowercase__ , lowercase__ = len(UpperCamelCase_ ), len(UpperCamelCase_ )
def lowerCamelCase_ ( self: Optional[int] , UpperCamelCase_: str ) -> int:
"""simple docstring"""
for i in range(self.patLen - 1 , -1 , -1 ):
if char == self.pattern[i]:
return i
return -1
def lowerCamelCase_ ( self: Optional[int] , UpperCamelCase_: int ) -> int:
"""simple docstring"""
for i in range(self.patLen - 1 , -1 , -1 ):
if self.pattern[i] != self.text[current_pos + i]:
return current_pos + i
return -1
def lowerCamelCase_ ( self: List[Any] ) -> list[int]:
"""simple docstring"""
lowercase__ = []
for i in range(self.textLen - self.patLen + 1 ):
lowercase__ = self.mismatch_in_text(UpperCamelCase_ )
if mismatch_index == -1:
positions.append(UpperCamelCase_ )
else:
lowercase__ = self.match_in_pattern(self.text[mismatch_index] )
lowercase__ = (
mismatch_index - match_index
) # shifting index lgtm [py/multiple-definition]
return positions
lowerCAmelCase = 'ABAABA'
lowerCAmelCase = 'AB'
lowerCAmelCase = BoyerMooreSearch(text, pattern)
lowerCAmelCase = bms.bad_character_heuristic()
if len(positions) == 0:
print('No match found')
else:
print('Pattern found in following positions: ')
print(positions)
| 110 | 1 |
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
_lowerCamelCase : List[Any] = logging.get_logger(__name__)
_lowerCamelCase : str = {
'''EleutherAI/gpt-j-6B''': '''https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json''',
# See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}
class __snake_case (lowerCamelCase_ ):
lowerCAmelCase__ = '''gptj'''
lowerCAmelCase__ = {
'''max_position_embeddings''': '''n_positions''',
'''hidden_size''': '''n_embd''',
'''num_attention_heads''': '''n_head''',
'''num_hidden_layers''': '''n_layer''',
}
def __init__( self : Dict , _UpperCAmelCase : List[Any]=5_0400 , _UpperCAmelCase : int=2048 , _UpperCAmelCase : List[str]=4096 , _UpperCAmelCase : List[Any]=28 , _UpperCAmelCase : List[Any]=16 , _UpperCAmelCase : Tuple=64 , _UpperCAmelCase : str=None , _UpperCAmelCase : str="gelu_new" , _UpperCAmelCase : Dict=0.0 , _UpperCAmelCase : int=0.0 , _UpperCAmelCase : Optional[Any]=0.0 , _UpperCAmelCase : Tuple=1E-5 , _UpperCAmelCase : Optional[int]=0.02 , _UpperCAmelCase : Union[str, Any]=True , _UpperCAmelCase : List[str]=5_0256 , _UpperCAmelCase : Union[str, Any]=5_0256 , _UpperCAmelCase : str=False , **_UpperCAmelCase : Union[str, Any] , ) -> Dict:
'''simple docstring'''
_lowerCAmelCase : Tuple = vocab_size
_lowerCAmelCase : Union[str, Any] = n_positions
_lowerCAmelCase : Tuple = n_embd
_lowerCAmelCase : int = n_layer
_lowerCAmelCase : str = n_head
_lowerCAmelCase : int = n_inner
_lowerCAmelCase : str = rotary_dim
_lowerCAmelCase : Tuple = activation_function
_lowerCAmelCase : str = resid_pdrop
_lowerCAmelCase : Union[str, Any] = embd_pdrop
_lowerCAmelCase : Any = attn_pdrop
_lowerCAmelCase : List[Any] = layer_norm_epsilon
_lowerCAmelCase : Union[str, Any] = initializer_range
_lowerCAmelCase : int = use_cache
_lowerCAmelCase : Optional[int] = bos_token_id
_lowerCAmelCase : List[str] = eos_token_id
super().__init__(
bos_token_id=__snake_case , eos_token_id=__snake_case , tie_word_embeddings=__snake_case , **__snake_case )
class __snake_case (lowerCamelCase_ ):
def __init__( self : Tuple , _UpperCAmelCase : PretrainedConfig , _UpperCAmelCase : str = "default" , _UpperCAmelCase : List[PatchingSpec] = None , _UpperCAmelCase : bool = False , ) -> str:
'''simple docstring'''
super().__init__(__snake_case , task=__snake_case , patching_specs=__snake_case , use_past=__snake_case )
if not getattr(self._config , """pad_token_id""" , __snake_case ):
# TODO: how to do that better?
_lowerCAmelCase : str = 0
@property
def SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
_lowerCAmelCase : Dict = OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}} )
if self.use_past:
self.fill_with_past_key_values_(__snake_case , direction="""inputs""" )
_lowerCAmelCase : Union[str, Any] = {0: """batch""", 1: """past_sequence + sequence"""}
else:
_lowerCAmelCase : Optional[int] = {0: """batch""", 1: """sequence"""}
return common_inputs
@property
def SCREAMING_SNAKE_CASE ( self : Tuple ) -> int:
'''simple docstring'''
return self._config.n_layer
@property
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> int:
'''simple docstring'''
return self._config.n_head
def SCREAMING_SNAKE_CASE ( self : Optional[int] , _UpperCAmelCase : PreTrainedTokenizer , _UpperCAmelCase : int = -1 , _UpperCAmelCase : int = -1 , _UpperCAmelCase : bool = False , _UpperCAmelCase : Optional[TensorType] = None , ) -> Mapping[str, Any]:
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = super(__snake_case , self ).generate_dummy_inputs(
__snake_case , batch_size=__snake_case , seq_length=__snake_case , is_pair=__snake_case , framework=__snake_case )
# We need to order the input in the way they appears in the forward()
_lowerCAmelCase : int = OrderedDict({"""input_ids""": common_inputs["""input_ids"""]} )
# Need to add the past_keys
if self.use_past:
if not is_torch_available():
raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed.""" )
else:
import torch
_lowerCAmelCase , _lowerCAmelCase : Dict = common_inputs["""input_ids"""].shape
# Not using the same length for past_key_values
_lowerCAmelCase : Dict = seqlen + 2
_lowerCAmelCase : int = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
_lowerCAmelCase : Optional[int] = [
(torch.zeros(__snake_case ), torch.zeros(__snake_case )) for _ in range(self.num_layers )
]
_lowerCAmelCase : int = common_inputs["""attention_mask"""]
if self.use_past:
_lowerCAmelCase : str = ordered_inputs["""attention_mask"""].dtype
_lowerCAmelCase : List[Any] = torch.cat(
[ordered_inputs["""attention_mask"""], torch.ones(__snake_case , __snake_case , dtype=__snake_case )] , dim=1 )
return ordered_inputs
@property
def SCREAMING_SNAKE_CASE ( self : Tuple ) -> int:
'''simple docstring'''
return 13
| 371 |
from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch
@require_torch
@require_tf
class __snake_case (_a ):
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> int:
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = SMALL_MODEL_IDENTIFIER
_lowerCAmelCase : str = """pt"""
_lowerCAmelCase : List[Any] = """tf"""
def SCREAMING_SNAKE_CASE ( self : Tuple , _UpperCAmelCase : Optional[Any] ) -> List[Any]:
'''simple docstring'''
_lowerCAmelCase : int = AutoModel.from_pretrained(self.test_model )
model_pt.save_pretrained(_UpperCAmelCase )
def SCREAMING_SNAKE_CASE ( self : Optional[int] , _UpperCAmelCase : Tuple ) -> Optional[int]:
'''simple docstring'''
_lowerCAmelCase : Optional[int] = TFAutoModel.from_pretrained(self.test_model , from_pt=_UpperCAmelCase )
model_tf.save_pretrained(_UpperCAmelCase )
def SCREAMING_SNAKE_CASE ( self : str ) -> List[Any]:
'''simple docstring'''
_lowerCAmelCase : int = """mock_framework"""
# Framework provided - return whatever the user provides
_lowerCAmelCase : Optional[int] = FeaturesManager.determine_framework(self.test_model , _UpperCAmelCase )
self.assertEqual(_UpperCAmelCase , _UpperCAmelCase )
# Local checkpoint and framework provided - return provided framework
# PyTorch checkpoint
with TemporaryDirectory() as local_pt_ckpt:
self._setup_pt_ckpt(_UpperCAmelCase )
_lowerCAmelCase : Optional[Any] = FeaturesManager.determine_framework(_UpperCAmelCase , _UpperCAmelCase )
self.assertEqual(_UpperCAmelCase , _UpperCAmelCase )
# TensorFlow checkpoint
with TemporaryDirectory() as local_tf_ckpt:
self._setup_tf_ckpt(_UpperCAmelCase )
_lowerCAmelCase : Optional[Any] = FeaturesManager.determine_framework(_UpperCAmelCase , _UpperCAmelCase )
self.assertEqual(_UpperCAmelCase , _UpperCAmelCase )
def SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> Tuple:
'''simple docstring'''
with TemporaryDirectory() as local_pt_ckpt:
self._setup_pt_ckpt(_UpperCAmelCase )
_lowerCAmelCase : str = FeaturesManager.determine_framework(_UpperCAmelCase )
self.assertEqual(_UpperCAmelCase , self.framework_pt )
# TensorFlow checkpoint
with TemporaryDirectory() as local_tf_ckpt:
self._setup_tf_ckpt(_UpperCAmelCase )
_lowerCAmelCase : Tuple = FeaturesManager.determine_framework(_UpperCAmelCase )
self.assertEqual(_UpperCAmelCase , self.framework_tf )
# Invalid local checkpoint
with TemporaryDirectory() as local_invalid_ckpt:
with self.assertRaises(_UpperCAmelCase ):
_lowerCAmelCase : Optional[int] = FeaturesManager.determine_framework(_UpperCAmelCase )
def SCREAMING_SNAKE_CASE ( self : Any ) -> Union[str, Any]:
'''simple docstring'''
_lowerCAmelCase : List[str] = MagicMock(return_value=_UpperCAmelCase )
with patch("""transformers.onnx.features.is_tf_available""" , _UpperCAmelCase ):
_lowerCAmelCase : Optional[int] = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(_UpperCAmelCase , self.framework_pt )
# PyTorch not in environment -> use TensorFlow
_lowerCAmelCase : str = MagicMock(return_value=_UpperCAmelCase )
with patch("""transformers.onnx.features.is_torch_available""" , _UpperCAmelCase ):
_lowerCAmelCase : Dict = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(_UpperCAmelCase , self.framework_tf )
# Both in environment -> use PyTorch
_lowerCAmelCase : List[Any] = MagicMock(return_value=_UpperCAmelCase )
_lowerCAmelCase : Tuple = MagicMock(return_value=_UpperCAmelCase )
with patch("""transformers.onnx.features.is_tf_available""" , _UpperCAmelCase ), patch(
"""transformers.onnx.features.is_torch_available""" , _UpperCAmelCase ):
_lowerCAmelCase : Optional[int] = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(_UpperCAmelCase , self.framework_pt )
# Both not in environment -> raise error
_lowerCAmelCase : List[str] = MagicMock(return_value=_UpperCAmelCase )
_lowerCAmelCase : List[Any] = MagicMock(return_value=_UpperCAmelCase )
with patch("""transformers.onnx.features.is_tf_available""" , _UpperCAmelCase ), patch(
"""transformers.onnx.features.is_torch_available""" , _UpperCAmelCase ):
with self.assertRaises(_UpperCAmelCase ):
_lowerCAmelCase : Dict = FeaturesManager.determine_framework(self.test_model )
| 159 | 0 |
import math
from numpy import inf
from scipy.integrate import quad
def _lowerCAmelCase ( __lowerCAmelCase ) -> float:
"""simple docstring"""
if num <= 0:
raise ValueError('''math domain error''' )
return quad(__lowerCAmelCase , 0 , __lowerCAmelCase , args=(__lowerCAmelCase) )[0]
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase ) -> float:
"""simple docstring"""
return math.pow(__lowerCAmelCase , z - 1 ) * math.exp(-x )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 230 |
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_simplify,
require_tf,
require_torch,
require_torch_gpu,
slow,
)
from .test_pipelines_common import ANY
@is_pipeline_test
class a ( unittest.TestCase ):
__lowerCAmelCase : Any = MODEL_FOR_MASKED_LM_MAPPING
__lowerCAmelCase : Optional[Any] = TF_MODEL_FOR_MASKED_LM_MAPPING
def __lowerCamelCase ( self :str ):
super().tearDown()
# clean-up as much as possible GPU memory occupied by PyTorch
gc.collect()
if is_torch_available():
import torch
torch.cuda.empty_cache()
@require_tf
def __lowerCamelCase ( self :Any ):
snake_case__ : Optional[Any] = pipeline(task='''fill-mask''' ,model='''sshleifer/tiny-distilroberta-base''' ,top_k=2 ,framework='''tf''' )
snake_case__ : int = unmasker('''My name is <mask>''' )
self.assertEqual(
nested_simplify(__lowercase ,decimals=6 ) ,[
{'''sequence''': '''My name is grouped''', '''score''': 2.1e-0_5, '''token''': 3_8_0_1_5, '''token_str''': ''' grouped'''},
{'''sequence''': '''My name is accuser''', '''score''': 2.1e-0_5, '''token''': 2_5_5_0_6, '''token_str''': ''' accuser'''},
] ,)
snake_case__ : int = unmasker('''The largest city in France is <mask>''' )
self.assertEqual(
nested_simplify(__lowercase ,decimals=6 ) ,[
{
'''sequence''': '''The largest city in France is grouped''',
'''score''': 2.1e-0_5,
'''token''': 3_8_0_1_5,
'''token_str''': ''' grouped''',
},
{
'''sequence''': '''The largest city in France is accuser''',
'''score''': 2.1e-0_5,
'''token''': 2_5_5_0_6,
'''token_str''': ''' accuser''',
},
] ,)
snake_case__ : Optional[int] = unmasker('''My name is <mask>''' ,targets=[''' Patrick''', ''' Clara''', ''' Teven'''] ,top_k=3 )
self.assertEqual(
nested_simplify(__lowercase ,decimals=6 ) ,[
{'''sequence''': '''My name is Clara''', '''score''': 2e-0_5, '''token''': 1_3_6_0_6, '''token_str''': ''' Clara'''},
{'''sequence''': '''My name is Patrick''', '''score''': 2e-0_5, '''token''': 3_4_9_9, '''token_str''': ''' Patrick'''},
{'''sequence''': '''My name is Te''', '''score''': 1.9e-0_5, '''token''': 2_9_4_1, '''token_str''': ''' Te'''},
] ,)
@require_torch
def __lowerCamelCase ( self :Optional[int] ):
snake_case__ : str = pipeline(task='''fill-mask''' ,model='''sshleifer/tiny-distilroberta-base''' ,top_k=2 ,framework='''pt''' )
snake_case__ : str = unmasker('''My name is <mask>''' )
self.assertEqual(
nested_simplify(__lowercase ,decimals=6 ) ,[
{'''sequence''': '''My name is Maul''', '''score''': 2.2e-0_5, '''token''': 3_5_6_7_6, '''token_str''': ''' Maul'''},
{'''sequence''': '''My name isELS''', '''score''': 2.2e-0_5, '''token''': 1_6_4_1_6, '''token_str''': '''ELS'''},
] ,)
snake_case__ : List[str] = unmasker('''The largest city in France is <mask>''' )
self.assertEqual(
nested_simplify(__lowercase ,decimals=6 ) ,[
{
'''sequence''': '''The largest city in France is Maul''',
'''score''': 2.2e-0_5,
'''token''': 3_5_6_7_6,
'''token_str''': ''' Maul''',
},
{'''sequence''': '''The largest city in France isELS''', '''score''': 2.2e-0_5, '''token''': 1_6_4_1_6, '''token_str''': '''ELS'''},
] ,)
snake_case__ : Union[str, Any] = unmasker('''My name is <mask>''' ,targets=[''' Patrick''', ''' Clara''', ''' Teven'''] ,top_k=3 )
self.assertEqual(
nested_simplify(__lowercase ,decimals=6 ) ,[
{'''sequence''': '''My name is Patrick''', '''score''': 2.1e-0_5, '''token''': 3_4_9_9, '''token_str''': ''' Patrick'''},
{'''sequence''': '''My name is Te''', '''score''': 2e-0_5, '''token''': 2_9_4_1, '''token_str''': ''' Te'''},
{'''sequence''': '''My name is Clara''', '''score''': 2e-0_5, '''token''': 1_3_6_0_6, '''token_str''': ''' Clara'''},
] ,)
snake_case__ : Optional[int] = unmasker('''My name is <mask> <mask>''' ,top_k=2 )
self.assertEqual(
nested_simplify(__lowercase ,decimals=6 ) ,[
[
{
'''score''': 2.2e-0_5,
'''token''': 3_5_6_7_6,
'''token_str''': ''' Maul''',
'''sequence''': '''<s>My name is Maul<mask></s>''',
},
{'''score''': 2.2e-0_5, '''token''': 1_6_4_1_6, '''token_str''': '''ELS''', '''sequence''': '''<s>My name isELS<mask></s>'''},
],
[
{
'''score''': 2.2e-0_5,
'''token''': 3_5_6_7_6,
'''token_str''': ''' Maul''',
'''sequence''': '''<s>My name is<mask> Maul</s>''',
},
{'''score''': 2.2e-0_5, '''token''': 1_6_4_1_6, '''token_str''': '''ELS''', '''sequence''': '''<s>My name is<mask>ELS</s>'''},
],
] ,)
@require_torch_gpu
def __lowerCamelCase ( self :int ):
snake_case__ : Optional[int] = pipeline('''fill-mask''' ,model='''hf-internal-testing/tiny-random-distilbert''' ,device=0 ,framework='''pt''' )
# convert model to fp16
pipe.model.half()
snake_case__ : List[str] = pipe('''Paris is the [MASK] of France.''' )
# We actually don't care about the result, we just want to make sure
# it works, meaning the float16 tensor got casted back to float32
# for postprocessing.
self.assertIsInstance(__lowercase ,__lowercase )
@slow
@require_torch
def __lowerCamelCase ( self :str ):
snake_case__ : List[str] = pipeline(task='''fill-mask''' ,model='''distilroberta-base''' ,top_k=2 ,framework='''pt''' )
self.run_large_test(__lowercase )
@slow
@require_tf
def __lowerCamelCase ( self :Any ):
snake_case__ : Optional[Any] = pipeline(task='''fill-mask''' ,model='''distilroberta-base''' ,top_k=2 ,framework='''tf''' )
self.run_large_test(__lowercase )
def __lowerCamelCase ( self :Optional[Any] ,__lowercase :List[Any] ):
snake_case__ : Optional[Any] = unmasker('''My name is <mask>''' )
self.assertEqual(
nested_simplify(__lowercase ) ,[
{'''sequence''': '''My name is John''', '''score''': 0.008, '''token''': 6_1_0, '''token_str''': ''' John'''},
{'''sequence''': '''My name is Chris''', '''score''': 0.007, '''token''': 1_5_7_3, '''token_str''': ''' Chris'''},
] ,)
snake_case__ : str = unmasker('''The largest city in France is <mask>''' )
self.assertEqual(
nested_simplify(__lowercase ) ,[
{
'''sequence''': '''The largest city in France is Paris''',
'''score''': 0.251,
'''token''': 2_2_0_1,
'''token_str''': ''' Paris''',
},
{
'''sequence''': '''The largest city in France is Lyon''',
'''score''': 0.214,
'''token''': 1_2_7_9_0,
'''token_str''': ''' Lyon''',
},
] ,)
snake_case__ : Dict = unmasker('''My name is <mask>''' ,targets=[''' Patrick''', ''' Clara''', ''' Teven'''] ,top_k=3 )
self.assertEqual(
nested_simplify(__lowercase ) ,[
{'''sequence''': '''My name is Patrick''', '''score''': 0.005, '''token''': 3_4_9_9, '''token_str''': ''' Patrick'''},
{'''sequence''': '''My name is Clara''', '''score''': 0.000, '''token''': 1_3_6_0_6, '''token_str''': ''' Clara'''},
{'''sequence''': '''My name is Te''', '''score''': 0.000, '''token''': 2_9_4_1, '''token_str''': ''' Te'''},
] ,)
@require_torch
def __lowerCamelCase ( self :List[str] ):
snake_case__ : List[Any] = pipeline(task='''fill-mask''' ,model='''sshleifer/tiny-distilroberta-base''' ,framework='''pt''' )
snake_case__ : str = None
snake_case__ : int = None
self.run_pipeline_test(__lowercase ,[] )
@require_tf
def __lowerCamelCase ( self :int ):
snake_case__ : Optional[int] = pipeline(task='''fill-mask''' ,model='''sshleifer/tiny-distilroberta-base''' ,framework='''tf''' )
snake_case__ : int = None
snake_case__ : List[str] = None
self.run_pipeline_test(__lowercase ,[] )
def __lowerCamelCase ( self :Any ,__lowercase :Any ,__lowercase :str ,__lowercase :Union[str, Any] ):
if tokenizer is None or tokenizer.mask_token_id is None:
self.skipTest('''The provided tokenizer has no mask token, (probably reformer or wav2vec2)''' )
snake_case__ : Optional[int] = FillMaskPipeline(model=__lowercase ,tokenizer=__lowercase )
snake_case__ : List[str] = [
F"""This is another {tokenizer.mask_token} test""",
]
return fill_masker, examples
def __lowerCamelCase ( self :Optional[Any] ,__lowercase :List[Any] ,__lowercase :Optional[Any] ):
snake_case__ : List[str] = fill_masker.tokenizer
snake_case__ : List[Any] = fill_masker.model
snake_case__ : Dict = fill_masker(
F"""This is a {tokenizer.mask_token}""" ,)
self.assertEqual(
__lowercase ,[
{'''sequence''': ANY(__lowercase ), '''score''': ANY(__lowercase ), '''token''': ANY(__lowercase ), '''token_str''': ANY(__lowercase )},
{'''sequence''': ANY(__lowercase ), '''score''': ANY(__lowercase ), '''token''': ANY(__lowercase ), '''token_str''': ANY(__lowercase )},
{'''sequence''': ANY(__lowercase ), '''score''': ANY(__lowercase ), '''token''': ANY(__lowercase ), '''token_str''': ANY(__lowercase )},
{'''sequence''': ANY(__lowercase ), '''score''': ANY(__lowercase ), '''token''': ANY(__lowercase ), '''token_str''': ANY(__lowercase )},
{'''sequence''': ANY(__lowercase ), '''score''': ANY(__lowercase ), '''token''': ANY(__lowercase ), '''token_str''': ANY(__lowercase )},
] ,)
snake_case__ : Tuple = fill_masker([F"""This is a {tokenizer.mask_token}"""] )
self.assertEqual(
__lowercase ,[
{'''sequence''': ANY(__lowercase ), '''score''': ANY(__lowercase ), '''token''': ANY(__lowercase ), '''token_str''': ANY(__lowercase )},
{'''sequence''': ANY(__lowercase ), '''score''': ANY(__lowercase ), '''token''': ANY(__lowercase ), '''token_str''': ANY(__lowercase )},
{'''sequence''': ANY(__lowercase ), '''score''': ANY(__lowercase ), '''token''': ANY(__lowercase ), '''token_str''': ANY(__lowercase )},
{'''sequence''': ANY(__lowercase ), '''score''': ANY(__lowercase ), '''token''': ANY(__lowercase ), '''token_str''': ANY(__lowercase )},
{'''sequence''': ANY(__lowercase ), '''score''': ANY(__lowercase ), '''token''': ANY(__lowercase ), '''token_str''': ANY(__lowercase )},
] ,)
snake_case__ : List[str] = fill_masker([F"""This is a {tokenizer.mask_token}""", F"""Another {tokenizer.mask_token} great test."""] )
self.assertEqual(
__lowercase ,[
[
{'''sequence''': ANY(__lowercase ), '''score''': ANY(__lowercase ), '''token''': ANY(__lowercase ), '''token_str''': ANY(__lowercase )},
{'''sequence''': ANY(__lowercase ), '''score''': ANY(__lowercase ), '''token''': ANY(__lowercase ), '''token_str''': ANY(__lowercase )},
{'''sequence''': ANY(__lowercase ), '''score''': ANY(__lowercase ), '''token''': ANY(__lowercase ), '''token_str''': ANY(__lowercase )},
{'''sequence''': ANY(__lowercase ), '''score''': ANY(__lowercase ), '''token''': ANY(__lowercase ), '''token_str''': ANY(__lowercase )},
{'''sequence''': ANY(__lowercase ), '''score''': ANY(__lowercase ), '''token''': ANY(__lowercase ), '''token_str''': ANY(__lowercase )},
],
[
{'''sequence''': ANY(__lowercase ), '''score''': ANY(__lowercase ), '''token''': ANY(__lowercase ), '''token_str''': ANY(__lowercase )},
{'''sequence''': ANY(__lowercase ), '''score''': ANY(__lowercase ), '''token''': ANY(__lowercase ), '''token_str''': ANY(__lowercase )},
{'''sequence''': ANY(__lowercase ), '''score''': ANY(__lowercase ), '''token''': ANY(__lowercase ), '''token_str''': ANY(__lowercase )},
{'''sequence''': ANY(__lowercase ), '''score''': ANY(__lowercase ), '''token''': ANY(__lowercase ), '''token_str''': ANY(__lowercase )},
{'''sequence''': ANY(__lowercase ), '''score''': ANY(__lowercase ), '''token''': ANY(__lowercase ), '''token_str''': ANY(__lowercase )},
],
] ,)
with self.assertRaises(__lowercase ):
fill_masker([None] )
# No mask_token is not supported
with self.assertRaises(__lowercase ):
fill_masker('''This is''' )
self.run_test_top_k(__lowercase ,__lowercase )
self.run_test_targets(__lowercase ,__lowercase )
self.run_test_top_k_targets(__lowercase ,__lowercase )
self.fill_mask_with_duplicate_targets_and_top_k(__lowercase ,__lowercase )
self.fill_mask_with_multiple_masks(__lowercase ,__lowercase )
def __lowerCamelCase ( self :Union[str, Any] ,__lowercase :Optional[int] ,__lowercase :int ):
snake_case__ : int = tokenizer.get_vocab()
snake_case__ : Dict = sorted(vocab.keys() )[:2]
# Pipeline argument
snake_case__ : List[Any] = FillMaskPipeline(model=__lowercase ,tokenizer=__lowercase ,targets=__lowercase )
snake_case__ : str = fill_masker(F"""This is a {tokenizer.mask_token}""" )
self.assertEqual(
__lowercase ,[
{'''sequence''': ANY(__lowercase ), '''score''': ANY(__lowercase ), '''token''': ANY(__lowercase ), '''token_str''': ANY(__lowercase )},
{'''sequence''': ANY(__lowercase ), '''score''': ANY(__lowercase ), '''token''': ANY(__lowercase ), '''token_str''': ANY(__lowercase )},
] ,)
snake_case__ : Optional[Any] = {vocab[el] for el in targets}
self.assertEqual({el['''token'''] for el in outputs} ,__lowercase )
snake_case__ : Any = [tokenizer.decode([x] ) for x in target_ids]
self.assertEqual({el['''token_str'''] for el in outputs} ,set(__lowercase ) )
# Call argument
snake_case__ : str = FillMaskPipeline(model=__lowercase ,tokenizer=__lowercase )
snake_case__ : int = fill_masker(F"""This is a {tokenizer.mask_token}""" ,targets=__lowercase )
self.assertEqual(
__lowercase ,[
{'''sequence''': ANY(__lowercase ), '''score''': ANY(__lowercase ), '''token''': ANY(__lowercase ), '''token_str''': ANY(__lowercase )},
{'''sequence''': ANY(__lowercase ), '''score''': ANY(__lowercase ), '''token''': ANY(__lowercase ), '''token_str''': ANY(__lowercase )},
] ,)
snake_case__ : str = {vocab[el] for el in targets}
self.assertEqual({el['''token'''] for el in outputs} ,__lowercase )
snake_case__ : Optional[Any] = [tokenizer.decode([x] ) for x in target_ids]
self.assertEqual({el['''token_str'''] for el in outputs} ,set(__lowercase ) )
# Score equivalence
snake_case__ : Dict = fill_masker(F"""This is a {tokenizer.mask_token}""" ,targets=__lowercase )
snake_case__ : Union[str, Any] = [top_mask['''token_str'''] for top_mask in outputs]
snake_case__ : Tuple = [top_mask['''score'''] for top_mask in outputs]
# For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
if set(__lowercase ) == set(__lowercase ):
snake_case__ : List[Any] = fill_masker(F"""This is a {tokenizer.mask_token}""" ,targets=__lowercase )
snake_case__ : int = [top_mask['''score'''] for top_mask in unmasked_targets]
self.assertEqual(nested_simplify(__lowercase ) ,nested_simplify(__lowercase ) )
# Raises with invalid
with self.assertRaises(__lowercase ):
snake_case__ : List[str] = fill_masker(F"""This is a {tokenizer.mask_token}""" ,targets=[] )
# For some tokenizers, `""` is actually in the vocabulary and the expected error won't raised
if "" not in tokenizer.get_vocab():
with self.assertRaises(__lowercase ):
snake_case__ : List[Any] = fill_masker(F"""This is a {tokenizer.mask_token}""" ,targets=[''''''] )
with self.assertRaises(__lowercase ):
snake_case__ : Optional[int] = fill_masker(F"""This is a {tokenizer.mask_token}""" ,targets='''''' )
def __lowerCamelCase ( self :Any ,__lowercase :Union[str, Any] ,__lowercase :Dict ):
snake_case__ : int = FillMaskPipeline(model=__lowercase ,tokenizer=__lowercase ,top_k=2 )
snake_case__ : Tuple = fill_masker(F"""This is a {tokenizer.mask_token}""" )
self.assertEqual(
__lowercase ,[
{'''sequence''': ANY(__lowercase ), '''score''': ANY(__lowercase ), '''token''': ANY(__lowercase ), '''token_str''': ANY(__lowercase )},
{'''sequence''': ANY(__lowercase ), '''score''': ANY(__lowercase ), '''token''': ANY(__lowercase ), '''token_str''': ANY(__lowercase )},
] ,)
snake_case__ : Any = FillMaskPipeline(model=__lowercase ,tokenizer=__lowercase )
snake_case__ : Optional[Any] = fill_masker(F"""This is a {tokenizer.mask_token}""" ,top_k=2 )
self.assertEqual(
__lowercase ,[
{'''sequence''': ANY(__lowercase ), '''score''': ANY(__lowercase ), '''token''': ANY(__lowercase ), '''token_str''': ANY(__lowercase )},
{'''sequence''': ANY(__lowercase ), '''score''': ANY(__lowercase ), '''token''': ANY(__lowercase ), '''token_str''': ANY(__lowercase )},
] ,)
self.assertEqual(nested_simplify(__lowercase ) ,nested_simplify(__lowercase ) )
def __lowerCamelCase ( self :List[Any] ,__lowercase :Tuple ,__lowercase :str ):
snake_case__ : Optional[int] = tokenizer.get_vocab()
snake_case__ : int = FillMaskPipeline(model=__lowercase ,tokenizer=__lowercase )
# top_k=2, ntargets=3
snake_case__ : int = sorted(vocab.keys() )[:3]
snake_case__ : List[Any] = fill_masker(F"""This is a {tokenizer.mask_token}""" ,top_k=2 ,targets=__lowercase )
# If we use the most probably targets, and filter differently, we should still
# have the same results
snake_case__ : Dict = [el['''token_str'''] for el in sorted(__lowercase ,key=lambda __lowercase : x["score"] ,reverse=__lowercase )]
# For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
if set(__lowercase ).issubset(__lowercase ):
snake_case__ : List[Any] = fill_masker(F"""This is a {tokenizer.mask_token}""" ,top_k=3 ,targets=__lowercase )
# They should yield exactly the same result
self.assertEqual(nested_simplify(__lowercase ) ,nested_simplify(__lowercase ) )
def __lowerCamelCase ( self :Union[str, Any] ,__lowercase :Dict ,__lowercase :Dict ):
snake_case__ : Union[str, Any] = FillMaskPipeline(model=__lowercase ,tokenizer=__lowercase )
snake_case__ : str = tokenizer.get_vocab()
# String duplicates + id duplicates
snake_case__ : int = sorted(vocab.keys() )[:3]
snake_case__ : Optional[Any] = [targets[0], targets[1], targets[0], targets[2], targets[1]]
snake_case__ : Optional[Any] = fill_masker(F"""My name is {tokenizer.mask_token}""" ,targets=__lowercase ,top_k=1_0 )
# The target list contains duplicates, so we can't output more
# than them
self.assertEqual(len(__lowercase ) ,3 )
def __lowerCamelCase ( self :Optional[Any] ,__lowercase :List[Any] ,__lowercase :Optional[Any] ):
snake_case__ : Any = FillMaskPipeline(model=__lowercase ,tokenizer=__lowercase )
snake_case__ : Tuple = fill_masker(
F"""This is a {tokenizer.mask_token} {tokenizer.mask_token} {tokenizer.mask_token}""" ,top_k=2 )
self.assertEqual(
__lowercase ,[
[
{'''sequence''': ANY(__lowercase ), '''score''': ANY(__lowercase ), '''token''': ANY(__lowercase ), '''token_str''': ANY(__lowercase )},
{'''sequence''': ANY(__lowercase ), '''score''': ANY(__lowercase ), '''token''': ANY(__lowercase ), '''token_str''': ANY(__lowercase )},
],
[
{'''sequence''': ANY(__lowercase ), '''score''': ANY(__lowercase ), '''token''': ANY(__lowercase ), '''token_str''': ANY(__lowercase )},
{'''sequence''': ANY(__lowercase ), '''score''': ANY(__lowercase ), '''token''': ANY(__lowercase ), '''token_str''': ANY(__lowercase )},
],
[
{'''sequence''': ANY(__lowercase ), '''score''': ANY(__lowercase ), '''token''': ANY(__lowercase ), '''token_str''': ANY(__lowercase )},
{'''sequence''': ANY(__lowercase ), '''score''': ANY(__lowercase ), '''token''': ANY(__lowercase ), '''token_str''': ANY(__lowercase )},
],
] ,)
| 230 | 1 |
import numpy as np
def _a ( SCREAMING_SNAKE_CASE : Optional[Any] , SCREAMING_SNAKE_CASE : List[Any] , SCREAMING_SNAKE_CASE : List[Any] , SCREAMING_SNAKE_CASE : List[Any] , SCREAMING_SNAKE_CASE : List[Any] ):
"""simple docstring"""
UpperCamelCase__ : int = int(np.ceil((x_end - xa) / h ) )
UpperCamelCase__ : List[str] = np.zeros((n + 1,) )
UpperCamelCase__ : List[str] = ya
UpperCamelCase__ : Any = xa
for k in range(SCREAMING_SNAKE_CASE ):
UpperCamelCase__ : Optional[int] = f(SCREAMING_SNAKE_CASE , y[k] )
UpperCamelCase__ : Any = f(x + 0.5 * h , y[k] + 0.5 * h * ka )
UpperCamelCase__ : Dict = f(x + 0.5 * h , y[k] + 0.5 * h * ka )
UpperCamelCase__ : int = f(x + h , y[k] + h * ka )
UpperCamelCase__ : Union[str, Any] = y[k] + (1 / 6) * h * (ka + 2 * ka + 2 * ka + ka)
x += h
return y
if __name__ == "__main__":
import doctest
doctest.testmod()
| 51 |
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
__UpperCamelCase : List[Any] = pytest.mark.integration
@pytest.mark.parametrize('''path''' , ['''paws''', '''csv'''] )
def _a ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : List[Any] ):
"""simple docstring"""
inspect_dataset(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
UpperCamelCase__ : List[str] = path + '''.py'''
assert script_name in os.listdir(SCREAMING_SNAKE_CASE )
assert "__pycache__" not in os.listdir(SCREAMING_SNAKE_CASE )
@pytest.mark.filterwarnings('''ignore:inspect_metric is deprecated:FutureWarning''' )
@pytest.mark.filterwarnings('''ignore:metric_module_factory is deprecated:FutureWarning''' )
@pytest.mark.parametrize('''path''' , ['''accuracy'''] )
def _a ( SCREAMING_SNAKE_CASE : Optional[int] , SCREAMING_SNAKE_CASE : Union[str, Any] ):
"""simple docstring"""
inspect_metric(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
UpperCamelCase__ : int = path + '''.py'''
assert script_name in os.listdir(SCREAMING_SNAKE_CASE )
assert "__pycache__" not in os.listdir(SCREAMING_SNAKE_CASE )
@pytest.mark.parametrize(
'''path, config_name, expected_splits''' , [
('''squad''', '''plain_text''', ['''train''', '''validation''']),
('''dalle-mini/wit''', '''dalle-mini--wit''', ['''train''']),
('''paws''', '''labeled_final''', ['''train''', '''test''', '''validation''']),
] , )
def _a ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : List[Any] , SCREAMING_SNAKE_CASE : str ):
"""simple docstring"""
UpperCamelCase__ : int = get_dataset_config_info(SCREAMING_SNAKE_CASE , config_name=SCREAMING_SNAKE_CASE )
assert info.config_name == config_name
assert list(info.splits.keys() ) == expected_splits
@pytest.mark.parametrize(
'''path, config_name, expected_exception''' , [
('''paws''', None, ValueError),
] , )
def _a ( SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : Tuple , SCREAMING_SNAKE_CASE : str ):
"""simple docstring"""
with pytest.raises(SCREAMING_SNAKE_CASE ):
get_dataset_config_info(SCREAMING_SNAKE_CASE , config_name=SCREAMING_SNAKE_CASE )
@pytest.mark.parametrize(
'''path, expected''' , [
('''squad''', '''plain_text'''),
('''acronym_identification''', '''default'''),
('''lhoestq/squad''', '''plain_text'''),
('''lhoestq/test''', '''default'''),
('''lhoestq/demo1''', '''lhoestq--demo1'''),
('''dalle-mini/wit''', '''dalle-mini--wit'''),
] , )
def _a ( SCREAMING_SNAKE_CASE : Tuple , SCREAMING_SNAKE_CASE : List[str] ):
"""simple docstring"""
UpperCamelCase__ : List[Any] = get_dataset_config_names(SCREAMING_SNAKE_CASE )
assert expected in config_names
@pytest.mark.parametrize(
'''path, expected_configs, expected_splits_in_first_config''' , [
('''squad''', ['''plain_text'''], ['''train''', '''validation''']),
('''dalle-mini/wit''', ['''dalle-mini--wit'''], ['''train''']),
('''paws''', ['''labeled_final''', '''labeled_swap''', '''unlabeled_final'''], ['''train''', '''test''', '''validation''']),
] , )
def _a ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : Optional[int] ):
"""simple docstring"""
UpperCamelCase__ : Optional[int] = get_dataset_infos(SCREAMING_SNAKE_CASE )
assert list(infos.keys() ) == expected_configs
UpperCamelCase__ : List[str] = expected_configs[0]
assert expected_config in infos
UpperCamelCase__ : Union[str, Any] = infos[expected_config]
assert info.config_name == expected_config
assert list(info.splits.keys() ) == expected_splits_in_first_config
@pytest.mark.parametrize(
'''path, expected_config, expected_splits''' , [
('''squad''', '''plain_text''', ['''train''', '''validation''']),
('''dalle-mini/wit''', '''dalle-mini--wit''', ['''train''']),
('''paws''', '''labeled_final''', ['''train''', '''test''', '''validation''']),
] , )
def _a ( SCREAMING_SNAKE_CASE : Tuple , SCREAMING_SNAKE_CASE : Union[str, Any] , SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
UpperCamelCase__ : Optional[Any] = get_dataset_infos(SCREAMING_SNAKE_CASE )
assert expected_config in infos
UpperCamelCase__ : Optional[int] = infos[expected_config]
assert info.config_name == expected_config
assert list(info.splits.keys() ) == expected_splits
@pytest.mark.parametrize(
'''path, config_name, expected_exception''' , [
('''paws''', None, ValueError),
] , )
def _a ( SCREAMING_SNAKE_CASE : Dict , SCREAMING_SNAKE_CASE : Optional[Any] , SCREAMING_SNAKE_CASE : Any ):
"""simple docstring"""
with pytest.raises(SCREAMING_SNAKE_CASE ):
get_dataset_split_names(SCREAMING_SNAKE_CASE , config_name=SCREAMING_SNAKE_CASE )
| 51 | 1 |
"""simple docstring"""
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = prime_factors(_UpperCamelCase )
if is_square_free(_UpperCamelCase ):
return -1 if len(_UpperCamelCase ) % 2 else 1
return 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 57 |
'''simple docstring'''
from pickle import UnpicklingError
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict
from ..utils import logging
UpperCAmelCase_ : List[Any] = logging.get_logger(__name__)
def snake_case_ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
try:
with open(SCREAMING_SNAKE_CASE__ , """rb""" ) as flax_state_f:
_SCREAMING_SNAKE_CASE : Dict = from_bytes(SCREAMING_SNAKE_CASE__ , flax_state_f.read() )
except UnpicklingError as e:
try:
with open(SCREAMING_SNAKE_CASE__ ) as f:
if f.read().startswith("""version""" ):
raise OSError(
"""You seem to have cloned a repository without having git-lfs installed. Please"""
""" install git-lfs and run `git lfs install` followed by `git lfs pull` in the"""
""" folder you cloned.""" )
else:
raise ValueError from e
except (UnicodeDecodeError, ValueError):
raise EnvironmentError(f"""Unable to convert {model_file} to Flax deserializable object. """ )
return load_flax_weights_in_pytorch_model(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def snake_case_ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
try:
import torch # noqa: F401
except ImportError:
logger.error(
"""Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see"""
""" https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"""
""" instructions.""" )
raise
# check if we have bf16 weights
_SCREAMING_SNAKE_CASE : List[Any] = flatten_dict(jax.tree_util.tree_map(lambda SCREAMING_SNAKE_CASE__ : x.dtype == jnp.bfloataa , SCREAMING_SNAKE_CASE__ ) ).values()
if any(SCREAMING_SNAKE_CASE__ ):
# convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16
# and bf16 is not fully supported in PT yet.
logger.warning(
"""Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` """
"""before loading those in PyTorch model.""" )
_SCREAMING_SNAKE_CASE : Dict = jax.tree_util.tree_map(
lambda SCREAMING_SNAKE_CASE__ : params.astype(np.floataa ) if params.dtype == jnp.bfloataa else params , SCREAMING_SNAKE_CASE__ )
_SCREAMING_SNAKE_CASE : Optional[Any] = """"""
_SCREAMING_SNAKE_CASE : str = flatten_dict(SCREAMING_SNAKE_CASE__ , sep=""".""" )
_SCREAMING_SNAKE_CASE : str = pt_model.state_dict()
# keep track of unexpected & missing keys
_SCREAMING_SNAKE_CASE : Tuple = []
_SCREAMING_SNAKE_CASE : int = set(pt_model_dict.keys() )
for flax_key_tuple, flax_tensor in flax_state_dict.items():
_SCREAMING_SNAKE_CASE : Any = flax_key_tuple.split(""".""" )
if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
_SCREAMING_SNAKE_CASE : Optional[Any] = flax_key_tuple_array[:-1] + ["""weight"""]
_SCREAMING_SNAKE_CASE : List[str] = jnp.transpose(SCREAMING_SNAKE_CASE__ , (3, 2, 0, 1) )
elif flax_key_tuple_array[-1] == "kernel":
_SCREAMING_SNAKE_CASE : Union[str, Any] = flax_key_tuple_array[:-1] + ["""weight"""]
_SCREAMING_SNAKE_CASE : Any = flax_tensor.T
elif flax_key_tuple_array[-1] == "scale":
_SCREAMING_SNAKE_CASE : Optional[int] = flax_key_tuple_array[:-1] + ["""weight"""]
if "time_embedding" not in flax_key_tuple_array:
for i, flax_key_tuple_string in enumerate(SCREAMING_SNAKE_CASE__ ):
_SCREAMING_SNAKE_CASE : Optional[int] = (
flax_key_tuple_string.replace("""_0""" , """.0""" )
.replace("""_1""" , """.1""" )
.replace("""_2""" , """.2""" )
.replace("""_3""" , """.3""" )
.replace("""_4""" , """.4""" )
.replace("""_5""" , """.5""" )
.replace("""_6""" , """.6""" )
.replace("""_7""" , """.7""" )
.replace("""_8""" , """.8""" )
.replace("""_9""" , """.9""" )
)
_SCREAMING_SNAKE_CASE : Tuple = """.""".join(SCREAMING_SNAKE_CASE__ )
if flax_key in pt_model_dict:
if flax_tensor.shape != pt_model_dict[flax_key].shape:
raise ValueError(
f"""Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected """
f"""to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.""" )
else:
# add weight to pytorch dict
_SCREAMING_SNAKE_CASE : Union[str, Any] = np.asarray(SCREAMING_SNAKE_CASE__ ) if not isinstance(SCREAMING_SNAKE_CASE__ , np.ndarray ) else flax_tensor
_SCREAMING_SNAKE_CASE : int = torch.from_numpy(SCREAMING_SNAKE_CASE__ )
# remove from missing keys
missing_keys.remove(SCREAMING_SNAKE_CASE__ )
else:
# weight is not expected by PyTorch model
unexpected_keys.append(SCREAMING_SNAKE_CASE__ )
pt_model.load_state_dict(SCREAMING_SNAKE_CASE__ )
# re-transform missing_keys to list
_SCREAMING_SNAKE_CASE : Optional[Any] = list(SCREAMING_SNAKE_CASE__ )
if len(SCREAMING_SNAKE_CASE__ ) > 0:
logger.warning(
"""Some weights of the Flax model were not used when initializing the PyTorch model"""
f""" {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing"""
f""" {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture"""
""" (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This"""
f""" IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect"""
""" to be exactly identical (e.g. initializing a BertForSequenceClassification model from a"""
""" FlaxBertForSequenceClassification model).""" )
if len(SCREAMING_SNAKE_CASE__ ) > 0:
logger.warning(
f"""Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly"""
f""" initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to"""
""" use it for predictions and inference.""" )
return pt_model
| 200 | 0 |
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_outputs import (
BaseModelOutputWithNoAttention,
BaseModelOutputWithPoolingAndNoAttention,
ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import logging
from .configuration_regnet import RegNetConfig
UpperCAmelCase : Union[str, Any] = logging.get_logger(__name__)
# General docstring
UpperCAmelCase : str = """RegNetConfig"""
# Base docstring
UpperCAmelCase : int = """facebook/regnet-y-040"""
UpperCAmelCase : Union[str, Any] = [1, 1088, 7, 7]
# Image classification docstring
UpperCAmelCase : List[str] = """facebook/regnet-y-040"""
UpperCAmelCase : str = """tabby, tabby cat"""
UpperCAmelCase : Optional[int] = [
"""facebook/regnet-y-040""",
# See all regnet models at https://huggingface.co/models?filter=regnet
]
class __lowerCAmelCase ( nn.Module):
def __init__( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = 3 , lowerCAmelCase__ = 1 , lowerCAmelCase__ = 1 , lowerCAmelCase__ = "relu" , ) -> Any:
'''simple docstring'''
super().__init__()
a__ : Dict =nn.Convad(
lowerCAmelCase__ , lowerCAmelCase__ , kernel_size=lowerCAmelCase__ , stride=lowerCAmelCase__ , padding=kernel_size // 2 , groups=lowerCAmelCase__ , bias=lowerCAmelCase__ , )
a__ : List[Any] =nn.BatchNormad(lowerCAmelCase__ )
a__ : Optional[Any] =ACTaFN[activation] if activation is not None else nn.Identity()
def _lowercase ( self , lowerCAmelCase__ ) -> Dict:
'''simple docstring'''
a__ : str =self.convolution(lowerCAmelCase__ )
a__ : str =self.normalization(lowerCAmelCase__ )
a__ : str =self.activation(lowerCAmelCase__ )
return hidden_state
class __lowerCAmelCase ( nn.Module):
def __init__( self , lowerCAmelCase__ ) -> Optional[Any]:
'''simple docstring'''
super().__init__()
a__ : Dict =RegNetConvLayer(
config.num_channels , config.embedding_size , kernel_size=3 , stride=2 , activation=config.hidden_act )
a__ : Optional[int] =config.num_channels
def _lowercase ( self , lowerCAmelCase__ ) -> List[Any]:
'''simple docstring'''
a__ : int =pixel_values.shape[1]
if num_channels != self.num_channels:
raise ValueError(
"Make sure that the channel dimension of the pixel values match with the one set in the configuration." )
a__ : Tuple =self.embedder(lowerCAmelCase__ )
return hidden_state
class __lowerCAmelCase ( nn.Module):
def __init__( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = 2 ) -> int:
'''simple docstring'''
super().__init__()
a__ : Optional[Any] =nn.Convad(lowerCAmelCase__ , lowerCAmelCase__ , kernel_size=1 , stride=lowerCAmelCase__ , bias=lowerCAmelCase__ )
a__ : int =nn.BatchNormad(lowerCAmelCase__ )
def _lowercase ( self , lowerCAmelCase__ ) -> Tensor:
'''simple docstring'''
a__ : Union[str, Any] =self.convolution(lowerCAmelCase__ )
a__ : Optional[Any] =self.normalization(lowerCAmelCase__ )
return hidden_state
class __lowerCAmelCase ( nn.Module):
def __init__( self , lowerCAmelCase__ , lowerCAmelCase__ ) -> List[str]:
'''simple docstring'''
super().__init__()
a__ : str =nn.AdaptiveAvgPoolad((1, 1) )
a__ : str =nn.Sequential(
nn.Convad(lowerCAmelCase__ , lowerCAmelCase__ , kernel_size=1 ) , nn.ReLU() , nn.Convad(lowerCAmelCase__ , lowerCAmelCase__ , kernel_size=1 ) , nn.Sigmoid() , )
def _lowercase ( self , lowerCAmelCase__ ) -> Optional[int]:
'''simple docstring'''
a__ : int =self.pooler(lowerCAmelCase__ )
a__ : Union[str, Any] =self.attention(lowerCAmelCase__ )
a__ : Any =hidden_state * attention
return hidden_state
class __lowerCAmelCase ( nn.Module):
def __init__( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = 1 ) -> Tuple:
'''simple docstring'''
super().__init__()
a__ : List[Any] =in_channels != out_channels or stride != 1
a__ : Dict =max(1 , out_channels // config.groups_width )
a__ : Optional[int] =(
RegNetShortCut(lowerCAmelCase__ , lowerCAmelCase__ , stride=lowerCAmelCase__ ) if should_apply_shortcut else nn.Identity()
)
a__ : Dict =nn.Sequential(
RegNetConvLayer(lowerCAmelCase__ , lowerCAmelCase__ , kernel_size=1 , activation=config.hidden_act ) , RegNetConvLayer(lowerCAmelCase__ , lowerCAmelCase__ , stride=lowerCAmelCase__ , groups=lowerCAmelCase__ , activation=config.hidden_act ) , RegNetConvLayer(lowerCAmelCase__ , lowerCAmelCase__ , kernel_size=1 , activation=lowerCAmelCase__ ) , )
a__ : int =ACTaFN[config.hidden_act]
def _lowercase ( self , lowerCAmelCase__ ) -> Any:
'''simple docstring'''
a__ : Optional[Any] =hidden_state
a__ : List[str] =self.layer(lowerCAmelCase__ )
a__ : Optional[Any] =self.shortcut(lowerCAmelCase__ )
hidden_state += residual
a__ : str =self.activation(lowerCAmelCase__ )
return hidden_state
class __lowerCAmelCase ( nn.Module):
def __init__( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = 1 ) -> List[str]:
'''simple docstring'''
super().__init__()
a__ : Optional[int] =in_channels != out_channels or stride != 1
a__ : Dict =max(1 , out_channels // config.groups_width )
a__ : List[Any] =(
RegNetShortCut(lowerCAmelCase__ , lowerCAmelCase__ , stride=lowerCAmelCase__ ) if should_apply_shortcut else nn.Identity()
)
a__ : Dict =nn.Sequential(
RegNetConvLayer(lowerCAmelCase__ , lowerCAmelCase__ , kernel_size=1 , activation=config.hidden_act ) , RegNetConvLayer(lowerCAmelCase__ , lowerCAmelCase__ , stride=lowerCAmelCase__ , groups=lowerCAmelCase__ , activation=config.hidden_act ) , RegNetSELayer(lowerCAmelCase__ , reduced_channels=int(round(in_channels / 4 ) ) ) , RegNetConvLayer(lowerCAmelCase__ , lowerCAmelCase__ , kernel_size=1 , activation=lowerCAmelCase__ ) , )
a__ : Optional[int] =ACTaFN[config.hidden_act]
def _lowercase ( self , lowerCAmelCase__ ) -> List[str]:
'''simple docstring'''
a__ : List[str] =hidden_state
a__ : Any =self.layer(lowerCAmelCase__ )
a__ : List[Any] =self.shortcut(lowerCAmelCase__ )
hidden_state += residual
a__ : Optional[int] =self.activation(lowerCAmelCase__ )
return hidden_state
class __lowerCAmelCase ( nn.Module):
def __init__( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = 2 , lowerCAmelCase__ = 2 , ) -> Union[str, Any]:
'''simple docstring'''
super().__init__()
a__ : Optional[int] =RegNetXLayer if config.layer_type == "x" else RegNetYLayer
a__ : List[Any] =nn.Sequential(
# downsampling is done in the first layer with stride of 2
layer(
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , stride=lowerCAmelCase__ , ) , *[layer(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) for _ in range(depth - 1 )] , )
def _lowercase ( self , lowerCAmelCase__ ) -> Tuple:
'''simple docstring'''
a__ : Any =self.layers(lowerCAmelCase__ )
return hidden_state
class __lowerCAmelCase ( nn.Module):
def __init__( self , lowerCAmelCase__ ) -> Union[str, Any]:
'''simple docstring'''
super().__init__()
a__ : List[Any] =nn.ModuleList([] )
# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
self.stages.append(
RegNetStage(
lowerCAmelCase__ , config.embedding_size , config.hidden_sizes[0] , stride=2 if config.downsample_in_first_stage else 1 , depth=config.depths[0] , ) )
a__ : str =zip(config.hidden_sizes , config.hidden_sizes[1:] )
for (in_channels, out_channels), depth in zip(lowerCAmelCase__ , config.depths[1:] ):
self.stages.append(RegNetStage(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , depth=lowerCAmelCase__ ) )
def _lowercase ( self , lowerCAmelCase__ , lowerCAmelCase__ = False , lowerCAmelCase__ = True ) -> BaseModelOutputWithNoAttention:
'''simple docstring'''
a__ : int =() if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
a__ : str =hidden_states + (hidden_state,)
a__ : str =stage_module(lowerCAmelCase__ )
if output_hidden_states:
a__ : Optional[Any] =hidden_states + (hidden_state,)
if not return_dict:
return tuple(v for v in [hidden_state, hidden_states] if v is not None )
return BaseModelOutputWithNoAttention(last_hidden_state=lowerCAmelCase__ , hidden_states=lowerCAmelCase__ )
class __lowerCAmelCase ( UpperCamelCase__):
_lowercase : Optional[int] = RegNetConfig
_lowercase : Optional[int] = """regnet"""
_lowercase : str = """pixel_values"""
_lowercase : List[Any] = True
def _lowercase ( self , lowerCAmelCase__ ) -> Optional[Any]:
'''simple docstring'''
if isinstance(lowerCAmelCase__ , nn.Convad ):
nn.init.kaiming_normal_(module.weight , mode="fan_out" , nonlinearity="relu" )
elif isinstance(lowerCAmelCase__ , (nn.BatchNormad, nn.GroupNorm) ):
nn.init.constant_(module.weight , 1 )
nn.init.constant_(module.bias , 0 )
def _lowercase ( self , lowerCAmelCase__ , lowerCAmelCase__=False ) -> int:
'''simple docstring'''
if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
a__ : Dict =value
UpperCAmelCase : List[Any] = r"""
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
UpperCAmelCase : List[str] = r"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`ConvNextImageProcessor.__call__`] for details.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
"""The bare RegNet model outputting raw features without any specific head on top.""" , UpperCamelCase__ , )
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class __lowerCAmelCase ( UpperCamelCase__):
def __init__( self , lowerCAmelCase__ ) -> Dict:
'''simple docstring'''
super().__init__(lowerCAmelCase__ )
a__ : List[str] =config
a__ : Dict =RegNetEmbeddings(lowerCAmelCase__ )
a__ : Any =RegNetEncoder(lowerCAmelCase__ )
a__ : Tuple =nn.AdaptiveAvgPoolad((1, 1) )
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(lowerCAmelCase__ )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=lowerCAmelCase__ , config_class=_CONFIG_FOR_DOC , modality="vision" , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def _lowercase ( self , lowerCAmelCase__ , lowerCAmelCase__ = None , lowerCAmelCase__ = None ) -> BaseModelOutputWithPoolingAndNoAttention:
'''simple docstring'''
a__ : Any =(
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
a__ : Optional[int] =return_dict if return_dict is not None else self.config.use_return_dict
a__ : List[str] =self.embedder(lowerCAmelCase__ )
a__ : Dict =self.encoder(
lowerCAmelCase__ , output_hidden_states=lowerCAmelCase__ , return_dict=lowerCAmelCase__ )
a__ : Tuple =encoder_outputs[0]
a__ : List[Any] =self.pooler(lowerCAmelCase__ )
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=lowerCAmelCase__ , pooler_output=lowerCAmelCase__ , hidden_states=encoder_outputs.hidden_states , )
@add_start_docstrings(
"""
RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
ImageNet.
""" , UpperCamelCase__ , )
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class __lowerCAmelCase ( UpperCamelCase__):
def __init__( self , lowerCAmelCase__ ) -> Optional[int]:
'''simple docstring'''
super().__init__(lowerCAmelCase__ )
a__ : List[str] =config.num_labels
a__ : Optional[Any] =RegNetModel(lowerCAmelCase__ )
# classification head
a__ : List[str] =nn.Sequential(
nn.Flatten() , nn.Linear(config.hidden_sizes[-1] , config.num_labels ) if config.num_labels > 0 else nn.Identity() , )
# initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(lowerCAmelCase__ )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=lowerCAmelCase__ , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def _lowercase ( self , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = None , ) -> ImageClassifierOutputWithNoAttention:
'''simple docstring'''
a__ : Optional[Any] =return_dict if return_dict is not None else self.config.use_return_dict
a__ : Union[str, Any] =self.regnet(lowerCAmelCase__ , output_hidden_states=lowerCAmelCase__ , return_dict=lowerCAmelCase__ )
a__ : int =outputs.pooler_output if return_dict else outputs[1]
a__ : int =self.classifier(lowerCAmelCase__ )
a__ : Tuple =None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
a__ : List[str] ="regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
a__ : Union[str, Any] ="single_label_classification"
else:
a__ : Optional[int] ="multi_label_classification"
if self.config.problem_type == "regression":
a__ : str =MSELoss()
if self.num_labels == 1:
a__ : Dict =loss_fct(logits.squeeze() , labels.squeeze() )
else:
a__ : Dict =loss_fct(lowerCAmelCase__ , lowerCAmelCase__ )
elif self.config.problem_type == "single_label_classification":
a__ : Dict =CrossEntropyLoss()
a__ : int =loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
a__ : List[str] =BCEWithLogitsLoss()
a__ : Any =loss_fct(lowerCAmelCase__ , lowerCAmelCase__ )
if not return_dict:
a__ : List[Any] =(logits,) + outputs[2:]
return (loss,) + output if loss is not None else output
return ImageClassifierOutputWithNoAttention(loss=lowerCAmelCase__ , logits=lowerCAmelCase__ , hidden_states=outputs.hidden_states )
| 148 |
import unittest
from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
NystromformerForMaskedLM,
NystromformerForMultipleChoice,
NystromformerForQuestionAnswering,
NystromformerForSequenceClassification,
NystromformerForTokenClassification,
NystromformerModel,
)
from transformers.models.nystromformer.modeling_nystromformer import NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
class __lowerCAmelCase :
def __init__( self , lowerCAmelCase__ , lowerCAmelCase__=1_3 , lowerCAmelCase__=7 , lowerCAmelCase__=True , lowerCAmelCase__=True , lowerCAmelCase__=True , lowerCAmelCase__=True , lowerCAmelCase__=9_9 , lowerCAmelCase__=3_2 , lowerCAmelCase__=5 , lowerCAmelCase__=4 , lowerCAmelCase__=3_7 , lowerCAmelCase__="gelu" , lowerCAmelCase__=0.1 , lowerCAmelCase__=0.1 , lowerCAmelCase__=5_1_2 , lowerCAmelCase__=1_6 , lowerCAmelCase__=2 , lowerCAmelCase__=0.02 , lowerCAmelCase__=3 , lowerCAmelCase__=4 , lowerCAmelCase__=None , ) -> List[Any]:
'''simple docstring'''
a__ : Optional[Any] =parent
a__ : Tuple =batch_size
a__ : List[Any] =seq_length
a__ : Dict =is_training
a__ : Any =use_input_mask
a__ : int =use_token_type_ids
a__ : Optional[Any] =use_labels
a__ : Optional[Any] =vocab_size
a__ : List[str] =hidden_size
a__ : int =num_hidden_layers
a__ : Tuple =num_attention_heads
a__ : Union[str, Any] =intermediate_size
a__ : Optional[int] =hidden_act
a__ : int =hidden_dropout_prob
a__ : Union[str, Any] =attention_probs_dropout_prob
a__ : List[Any] =max_position_embeddings
a__ : str =type_vocab_size
a__ : Optional[Any] =type_sequence_label_size
a__ : Union[str, Any] =initializer_range
a__ : List[Any] =num_labels
a__ : str =num_choices
a__ : int =scope
def _lowercase ( self ) -> int:
'''simple docstring'''
a__ : List[Any] =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
a__ : str =None
if self.use_input_mask:
a__ : List[Any] =random_attention_mask([self.batch_size, self.seq_length] )
a__ : str =None
if self.use_token_type_ids:
a__ : List[str] =ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
a__ : Dict =None
a__ : str =None
a__ : str =None
if self.use_labels:
a__ : List[str] =ids_tensor([self.batch_size] , self.type_sequence_label_size )
a__ : Tuple =ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
a__ : Dict =ids_tensor([self.batch_size] , self.num_choices )
a__ : Tuple =self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def _lowercase ( self ) -> Tuple:
'''simple docstring'''
return NystromformerConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=lowerCAmelCase__ , initializer_range=self.initializer_range , )
def _lowercase ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> List[Any]:
'''simple docstring'''
a__ : Tuple =NystromformerModel(config=lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
a__ : Optional[Any] =model(lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , token_type_ids=lowerCAmelCase__ )
a__ : str =model(lowerCAmelCase__ , token_type_ids=lowerCAmelCase__ )
a__ : Optional[int] =model(lowerCAmelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _lowercase ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> int:
'''simple docstring'''
a__ : int =NystromformerForMaskedLM(config=lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
a__ : Dict =model(lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , token_type_ids=lowerCAmelCase__ , labels=lowerCAmelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _lowercase ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> Any:
'''simple docstring'''
a__ : Optional[int] =NystromformerForQuestionAnswering(config=lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
a__ : str =model(
lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , token_type_ids=lowerCAmelCase__ , start_positions=lowerCAmelCase__ , end_positions=lowerCAmelCase__ , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def _lowercase ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> Optional[int]:
'''simple docstring'''
a__ : Optional[Any] =self.num_labels
a__ : Dict =NystromformerForSequenceClassification(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
a__ : List[str] =model(lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , token_type_ids=lowerCAmelCase__ , labels=lowerCAmelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _lowercase ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> List[str]:
'''simple docstring'''
a__ : Tuple =self.num_labels
a__ : List[str] =NystromformerForTokenClassification(config=lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
a__ : List[Any] =model(lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , token_type_ids=lowerCAmelCase__ , labels=lowerCAmelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def _lowercase ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> Tuple:
'''simple docstring'''
a__ : List[Any] =self.num_choices
a__ : Optional[Any] =NystromformerForMultipleChoice(config=lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
a__ : List[str] =input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
a__ : List[Any] =token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
a__ : List[Any] =input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
a__ : Dict =model(
lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , token_type_ids=lowerCAmelCase__ , labels=lowerCAmelCase__ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def _lowercase ( self ) -> Tuple:
'''simple docstring'''
a__ : Optional[Any] =self.prepare_config_and_inputs()
(
(
a__
) , (
a__
) , (
a__
) , (
a__
) , (
a__
) , (
a__
) , (
a__
) ,
) : List[str] =config_and_inputs
a__ : str ={"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class __lowerCAmelCase ( UpperCamelCase__ , UpperCamelCase__ , unittest.TestCase):
_lowercase : int = (
(
NystromformerModel,
NystromformerForMaskedLM,
NystromformerForMultipleChoice,
NystromformerForQuestionAnswering,
NystromformerForSequenceClassification,
NystromformerForTokenClassification,
)
if is_torch_available()
else ()
)
_lowercase : Union[str, Any] = (
{
"""feature-extraction""": NystromformerModel,
"""fill-mask""": NystromformerForMaskedLM,
"""question-answering""": NystromformerForQuestionAnswering,
"""text-classification""": NystromformerForSequenceClassification,
"""token-classification""": NystromformerForTokenClassification,
"""zero-shot""": NystromformerForSequenceClassification,
}
if is_torch_available()
else {}
)
_lowercase : Union[str, Any] = False
_lowercase : Union[str, Any] = False
def _lowercase ( self ) -> List[str]:
'''simple docstring'''
a__ : Optional[int] =NystromformerModelTester(self )
a__ : Optional[int] =ConfigTester(self , config_class=lowerCAmelCase__ , hidden_size=3_7 )
def _lowercase ( self ) -> int:
'''simple docstring'''
self.config_tester.run_common_tests()
def _lowercase ( self ) -> Dict:
'''simple docstring'''
a__ : List[str] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCAmelCase__ )
def _lowercase ( self ) -> Optional[Any]:
'''simple docstring'''
a__ : Tuple =self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
a__ : int =type
self.model_tester.create_and_check_model(*lowerCAmelCase__ )
def _lowercase ( self ) -> Optional[Any]:
'''simple docstring'''
a__ : Optional[Any] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*lowerCAmelCase__ )
def _lowercase ( self ) -> Dict:
'''simple docstring'''
a__ : str =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*lowerCAmelCase__ )
def _lowercase ( self ) -> List[str]:
'''simple docstring'''
a__ : Tuple =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase__ )
def _lowercase ( self ) -> Union[str, Any]:
'''simple docstring'''
a__ : Tuple =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase__ )
def _lowercase ( self ) -> Optional[Any]:
'''simple docstring'''
a__ : int =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase__ )
@slow
def _lowercase ( self ) -> str:
'''simple docstring'''
for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a__ : int =NystromformerModel.from_pretrained(lowerCAmelCase__ )
self.assertIsNotNone(lowerCAmelCase__ )
@require_torch
class __lowerCAmelCase ( unittest.TestCase):
@slow
def _lowercase ( self ) -> str:
'''simple docstring'''
a__ : str =NystromformerModel.from_pretrained("uw-madison/nystromformer-512" )
a__ : int =torch.tensor([[0, 1, 2, 3, 4, 5]] )
with torch.no_grad():
a__ : Tuple =model(lowerCAmelCase__ )[0]
a__ : List[str] =torch.Size((1, 6, 7_6_8) )
self.assertEqual(output.shape , lowerCAmelCase__ )
a__ : int =torch.tensor(
[[[-0.45_32, -0.09_36, 0.51_37], [-0.26_76, 0.06_28, 0.61_86], [-0.36_29, -0.17_26, 0.47_16]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , lowerCAmelCase__ , atol=1E-4 ) )
@slow
def _lowercase ( self ) -> Optional[Any]:
'''simple docstring'''
a__ : Union[str, Any] ="the [MASK] of Belgium is Brussels"
a__ : str =AutoTokenizer.from_pretrained("uw-madison/nystromformer-512" )
a__ : int =NystromformerForMaskedLM.from_pretrained("uw-madison/nystromformer-512" )
a__ : List[Any] =tokenizer(lowerCAmelCase__ , return_tensors="pt" )
with torch.no_grad():
a__ : str =model(encoding.input_ids ).logits
a__ : List[str] =token_logits[:, 2, :].argmax(-1 )[0]
self.assertEqual(tokenizer.decode(lowerCAmelCase__ ) , "capital" )
| 148 | 1 |
'''simple docstring'''
from typing import List
import numpy as np
def UpperCamelCase( UpperCAmelCase_ ):
UpperCAmelCase : Union[str, Any] = {key: len(UpperCAmelCase_ ) for key, value in gen_kwargs.items() if isinstance(UpperCAmelCase_ , UpperCAmelCase_ )}
if len(set(lists_lengths.values() ) ) > 1:
raise RuntimeError(
(
'Sharding is ambiguous for this dataset: '
+ 'we found several data sources lists of different lengths, and we don\'t know over which list we should parallelize:\n'
+ '\n'.join(F"""\t- key {key} has length {length}""" for key, length in lists_lengths.items() )
+ '\nTo fix this, check the \'gen_kwargs\' and make sure to use lists only for data sources, '
+ 'and use tuples otherwise. In the end there should only be one single list, or several lists with the same length.'
) )
UpperCAmelCase : Union[str, Any] = max(lists_lengths.values() , default=0 )
return max(1 , UpperCAmelCase_ )
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ ):
UpperCAmelCase : int = []
for group_idx in range(UpperCAmelCase_ ):
UpperCAmelCase : List[Any] = num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
if num_shards_to_add == 0:
break
UpperCAmelCase : Union[str, Any] = shards_indices_per_group[-1].stop if shards_indices_per_group else 0
UpperCAmelCase : List[str] = range(UpperCAmelCase_ , start + num_shards_to_add )
shards_indices_per_group.append(UpperCAmelCase_ )
return shards_indices_per_group
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ ):
UpperCAmelCase : Union[str, Any] = _number_of_shards_in_gen_kwargs(UpperCAmelCase_ )
if num_shards == 1:
return [dict(UpperCAmelCase_ )]
else:
UpperCAmelCase : Optional[int] = _distribute_shards(num_shards=UpperCAmelCase_ , max_num_jobs=UpperCAmelCase_ )
return [
{
key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
if isinstance(UpperCAmelCase_ , UpperCAmelCase_ )
else value
for key, value in gen_kwargs.items()
}
for group_idx in range(len(UpperCAmelCase_ ) )
]
def UpperCamelCase( UpperCAmelCase_ ):
return {
key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
if isinstance(gen_kwargs_list[0][key] , UpperCAmelCase_ )
else gen_kwargs_list[0][key]
for key in gen_kwargs_list[0]
}
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ ):
UpperCAmelCase : Dict = {len(UpperCAmelCase_ ) for value in gen_kwargs.values() if isinstance(UpperCAmelCase_ , UpperCAmelCase_ )}
UpperCAmelCase : Any = {}
for size in list_sizes:
UpperCAmelCase : Optional[Any] = list(range(UpperCAmelCase_ ) )
rng.shuffle(indices_per_size[size] )
# Now let's copy the gen_kwargs and shuffle the lists based on their sizes
UpperCAmelCase : Union[str, Any] = dict(UpperCAmelCase_ )
for key, value in shuffled_kwargs.items():
if isinstance(UpperCAmelCase_ , UpperCAmelCase_ ):
UpperCAmelCase : List[Any] = [value[i] for i in indices_per_size[len(UpperCAmelCase_ )]]
return shuffled_kwargs
| 151 |
'''simple docstring'''
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
lowercase__ = "\\n@INPROCEEDINGS{Papineni02bleu:a,\n author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n title = {BLEU: a Method for Automatic Evaluation of Machine Translation},\n booktitle = {},\n year = {2002},\n pages = {311--318}\n}\n@inproceedings{lin-och-2004-orange,\n title = \"{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation\",\n author = \"Lin, Chin-Yew and\n Och, Franz Josef\",\n booktitle = \"{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics\",\n month = \"aug 23{--}aug 27\",\n year = \"2004\",\n address = \"Geneva, Switzerland\",\n publisher = \"COLING\",\n url = \"https://www.aclweb.org/anthology/C04-1072\",\n pages = \"501--507\",\n}\n"
lowercase__ = "\\nBLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.\nQuality is considered to be the correspondence between a machine's output and that of a human: \"the closer a machine translation is to a professional human translation,\nthe better it is\" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and\nremains one of the most popular automated and inexpensive metrics.\n\nScores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.\nThose scores are then averaged over the whole corpus to reach an estimate of the translation's overall quality. Intelligibility or grammatical correctness\nare not taken into account[citation needed].\n\nBLEU's output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1\nrepresenting more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the\nreference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional\nreference translations will increase the BLEU score.\n"
lowercase__ = "\nComputes BLEU score of translated segments against one or more references.\nArgs:\n predictions: list of translations to score.\n Each translation should be tokenized into a list of tokens.\n references: list of lists of references for each translation.\n Each reference should be tokenized into a list of tokens.\n max_order: Maximum n-gram order to use when computing BLEU score.\n smooth: Whether or not to apply Lin et al. 2004 smoothing.\nReturns:\n 'bleu': bleu score,\n 'precisions': geometric mean of n-gram precisions,\n 'brevity_penalty': brevity penalty,\n 'length_ratio': ratio of lengths,\n 'translation_length': translation_length,\n 'reference_length': reference_length\nExamples:\n\n >>> predictions = [\n ... [\"hello\", \"there\", \"general\", \"kenobi\"], # tokenized prediction of the first sample\n ... [\"foo\", \"bar\", \"foobar\"] # tokenized prediction of the second sample\n ... ]\n >>> references = [\n ... [[\"hello\", \"there\", \"general\", \"kenobi\"], [\"hello\", \"there\", \"!\"]], # tokenized references for the first sample (2 references)\n ... [[\"foo\", \"bar\", \"foobar\"]] # tokenized references for the second sample (1 reference)\n ... ]\n >>> bleu = datasets.load_metric(\"bleu\")\n >>> results = bleu.compute(predictions=predictions, references=references)\n >>> print(results[\"bleu\"])\n 1.0\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class A_ ( datasets.Metric ):
'''simple docstring'''
def UpperCAmelCase_ ( self : Tuple ) -> List[Any]:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Sequence(datasets.Value('string' , id='token' ) , id='sequence' ),
'references': datasets.Sequence(
datasets.Sequence(datasets.Value('string' , id='token' ) , id='sequence' ) , id='references' ),
} ) , codebase_urls=['https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py'] , reference_urls=[
'https://en.wikipedia.org/wiki/BLEU',
'https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213',
] , )
def UpperCAmelCase_ ( self : Union[str, Any] , lowercase_ : Tuple , lowercase_ : Tuple , lowercase_ : Tuple=4 , lowercase_ : List[str]=False ) -> Union[str, Any]:
UpperCAmelCase : Tuple = compute_bleu(
reference_corpus=lowercase_ , translation_corpus=lowercase_ , max_order=lowercase_ , smooth=lowercase_ )
((UpperCAmelCase) , (UpperCAmelCase) , (UpperCAmelCase) , (UpperCAmelCase) , (UpperCAmelCase) , (UpperCAmelCase)) : Dict = score
return {
"bleu": bleu,
"precisions": precisions,
"brevity_penalty": bp,
"length_ratio": ratio,
"translation_length": translation_length,
"reference_length": reference_length,
}
| 151 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'facebook/xmod-base': 'https://huggingface.co/facebook/xmod-base/resolve/main/config.json',
'facebook/xmod-large-prenorm': 'https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json',
'facebook/xmod-base-13-125k': 'https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json',
'facebook/xmod-base-30-125k': 'https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json',
'facebook/xmod-base-30-195k': 'https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json',
'facebook/xmod-base-60-125k': 'https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json',
'facebook/xmod-base-60-265k': 'https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json',
'facebook/xmod-base-75-125k': 'https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json',
'facebook/xmod-base-75-269k': 'https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json',
}
class __a( _a ):
"""simple docstring"""
lowerCAmelCase = '''xmod'''
def __init__( self ,_SCREAMING_SNAKE_CASE=30_522 ,_SCREAMING_SNAKE_CASE=768 ,_SCREAMING_SNAKE_CASE=12 ,_SCREAMING_SNAKE_CASE=12 ,_SCREAMING_SNAKE_CASE=3_072 ,_SCREAMING_SNAKE_CASE="gelu" ,_SCREAMING_SNAKE_CASE=0.1 ,_SCREAMING_SNAKE_CASE=0.1 ,_SCREAMING_SNAKE_CASE=512 ,_SCREAMING_SNAKE_CASE=2 ,_SCREAMING_SNAKE_CASE=0.02 ,_SCREAMING_SNAKE_CASE=1e-12 ,_SCREAMING_SNAKE_CASE=1 ,_SCREAMING_SNAKE_CASE=0 ,_SCREAMING_SNAKE_CASE=2 ,_SCREAMING_SNAKE_CASE="absolute" ,_SCREAMING_SNAKE_CASE=True ,_SCREAMING_SNAKE_CASE=None ,_SCREAMING_SNAKE_CASE=False ,_SCREAMING_SNAKE_CASE=2 ,_SCREAMING_SNAKE_CASE=False ,_SCREAMING_SNAKE_CASE=True ,_SCREAMING_SNAKE_CASE=True ,_SCREAMING_SNAKE_CASE=("en_XX",) ,_SCREAMING_SNAKE_CASE=None ,**_SCREAMING_SNAKE_CASE ,) -> Tuple:
super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE ,bos_token_id=_SCREAMING_SNAKE_CASE ,eos_token_id=_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : Union[str, Any] = vocab_size
UpperCAmelCase_ : List[Any] = hidden_size
UpperCAmelCase_ : Dict = num_hidden_layers
UpperCAmelCase_ : List[str] = num_attention_heads
UpperCAmelCase_ : str = hidden_act
UpperCAmelCase_ : Optional[Any] = intermediate_size
UpperCAmelCase_ : Any = hidden_dropout_prob
UpperCAmelCase_ : Union[str, Any] = attention_probs_dropout_prob
UpperCAmelCase_ : Union[str, Any] = max_position_embeddings
UpperCAmelCase_ : List[Any] = type_vocab_size
UpperCAmelCase_ : Tuple = initializer_range
UpperCAmelCase_ : List[str] = layer_norm_eps
UpperCAmelCase_ : Union[str, Any] = position_embedding_type
UpperCAmelCase_ : Optional[Any] = use_cache
UpperCAmelCase_ : List[Any] = classifier_dropout
UpperCAmelCase_ : int = pre_norm
UpperCAmelCase_ : int = adapter_reduction_factor
UpperCAmelCase_ : List[Any] = adapter_layer_norm
UpperCAmelCase_ : Dict = adapter_reuse_layer_norm
UpperCAmelCase_ : Tuple = ln_before_adapter
UpperCAmelCase_ : str = list(_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : Any = default_language
class __a( _a ):
"""simple docstring"""
@property
def a__ ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
UpperCAmelCase_ : List[str] = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
UpperCAmelCase_ : int = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] ) | 235 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
__a = logging.get_logger(__name__)
__a = {
'microsoft/layoutlmv3-base': 'https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json',
}
class __a( _a ):
"""simple docstring"""
lowerCAmelCase = '''layoutlmv3'''
def __init__( self ,_SCREAMING_SNAKE_CASE=50_265 ,_SCREAMING_SNAKE_CASE=768 ,_SCREAMING_SNAKE_CASE=12 ,_SCREAMING_SNAKE_CASE=12 ,_SCREAMING_SNAKE_CASE=3_072 ,_SCREAMING_SNAKE_CASE="gelu" ,_SCREAMING_SNAKE_CASE=0.1 ,_SCREAMING_SNAKE_CASE=0.1 ,_SCREAMING_SNAKE_CASE=512 ,_SCREAMING_SNAKE_CASE=2 ,_SCREAMING_SNAKE_CASE=0.02 ,_SCREAMING_SNAKE_CASE=1e-5 ,_SCREAMING_SNAKE_CASE=1 ,_SCREAMING_SNAKE_CASE=0 ,_SCREAMING_SNAKE_CASE=2 ,_SCREAMING_SNAKE_CASE=1_024 ,_SCREAMING_SNAKE_CASE=128 ,_SCREAMING_SNAKE_CASE=128 ,_SCREAMING_SNAKE_CASE=True ,_SCREAMING_SNAKE_CASE=32 ,_SCREAMING_SNAKE_CASE=128 ,_SCREAMING_SNAKE_CASE=64 ,_SCREAMING_SNAKE_CASE=256 ,_SCREAMING_SNAKE_CASE=True ,_SCREAMING_SNAKE_CASE=True ,_SCREAMING_SNAKE_CASE=True ,_SCREAMING_SNAKE_CASE=224 ,_SCREAMING_SNAKE_CASE=3 ,_SCREAMING_SNAKE_CASE=16 ,_SCREAMING_SNAKE_CASE=None ,**_SCREAMING_SNAKE_CASE ,) -> Dict:
super().__init__(
vocab_size=_SCREAMING_SNAKE_CASE ,hidden_size=_SCREAMING_SNAKE_CASE ,num_hidden_layers=_SCREAMING_SNAKE_CASE ,num_attention_heads=_SCREAMING_SNAKE_CASE ,intermediate_size=_SCREAMING_SNAKE_CASE ,hidden_act=_SCREAMING_SNAKE_CASE ,hidden_dropout_prob=_SCREAMING_SNAKE_CASE ,attention_probs_dropout_prob=_SCREAMING_SNAKE_CASE ,max_position_embeddings=_SCREAMING_SNAKE_CASE ,type_vocab_size=_SCREAMING_SNAKE_CASE ,initializer_range=_SCREAMING_SNAKE_CASE ,layer_norm_eps=_SCREAMING_SNAKE_CASE ,pad_token_id=_SCREAMING_SNAKE_CASE ,bos_token_id=_SCREAMING_SNAKE_CASE ,eos_token_id=_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE ,)
UpperCAmelCase_ : Dict = max_ad_position_embeddings
UpperCAmelCase_ : Any = coordinate_size
UpperCAmelCase_ : Tuple = shape_size
UpperCAmelCase_ : Optional[int] = has_relative_attention_bias
UpperCAmelCase_ : Union[str, Any] = rel_pos_bins
UpperCAmelCase_ : Dict = max_rel_pos
UpperCAmelCase_ : Union[str, Any] = has_spatial_attention_bias
UpperCAmelCase_ : Any = rel_ad_pos_bins
UpperCAmelCase_ : Tuple = max_rel_ad_pos
UpperCAmelCase_ : List[str] = text_embed
UpperCAmelCase_ : int = visual_embed
UpperCAmelCase_ : int = input_size
UpperCAmelCase_ : Dict = num_channels
UpperCAmelCase_ : int = patch_size
UpperCAmelCase_ : Dict = classifier_dropout
class __a( _a ):
"""simple docstring"""
lowerCAmelCase = version.parse('''1.12''' )
@property
def a__ ( self ) -> Mapping[str, Mapping[int, str]]:
# The order of inputs is different for question answering and sequence classification
if self.task in ["question-answering", "sequence-classification"]:
return OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
('''bbox''', {0: '''batch''', 1: '''sequence'''}),
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
else:
return OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
('''bbox''', {0: '''batch''', 1: '''sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
('''pixel_values''', {0: '''batch''', 1: '''num_channels'''}),
] )
@property
def a__ ( self ) -> float:
return 1e-5
@property
def a__ ( self ) -> int:
return 12
def a__ ( self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = -1 ,_SCREAMING_SNAKE_CASE = -1 ,_SCREAMING_SNAKE_CASE = False ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = 3 ,_SCREAMING_SNAKE_CASE = 40 ,_SCREAMING_SNAKE_CASE = 40 ,) -> Mapping[str, Any]:
setattr(processor.image_processor ,'''apply_ocr''' ,_SCREAMING_SNAKE_CASE )
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
UpperCAmelCase_ : List[str] = compute_effective_axis_dimension(
_SCREAMING_SNAKE_CASE ,fixed_dimension=OnnxConfig.default_fixed_batch ,num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
UpperCAmelCase_ : Optional[Any] = processor.tokenizer.num_special_tokens_to_add(_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : int = compute_effective_axis_dimension(
_SCREAMING_SNAKE_CASE ,fixed_dimension=OnnxConfig.default_fixed_sequence ,num_token_to_add=_SCREAMING_SNAKE_CASE )
# Generate dummy inputs according to compute batch and sequence
UpperCAmelCase_ : Optional[Any] = [[''' '''.join([processor.tokenizer.unk_token] ) * seq_length]] * batch_size
# Generate dummy bounding boxes
UpperCAmelCase_ : Tuple = [[[48, 84, 73, 128]]] * batch_size
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
# batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
UpperCAmelCase_ : Union[str, Any] = self._generate_dummy_images(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : int = dict(
processor(
_SCREAMING_SNAKE_CASE ,text=_SCREAMING_SNAKE_CASE ,boxes=_SCREAMING_SNAKE_CASE ,return_tensors=_SCREAMING_SNAKE_CASE ,) )
return inputs | 235 | 1 |
"""simple docstring"""
from typing import Dict
from .base import GenericTensor, Pipeline
class UpperCamelCase ( lowerCamelCase__ ):
def _UpperCAmelCase ( self ,__UpperCamelCase=None ,__UpperCamelCase=None ,__UpperCamelCase=None ,**__UpperCamelCase ) -> Union[str, Any]:
'''simple docstring'''
if tokenize_kwargs is None:
lowercase_ : Dict = {}
if truncation is not None:
if "truncation" in tokenize_kwargs:
raise ValueError(
'truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)' )
lowercase_ : Optional[int] = truncation
lowercase_ : int = tokenize_kwargs
lowercase_ : str = {}
if return_tensors is not None:
lowercase_ : List[str] = return_tensors
return preprocess_params, {}, postprocess_params
def _UpperCAmelCase ( self ,__UpperCamelCase ,**__UpperCamelCase ) -> Dict[str, GenericTensor]:
'''simple docstring'''
lowercase_ : Tuple = self.framework
lowercase_ : int = self.tokenizer(__lowerCamelCase ,return_tensors=__lowerCamelCase ,**__lowerCamelCase )
return model_inputs
def _UpperCAmelCase ( self ,__UpperCamelCase ) -> Optional[int]:
'''simple docstring'''
lowercase_ : int = self.model(**__lowerCamelCase )
return model_outputs
def _UpperCAmelCase ( self ,__UpperCamelCase ,__UpperCamelCase=False ) -> Dict:
'''simple docstring'''
if return_tensors:
return model_outputs[0]
if self.framework == "pt":
return model_outputs[0].tolist()
elif self.framework == "tf":
return model_outputs[0].numpy().tolist()
def __call__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> str:
'''simple docstring'''
return super().__call__(*__lowerCamelCase ,**__lowerCamelCase )
| 213 |
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class lowerCamelCase__ ( lowerCamelCase__):
'''simple docstring'''
snake_case_ =(DPMSolverSDEScheduler,)
snake_case_ =10
def lowerCAmelCase__ (self ,**__lowerCamelCase ) -> List[str]:
"""simple docstring"""
lowerCAmelCase__ : List[str] = {
'''num_train_timesteps''': 11_00,
'''beta_start''': 0.0001,
'''beta_end''': 0.02,
'''beta_schedule''': '''linear''',
'''noise_sampler_seed''': 0,
}
config.update(**__lowerCamelCase )
return config
def lowerCAmelCase__ (self ) -> int:
"""simple docstring"""
for timesteps in [10, 50, 1_00, 10_00]:
self.check_over_configs(num_train_timesteps=__lowerCamelCase )
def lowerCAmelCase__ (self ) -> Optional[Any]:
"""simple docstring"""
for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001] ,[0.0002, 0.002, 0.02] ):
self.check_over_configs(beta_start=__lowerCamelCase ,beta_end=__lowerCamelCase )
def lowerCAmelCase__ (self ) -> Union[str, Any]:
"""simple docstring"""
for schedule in ["linear", "scaled_linear"]:
self.check_over_configs(beta_schedule=__lowerCamelCase )
def lowerCAmelCase__ (self ) -> Optional[Any]:
"""simple docstring"""
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=__lowerCamelCase )
def lowerCAmelCase__ (self ) -> int:
"""simple docstring"""
lowerCAmelCase__ : List[str] = self.scheduler_classes[0]
lowerCAmelCase__ : str = self.get_scheduler_config()
lowerCAmelCase__ : Optional[Any] = scheduler_class(**__lowerCamelCase )
scheduler.set_timesteps(self.num_inference_steps )
lowerCAmelCase__ : Union[str, Any] = self.dummy_model()
lowerCAmelCase__ : Tuple = self.dummy_sample_deter * scheduler.init_noise_sigma
lowerCAmelCase__ : Union[str, Any] = sample.to(__lowerCamelCase )
for i, t in enumerate(scheduler.timesteps ):
lowerCAmelCase__ : Dict = scheduler.scale_model_input(__lowerCamelCase ,__lowerCamelCase )
lowerCAmelCase__ : Any = model(__lowerCamelCase ,__lowerCamelCase )
lowerCAmelCase__ : List[Any] = scheduler.step(__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase )
lowerCAmelCase__ : Optional[int] = output.prev_sample
lowerCAmelCase__ : List[Any] = torch.sum(torch.abs(__lowerCamelCase ) )
lowerCAmelCase__ : Dict = torch.mean(torch.abs(__lowerCamelCase ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 167.47_8210_4492_1875 ) < 1e-2
assert abs(result_mean.item() - 0.2178_7059_6456_5277 ) < 1e-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 171.59_3521_1181_6406 ) < 1e-2
assert abs(result_mean.item() - 0.2_2342_9068_9229_9652 ) < 1e-3
else:
assert abs(result_sum.item() - 162.52_3834_2285_1562 ) < 1e-2
assert abs(result_mean.item() - 0.211_6195_7085_1326 ) < 1e-3
def lowerCAmelCase__ (self ) -> Union[str, Any]:
"""simple docstring"""
lowerCAmelCase__ : Dict = self.scheduler_classes[0]
lowerCAmelCase__ : Any = self.get_scheduler_config(prediction_type='''v_prediction''' )
lowerCAmelCase__ : List[Any] = scheduler_class(**__lowerCamelCase )
scheduler.set_timesteps(self.num_inference_steps )
lowerCAmelCase__ : Optional[int] = self.dummy_model()
lowerCAmelCase__ : Optional[Any] = self.dummy_sample_deter * scheduler.init_noise_sigma
lowerCAmelCase__ : Tuple = sample.to(__lowerCamelCase )
for i, t in enumerate(scheduler.timesteps ):
lowerCAmelCase__ : Optional[Any] = scheduler.scale_model_input(__lowerCamelCase ,__lowerCamelCase )
lowerCAmelCase__ : Optional[Any] = model(__lowerCamelCase ,__lowerCamelCase )
lowerCAmelCase__ : Optional[int] = scheduler.step(__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase )
lowerCAmelCase__ : Union[str, Any] = output.prev_sample
lowerCAmelCase__ : Any = torch.sum(torch.abs(__lowerCamelCase ) )
lowerCAmelCase__ : Union[str, Any] = torch.mean(torch.abs(__lowerCamelCase ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 124.77_1492_0043_9453 ) < 1e-2
assert abs(result_mean.item() - 0.1_6226_2890_1481_6284 ) < 1e-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 128.1_6633_6059_5703 ) < 1e-2
assert abs(result_mean.item() - 0.1_6688_3260_0116_7297 ) < 1e-3
else:
assert abs(result_sum.item() - 119.8_4875_4882_8125 ) < 1e-2
assert abs(result_mean.item() - 0.1560_5306_6253_6621 ) < 1e-3
def lowerCAmelCase__ (self ) -> List[Any]:
"""simple docstring"""
lowerCAmelCase__ : Any = self.scheduler_classes[0]
lowerCAmelCase__ : Tuple = self.get_scheduler_config()
lowerCAmelCase__ : str = scheduler_class(**__lowerCamelCase )
scheduler.set_timesteps(self.num_inference_steps ,device=__lowerCamelCase )
lowerCAmelCase__ : Optional[Any] = self.dummy_model()
lowerCAmelCase__ : List[Any] = self.dummy_sample_deter.to(__lowerCamelCase ) * scheduler.init_noise_sigma
for t in scheduler.timesteps:
lowerCAmelCase__ : List[Any] = scheduler.scale_model_input(__lowerCamelCase ,__lowerCamelCase )
lowerCAmelCase__ : Any = model(__lowerCamelCase ,__lowerCamelCase )
lowerCAmelCase__ : List[Any] = scheduler.step(__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase )
lowerCAmelCase__ : List[Any] = output.prev_sample
lowerCAmelCase__ : List[str] = torch.sum(torch.abs(__lowerCamelCase ) )
lowerCAmelCase__ : Dict = torch.mean(torch.abs(__lowerCamelCase ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 167.46_9573_9746_0938 ) < 1e-2
assert abs(result_mean.item() - 0.2_1805_9346_0798_2635 ) < 1e-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 171.59_3536_3769_5312 ) < 1e-2
assert abs(result_mean.item() - 0.2_2342_9083_8241_5771 ) < 1e-3
else:
assert abs(result_sum.item() - 162.52_3834_2285_1562 ) < 1e-2
assert abs(result_mean.item() - 0.211_6195_7085_1326 ) < 1e-3
def lowerCAmelCase__ (self ) -> int:
"""simple docstring"""
lowerCAmelCase__ : str = self.scheduler_classes[0]
lowerCAmelCase__ : List[Any] = self.get_scheduler_config()
lowerCAmelCase__ : Union[str, Any] = scheduler_class(**__lowerCamelCase ,use_karras_sigmas=__lowerCamelCase )
scheduler.set_timesteps(self.num_inference_steps ,device=__lowerCamelCase )
lowerCAmelCase__ : str = self.dummy_model()
lowerCAmelCase__ : Union[str, Any] = self.dummy_sample_deter.to(__lowerCamelCase ) * scheduler.init_noise_sigma
lowerCAmelCase__ : Union[str, Any] = sample.to(__lowerCamelCase )
for t in scheduler.timesteps:
lowerCAmelCase__ : List[Any] = scheduler.scale_model_input(__lowerCamelCase ,__lowerCamelCase )
lowerCAmelCase__ : str = model(__lowerCamelCase ,__lowerCamelCase )
lowerCAmelCase__ : Tuple = scheduler.step(__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase )
lowerCAmelCase__ : str = output.prev_sample
lowerCAmelCase__ : Tuple = torch.sum(torch.abs(__lowerCamelCase ) )
lowerCAmelCase__ : List[Any] = torch.mean(torch.abs(__lowerCamelCase ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 176.66_9741_3574_2188 ) < 1e-2
assert abs(result_mean.item() - 0.2_3003_8727_3098_1811 ) < 1e-2
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 177.63_6535_6445_3125 ) < 1e-2
assert abs(result_mean.item() - 0.2_3003_8727_3098_1811 ) < 1e-2
else:
assert abs(result_sum.item() - 170.3_1352_2338_8672 ) < 1e-2
assert abs(result_mean.item() - 0.2_3003_8727_3098_1811 ) < 1e-2
| 129 | 0 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class SCREAMING_SNAKE_CASE__ ( metaclass=snake_case_):
lowerCAmelCase_ = ["""transformers""", """torch""", """note_seq"""]
def __init__( self , *A_ , **A_ )-> Optional[int]:
'''simple docstring'''
requires_backends(self , ['transformers', 'torch', 'note_seq'] )
@classmethod
def UpperCAmelCase_ ( cls , *A_ , **A_ )-> Optional[int]:
'''simple docstring'''
requires_backends(cls , ['transformers', 'torch', 'note_seq'] )
@classmethod
def UpperCAmelCase_ ( cls , *A_ , **A_ )-> Dict:
'''simple docstring'''
requires_backends(cls , ['transformers', 'torch', 'note_seq'] )
| 251 |
'''simple docstring'''
from __future__ import annotations
import time
import numpy as np
lowerCAmelCase : List[Any] = [8, 5, 9, 7]
lowerCAmelCase : str = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
lowerCAmelCase : Tuple = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1, 0, 5],
[1, 5, 3, 0],
[3, 0, 3, 3],
]
class SCREAMING_SNAKE_CASE__ :
def __init__( self , A_ , A_ , A_ , )-> None:
'''simple docstring'''
UpperCamelCase = claim_vector
UpperCamelCase = allocated_resources_table
UpperCamelCase = maximum_claim_table
def UpperCAmelCase_ ( self )-> list[int]:
'''simple docstring'''
return [
sum(p_item[i] for p_item in self.__allocated_resources_table )
for i in range(len(self.__allocated_resources_table[0] ) )
]
def UpperCAmelCase_ ( self )-> list[int]:
'''simple docstring'''
return np.array(self.__claim_vector ) - np.array(
self.__processes_resource_summation() )
def UpperCAmelCase_ ( self )-> list[list[int]]:
'''simple docstring'''
return [
list(np.array(self.__maximum_claim_table[i] ) - np.array(A_ ) )
for i, allocated_resource in enumerate(self.__allocated_resources_table )
]
def UpperCAmelCase_ ( self )-> dict[int, list[int]]:
'''simple docstring'''
return {self.__need().index(A_ ): i for i in self.__need()}
def UpperCAmelCase_ ( self , **A_ )-> None:
'''simple docstring'''
UpperCamelCase = self.__need()
UpperCamelCase = self.__allocated_resources_table
UpperCamelCase = self.__available_resources()
UpperCamelCase = self.__need_index_manager()
for kw, val in kwargs.items():
if kw and val is True:
self.__pretty_data()
print('_' * 50 + '\n' )
while need_list:
UpperCamelCase = False
for each_need in need_list:
UpperCamelCase = True
for index, need in enumerate(A_ ):
if need > available_resources[index]:
UpperCamelCase = False
break
if execution:
UpperCamelCase = True
# get the original index of the process from ind_ctrl db
for original_need_index, need_clone in need_index_manager.items():
if each_need == need_clone:
UpperCamelCase = original_need_index
print(F'''Process {process_number + 1} is executing.''' )
# remove the process run from stack
need_list.remove(A_ )
# update available/freed resources stack
UpperCamelCase = np.array(A_ ) + np.array(
alloc_resources_table[process_number] )
print(
'Updated available resource stack for processes: '
+ ' '.join([str(A_ ) for x in available_resources] ) )
break
if safe:
print('The process is in a safe state.\n' )
else:
print('System in unsafe state. Aborting...\n' )
break
def UpperCAmelCase_ ( self )-> Union[str, Any]:
'''simple docstring'''
print(' ' * 9 + 'Allocated Resource Table' )
for item in self.__allocated_resources_table:
print(
F'''P{self.__allocated_resources_table.index(A_ ) + 1}'''
+ ' '.join(F'''{it:>8}''' for it in item )
+ '\n' )
print(' ' * 9 + 'System Resource Table' )
for item in self.__maximum_claim_table:
print(
F'''P{self.__maximum_claim_table.index(A_ ) + 1}'''
+ ' '.join(F'''{it:>8}''' for it in item )
+ '\n' )
print(
'Current Usage by Active Processes: '
+ ' '.join(str(A_ ) for x in self.__claim_vector ) )
print(
'Initial Available Resources: '
+ ' '.join(str(A_ ) for x in self.__available_resources() ) )
time.sleep(1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 251 | 1 |
"""simple docstring"""
import argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logging.set_verbosity_info()
a = logging.get_logger(__name__)
a = {name: getattr(transformers, name + 'Fast') for name in SLOW_TO_FAST_CONVERTERS}
def lowercase (snake_case__ : Optional[int] , snake_case__ : List[str] , snake_case__ : Tuple , snake_case__ : List[str] ) -> Any:
'''simple docstring'''
if tokenizer_name is not None and tokenizer_name not in TOKENIZER_CLASSES:
raise ValueError(f'''Unrecognized tokenizer name, should be one of {list(TOKENIZER_CLASSES.keys() )}.''' )
if tokenizer_name is None:
lowerCAmelCase = TOKENIZER_CLASSES
else:
lowerCAmelCase = {tokenizer_name: getattr(snake_case__ , tokenizer_name + """Fast""" )}
logger.info(f'''Loading tokenizer classes: {tokenizer_names}''' )
for tokenizer_name in tokenizer_names:
lowerCAmelCase = TOKENIZER_CLASSES[tokenizer_name]
lowerCAmelCase = True
if checkpoint_name is None:
lowerCAmelCase = list(tokenizer_class.max_model_input_sizes.keys() )
else:
lowerCAmelCase = [checkpoint_name]
logger.info(f'''For tokenizer {tokenizer_class.__class__.__name__} loading checkpoints: {checkpoint_names}''' )
for checkpoint in checkpoint_names:
logger.info(f'''Loading {tokenizer_class.__class__.__name__} {checkpoint}''' )
# Load tokenizer
lowerCAmelCase = tokenizer_class.from_pretrained(snake_case__ , force_download=snake_case__ )
# Save fast tokenizer
logger.info(f'''Save fast tokenizer to {dump_path} with prefix {checkpoint} add_prefix {add_prefix}''' )
# For organization names we create sub-directories
if "/" in checkpoint:
lowerCAmelCase , lowerCAmelCase = checkpoint.split("""/""" )
lowerCAmelCase = os.path.join(snake_case__ , snake_case__ )
elif add_prefix:
lowerCAmelCase = checkpoint
lowerCAmelCase = dump_path
else:
lowerCAmelCase = None
lowerCAmelCase = dump_path
logger.info(f'''=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}''' )
if checkpoint in list(tokenizer.pretrained_vocab_files_map.values() )[0]:
lowerCAmelCase = list(tokenizer.pretrained_vocab_files_map.values() )[0][checkpoint]
lowerCAmelCase = file_path.split(snake_case__ )[-1][0]
if next_char == "/":
lowerCAmelCase = os.path.join(snake_case__ , snake_case__ )
lowerCAmelCase = None
logger.info(f'''=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}''' )
lowerCAmelCase = tokenizer.save_pretrained(
snake_case__ , legacy_format=snake_case__ , filename_prefix=snake_case__ )
logger.info(f'''=> File names {file_names}''' )
for file_name in file_names:
if not file_name.endswith("""tokenizer.json""" ):
os.remove(snake_case__ )
logger.info(f'''=> removing {file_name}''' )
if __name__ == "__main__":
a = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--dump_path', default=None, type=str, required=True, help='Path to output generated fast tokenizer files.'
)
parser.add_argument(
'--tokenizer_name',
default=None,
type=str,
help=(
f"""Optional tokenizer type selected in the list of {list(TOKENIZER_CLASSES.keys())}. If not given, will """
'download and convert all the checkpoints from AWS.'
),
)
parser.add_argument(
'--checkpoint_name',
default=None,
type=str,
help='Optional checkpoint name. If not given, will download and convert the canonical checkpoints from AWS.',
)
parser.add_argument(
'--force_download',
action='store_true',
help='Re-download checkpoints.',
)
a = parser.parse_args()
convert_slow_checkpoint_to_fast(args.tokenizer_name, args.checkpoint_name, args.dump_path, args.force_download)
| 155 |
"""simple docstring"""
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=_a )
class SCREAMING_SNAKE_CASE__ ( _a ):
_a = field(default='image-classification' , metadata={'include_in_asdict_even_if_is_default': True} )
_a = Features({'image': Image()} )
_a = Features({'labels': ClassLabel} )
_a = "image"
_a = "labels"
def __lowercase ( self : List[str] , lowerCAmelCase : Tuple ):
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] , lowerCAmelCase ):
raise ValueError(f'''Column {self.label_column} is not a ClassLabel.''' )
lowerCAmelCase = copy.deepcopy(self )
lowerCAmelCase = self.label_schema.copy()
lowerCAmelCase = features[self.label_column]
lowerCAmelCase = label_schema
return task_template
@property
def __lowercase ( self : Optional[Any] ):
return {
self.image_column: "image",
self.label_column: "labels",
}
| 155 | 1 |
'''simple docstring'''
from math import sqrt
def __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE ):
_snake_case = 0
for i in range(1 , int(sqrt(_SCREAMING_SNAKE_CASE ) + 1 ) ):
if n % i == 0 and i != sqrt(_SCREAMING_SNAKE_CASE ):
total += i + n // i
elif i == sqrt(_SCREAMING_SNAKE_CASE ):
total += i
return total - n
def __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE = 1_0000 ):
_snake_case = sum(
i
for i in range(1 , _SCREAMING_SNAKE_CASE )
if sum_of_divisors(sum_of_divisors(_SCREAMING_SNAKE_CASE ) ) == i and sum_of_divisors(_SCREAMING_SNAKE_CASE ) != i )
return total
if __name__ == "__main__":
print(solution(int(str(input()).strip()))) | 270 |
'''simple docstring'''
def __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
if not (isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) and isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )):
raise ValueError("""longest_common_substring() takes two strings for inputs""" )
_snake_case = len(_SCREAMING_SNAKE_CASE )
_snake_case = len(_SCREAMING_SNAKE_CASE )
_snake_case = [[0] * (texta_length + 1) for _ in range(texta_length + 1 )]
_snake_case = 0
_snake_case = 0
for i in range(1 , texta_length + 1 ):
for j in range(1 , texta_length + 1 ):
if texta[i - 1] == texta[j - 1]:
_snake_case = 1 + dp[i - 1][j - 1]
if dp[i][j] > ans_length:
_snake_case = i
_snake_case = dp[i][j]
return texta[ans_index - ans_length : ans_index]
if __name__ == "__main__":
import doctest
doctest.testmod() | 270 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCAmelCase = {"""configuration_opt""": ["""OPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OPTConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
"""OPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""OPTForCausalLM""",
"""OPTModel""",
"""OPTPreTrainedModel""",
"""OPTForSequenceClassification""",
"""OPTForQuestionAnswering""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = ["""TFOPTForCausalLM""", """TFOPTModel""", """TFOPTPreTrainedModel"""]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
"""FlaxOPTForCausalLM""",
"""FlaxOPTModel""",
"""FlaxOPTPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_opt import (
OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
OPTForCausalLM,
OPTForQuestionAnswering,
OPTForSequenceClassification,
OPTModel,
OPTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel
else:
import sys
__UpperCAmelCase = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 323 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
__UpperCAmelCase = logging.getLogger(__name__)
def __A ( lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
return (preds == labels).mean()
@dataclass
class UpperCamelCase__ :
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = field(
metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
SCREAMING_SNAKE_CASE__ = field(
default=lowercase_ , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
SCREAMING_SNAKE_CASE__ = field(
default=lowercase_ , metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
SCREAMING_SNAKE_CASE__ = field(
default=lowercase_ , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} , )
@dataclass
class UpperCamelCase__ :
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = field(metadata={'''help''': '''The name of the task to train on: ''' + ''', '''.join(processors.keys() )} )
SCREAMING_SNAKE_CASE__ = field(metadata={'''help''': '''Should contain the data files for the 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=lowercase_ , metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )
def __A ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : Any = parser.parse_args_into_dataclasses()
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
f'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
""" --overwrite_output_dir to overcome.""" )
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
"""Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s""" , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1 ) , training_args.fpaa , )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info("""Training/evaluation parameters %s""" , lowerCamelCase_ )
# Set seed
set_seed(training_args.seed )
try:
SCREAMING_SNAKE_CASE : Dict = processors[data_args.task_name]()
SCREAMING_SNAKE_CASE : Optional[int] = processor.get_labels()
SCREAMING_SNAKE_CASE : List[str] = len(lowerCamelCase_ )
except KeyError:
raise ValueError("""Task not found: %s""" % (data_args.task_name) )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
SCREAMING_SNAKE_CASE : Any = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=lowerCamelCase_ , finetuning_task=data_args.task_name , cache_dir=model_args.cache_dir , )
SCREAMING_SNAKE_CASE : List[Any] = 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 , )
SCREAMING_SNAKE_CASE : Union[str, Any] = AutoModelForMultipleChoice.from_pretrained(
model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=lowerCamelCase_ , cache_dir=model_args.cache_dir , )
# Get datasets
SCREAMING_SNAKE_CASE : Optional[Any] = (
MultipleChoiceDataset(
data_dir=data_args.data_dir , tokenizer=lowerCamelCase_ , task=data_args.task_name , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.train , )
if training_args.do_train
else None
)
SCREAMING_SNAKE_CASE : Dict = (
MultipleChoiceDataset(
data_dir=data_args.data_dir , tokenizer=lowerCamelCase_ , task=data_args.task_name , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.dev , )
if training_args.do_eval
else None
)
def compute_metrics(lowerCamelCase_ ) -> Dict:
SCREAMING_SNAKE_CASE : str = np.argmax(p.predictions , axis=1 )
return {"acc": simple_accuracy(lowerCamelCase_ , p.label_ids )}
# Data collator
SCREAMING_SNAKE_CASE : List[Any] = DataCollatorWithPadding(lowerCamelCase_ , pad_to_multiple_of=8 ) if training_args.fpaa else None
# Initialize our Trainer
SCREAMING_SNAKE_CASE : Any = Trainer(
model=lowerCamelCase_ , args=lowerCamelCase_ , train_dataset=lowerCamelCase_ , eval_dataset=lowerCamelCase_ , compute_metrics=lowerCamelCase_ , data_collator=lowerCamelCase_ , )
# Training
if training_args.do_train:
trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_master():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
SCREAMING_SNAKE_CASE : Optional[Any] = {}
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
SCREAMING_SNAKE_CASE : Optional[Any] = trainer.evaluate()
SCREAMING_SNAKE_CASE : str = os.path.join(training_args.output_dir , """eval_results.txt""" )
if trainer.is_world_master():
with open(lowerCamelCase_ , """w""" ) as writer:
logger.info("""***** Eval results *****""" )
for key, value in result.items():
logger.info(""" %s = %s""" , lowerCamelCase_ , lowerCamelCase_ )
writer.write("""%s = %s\n""" % (key, value) )
results.update(lowerCamelCase_ )
return results
def __A ( lowerCamelCase_ ):
"""simple docstring"""
main()
if __name__ == "__main__":
main()
| 323 | 1 |
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFDeiTForImageClassification,
TFDeiTForImageClassificationWithTeacher,
TFDeiTForMaskedImageModeling,
TFDeiTModel,
)
from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import DeiTImageProcessor
class SCREAMING_SNAKE_CASE_ :
"""simple docstring"""
def __init__( self , lowerCAmelCase__ , lowerCAmelCase__=1_3 , lowerCAmelCase__=3_0 , lowerCAmelCase__=2 , lowerCAmelCase__=3 , lowerCAmelCase__=True , lowerCAmelCase__=True , lowerCAmelCase__=3_2 , lowerCAmelCase__=2 , lowerCAmelCase__=4 , lowerCAmelCase__=3_7 , lowerCAmelCase__="gelu" , lowerCAmelCase__=0.1 , lowerCAmelCase__=0.1 , lowerCAmelCase__=1_0 , lowerCAmelCase__=0.02 , lowerCAmelCase__=3 , lowerCAmelCase__=None , lowerCAmelCase__=2 , ):
__SCREAMING_SNAKE_CASE = parent
__SCREAMING_SNAKE_CASE = batch_size
__SCREAMING_SNAKE_CASE = image_size
__SCREAMING_SNAKE_CASE = patch_size
__SCREAMING_SNAKE_CASE = num_channels
__SCREAMING_SNAKE_CASE = is_training
__SCREAMING_SNAKE_CASE = use_labels
__SCREAMING_SNAKE_CASE = hidden_size
__SCREAMING_SNAKE_CASE = num_hidden_layers
__SCREAMING_SNAKE_CASE = num_attention_heads
__SCREAMING_SNAKE_CASE = intermediate_size
__SCREAMING_SNAKE_CASE = hidden_act
__SCREAMING_SNAKE_CASE = hidden_dropout_prob
__SCREAMING_SNAKE_CASE = attention_probs_dropout_prob
__SCREAMING_SNAKE_CASE = type_sequence_label_size
__SCREAMING_SNAKE_CASE = initializer_range
__SCREAMING_SNAKE_CASE = scope
__SCREAMING_SNAKE_CASE = encoder_stride
# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
__SCREAMING_SNAKE_CASE = (image_size // patch_size) ** 2
__SCREAMING_SNAKE_CASE = num_patches + 2
def snake_case_ ( self):
__SCREAMING_SNAKE_CASE = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
__SCREAMING_SNAKE_CASE = None
if self.use_labels:
__SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size] , self.type_sequence_label_size)
__SCREAMING_SNAKE_CASE = self.get_config()
return config, pixel_values, labels
def snake_case_ ( self):
return DeiTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=lowerCAmelCase__ , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , )
def snake_case_ ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__):
__SCREAMING_SNAKE_CASE = TFDeiTModel(config=lowerCAmelCase__)
__SCREAMING_SNAKE_CASE = model(lowerCAmelCase__)
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size))
def snake_case_ ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__):
__SCREAMING_SNAKE_CASE = TFDeiTForMaskedImageModeling(config=lowerCAmelCase__)
__SCREAMING_SNAKE_CASE = model(lowerCAmelCase__)
self.parent.assertEqual(
result.reconstruction.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size))
# test greyscale images
__SCREAMING_SNAKE_CASE = 1
__SCREAMING_SNAKE_CASE = TFDeiTForMaskedImageModeling(lowerCAmelCase__)
__SCREAMING_SNAKE_CASE = floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
__SCREAMING_SNAKE_CASE = model(lowerCAmelCase__)
self.parent.assertEqual(result.reconstruction.shape , (self.batch_size, 1, self.image_size, self.image_size))
def snake_case_ ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__):
__SCREAMING_SNAKE_CASE = self.type_sequence_label_size
__SCREAMING_SNAKE_CASE = TFDeiTForImageClassification(lowerCAmelCase__)
__SCREAMING_SNAKE_CASE = model(lowerCAmelCase__ , labels=lowerCAmelCase__)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size))
# test greyscale images
__SCREAMING_SNAKE_CASE = 1
__SCREAMING_SNAKE_CASE = TFDeiTForImageClassification(lowerCAmelCase__)
__SCREAMING_SNAKE_CASE = floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
__SCREAMING_SNAKE_CASE = model(lowerCAmelCase__ , labels=lowerCAmelCase__)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size))
def snake_case_ ( self):
__SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs()
__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE = config_and_inputs
__SCREAMING_SNAKE_CASE = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_tf
class SCREAMING_SNAKE_CASE_ ( __a , __a , unittest.TestCase ):
"""simple docstring"""
__lowercase : Tuple = (
(
TFDeiTModel,
TFDeiTForImageClassification,
TFDeiTForImageClassificationWithTeacher,
TFDeiTForMaskedImageModeling,
)
if is_tf_available()
else ()
)
__lowercase : Union[str, Any] = (
{
'''feature-extraction''': TFDeiTModel,
'''image-classification''': (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
}
if is_tf_available()
else {}
)
__lowercase : Tuple = False
__lowercase : Any = False
__lowercase : Union[str, Any] = False
__lowercase : int = False
def snake_case_ ( self):
__SCREAMING_SNAKE_CASE = TFDeiTModelTester(self)
__SCREAMING_SNAKE_CASE = ConfigTester(self , config_class=lowerCAmelCase__ , has_text_modality=lowerCAmelCase__ , hidden_size=3_7)
def snake_case_ ( self):
self.config_tester.run_common_tests()
@unittest.skip(reason="""DeiT does not use inputs_embeds""")
def snake_case_ ( self):
pass
def snake_case_ ( self):
__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__SCREAMING_SNAKE_CASE = model_class(lowerCAmelCase__)
self.assertIsInstance(model.get_input_embeddings() , (tf.keras.layers.Layer))
__SCREAMING_SNAKE_CASE = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(lowerCAmelCase__ , tf.keras.layers.Dense))
def snake_case_ ( self):
__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__SCREAMING_SNAKE_CASE = model_class(lowerCAmelCase__)
__SCREAMING_SNAKE_CASE = inspect.signature(model.call)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__SCREAMING_SNAKE_CASE = [*signature.parameters.keys()]
__SCREAMING_SNAKE_CASE = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , lowerCAmelCase__)
def snake_case_ ( self):
__SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCAmelCase__)
def snake_case_ ( self):
__SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*lowerCAmelCase__)
def snake_case_ ( self):
__SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__)
def snake_case_ ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__=False):
__SCREAMING_SNAKE_CASE = super()._prepare_for_class(lowerCAmelCase__ , lowerCAmelCase__ , return_labels=lowerCAmelCase__)
if return_labels:
if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call).parameters:
del inputs_dict["labels"]
return inputs_dict
@slow
def snake_case_ ( self):
for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__SCREAMING_SNAKE_CASE = TFDeiTModel.from_pretrained(lowerCAmelCase__)
self.assertIsNotNone(lowerCAmelCase__)
def _lowerCAmelCase ( ):
__SCREAMING_SNAKE_CASE = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_tf
@require_vision
class SCREAMING_SNAKE_CASE_ ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def snake_case_ ( self):
return (
DeiTImageProcessor.from_pretrained("""facebook/deit-base-distilled-patch16-224""")
if is_vision_available()
else None
)
@slow
def snake_case_ ( self):
__SCREAMING_SNAKE_CASE = TFDeiTForImageClassificationWithTeacher.from_pretrained("""facebook/deit-base-distilled-patch16-224""")
__SCREAMING_SNAKE_CASE = self.default_image_processor
__SCREAMING_SNAKE_CASE = prepare_img()
__SCREAMING_SNAKE_CASE = image_processor(images=lowerCAmelCase__ , return_tensors="""tf""")
# forward pass
__SCREAMING_SNAKE_CASE = model(**lowerCAmelCase__)
# verify the logits
__SCREAMING_SNAKE_CASE = tf.TensorShape((1, 1_0_0_0))
self.assertEqual(outputs.logits.shape , lowerCAmelCase__)
__SCREAMING_SNAKE_CASE = tf.constant([-1.02_66, 0.19_12, -1.28_61])
self.assertTrue(np.allclose(outputs.logits[0, :3] , lowerCAmelCase__ , atol=1E-4))
| 255 |
"""simple docstring"""
import unittest
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import BridgeTowerImageProcessor
class SCREAMING_SNAKE_CASE_ ( unittest.TestCase ):
"""simple docstring"""
def __init__( self , lowerCAmelCase__ , lowerCAmelCase__ = True , lowerCAmelCase__ = None , lowerCAmelCase__ = 3_2 , lowerCAmelCase__ = True , lowerCAmelCase__ = 1 / 2_5_5 , lowerCAmelCase__ = True , lowerCAmelCase__ = True , lowerCAmelCase__ = [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73] , lowerCAmelCase__ = [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11] , lowerCAmelCase__ = True , lowerCAmelCase__=7 , lowerCAmelCase__=3_0 , lowerCAmelCase__=4_0_0 , lowerCAmelCase__=3 , ):
__SCREAMING_SNAKE_CASE = parent
__SCREAMING_SNAKE_CASE = do_resize
__SCREAMING_SNAKE_CASE = size if size is not None else {"""shortest_edge""": 2_8_8}
__SCREAMING_SNAKE_CASE = size_divisor
__SCREAMING_SNAKE_CASE = do_rescale
__SCREAMING_SNAKE_CASE = rescale_factor
__SCREAMING_SNAKE_CASE = do_normalize
__SCREAMING_SNAKE_CASE = do_center_crop
__SCREAMING_SNAKE_CASE = image_mean
__SCREAMING_SNAKE_CASE = image_std
__SCREAMING_SNAKE_CASE = do_pad
__SCREAMING_SNAKE_CASE = batch_size
__SCREAMING_SNAKE_CASE = num_channels
__SCREAMING_SNAKE_CASE = min_resolution
__SCREAMING_SNAKE_CASE = max_resolution
def snake_case_ ( self):
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"size": self.size,
"size_divisor": self.size_divisor,
}
def snake_case_ ( self , lowerCAmelCase__ , lowerCAmelCase__=False):
if not batched:
__SCREAMING_SNAKE_CASE = self.size["""shortest_edge"""]
__SCREAMING_SNAKE_CASE = image_inputs[0]
if isinstance(lowerCAmelCase__ , Image.Image):
__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE = image.size
else:
__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE = image.shape[1], image.shape[2]
__SCREAMING_SNAKE_CASE = size / min(lowerCAmelCase__ , lowerCAmelCase__)
if h < w:
__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE = size, scale * w
else:
__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE = scale * h, size
__SCREAMING_SNAKE_CASE = int((1_3_3_3 / 8_0_0) * size)
if max(lowerCAmelCase__ , lowerCAmelCase__) > max_size:
__SCREAMING_SNAKE_CASE = max_size / max(lowerCAmelCase__ , lowerCAmelCase__)
__SCREAMING_SNAKE_CASE = newh * scale
__SCREAMING_SNAKE_CASE = neww * scale
__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE = int(newh + 0.5), int(neww + 0.5)
__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE = (
newh // self.size_divisor * self.size_divisor,
neww // self.size_divisor * self.size_divisor,
)
else:
__SCREAMING_SNAKE_CASE = []
for image in image_inputs:
__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE = self.get_expected_values([image])
expected_values.append((expected_height, expected_width))
__SCREAMING_SNAKE_CASE = max(lowerCAmelCase__ , key=lambda lowerCAmelCase__: item[0])[0]
__SCREAMING_SNAKE_CASE = max(lowerCAmelCase__ , key=lambda lowerCAmelCase__: item[1])[1]
return expected_height, expected_width
@require_torch
@require_vision
class SCREAMING_SNAKE_CASE_ ( __a , unittest.TestCase ):
"""simple docstring"""
__lowercase : Tuple = BridgeTowerImageProcessor if is_vision_available() else None
def snake_case_ ( self):
__SCREAMING_SNAKE_CASE = BridgeTowerImageProcessingTester(self)
@property
def snake_case_ ( self):
return self.image_processor_tester.prepare_image_processor_dict()
def snake_case_ ( self):
__SCREAMING_SNAKE_CASE = self.image_processing_class(**self.image_processor_dict)
self.assertTrue(hasattr(lowerCAmelCase__ , """image_mean"""))
self.assertTrue(hasattr(lowerCAmelCase__ , """image_std"""))
self.assertTrue(hasattr(lowerCAmelCase__ , """do_normalize"""))
self.assertTrue(hasattr(lowerCAmelCase__ , """do_resize"""))
self.assertTrue(hasattr(lowerCAmelCase__ , """size"""))
self.assertTrue(hasattr(lowerCAmelCase__ , """size_divisor"""))
def snake_case_ ( self):
pass
def snake_case_ ( self):
# Initialize image processor
__SCREAMING_SNAKE_CASE = self.image_processing_class(**self.image_processor_dict)
# create random PIL images
__SCREAMING_SNAKE_CASE = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__)
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , Image.Image)
# Test not batched input
__SCREAMING_SNAKE_CASE = image_processing(image_inputs[0] , return_tensors="""pt""").pixel_values
__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE = self.image_processor_tester.get_expected_values(lowerCAmelCase__)
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
__SCREAMING_SNAKE_CASE = image_processing(lowerCAmelCase__ , return_tensors="""pt""").pixel_values
__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE = self.image_processor_tester.get_expected_values(lowerCAmelCase__ , batched=lowerCAmelCase__)
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def snake_case_ ( self):
# Initialize image processor
__SCREAMING_SNAKE_CASE = self.image_processing_class(**self.image_processor_dict)
# create random numpy tensors
__SCREAMING_SNAKE_CASE = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ , numpify=lowerCAmelCase__)
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , np.ndarray)
# Test not batched input
__SCREAMING_SNAKE_CASE = image_processing(image_inputs[0] , return_tensors="""pt""").pixel_values
__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE = self.image_processor_tester.get_expected_values(lowerCAmelCase__)
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
__SCREAMING_SNAKE_CASE = image_processing(lowerCAmelCase__ , return_tensors="""pt""").pixel_values
__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE = self.image_processor_tester.get_expected_values(lowerCAmelCase__ , batched=lowerCAmelCase__)
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def snake_case_ ( self):
# Initialize image processor
__SCREAMING_SNAKE_CASE = self.image_processing_class(**self.image_processor_dict)
# create random PyTorch tensors
__SCREAMING_SNAKE_CASE = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ , torchify=lowerCAmelCase__)
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , torch.Tensor)
# Test not batched input
__SCREAMING_SNAKE_CASE = image_processing(image_inputs[0] , return_tensors="""pt""").pixel_values
__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE = self.image_processor_tester.get_expected_values(lowerCAmelCase__)
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
__SCREAMING_SNAKE_CASE = image_processing(lowerCAmelCase__ , return_tensors="""pt""").pixel_values
__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE = self.image_processor_tester.get_expected_values(lowerCAmelCase__ , batched=lowerCAmelCase__)
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
| 255 | 1 |
'''simple docstring'''
import importlib
import os
import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry
from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem
from .utils import require_lza, require_zstandard
def _SCREAMING_SNAKE_CASE (A ) -> Any:
"""simple docstring"""
assert "mock" in _fsspec_registry
assert "bz2" in _fsspec_registry
def _SCREAMING_SNAKE_CASE () -> List[Any]:
"""simple docstring"""
assert "mock" not in _fsspec_registry
assert "bz2" in _fsspec_registry
def _SCREAMING_SNAKE_CASE () -> Optional[Any]:
"""simple docstring"""
lowercase__ = '''mock-s3-bucket'''
lowercase__ = f"s3://{mock_bucket}"
lowercase__ = extract_path_from_uri(A )
assert dataset_path.startswith('''s3://''' ) is False
lowercase__ = '''./local/path'''
lowercase__ = extract_path_from_uri(A )
assert dataset_path == new_dataset_path
def _SCREAMING_SNAKE_CASE (A ) -> Dict:
"""simple docstring"""
lowercase__ = is_remote_filesystem(A )
assert is_remote is True
lowercase__ = fsspec.filesystem('''file''' )
lowercase__ = is_remote_filesystem(A )
assert is_remote is False
@pytest.mark.parametrize('''compression_fs_class''' , A )
def _SCREAMING_SNAKE_CASE (A , A , A , A , A , A , A ) -> List[str]:
"""simple docstring"""
lowercase__ = {'''gzip''': gz_file, '''xz''': xz_file, '''zstd''': zstd_file, '''bz2''': bza_file, '''lz4''': lza_file}
lowercase__ = input_paths[compression_fs_class.protocol]
if input_path is None:
lowercase__ = f"for '{compression_fs_class.protocol}' compression protocol, "
if compression_fs_class.protocol == "lz4":
reason += require_lza.kwargs["reason"]
elif compression_fs_class.protocol == "zstd":
reason += require_zstandard.kwargs["reason"]
pytest.skip(A )
lowercase__ = fsspec.filesystem(compression_fs_class.protocol , fo=A )
assert isinstance(A , A )
lowercase__ = os.path.basename(A )
lowercase__ = expected_filename[: expected_filename.rindex('''.''' )]
assert fs.glob('''*''' ) == [expected_filename]
with fs.open(A , '''r''' , encoding='''utf-8''' ) as f, open(A , encoding='''utf-8''' ) as expected_file:
assert f.read() == expected_file.read()
@pytest.mark.parametrize('''protocol''' , ['''zip''', '''gzip'''] )
def _SCREAMING_SNAKE_CASE (A , A , A ) -> Optional[int]:
"""simple docstring"""
lowercase__ = {'''zip''': zip_jsonl_path, '''gzip''': jsonl_gz_path}
lowercase__ = compressed_file_paths[protocol]
lowercase__ = '''dataset.jsonl'''
lowercase__ = f"{protocol}://{member_file_path}::{compressed_file_path}"
lowercase__ ,*lowercase__ = fsspec.get_fs_token_paths(A )
assert fs.isfile(A )
assert not fs.isfile('''non_existing_''' + member_file_path )
@pytest.mark.integration
def _SCREAMING_SNAKE_CASE (A , A , A , A ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = hf_api.dataset_info(A , token=A )
lowercase__ = HfFileSystem(repo_info=A , token=A )
assert sorted(hffs.glob('''*''' ) ) == [".gitattributes", "data"]
assert hffs.isdir('''data''' )
assert hffs.isfile('''.gitattributes''' ) and hffs.isfile('''data/text_data.txt''' )
with open(A ) as f:
assert hffs.open('''data/text_data.txt''' , '''r''' ).read() == f.read()
def _SCREAMING_SNAKE_CASE () -> Optional[int]:
"""simple docstring"""
lowercase__ = '''bz2'''
# Import module
import datasets.filesystems
# Overwrite protocol and reload
register_implementation(A , A , clobber=A )
with pytest.warns(A ) as warning_info:
importlib.reload(datasets.filesystems )
assert len(A ) == 1
assert (
str(warning_info[0].message )
== f"A filesystem protocol was already set for {protocol} and will be overwritten."
)
| 2 |
'''simple docstring'''
import torch
from torch import nn
class _snake_case ( nn.Module ):
def __init__( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase=1 , _lowerCamelCase=False):
super().__init__()
UpperCAmelCase__ : List[Any] = n_token
UpperCAmelCase__ : Tuple = d_embed
UpperCAmelCase__ : str = d_proj
UpperCAmelCase__ : str = cutoffs + [n_token]
UpperCAmelCase__ : List[Any] = [0] + self.cutoffs
UpperCAmelCase__ : Optional[Any] = div_val
UpperCAmelCase__ : Optional[int] = self.cutoffs[0]
UpperCAmelCase__ : Optional[int] = len(self.cutoffs) - 1
UpperCAmelCase__ : Union[str, Any] = self.shortlist_size + self.n_clusters
if self.n_clusters > 0:
UpperCAmelCase__ : int = nn.Parameter(torch.zeros(self.n_clusters , self.d_embed))
UpperCAmelCase__ : Optional[Any] = nn.Parameter(torch.zeros(self.n_clusters))
UpperCAmelCase__ : int = nn.ModuleList()
UpperCAmelCase__ : List[Any] = nn.ParameterList()
if div_val == 1:
for i in range(len(self.cutoffs)):
if d_proj != d_embed:
self.out_projs.append(nn.Parameter(torch.FloatTensor(_lowerCamelCase , _lowerCamelCase)))
else:
self.out_projs.append(_lowerCamelCase)
self.out_layers.append(nn.Linear(_lowerCamelCase , _lowerCamelCase))
else:
for i in range(len(self.cutoffs)):
UpperCAmelCase__ , UpperCAmelCase__ : List[str] = self.cutoff_ends[i], self.cutoff_ends[i + 1]
UpperCAmelCase__ : Union[str, Any] = d_embed // (div_val**i)
self.out_projs.append(nn.Parameter(torch.FloatTensor(_lowerCamelCase , _lowerCamelCase)))
self.out_layers.append(nn.Linear(_lowerCamelCase , r_idx - l_idx))
UpperCAmelCase__ : Optional[int] = keep_order
def snake_case__ ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase):
if proj is None:
UpperCAmelCase__ : Dict = nn.functional.linear(_lowerCamelCase , _lowerCamelCase , bias=_lowerCamelCase)
else:
# if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
UpperCAmelCase__ : Optional[int] = nn.functional.linear(_lowerCamelCase , proj.t().contiguous())
UpperCAmelCase__ : List[str] = nn.functional.linear(_lowerCamelCase , _lowerCamelCase , bias=_lowerCamelCase)
# else:
# logit = torch.einsum('bd,de,ev->bv', (hidden, proj, weight.t()))
# if bias is not None:
# logit = logit + bias
return logit
def snake_case__ ( self , _lowerCamelCase , _lowerCamelCase=None , _lowerCamelCase=False):
if labels is not None:
# Shift so that tokens < n predict n
UpperCAmelCase__ : Optional[int] = hidden[..., :-1, :].contiguous()
UpperCAmelCase__ : int = labels[..., 1:].contiguous()
UpperCAmelCase__ : List[str] = hidden.view(-1 , hidden.size(-1))
UpperCAmelCase__ : Optional[int] = labels.view(-1)
if hidden.size(0) != labels.size(0):
raise RuntimeError("""Input and labels should have the same size in the batch dimension.""")
else:
UpperCAmelCase__ : Optional[int] = hidden.view(-1 , hidden.size(-1))
if self.n_clusters == 0:
UpperCAmelCase__ : Tuple = self._compute_logit(_lowerCamelCase , self.out_layers[0].weight , self.out_layers[0].bias , self.out_projs[0])
if labels is not None:
UpperCAmelCase__ : Dict = labels != -100
UpperCAmelCase__ : Tuple = torch.zeros_like(_lowerCamelCase , dtype=hidden.dtype , device=hidden.device)
UpperCAmelCase__ : List[Any] = (
-nn.functional.log_softmax(_lowerCamelCase , dim=-1)[mask].gather(1 , labels[mask].unsqueeze(1)).squeeze(1)
)
else:
UpperCAmelCase__ : List[str] = nn.functional.log_softmax(_lowerCamelCase , dim=-1)
else:
# construct weights and biases
UpperCAmelCase__ , UpperCAmelCase__ : Union[str, Any] = [], []
for i in range(len(self.cutoffs)):
if self.div_val == 1:
UpperCAmelCase__ , UpperCAmelCase__ : int = self.cutoff_ends[i], self.cutoff_ends[i + 1]
UpperCAmelCase__ : Dict = self.out_layers[0].weight[l_idx:r_idx]
UpperCAmelCase__ : Any = self.out_layers[0].bias[l_idx:r_idx]
else:
UpperCAmelCase__ : Union[str, Any] = self.out_layers[i].weight
UpperCAmelCase__ : Any = self.out_layers[i].bias
if i == 0:
UpperCAmelCase__ : Optional[Any] = torch.cat([weight_i, self.cluster_weight] , dim=0)
UpperCAmelCase__ : List[Any] = torch.cat([bias_i, self.cluster_bias] , dim=0)
weights.append(_lowerCamelCase)
biases.append(_lowerCamelCase)
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ : Optional[int] = weights[0], biases[0], self.out_projs[0]
UpperCAmelCase__ : Optional[int] = self._compute_logit(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase)
UpperCAmelCase__ : Union[str, Any] = nn.functional.log_softmax(_lowerCamelCase , dim=1)
if labels is None:
UpperCAmelCase__ : str = hidden.new_empty((head_logit.size(0), self.n_token))
else:
UpperCAmelCase__ : Optional[Any] = torch.zeros_like(_lowerCamelCase , dtype=hidden.dtype , device=hidden.device)
UpperCAmelCase__ : Optional[int] = 0
UpperCAmelCase__ : List[str] = [0] + self.cutoffs
for i in range(len(_lowerCamelCase) - 1):
UpperCAmelCase__ , UpperCAmelCase__ : Dict = cutoff_values[i], cutoff_values[i + 1]
if labels is not None:
UpperCAmelCase__ : List[str] = (labels >= l_idx) & (labels < r_idx)
UpperCAmelCase__ : str = mask_i.nonzero().squeeze()
if indices_i.numel() == 0:
continue
UpperCAmelCase__ : List[Any] = labels.index_select(0 , _lowerCamelCase) - l_idx
UpperCAmelCase__ : List[str] = head_logprob.index_select(0 , _lowerCamelCase)
UpperCAmelCase__ : Optional[Any] = hidden.index_select(0 , _lowerCamelCase)
else:
UpperCAmelCase__ : Any = hidden
if i == 0:
if labels is not None:
UpperCAmelCase__ : List[Any] = head_logprob_i.gather(1 , target_i[:, None]).squeeze(1)
else:
UpperCAmelCase__ : Tuple = head_logprob[:, : self.cutoffs[0]]
else:
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ : str = weights[i], biases[i], self.out_projs[i]
UpperCAmelCase__ : int = self._compute_logit(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase)
UpperCAmelCase__ : str = nn.functional.log_softmax(_lowerCamelCase , dim=1)
UpperCAmelCase__ : int = self.cutoffs[0] + i - 1 # No probability for the head cluster
if labels is not None:
UpperCAmelCase__ : Dict = head_logprob_i[:, cluster_prob_idx] + tail_logprob_i.gather(
1 , target_i[:, None]).squeeze(1)
else:
UpperCAmelCase__ : List[str] = head_logprob[:, cluster_prob_idx, None] + tail_logprob_i
UpperCAmelCase__ : Tuple = logprob_i
if labels is not None:
if (hasattr(self , """keep_order""") and self.keep_order) or keep_order:
out.index_copy_(0 , _lowerCamelCase , -logprob_i)
else:
out[offset : offset + logprob_i.size(0)].copy_(-logprob_i)
offset += logprob_i.size(0)
return out
def snake_case__ ( self , _lowerCamelCase):
if self.n_clusters == 0:
UpperCAmelCase__ : Union[str, Any] = self._compute_logit(_lowerCamelCase , self.out_layers[0].weight , self.out_layers[0].bias , self.out_projs[0])
return nn.functional.log_softmax(_lowerCamelCase , dim=-1)
else:
# construct weights and biases
UpperCAmelCase__ , UpperCAmelCase__ : Optional[int] = [], []
for i in range(len(self.cutoffs)):
if self.div_val == 1:
UpperCAmelCase__ , UpperCAmelCase__ : Any = self.cutoff_ends[i], self.cutoff_ends[i + 1]
UpperCAmelCase__ : Union[str, Any] = self.out_layers[0].weight[l_idx:r_idx]
UpperCAmelCase__ : Any = self.out_layers[0].bias[l_idx:r_idx]
else:
UpperCAmelCase__ : int = self.out_layers[i].weight
UpperCAmelCase__ : List[str] = self.out_layers[i].bias
if i == 0:
UpperCAmelCase__ : List[Any] = torch.cat([weight_i, self.cluster_weight] , dim=0)
UpperCAmelCase__ : Optional[int] = torch.cat([bias_i, self.cluster_bias] , dim=0)
weights.append(_lowerCamelCase)
biases.append(_lowerCamelCase)
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ : str = weights[0], biases[0], self.out_projs[0]
UpperCAmelCase__ : List[Any] = self._compute_logit(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase)
UpperCAmelCase__ : List[Any] = hidden.new_empty((head_logit.size(0), self.n_token))
UpperCAmelCase__ : int = nn.functional.log_softmax(_lowerCamelCase , dim=1)
UpperCAmelCase__ : str = [0] + self.cutoffs
for i in range(len(_lowerCamelCase) - 1):
UpperCAmelCase__ , UpperCAmelCase__ : Union[str, Any] = cutoff_values[i], cutoff_values[i + 1]
if i == 0:
UpperCAmelCase__ : List[Any] = head_logprob[:, : self.cutoffs[0]]
else:
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ : Union[str, Any] = weights[i], biases[i], self.out_projs[i]
UpperCAmelCase__ : Union[str, Any] = self._compute_logit(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase)
UpperCAmelCase__ : List[str] = nn.functional.log_softmax(_lowerCamelCase , dim=1)
UpperCAmelCase__ : Union[str, Any] = head_logprob[:, -i] + tail_logprob_i
UpperCAmelCase__ : Dict = logprob_i
return out | 163 | 0 |
"""simple docstring"""
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class _SCREAMING_SNAKE_CASE ( __snake_case ):
@staticmethod
@abstractmethod
def __lowerCAmelCase ( __A ) -> int:
raise NotImplementedError()
@abstractmethod
def __lowerCAmelCase ( self ) -> Tuple:
raise NotImplementedError()
| 361 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@require_torchaudio
@require_sentencepiece
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __lowerCAmelCase ( self ) -> Optional[int]:
lowerCAmelCase_ :Any = """laion/clap-htsat-unfused"""
lowerCAmelCase_ :Optional[Any] = tempfile.mkdtemp()
def __lowerCAmelCase ( self , **__A ) -> List[Any]:
return RobertaTokenizer.from_pretrained(self.checkpoint , **__A )
def __lowerCAmelCase ( self , **__A ) -> Tuple:
return ClapFeatureExtractor.from_pretrained(self.checkpoint , **__A )
def __lowerCAmelCase ( self ) -> int:
shutil.rmtree(self.tmpdirname )
def __lowerCAmelCase ( self ) -> List[str]:
lowerCAmelCase_ :Optional[Any] = self.get_tokenizer()
lowerCAmelCase_ :Optional[Any] = self.get_feature_extractor()
lowerCAmelCase_ :Optional[int] = ClapProcessor(tokenizer=__A , feature_extractor=__A )
processor.save_pretrained(self.tmpdirname )
lowerCAmelCase_ :Optional[Any] = ClapProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , __A )
self.assertEqual(processor.feature_extractor.to_json_string() , feature_extractor.to_json_string() )
self.assertIsInstance(processor.feature_extractor , __A )
def __lowerCAmelCase ( self ) -> Tuple:
lowerCAmelCase_ :Dict = ClapProcessor(tokenizer=self.get_tokenizer() , feature_extractor=self.get_feature_extractor() )
processor.save_pretrained(self.tmpdirname )
lowerCAmelCase_ :str = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
lowerCAmelCase_ :Dict = self.get_feature_extractor(do_normalize=__A , padding_value=1.0 )
lowerCAmelCase_ :Union[str, Any] = ClapProcessor.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.feature_extractor.to_json_string() , feature_extractor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.feature_extractor , __A )
def __lowerCAmelCase ( self ) -> Union[str, Any]:
lowerCAmelCase_ :Dict = self.get_feature_extractor()
lowerCAmelCase_ :str = self.get_tokenizer()
lowerCAmelCase_ :List[Any] = ClapProcessor(tokenizer=__A , feature_extractor=__A )
lowerCAmelCase_ :Optional[Any] = floats_list((3, 1000) )
lowerCAmelCase_ :Optional[Any] = feature_extractor(__A , return_tensors="""np""" )
lowerCAmelCase_ :str = processor(audios=__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 __lowerCAmelCase ( self ) -> List[str]:
lowerCAmelCase_ :List[Any] = self.get_feature_extractor()
lowerCAmelCase_ :Any = self.get_tokenizer()
lowerCAmelCase_ :Optional[int] = ClapProcessor(tokenizer=__A , feature_extractor=__A )
lowerCAmelCase_ :List[Any] = """This is a test string"""
lowerCAmelCase_ :Dict = processor(text=__A )
lowerCAmelCase_ :List[str] = tokenizer(__A )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def __lowerCAmelCase ( self ) -> int:
lowerCAmelCase_ :int = self.get_feature_extractor()
lowerCAmelCase_ :Tuple = self.get_tokenizer()
lowerCAmelCase_ :Optional[Any] = ClapProcessor(tokenizer=__A , feature_extractor=__A )
lowerCAmelCase_ :int = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
lowerCAmelCase_ :Tuple = processor.batch_decode(__A )
lowerCAmelCase_ :Optional[Any] = tokenizer.batch_decode(__A )
self.assertListEqual(__A , __A )
def __lowerCAmelCase ( self ) -> List[Any]:
lowerCAmelCase_ :Optional[Any] = self.get_feature_extractor()
lowerCAmelCase_ :Any = self.get_tokenizer()
lowerCAmelCase_ :Optional[int] = ClapProcessor(tokenizer=__A , feature_extractor=__A )
self.assertListEqual(
processor.model_input_names[2:] , feature_extractor.model_input_names , msg="""`processor` and `feature_extractor` model input names do not match""" , )
| 1 | 0 |
'''simple docstring'''
import os
import pickle
import unittest
from transformers import AutoTokenizer
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.models.bert_japanese.tokenization_bert_japanese import (
VOCAB_FILES_NAMES,
BertJapaneseTokenizer,
CharacterTokenizer,
JumanppTokenizer,
MecabTokenizer,
SudachiTokenizer,
WordpieceTokenizer,
)
from transformers.testing_utils import custom_tokenizers, require_jumanpp, require_sudachi
from ...test_tokenization_common import TokenizerTesterMixin
@custom_tokenizers
class __UpperCAmelCase ( _lowerCAmelCase , unittest.TestCase ):
__lowercase = BertJapaneseTokenizer
__lowercase = False
__lowercase = True
def lowerCamelCase ( self ):
"""simple docstring"""
super().setUp()
_snake_case = [
'[UNK]',
'[CLS]',
'[SEP]',
'こんにちは',
'こん',
'にちは',
'ばんは',
'##こん',
'##にちは',
'##ばんは',
'世界',
'##世界',
'、',
'##、',
'。',
'##。',
]
_snake_case = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in vocab_tokens] ) )
def lowerCamelCase ( self , lowerCAmelCase_ ):
"""simple docstring"""
_snake_case = 'こんにちは、世界。 \nこんばんは、世界。'
_snake_case = 'こんにちは 、 世界 。 こんばんは 、 世界 。'
return input_text, output_text
def lowerCamelCase ( self , lowerCAmelCase_ ):
"""simple docstring"""
_snake_case , _snake_case = self.get_input_output_texts(_lowerCamelCase )
_snake_case = tokenizer.encode(_lowerCamelCase , add_special_tokens=_lowerCamelCase )
_snake_case = tokenizer.decode(_lowerCamelCase , clean_up_tokenization_spaces=_lowerCamelCase )
return text, ids
def lowerCamelCase ( self ):
"""simple docstring"""
pass # TODO add if relevant
def lowerCamelCase ( self ):
"""simple docstring"""
pass # TODO add if relevant
def lowerCamelCase ( self ):
"""simple docstring"""
pass # TODO add if relevant
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = self.tokenizer_class(self.vocab_file )
_snake_case = tokenizer.tokenize('こんにちは、世界。\nこんばんは、世界。' )
self.assertListEqual(_lowerCamelCase , ['こんにちは', '、', '世界', '。', 'こん', '##ばんは', '、', '世界', '。'] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase ) , [3, 12, 10, 14, 4, 9, 12, 10, 14] )
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = self.tokenizer_class(self.vocab_file , word_tokenizer_type='mecab' )
self.assertIsNotNone(_lowerCamelCase )
_snake_case = 'こんにちは、世界。\nこんばんは、世界。'
_snake_case = tokenizer.tokenize(_lowerCamelCase )
self.assertListEqual(_lowerCamelCase , ['こんにちは', '、', '世界', '。', 'こん', '##ばんは', '、', '世界', '。'] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase ) , [3, 12, 10, 14, 4, 9, 12, 10, 14] )
_snake_case = os.path.join(self.tmpdirname , 'tokenizer.bin' )
with open(_lowerCamelCase , 'wb' ) as handle:
pickle.dump(_lowerCamelCase , _lowerCamelCase )
with open(_lowerCamelCase , 'rb' ) as handle:
_snake_case = pickle.load(_lowerCamelCase )
_snake_case = tokenizer_new.tokenize(_lowerCamelCase )
self.assertListEqual(_lowerCamelCase , _lowerCamelCase )
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = MecabTokenizer(mecab_dic='ipadic' )
self.assertListEqual(
tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ' ) , ['アップルストア', 'で', 'iPhone', '8', 'が', '発売', 'さ', 'れ', 'た', '。'] , )
def lowerCamelCase ( self ):
"""simple docstring"""
try:
_snake_case = MecabTokenizer(mecab_dic='unidic_lite' )
except ModuleNotFoundError:
return
self.assertListEqual(
tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ' ) , ['アップル', 'ストア', 'で', 'iPhone', '8', 'が', '発売', 'さ', 'れ', 'た', '。'] , )
def lowerCamelCase ( self ):
"""simple docstring"""
try:
_snake_case = MecabTokenizer(mecab_dic='unidic' )
except ModuleNotFoundError:
return
self.assertListEqual(
tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ' ) , ['アップル', 'ストア', 'で', 'iPhone', '8', 'が', '発売', 'さ', 'れ', 'た', '。'] , )
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = MecabTokenizer(do_lower_case=_lowerCamelCase , mecab_dic='ipadic' )
self.assertListEqual(
tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ' ) , ['アップルストア', 'で', 'iphone', '8', 'が', '発売', 'さ', 'れ', 'た', '。'] , )
def lowerCamelCase ( self ):
"""simple docstring"""
try:
_snake_case = MecabTokenizer(
do_lower_case=_lowerCamelCase , normalize_text=_lowerCamelCase , mecab_option='-d /usr/local/lib/mecab/dic/jumandic' )
except RuntimeError:
# if dict doesn't exist in the system, previous code raises this error.
return
self.assertListEqual(
tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ' ) , ['ｱｯﾌﾟﾙストア', 'で', 'iPhone', '８', 'が', '発売', 'さ', 'れた', '\u3000', '。'] , )
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = MecabTokenizer(normalize_text=_lowerCamelCase , mecab_dic='ipadic' )
self.assertListEqual(
tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ' ) , ['ｱｯﾌﾟﾙストア', 'で', 'iPhone', '８', 'が', '発売', 'さ', 'れ', 'た', ' ', '。'] , )
@require_sudachi
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = self.tokenizer_class(self.vocab_file , word_tokenizer_type='sudachi' )
self.assertIsNotNone(_lowerCamelCase )
_snake_case = 'こんにちは、世界。\nこんばんは、世界。'
_snake_case = tokenizer.tokenize(_lowerCamelCase )
self.assertListEqual(_lowerCamelCase , ['こんにちは', '、', '世界', '。', 'こん', '##ばんは', '、', '世界', '。'] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase ) , [3, 12, 10, 14, 4, 9, 12, 10, 14] )
_snake_case = os.path.join(self.tmpdirname , 'tokenizer.bin' )
with open(_lowerCamelCase , 'wb' ) as handle:
pickle.dump(_lowerCamelCase , _lowerCamelCase )
with open(_lowerCamelCase , 'rb' ) as handle:
_snake_case = pickle.load(_lowerCamelCase )
_snake_case = tokenizer_new.tokenize(_lowerCamelCase )
self.assertListEqual(_lowerCamelCase , _lowerCamelCase )
@require_sudachi
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = SudachiTokenizer(sudachi_dict_type='core' )
self.assertListEqual(
tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ' ) , [' ', '\t', 'アップル', 'ストア', 'で', 'iPhone', '8', ' ', 'が', ' ', ' ', '\n ', '発売', 'さ', 'れ', 'た', ' ', '。', ' ', ' '] , )
@require_sudachi
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = SudachiTokenizer(sudachi_dict_type='core' , sudachi_split_mode='A' )
self.assertListEqual(tokenizer.tokenize('外国人参政権' ) , ['外国', '人', '参政', '権'] )
@require_sudachi
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = SudachiTokenizer(sudachi_dict_type='core' , sudachi_split_mode='B' )
self.assertListEqual(tokenizer.tokenize('外国人参政権' ) , ['外国人', '参政権'] )
@require_sudachi
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = SudachiTokenizer(sudachi_dict_type='core' , sudachi_split_mode='C' )
self.assertListEqual(tokenizer.tokenize('外国人参政権' ) , ['外国人参政権'] )
@require_sudachi
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = SudachiTokenizer(do_lower_case=_lowerCamelCase , sudachi_dict_type='core' )
self.assertListEqual(
tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ' ) , [' ', '\t', 'アップル', 'ストア', 'で', 'iphone', '8', ' ', 'が', ' ', ' ', '\n ', '発売', 'さ', 'れ', 'た', ' ', '。', ' ', ' '] , )
@require_sudachi
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = SudachiTokenizer(normalize_text=_lowerCamelCase , sudachi_dict_type='core' )
self.assertListEqual(
tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ' ) , [' ', '\t', 'ｱｯﾌﾟﾙ', 'ストア', 'で', 'iPhone', '８', ' ', 'が', ' ', ' ', '\n ', '発売', 'さ', 'れ', 'た', '\u3000', '。', ' ', ' '] , )
@require_sudachi
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = SudachiTokenizer(trim_whitespace=_lowerCamelCase , sudachi_dict_type='core' )
self.assertListEqual(
tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ' ) , ['アップル', 'ストア', 'で', 'iPhone', '8', 'が', '発売', 'さ', 'れ', 'た', '。'] , )
@require_jumanpp
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = self.tokenizer_class(self.vocab_file , word_tokenizer_type='jumanpp' )
self.assertIsNotNone(_lowerCamelCase )
_snake_case = 'こんにちは、世界。\nこんばんは、世界。'
_snake_case = tokenizer.tokenize(_lowerCamelCase )
self.assertListEqual(_lowerCamelCase , ['こんにちは', '、', '世界', '。', 'こん', '##ばんは', '、', '世界', '。'] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase ) , [3, 12, 10, 14, 4, 9, 12, 10, 14] )
_snake_case = os.path.join(self.tmpdirname , 'tokenizer.bin' )
with open(_lowerCamelCase , 'wb' ) as handle:
pickle.dump(_lowerCamelCase , _lowerCamelCase )
with open(_lowerCamelCase , 'rb' ) as handle:
_snake_case = pickle.load(_lowerCamelCase )
_snake_case = tokenizer_new.tokenize(_lowerCamelCase )
self.assertListEqual(_lowerCamelCase , _lowerCamelCase )
@require_jumanpp
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = JumanppTokenizer()
self.assertListEqual(
tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ' ) , ['アップル', 'ストア', 'で', 'iPhone', '8', '\u3000', 'が', '\u3000', '\u3000', '\u3000', '発売', 'さ', 'れた', '\u3000', '。'] , )
@require_jumanpp
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = JumanppTokenizer(do_lower_case=_lowerCamelCase )
self.assertListEqual(
tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ' ) , ['アップル', 'ストア', 'で', 'iphone', '8', '\u3000', 'が', '\u3000', '\u3000', '\u3000', '発売', 'さ', 'れた', '\u3000', '。'] , )
@require_jumanpp
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = JumanppTokenizer(normalize_text=_lowerCamelCase )
self.assertListEqual(
tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ' ) , ['ｱ', 'ｯ', 'ﾌ', 'ﾟ', 'ﾙ', 'ストア', 'で', 'iPhone', '８', '\u3000', 'が', '\u3000', '\u3000', '\u3000', '発売', 'さ', 'れた', '\u3000', '。'] , )
@require_jumanpp
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = JumanppTokenizer(trim_whitespace=_lowerCamelCase )
self.assertListEqual(
tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ' ) , ['アップル', 'ストア', 'で', 'iPhone', '8', 'が', '発売', 'さ', 'れた', '。'] , )
@require_jumanpp
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = JumanppTokenizer()
self.assertListEqual(
tokenizer.tokenize('ありがとうございますm(_ _)ｍ見つけるのが大変です。' ) , ['ありがとう', 'ございます', 'm(_ _)m', '見つける', 'の', 'が', '大変です', '。'] , )
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = ['[UNK]', '[CLS]', '[SEP]', 'こんにちは', 'こん', 'にちは', 'ばんは', '##こん', '##にちは', '##ばんは']
_snake_case = {}
for i, token in enumerate(_lowerCamelCase ):
_snake_case = i
_snake_case = WordpieceTokenizer(vocab=_lowerCamelCase , unk_token='[UNK]' )
self.assertListEqual(tokenizer.tokenize('' ) , [] )
self.assertListEqual(tokenizer.tokenize('こんにちは' ) , ['こんにちは'] )
self.assertListEqual(tokenizer.tokenize('こんばんは' ) , ['こん', '##ばんは'] )
self.assertListEqual(tokenizer.tokenize('こんばんは こんばんにちは こんにちは' ) , ['こん', '##ばんは', '[UNK]', 'こんにちは'] )
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = BertJapaneseTokenizer.from_pretrained('nlp-waseda/roberta-base-japanese-with-auto-jumanpp' )
_snake_case = tokenizer.subword_tokenizer
_snake_case = subword_tokenizer.tokenize('国境 の 長い トンネル を 抜ける と 雪国 であった 。' )
self.assertListEqual(_lowerCamelCase , ['▁国境', '▁の', '▁長い', '▁トンネル', '▁を', '▁抜ける', '▁と', '▁雪', '国', '▁であった', '▁。'] )
_snake_case = subword_tokenizer.tokenize('こんばんは こんばん にち は こんにちは' )
self.assertListEqual(_lowerCamelCase , ['▁こん', 'ばん', 'は', '▁こん', 'ばん', '▁に', 'ち', '▁は', '▁こんにちは'] )
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = self.tokenizer_class.from_pretrained('cl-tohoku/bert-base-japanese' )
_snake_case = tokenizer.encode('ありがとう。' , add_special_tokens=_lowerCamelCase )
_snake_case = tokenizer.encode('どういたしまして。' , add_special_tokens=_lowerCamelCase )
_snake_case = tokenizer.build_inputs_with_special_tokens(_lowerCamelCase )
_snake_case = tokenizer.build_inputs_with_special_tokens(_lowerCamelCase , _lowerCamelCase )
# 2 is for "[CLS]", 3 is for "[SEP]"
assert encoded_sentence == [2] + text + [3]
assert encoded_pair == [2] + text + [3] + text_a + [3]
@custom_tokenizers
class __UpperCAmelCase ( _lowerCAmelCase , unittest.TestCase ):
__lowercase = BertJapaneseTokenizer
__lowercase = False
def lowerCamelCase ( self ):
"""simple docstring"""
super().setUp()
_snake_case = ['[UNK]', '[CLS]', '[SEP]', 'こ', 'ん', 'に', 'ち', 'は', 'ば', '世', '界', '、', '。']
_snake_case = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in vocab_tokens] ) )
def lowerCamelCase ( self , **lowerCAmelCase_ ):
"""simple docstring"""
return BertJapaneseTokenizer.from_pretrained(self.tmpdirname , subword_tokenizer_type='character' , **_lowerCamelCase )
def lowerCamelCase ( self , lowerCAmelCase_ ):
"""simple docstring"""
_snake_case = 'こんにちは、世界。 \nこんばんは、世界。'
_snake_case = 'こ ん に ち は 、 世 界 。 こ ん ば ん は 、 世 界 。'
return input_text, output_text
def lowerCamelCase ( self ):
"""simple docstring"""
pass # TODO add if relevant
def lowerCamelCase ( self ):
"""simple docstring"""
pass # TODO add if relevant
def lowerCamelCase ( self ):
"""simple docstring"""
pass # TODO add if relevant
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = self.tokenizer_class(self.vocab_file , subword_tokenizer_type='character' )
_snake_case = tokenizer.tokenize('こんにちは、世界。 \nこんばんは、世界。' )
self.assertListEqual(
_lowerCamelCase , ['こ', 'ん', 'に', 'ち', 'は', '、', '世', '界', '。', 'こ', 'ん', 'ば', 'ん', 'は', '、', '世', '界', '。'] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(_lowerCamelCase ) , [3, 4, 5, 6, 7, 11, 9, 10, 12, 3, 4, 8, 4, 7, 11, 9, 10, 12] )
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = ['[UNK]', '[CLS]', '[SEP]', 'こ', 'ん', 'に', 'ち', 'は', 'ば', '世', '界', '、', '。']
_snake_case = {}
for i, token in enumerate(_lowerCamelCase ):
_snake_case = i
_snake_case = CharacterTokenizer(vocab=_lowerCamelCase , unk_token='[UNK]' )
self.assertListEqual(tokenizer.tokenize('' ) , [] )
self.assertListEqual(tokenizer.tokenize('こんにちは' ) , ['こ', 'ん', 'に', 'ち', 'は'] )
self.assertListEqual(tokenizer.tokenize('こんにちほ' ) , ['こ', 'ん', 'に', 'ち', '[UNK]'] )
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = self.tokenizer_class.from_pretrained('cl-tohoku/bert-base-japanese-char' )
_snake_case = tokenizer.encode('ありがとう。' , add_special_tokens=_lowerCamelCase )
_snake_case = tokenizer.encode('どういたしまして。' , add_special_tokens=_lowerCamelCase )
_snake_case = tokenizer.build_inputs_with_special_tokens(_lowerCamelCase )
_snake_case = tokenizer.build_inputs_with_special_tokens(_lowerCamelCase , _lowerCamelCase )
# 2 is for "[CLS]", 3 is for "[SEP]"
assert encoded_sentence == [2] + text + [3]
assert encoded_pair == [2] + text + [3] + text_a + [3]
@custom_tokenizers
class __UpperCAmelCase ( unittest.TestCase ):
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = 'cl-tohoku/bert-base-japanese'
_snake_case = AutoTokenizer.from_pretrained(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
class __UpperCAmelCase ( unittest.TestCase ):
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = 'cl-tohoku/bert-base-japanese'
with self.assertLogs('transformers' , level='WARNING' ) as cm:
BertTokenizer.from_pretrained(_lowerCamelCase )
self.assertTrue(
cm.records[0].message.startswith(
'The tokenizer class you load from this checkpoint is not the same type as the class this function'
' is called from.' ) )
_snake_case = 'bert-base-cased'
with self.assertLogs('transformers' , level='WARNING' ) as cm:
BertJapaneseTokenizer.from_pretrained(_lowerCamelCase )
self.assertTrue(
cm.records[0].message.startswith(
'The tokenizer class you load from this checkpoint is not the same type as the class this function'
' is called from.' ) )
| 42 |
"""simple docstring"""
class snake_case :
'''simple docstring'''
def __init__( self : List[str], _lowerCamelCase : list[int] ):
'''simple docstring'''
__A = len(_lowerCamelCase )
__A = [0] * len_array
if len_array > 0:
__A = array[0]
for i in range(1, _lowerCamelCase ):
__A = self.prefix_sum[i - 1] + array[i]
def _SCREAMING_SNAKE_CASE ( self : List[Any], _lowerCamelCase : int, _lowerCamelCase : int ):
'''simple docstring'''
if start == 0:
return self.prefix_sum[end]
return self.prefix_sum[end] - self.prefix_sum[start - 1]
def _SCREAMING_SNAKE_CASE ( self : List[str], _lowerCamelCase : int ):
'''simple docstring'''
__A = {0}
for sum_item in self.prefix_sum:
if sum_item - target_sum in sums:
return True
sums.add(_lowerCamelCase )
return False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 266 | 0 |
"""simple docstring"""
import argparse
from collections import defaultdict
import yaml
snake_case__ : Any = '''docs/source/en/_toctree.yml'''
def _snake_case ( _snake_case : Union[str, Any] ):
lowerCAmelCase : Any = defaultdict(_snake_case )
for doc in model_doc:
counts[doc["local"]] += 1
lowerCAmelCase : List[Any] = [key for key, value in counts.items() if value > 1]
lowerCAmelCase : int = []
for duplicate_key in duplicates:
lowerCAmelCase : Dict = list({doc['''title'''] for doc in model_doc if doc['''local'''] == duplicate_key} )
if len(_snake_case ) > 1:
raise ValueError(
f'''{duplicate_key} is present several times in the documentation table of content at '''
'''`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '''
'''others.''' )
# Only add this once
new_doc.append({'''local''': duplicate_key, '''title''': titles[0]} )
# Add none duplicate-keys
new_doc.extend([doc for doc in model_doc if counts[doc['''local''']] == 1] )
# Sort
return sorted(_snake_case , key=lambda _snake_case : s["title"].lower() )
def _snake_case ( _snake_case : Dict=False ):
with open(_snake_case , encoding='''utf-8''' ) as f:
lowerCAmelCase : str = yaml.safe_load(f.read() )
# Get to the API doc
lowerCAmelCase : Optional[int] = 0
while content[api_idx]["title"] != "API":
api_idx += 1
lowerCAmelCase : str = content[api_idx]['''sections''']
# Then to the model doc
lowerCAmelCase : int = 0
while api_doc[model_idx]["title"] != "Models":
model_idx += 1
lowerCAmelCase : List[Any] = api_doc[model_idx]['''sections''']
lowerCAmelCase : Any = [(idx, section) for idx, section in enumerate(_snake_case ) if '''sections''' in section]
lowerCAmelCase : Dict = False
for idx, modality_doc in modalities_docs:
lowerCAmelCase : List[Any] = modality_doc['''sections''']
lowerCAmelCase : List[Any] = clean_model_doc_toc(_snake_case )
if old_modality_doc != new_modality_doc:
lowerCAmelCase : List[Any] = True
if overwrite:
lowerCAmelCase : Dict = new_modality_doc
if diff:
if overwrite:
lowerCAmelCase : Dict = model_doc
lowerCAmelCase : Any = api_doc
with open(_snake_case , '''w''' , encoding='''utf-8''' ) as f:
f.write(yaml.dump(_snake_case , allow_unicode=_snake_case ) )
else:
raise ValueError(
'''The model doc part of the table of content is not properly sorted, run `make style` to fix this.''' )
if __name__ == "__main__":
snake_case__ : Dict = argparse.ArgumentParser()
parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
snake_case__ : List[str] = parser.parse_args()
check_model_doc(args.fix_and_overwrite)
| 352 |
"""simple docstring"""
def _snake_case ( _snake_case : int ):
assert isinstance(_snake_case , _snake_case ), f'''The input value of [n={number}] is not an integer'''
if number == 1:
return 2
elif number < 1:
lowerCAmelCase : Tuple = f'''The input value of [n={number}] has to be > 0'''
raise ValueError(_snake_case )
else:
lowerCAmelCase : str = sylvester(number - 1 )
lowerCAmelCase : Optional[Any] = num - 1
lowerCAmelCase : Optional[Any] = num
return lower * upper + 1
if __name__ == "__main__":
print(f"""The 8th number in Sylvester's sequence: {sylvester(8)}""")
| 314 | 0 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {"vocab_file": "sentencepiece.bpe.model"}
__magic_name__ = {
"vocab_file": {
"camembert-base": "https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model",
}
}
__magic_name__ = {
"camembert-base": 512,
}
__magic_name__ = "▁"
class SCREAMING_SNAKE_CASE_ ( __a ):
"""simple docstring"""
__lowercase : List[str] = VOCAB_FILES_NAMES
__lowercase : List[str] = PRETRAINED_VOCAB_FILES_MAP
__lowercase : Optional[int] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowercase : int = ['''input_ids''', '''attention_mask''']
def __init__( self , lowerCAmelCase__ , lowerCAmelCase__="<s>" , lowerCAmelCase__="</s>" , lowerCAmelCase__="</s>" , lowerCAmelCase__="<s>" , lowerCAmelCase__="<unk>" , lowerCAmelCase__="<pad>" , lowerCAmelCase__="<mask>" , lowerCAmelCase__=["<s>NOTUSED", "</s>NOTUSED"] , lowerCAmelCase__ = None , **lowerCAmelCase__ , ):
__SCREAMING_SNAKE_CASE = AddedToken(lowerCAmelCase__ , lstrip=lowerCAmelCase__ , rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__ , lowerCAmelCase__) else mask_token
__SCREAMING_SNAKE_CASE = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=lowerCAmelCase__ , eos_token=lowerCAmelCase__ , unk_token=lowerCAmelCase__ , sep_token=lowerCAmelCase__ , cls_token=lowerCAmelCase__ , pad_token=lowerCAmelCase__ , mask_token=lowerCAmelCase__ , additional_special_tokens=lowerCAmelCase__ , sp_model_kwargs=self.sp_model_kwargs , **lowerCAmelCase__ , )
__SCREAMING_SNAKE_CASE = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(str(lowerCAmelCase__))
__SCREAMING_SNAKE_CASE = vocab_file
# HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
# sentencepiece vocabulary (this is the case for <s> and </s>
__SCREAMING_SNAKE_CASE = {"""<s>NOTUSED""": 0, """<pad>""": 1, """</s>NOTUSED""": 2, """<unk>""": 3}
__SCREAMING_SNAKE_CASE = len(self.fairseq_tokens_to_ids)
__SCREAMING_SNAKE_CASE = len(self.sp_model) + len(self.fairseq_tokens_to_ids)
__SCREAMING_SNAKE_CASE = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def snake_case_ ( self , lowerCAmelCase__ , lowerCAmelCase__ = None):
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
__SCREAMING_SNAKE_CASE = [self.cls_token_id]
__SCREAMING_SNAKE_CASE = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def snake_case_ ( self , lowerCAmelCase__ , lowerCAmelCase__ = None , lowerCAmelCase__ = False):
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 snake_case_ ( self , lowerCAmelCase__ , lowerCAmelCase__ = None):
__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 + sep + token_ids_a + sep) * [0]
@property
def snake_case_ ( self):
return len(self.fairseq_tokens_to_ids) + len(self.sp_model)
def snake_case_ ( self):
__SCREAMING_SNAKE_CASE = {self.convert_ids_to_tokens(lowerCAmelCase__): i for i in range(self.vocab_size)}
vocab.update(self.added_tokens_encoder)
return vocab
def snake_case_ ( self , lowerCAmelCase__):
return self.sp_model.encode(lowerCAmelCase__ , out_type=lowerCAmelCase__)
def snake_case_ ( self , lowerCAmelCase__):
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
elif self.sp_model.PieceToId(lowerCAmelCase__) == 0:
# Convert sentence piece unk token to fairseq unk token index
return self.unk_token_id
return self.fairseq_offset + self.sp_model.PieceToId(lowerCAmelCase__)
def snake_case_ ( self , lowerCAmelCase__):
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset)
def snake_case_ ( self , lowerCAmelCase__):
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = """"""
__SCREAMING_SNAKE_CASE = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(lowerCAmelCase__) + token
__SCREAMING_SNAKE_CASE = True
__SCREAMING_SNAKE_CASE = []
else:
current_sub_tokens.append(lowerCAmelCase__)
__SCREAMING_SNAKE_CASE = False
out_string += self.sp_model.decode(lowerCAmelCase__)
return out_string.strip()
def __getstate__( self):
__SCREAMING_SNAKE_CASE = self.__dict__.copy()
__SCREAMING_SNAKE_CASE = None
return state
def __setstate__( self , lowerCAmelCase__):
__SCREAMING_SNAKE_CASE = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs"""):
__SCREAMING_SNAKE_CASE = {}
__SCREAMING_SNAKE_CASE = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(self.vocab_file)
def snake_case_ ( self , lowerCAmelCase__ , lowerCAmelCase__ = None):
if not os.path.isdir(lowerCAmelCase__):
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
return
__SCREAMING_SNAKE_CASE = os.path.join(
lowerCAmelCase__ , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""])
if os.path.abspath(self.vocab_file) != os.path.abspath(lowerCAmelCase__) and os.path.isfile(self.vocab_file):
copyfile(self.vocab_file , lowerCAmelCase__)
elif not os.path.isfile(self.vocab_file):
with open(lowerCAmelCase__ , """wb""") as fi:
__SCREAMING_SNAKE_CASE = self.sp_model.serialized_model_proto()
fi.write(lowerCAmelCase__)
return (out_vocab_file,)
| 100 |
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
UpperCAmelCase__ = datasets.utils.logging.get_logger(__name__)
class __lowerCAmelCase ( folder_based_builder.FolderBasedBuilderConfig ):
UpperCamelCase = None
UpperCamelCase = None
class __lowerCAmelCase ( folder_based_builder.FolderBasedBuilder ):
UpperCamelCase = datasets.Audio()
UpperCamelCase = '''audio'''
UpperCamelCase = AudioFolderConfig
UpperCamelCase = 42 # definition at the bottom of the script
UpperCamelCase = AudioClassification(audio_column='''audio''' , label_column='''label''' )
UpperCAmelCase__ = [
".aiff",
".au",
".avr",
".caf",
".flac",
".htk",
".svx",
".mat4",
".mat5",
".mpc2k",
".ogg",
".paf",
".pvf",
".raw",
".rf64",
".sd2",
".sds",
".ircam",
".voc",
".w64",
".wav",
".nist",
".wavex",
".wve",
".xi",
".mp3",
".opus",
]
UpperCAmelCase__ = AUDIO_EXTENSIONS
| 339 | 0 |
from . import (
albert,
align,
altclip,
audio_spectrogram_transformer,
auto,
autoformer,
bark,
bart,
barthez,
bartpho,
beit,
bert,
bert_generation,
bert_japanese,
bertweet,
big_bird,
bigbird_pegasus,
biogpt,
bit,
blenderbot,
blenderbot_small,
blip,
blip_a,
bloom,
bridgetower,
byta,
camembert,
canine,
chinese_clip,
clap,
clip,
clipseg,
codegen,
conditional_detr,
convbert,
convnext,
convnextva,
cpm,
cpmant,
ctrl,
cvt,
dataavec,
deberta,
deberta_va,
decision_transformer,
deformable_detr,
deit,
deprecated,
deta,
detr,
dialogpt,
dinat,
distilbert,
dit,
donut,
dpr,
dpt,
efficientformer,
efficientnet,
electra,
encodec,
encoder_decoder,
ernie,
ernie_m,
esm,
falcon,
flaubert,
flava,
fnet,
focalnet,
fsmt,
funnel,
git,
glpn,
gpta,
gpt_bigcode,
gpt_neo,
gpt_neox,
gpt_neox_japanese,
gpt_swa,
gptj,
gptsan_japanese,
graphormer,
groupvit,
herbert,
hubert,
ibert,
imagegpt,
informer,
instructblip,
jukebox,
layoutlm,
layoutlmva,
layoutlmva,
layoutxlm,
led,
levit,
lilt,
llama,
longformer,
longta,
luke,
lxmert,
mam_aaa,
marian,
markuplm,
maskaformer,
maskformer,
mbart,
mbartaa,
mega,
megatron_bert,
megatron_gpta,
mgp_str,
mluke,
mobilebert,
mobilenet_va,
mobilenet_va,
mobilevit,
mobilevitva,
mpnet,
mra,
mta,
musicgen,
mvp,
nat,
nezha,
nllb,
nllb_moe,
nystromformer,
oneformer,
open_llama,
openai,
opt,
owlvit,
pegasus,
pegasus_x,
perceiver,
phobert,
pixastruct,
plbart,
poolformer,
prophetnet,
qdqbert,
rag,
realm,
reformer,
regnet,
rembert,
resnet,
roberta,
roberta_prelayernorm,
roc_bert,
roformer,
rwkv,
sam,
segformer,
sew,
sew_d,
speech_encoder_decoder,
speech_to_text,
speech_to_text_a,
speechta,
splinter,
squeezebert,
swiftformer,
swin,
swinasr,
swinva,
switch_transformers,
ta,
table_transformer,
tapas,
time_series_transformer,
timesformer,
timm_backbone,
transfo_xl,
trocr,
tvlt,
umta,
unispeech,
unispeech_sat,
upernet,
videomae,
vilt,
vision_encoder_decoder,
vision_text_dual_encoder,
visual_bert,
vit,
vit_hybrid,
vit_mae,
vit_msn,
vivit,
wavaveca,
wavaveca_conformer,
wavaveca_phoneme,
wavaveca_with_lm,
wavlm,
whisper,
x_clip,
xglm,
xlm,
xlm_prophetnet,
xlm_roberta,
xlm_roberta_xl,
xlnet,
xmod,
yolos,
yoso,
)
| 28 |
from datetime import datetime as dt
import os
from github import Github
__UpperCAmelCase = [
'good first issue',
'good second issue',
'good difficult issue',
'feature request',
'new model',
'wip',
]
def __UpperCamelCase ( ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase_ : Dict = Github(os.environ["""GITHUB_TOKEN"""] )
lowerCAmelCase_ : Tuple = g.get_repo("""huggingface/transformers""" )
lowerCAmelCase_ : Any = repo.get_issues(state="""open""" )
for issue in open_issues:
lowerCAmelCase_ : Union[str, Any] = sorted([comment for comment in issue.get_comments()] , key=lambda lowercase__ : i.created_at , reverse=lowercase__ )
lowerCAmelCase_ : str = comments[0] if len(lowercase__ ) > 0 else None
if (
last_comment is not None
and last_comment.user.login == "github-actions[bot]"
and (dt.utcnow() - issue.updated_at).days > 7
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
issue.edit(state="""closed""" )
elif (
(dt.utcnow() - issue.updated_at).days > 23
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# print(f"Would add stale comment to {issue.number}")
issue.create_comment(
"""This issue has been automatically marked as stale because it has not had """
"""recent activity. If you think this still needs to be addressed """
"""please comment on this thread.\n\nPlease note that issues that do not follow the """
"""[contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) """
"""are likely to be ignored.""" )
if __name__ == "__main__":
main()
| 28 | 1 |
"""simple docstring"""
from collections.abc import Iterable
from typing import Any
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self : Optional[int] ,lowercase_ : int | None = None ):
lowerCAmelCase__ : List[str] = value
lowerCAmelCase__ : Node | None = None # Added in order to delete a node easier
lowerCAmelCase__ : Node | None = None
lowerCAmelCase__ : Node | None = None
def __repr__( self : List[str] ):
from pprint import pformat
if self.left is None and self.right is None:
return str(self.value )
return pformat({F'{self.value}': (self.left, self.right)} ,indent=1 )
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self : int ,lowercase_ : Node | None = None ):
lowerCAmelCase__ : Union[str, Any] = root
def __str__( self : Union[str, Any] ):
return str(self.root )
def __lowerCAmelCase ( self : Dict ,lowercase_ : Node ,lowercase_ : Node | None ):
if new_children is not None: # reset its kids
lowerCAmelCase__ : List[str] = node.parent
if node.parent is not None: # reset its parent
if self.is_right(lowercase_ ): # If it is the right children
lowerCAmelCase__ : Union[str, Any] = new_children
else:
lowerCAmelCase__ : Optional[Any] = new_children
else:
lowerCAmelCase__ : List[str] = new_children
def __lowerCAmelCase ( self : List[Any] ,lowercase_ : Node ):
if node.parent and node.parent.right:
return node == node.parent.right
return False
def __lowerCAmelCase ( self : List[str] ):
return self.root is None
def __lowerCAmelCase ( self : Tuple ,lowercase_ : Any ):
lowerCAmelCase__ : Union[str, Any] = Node(lowercase_ ) # create a new Node
if self.empty(): # if Tree is empty
lowerCAmelCase__ : Dict = new_node # set its root
else: # Tree is not empty
lowerCAmelCase__ : Union[str, Any] = self.root # from root
if parent_node is None:
return
while True: # While we don't get to a leaf
if value < parent_node.value: # We go left
if parent_node.left is None:
lowerCAmelCase__ : int = new_node # We insert the new node in a leaf
break
else:
lowerCAmelCase__ : Any = parent_node.left
else:
if parent_node.right is None:
lowerCAmelCase__ : Any = new_node
break
else:
lowerCAmelCase__ : List[str] = parent_node.right
lowerCAmelCase__ : Dict = parent_node
def __lowerCAmelCase ( self : str ,*lowercase_ : Dict ):
for value in values:
self.__insert(lowercase_ )
def __lowerCAmelCase ( self : Union[str, Any] ,lowercase_ : Any ):
if self.empty():
raise IndexError('''Warning: Tree is empty! please use another.''' )
else:
lowerCAmelCase__ : str = self.root
# use lazy evaluation here to avoid NoneType Attribute error
while node is not None and node.value is not value:
lowerCAmelCase__ : Tuple = node.left if value < node.value else node.right
return node
def __lowerCAmelCase ( self : int ,lowercase_ : Node | None = None ):
if node is None:
if self.root is None:
return None
lowerCAmelCase__ : str = self.root
if not self.empty():
while node.right is not None:
lowerCAmelCase__ : List[Any] = node.right
return node
def __lowerCAmelCase ( self : Any ,lowercase_ : Node | None = None ):
if node is None:
lowerCAmelCase__ : Dict = self.root
if self.root is None:
return None
if not self.empty():
lowerCAmelCase__ : str = self.root
while node.left is not None:
lowerCAmelCase__ : int = node.left
return node
def __lowerCAmelCase ( self : Dict ,lowercase_ : int ):
lowerCAmelCase__ : Tuple = self.search(lowercase_ ) # Look for the node with that label
if node is not None:
if node.left is None and node.right is None: # If it has no children
self.__reassign_nodes(lowercase_ ,lowercase_ )
elif node.left is None: # Has only right children
self.__reassign_nodes(lowercase_ ,node.right )
elif node.right is None: # Has only left children
self.__reassign_nodes(lowercase_ ,node.left )
else:
lowerCAmelCase__ : Dict = self.get_max(
node.left ) # Gets the max value of the left branch
self.remove(tmp_node.value ) # type: ignore
lowerCAmelCase__ : List[str] = (
tmp_node.value # type: ignore
) # Assigns the value to the node to delete and keep tree structure
def __lowerCAmelCase ( self : Optional[int] ,lowercase_ : Node | None ):
if node is not None:
yield node # Preorder Traversal
yield from self.preorder_traverse(node.left )
yield from self.preorder_traverse(node.right )
def __lowerCAmelCase ( self : str ,lowercase_ : List[str]=None ):
if traversal_function is None:
return self.preorder_traverse(self.root )
else:
return traversal_function(self.root )
def __lowerCAmelCase ( self : Dict ,lowercase_ : list ,lowercase_ : Node | None ):
if node:
self.inorder(lowercase_ ,node.left )
arr.append(node.value )
self.inorder(lowercase_ ,node.right )
def __lowerCAmelCase ( self : Tuple ,lowercase_ : int ,lowercase_ : Node ):
lowerCAmelCase__ : list[int] = []
self.inorder(lowercase_ ,lowercase_ ) # append all values to list using inorder traversal
return arr[k - 1]
def __SCREAMING_SNAKE_CASE ( A_ ):
lowerCAmelCase__ : Union[str, Any] = []
if curr_node is not None:
lowerCAmelCase__ : int = postorder(curr_node.left ) + postorder(curr_node.right ) + [curr_node]
return node_list
def __SCREAMING_SNAKE_CASE ( ):
lowerCAmelCase__ : str = (8, 3, 6, 1, 10, 14, 13, 4, 7)
lowerCAmelCase__ : Optional[int] = BinarySearchTree()
for i in testlist:
t.insert(A_ )
# Prints all the elements of the list in order traversal
print(A_ )
if t.search(6 ) is not None:
print('''The value 6 exists''' )
else:
print('''The value 6 doesn\'t exist''' )
if t.search(-1 ) is not None:
print('''The value -1 exists''' )
else:
print('''The value -1 doesn\'t exist''' )
if not t.empty():
print('''Max Value: ''' , t.get_max().value ) # type: ignore
print('''Min Value: ''' , t.get_min().value ) # type: ignore
for i in testlist:
t.remove(A_ )
print(A_ )
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
| 106 |
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 __lowerCAmelCase ( a ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = 42
_SCREAMING_SNAKE_CASE = 42
class __lowerCAmelCase ( nn.Module ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = 42
_SCREAMING_SNAKE_CASE = (16, 32, 96, 256)
_SCREAMING_SNAKE_CASE = jnp.floataa
def lowerCAmelCase__ ( self : Optional[Any] ) -> Dict:
"""simple docstring"""
snake_case_ = nn.Conv(
self.block_out_channels[0] , kernel_size=(3, 3) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
snake_case_ = []
for i in range(len(self.block_out_channels ) - 1 ):
snake_case_ = self.block_out_channels[i]
snake_case_ = self.block_out_channels[i + 1]
snake_case_ = nn.Conv(
_lowerCAmelCase , kernel_size=(3, 3) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
blocks.append(_lowerCAmelCase )
snake_case_ = nn.Conv(
_lowerCAmelCase , kernel_size=(3, 3) , strides=(2, 2) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
blocks.append(_lowerCAmelCase )
snake_case_ = blocks
snake_case_ = 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 : Union[str, Any] , _lowerCAmelCase : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
snake_case_ = self.conv_in(_lowerCAmelCase )
snake_case_ = nn.silu(_lowerCAmelCase )
for block in self.blocks:
snake_case_ = block(_lowerCAmelCase )
snake_case_ = nn.silu(_lowerCAmelCase )
snake_case_ = self.conv_out(_lowerCAmelCase )
return embedding
@flax_register_to_config
class __lowerCAmelCase ( nn.Module , a , a ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = 32
_SCREAMING_SNAKE_CASE = 4
_SCREAMING_SNAKE_CASE = (
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"DownBlock2D",
)
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = (320, 640, 1280, 1280)
_SCREAMING_SNAKE_CASE = 2
_SCREAMING_SNAKE_CASE = 8
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = 1280
_SCREAMING_SNAKE_CASE = 0.0
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = jnp.floataa
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAKE_CASE = "rgb"
_SCREAMING_SNAKE_CASE = (16, 32, 96, 256)
def lowerCAmelCase__ ( self : str , _lowerCAmelCase : jax.random.KeyArray ) -> FrozenDict:
"""simple docstring"""
# init input tensors
snake_case_ = (1, self.in_channels, self.sample_size, self.sample_size)
snake_case_ = jnp.zeros(_lowerCAmelCase , dtype=jnp.floataa )
snake_case_ = jnp.ones((1,) , dtype=jnp.intaa )
snake_case_ = jnp.zeros((1, 1, self.cross_attention_dim) , dtype=jnp.floataa )
snake_case_ = (1, 3, self.sample_size * 8, self.sample_size * 8)
snake_case_ = jnp.zeros(_lowerCAmelCase , dtype=jnp.floataa )
snake_case_ , snake_case_ = jax.random.split(_lowerCAmelCase )
snake_case_ = {"params": params_rng, "dropout": dropout_rng}
return self.init(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )["params"]
def lowerCAmelCase__ ( self : List[Any] ) -> Any:
"""simple docstring"""
snake_case_ = self.block_out_channels
snake_case_ = 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.
snake_case_ = self.num_attention_heads or self.attention_head_dim
# input
snake_case_ = nn.Conv(
block_out_channels[0] , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
# time
snake_case_ = FlaxTimesteps(
block_out_channels[0] , flip_sin_to_cos=self.flip_sin_to_cos , freq_shift=self.config.freq_shift )
snake_case_ = FlaxTimestepEmbedding(_lowerCAmelCase , dtype=self.dtype )
snake_case_ = FlaxControlNetConditioningEmbedding(
conditioning_embedding_channels=block_out_channels[0] , block_out_channels=self.conditioning_embedding_out_channels , )
snake_case_ = self.only_cross_attention
if isinstance(_lowerCAmelCase , _lowerCAmelCase ):
snake_case_ = (only_cross_attention,) * len(self.down_block_types )
if isinstance(_lowerCAmelCase , _lowerCAmelCase ):
snake_case_ = (num_attention_heads,) * len(self.down_block_types )
# down
snake_case_ = []
snake_case_ = []
snake_case_ = block_out_channels[0]
snake_case_ = nn.Conv(
_lowerCAmelCase , 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(_lowerCAmelCase )
for i, down_block_type in enumerate(self.down_block_types ):
snake_case_ = output_channel
snake_case_ = block_out_channels[i]
snake_case_ = i == len(_lowerCAmelCase ) - 1
if down_block_type == "CrossAttnDownBlock2D":
snake_case_ = FlaxCrossAttnDownBlockaD(
in_channels=_lowerCAmelCase , out_channels=_lowerCAmelCase , 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:
snake_case_ = FlaxDownBlockaD(
in_channels=_lowerCAmelCase , out_channels=_lowerCAmelCase , dropout=self.dropout , num_layers=self.layers_per_block , add_downsample=not is_final_block , dtype=self.dtype , )
down_blocks.append(_lowerCAmelCase )
for _ in range(self.layers_per_block ):
snake_case_ = nn.Conv(
_lowerCAmelCase , 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(_lowerCAmelCase )
if not is_final_block:
snake_case_ = nn.Conv(
_lowerCAmelCase , 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(_lowerCAmelCase )
snake_case_ = down_blocks
snake_case_ = controlnet_down_blocks
# mid
snake_case_ = block_out_channels[-1]
snake_case_ = FlaxUNetMidBlockaDCrossAttn(
in_channels=_lowerCAmelCase , dropout=self.dropout , num_attention_heads=num_attention_heads[-1] , use_linear_projection=self.use_linear_projection , dtype=self.dtype , )
snake_case_ = nn.Conv(
_lowerCAmelCase , kernel_size=(1, 1) , padding="VALID" , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
def __call__( self : Any , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Any , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : float = 1.0 , _lowerCAmelCase : bool = True , _lowerCAmelCase : bool = False , ) -> Union[FlaxControlNetOutput, Tuple]:
"""simple docstring"""
snake_case_ = self.controlnet_conditioning_channel_order
if channel_order == "bgr":
snake_case_ = jnp.flip(_lowerCAmelCase , axis=1 )
# 1. time
if not isinstance(_lowerCAmelCase , jnp.ndarray ):
snake_case_ = jnp.array([timesteps] , dtype=jnp.intaa )
elif isinstance(_lowerCAmelCase , jnp.ndarray ) and len(timesteps.shape ) == 0:
snake_case_ = timesteps.astype(dtype=jnp.floataa )
snake_case_ = jnp.expand_dims(_lowerCAmelCase , 0 )
snake_case_ = self.time_proj(_lowerCAmelCase )
snake_case_ = self.time_embedding(_lowerCAmelCase )
# 2. pre-process
snake_case_ = jnp.transpose(_lowerCAmelCase , (0, 2, 3, 1) )
snake_case_ = self.conv_in(_lowerCAmelCase )
snake_case_ = jnp.transpose(_lowerCAmelCase , (0, 2, 3, 1) )
snake_case_ = self.controlnet_cond_embedding(_lowerCAmelCase )
sample += controlnet_cond
# 3. down
snake_case_ = (sample,)
for down_block in self.down_blocks:
if isinstance(_lowerCAmelCase , _lowerCAmelCase ):
snake_case_ , snake_case_ = down_block(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , deterministic=not train )
else:
snake_case_ , snake_case_ = down_block(_lowerCAmelCase , _lowerCAmelCase , deterministic=not train )
down_block_res_samples += res_samples
# 4. mid
snake_case_ = self.mid_block(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , deterministic=not train )
# 5. contronet blocks
snake_case_ = ()
for down_block_res_sample, controlnet_block in zip(_lowerCAmelCase , self.controlnet_down_blocks ):
snake_case_ = controlnet_block(_lowerCAmelCase )
controlnet_down_block_res_samples += (down_block_res_sample,)
snake_case_ = controlnet_down_block_res_samples
snake_case_ = self.controlnet_mid_block(_lowerCAmelCase )
# 6. scaling
snake_case_ = [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=_lowerCAmelCase , mid_block_res_sample=_lowerCAmelCase )
| 159 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowerCAmelCase_ : Dict = {'''configuration_vit''': ['''VIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTConfig''', '''ViTOnnxConfig''']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ : int = ['''ViTFeatureExtractor''']
lowerCAmelCase_ : Any = ['''ViTImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ : List[Any] = [
'''VIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ViTForImageClassification''',
'''ViTForMaskedImageModeling''',
'''ViTModel''',
'''ViTPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ : int = [
'''TFViTForImageClassification''',
'''TFViTModel''',
'''TFViTPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ : List[Any] = [
'''FlaxViTForImageClassification''',
'''FlaxViTModel''',
'''FlaxViTPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_vit import ViTFeatureExtractor
from .image_processing_vit import ViTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vit import (
VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
ViTForImageClassification,
ViTForMaskedImageModeling,
ViTModel,
ViTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel
else:
import sys
lowerCAmelCase_ : int = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 248 |
"""simple docstring"""
from __future__ import annotations
def _lowerCAmelCase ( lowerCAmelCase = 4 ):
'''simple docstring'''
UpperCAmelCase = abs(lowerCAmelCase ) or 4
return [[1 + x + y * row_size for x in range(lowerCAmelCase )] for y in range(lowerCAmelCase )]
def _lowerCAmelCase ( lowerCAmelCase ):
'''simple docstring'''
return reverse_row(transpose(lowerCAmelCase ) )
# OR.. transpose(reverse_column(matrix))
def _lowerCAmelCase ( lowerCAmelCase ):
'''simple docstring'''
return reverse_row(reverse_column(lowerCAmelCase ) )
# OR.. reverse_column(reverse_row(matrix))
def _lowerCAmelCase ( lowerCAmelCase ):
'''simple docstring'''
return reverse_column(transpose(lowerCAmelCase ) )
# OR.. transpose(reverse_row(matrix))
def _lowerCAmelCase ( lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase = [list(lowerCAmelCase ) for x in zip(*lowerCAmelCase )]
return matrix
def _lowerCAmelCase ( lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase = matrix[::-1]
return matrix
def _lowerCAmelCase ( lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase = [x[::-1] for x in matrix]
return matrix
def _lowerCAmelCase ( lowerCAmelCase ):
'''simple docstring'''
for i in matrix:
print(*lowerCAmelCase )
if __name__ == "__main__":
lowerCAmelCase_ : Any = make_matrix()
print('''\norigin:\n''')
print_matrix(matrix)
print('''\nrotate 90 counterclockwise:\n''')
print_matrix(rotate_aa(matrix))
lowerCAmelCase_ : Union[str, Any] = make_matrix()
print('''\norigin:\n''')
print_matrix(matrix)
print('''\nrotate 180:\n''')
print_matrix(rotate_aaa(matrix))
lowerCAmelCase_ : Optional[Any] = make_matrix()
print('''\norigin:\n''')
print_matrix(matrix)
print('''\nrotate 270 counterclockwise:\n''')
print_matrix(rotate_aaa(matrix))
| 248 | 1 |
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
snake_case_ : str = 0
snake_case_ : Union[str, Any] = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0],
]
snake_case_ : str = [[-1, 0], [0, -1], [1, 0], [0, 1]] # up, left, down, right
snake_case_ : List[Any] = tuple[int, int]
class __snake_case :
def __init__( self : Any , _snake_case : int , _snake_case : int , _snake_case : int , _snake_case : int , _snake_case : int , _snake_case : Node | None , ):
"""simple docstring"""
UpperCAmelCase_ = pos_x
UpperCAmelCase_ = pos_y
UpperCAmelCase_ = (pos_y, pos_x)
UpperCAmelCase_ = goal_x
UpperCAmelCase_ = goal_y
UpperCAmelCase_ = g_cost
UpperCAmelCase_ = parent
UpperCAmelCase_ = self.calculate_heuristic()
UpperCAmelCase_ = self.g_cost + self.h_cost
def lowerCamelCase ( self : List[str]):
"""simple docstring"""
UpperCAmelCase_ = self.pos_x - self.goal_x
UpperCAmelCase_ = self.pos_y - self.goal_y
if HEURISTIC == 1:
return abs(_snake_case) + abs(_snake_case)
else:
return sqrt(dy**2 + dx**2)
def __lt__( self : Union[str, Any] , _snake_case : Node):
"""simple docstring"""
return self.f_cost < other.f_cost
class __snake_case :
def __init__( self : str , _snake_case : TPosition , _snake_case : TPosition):
"""simple docstring"""
UpperCAmelCase_ = Node(start[1] , start[0] , goal[1] , goal[0] , 0 , _snake_case)
UpperCAmelCase_ = Node(goal[1] , goal[0] , goal[1] , goal[0] , 99999 , _snake_case)
UpperCAmelCase_ = [self.start]
UpperCAmelCase_ = []
UpperCAmelCase_ = False
def lowerCamelCase ( self : Optional[int]):
"""simple docstring"""
while self.open_nodes:
# Open Nodes are sorted using __lt__
self.open_nodes.sort()
UpperCAmelCase_ = self.open_nodes.pop(0)
if current_node.pos == self.target.pos:
return self.retrace_path(_snake_case)
self.closed_nodes.append(_snake_case)
UpperCAmelCase_ = self.get_successors(_snake_case)
for child_node in successors:
if child_node in self.closed_nodes:
continue
if child_node not in self.open_nodes:
self.open_nodes.append(_snake_case)
else:
# retrieve the best current path
UpperCAmelCase_ = self.open_nodes.pop(self.open_nodes.index(_snake_case))
if child_node.g_cost < better_node.g_cost:
self.open_nodes.append(_snake_case)
else:
self.open_nodes.append(_snake_case)
return [self.start.pos]
def lowerCamelCase ( self : Tuple , _snake_case : Node):
"""simple docstring"""
UpperCAmelCase_ = []
for action in delta:
UpperCAmelCase_ = parent.pos_x + action[1]
UpperCAmelCase_ = parent.pos_y + action[0]
if not (0 <= pos_x <= len(grid[0]) - 1 and 0 <= pos_y <= len(_snake_case) - 1):
continue
if grid[pos_y][pos_x] != 0:
continue
successors.append(
Node(
_snake_case , _snake_case , self.target.pos_y , self.target.pos_x , parent.g_cost + 1 , _snake_case , ))
return successors
def lowerCamelCase ( self : Any , _snake_case : Node | None):
"""simple docstring"""
UpperCAmelCase_ = node
UpperCAmelCase_ = []
while current_node is not None:
path.append((current_node.pos_y, current_node.pos_x))
UpperCAmelCase_ = current_node.parent
path.reverse()
return path
class __snake_case :
def __init__( self : Any , _snake_case : TPosition , _snake_case : TPosition):
"""simple docstring"""
UpperCAmelCase_ = AStar(_snake_case , _snake_case)
UpperCAmelCase_ = AStar(_snake_case , _snake_case)
UpperCAmelCase_ = False
def lowerCamelCase ( self : List[Any]):
"""simple docstring"""
while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
self.fwd_astar.open_nodes.sort()
self.bwd_astar.open_nodes.sort()
UpperCAmelCase_ = self.fwd_astar.open_nodes.pop(0)
UpperCAmelCase_ = self.bwd_astar.open_nodes.pop(0)
if current_bwd_node.pos == current_fwd_node.pos:
return self.retrace_bidirectional_path(
_snake_case , _snake_case)
self.fwd_astar.closed_nodes.append(_snake_case)
self.bwd_astar.closed_nodes.append(_snake_case)
UpperCAmelCase_ = current_bwd_node
UpperCAmelCase_ = current_fwd_node
UpperCAmelCase_ = {
self.fwd_astar: self.fwd_astar.get_successors(_snake_case),
self.bwd_astar: self.bwd_astar.get_successors(_snake_case),
}
for astar in [self.fwd_astar, self.bwd_astar]:
for child_node in successors[astar]:
if child_node in astar.closed_nodes:
continue
if child_node not in astar.open_nodes:
astar.open_nodes.append(_snake_case)
else:
# retrieve the best current path
UpperCAmelCase_ = astar.open_nodes.pop(
astar.open_nodes.index(_snake_case))
if child_node.g_cost < better_node.g_cost:
astar.open_nodes.append(_snake_case)
else:
astar.open_nodes.append(_snake_case)
return [self.fwd_astar.start.pos]
def lowerCamelCase ( self : int , _snake_case : Node , _snake_case : Node):
"""simple docstring"""
UpperCAmelCase_ = self.fwd_astar.retrace_path(_snake_case)
UpperCAmelCase_ = self.bwd_astar.retrace_path(_snake_case)
bwd_path.pop()
bwd_path.reverse()
UpperCAmelCase_ = fwd_path + bwd_path
return path
if __name__ == "__main__":
# all coordinates are given in format [y,x]
snake_case_ : Any = (0, 0)
snake_case_ : Union[str, Any] = (len(grid) - 1, len(grid[0]) - 1)
for elem in grid:
print(elem)
snake_case_ : str = time.time()
snake_case_ : List[str] = AStar(init, goal)
snake_case_ : Optional[int] = a_star.search()
snake_case_ : Optional[Any] = time.time() - start_time
print(f"AStar execution time = {end_time:f} seconds")
snake_case_ : int = time.time()
snake_case_ : Dict = BidirectionalAStar(init, goal)
snake_case_ : str = time.time() - bd_start_time
print(f"BidirectionalAStar execution time = {bd_end_time:f} seconds")
| 51 |
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class __snake_case :
pass
| 51 | 1 |
"""simple docstring"""
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class _lowercase ( a__ ):
# to overwrite at feature extractactor specific tests
lowercase_ = None
lowercase_ = None
@property
def _UpperCamelCase ( self ) -> Optional[Any]:
return self.feat_extract_tester.prepare_feat_extract_dict()
def _UpperCamelCase ( self ) -> int:
lowerCamelCase : int = self.feature_extraction_class(**self.feat_extract_dict )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , 'feature_size' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , 'sampling_rate' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , 'padding_value' ) )
def _UpperCamelCase ( self ) -> Union[str, Any]:
lowerCamelCase : Union[str, Any] = self.feat_extract_tester.prepare_inputs_for_common()
lowerCamelCase : str = self.feature_extraction_class(**self.feat_extract_dict )
lowerCamelCase : Optional[int] = feat_extract.model_input_names[0]
lowerCamelCase : List[Any] = BatchFeature({input_name: speech_inputs} )
self.assertTrue(all(len(SCREAMING_SNAKE_CASE_ ) == len(SCREAMING_SNAKE_CASE_ ) for x, y in zip(SCREAMING_SNAKE_CASE_ , processed_features[input_name] ) ) )
lowerCamelCase : List[str] = self.feat_extract_tester.prepare_inputs_for_common(equal_length=SCREAMING_SNAKE_CASE_ )
lowerCamelCase : int = BatchFeature({input_name: speech_inputs} , tensor_type='np' )
lowerCamelCase : Optional[int] = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
lowerCamelCase : List[Any] = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
@require_torch
def _UpperCamelCase ( self ) -> Tuple:
lowerCamelCase : str = self.feat_extract_tester.prepare_inputs_for_common(equal_length=SCREAMING_SNAKE_CASE_ )
lowerCamelCase : int = self.feature_extraction_class(**self.feat_extract_dict )
lowerCamelCase : Dict = feat_extract.model_input_names[0]
lowerCamelCase : Union[str, Any] = BatchFeature({input_name: speech_inputs} , tensor_type='pt' )
lowerCamelCase : Dict = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
lowerCamelCase : Union[str, Any] = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
@require_tf
def _UpperCamelCase ( self ) -> Any:
lowerCamelCase : Optional[Any] = self.feat_extract_tester.prepare_inputs_for_common(equal_length=SCREAMING_SNAKE_CASE_ )
lowerCamelCase : Union[str, Any] = self.feature_extraction_class(**self.feat_extract_dict )
lowerCamelCase : int = feat_extract.model_input_names[0]
lowerCamelCase : str = BatchFeature({input_name: speech_inputs} , tensor_type='tf' )
lowerCamelCase : Dict = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
lowerCamelCase : Union[str, Any] = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
def _UpperCamelCase ( self , UpperCAmelCase_=False ) -> Any:
def _inputs_have_equal_length(UpperCAmelCase_ ):
lowerCamelCase : Tuple = len(input[0] )
for input_slice in input[1:]:
if len(SCREAMING_SNAKE_CASE_ ) != length:
return False
return True
def _inputs_are_equal(UpperCAmelCase_ , UpperCAmelCase_ ):
if len(SCREAMING_SNAKE_CASE_ ) != len(SCREAMING_SNAKE_CASE_ ):
return False
for input_slice_a, input_slice_a in zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
if not np.allclose(np.asarray(SCREAMING_SNAKE_CASE_ ) , np.asarray(SCREAMING_SNAKE_CASE_ ) , atol=1E-3 ):
return False
return True
lowerCamelCase : Optional[int] = self.feature_extraction_class(**self.feat_extract_dict )
lowerCamelCase : int = self.feat_extract_tester.prepare_inputs_for_common(numpify=SCREAMING_SNAKE_CASE_ )
lowerCamelCase : str = feat_extract.model_input_names[0]
lowerCamelCase : Tuple = BatchFeature({input_name: speech_inputs} )
lowerCamelCase : List[Any] = self.feat_extract_tester.seq_length_diff
lowerCamelCase : Any = self.feat_extract_tester.max_seq_length + pad_diff
lowerCamelCase : Tuple = self.feat_extract_tester.min_seq_length
lowerCamelCase : Dict = self.feat_extract_tester.batch_size
lowerCamelCase : str = self.feat_extract_tester.feature_size
# test padding for List[int] + numpy
lowerCamelCase : Dict = feat_extract.pad(SCREAMING_SNAKE_CASE_ , padding=SCREAMING_SNAKE_CASE_ )
lowerCamelCase : str = input_a[input_name]
lowerCamelCase : str = feat_extract.pad(SCREAMING_SNAKE_CASE_ , padding='longest' )
lowerCamelCase : Union[str, Any] = input_a[input_name]
lowerCamelCase : str = feat_extract.pad(SCREAMING_SNAKE_CASE_ , padding='max_length' , max_length=len(speech_inputs[-1] ) )
lowerCamelCase : Any = input_a[input_name]
lowerCamelCase : Tuple = feat_extract.pad(SCREAMING_SNAKE_CASE_ , padding='longest' , return_tensors='np' )
lowerCamelCase : Any = input_a[input_name]
# max_length parameter has to be provided when setting `padding="max_length"`
with self.assertRaises(SCREAMING_SNAKE_CASE_ ):
feat_extract.pad(SCREAMING_SNAKE_CASE_ , padding='max_length' )[input_name]
lowerCamelCase : int = feat_extract.pad(
SCREAMING_SNAKE_CASE_ , padding='max_length' , max_length=SCREAMING_SNAKE_CASE_ , return_tensors='np' )
lowerCamelCase : Optional[int] = input_a[input_name]
self.assertFalse(_inputs_have_equal_length(SCREAMING_SNAKE_CASE_ ) )
self.assertTrue(_inputs_have_equal_length(SCREAMING_SNAKE_CASE_ ) )
self.assertTrue(_inputs_have_equal_length(SCREAMING_SNAKE_CASE_ ) )
self.assertTrue(_inputs_are_equal(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
self.assertTrue(len(input_a[0] ) == pad_min_length )
self.assertTrue(len(input_a[1] ) == pad_min_length + pad_diff )
self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0] )) )
self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length) )
if feature_size > 1:
self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size )
# test padding for `pad_to_multiple_of` for List[int] + numpy
lowerCamelCase : List[Any] = feat_extract.pad(SCREAMING_SNAKE_CASE_ , pad_to_multiple_of=10 )
lowerCamelCase : List[str] = input_a[input_name]
lowerCamelCase : Dict = feat_extract.pad(SCREAMING_SNAKE_CASE_ , padding='longest' , pad_to_multiple_of=10 )
lowerCamelCase : Dict = input_a[input_name]
lowerCamelCase : List[Any] = feat_extract.pad(
SCREAMING_SNAKE_CASE_ , padding='max_length' , pad_to_multiple_of=10 , max_length=SCREAMING_SNAKE_CASE_ )
lowerCamelCase : List[str] = input_a[input_name]
lowerCamelCase : Optional[int] = feat_extract.pad(
SCREAMING_SNAKE_CASE_ , padding='max_length' , pad_to_multiple_of=10 , max_length=SCREAMING_SNAKE_CASE_ , return_tensors='np' , )
lowerCamelCase : Dict = input_a[input_name]
self.assertTrue(all(len(SCREAMING_SNAKE_CASE_ ) % 10 == 0 for x in input_a ) )
self.assertTrue(_inputs_are_equal(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
lowerCamelCase : int = pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
self.assertTrue(all(len(SCREAMING_SNAKE_CASE_ ) == expected_mult_pad_length for x in input_a ) )
self.assertEqual(input_a.shape[:2] , (batch_size, expected_mult_pad_length) )
if feature_size > 1:
self.assertTrue(input_a.shape[2] == feature_size )
# Check padding value is correct
lowerCamelCase : List[Any] = (np.ones(self.feat_extract_tester.feature_size ) * feat_extract.padding_value).sum()
self.assertTrue(
abs(np.asarray(input_a[0] )[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) )
< 1E-3 )
self.assertTrue(
abs(
np.asarray(input_a[1] )[pad_min_length + pad_diff :].sum()
- padding_vector_sum * (pad_max_length - pad_min_length - pad_diff) )
< 1E-3 )
self.assertTrue(
abs(
np.asarray(input_a[2] )[pad_min_length + 2 * pad_diff :].sum()
- padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff) )
< 1E-3 )
self.assertTrue(
abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) ) < 1E-3 )
self.assertTrue(
abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length) )
< 1E-3 )
def _UpperCamelCase ( self , UpperCAmelCase_=False ) -> int:
def _inputs_have_equal_length(UpperCAmelCase_ ):
lowerCamelCase : Optional[int] = len(input[0] )
for input_slice in input[1:]:
if len(SCREAMING_SNAKE_CASE_ ) != length:
return False
return True
def _inputs_are_equal(UpperCAmelCase_ , UpperCAmelCase_ ):
if len(SCREAMING_SNAKE_CASE_ ) != len(SCREAMING_SNAKE_CASE_ ):
return False
for input_slice_a, input_slice_a in zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
if not np.allclose(np.asarray(SCREAMING_SNAKE_CASE_ ) , np.asarray(SCREAMING_SNAKE_CASE_ ) , atol=1E-3 ):
return False
return True
lowerCamelCase : int = self.feature_extraction_class(**self.feat_extract_dict )
lowerCamelCase : List[str] = self.feat_extract_tester.prepare_inputs_for_common(numpify=SCREAMING_SNAKE_CASE_ )
lowerCamelCase : List[Any] = feat_extract.model_input_names[0]
lowerCamelCase : Any = BatchFeature({input_name: speech_inputs} )
# truncate to smallest
lowerCamelCase : Union[str, Any] = feat_extract.pad(
SCREAMING_SNAKE_CASE_ , padding='max_length' , max_length=len(speech_inputs[0] ) , truncation=SCREAMING_SNAKE_CASE_ )
lowerCamelCase : List[Any] = input_a[input_name]
lowerCamelCase : Optional[int] = feat_extract.pad(SCREAMING_SNAKE_CASE_ , padding='max_length' , max_length=len(speech_inputs[0] ) )
lowerCamelCase : Any = input_a[input_name]
self.assertTrue(_inputs_have_equal_length(SCREAMING_SNAKE_CASE_ ) )
self.assertFalse(_inputs_have_equal_length(SCREAMING_SNAKE_CASE_ ) )
# truncate to smallest with np
lowerCamelCase : Optional[int] = feat_extract.pad(
SCREAMING_SNAKE_CASE_ , padding='max_length' , max_length=len(speech_inputs[0] ) , return_tensors='np' , truncation=SCREAMING_SNAKE_CASE_ , )
lowerCamelCase : Tuple = input_a[input_name]
lowerCamelCase : List[str] = feat_extract.pad(
SCREAMING_SNAKE_CASE_ , padding='max_length' , max_length=len(speech_inputs[0] ) , return_tensors='np' )
lowerCamelCase : Optional[Any] = input_a[input_name]
self.assertTrue(_inputs_have_equal_length(SCREAMING_SNAKE_CASE_ ) )
self.assertTrue(input_a.shape[1] == len(speech_inputs[0] ) )
# since truncation forces padding to be smaller than longest input
# function can't return `np.ndarray`, but has to return list
self.assertFalse(_inputs_have_equal_length(SCREAMING_SNAKE_CASE_ ) )
# truncate to middle
lowerCamelCase : Optional[int] = feat_extract.pad(
SCREAMING_SNAKE_CASE_ , padding='max_length' , max_length=len(speech_inputs[1] ) , truncation=SCREAMING_SNAKE_CASE_ , return_tensors='np' , )
lowerCamelCase : Any = input_a[input_name]
lowerCamelCase : Union[str, Any] = feat_extract.pad(
SCREAMING_SNAKE_CASE_ , padding='max_length' , max_length=len(speech_inputs[1] ) , truncation=SCREAMING_SNAKE_CASE_ )
lowerCamelCase : Any = input_a[input_name]
lowerCamelCase : str = feat_extract.pad(
SCREAMING_SNAKE_CASE_ , padding='max_length' , max_length=len(speech_inputs[1] ) , return_tensors='np' )
lowerCamelCase : List[str] = input_a[input_name]
self.assertTrue(input_a.shape[1] == len(speech_inputs[1] ) )
self.assertTrue(_inputs_have_equal_length(SCREAMING_SNAKE_CASE_ ) )
self.assertTrue(_inputs_have_equal_length(SCREAMING_SNAKE_CASE_ ) )
self.assertTrue(_inputs_are_equal(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
# since truncation forces padding to be smaller than longest input
# function can't return `np.ndarray`, but has to return list
self.assertFalse(_inputs_have_equal_length(SCREAMING_SNAKE_CASE_ ) )
self.assertTrue(len(input_a[-1] ) == len(speech_inputs[-1] ) )
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(SCREAMING_SNAKE_CASE_ ):
feat_extract.pad(SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ )[input_name]
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(SCREAMING_SNAKE_CASE_ ):
feat_extract.pad(SCREAMING_SNAKE_CASE_ , padding='longest' , truncation=SCREAMING_SNAKE_CASE_ )[input_name]
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(SCREAMING_SNAKE_CASE_ ):
feat_extract.pad(SCREAMING_SNAKE_CASE_ , padding='longest' , truncation=SCREAMING_SNAKE_CASE_ )[input_name]
# max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
with self.assertRaises(SCREAMING_SNAKE_CASE_ ):
feat_extract.pad(SCREAMING_SNAKE_CASE_ , padding='max_length' , truncation=SCREAMING_SNAKE_CASE_ )[input_name]
# test truncation for `pad_to_multiple_of` for List[int] + numpy
lowerCamelCase : Optional[Any] = 12
lowerCamelCase : Optional[Any] = feat_extract.pad(
SCREAMING_SNAKE_CASE_ , padding='max_length' , max_length=len(speech_inputs[0] ) , pad_to_multiple_of=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ , )
lowerCamelCase : Tuple = input_a[input_name]
lowerCamelCase : List[Any] = feat_extract.pad(
SCREAMING_SNAKE_CASE_ , padding='max_length' , max_length=len(speech_inputs[0] ) , pad_to_multiple_of=SCREAMING_SNAKE_CASE_ , )
lowerCamelCase : Union[str, Any] = input_a[input_name]
# retrieve expected_length as multiple of pad_to_multiple_of
lowerCamelCase : Optional[int] = len(speech_inputs[0] )
if expected_length % pad_to_multiple_of != 0:
lowerCamelCase : Tuple = ((len(speech_inputs[0] ) // pad_to_multiple_of) + 1) * pad_to_multiple_of
self.assertTrue(len(input_a[0] ) == expected_length )
self.assertTrue(_inputs_have_equal_length(SCREAMING_SNAKE_CASE_ ) )
self.assertFalse(_inputs_have_equal_length(SCREAMING_SNAKE_CASE_ ) )
def _UpperCamelCase ( self ) -> Optional[Any]:
self._check_padding(numpify=SCREAMING_SNAKE_CASE_ )
def _UpperCamelCase ( self ) -> List[Any]:
self._check_padding(numpify=SCREAMING_SNAKE_CASE_ )
def _UpperCamelCase ( self ) -> Any:
self._check_truncation(numpify=SCREAMING_SNAKE_CASE_ )
def _UpperCamelCase ( self ) -> Any:
self._check_truncation(numpify=SCREAMING_SNAKE_CASE_ )
@require_torch
def _UpperCamelCase ( self ) -> Union[str, Any]:
lowerCamelCase : Optional[Any] = self.feature_extraction_class(**self.feat_extract_dict )
lowerCamelCase : Optional[int] = self.feat_extract_tester.prepare_inputs_for_common()
lowerCamelCase : Tuple = feat_extract.model_input_names[0]
lowerCamelCase : str = BatchFeature({input_name: speech_inputs} )
lowerCamelCase : Optional[int] = feat_extract.pad(SCREAMING_SNAKE_CASE_ , padding='longest' , return_tensors='np' )[input_name]
lowerCamelCase : Union[str, Any] = feat_extract.pad(SCREAMING_SNAKE_CASE_ , padding='longest' , return_tensors='pt' )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_pt.numpy().astype(np.floataa ).sum() ) < 1E-2 )
@require_tf
def _UpperCamelCase ( self ) -> Tuple:
lowerCamelCase : Optional[Any] = self.feature_extraction_class(**self.feat_extract_dict )
lowerCamelCase : Dict = self.feat_extract_tester.prepare_inputs_for_common()
lowerCamelCase : str = feat_extract.model_input_names[0]
lowerCamelCase : Optional[Any] = BatchFeature({input_name: speech_inputs} )
lowerCamelCase : int = feat_extract.pad(SCREAMING_SNAKE_CASE_ , padding='longest' , return_tensors='np' )[input_name]
lowerCamelCase : Union[str, Any] = feat_extract.pad(SCREAMING_SNAKE_CASE_ , padding='longest' , return_tensors='tf' )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_tf.numpy().astype(np.floataa ).sum() ) < 1E-2 )
def _UpperCamelCase ( self ) -> List[Any]:
lowerCamelCase : List[Any] = self.feat_extract_dict
lowerCamelCase : Optional[Any] = True
lowerCamelCase : List[str] = self.feature_extraction_class(**SCREAMING_SNAKE_CASE_ )
lowerCamelCase : Optional[int] = self.feat_extract_tester.prepare_inputs_for_common()
lowerCamelCase : Optional[int] = [len(SCREAMING_SNAKE_CASE_ ) for x in speech_inputs]
lowerCamelCase : Union[str, Any] = feat_extract.model_input_names[0]
lowerCamelCase : Optional[Any] = BatchFeature({input_name: speech_inputs} )
lowerCamelCase : List[Any] = feat_extract.pad(SCREAMING_SNAKE_CASE_ , padding='longest' , return_tensors='np' )
self.assertIn('attention_mask' , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(list(processed.attention_mask.shape ) , list(processed[input_name].shape[:2] ) )
self.assertListEqual(processed.attention_mask.sum(-1 ).tolist() , SCREAMING_SNAKE_CASE_ )
def _UpperCamelCase ( self ) -> Dict:
lowerCamelCase : List[str] = self.feat_extract_dict
lowerCamelCase : Optional[int] = True
lowerCamelCase : int = self.feature_extraction_class(**SCREAMING_SNAKE_CASE_ )
lowerCamelCase : Optional[Any] = self.feat_extract_tester.prepare_inputs_for_common()
lowerCamelCase : Union[str, Any] = [len(SCREAMING_SNAKE_CASE_ ) for x in speech_inputs]
lowerCamelCase : int = feat_extract.model_input_names[0]
lowerCamelCase : str = BatchFeature({input_name: speech_inputs} )
lowerCamelCase : Dict = min(SCREAMING_SNAKE_CASE_ )
lowerCamelCase : Dict = feat_extract.pad(
SCREAMING_SNAKE_CASE_ , padding='max_length' , max_length=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ , return_tensors='np' )
self.assertIn('attention_mask' , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
list(processed_pad.attention_mask.shape ) , [processed_pad[input_name].shape[0], max_length] )
self.assertListEqual(
processed_pad.attention_mask[:, :max_length].sum(-1 ).tolist() , [max_length for x in speech_inputs] )
| 365 |
"""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_bart import BartTokenizer
_A = logging.get_logger(__name__)
_A = {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}
# See all BART models at https://huggingface.co/models?filter=bart
_A = {
'vocab_file': {
'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/vocab.json',
'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/vocab.json',
'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json',
'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json',
'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json',
'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json',
},
'merges_file': {
'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/merges.txt',
'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/merges.txt',
'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt',
'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt',
'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt',
'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt',
},
'tokenizer_file': {
'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json',
'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json',
'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json',
'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json',
'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json',
'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json',
},
}
_A = {
'facebook/bart-base': 1_0_2_4,
'facebook/bart-large': 1_0_2_4,
'facebook/bart-large-mnli': 1_0_2_4,
'facebook/bart-large-cnn': 1_0_2_4,
'facebook/bart-large-xsum': 1_0_2_4,
'yjernite/bart_eli5': 1_0_2_4,
}
class _lowercase ( __UpperCAmelCase ):
lowercase_ = VOCAB_FILES_NAMES
lowercase_ = PRETRAINED_VOCAB_FILES_MAP
lowercase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase_ = ['input_ids', 'attention_mask']
lowercase_ = BartTokenizer
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_ , ) -> Union[str, Any]:
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 : List[Any] = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get('add_prefix_space' , UpperCAmelCase_ ) != add_prefix_space:
lowerCamelCase : Tuple = getattr(UpperCAmelCase_ , pre_tok_state.pop('type' ) )
lowerCamelCase : Optional[Any] = add_prefix_space
lowerCamelCase : str = pre_tok_class(**UpperCAmelCase_ )
lowerCamelCase : Optional[Any] = add_prefix_space
# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
lowerCamelCase : Dict = 'post_processor'
lowerCamelCase : str = getattr(self.backend_tokenizer , UpperCAmelCase_ , UpperCAmelCase_ )
if tokenizer_component_instance:
lowerCamelCase : Optional[Any] = 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 : int = tuple(state['sep'] )
if "cls" in state:
lowerCamelCase : str = tuple(state['cls'] )
lowerCamelCase : Optional[Any] = False
if state.get('add_prefix_space' , UpperCAmelCase_ ) != add_prefix_space:
lowerCamelCase : Dict = add_prefix_space
lowerCamelCase : Tuple = True
if state.get('trim_offsets' , UpperCAmelCase_ ) != trim_offsets:
lowerCamelCase : Tuple = trim_offsets
lowerCamelCase : Dict = True
if changes_to_apply:
lowerCamelCase : Optional[int] = getattr(UpperCAmelCase_ , state.pop('type' ) )
lowerCamelCase : Any = component_class(**UpperCAmelCase_ )
setattr(self.backend_tokenizer , UpperCAmelCase_ , UpperCAmelCase_ )
@property
def _UpperCamelCase ( self ) -> str:
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 _UpperCamelCase ( self , UpperCAmelCase_ ) -> List[Any]:
lowerCamelCase : Optional[int] = AddedToken(UpperCAmelCase_ , lstrip=UpperCAmelCase_ , rstrip=UpperCAmelCase_ ) if isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) else value
lowerCamelCase : int = value
def _UpperCamelCase ( self , *UpperCAmelCase_ , **UpperCAmelCase_ ) -> BatchEncoding:
lowerCamelCase : str = kwargs.get('is_split_into_words' , UpperCAmelCase_ )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
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 _UpperCamelCase ( self , *UpperCAmelCase_ , **UpperCAmelCase_ ) -> BatchEncoding:
lowerCamelCase : Optional[Any] = kwargs.get('is_split_into_words' , UpperCAmelCase_ )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
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 _UpperCamelCase ( self , UpperCAmelCase_ , UpperCAmelCase_ = None ) -> Tuple[str]:
lowerCamelCase : Any = self._tokenizer.model.save(UpperCAmelCase_ , name=UpperCAmelCase_ )
return tuple(UpperCAmelCase_ )
def _UpperCamelCase ( self , UpperCAmelCase_ , UpperCAmelCase_=None ) -> List[Any]:
lowerCamelCase : Union[str, Any] = [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 _UpperCamelCase ( self , UpperCAmelCase_ , UpperCAmelCase_ = None ) -> List[int]:
lowerCamelCase : List[Any] = [self.sep_token_id]
lowerCamelCase : str = [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]
| 205 | 0 |
"""simple docstring"""
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
# TODO Update this
__A = {
"facebook/esm-1b": "https://huggingface.co/facebook/esm-1b/resolve/main/config.json",
# See all ESM models at https://huggingface.co/models?filter=esm
}
class lowerCamelCase__ ( lowerCamelCase_ ):
a__ : str = """esm"""
def __init__( self , SCREAMING_SNAKE_CASE=None , SCREAMING_SNAKE_CASE=None , SCREAMING_SNAKE_CASE=None , SCREAMING_SNAKE_CASE=768 , SCREAMING_SNAKE_CASE=12 , SCREAMING_SNAKE_CASE=12 , SCREAMING_SNAKE_CASE=3_072 , SCREAMING_SNAKE_CASE=0.1 , SCREAMING_SNAKE_CASE=0.1 , SCREAMING_SNAKE_CASE=1_026 , SCREAMING_SNAKE_CASE=0.02 , SCREAMING_SNAKE_CASE=1E-12 , SCREAMING_SNAKE_CASE="absolute" , SCREAMING_SNAKE_CASE=True , SCREAMING_SNAKE_CASE=None , SCREAMING_SNAKE_CASE=False , SCREAMING_SNAKE_CASE=False , SCREAMING_SNAKE_CASE=None , SCREAMING_SNAKE_CASE=None , **SCREAMING_SNAKE_CASE , ):
"""simple docstring"""
super().__init__(pad_token_id=SCREAMING_SNAKE_CASE , mask_token_id=SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
snake_case : Optional[int] = vocab_size
snake_case : Dict = hidden_size
snake_case : str = num_hidden_layers
snake_case : Any = num_attention_heads
snake_case : int = intermediate_size
snake_case : Optional[Any] = hidden_dropout_prob
snake_case : Dict = attention_probs_dropout_prob
snake_case : int = max_position_embeddings
snake_case : Optional[Any] = initializer_range
snake_case : List[str] = layer_norm_eps
snake_case : str = position_embedding_type
snake_case : Any = use_cache
snake_case : Any = emb_layer_norm_before
snake_case : int = token_dropout
snake_case : Tuple = is_folding_model
if is_folding_model:
if esmfold_config is None:
logger.info("No esmfold_config supplied for folding model, using default values." )
snake_case : List[Any] = EsmFoldConfig()
elif isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
snake_case : List[Any] = EsmFoldConfig(**SCREAMING_SNAKE_CASE )
snake_case : Any = esmfold_config
if vocab_list is None:
logger.warning("No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!" )
snake_case : Dict = get_default_vocab_list()
else:
snake_case : Union[str, Any] = vocab_list
else:
snake_case : List[str] = None
snake_case : Union[str, Any] = None
if self.esmfold_config is not None and getattr(self.esmfold_config , "use_esm_attn_map" , SCREAMING_SNAKE_CASE ):
raise ValueError("The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!" )
def lowerCamelCase_ ( self ):
"""simple docstring"""
snake_case : List[str] = super().to_dict()
if isinstance(self.esmfold_config , SCREAMING_SNAKE_CASE ):
snake_case : Optional[int] = self.esmfold_config.to_dict()
return output
@dataclass
class lowerCamelCase__ :
a__ : str = None
a__ : bool = True
a__ : bool = False
a__ : bool = False
a__ : bool = False
a__ : float = 0
a__ : bool = True
a__ : bool = False
a__ : int = 1_28
a__ : "TrunkConfig" = None
def lowerCamelCase_ ( self ):
"""simple docstring"""
if self.trunk is None:
snake_case : Optional[Any] = TrunkConfig()
elif isinstance(self.trunk , SCREAMING_SNAKE_CASE ):
snake_case : Tuple = TrunkConfig(**self.trunk )
def lowerCamelCase_ ( self ):
"""simple docstring"""
snake_case : Union[str, Any] = asdict(self )
snake_case : str = self.trunk.to_dict()
return output
@dataclass
class lowerCamelCase__ :
a__ : int = 48
a__ : int = 10_24
a__ : int = 1_28
a__ : int = 32
a__ : int = 32
a__ : int = 32
a__ : float = 0
a__ : float = 0
a__ : bool = False
a__ : int = 4
a__ : Optional[int] = 1_28
a__ : "StructureModuleConfig" = None
def lowerCamelCase_ ( self ):
"""simple docstring"""
if self.structure_module is None:
snake_case : List[Any] = StructureModuleConfig()
elif isinstance(self.structure_module , SCREAMING_SNAKE_CASE ):
snake_case : int = StructureModuleConfig(**self.structure_module )
if self.max_recycles <= 0:
raise ValueError(F'''`max_recycles` should be positive, got {self.max_recycles}.''' )
if self.sequence_state_dim % self.sequence_state_dim != 0:
raise ValueError(
"`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got"
F''' {self.sequence_state_dim} and {self.sequence_state_dim}.''' )
if self.pairwise_state_dim % self.pairwise_state_dim != 0:
raise ValueError(
"`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got"
F''' {self.pairwise_state_dim} and {self.pairwise_state_dim}.''' )
snake_case : Dict = self.sequence_state_dim // self.sequence_head_width
snake_case : List[Any] = self.pairwise_state_dim // self.pairwise_head_width
if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
raise ValueError(
"`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got"
F''' {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}.''' )
if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
raise ValueError(
"`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got"
F''' {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}.''' )
if self.pairwise_state_dim % 2 != 0:
raise ValueError(F'''`pairwise_state_dim` should be even, got {self.pairwise_state_dim}.''' )
if self.dropout >= 0.4:
raise ValueError(F'''`dropout` should not be greater than 0.4, got {self.dropout}.''' )
def lowerCamelCase_ ( self ):
"""simple docstring"""
snake_case : Tuple = asdict(self )
snake_case : int = self.structure_module.to_dict()
return output
@dataclass
class lowerCamelCase__ :
a__ : int = 3_84
a__ : int = 1_28
a__ : int = 16
a__ : int = 1_28
a__ : int = 12
a__ : int = 4
a__ : int = 8
a__ : float = 0.1
a__ : int = 8
a__ : int = 1
a__ : int = 2
a__ : int = 7
a__ : int = 10
a__ : float = 1e-8
a__ : float = 1e5
def lowerCamelCase_ ( self ):
"""simple docstring"""
return asdict(self )
def UpperCamelCase__ ( ):
return (
"<cls>",
"<pad>",
"<eos>",
"<unk>",
"L",
"A",
"G",
"V",
"S",
"E",
"R",
"T",
"I",
"D",
"P",
"K",
"Q",
"N",
"F",
"Y",
"M",
"H",
"W",
"C",
"X",
"B",
"U",
"Z",
"O",
".",
"-",
"<null_1>",
"<mask>",
)
| 148 |
"""simple docstring"""
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
__A = abspath(join(dirname(dirname(dirname(__file__))), "src"))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action="ignore", category=FutureWarning)
def UpperCamelCase__ ( lowercase__ : Any ):
from transformers.testing_utils import pytest_addoption_shared
pytest_addoption_shared(lowercase__ )
def UpperCamelCase__ ( lowercase__ : Optional[int] ):
from transformers.testing_utils import pytest_terminal_summary_main
snake_case : Any = terminalreporter.config.getoption("--make-reports" )
if make_reports:
pytest_terminal_summary_main(lowercase__ , id=lowercase__ )
| 148 | 1 |
import os
def lowerCamelCase (a_ :List[str]) -> int:
lowercase :int = len(grid[0])
lowercase :Dict = len(a_)
lowercase :List[str] = 0
lowercase :List[str] = 0
lowercase :Optional[int] = 0
# Check vertically, horizontally, diagonally at the same time (only works
# for nxn grid)
for i in range(a_):
for j in range(n_rows - 3):
lowercase :Tuple = grid[j][i] * grid[j + 1][i] * grid[j + 2][i] * grid[j + 3][i]
lowercase :int = grid[i][j] * grid[i][j + 1] * grid[i][j + 2] * grid[i][j + 3]
# Left-to-right diagonal (\) product
if i < n_columns - 3:
lowercase :List[str] = (
grid[i][j]
* grid[i + 1][j + 1]
* grid[i + 2][j + 2]
* grid[i + 3][j + 3]
)
# Right-to-left diagonal(/) product
if i > 2:
lowercase :List[Any] = (
grid[i][j]
* grid[i - 1][j + 1]
* grid[i - 2][j + 2]
* grid[i - 3][j + 3]
)
lowercase :Any = max(
a_ , a_ , a_ , a_)
if max_product > largest:
lowercase :str = max_product
return largest
def lowerCamelCase () -> List[str]:
lowercase :Dict = []
with open(os.path.dirname(a_) + '''/grid.txt''') as file:
for line in file:
grid.append(line.strip('''\n''').split(''' '''))
lowercase :Any = [[int(a_) for i in grid[j]] for j in range(len(a_))]
return largest_product(a_)
if __name__ == "__main__":
print(solution())
| 364 |
"""simple docstring"""
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
UpperCAmelCase = '''\
@misc{chen2021evaluating,
title={Evaluating Large Language Models Trained on Code},
author={Mark Chen and Jerry Tworek and Heewoo Jun and Qiming Yuan \
and Henrique Ponde de Oliveira Pinto and Jared Kaplan and Harri Edwards \
and Yuri Burda and Nicholas Joseph and Greg Brockman and Alex Ray \
and Raul Puri and Gretchen Krueger and Michael Petrov and Heidy Khlaaf \
and Girish Sastry and Pamela Mishkin and Brooke Chan and Scott Gray \
and Nick Ryder and Mikhail Pavlov and Alethea Power and Lukasz Kaiser \
and Mohammad Bavarian and Clemens Winter and Philippe Tillet \
and Felipe Petroski Such and Dave Cummings and Matthias Plappert \
and Fotios Chantzis and Elizabeth Barnes and Ariel Herbert-Voss \
and William Hebgen Guss and Alex Nichol and Alex Paino and Nikolas Tezak \
and Jie Tang and Igor Babuschkin and Suchir Balaji and Shantanu Jain \
and William Saunders and Christopher Hesse and Andrew N. Carr \
and Jan Leike and Josh Achiam and Vedant Misra and Evan Morikawa \
and Alec Radford and Matthew Knight and Miles Brundage and Mira Murati \
and Katie Mayer and Peter Welinder and Bob McGrew and Dario Amodei \
and Sam McCandlish and Ilya Sutskever and Wojciech Zaremba},
year={2021},
eprint={2107.03374},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
'''
UpperCAmelCase = '''\
This metric implements the evaluation harness for the HumanEval problem solving dataset
described in the paper "Evaluating Large Language Models Trained on Code"
(https://arxiv.org/abs/2107.03374).
'''
UpperCAmelCase = '''
Calculates how good are predictions given some references, using certain scores
Args:
predictions: list of candidates to evaluate. Each candidates should be a list
of strings with several code candidates to solve the problem.
references: a list with a test for each prediction. Each test should evaluate the
correctness of a code candidate.
k: number of code candidates to consider in the evaluation (Default: [1, 10, 100])
num_workers: number of workers used to evaluate the canidate programs (Default: 4).
timeout:
Returns:
pass_at_k: dict with pass rates for each k
results: dict with granular results of each unittest
Examples:
>>> code_eval = datasets.load_metric("code_eval")
>>> test_cases = ["assert add(2,3)==5"]
>>> candidates = [["def add(a,b): return a*b", "def add(a, b): return a+b"]]
>>> pass_at_k, results = code_eval.compute(references=test_cases, predictions=candidates, k=[1, 2])
>>> print(pass_at_k)
{\'pass@1\': 0.5, \'pass@2\': 1.0}
'''
UpperCAmelCase = '''
################################################################################
!!!WARNING!!!
################################################################################
The "code_eval" metric executes untrusted model-generated code in Python.
Although it is highly unlikely that model-generated code will do something
overtly malicious in response to this test suite, model-generated code may act
destructively due to a lack of model capability or alignment.
Users are strongly encouraged to sandbox this evaluation suite so that it
does not perform destructive actions on their host or network. For more
information on how OpenAI sandboxes its code, see the paper "Evaluating Large
Language Models Trained on Code" (https://arxiv.org/abs/2107.03374).
Once you have read this disclaimer and taken appropriate precautions,
set the environment variable HF_ALLOW_CODE_EVAL="1". Within Python you can to this
with:
>>> import os
>>> os.environ["HF_ALLOW_CODE_EVAL"] = "1"
################################################################################\
'''
UpperCAmelCase = '''The MIT License
Copyright (c) OpenAI (https://openai.com)
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __magic_name__ ( datasets.Metric ):
def __snake_case ( self : Any ):
'''simple docstring'''
return datasets.MetricInfo(
# This is the description that will appear on the metrics page.
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Sequence(datasets.Value('''string''' ) ),
'''references''': datasets.Value('''string''' ),
} ) , homepage='''https://github.com/openai/human-eval''' , codebase_urls=['''https://github.com/openai/human-eval'''] , reference_urls=['''https://github.com/openai/human-eval'''] , license=_LICENSE , )
def __snake_case ( self : Union[str, Any] , snake_case__ : List[Any] , snake_case__ : Optional[Any] , snake_case__ : Tuple=[1, 1_0, 1_0_0] , snake_case__ : List[str]=4 , snake_case__ : Tuple=3.0 ):
'''simple docstring'''
if os.getenv('''HF_ALLOW_CODE_EVAL''' , 0 ) != "1":
raise ValueError(_WARNING )
if os.name == "nt":
raise NotImplementedError('''This metric is currently not supported on Windows.''' )
with ThreadPoolExecutor(max_workers=snake_case__ ) as executor:
lowercase :Optional[Any] = []
lowercase :Optional[Any] = Counter()
lowercase :Optional[int] = 0
lowercase :int = defaultdict(snake_case__ )
for task_id, (candidates, test_case) in enumerate(zip(snake_case__ , snake_case__ ) ):
for candidate in candidates:
lowercase :int = candidate + '''\n''' + test_case
lowercase :int = (test_program, timeout, task_id, completion_id[task_id])
lowercase :Optional[int] = executor.submit(snake_case__ , *snake_case__ )
futures.append(snake_case__ )
completion_id[task_id] += 1
n_samples += 1
for future in as_completed(snake_case__ ):
lowercase :Dict = future.result()
results[result["task_id"]].append((result['''completion_id'''], result) )
lowercase , lowercase :List[str] = [], []
for result in results.values():
result.sort()
lowercase :int = [r[1]['''passed'''] for r in result]
total.append(len(snake_case__ ) )
correct.append(sum(snake_case__ ) )
lowercase :List[str] = np.array(snake_case__ )
lowercase :Optional[Any] = np.array(snake_case__ )
lowercase :str = k
lowercase :int = {f"""pass@{k}""": estimate_pass_at_k(snake_case__ , snake_case__ , snake_case__ ).mean() for k in ks if (total >= k).all()}
return pass_at_k, results
def lowerCamelCase (a_ :Optional[Any] , a_ :Any , a_ :Any) -> List[Any]:
def estimator(a_ :int , a_ :int , a_ :int) -> float:
if n - c < k:
return 1.0
return 1.0 - np.prod(1.0 - k / np.arange(n - c + 1 , n + 1))
if isinstance(a_ , a_):
lowercase :Optional[int] = itertools.repeat(a_ , len(a_))
else:
assert len(a_) == len(a_)
lowercase :List[Any] = iter(a_)
return np.array([estimator(int(a_) , int(a_) , a_) for n, c in zip(a_ , a_)])
| 172 | 0 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
UpperCAmelCase__ : Dict = ["image_processor", "tokenizer"]
UpperCAmelCase__ : Dict = "AutoImageProcessor"
UpperCAmelCase__ : Union[str, Any] = "AutoTokenizer"
def __init__( self , _a , _a ) -> int:
super().__init__(_a , _a )
_a : List[Any] = self.image_processor
def __call__( self , _a=None , _a=None , _a=None , **_a ) -> int:
if text is None and images is None:
raise ValueError('''You have to specify either text or images. Both cannot be none.''' )
if text is not None:
_a : List[str] = self.tokenizer(_a , return_tensors=_a , **_a )
if images is not None:
_a : Dict = self.image_processor(_a , return_tensors=_a , **_a )
if text is not None and images is not None:
_a : str = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**_a ) , tensor_type=_a )
def __lowercase ( self , *_a , **_a ) -> Dict:
return self.tokenizer.batch_decode(*_a , **_a )
def __lowercase ( self , *_a , **_a ) -> Optional[int]:
return self.tokenizer.decode(*_a , **_a )
@property
def __lowercase ( self ) -> Dict:
return ["input_ids", "attention_mask", "pixel_values"]
| 235 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
a__ = logging.get_logger(__name__)
a__ = {
'''google/bit-50''': '''https://huggingface.co/google/bit-50/resolve/main/config.json''',
}
class UpperCAmelCase_ ( __lowercase , __lowercase ):
"""simple docstring"""
UpperCAmelCase__ : Any = "bit"
UpperCAmelCase__ : Optional[int] = ["preactivation", "bottleneck"]
UpperCAmelCase__ : Optional[Any] = ["SAME", "VALID"]
def __init__( self , _a=3 , _a=6_4 , _a=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8] , _a=[3, 4, 6, 3] , _a="preactivation" , _a="relu" , _a=None , _a=3_2 , _a=0.0 , _a=False , _a=3_2 , _a=1 , _a=None , _a=None , **_a , ) -> Union[str, Any]:
super().__init__(**_a )
if layer_type not in self.layer_types:
raise ValueError(F"""layer_type={layer_type} is not one of {','.join(self.layer_types )}""" )
if global_padding is not None:
if global_padding.upper() in self.supported_padding:
_a : Any = global_padding.upper()
else:
raise ValueError(F"""Padding strategy {global_padding} not supported""" )
_a : Optional[int] = num_channels
_a : List[Any] = embedding_size
_a : Any = hidden_sizes
_a : int = depths
_a : Dict = layer_type
_a : int = hidden_act
_a : Optional[Any] = global_padding
_a : Optional[Any] = num_groups
_a : Union[str, Any] = drop_path_rate
_a : Tuple = embedding_dynamic_padding
_a : Union[str, Any] = output_stride
_a : Any = width_factor
_a : Any = ['''stem'''] + [F"""stage{idx}""" for idx in range(1 , len(_a ) + 1 )]
_a , _a : List[str] = get_aligned_output_features_output_indices(
out_features=_a , out_indices=_a , stage_names=self.stage_names )
| 235 | 1 |
"""simple docstring"""
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import (
AutoTokenizer,
BlipaConfig,
BlipaForConditionalGeneration,
BlipaProcessor,
BlipaVisionConfig,
BlipImageProcessor,
OPTConfig,
TaConfig,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
def a__ ( ) -> List[str]:
_A = "https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png"
_A = Image.open(requests.get(__lowercase , stream=__lowercase ).raw ).convert("RGB" )
return image
def a__ ( __lowercase ) -> str:
_A = []
# fmt: off
# vision encoder
rename_keys.append(("visual_encoder.cls_token", "vision_model.embeddings.class_embedding") )
rename_keys.append(("visual_encoder.pos_embed", "vision_model.embeddings.position_embedding") )
rename_keys.append(("visual_encoder.patch_embed.proj.weight", "vision_model.embeddings.patch_embedding.weight") )
rename_keys.append(("visual_encoder.patch_embed.proj.bias", "vision_model.embeddings.patch_embedding.bias") )
rename_keys.append(("ln_vision.weight", "vision_model.post_layernorm.weight") )
rename_keys.append(("ln_vision.bias", "vision_model.post_layernorm.bias") )
for i in range(config.vision_config.num_hidden_layers ):
rename_keys.append((f"""visual_encoder.blocks.{i}.norm1.weight""", f"""vision_model.encoder.layers.{i}.layer_norm1.weight""") )
rename_keys.append((f"""visual_encoder.blocks.{i}.norm1.bias""", f"""vision_model.encoder.layers.{i}.layer_norm1.bias""") )
rename_keys.append((f"""visual_encoder.blocks.{i}.norm2.weight""", f"""vision_model.encoder.layers.{i}.layer_norm2.weight""") )
rename_keys.append((f"""visual_encoder.blocks.{i}.norm2.bias""", f"""vision_model.encoder.layers.{i}.layer_norm2.bias""") )
rename_keys.append((f"""visual_encoder.blocks.{i}.attn.qkv.weight""", f"""vision_model.encoder.layers.{i}.self_attn.qkv.weight""") )
rename_keys.append((f"""visual_encoder.blocks.{i}.attn.proj.weight""", f"""vision_model.encoder.layers.{i}.self_attn.projection.weight""",) )
rename_keys.append((f"""visual_encoder.blocks.{i}.attn.proj.bias""", f"""vision_model.encoder.layers.{i}.self_attn.projection.bias""") )
rename_keys.append((f"""visual_encoder.blocks.{i}.mlp.fc1.weight""", f"""vision_model.encoder.layers.{i}.mlp.fc1.weight""") )
rename_keys.append((f"""visual_encoder.blocks.{i}.mlp.fc1.bias""", f"""vision_model.encoder.layers.{i}.mlp.fc1.bias""") )
rename_keys.append((f"""visual_encoder.blocks.{i}.mlp.fc2.weight""", f"""vision_model.encoder.layers.{i}.mlp.fc2.weight""") )
rename_keys.append((f"""visual_encoder.blocks.{i}.mlp.fc2.bias""", f"""vision_model.encoder.layers.{i}.mlp.fc2.bias""") )
# QFormer
rename_keys.append(("Qformer.bert.embeddings.LayerNorm.weight", "qformer.layernorm.weight") )
rename_keys.append(("Qformer.bert.embeddings.LayerNorm.bias", "qformer.layernorm.bias") )
# fmt: on
return rename_keys
def a__ ( __lowercase , __lowercase , __lowercase ) -> Dict:
_A = dct.pop(__lowercase )
_A = val
def a__ ( __lowercase , __lowercase ) -> Tuple:
for i in range(config.vision_config.num_hidden_layers ):
# read in original q and v biases
_A = state_dict.pop(f"""visual_encoder.blocks.{i}.attn.q_bias""" )
_A = state_dict.pop(f"""visual_encoder.blocks.{i}.attn.v_bias""" )
# next, set bias in the state dict
_A = torch.cat((q_bias, torch.zeros_like(__lowercase , requires_grad=__lowercase ), v_bias) )
_A = qkv_bias
def a__ ( __lowercase , __lowercase ) -> Optional[Any]:
_A = 364 if "coco" in model_name else 224
_A = BlipaVisionConfig(image_size=__lowercase ).to_dict()
# make sure the models have proper bos_token_id and eos_token_id set (important for generation)
# seems like flan-T5 models don't have bos_token_id properly set?
if "opt-2.7b" in model_name:
_A = OPTConfig.from_pretrained("facebook/opt-2.7b" , eos_token_id=__lowercase ).to_dict()
elif "opt-6.7b" in model_name:
_A = OPTConfig.from_pretrained("facebook/opt-6.7b" , eos_token_id=__lowercase ).to_dict()
elif "t5-xl" in model_name:
_A = TaConfig.from_pretrained("google/flan-t5-xl" , dense_act_fn="gelu" , bos_token_id=1 ).to_dict()
elif "t5-xxl" in model_name:
_A = TaConfig.from_pretrained("google/flan-t5-xxl" , dense_act_fn="gelu" , bos_token_id=1 ).to_dict()
_A = BlipaConfig(vision_config=__lowercase , text_config=__lowercase )
return config, image_size
@torch.no_grad()
def a__ ( __lowercase , __lowercase=None , __lowercase=False ) -> str:
_A = (
AutoTokenizer.from_pretrained("facebook/opt-2.7b" )
if "opt" in model_name
else AutoTokenizer.from_pretrained("google/flan-t5-xl" )
)
_A = tokenizer("\n" , add_special_tokens=__lowercase ).input_ids[0]
_A , _A = get_blipa_config(__lowercase , eos_token_id=__lowercase )
_A = BlipaForConditionalGeneration(__lowercase ).eval()
_A = {
"blip2-opt-2.7b": ("blip2_opt", "pretrain_opt2.7b"),
"blip2-opt-6.7b": ("blip2_opt", "pretrain_opt6.7b"),
"blip2-opt-2.7b-coco": ("blip2_opt", "caption_coco_opt2.7b"),
"blip2-opt-6.7b-coco": ("blip2_opt", "caption_coco_opt6.7b"),
"blip2-flan-t5-xl": ("blip2_t5", "pretrain_flant5xl"),
"blip2-flan-t5-xl-coco": ("blip2_t5", "caption_coco_flant5xl"),
"blip2-flan-t5-xxl": ("blip2_t5", "pretrain_flant5xxl"),
}
_A , _A = model_name_to_original[model_name]
# load original model
print("Loading original model..." )
_A = "cuda" if torch.cuda.is_available() else "cpu"
_A , _A , _A = load_model_and_preprocess(
name=__lowercase , model_type=__lowercase , is_eval=__lowercase , device=__lowercase )
original_model.eval()
print("Done!" )
# update state dict keys
_A = original_model.state_dict()
_A = create_rename_keys(__lowercase )
for src, dest in rename_keys:
rename_key(__lowercase , __lowercase , __lowercase )
# some keys can be renamed efficiently
for key, val in state_dict.copy().items():
_A = state_dict.pop(__lowercase )
if key.startswith("Qformer.bert" ):
_A = key.replace("Qformer.bert" , "qformer" )
if "attention.self" in key:
_A = key.replace("self" , "attention" )
if "opt_proj" in key:
_A = key.replace("opt_proj" , "language_projection" )
if "t5_proj" in key:
_A = key.replace("t5_proj" , "language_projection" )
if key.startswith("opt" ):
_A = key.replace("opt" , "language" )
if key.startswith("t5" ):
_A = key.replace("t5" , "language" )
_A = val
# read in qv biases
read_in_q_v_bias(__lowercase , __lowercase )
_A , _A = hf_model.load_state_dict(__lowercase , strict=__lowercase )
assert len(__lowercase ) == 0
assert unexpected_keys == ["qformer.embeddings.position_ids"]
_A = load_demo_image()
_A = vis_processors["eval"](__lowercase ).unsqueeze(0 ).to(__lowercase )
_A = tokenizer(["\n"] , return_tensors="pt" ).input_ids.to(__lowercase )
# create processor
_A = BlipImageProcessor(
size={"height": image_size, "width": image_size} , image_mean=__lowercase , image_std=__lowercase )
_A = BlipaProcessor(image_processor=__lowercase , tokenizer=__lowercase )
_A = processor(images=__lowercase , return_tensors="pt" ).pixel_values.to(__lowercase )
# make sure processor creates exact same pixel values
assert torch.allclose(__lowercase , __lowercase )
original_model.to(__lowercase )
hf_model.to(__lowercase )
with torch.no_grad():
if "opt" in model_name:
_A = original_model({"image": original_pixel_values, "text_input": [""]} ).logits
_A = hf_model(__lowercase , __lowercase ).logits
else:
_A = original_model(
{"image": original_pixel_values, "text_input": ["\n"], "text_output": ["\n"]} ).logits
_A = input_ids.masked_fill(input_ids == tokenizer.pad_token_id , -100 )
_A = hf_model(__lowercase , __lowercase , labels=__lowercase ).logits
assert original_logits.shape == logits.shape
print("First values of original logits:" , original_logits[0, :3, :3] )
print("First values of HF logits:" , logits[0, :3, :3] )
# assert values
if model_name == "blip2-flan-t5-xl":
_A = torch.tensor(
[[-41.5_850, -4.4_440, -8.9_922], [-47.4_322, -5.9_143, -1.7_340]] , device=__lowercase )
assert torch.allclose(logits[0, :3, :3] , __lowercase , atol=1E-4 )
elif model_name == "blip2-flan-t5-xl-coco":
_A = torch.tensor(
[[-57.0_109, -9.8_967, -12.6_280], [-68.6_578, -12.7_191, -10.5_065]] , device=__lowercase )
else:
# cast to same type
_A = logits.dtype
assert torch.allclose(original_logits.to(__lowercase ) , __lowercase , atol=1E-2 )
print("Looks ok!" )
print("Generating a caption..." )
_A = ""
_A = tokenizer(__lowercase , return_tensors="pt" ).input_ids.to(__lowercase )
_A = original_model.generate({"image": original_pixel_values} )
_A = hf_model.generate(
__lowercase , __lowercase , do_sample=__lowercase , num_beams=5 , max_length=30 , min_length=1 , top_p=0.9 , repetition_penalty=1.0 , length_penalty=1.0 , temperature=1 , )
print("Original generation:" , __lowercase )
_A = input_ids.shape[1]
_A = processor.batch_decode(outputs[:, prompt_length:] , skip_special_tokens=__lowercase )
_A = [text.strip() for text in output_text]
print("HF generation:" , __lowercase )
if pytorch_dump_folder_path is not None:
processor.save_pretrained(__lowercase )
hf_model.save_pretrained(__lowercase )
if push_to_hub:
processor.push_to_hub(f"""nielsr/{model_name}""" )
hf_model.push_to_hub(f"""nielsr/{model_name}""" )
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
a_ = [
"blip2-opt-2.7b",
"blip2-opt-6.7b",
"blip2-opt-2.7b-coco",
"blip2-opt-6.7b-coco",
"blip2-flan-t5-xl",
"blip2-flan-t5-xl-coco",
"blip2-flan-t5-xxl",
]
parser.add_argument(
"--model_name",
default="blip2-opt-2.7b",
choices=choices,
type=str,
help="Path to hf config.json of model to convert",
)
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument(
"--push_to_hub",
action="store_true",
help="Whether to push the model and processor to the hub after converting",
)
a_ = parser.parse_args()
convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub) | 163 |
"""simple docstring"""
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
"kwargs, expected" , [
({"num_shards": 0, "max_num_jobs": 1}, []),
({"num_shards": 10, "max_num_jobs": 1}, [range(10 )]),
({"num_shards": 10, "max_num_jobs": 10}, [range(__lowercase , i + 1 ) for i in range(10 )]),
({"num_shards": 1, "max_num_jobs": 10}, [range(1 )]),
({"num_shards": 10, "max_num_jobs": 3}, [range(0 , 4 ), range(4 , 7 ), range(7 , 10 )]),
({"num_shards": 3, "max_num_jobs": 10}, [range(0 , 1 ), range(1 , 2 ), range(2 , 3 )]),
] , )
def a__ ( __lowercase , __lowercase ) -> Optional[Any]:
_A = _distribute_shards(**__lowercase )
assert out == expected
@pytest.mark.parametrize(
"gen_kwargs, max_num_jobs, expected" , [
({"foo": 0}, 10, [{"foo": 0}]),
({"shards": [0, 1, 2, 3]}, 1, [{"shards": [0, 1, 2, 3]}]),
({"shards": [0, 1, 2, 3]}, 4, [{"shards": [0]}, {"shards": [1]}, {"shards": [2]}, {"shards": [3]}]),
({"shards": [0, 1]}, 4, [{"shards": [0]}, {"shards": [1]}]),
({"shards": [0, 1, 2, 3]}, 2, [{"shards": [0, 1]}, {"shards": [2, 3]}]),
] , )
def a__ ( __lowercase , __lowercase , __lowercase ) -> List[str]:
_A = _split_gen_kwargs(__lowercase , __lowercase )
assert out == expected
@pytest.mark.parametrize(
"gen_kwargs, expected" , [
({"foo": 0}, 1),
({"shards": [0]}, 1),
({"shards": [0, 1, 2, 3]}, 4),
({"shards": [0, 1, 2, 3], "foo": 0}, 4),
({"shards": [0, 1, 2, 3], "other": (0, 1)}, 4),
({"shards": [0, 1, 2, 3], "shards2": [0, 1]}, RuntimeError),
] , )
def a__ ( __lowercase , __lowercase ) -> List[Any]:
if expected is RuntimeError:
with pytest.raises(__lowercase ):
_number_of_shards_in_gen_kwargs(__lowercase )
else:
_A = _number_of_shards_in_gen_kwargs(__lowercase )
assert out == expected | 163 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {"vocab_file": "sentencepiece.bpe.model"}
UpperCamelCase_ = {
"vocab_file": {
"moussaKam/mbarthez": "https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model",
"moussaKam/barthez": "https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model",
"moussaKam/barthez-orangesum-title": (
"https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"
),
},
}
UpperCamelCase_ = {
"moussaKam/mbarthez": 1_0_2_4,
"moussaKam/barthez": 1_0_2_4,
"moussaKam/barthez-orangesum-title": 1_0_2_4,
}
UpperCamelCase_ = "▁"
class _a ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
A : Dict = VOCAB_FILES_NAMES
A : Optional[Any] = PRETRAINED_VOCAB_FILES_MAP
A : Optional[int] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A : List[str] = ['''input_ids''', '''attention_mask''']
def __init__( self, A, A="<s>", A="</s>", A="</s>", A="<s>", A="<unk>", A="<pad>", A="<mask>", A = None, **A, ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = AddedToken(A, lstrip=A, rstrip=A ) if isinstance(A, A ) else mask_token
SCREAMING_SNAKE_CASE : List[Any] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=A, eos_token=A, unk_token=A, sep_token=A, cls_token=A, pad_token=A, mask_token=A, sp_model_kwargs=self.sp_model_kwargs, **A, )
SCREAMING_SNAKE_CASE : List[str] = vocab_file
SCREAMING_SNAKE_CASE : Optional[int] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(A ) )
SCREAMING_SNAKE_CASE : Any = {'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}
SCREAMING_SNAKE_CASE : Optional[Any] = len(self.sp_model ) - 1
SCREAMING_SNAKE_CASE : Optional[int] = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def UpperCamelCase_ ( self, A, A = None ):
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
SCREAMING_SNAKE_CASE : int = [self.cls_token_id]
SCREAMING_SNAKE_CASE : List[Any] = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def UpperCamelCase_ ( self, A, A = None, A = False ):
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=A, token_ids_a=A, already_has_special_tokens=A )
if token_ids_a is None:
return [1] + ([0] * len(A )) + [1]
return [1] + ([0] * len(A )) + [1, 1] + ([0] * len(A )) + [1]
def UpperCamelCase_ ( self, A, A = None ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = [self.sep_token_id]
SCREAMING_SNAKE_CASE : Any = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
@property
def UpperCamelCase_ ( self ):
'''simple docstring'''
return len(self.sp_model )
def UpperCamelCase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = {self.convert_ids_to_tokens(A ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def UpperCamelCase_ ( self, A ):
'''simple docstring'''
return self.sp_model.encode(A, out_type=A )
def UpperCamelCase_ ( self, A ):
'''simple docstring'''
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
SCREAMING_SNAKE_CASE : List[str] = self.sp_model.PieceToId(A )
return spm_id if spm_id else self.unk_token_id
def UpperCamelCase_ ( self, A ):
'''simple docstring'''
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(A )
def UpperCamelCase_ ( self, A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = []
SCREAMING_SNAKE_CASE : Tuple = ''
SCREAMING_SNAKE_CASE : Tuple = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(A ) + token
SCREAMING_SNAKE_CASE : List[str] = True
SCREAMING_SNAKE_CASE : List[Any] = []
else:
current_sub_tokens.append(A )
SCREAMING_SNAKE_CASE : Dict = False
out_string += self.sp_model.decode(A )
return out_string.strip()
def __getstate__( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = self.__dict__.copy()
SCREAMING_SNAKE_CASE : Dict = None
return state
def __setstate__( self, A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : int = d
# for backward compatibility
if not hasattr(self, 'sp_model_kwargs' ):
SCREAMING_SNAKE_CASE : Union[str, Any] = {}
SCREAMING_SNAKE_CASE : Union[str, Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def UpperCamelCase_ ( self, A, A = None ):
'''simple docstring'''
if not os.path.isdir(A ):
logger.error(F"Vocabulary path ({save_directory}) should be a directory" )
return
SCREAMING_SNAKE_CASE : List[Any] = os.path.join(
A, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(A ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file, A )
elif not os.path.isfile(self.vocab_file ):
with open(A, 'wb' ) as fi:
SCREAMING_SNAKE_CASE : Dict = self.sp_model.serialized_model_proto()
fi.write(A )
return (out_vocab_file,)
| 251 |
'''simple docstring'''
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
class _a ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
A : Union[str, Any] = '''linear'''
A : int = '''cosine'''
A : Optional[Any] = '''cosine_with_restarts'''
A : Optional[int] = '''polynomial'''
A : str = '''constant'''
A : Union[str, Any] = '''constant_with_warmup'''
A : Optional[Any] = '''piecewise_constant'''
def lowercase__( __UpperCamelCase: Optimizer ,__UpperCamelCase: int = -1 ):
"""simple docstring"""
return LambdaLR(__UpperCamelCase ,lambda __UpperCamelCase : 1 ,last_epoch=__UpperCamelCase )
def lowercase__( __UpperCamelCase: Optimizer ,__UpperCamelCase: int ,__UpperCamelCase: int = -1 ):
"""simple docstring"""
def lr_lambda(__UpperCamelCase: int ):
if current_step < num_warmup_steps:
return float(__UpperCamelCase ) / float(max(1.0 ,__UpperCamelCase ) )
return 1.0
return LambdaLR(__UpperCamelCase ,__UpperCamelCase ,last_epoch=__UpperCamelCase )
def lowercase__( __UpperCamelCase: Optimizer ,__UpperCamelCase: str ,__UpperCamelCase: int = -1 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = {}
SCREAMING_SNAKE_CASE : Optional[Any] = step_rules.split(',' )
for rule_str in rule_list[:-1]:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Optional[int] = rule_str.split(':' )
SCREAMING_SNAKE_CASE : int = int(__UpperCamelCase )
SCREAMING_SNAKE_CASE : Any = float(__UpperCamelCase )
SCREAMING_SNAKE_CASE : List[str] = value
SCREAMING_SNAKE_CASE : Any = float(rule_list[-1] )
def create_rules_function(__UpperCamelCase: Union[str, Any] ,__UpperCamelCase: Optional[Any] ):
def rule_func(__UpperCamelCase: int ) -> float:
SCREAMING_SNAKE_CASE : Union[str, Any] = sorted(rules_dict.keys() )
for i, sorted_step in enumerate(__UpperCamelCase ):
if steps < sorted_step:
return rules_dict[sorted_steps[i]]
return last_lr_multiple
return rule_func
SCREAMING_SNAKE_CASE : Any = create_rules_function(__UpperCamelCase ,__UpperCamelCase )
return LambdaLR(__UpperCamelCase ,__UpperCamelCase ,last_epoch=__UpperCamelCase )
def lowercase__( __UpperCamelCase: int ,__UpperCamelCase: List[Any] ,__UpperCamelCase: Dict ,__UpperCamelCase: int=-1 ):
"""simple docstring"""
def lr_lambda(__UpperCamelCase: int ):
if current_step < num_warmup_steps:
return float(__UpperCamelCase ) / float(max(1 ,__UpperCamelCase ) )
return max(
0.0 ,float(num_training_steps - current_step ) / float(max(1 ,num_training_steps - num_warmup_steps ) ) )
return LambdaLR(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
def lowercase__( __UpperCamelCase: Optimizer ,__UpperCamelCase: int ,__UpperCamelCase: int ,__UpperCamelCase: float = 0.5 ,__UpperCamelCase: int = -1 ):
"""simple docstring"""
def lr_lambda(__UpperCamelCase: Any ):
if current_step < num_warmup_steps:
return float(__UpperCamelCase ) / float(max(1 ,__UpperCamelCase ) )
SCREAMING_SNAKE_CASE : str = float(current_step - num_warmup_steps ) / float(max(1 ,num_training_steps - num_warmup_steps ) )
return max(0.0 ,0.5 * (1.0 + math.cos(math.pi * float(__UpperCamelCase ) * 2.0 * progress )) )
return LambdaLR(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
def lowercase__( __UpperCamelCase: Optimizer ,__UpperCamelCase: int ,__UpperCamelCase: int ,__UpperCamelCase: int = 1 ,__UpperCamelCase: int = -1 ):
"""simple docstring"""
def lr_lambda(__UpperCamelCase: Dict ):
if current_step < num_warmup_steps:
return float(__UpperCamelCase ) / float(max(1 ,__UpperCamelCase ) )
SCREAMING_SNAKE_CASE : int = float(current_step - num_warmup_steps ) / float(max(1 ,num_training_steps - num_warmup_steps ) )
if progress >= 1.0:
return 0.0
return max(0.0 ,0.5 * (1.0 + math.cos(math.pi * ((float(__UpperCamelCase ) * progress) % 1.0) )) )
return LambdaLR(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
def lowercase__( __UpperCamelCase: Optional[int] ,__UpperCamelCase: Any ,__UpperCamelCase: Optional[int] ,__UpperCamelCase: Optional[Any]=1e-7 ,__UpperCamelCase: Dict=1.0 ,__UpperCamelCase: Optional[Any]=-1 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = optimizer.defaults['lr']
if not (lr_init > lr_end):
raise ValueError(f"lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})" )
def lr_lambda(__UpperCamelCase: int ):
if current_step < num_warmup_steps:
return float(__UpperCamelCase ) / float(max(1 ,__UpperCamelCase ) )
elif current_step > num_training_steps:
return lr_end / lr_init # as LambdaLR multiplies by lr_init
else:
SCREAMING_SNAKE_CASE : List[str] = lr_init - lr_end
SCREAMING_SNAKE_CASE : Optional[Any] = num_training_steps - num_warmup_steps
SCREAMING_SNAKE_CASE : Union[str, Any] = 1 - (current_step - num_warmup_steps) / decay_steps
SCREAMING_SNAKE_CASE : str = lr_range * pct_remaining**power + lr_end
return decay / lr_init # as LambdaLR multiplies by lr_init
return LambdaLR(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
UpperCamelCase_ = {
SchedulerType.LINEAR: get_linear_schedule_with_warmup,
SchedulerType.COSINE: get_cosine_schedule_with_warmup,
SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
SchedulerType.CONSTANT: get_constant_schedule,
SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}
def lowercase__( __UpperCamelCase: Union[str, SchedulerType] ,__UpperCamelCase: Optimizer ,__UpperCamelCase: Optional[str] = None ,__UpperCamelCase: Optional[int] = None ,__UpperCamelCase: Optional[int] = None ,__UpperCamelCase: int = 1 ,__UpperCamelCase: float = 1.0 ,__UpperCamelCase: int = -1 ,):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = SchedulerType(__UpperCamelCase )
SCREAMING_SNAKE_CASE : str = TYPE_TO_SCHEDULER_FUNCTION[name]
if name == SchedulerType.CONSTANT:
return schedule_func(__UpperCamelCase ,last_epoch=__UpperCamelCase )
if name == SchedulerType.PIECEWISE_CONSTANT:
return schedule_func(__UpperCamelCase ,step_rules=__UpperCamelCase ,last_epoch=__UpperCamelCase )
# All other schedulers require `num_warmup_steps`
if num_warmup_steps is None:
raise ValueError(f"{name} requires `num_warmup_steps`, please provide that argument." )
if name == SchedulerType.CONSTANT_WITH_WARMUP:
return schedule_func(__UpperCamelCase ,num_warmup_steps=__UpperCamelCase ,last_epoch=__UpperCamelCase )
# All other schedulers require `num_training_steps`
if num_training_steps is None:
raise ValueError(f"{name} requires `num_training_steps`, please provide that argument." )
if name == SchedulerType.COSINE_WITH_RESTARTS:
return schedule_func(
__UpperCamelCase ,num_warmup_steps=__UpperCamelCase ,num_training_steps=__UpperCamelCase ,num_cycles=__UpperCamelCase ,last_epoch=__UpperCamelCase ,)
if name == SchedulerType.POLYNOMIAL:
return schedule_func(
__UpperCamelCase ,num_warmup_steps=__UpperCamelCase ,num_training_steps=__UpperCamelCase ,power=__UpperCamelCase ,last_epoch=__UpperCamelCase ,)
return schedule_func(
__UpperCamelCase ,num_warmup_steps=__UpperCamelCase ,num_training_steps=__UpperCamelCase ,last_epoch=__UpperCamelCase )
| 251 | 1 |
import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch
from parameterized import parameterized
from transformers.testing_utils import (
CaptureStderr,
ExtendSysPath,
TestCasePlus,
execute_subprocess_async,
get_gpu_count,
get_torch_dist_unique_port,
require_apex,
require_bitsandbytes,
require_fairscale,
require_torch,
require_torch_gpu,
require_torch_multi_gpu,
require_torch_non_multi_gpu,
slow,
)
from transformers.trainer_callback import TrainerState
from transformers.trainer_utils import set_seed
lowercase_ = os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(F'{bindir}/../../examples/pytorch/translation'):
from run_translation import main # noqa
set_seed(42)
lowercase_ = """sshleifer/student_marian_en_ro_6_1"""
lowercase_ = """sshleifer/tiny-mbart"""
@require_torch
class _snake_case ( lowercase__):
def A__ ( self : Union[str, Any], __lowercase : Optional[int]=False, __lowercase : Union[str, Any]=None, __lowercase : Tuple=True, __lowercase : Union[str, Any]=True, __lowercase : Union[str, Any]=True, __lowercase : List[Any]=True, ):
lowercase__ = self.run_trainer(
eval_steps=1, max_len=12, model_name=__lowercase, num_train_epochs=1, distributed=__lowercase, extra_args_str=__lowercase, predict_with_generate=__lowercase, do_train=__lowercase, do_eval=__lowercase, do_predict=__lowercase, )
lowercase__ = TrainerState.load_from_json(os.path.join(__lowercase, "trainer_state.json" ) ).log_history
if not do_eval:
return
lowercase__ = [log for log in logs if "eval_loss" in log.keys()]
lowercase__ = eval_metrics[0]
if predict_with_generate:
assert "eval_bleu" in first_step_stats
lowercase__ = eval_metrics[-1]
assert isinstance(last_step_stats["eval_bleu"], __lowercase )
assert not math.isnan(float(last_step_stats["eval_loss"] ) ), "eval_loss must not be `nan`"
@require_torch_non_multi_gpu
def A__ ( self : Any ):
self.run_seqaseq_quick()
@require_torch_multi_gpu
def A__ ( self : List[str] ):
self.run_seqaseq_quick(distributed=__lowercase )
@require_torch_multi_gpu
def A__ ( self : Union[str, Any] ):
self.run_seqaseq_quick(distributed=__lowercase )
@unittest.skip("Requires an update of the env running those tests" )
@require_torch_multi_gpu
@require_fairscale
def A__ ( self : Optional[int] ):
self.run_seqaseq_quick(distributed=__lowercase, extra_args_str="--sharded_ddp simple" )
@unittest.skip("Requires an update of the env running those tests" )
@require_torch_multi_gpu
@require_fairscale
def A__ ( self : Union[str, Any] ):
self.run_seqaseq_quick(distributed=__lowercase, extra_args_str="--sharded_ddp simple --fp16" )
@unittest.skip("Requires an update of the env running those tests" )
@require_torch_multi_gpu
@require_fairscale
def A__ ( self : List[str] ):
self.run_seqaseq_quick(distributed=__lowercase, extra_args_str="--sharded_ddp zero_dp_2", predict_with_generate=__lowercase )
@unittest.skip("Requires an update of the env running those tests" )
@require_torch_multi_gpu
@require_fairscale
def A__ ( self : Optional[Any] ):
self.run_seqaseq_quick(
distributed=__lowercase, extra_args_str="--sharded_ddp zero_dp_2 --fp16", predict_with_generate=__lowercase )
@require_apex
@require_torch_gpu
def A__ ( self : List[str] ):
# XXX: apex breaks the trainer if it's run twice e.g. run_seq2seq.main() from the same
# program and it breaks other tests that run from the same pytest worker, therefore until this is
# sorted out it must be run only in an external program, that is distributed=True in this
# test and only under one or more gpus - if we want cpu will need to make a special test
#
# specifically to the problem traced it to self.optimizer.step() - if it's run 2nd time via
# 2nd main() call it botches the future eval.
#
self.run_seqaseq_quick(distributed=__lowercase, extra_args_str="--fp16 --fp16_backend=apex" )
# test 2nd time - was getting eval_loss': nan'
# to reproduce the problem set distributed=False
self.run_seqaseq_quick(distributed=__lowercase, extra_args_str="--fp16 --fp16_backend=apex" )
@parameterized.expand(["base", "low", "high", "mixed"] )
@require_torch_multi_gpu
def A__ ( self : Tuple, __lowercase : Tuple ):
# as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
lowercase__ = {
# test with the default log_level - should be info and thus log info once
"base": {"extra_args_str": "", "n_matches": 1},
# test with low log_level and log_level_replica - should be noisy on all processes
# now the info string should appear twice on 2 processes
"low": {"extra_args_str": "--log_level debug --log_level_replica debug", "n_matches": 2},
# test with high log_level and low log_level_replica
# now the info string should appear once only on the replica
"high": {"extra_args_str": "--log_level error --log_level_replica debug", "n_matches": 1},
# test with high log_level and log_level_replica - should be quiet on all processes
"mixed": {"extra_args_str": "--log_level error --log_level_replica error", "n_matches": 0},
}
lowercase__ = experiments[experiment_id]
lowercase__ = {"distributed": True, "predict_with_generate": False, "do_eval": False, "do_predict": False}
lowercase__ = "Running training"
with CaptureStderr() as cl:
self.run_seqaseq_quick(**__lowercase, extra_args_str=data["extra_args_str"] )
lowercase__ = len(re.findall(__lowercase, cl.err ) )
self.assertEqual(__lowercase, data["n_matches"] )
@slow
def A__ ( self : int ):
lowercase__ = self.run_trainer(
eval_steps=2, max_len=128, model_name=__lowercase, learning_rate=3e-4, num_train_epochs=10, distributed=__lowercase, )
# Check metrics
lowercase__ = TrainerState.load_from_json(os.path.join(__lowercase, "trainer_state.json" ) ).log_history
lowercase__ = [log for log in logs if "eval_loss" in log.keys()]
lowercase__ = eval_metrics[0]
lowercase__ = eval_metrics[-1]
assert first_step_stats["eval_loss"] > last_step_stats["eval_loss"], "model learned nothing"
assert isinstance(last_step_stats["eval_bleu"], __lowercase )
# test if do_predict saves generations and metrics
lowercase__ = os.listdir(__lowercase )
lowercase__ = {os.path.basename(__lowercase ) for p in contents}
assert "generated_predictions.txt" in contents
assert "predict_results.json" in contents
@slow
@require_bitsandbytes
def A__ ( self : List[str] ):
from transformers.training_args import OptimizerNames
def train_and_return_metrics(__lowercase : str ) -> Tuple[int, float]:
lowercase__ = "--skip_memory_metrics 0"
lowercase__ = self.run_trainer(
max_len=128, model_name=__lowercase, learning_rate=3e-4, num_train_epochs=1, optim=__lowercase, distributed=__lowercase, extra_args_str=__lowercase, do_eval=__lowercase, do_predict=__lowercase, n_gpus_to_use=1, )
# Check metrics
lowercase__ = TrainerState.load_from_json(Path(__lowercase, "trainer_state.json" ) ).log_history
lowercase__ = int(logs[0]["train_mem_gpu_peaked_delta"] / 2**20 )
lowercase__ = int(logs[0]["train_mem_gpu_alloc_delta"] / 2**20 )
lowercase__ = logs[0]["train_loss"]
return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss
lowercase__ , lowercase__ , lowercase__ = train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value )
lowercase__ , lowercase__ , lowercase__ = train_and_return_metrics(OptimizerNames.ADAMW_BNB.value )
lowercase__ = gpu_alloc_mem_orig - gpu_alloc_mem_bnb
lowercase__ = gpu_peak_mem_orig + gpu_alloc_mem_orig
lowercase__ = gpu_peak_mem_bnb + gpu_alloc_mem_bnb
lowercase__ = gpu_total_mem_orig - gpu_total_mem_bnb
# sshleifer/student_marian_en_ro_6_1 has 54M parameter, 29M of which is `nn.Embedding` which
# doesn't get quantized and remains in fp32. Therefore we only have 25M parameters quantized
# in 2 bytes and the diff in optim memory usage is derived as so:
#
# - normal 25*8=~200MB (8 bytes per param)
# - bnb 25*2= ~50MB (2 bytes per param)
#
# Thus we should expect ~150MB total memory saved.
#
# Peak memory should be the same - the total should be different by about that same margin
#
# After leaving a small margin to accommodate for differences between gpus let's check
# that we have at least 120MB in savings
lowercase__ = 120
# uncomment the following if this test starts failing - requires py38 for a new print feature
# gpu_peak_mem_diff = gpu_peak_mem_orig - gpu_peak_mem_bnb
# print(f"{gpu_alloc_mem_orig=}MB {gpu_peak_mem_orig=}MB {gpu_alloc_mem_orig+gpu_peak_mem_orig=}MB")
# print(f" {gpu_alloc_mem_bnb=}MB {gpu_peak_mem_bnb=}MB {gpu_alloc_mem_bnb+gpu_peak_mem_bnb=}MB")
# print(f"{gpu_alloc_mem_diff=}MB")
# print(f"{gpu_peak_mem_diff=}MB")
# print(f"{gpu_total_mem_orig=}MB, {gpu_total_mem_bnb=}MB")
# print(f"{gpu_total_mem_diff=}MB, {gpu_total_mem_diff=}MB")
self.assertGreater(
__lowercase, __lowercase, "should use ~150MB less alloc gpu memory with BNB, compared to without it for this model but got"
F''' a difference of {gpu_alloc_mem_diff}MB, with gpu_alloc_mem_orig={gpu_alloc_mem_orig}MB and'''
F''' gpu_alloc_mem_bnb={gpu_alloc_mem_bnb}MB''', )
self.assertGreater(
__lowercase, __lowercase, "should use ~150MB less total gpu memory with BNB, compared to without it for this model but got"
F''' a difference of {gpu_total_mem_diff}MB, with gpu_total_mem_orig={gpu_total_mem_orig}MB and'''
F''' gpu_total_mem_bnb={gpu_total_mem_bnb}MB''', )
self.assertEqual(
__lowercase, __lowercase, F'''loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}''' )
def A__ ( self : str, __lowercase : int, __lowercase : str, __lowercase : int, __lowercase : float = 3e-3, __lowercase : str = "adafactor", __lowercase : bool = False, __lowercase : str = None, __lowercase : int = 0, __lowercase : bool = True, __lowercase : bool = True, __lowercase : bool = True, __lowercase : bool = True, __lowercase : int = None, ):
lowercase__ = self.test_file_dir / "../fixtures/tests_samples/wmt_en_ro"
lowercase__ = self.get_auto_remove_tmp_dir()
lowercase__ = F'''
--model_name_or_path {model_name}
--train_file {data_dir}/train.json
--validation_file {data_dir}/val.json
--test_file {data_dir}/test.json
--output_dir {output_dir}
--overwrite_output_dir
--max_train_samples 8
--max_source_length {max_len}
--max_target_length {max_len}
--do_train
--num_train_epochs {str(__lowercase )}
--per_device_train_batch_size 4
--learning_rate {learning_rate}
--warmup_steps 8
--logging_steps 0
--logging_strategy no
--save_steps {str(__lowercase )}
--group_by_length
--label_smoothing_factor 0.1
--target_lang ro_RO
--source_lang en_XX
'''.split()
lowercase__ = F'''
--do_eval
--per_device_eval_batch_size 4
--max_eval_samples 8
--val_max_target_length {max_len}
--evaluation_strategy steps
--eval_steps {str(__lowercase )}
'''.split()
lowercase__ = "\n --do_predict\n ".split()
lowercase__ = []
if do_train:
args += args_train
if do_eval:
args += args_eval
if do_predict:
args += args_predict
if predict_with_generate:
args += "--predict_with_generate".split()
if do_train:
if optim == "adafactor":
args += "--adafactor".split()
else:
args += F'''--optim {optim}'''.split()
if extra_args_str is not None:
args += extra_args_str.split()
if distributed:
if n_gpus_to_use is None:
lowercase__ = get_gpu_count()
lowercase__ = get_torch_dist_unique_port()
lowercase__ = F'''
-m torch.distributed.run
--nproc_per_node={n_gpus_to_use}
--master_port={master_port}
{self.examples_dir_str}/pytorch/translation/run_translation.py
'''.split()
lowercase__ = [sys.executable] + distributed_args + args
# keep for quick debug
# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
execute_subprocess_async(__lowercase, env=self.get_env() )
else:
lowercase__ = ["run_translation.py"] + args
with patch.object(__lowercase, "argv", __lowercase ):
main()
return output_dir
| 224 |
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"""deepmind/language-perceiver""": """https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json""",
# See all Perceiver models at https://huggingface.co/models?filter=perceiver
}
class _snake_case ( lowercase__):
UpperCamelCase__ : Any ="""perceiver"""
def __init__( self : Any, __lowercase : Optional[Any]=256, __lowercase : List[str]=1280, __lowercase : Dict=768, __lowercase : int=1, __lowercase : Dict=26, __lowercase : Any=8, __lowercase : List[Any]=8, __lowercase : Dict=None, __lowercase : List[Any]=None, __lowercase : str="kv", __lowercase : str=1, __lowercase : Optional[Any]=1, __lowercase : str="gelu", __lowercase : List[str]=0.1, __lowercase : int=0.02, __lowercase : Union[str, Any]=1e-1_2, __lowercase : Optional[Any]=True, __lowercase : Optional[Any]=262, __lowercase : str=2048, __lowercase : Optional[Any]=56, __lowercase : str=[368, 496], __lowercase : str=16, __lowercase : int=1920, __lowercase : Dict=16, __lowercase : List[Any]=[1, 16, 224, 224], **__lowercase : str, ):
super().__init__(**__lowercase )
lowercase__ = num_latents
lowercase__ = d_latents
lowercase__ = d_model
lowercase__ = num_blocks
lowercase__ = num_self_attends_per_block
lowercase__ = num_self_attention_heads
lowercase__ = num_cross_attention_heads
lowercase__ = qk_channels
lowercase__ = v_channels
lowercase__ = cross_attention_shape_for_attention
lowercase__ = self_attention_widening_factor
lowercase__ = cross_attention_widening_factor
lowercase__ = hidden_act
lowercase__ = attention_probs_dropout_prob
lowercase__ = initializer_range
lowercase__ = layer_norm_eps
lowercase__ = use_query_residual
# masked language modeling attributes
lowercase__ = vocab_size
lowercase__ = max_position_embeddings
# image classification attributes
lowercase__ = image_size
# flow attributes
lowercase__ = train_size
# multimodal autoencoding attributes
lowercase__ = num_frames
lowercase__ = audio_samples_per_frame
lowercase__ = samples_per_patch
lowercase__ = output_shape
class _snake_case ( lowercase__):
@property
def A__ ( self : Optional[int] ):
if self.task == "multiple-choice":
lowercase__ = {0: "batch", 1: "choice", 2: "sequence"}
else:
lowercase__ = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("inputs", dynamic_axis),
("attention_mask", dynamic_axis),
] )
@property
def A__ ( self : Optional[Any] ):
return 1e-4
def A__ ( self : Tuple, __lowercase : Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"], __lowercase : int = -1, __lowercase : int = -1, __lowercase : int = -1, __lowercase : bool = False, __lowercase : Optional[TensorType] = None, __lowercase : int = 3, __lowercase : int = 40, __lowercase : int = 40, ):
# copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified
if isinstance(__lowercase, __lowercase ):
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
lowercase__ = compute_effective_axis_dimension(
__lowercase, fixed_dimension=OnnxConfig.default_fixed_batch, num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
lowercase__ = preprocessor.num_special_tokens_to_add(__lowercase )
lowercase__ = compute_effective_axis_dimension(
__lowercase, fixed_dimension=OnnxConfig.default_fixed_sequence, num_token_to_add=__lowercase )
# Generate dummy inputs according to compute batch and sequence
lowercase__ = [" ".join(["a"] ) * seq_length] * batch_size
lowercase__ = dict(preprocessor(__lowercase, return_tensors=__lowercase ) )
lowercase__ = inputs.pop("input_ids" )
return inputs
elif isinstance(__lowercase, __lowercase ) and preprocessor.model_input_names[0] == "pixel_values":
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
lowercase__ = compute_effective_axis_dimension(__lowercase, fixed_dimension=OnnxConfig.default_fixed_batch )
lowercase__ = self._generate_dummy_images(__lowercase, __lowercase, __lowercase, __lowercase )
lowercase__ = dict(preprocessor(images=__lowercase, return_tensors=__lowercase ) )
lowercase__ = inputs.pop("pixel_values" )
return inputs
else:
raise ValueError(
"Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor." )
| 224 | 1 |
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def __magic_name__ ( __lowerCAmelCase : Tuple ) -> Optional[int]:
__lowerCamelCase = {}
__lowerCamelCase = job['''started_at''']
__lowerCamelCase = job['''completed_at''']
__lowerCamelCase = date_parser.parse(__lowerCAmelCase )
__lowerCamelCase = date_parser.parse(__lowerCAmelCase )
__lowerCamelCase = round((end_datetime - start_datetime).total_seconds() / 60.0 )
__lowerCamelCase = start
__lowerCamelCase = end
__lowerCamelCase = duration_in_min
return job_info
def __magic_name__ ( __lowerCAmelCase : Dict , __lowerCAmelCase : Optional[int]=None ) -> Tuple:
__lowerCamelCase = None
if token is not None:
__lowerCamelCase = {'''Accept''': '''application/vnd.github+json''', '''Authorization''': f'''Bearer {token}'''}
__lowerCamelCase = f'''https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'''
__lowerCamelCase = requests.get(__lowerCAmelCase , headers=__lowerCAmelCase ).json()
__lowerCamelCase = {}
try:
job_time.update({job['''name''']: extract_time_from_single_job(__lowerCAmelCase ) for job in result['''jobs''']} )
__lowerCamelCase = math.ceil((result['''total_count'''] - 100) / 100 )
for i in range(__lowerCAmelCase ):
__lowerCamelCase = requests.get(url + f'''&page={i + 2}''' , headers=__lowerCAmelCase ).json()
job_time.update({job['''name''']: extract_time_from_single_job(__lowerCAmelCase ) for job in result['''jobs''']} )
return job_time
except Exception:
print(f'''Unknown error, could not fetch links:\n{traceback.format_exc()}''' )
return {}
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument("--workflow_run_id", type=str, required=True, help="A GitHub Actions workflow run id.")
SCREAMING_SNAKE_CASE__ : List[Any] = parser.parse_args()
SCREAMING_SNAKE_CASE__ : List[str] = get_job_time(args.workflow_run_id)
SCREAMING_SNAKE_CASE__ : List[Any] = dict(sorted(job_time.items(), key=lambda item: item[1]["duration"], reverse=True))
for k, v in job_time.items():
print(F'{k}: {v["duration"]}')
| 270 |
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
SCREAMING_SNAKE_CASE__ : List[Any] = TypeVar("T")
def __magic_name__ ( __lowerCAmelCase : int ) -> int:
return (position - 1) // 2
def __magic_name__ ( __lowerCAmelCase : int ) -> int:
return (2 * position) + 1
def __magic_name__ ( __lowerCAmelCase : int ) -> int:
return (2 * position) + 2
class lowerCAmelCase__ ( Generic[T] ):
def __init__( self : List[str] ) -> None:
__lowerCamelCase = []
__lowerCamelCase = {}
__lowerCamelCase = 0
def __len__( self : Optional[int] ) -> int:
return self.elements
def __repr__( self : Optional[int] ) -> str:
return str(self.heap )
def __A ( self : Union[str, Any] ) -> bool:
# Check if the priority queue is empty
return self.elements == 0
def __A ( self : str , SCREAMING_SNAKE_CASE__ : T , SCREAMING_SNAKE_CASE__ : int ) -> None:
# Add an element with given priority to the queue
self.heap.append((elem, weight) )
__lowerCamelCase = self.elements
self.elements += 1
self._bubble_up(SCREAMING_SNAKE_CASE__ )
def __A ( self : Any ) -> T:
# Remove and return the element with lowest weight (highest priority)
if self.elements > 1:
self._swap_nodes(0 , self.elements - 1 )
__lowerCamelCase , __lowerCamelCase = self.heap.pop()
del self.position_map[elem]
self.elements -= 1
if self.elements > 0:
__lowerCamelCase , __lowerCamelCase = self.heap[0]
self._bubble_down(SCREAMING_SNAKE_CASE__ )
return elem
def __A ( self : Dict , SCREAMING_SNAKE_CASE__ : T , SCREAMING_SNAKE_CASE__ : int ) -> None:
# Update the weight of the given key
__lowerCamelCase = self.position_map[elem]
__lowerCamelCase = (elem, weight)
if position > 0:
__lowerCamelCase = get_parent_position(SCREAMING_SNAKE_CASE__ )
__lowerCamelCase , __lowerCamelCase = self.heap[parent_position]
if parent_weight > weight:
self._bubble_up(SCREAMING_SNAKE_CASE__ )
else:
self._bubble_down(SCREAMING_SNAKE_CASE__ )
else:
self._bubble_down(SCREAMING_SNAKE_CASE__ )
def __A ( self : Optional[int] , SCREAMING_SNAKE_CASE__ : T ) -> None:
# Place a node at the proper position (upward movement) [to be used internally
# only]
__lowerCamelCase = self.position_map[elem]
if curr_pos == 0:
return None
__lowerCamelCase = get_parent_position(SCREAMING_SNAKE_CASE__ )
__lowerCamelCase , __lowerCamelCase = self.heap[curr_pos]
__lowerCamelCase , __lowerCamelCase = self.heap[parent_position]
if parent_weight > weight:
self._swap_nodes(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
return self._bubble_up(SCREAMING_SNAKE_CASE__ )
return None
def __A ( self : Optional[int] , SCREAMING_SNAKE_CASE__ : T ) -> None:
# Place a node at the proper position (downward movement) [to be used
# internally only]
__lowerCamelCase = self.position_map[elem]
__lowerCamelCase , __lowerCamelCase = self.heap[curr_pos]
__lowerCamelCase = get_child_left_position(SCREAMING_SNAKE_CASE__ )
__lowerCamelCase = get_child_right_position(SCREAMING_SNAKE_CASE__ )
if child_left_position < self.elements and child_right_position < self.elements:
__lowerCamelCase , __lowerCamelCase = self.heap[child_left_position]
__lowerCamelCase , __lowerCamelCase = self.heap[child_right_position]
if child_right_weight < child_left_weight and child_right_weight < weight:
self._swap_nodes(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
return self._bubble_down(SCREAMING_SNAKE_CASE__ )
if child_left_position < self.elements:
__lowerCamelCase , __lowerCamelCase = self.heap[child_left_position]
if child_left_weight < weight:
self._swap_nodes(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
return self._bubble_down(SCREAMING_SNAKE_CASE__ )
else:
return None
if child_right_position < self.elements:
__lowerCamelCase , __lowerCamelCase = self.heap[child_right_position]
if child_right_weight < weight:
self._swap_nodes(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
return self._bubble_down(SCREAMING_SNAKE_CASE__ )
return None
def __A ( self : List[Any] , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int ) -> None:
# Swap the nodes at the given positions
__lowerCamelCase = self.heap[nodea_pos][0]
__lowerCamelCase = self.heap[nodea_pos][0]
__lowerCamelCase , __lowerCamelCase = (
self.heap[nodea_pos],
self.heap[nodea_pos],
)
__lowerCamelCase = nodea_pos
__lowerCamelCase = nodea_pos
class lowerCAmelCase__ ( Generic[T] ):
def __init__( self : Tuple ) -> None:
__lowerCamelCase = {}
__lowerCamelCase = 0
def __repr__( self : Optional[int] ) -> str:
return str(self.connections )
def __len__( self : List[str] ) -> int:
return self.nodes
def __A ( self : List[str] , SCREAMING_SNAKE_CASE__ : T ) -> None:
# Add a node in the graph if it is not in the graph
if node not in self.connections:
__lowerCamelCase = {}
self.nodes += 1
def __A ( self : Dict , SCREAMING_SNAKE_CASE__ : T , SCREAMING_SNAKE_CASE__ : T , SCREAMING_SNAKE_CASE__ : int ) -> None:
# Add an edge between 2 nodes in the graph
self.add_node(SCREAMING_SNAKE_CASE__ )
self.add_node(SCREAMING_SNAKE_CASE__ )
__lowerCamelCase = weight
__lowerCamelCase = weight
def __magic_name__ ( __lowerCAmelCase : GraphUndirectedWeighted[T] , ) -> tuple[dict[T, int], dict[T, T | None]]:
__lowerCamelCase = {node: maxsize for node in graph.connections}
__lowerCamelCase = {node: None for node in graph.connections}
__lowerCamelCase = MinPriorityQueue()
for node, weight in dist.items():
priority_queue.push(__lowerCAmelCase , __lowerCAmelCase )
if priority_queue.is_empty():
return dist, parent
# initialization
__lowerCamelCase = priority_queue.extract_min()
__lowerCamelCase = 0
for neighbour in graph.connections[node]:
if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
__lowerCamelCase = dist[node] + graph.connections[node][neighbour]
priority_queue.update_key(__lowerCAmelCase , dist[neighbour] )
__lowerCamelCase = node
# running prim's algorithm
while not priority_queue.is_empty():
__lowerCamelCase = priority_queue.extract_min()
for neighbour in graph.connections[node]:
if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
__lowerCamelCase = dist[node] + graph.connections[node][neighbour]
priority_queue.update_key(__lowerCAmelCase , dist[neighbour] )
__lowerCamelCase = node
return dist, parent
| 270 | 1 |
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from ..image_utils import load_image
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = Dict[str, Any]
UpperCamelCase__ = List[Prediction]
@add_end_docstrings(UpperCAmelCase_ )
class A ( UpperCAmelCase_ ):
def __init__(self : Any , *__UpperCAmelCase : List[str] , **__UpperCAmelCase : Any ) -> Union[str, Any]:
"""simple docstring"""
super().__init__(*__UpperCAmelCase , **__UpperCAmelCase )
if self.framework == "tf":
raise ValueError(f"""The {self.__class__} is only available in PyTorch.""" )
requires_backends(self , "vision" )
self.check_model_type(
dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items() ) )
def lowercase_ (self : Optional[Any] , **__UpperCAmelCase : Dict ) -> Any:
"""simple docstring"""
UpperCAmelCase__ = {}
if "threshold" in kwargs:
UpperCAmelCase__ = kwargs["threshold"]
return {}, {}, postprocess_kwargs
def __call__(self : Optional[int] , *__UpperCAmelCase : List[Any] , **__UpperCAmelCase : Dict ) -> Union[Predictions, List[Prediction]]:
"""simple docstring"""
return super().__call__(*__UpperCAmelCase , **__UpperCAmelCase )
def lowercase_ (self : Optional[int] , __UpperCAmelCase : Any ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase__ = load_image(__UpperCAmelCase )
UpperCAmelCase__ = torch.IntTensor([[image.height, image.width]] )
UpperCAmelCase__ = self.image_processor(images=[image] , return_tensors="pt" )
if self.tokenizer is not None:
UpperCAmelCase__ = self.tokenizer(text=inputs["words"] , boxes=inputs["boxes"] , return_tensors="pt" )
UpperCAmelCase__ = target_size
return inputs
def lowercase_ (self : List[str] , __UpperCAmelCase : str ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase__ = model_inputs.pop("target_size" )
UpperCAmelCase__ = self.model(**__UpperCAmelCase )
UpperCAmelCase__ = outputs.__class__({"target_size": target_size, **outputs} )
if self.tokenizer is not None:
UpperCAmelCase__ = model_inputs["bbox"]
return model_outputs
def lowercase_ (self : List[Any] , __UpperCAmelCase : Any , __UpperCAmelCase : Dict=0.9 ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase__ = model_outputs["target_size"]
if self.tokenizer is not None:
# This is a LayoutLMForTokenClassification variant.
# The OCR got the boxes and the model classified the words.
UpperCAmelCase__ , UpperCAmelCase__ = target_size[0].tolist()
def unnormalize(__UpperCAmelCase : List[str] ):
return self._get_bounding_box(
torch.Tensor(
[
(width * bbox[0] / 1_0_0_0),
(height * bbox[1] / 1_0_0_0),
(width * bbox[2] / 1_0_0_0),
(height * bbox[3] / 1_0_0_0),
] ) )
UpperCAmelCase__ , UpperCAmelCase__ = model_outputs["logits"].squeeze(0 ).softmax(dim=-1 ).max(dim=-1 )
UpperCAmelCase__ = [self.model.config.idalabel[prediction] for prediction in classes.tolist()]
UpperCAmelCase__ = [unnormalize(__UpperCAmelCase ) for bbox in model_outputs["bbox"].squeeze(0 )]
UpperCAmelCase__ = ["score", "label", "box"]
UpperCAmelCase__ = [dict(zip(__UpperCAmelCase , __UpperCAmelCase ) ) for vals in zip(scores.tolist() , __UpperCAmelCase , __UpperCAmelCase ) if vals[0] > threshold]
else:
# This is a regular ForObjectDetectionModel
UpperCAmelCase__ = self.image_processor.post_process_object_detection(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
UpperCAmelCase__ = raw_annotations[0]
UpperCAmelCase__ = raw_annotation["scores"]
UpperCAmelCase__ = raw_annotation["labels"]
UpperCAmelCase__ = raw_annotation["boxes"]
UpperCAmelCase__ = scores.tolist()
UpperCAmelCase__ = [self.model.config.idalabel[label.item()] for label in labels]
UpperCAmelCase__ = [self._get_bounding_box(__UpperCAmelCase ) for box in boxes]
# {"scores": [...], ...} --> [{"score":x, ...}, ...]
UpperCAmelCase__ = ["score", "label", "box"]
UpperCAmelCase__ = [
dict(zip(__UpperCAmelCase , __UpperCAmelCase ) )
for vals in zip(raw_annotation["scores"] , raw_annotation["labels"] , raw_annotation["boxes"] )
]
return annotation
def lowercase_ (self : List[str] , __UpperCAmelCase : "torch.Tensor" ) -> Dict[str, int]:
"""simple docstring"""
if self.framework != "pt":
raise ValueError("The ObjectDetectionPipeline is only available in PyTorch." )
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = box.int().tolist()
UpperCAmelCase__ = {
"xmin": xmin,
"ymin": ymin,
"xmax": xmax,
"ymax": ymax,
}
return bbox
| 143 | from __future__ import annotations
import unittest
from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForMultipleChoice,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerModel,
)
from transformers.models.roformer.modeling_tf_roformer import (
TFRoFormerSelfAttention,
TFRoFormerSinusoidalPositionalEmbedding,
)
class A :
def __init__(self : int , __UpperCAmelCase : Tuple , __UpperCAmelCase : List[str]=1_3 , __UpperCAmelCase : Optional[Any]=7 , __UpperCAmelCase : Dict=True , __UpperCAmelCase : int=True , __UpperCAmelCase : Any=True , __UpperCAmelCase : int=True , __UpperCAmelCase : Dict=9_9 , __UpperCAmelCase : Optional[int]=3_2 , __UpperCAmelCase : Dict=2 , __UpperCAmelCase : Any=4 , __UpperCAmelCase : Tuple=3_7 , __UpperCAmelCase : List[Any]="gelu" , __UpperCAmelCase : Dict=0.1 , __UpperCAmelCase : int=0.1 , __UpperCAmelCase : str=5_1_2 , __UpperCAmelCase : Union[str, Any]=1_6 , __UpperCAmelCase : Tuple=2 , __UpperCAmelCase : Tuple=0.02 , __UpperCAmelCase : Optional[int]=3 , __UpperCAmelCase : str=4 , __UpperCAmelCase : Any=None , ) -> Dict:
"""simple docstring"""
UpperCAmelCase__ = parent
UpperCAmelCase__ = 1_3
UpperCAmelCase__ = 7
UpperCAmelCase__ = True
UpperCAmelCase__ = True
UpperCAmelCase__ = True
UpperCAmelCase__ = True
UpperCAmelCase__ = 9_9
UpperCAmelCase__ = 3_2
UpperCAmelCase__ = 2
UpperCAmelCase__ = 4
UpperCAmelCase__ = 3_7
UpperCAmelCase__ = "gelu"
UpperCAmelCase__ = 0.1
UpperCAmelCase__ = 0.1
UpperCAmelCase__ = 5_1_2
UpperCAmelCase__ = 1_6
UpperCAmelCase__ = 2
UpperCAmelCase__ = 0.02
UpperCAmelCase__ = 3
UpperCAmelCase__ = 4
UpperCAmelCase__ = None
def lowercase_ (self : Union[str, Any] ) -> str:
"""simple docstring"""
UpperCAmelCase__ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCAmelCase__ = None
if self.use_input_mask:
UpperCAmelCase__ = random_attention_mask([self.batch_size, self.seq_length] )
UpperCAmelCase__ = None
if self.use_token_type_ids:
UpperCAmelCase__ = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
UpperCAmelCase__ = None
UpperCAmelCase__ = None
UpperCAmelCase__ = None
if self.use_labels:
UpperCAmelCase__ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCAmelCase__ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCAmelCase__ = ids_tensor([self.batch_size] , self.num_choices )
UpperCAmelCase__ = RoFormerConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , return_dict=__UpperCAmelCase , )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def lowercase_ (self : List[Any] , __UpperCAmelCase : str , __UpperCAmelCase : List[str] , __UpperCAmelCase : List[Any] , __UpperCAmelCase : List[str] , __UpperCAmelCase : List[str] , __UpperCAmelCase : Union[str, Any] , __UpperCAmelCase : Any ) -> List[str]:
"""simple docstring"""
UpperCAmelCase__ = TFRoFormerModel(config=__UpperCAmelCase )
UpperCAmelCase__ = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
UpperCAmelCase__ = [input_ids, input_mask]
UpperCAmelCase__ = model(__UpperCAmelCase )
UpperCAmelCase__ = model(__UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowercase_ (self : int , __UpperCAmelCase : Optional[int] , __UpperCAmelCase : Any , __UpperCAmelCase : Any , __UpperCAmelCase : List[str] , __UpperCAmelCase : Tuple , __UpperCAmelCase : Optional[int] , __UpperCAmelCase : List[Any] ) -> Any:
"""simple docstring"""
UpperCAmelCase__ = True
UpperCAmelCase__ = TFRoFormerForCausalLM(config=__UpperCAmelCase )
UpperCAmelCase__ = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
UpperCAmelCase__ = model(__UpperCAmelCase )["logits"]
self.parent.assertListEqual(
list(prediction_scores.numpy().shape ) , [self.batch_size, self.seq_length, self.vocab_size] )
def lowercase_ (self : str , __UpperCAmelCase : Union[str, Any] , __UpperCAmelCase : int , __UpperCAmelCase : Optional[int] , __UpperCAmelCase : Any , __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : Any , __UpperCAmelCase : int ) -> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase__ = TFRoFormerForMaskedLM(config=__UpperCAmelCase )
UpperCAmelCase__ = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
UpperCAmelCase__ = model(__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowercase_ (self : Optional[Any] , __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : Optional[int] , __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : List[str] , __UpperCAmelCase : List[Any] , __UpperCAmelCase : Tuple , __UpperCAmelCase : str ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase__ = self.num_labels
UpperCAmelCase__ = TFRoFormerForSequenceClassification(config=__UpperCAmelCase )
UpperCAmelCase__ = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
UpperCAmelCase__ = model(__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowercase_ (self : str , __UpperCAmelCase : Union[str, Any] , __UpperCAmelCase : int , __UpperCAmelCase : Any , __UpperCAmelCase : Tuple , __UpperCAmelCase : List[Any] , __UpperCAmelCase : Union[str, Any] , __UpperCAmelCase : Optional[Any] ) -> Any:
"""simple docstring"""
UpperCAmelCase__ = self.num_choices
UpperCAmelCase__ = TFRoFormerForMultipleChoice(config=__UpperCAmelCase )
UpperCAmelCase__ = tf.tile(tf.expand_dims(__UpperCAmelCase , 1 ) , (1, self.num_choices, 1) )
UpperCAmelCase__ = tf.tile(tf.expand_dims(__UpperCAmelCase , 1 ) , (1, self.num_choices, 1) )
UpperCAmelCase__ = tf.tile(tf.expand_dims(__UpperCAmelCase , 1 ) , (1, self.num_choices, 1) )
UpperCAmelCase__ = {
"input_ids": multiple_choice_inputs_ids,
"attention_mask": multiple_choice_input_mask,
"token_type_ids": multiple_choice_token_type_ids,
}
UpperCAmelCase__ = model(__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def lowercase_ (self : int , __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : Tuple , __UpperCAmelCase : int , __UpperCAmelCase : int , __UpperCAmelCase : List[str] , __UpperCAmelCase : Union[str, Any] , __UpperCAmelCase : int ) -> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase__ = self.num_labels
UpperCAmelCase__ = TFRoFormerForTokenClassification(config=__UpperCAmelCase )
UpperCAmelCase__ = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
UpperCAmelCase__ = model(__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def lowercase_ (self : Dict , __UpperCAmelCase : Tuple , __UpperCAmelCase : Any , __UpperCAmelCase : Any , __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : Tuple , __UpperCAmelCase : Dict ) -> List[str]:
"""simple docstring"""
UpperCAmelCase__ = TFRoFormerForQuestionAnswering(config=__UpperCAmelCase )
UpperCAmelCase__ = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
UpperCAmelCase__ = model(__UpperCAmelCase )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def lowercase_ (self : Dict ) -> Dict:
"""simple docstring"""
UpperCAmelCase__ = self.prepare_config_and_inputs()
(
(
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) ,
) = config_and_inputs
UpperCAmelCase__ = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_tf
class A ( UpperCAmelCase_ , UpperCAmelCase_ , unittest.TestCase ):
__UpperCAmelCase : str = (
(
TFRoFormerModel,
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerForMultipleChoice,
)
if is_tf_available()
else ()
)
__UpperCAmelCase : List[str] = (
{
'feature-extraction': TFRoFormerModel,
'fill-mask': TFRoFormerForMaskedLM,
'question-answering': TFRoFormerForQuestionAnswering,
'text-classification': TFRoFormerForSequenceClassification,
'text-generation': TFRoFormerForCausalLM,
'token-classification': TFRoFormerForTokenClassification,
'zero-shot': TFRoFormerForSequenceClassification,
}
if is_tf_available()
else {}
)
__UpperCAmelCase : Optional[int] = False
__UpperCAmelCase : List[Any] = False
def lowercase_ (self : List[str] , __UpperCAmelCase : Union[str, Any] , __UpperCAmelCase : Dict , __UpperCAmelCase : Any , __UpperCAmelCase : Dict , __UpperCAmelCase : Any ) -> int:
"""simple docstring"""
if pipeline_test_casse_name == "TextGenerationPipelineTests":
return True
return False
def lowercase_ (self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase__ = TFRoFormerModelTester(self )
UpperCAmelCase__ = ConfigTester(self , config_class=__UpperCAmelCase , hidden_size=3_7 )
def lowercase_ (self : Optional[int] ) -> Any:
"""simple docstring"""
self.config_tester.run_common_tests()
def lowercase_ (self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCAmelCase )
def lowercase_ (self : Dict ) -> Any:
"""simple docstring"""
UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*__UpperCAmelCase )
def lowercase_ (self : Union[str, Any] ) -> str:
"""simple docstring"""
UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head(*__UpperCAmelCase )
def lowercase_ (self : Optional[Any] ) -> Dict:
"""simple docstring"""
UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*__UpperCAmelCase )
def lowercase_ (self : List[Any] ) -> List[str]:
"""simple docstring"""
UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*__UpperCAmelCase )
def lowercase_ (self : int ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*__UpperCAmelCase )
def lowercase_ (self : List[str] ) -> Tuple:
"""simple docstring"""
UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*__UpperCAmelCase )
@slow
def lowercase_ (self : Any ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase__ = TFRoFormerModel.from_pretrained("junnyu/roformer_chinese_base" )
self.assertIsNotNone(__UpperCAmelCase )
@require_tf
class A ( unittest.TestCase ):
@slow
def lowercase_ (self : List[Any] ) -> Any:
"""simple docstring"""
UpperCAmelCase__ = TFRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base" )
UpperCAmelCase__ = tf.constant([[0, 1, 2, 3, 4, 5]] )
UpperCAmelCase__ = model(__UpperCAmelCase )[0]
# TODO Replace vocab size
UpperCAmelCase__ = 5_0_0_0_0
UpperCAmelCase__ = [1, 6, vocab_size]
self.assertEqual(output.shape , __UpperCAmelCase )
print(output[:, :3, :3] )
# TODO Replace values below with what was printed above.
UpperCAmelCase__ = tf.constant(
[
[
[-0.12053341, -1.0264901, 0.29221946],
[-1.5133783, 0.197433, 0.15190607],
[-5.0135403, -3.900256, -0.84038764],
]
] )
tf.debugging.assert_near(output[:, :3, :3] , __UpperCAmelCase , atol=1E-4 )
@require_tf
class A ( unittest.TestCase ):
__UpperCAmelCase : Tuple = 1E-4
def lowercase_ (self : List[str] ) -> str:
"""simple docstring"""
UpperCAmelCase__ = tf.constant([[4, 1_0]] )
UpperCAmelCase__ = TFRoFormerSinusoidalPositionalEmbedding(num_positions=6 , embedding_dim=6 )
UpperCAmelCase__ = emba(input_ids.shape )
UpperCAmelCase__ = tf.constant(
[[0.0000, 0.0000, 0.0000, 1.0000, 1.0000, 1.0000], [0.8415, 0.0464, 0.0022, 0.5403, 0.9989, 1.0000]] )
tf.debugging.assert_near(__UpperCAmelCase , __UpperCAmelCase , atol=self.tolerance )
def lowercase_ (self : List[Any] ) -> int:
"""simple docstring"""
UpperCAmelCase__ = tf.constant(
[
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
[0.8415, 0.8219, 0.8020, 0.7819, 0.7617],
[0.9093, 0.9364, 0.9581, 0.9749, 0.9870],
] )
UpperCAmelCase__ = TFRoFormerSinusoidalPositionalEmbedding(num_positions=5_1_2 , embedding_dim=5_1_2 )
emba([2, 1_6, 5_1_2] )
UpperCAmelCase__ = emba.weight[:3, :5]
tf.debugging.assert_near(__UpperCAmelCase , __UpperCAmelCase , atol=self.tolerance )
@require_tf
class A ( unittest.TestCase ):
__UpperCAmelCase : Any = 1E-4
def lowercase_ (self : Tuple ) -> int:
"""simple docstring"""
UpperCAmelCase__ = tf.reshape(tf.range(2 * 1_2 * 1_6 * 6_4 , dtype=tf.floataa ) , shape=(2, 1_2, 1_6, 6_4) ) / 1_0_0
UpperCAmelCase__ = -tf.reshape(tf.range(2 * 1_2 * 1_6 * 6_4 , dtype=tf.floataa ) , shape=(2, 1_2, 1_6, 6_4) ) / 1_0_0
UpperCAmelCase__ = TFRoFormerSinusoidalPositionalEmbedding(num_positions=3_2 , embedding_dim=6_4 )
UpperCAmelCase__ = embed_positions([2, 1_6, 7_6_8] )[None, None, :, :]
UpperCAmelCase__ , UpperCAmelCase__ = TFRoFormerSelfAttention.apply_rotary_position_embeddings(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
UpperCAmelCase__ = tf.constant(
[
[0.0000, 0.0100, 0.0200, 0.0300, 0.0400, 0.0500, 0.0600, 0.0700],
[-0.2012, 0.8897, 0.0263, 0.9401, 0.2074, 0.9463, 0.3481, 0.9343],
[-1.7057, 0.6271, -1.2145, 1.3897, -0.6303, 1.7647, -0.1173, 1.8985],
[-2.1731, -1.6397, -2.7358, 0.2854, -2.1840, 1.7183, -1.3018, 2.4871],
[0.2717, -3.6173, -2.9206, -2.1988, -3.6638, 0.3858, -2.9155, 2.2980],
[3.9859, -2.1580, -0.7984, -4.4904, -4.1181, -2.0252, -4.4782, 1.1253],
] )
UpperCAmelCase__ = tf.constant(
[
[0.0000, -0.0100, -0.0200, -0.0300, -0.0400, -0.0500, -0.0600, -0.0700],
[0.2012, -0.8897, -0.0263, -0.9401, -0.2074, -0.9463, -0.3481, -0.9343],
[1.7057, -0.6271, 1.2145, -1.3897, 0.6303, -1.7647, 0.1173, -1.8985],
[2.1731, 1.6397, 2.7358, -0.2854, 2.1840, -1.7183, 1.3018, -2.4871],
[-0.2717, 3.6173, 2.9206, 2.1988, 3.6638, -0.3858, 2.9155, -2.2980],
[-3.9859, 2.1580, 0.7984, 4.4904, 4.1181, 2.0252, 4.4782, -1.1253],
] )
tf.debugging.assert_near(query_layer[0, 0, :6, :8] , __UpperCAmelCase , atol=self.tolerance )
tf.debugging.assert_near(key_layer[0, 0, :6, :8] , __UpperCAmelCase , atol=self.tolerance )
| 143 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_UpperCamelCase: Any = {
'configuration_mega': ['MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegaConfig', 'MegaOnnxConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCamelCase: List[Any] = [
'MEGA_PRETRAINED_MODEL_ARCHIVE_LIST',
'MegaForCausalLM',
'MegaForMaskedLM',
'MegaForMultipleChoice',
'MegaForQuestionAnswering',
'MegaForSequenceClassification',
'MegaForTokenClassification',
'MegaModel',
'MegaPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mega import (
MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
MegaForCausalLM,
MegaForMaskedLM,
MegaForMultipleChoice,
MegaForQuestionAnswering,
MegaForSequenceClassification,
MegaForTokenClassification,
MegaModel,
MegaPreTrainedModel,
)
else:
import sys
_UpperCamelCase: Tuple = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 255 |
"""simple docstring"""
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor
from accelerate import Accelerator
def lowercase__ ( _UpperCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
lowercase : Any = fname.split(os.path.sep )[-1]
return re.search(R'^(.*)_\d+\.jpg$' , _UpperCAmelCase ).groups()[0]
class a__ ( SCREAMING_SNAKE_CASE__ ):
def __init__( self : Union[str, Any], lowerCAmelCase : Tuple, lowerCAmelCase : Tuple=None, lowerCAmelCase : List[Any]=None ) -> Optional[Any]:
lowercase : str = file_names
lowercase : Optional[Any] = image_transform
lowercase : int = label_to_id
def __len__( self : List[Any] ) -> Any:
return len(self.file_names )
def __getitem__( self : str, lowerCAmelCase : Optional[int] ) -> Optional[Any]:
lowercase : List[Any] = self.file_names[idx]
lowercase : Tuple = PIL.Image.open(lowerCAmelCase )
lowercase : Tuple = raw_image.convert('RGB' )
if self.image_transform is not None:
lowercase : Optional[Any] = self.image_transform(lowerCAmelCase )
lowercase : Any = extract_label(lowerCAmelCase )
if self.label_to_id is not None:
lowercase : List[Any] = self.label_to_id[label]
return {"image": image, "label": label}
def lowercase__ ( _UpperCAmelCase , _UpperCAmelCase ) -> List[str]:
'''simple docstring'''
if args.with_tracking:
lowercase : Optional[int] = Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , log_with='all' , project_dir=args.project_dir )
else:
lowercase : Union[str, Any] = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
lowercase : Union[str, Any] = config['lr']
lowercase : Any = int(config['num_epochs'] )
lowercase : Union[str, Any] = int(config['seed'] )
lowercase : List[Any] = int(config['batch_size'] )
lowercase : str = config['image_size']
if not isinstance(_UpperCAmelCase , (list, tuple) ):
lowercase : Dict = (image_size, image_size)
# Parse out whether we are saving every epoch or after a certain number of batches
if hasattr(args.checkpointing_steps , 'isdigit' ):
if args.checkpointing_steps == "epoch":
lowercase : Dict = args.checkpointing_steps
elif args.checkpointing_steps.isdigit():
lowercase : Dict = int(args.checkpointing_steps )
else:
raise ValueError(
f'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.''' )
else:
lowercase : Tuple = None
# We need to initialize the trackers we use, and also store our configuration
if args.with_tracking:
lowercase : Optional[int] = os.path.split(_UpperCAmelCase )[-1].split('.' )[0]
accelerator.init_trackers(_UpperCAmelCase , _UpperCAmelCase )
# Grab all the image filenames
lowercase : Optional[Any] = [os.path.join(args.data_dir , _UpperCAmelCase ) for fname in os.listdir(args.data_dir ) if fname.endswith('.jpg' )]
# Build the label correspondences
lowercase : str = [extract_label(_UpperCAmelCase ) for fname in file_names]
lowercase : List[Any] = list(set(_UpperCAmelCase ) )
id_to_label.sort()
lowercase : Optional[Any] = {lbl: i for i, lbl in enumerate(_UpperCAmelCase )}
# Set the seed before splitting the data.
np.random.seed(_UpperCAmelCase )
torch.manual_seed(_UpperCAmelCase )
torch.cuda.manual_seed_all(_UpperCAmelCase )
# Split our filenames between train and validation
lowercase : List[Any] = np.random.permutation(len(_UpperCAmelCase ) )
lowercase : Optional[Any] = int(0.8 * len(_UpperCAmelCase ) )
lowercase : int = random_perm[:cut]
lowercase : Any = random_perm[cut:]
# For training we use a simple RandomResizedCrop
lowercase : Dict = Compose([RandomResizedCrop(_UpperCAmelCase , scale=(0.5, 1.0) ), ToTensor()] )
lowercase : List[Any] = PetsDataset(
[file_names[i] for i in train_split] , image_transform=_UpperCAmelCase , label_to_id=_UpperCAmelCase )
# For evaluation, we use a deterministic Resize
lowercase : List[Any] = Compose([Resize(_UpperCAmelCase ), ToTensor()] )
lowercase : List[str] = PetsDataset([file_names[i] for i in eval_split] , image_transform=_UpperCAmelCase , label_to_id=_UpperCAmelCase )
# Instantiate dataloaders.
lowercase : Dict = DataLoader(_UpperCAmelCase , shuffle=_UpperCAmelCase , batch_size=_UpperCAmelCase , num_workers=4 )
lowercase : Any = DataLoader(_UpperCAmelCase , shuffle=_UpperCAmelCase , batch_size=_UpperCAmelCase , num_workers=4 )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
lowercase : List[Any] = create_model('resnet50d' , pretrained=_UpperCAmelCase , num_classes=len(_UpperCAmelCase ) )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
lowercase : Union[str, Any] = model.to(accelerator.device )
# Freezing the base model
for param in model.parameters():
lowercase : Dict = False
for param in model.get_classifier().parameters():
lowercase : Dict = True
# We normalize the batches of images to be a bit faster.
lowercase : int = torch.tensor(model.default_cfg['mean'] )[None, :, None, None].to(accelerator.device )
lowercase : Dict = torch.tensor(model.default_cfg['std'] )[None, :, None, None].to(accelerator.device )
# Instantiate optimizer
lowercase : Any = torch.optim.Adam(params=model.parameters() , lr=lr / 25 )
# Instantiate learning rate scheduler
lowercase : List[Any] = OneCycleLR(optimizer=_UpperCAmelCase , max_lr=_UpperCAmelCase , epochs=_UpperCAmelCase , steps_per_epoch=len(_UpperCAmelCase ) )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
lowercase , lowercase , lowercase , lowercase , lowercase : Optional[int] = accelerator.prepare(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
# We need to keep track of how many total steps we have iterated over
lowercase : Tuple = 0
# We also need to keep track of the starting epoch so files are named properly
lowercase : List[str] = 0
# Potentially load in the weights and states from a previous save
if args.resume_from_checkpoint:
if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
accelerator.print(f'''Resumed from checkpoint: {args.resume_from_checkpoint}''' )
accelerator.load_state(args.resume_from_checkpoint )
lowercase : Any = os.path.basename(args.resume_from_checkpoint )
else:
# Get the most recent checkpoint
lowercase : List[str] = [f.name for f in os.scandir(os.getcwd() ) if f.is_dir()]
dirs.sort(key=os.path.getctime )
lowercase : Dict = dirs[-1] # Sorts folders by date modified, most recent checkpoint is the last
# Extract `epoch_{i}` or `step_{i}`
lowercase : Any = os.path.splitext(_UpperCAmelCase )[0]
if "epoch" in training_difference:
lowercase : List[Any] = int(training_difference.replace('epoch_' , '' ) ) + 1
lowercase : List[Any] = None
else:
lowercase : Optional[Any] = int(training_difference.replace('step_' , '' ) )
lowercase : int = resume_step // len(_UpperCAmelCase )
resume_step -= starting_epoch * len(_UpperCAmelCase )
# Now we train the model
for epoch in range(_UpperCAmelCase , _UpperCAmelCase ):
model.train()
if args.with_tracking:
lowercase : str = 0
if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
# We need to skip steps until we reach the resumed step
lowercase : Any = accelerator.skip_first_batches(_UpperCAmelCase , _UpperCAmelCase )
overall_step += resume_step
else:
# After the first iteration though, we need to go back to the original dataloader
lowercase : Union[str, Any] = train_dataloader
for batch in active_dataloader:
# We could avoid this line since we set the accelerator with `device_placement=True`.
lowercase : Any = {k: v.to(accelerator.device ) for k, v in batch.items()}
lowercase : List[str] = (batch['image'] - mean) / std
lowercase : Union[str, Any] = model(_UpperCAmelCase )
lowercase : Optional[int] = torch.nn.functional.cross_entropy(_UpperCAmelCase , batch['label'] )
# We keep track of the loss at each epoch
if args.with_tracking:
total_loss += loss.detach().float()
accelerator.backward(_UpperCAmelCase )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
lowercase : Union[str, Any] = f'''step_{overall_step}'''
if overall_step % checkpointing_steps == 0:
if args.output_dir is not None:
lowercase : Optional[Any] = os.path.join(args.output_dir , _UpperCAmelCase )
accelerator.save_state(_UpperCAmelCase )
model.eval()
lowercase : int = 0
lowercase : List[Any] = 0
for step, batch in enumerate(_UpperCAmelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
lowercase : List[str] = {k: v.to(accelerator.device ) for k, v in batch.items()}
lowercase : Optional[Any] = (batch['image'] - mean) / std
with torch.no_grad():
lowercase : int = model(_UpperCAmelCase )
lowercase : Tuple = outputs.argmax(dim=-1 )
lowercase , lowercase : Optional[Any] = accelerator.gather_for_metrics((predictions, batch['label']) )
lowercase : Union[str, Any] = predictions == references
num_elems += accurate_preds.shape[0]
accurate += accurate_preds.long().sum()
lowercase : List[str] = accurate.item() / num_elems
# Use accelerator.print to print only on the main process.
accelerator.print(f'''epoch {epoch}: {1_00 * eval_metric:.2f}''' )
if args.with_tracking:
accelerator.log(
{
'accuracy': 1_00 * eval_metric,
'train_loss': total_loss.item() / len(_UpperCAmelCase ),
'epoch': epoch,
} , step=_UpperCAmelCase , )
if checkpointing_steps == "epoch":
lowercase : str = f'''epoch_{epoch}'''
if args.output_dir is not None:
lowercase : Any = os.path.join(args.output_dir , _UpperCAmelCase )
accelerator.save_state(_UpperCAmelCase )
if args.with_tracking:
accelerator.end_training()
def lowercase__ ( ) -> Tuple:
'''simple docstring'''
lowercase : str = argparse.ArgumentParser(description='Simple example of training script.' )
parser.add_argument('--data_dir' , required=_UpperCAmelCase , help='The data folder on disk.' )
parser.add_argument('--fp16' , action='store_true' , help='If passed, will use FP16 training.' )
parser.add_argument(
'--mixed_precision' , type=_UpperCAmelCase , default=_UpperCAmelCase , choices=['no', 'fp16', 'bf16', 'fp8'] , help='Whether to use mixed precision. Choose'
'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
'and an Nvidia Ampere GPU.' , )
parser.add_argument('--cpu' , action='store_true' , help='If passed, will train on the CPU.' )
parser.add_argument(
'--checkpointing_steps' , type=_UpperCAmelCase , default=_UpperCAmelCase , help='Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.' , )
parser.add_argument(
'--output_dir' , type=_UpperCAmelCase , default='.' , help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.' , )
parser.add_argument(
'--resume_from_checkpoint' , type=_UpperCAmelCase , default=_UpperCAmelCase , help='If the training should continue from a checkpoint folder.' , )
parser.add_argument(
'--with_tracking' , action='store_true' , help='Whether to load in all available experiment trackers from the environment and use them for logging.' , )
parser.add_argument(
'--project_dir' , type=_UpperCAmelCase , default='logs' , help='Location on where to store experiment tracking logs` and relevent project information' , )
lowercase : int = parser.parse_args()
lowercase : List[Any] = {'lr': 3e-2, 'num_epochs': 3, 'seed': 42, 'batch_size': 64, 'image_size': 2_24}
training_function(_UpperCAmelCase , _UpperCAmelCase )
if __name__ == "__main__":
main()
| 255 | 1 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFResNetForImageClassification, TFResNetModel
from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class a :
def __init__( self , A_ , A_=3 , A_=32 , A_=3 , A_=10 , A_=[10, 20, 30, 40] , A_=[1, 1, 2, 1] , A_=True , A_=True , A_="relu" , A_=3 , A_=None , ):
'''simple docstring'''
_UpperCAmelCase : str = parent
_UpperCAmelCase : Tuple = batch_size
_UpperCAmelCase : str = image_size
_UpperCAmelCase : Optional[int] = num_channels
_UpperCAmelCase : List[str] = embeddings_size
_UpperCAmelCase : Optional[Any] = hidden_sizes
_UpperCAmelCase : Union[str, Any] = depths
_UpperCAmelCase : Optional[Any] = is_training
_UpperCAmelCase : str = use_labels
_UpperCAmelCase : Dict = hidden_act
_UpperCAmelCase : str = num_labels
_UpperCAmelCase : Tuple = scope
_UpperCAmelCase : Dict = len(A_ )
def _UpperCAmelCase ( self ):
'''simple docstring'''
_UpperCAmelCase : List[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_UpperCAmelCase : int = None
if self.use_labels:
_UpperCAmelCase : Optional[Any] = ids_tensor([self.batch_size] , self.num_labels )
_UpperCAmelCase : int = self.get_config()
return config, pixel_values, labels
def _UpperCAmelCase ( self ):
'''simple docstring'''
return ResNetConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , image_size=self.image_size , )
def _UpperCAmelCase ( self , A_ , A_ , A_ ):
'''simple docstring'''
_UpperCAmelCase : int = TFResNetModel(config=A_ )
_UpperCAmelCase : Dict = 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 _UpperCAmelCase ( self , A_ , A_ , A_ ):
'''simple docstring'''
_UpperCAmelCase : Tuple = self.num_labels
_UpperCAmelCase : Any = TFResNetForImageClassification(A_ )
_UpperCAmelCase : str = model(A_ , labels=A_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _UpperCAmelCase ( self ):
'''simple docstring'''
_UpperCAmelCase : Dict = self.prepare_config_and_inputs()
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : Dict = config_and_inputs
_UpperCAmelCase : Union[str, Any] = {"pixel_values": pixel_values}
return config, inputs_dict
@require_tf
class a ( UpperCAmelCase , UpperCAmelCase , unittest.TestCase ):
_lowercase = (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
_lowercase = (
{"feature-extraction": TFResNetModel, "image-classification": TFResNetForImageClassification}
if is_tf_available()
else {}
)
_lowercase = False
_lowercase = False
_lowercase = False
_lowercase = False
_lowercase = False
def _UpperCAmelCase ( self ):
'''simple docstring'''
_UpperCAmelCase : Union[str, Any] = TFResNetModelTester(self )
_UpperCAmelCase : Optional[Any] = ConfigTester(self , config_class=A_ , has_text_modality=A_ )
def _UpperCAmelCase ( self ):
'''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 _UpperCAmelCase ( self ):
'''simple docstring'''
return
@unittest.skip(reason="ResNet does not use inputs_embeds" )
def _UpperCAmelCase ( self ):
'''simple docstring'''
pass
@unittest.skip(reason="ResNet does not support input and output embeddings" )
def _UpperCAmelCase ( self ):
'''simple docstring'''
pass
def _UpperCAmelCase ( self ):
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase : Union[str, Any] = model_class(A_ )
_UpperCAmelCase : Any = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_UpperCAmelCase : Optional[Any] = [*signature.parameters.keys()]
_UpperCAmelCase : List[str] = ["pixel_values"]
self.assertListEqual(arg_names[:1] , A_ )
def _UpperCAmelCase ( self ):
'''simple docstring'''
_UpperCAmelCase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A_ )
def _UpperCAmelCase ( self ):
'''simple docstring'''
def check_hidden_states_output(A_ , A_ , A_ ):
_UpperCAmelCase : Union[str, Any] = model_class(A_ )
_UpperCAmelCase : List[str] = model(**self._prepare_for_class(A_ , A_ ) )
_UpperCAmelCase : List[str] = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
_UpperCAmelCase : List[Any] = self.model_tester.num_stages
self.assertEqual(len(A_ ) , expected_num_stages + 1 )
# ResNet'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] , )
_UpperCAmelCase , _UpperCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
_UpperCAmelCase : List[Any] = ["basic", "bottleneck"]
for model_class in self.all_model_classes:
for layer_type in layers_type:
_UpperCAmelCase : Union[str, Any] = layer_type
_UpperCAmelCase : List[str] = True
check_hidden_states_output(A_ , A_ , A_ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_UpperCAmelCase : Union[str, Any] = True
check_hidden_states_output(A_ , A_ , A_ )
def _UpperCAmelCase ( self ):
'''simple docstring'''
_UpperCAmelCase : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*A_ )
@slow
def _UpperCAmelCase ( self ):
'''simple docstring'''
for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase : List[str] = TFResNetModel.from_pretrained(A_ )
self.assertIsNotNone(A_ )
def __SCREAMING_SNAKE_CASE ( ) -> Optional[int]:
_UpperCAmelCase : int = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_tf
@require_vision
class a ( unittest.TestCase ):
@cached_property
def _UpperCAmelCase ( self ):
'''simple docstring'''
return (
AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def _UpperCAmelCase ( self ):
'''simple docstring'''
_UpperCAmelCase : Any = TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
_UpperCAmelCase : Union[str, Any] = self.default_image_processor
_UpperCAmelCase : Dict = prepare_img()
_UpperCAmelCase : List[str] = image_processor(images=A_ , return_tensors="tf" )
# forward pass
_UpperCAmelCase : Optional[Any] = model(**A_ )
# verify the logits
_UpperCAmelCase : Any = tf.TensorShape((1, 1000) )
self.assertEqual(outputs.logits.shape , A_ )
_UpperCAmelCase : Union[str, Any] = tf.constant([-11.10_69, -9.78_77, -8.37_77] )
self.assertTrue(np.allclose(outputs.logits[0, :3].numpy() , A_ , atol=1e-4 ) )
| 189 |
from typing import Any
class a :
def __init__( self , A_ ):
'''simple docstring'''
_UpperCAmelCase : List[Any] = data
_UpperCAmelCase : Any = None
class a :
def __init__( self ):
'''simple docstring'''
_UpperCAmelCase : Union[str, Any] = None
def _UpperCAmelCase ( self ):
'''simple docstring'''
_UpperCAmelCase : str = self.head
while temp is not None:
print(temp.data , end=" " )
_UpperCAmelCase : str = temp.next
print()
def _UpperCAmelCase ( self , A_ ):
'''simple docstring'''
_UpperCAmelCase : Optional[int] = Node(A_ )
_UpperCAmelCase : Tuple = self.head
_UpperCAmelCase : Tuple = new_node
def _UpperCAmelCase ( self , A_ , A_ ):
'''simple docstring'''
if node_data_a == node_data_a:
return
else:
_UpperCAmelCase : int = self.head
while node_a is not None and node_a.data != node_data_a:
_UpperCAmelCase : Tuple = node_a.next
_UpperCAmelCase : Dict = self.head
while node_a is not None and node_a.data != node_data_a:
_UpperCAmelCase : List[Any] = node_a.next
if node_a is None or node_a is None:
return
_UpperCAmelCase , _UpperCAmelCase : Optional[int] = node_a.data, node_a.data
if __name__ == "__main__":
SCREAMING_SNAKE_CASE_ = LinkedList()
for i in range(5, 0, -1):
ll.push(i)
ll.print_list()
ll.swap_nodes(1, 4)
print('After swapping')
ll.print_list()
| 189 | 1 |
'''simple docstring'''
from torch import nn
def snake_case_ (_a : List[Any] ):
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise ValueError(F"Unsupported activation function: {act_fn}" )
| 34 | '''simple docstring'''
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class __A ( UpperCamelCase__ ):
a__ : Optional[Any] = DistilBertTokenizer
a__ : Any = DistilBertTokenizerFast
a__ : str = True
@slow
def _lowercase (self : int ):
UpperCAmelCase_ = DistilBertTokenizer.from_pretrained("distilbert-base-uncased" )
UpperCAmelCase_ = tokenizer.encode("sequence builders" , add_special_tokens=__a )
UpperCAmelCase_ = tokenizer.encode("multi-sequence build" , add_special_tokens=__a )
UpperCAmelCase_ = tokenizer.build_inputs_with_special_tokens(__a )
UpperCAmelCase_ = tokenizer.build_inputs_with_special_tokens(__a , __a )
assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
tokenizer.sep_token_id
]
| 1 | 0 |
"""simple docstring"""
from __future__ import annotations
import math
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
if len(_lowerCamelCase ) != 2 or len(a[0] ) != 2 or len(_lowerCamelCase ) != 2 or len(b[0] ) != 2:
raise Exception('Matrices are not 2x2' )
lowerCamelCase__ : Optional[int] = [
[a[0][0] * b[0][0] + a[0][1] * b[1][0], a[0][0] * b[0][1] + a[0][1] * b[1][1]],
[a[1][0] * b[0][0] + a[1][1] * b[1][0], a[1][0] * b[0][1] + a[1][1] * b[1][1]],
]
return new_matrix
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
return [
[matrix_a[row][col] + matrix_b[row][col] for col in range(len(matrix_a[row] ) )]
for row in range(len(_lowerCamelCase ) )
]
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
return [
[matrix_a[row][col] - matrix_b[row][col] for col in range(len(matrix_a[row] ) )]
for row in range(len(_lowerCamelCase ) )
]
def lowerCamelCase_ ( _lowerCamelCase ):
if len(_lowerCamelCase ) % 2 != 0 or len(a[0] ) % 2 != 0:
raise Exception('Odd matrices are not supported!' )
lowerCamelCase__ : Union[str, Any] = len(_lowerCamelCase )
lowerCamelCase__ : Any = matrix_length // 2
lowerCamelCase__ : Tuple = [[a[i][j] for j in range(_lowerCamelCase , _lowerCamelCase )] for i in range(_lowerCamelCase )]
lowerCamelCase__ : str = [
[a[i][j] for j in range(_lowerCamelCase , _lowerCamelCase )] for i in range(_lowerCamelCase , _lowerCamelCase )
]
lowerCamelCase__ : Dict = [[a[i][j] for j in range(_lowerCamelCase )] for i in range(_lowerCamelCase )]
lowerCamelCase__ : List[str] = [[a[i][j] for j in range(_lowerCamelCase )] for i in range(_lowerCamelCase , _lowerCamelCase )]
return top_left, top_right, bot_left, bot_right
def lowerCamelCase_ ( _lowerCamelCase ):
return len(_lowerCamelCase ), len(matrix[0] )
def lowerCamelCase_ ( _lowerCamelCase ):
print('\n'.join(str(_lowerCamelCase ) for line in matrix ) )
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
if matrix_dimensions(_lowerCamelCase ) == (2, 2):
return default_matrix_multiplication(_lowerCamelCase , _lowerCamelCase )
lowerCamelCase__ : Tuple = split_matrix(_lowerCamelCase )
lowerCamelCase__ : int = split_matrix(_lowerCamelCase )
lowerCamelCase__ : Dict = actual_strassen(_lowerCamelCase , matrix_subtraction(_lowerCamelCase , _lowerCamelCase ) )
lowerCamelCase__ : Tuple = actual_strassen(matrix_addition(_lowerCamelCase , _lowerCamelCase ) , _lowerCamelCase )
lowerCamelCase__ : Optional[int] = actual_strassen(matrix_addition(_lowerCamelCase , _lowerCamelCase ) , _lowerCamelCase )
lowerCamelCase__ : int = actual_strassen(_lowerCamelCase , matrix_subtraction(_lowerCamelCase , _lowerCamelCase ) )
lowerCamelCase__ : Any = actual_strassen(matrix_addition(_lowerCamelCase , _lowerCamelCase ) , matrix_addition(_lowerCamelCase , _lowerCamelCase ) )
lowerCamelCase__ : Any = actual_strassen(matrix_subtraction(_lowerCamelCase , _lowerCamelCase ) , matrix_addition(_lowerCamelCase , _lowerCamelCase ) )
lowerCamelCase__ : List[str] = actual_strassen(matrix_subtraction(_lowerCamelCase , _lowerCamelCase ) , matrix_addition(_lowerCamelCase , _lowerCamelCase ) )
lowerCamelCase__ : int = matrix_addition(matrix_subtraction(matrix_addition(_lowerCamelCase , _lowerCamelCase ) , _lowerCamelCase ) , _lowerCamelCase )
lowerCamelCase__ : Dict = matrix_addition(_lowerCamelCase , _lowerCamelCase )
lowerCamelCase__ : List[Any] = matrix_addition(_lowerCamelCase , _lowerCamelCase )
lowerCamelCase__ : Dict = matrix_subtraction(matrix_subtraction(matrix_addition(_lowerCamelCase , _lowerCamelCase ) , _lowerCamelCase ) , _lowerCamelCase )
# construct the new matrix from our 4 quadrants
lowerCamelCase__ : Optional[int] = []
for i in range(len(_lowerCamelCase ) ):
new_matrix.append(top_left[i] + top_right[i] )
for i in range(len(_lowerCamelCase ) ):
new_matrix.append(bot_left[i] + bot_right[i] )
return new_matrix
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
if matrix_dimensions(_lowerCamelCase )[1] != matrix_dimensions(_lowerCamelCase )[0]:
lowerCamelCase__ : List[Any] = (
'Unable to multiply these matrices, please check the dimensions.\n'
f'''Matrix A: {matrixa}\n'''
f'''Matrix B: {matrixa}'''
)
raise Exception(_lowerCamelCase )
lowerCamelCase__ : Optional[int] = matrix_dimensions(_lowerCamelCase )
lowerCamelCase__ : Dict = matrix_dimensions(_lowerCamelCase )
if dimensiona[0] == dimensiona[1] and dimensiona[0] == dimensiona[1]:
return [matrixa, matrixa]
lowerCamelCase__ : int = max(*_lowerCamelCase , *_lowerCamelCase )
lowerCamelCase__ : Optional[Any] = int(math.pow(2 , math.ceil(math.loga(_lowerCamelCase ) ) ) )
lowerCamelCase__ : Union[str, Any] = matrixa
lowerCamelCase__ : Union[str, Any] = matrixa
# Adding zeros to the matrices so that the arrays dimensions are the same and also
# power of 2
for i in range(0 , _lowerCamelCase ):
if i < dimensiona[0]:
for _ in range(dimensiona[1] , _lowerCamelCase ):
new_matrixa[i].append(0 )
else:
new_matrixa.append([0] * maxim )
if i < dimensiona[0]:
for _ in range(dimensiona[1] , _lowerCamelCase ):
new_matrixa[i].append(0 )
else:
new_matrixa.append([0] * maxim )
lowerCamelCase__ : List[str] = actual_strassen(_lowerCamelCase , _lowerCamelCase )
# Removing the additional zeros
for i in range(0 , _lowerCamelCase ):
if i < dimensiona[0]:
for _ in range(dimensiona[1] , _lowerCamelCase ):
final_matrix[i].pop()
else:
final_matrix.pop()
return final_matrix
if __name__ == "__main__":
A_ : Dict = [
[2, 3, 4, 5],
[6, 4, 3, 1],
[2, 3, 6, 7],
[3, 1, 2, 4],
[2, 3, 4, 5],
[6, 4, 3, 1],
[2, 3, 6, 7],
[3, 1, 2, 4],
[2, 3, 4, 5],
[6, 2, 3, 1],
]
A_ : Union[str, Any] = [[0, 2, 1, 1], [16, 2, 3, 3], [2, 2, 7, 7], [13, 11, 22, 4]]
print(strassen(matrixa, matrixa))
| 362 |
"""simple docstring"""
from __future__ import annotations
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
lowerCamelCase__ : list[list[int]] = []
lowerCamelCase__ : list[int] = []
lowerCamelCase__ : List[str] = 0
lowerCamelCase__ : List[Any] = sum(_lowerCamelCase )
create_state_space_tree(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
return result
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , ):
if sum(_lowerCamelCase ) > max_sum or (remaining_nums_sum + sum(_lowerCamelCase )) < max_sum:
return
if sum(_lowerCamelCase ) == max_sum:
result.append(_lowerCamelCase )
return
for index in range(_lowerCamelCase , len(_lowerCamelCase ) ):
create_state_space_tree(
_lowerCamelCase , _lowerCamelCase , index + 1 , [*path, nums[index]] , _lowerCamelCase , remaining_nums_sum - nums[index] , )
A_ : Optional[Any] = [3, 34, 4, 12, 5, 2]
A_ : List[str] = 9
A_ : List[Any] = generate_sum_of_subsets_soln(nums, max_sum)
print(*result)
| 316 | 0 |
'''simple docstring'''
import numpy
class A_ :
def __init__( self : List[str] , snake_case_ : numpy.ndarray , snake_case_ : numpy.ndarray ):
_UpperCAmelCase = input_array
# Random initial weights are assigned where first argument is the
# number of nodes in previous layer and second argument is the
# number of nodes in the next layer.
# Random initial weights are assigned.
# self.input_array.shape[1] is used to represent number of nodes in input layer.
# First hidden layer consists of 4 nodes.
_UpperCAmelCase = numpy.random.rand(
self.input_array.shape[1] , 4 )
# Random initial values for the first hidden layer.
# First hidden layer has 4 nodes.
# Second hidden layer has 3 nodes.
_UpperCAmelCase = numpy.random.rand(
4 , 3 )
# Random initial values for the second hidden layer.
# Second hidden layer has 3 nodes.
# Output layer has 1 node.
_UpperCAmelCase = numpy.random.rand(3 , 1 )
# Real output values provided.
_UpperCAmelCase = output_array
# Predicted output values by the neural network.
# Predicted_output array initially consists of zeroes.
_UpperCAmelCase = numpy.zeros(output_array.shape )
def lowercase ( self : List[str] ):
_UpperCAmelCase = sigmoid(
numpy.dot(self.input_array , self.input_layer_and_first_hidden_layer_weights ) )
# layer_between_first_hidden_layer_and_second_hidden_layer is the layer
# connecting the first hidden set of nodes with the second hidden set of nodes.
_UpperCAmelCase = sigmoid(
numpy.dot(
self.layer_between_input_and_first_hidden_layer , self.first_hidden_layer_and_second_hidden_layer_weights , ) )
# layer_between_second_hidden_layer_and_output is the layer connecting
# second hidden layer with the output node.
_UpperCAmelCase = sigmoid(
numpy.dot(
self.layer_between_first_hidden_layer_and_second_hidden_layer , self.second_hidden_layer_and_output_layer_weights , ) )
return self.layer_between_second_hidden_layer_and_output
def lowercase ( self : List[Any] ):
_UpperCAmelCase = numpy.dot(
self.layer_between_first_hidden_layer_and_second_hidden_layer.T , 2
* (self.output_array - self.predicted_output)
* sigmoid_derivative(self.predicted_output ) , )
_UpperCAmelCase = numpy.dot(
self.layer_between_input_and_first_hidden_layer.T , numpy.dot(
2
* (self.output_array - self.predicted_output)
* sigmoid_derivative(self.predicted_output ) , self.second_hidden_layer_and_output_layer_weights.T , )
* sigmoid_derivative(
self.layer_between_first_hidden_layer_and_second_hidden_layer ) , )
_UpperCAmelCase = numpy.dot(
self.input_array.T , numpy.dot(
numpy.dot(
2
* (self.output_array - self.predicted_output)
* sigmoid_derivative(self.predicted_output ) , self.second_hidden_layer_and_output_layer_weights.T , )
* sigmoid_derivative(
self.layer_between_first_hidden_layer_and_second_hidden_layer ) , self.first_hidden_layer_and_second_hidden_layer_weights.T , )
* sigmoid_derivative(self.layer_between_input_and_first_hidden_layer ) , )
self.input_layer_and_first_hidden_layer_weights += (
updated_input_layer_and_first_hidden_layer_weights
)
self.first_hidden_layer_and_second_hidden_layer_weights += (
updated_first_hidden_layer_and_second_hidden_layer_weights
)
self.second_hidden_layer_and_output_layer_weights += (
updated_second_hidden_layer_and_output_layer_weights
)
def lowercase ( self : Tuple , snake_case_ : numpy.ndarray , snake_case_ : int , snake_case_ : bool ):
for iteration in range(1 , iterations + 1 ):
_UpperCAmelCase = self.feedforward()
self.back_propagation()
if give_loss:
_UpperCAmelCase = numpy.mean(numpy.square(output - self.feedforward() ) )
print(f'Iteration {iteration} Loss: {loss}' )
def lowercase ( self : Dict , snake_case_ : numpy.ndarray ):
_UpperCAmelCase = input_arr
_UpperCAmelCase = sigmoid(
numpy.dot(self.array , self.input_layer_and_first_hidden_layer_weights ) )
_UpperCAmelCase = sigmoid(
numpy.dot(
self.layer_between_input_and_first_hidden_layer , self.first_hidden_layer_and_second_hidden_layer_weights , ) )
_UpperCAmelCase = sigmoid(
numpy.dot(
self.layer_between_first_hidden_layer_and_second_hidden_layer , self.second_hidden_layer_and_output_layer_weights , ) )
return int(self.layer_between_second_hidden_layer_and_output > 0.6 )
def UpperCAmelCase_ ( __lowercase : List[Any] ) -> str:
'''simple docstring'''
return 1 / (1 + numpy.exp(-value ))
def UpperCAmelCase_ ( __lowercase : Optional[Any] ) -> Tuple:
'''simple docstring'''
return (value) * (1 - (value))
def UpperCAmelCase_ ( ) -> Optional[Any]:
'''simple docstring'''
_UpperCAmelCase = numpy.array(
(
[0, 0, 0],
[0, 0, 1],
[0, 1, 0],
[0, 1, 1],
[1, 0, 0],
[1, 0, 1],
[1, 1, 0],
[1, 1, 1],
) , dtype=numpy.floataa , )
# True output values for the given input values.
_UpperCAmelCase = numpy.array(([0], [1], [1], [0], [1], [0], [0], [1]) , dtype=numpy.floataa )
# Calling neural network class.
_UpperCAmelCase = TwoHiddenLayerNeuralNetwork(
input_array=_A , output_array=_A )
# Calling training function.
# Set give_loss to True if you want to see loss in every iteration.
neural_network.train(output=_A , iterations=10 , give_loss=_A )
return neural_network.predict(numpy.array(([1, 1, 1]) , dtype=numpy.floataa ) )
if __name__ == "__main__":
example()
| 22 |
from ...configuration_utils import PretrainedConfig
_SCREAMING_SNAKE_CASE : Optional[Any] = {
'''google/tapas-base-finetuned-sqa''': (
'''https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json'''
),
'''google/tapas-base-finetuned-wtq''': (
'''https://huggingface.co/google/tapas-base-finetuned-wtq/resolve/main/config.json'''
),
'''google/tapas-base-finetuned-wikisql-supervised''': (
'''https://huggingface.co/google/tapas-base-finetuned-wikisql-supervised/resolve/main/config.json'''
),
'''google/tapas-base-finetuned-tabfact''': (
'''https://huggingface.co/google/tapas-base-finetuned-tabfact/resolve/main/config.json'''
),
}
class UpperCAmelCase__ ( A__ ):
"""simple docstring"""
a = "tapas"
def __init__( self : int , __lowerCamelCase : Optional[Any]=3_0522 , __lowerCamelCase : Tuple=768 , __lowerCamelCase : int=12 , __lowerCamelCase : Any=12 , __lowerCamelCase : Union[str, Any]=3072 , __lowerCamelCase : Optional[int]="gelu" , __lowerCamelCase : Optional[int]=0.1 , __lowerCamelCase : Dict=0.1 , __lowerCamelCase : str=1024 , __lowerCamelCase : Union[str, Any]=[3, 256, 256, 2, 256, 256, 10] , __lowerCamelCase : Optional[int]=0.02 , __lowerCamelCase : List[str]=1e-12 , __lowerCamelCase : Optional[Any]=0 , __lowerCamelCase : Optional[Any]=10.0 , __lowerCamelCase : Optional[Any]=0 , __lowerCamelCase : str=1.0 , __lowerCamelCase : Union[str, Any]=None , __lowerCamelCase : List[Any]=1.0 , __lowerCamelCase : Optional[Any]=False , __lowerCamelCase : Union[str, Any]=None , __lowerCamelCase : int=1.0 , __lowerCamelCase : Dict=1.0 , __lowerCamelCase : Optional[int]=False , __lowerCamelCase : int=False , __lowerCamelCase : List[str]="ratio" , __lowerCamelCase : Tuple=None , __lowerCamelCase : List[Any]=None , __lowerCamelCase : List[Any]=64 , __lowerCamelCase : Any=32 , __lowerCamelCase : Tuple=False , __lowerCamelCase : Union[str, Any]=True , __lowerCamelCase : Optional[int]=False , __lowerCamelCase : Tuple=False , __lowerCamelCase : Tuple=True , __lowerCamelCase : Optional[Any]=False , __lowerCamelCase : Union[str, Any]=None , __lowerCamelCase : Optional[Any]=None , **__lowerCamelCase : str , ) -> str:
super().__init__(pad_token_id=__lowerCamelCase , **__lowerCamelCase )
# BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes)
SCREAMING_SNAKE_CASE__ = vocab_size
SCREAMING_SNAKE_CASE__ = hidden_size
SCREAMING_SNAKE_CASE__ = num_hidden_layers
SCREAMING_SNAKE_CASE__ = num_attention_heads
SCREAMING_SNAKE_CASE__ = hidden_act
SCREAMING_SNAKE_CASE__ = intermediate_size
SCREAMING_SNAKE_CASE__ = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ = max_position_embeddings
SCREAMING_SNAKE_CASE__ = type_vocab_sizes
SCREAMING_SNAKE_CASE__ = initializer_range
SCREAMING_SNAKE_CASE__ = layer_norm_eps
# Fine-tuning task hyperparameters
SCREAMING_SNAKE_CASE__ = positive_label_weight
SCREAMING_SNAKE_CASE__ = num_aggregation_labels
SCREAMING_SNAKE_CASE__ = aggregation_loss_weight
SCREAMING_SNAKE_CASE__ = use_answer_as_supervision
SCREAMING_SNAKE_CASE__ = answer_loss_importance
SCREAMING_SNAKE_CASE__ = use_normalized_answer_loss
SCREAMING_SNAKE_CASE__ = huber_loss_delta
SCREAMING_SNAKE_CASE__ = temperature
SCREAMING_SNAKE_CASE__ = aggregation_temperature
SCREAMING_SNAKE_CASE__ = use_gumbel_for_cells
SCREAMING_SNAKE_CASE__ = use_gumbel_for_aggregation
SCREAMING_SNAKE_CASE__ = average_approximation_function
SCREAMING_SNAKE_CASE__ = cell_selection_preference
SCREAMING_SNAKE_CASE__ = answer_loss_cutoff
SCREAMING_SNAKE_CASE__ = max_num_rows
SCREAMING_SNAKE_CASE__ = max_num_columns
SCREAMING_SNAKE_CASE__ = average_logits_per_cell
SCREAMING_SNAKE_CASE__ = select_one_column
SCREAMING_SNAKE_CASE__ = allow_empty_column_selection
SCREAMING_SNAKE_CASE__ = init_cell_selection_weights_to_zero
SCREAMING_SNAKE_CASE__ = reset_position_index_per_cell
SCREAMING_SNAKE_CASE__ = disable_per_token_loss
# Aggregation hyperparameters
SCREAMING_SNAKE_CASE__ = aggregation_labels
SCREAMING_SNAKE_CASE__ = no_aggregation_label_index
if isinstance(self.aggregation_labels , __lowerCamelCase ):
SCREAMING_SNAKE_CASE__ = {int(__lowerCamelCase ): v for k, v in aggregation_labels.items()}
| 314 | 0 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_camembert import CamembertTokenizer
else:
lowerCAmelCase : str = None
lowerCAmelCase : Optional[Any] = logging.get_logger(__name__)
lowerCAmelCase : Optional[int] = {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}
lowerCAmelCase : Union[str, Any] = {
"vocab_file": {
"camembert-base": "https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model",
},
"tokenizer_file": {
"camembert-base": "https://huggingface.co/camembert-base/resolve/main/tokenizer.json",
},
}
lowerCAmelCase : List[str] = {
"camembert-base": 512,
}
lowerCAmelCase : int = "▁"
class __magic_name__ ( a_ ):
'''simple docstring'''
__UpperCamelCase = VOCAB_FILES_NAMES
__UpperCamelCase = PRETRAINED_VOCAB_FILES_MAP
__UpperCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCamelCase = ["input_ids", "attention_mask"]
__UpperCamelCase = CamembertTokenizer
def __init__( self , _a=None , _a=None , _a="<s>" , _a="</s>" , _a="</s>" , _a="<s>" , _a="<unk>" , _a="<pad>" , _a="<mask>" , _a=["<s>NOTUSED", "</s>NOTUSED"] , **_a , ):
"""simple docstring"""
lowerCamelCase = AddedToken(_a , lstrip=_a , rstrip=_a ) if isinstance(_a , _a ) else mask_token
super().__init__(
_a , tokenizer_file=_a , bos_token=_a , eos_token=_a , sep_token=_a , cls_token=_a , unk_token=_a , pad_token=_a , mask_token=_a , additional_special_tokens=_a , **_a , )
lowerCamelCase = vocab_file
lowerCamelCase = False if not self.vocab_file else True
def _lowerCAmelCase ( self , _a , _a = None ):
"""simple docstring"""
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
lowerCamelCase = [self.cls_token_id]
lowerCamelCase = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def _lowerCAmelCase ( self , _a , _a = None ):
"""simple docstring"""
lowerCamelCase = [self.sep_token_id]
lowerCamelCase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def _lowerCAmelCase ( self , _a , _a = None ):
"""simple docstring"""
if not self.can_save_slow_tokenizer:
raise ValueError(
"""Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
"""tokenizer.""" )
if not os.path.isdir(_a ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
return
lowerCamelCase = os.path.join(
_a , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_a ):
copyfile(self.vocab_file , _a )
return (out_vocab_file,)
| 350 |
"""simple docstring"""
import json
import os
import torch
from diffusers import UNetaDModel
os.makedirs("""hub/hopper-medium-v2/unet/hor32""", exist_ok=True)
os.makedirs("""hub/hopper-medium-v2/unet/hor128""", exist_ok=True)
os.makedirs("""hub/hopper-medium-v2/value_function""", exist_ok=True)
def a__ ( snake_case__ ) -> Tuple:
if hor == 1_28:
lowerCamelCase = ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
lowerCamelCase = (32, 1_28, 2_56)
lowerCamelCase = ("""UpResnetBlock1D""", """UpResnetBlock1D""")
elif hor == 32:
lowerCamelCase = ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
lowerCamelCase = (32, 64, 1_28, 2_56)
lowerCamelCase = ("""UpResnetBlock1D""", """UpResnetBlock1D""", """UpResnetBlock1D""")
lowerCamelCase = torch.load(F'/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch' )
lowerCamelCase = model.state_dict()
lowerCamelCase = {
"""down_block_types""": down_block_types,
"""block_out_channels""": block_out_channels,
"""up_block_types""": up_block_types,
"""layers_per_block""": 1,
"""use_timestep_embedding""": True,
"""out_block_type""": """OutConv1DBlock""",
"""norm_num_groups""": 8,
"""downsample_each_block""": False,
"""in_channels""": 14,
"""out_channels""": 14,
"""extra_in_channels""": 0,
"""time_embedding_type""": """positional""",
"""flip_sin_to_cos""": False,
"""freq_shift""": 1,
"""sample_size""": 6_55_36,
"""mid_block_type""": """MidResTemporalBlock1D""",
"""act_fn""": """mish""",
}
lowerCamelCase = UNetaDModel(**snake_case__ )
print(F'length of state dict: {len(state_dict.keys() )}' )
print(F'length of value function dict: {len(hf_value_function.state_dict().keys() )}' )
lowerCamelCase = dict(zip(model.state_dict().keys() , hf_value_function.state_dict().keys() ) )
for k, v in mapping.items():
lowerCamelCase = state_dict.pop(snake_case__ )
hf_value_function.load_state_dict(snake_case__ )
torch.save(hf_value_function.state_dict() , F'hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin' )
with open(F'hub/hopper-medium-v2/unet/hor{hor}/config.json' , """w""" ) as f:
json.dump(snake_case__ , snake_case__ )
def a__ ( ) -> Optional[int]:
lowerCamelCase = {
"""in_channels""": 14,
"""down_block_types""": ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D"""),
"""up_block_types""": (),
"""out_block_type""": """ValueFunction""",
"""mid_block_type""": """ValueFunctionMidBlock1D""",
"""block_out_channels""": (32, 64, 1_28, 2_56),
"""layers_per_block""": 1,
"""downsample_each_block""": True,
"""sample_size""": 6_55_36,
"""out_channels""": 14,
"""extra_in_channels""": 0,
"""time_embedding_type""": """positional""",
"""use_timestep_embedding""": True,
"""flip_sin_to_cos""": False,
"""freq_shift""": 1,
"""norm_num_groups""": 8,
"""act_fn""": """mish""",
}
lowerCamelCase = torch.load("""/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch""" )
lowerCamelCase = model
lowerCamelCase = UNetaDModel(**snake_case__ )
print(F'length of state dict: {len(state_dict.keys() )}' )
print(F'length of value function dict: {len(hf_value_function.state_dict().keys() )}' )
lowerCamelCase = dict(zip(state_dict.keys() , hf_value_function.state_dict().keys() ) )
for k, v in mapping.items():
lowerCamelCase = state_dict.pop(snake_case__ )
hf_value_function.load_state_dict(snake_case__ )
torch.save(hf_value_function.state_dict() , """hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin""" )
with open("""hub/hopper-medium-v2/value_function/config.json""" , """w""" ) as f:
json.dump(snake_case__ , snake_case__ )
if __name__ == "__main__":
unet(32)
# unet(128)
value_function()
| 168 | 0 |
'''simple docstring'''
def __lowerCamelCase ( A__ ) -> bool:
"""simple docstring"""
if not isinstance(A__ , A__ ):
UpperCamelCase = F"""Input value of [number={number}] must be an integer"""
raise TypeError(A__ )
if number < 0:
return False
UpperCamelCase = number * number
while number > 0:
if number % 10 != number_square % 10:
return False
number //= 10
number_square //= 10
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
| 28 |
'''simple docstring'''
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
_lowerCamelCase : List[str] = 5_0000
_lowerCamelCase : Optional[int] = 5000
_lowerCamelCase ,_lowerCamelCase : int = os.path.split(__file__)
_lowerCamelCase : str = os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))
@get_duration
def __lowerCamelCase ( A__ , A__ ) -> Any:
"""simple docstring"""
for i in range(A__ ):
UpperCamelCase = dataset[i]
@get_duration
def __lowerCamelCase ( A__ , A__ , A__ ) -> int:
"""simple docstring"""
for i in range(0 , len(A__ ) , A__ ):
UpperCamelCase = dataset[i : i + batch_size]
@get_duration
def __lowerCamelCase ( A__ , A__ , A__ ) -> List[Any]:
"""simple docstring"""
with dataset.formatted_as(type=A__ ):
for i in range(A__ ):
UpperCamelCase = dataset[i]
@get_duration
def __lowerCamelCase ( A__ , A__ , A__ , A__ ) -> int:
"""simple docstring"""
with dataset.formatted_as(type=A__ ):
for i in range(0 , A__ , A__ ):
UpperCamelCase = dataset[i : i + batch_size]
def __lowerCamelCase ( ) -> List[str]:
"""simple docstring"""
UpperCamelCase = {'num examples': SPEED_TEST_N_EXAMPLES}
UpperCamelCase = [
(read, {'length': SMALL_TEST}),
(read, {'length': SPEED_TEST_N_EXAMPLES}),
(read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
(read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 100}),
(read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1_000}),
(read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
(read_formatted, {'type': 'pandas', 'length': SMALL_TEST}),
(read_formatted, {'type': 'torch', 'length': SMALL_TEST}),
(read_formatted, {'type': 'tensorflow', 'length': SMALL_TEST}),
(read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
(read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 1_000}),
]
UpperCamelCase = [
(read, {'length': SMALL_TEST}),
(read, {'length': SPEED_TEST_N_EXAMPLES}),
(read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
(read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 100}),
(read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1_000}),
(read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
(read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
(read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 1_000}),
]
with tempfile.TemporaryDirectory() as tmp_dir:
print('generating dataset' )
UpperCamelCase = datasets.Features(
{'list': datasets.Sequence(datasets.Value('float32' ) ), 'numbers': datasets.Value('float32' )} )
UpperCamelCase = generate_example_dataset(
os.path.join(A__ , 'dataset.arrow' ) , A__ , num_examples=A__ , seq_shapes={'list': (100,)} , )
print('first set of iterations' )
for func, kwargs in functions:
print(func.__name__ , str(A__ ) )
UpperCamelCase = func(A__ , **A__ )
print('shuffling dataset' )
UpperCamelCase = dataset.shuffle()
print('Second set of iterations (after shuffling' )
for func, kwargs in functions_shuffled:
print('shuffled ' , func.__name__ , str(A__ ) )
UpperCamelCase = func(
A__ , **A__ )
with open(A__ , 'wb' ) as f:
f.write(json.dumps(A__ ).encode('utf-8' ) )
if __name__ == "__main__": # useful to run the profiler
benchmark_iterating()
| 28 | 1 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = torch.device('cpu')
def __UpperCAmelCase ( ) -> Any:
lowercase__ : List[str] = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
lowercase__ : Dict = Image.open(requests.get(snake_case_ , stream=snake_case_ ).raw )
return im
def __UpperCAmelCase ( __lowerCamelCase ) -> List[Any]:
if swiftformer_name == "swiftformer_xs":
return torch.tensor([-2.1703E00, 2.1107E00, -2.0811E00, 8.8685E-01, 2.4360E-01] )
elif swiftformer_name == "swiftformer_s":
return torch.tensor([3.9636E-01, 2.3478E-01, -1.6963E00, -1.7381E00, -8.6337E-01] )
elif swiftformer_name == "swiftformer_l1":
return torch.tensor([-4.2768E-01, -4.7429E-01, -1.0897E00, -1.0248E00, 3.5523E-02] )
elif swiftformer_name == "swiftformer_l3":
return torch.tensor([-2.5330E-01, 2.4211E-01, -6.0185E-01, -8.2789E-01, -6.0446E-02] )
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> int:
lowercase__ : Optional[Any] = dct.pop(snake_case_ )
lowercase__ : Tuple = val
def __UpperCAmelCase ( __lowerCamelCase ) -> Optional[int]:
lowercase__ : Optional[Any] = []
for k in state_dict.keys():
lowercase__ : Dict = k
if ".pwconv" in k:
lowercase__ : int = k_new.replace('''.pwconv''' , '''.point_wise_conv''' )
if ".dwconv" in k:
lowercase__ : List[Any] = k_new.replace('''.dwconv''' , '''.depth_wise_conv''' )
if ".Proj." in k:
lowercase__ : List[str] = k_new.replace('''.Proj.''' , '''.proj.''' )
if "patch_embed" in k_new:
lowercase__ : int = k_new.replace('''patch_embed''' , '''swiftformer.patch_embed.patch_embedding''' )
if "network" in k_new:
lowercase__ : Any = k_new.split('''.''' )
if ls[2].isdigit():
lowercase__ : Dict = '''swiftformer.encoder.network.''' + ls[1] + '''.blocks.''' + ls[2] + '''.''' + '''.'''.join(ls[3:] )
else:
lowercase__ : Optional[int] = k_new.replace('''network''' , '''swiftformer.encoder.network''' )
rename_keys.append((k, k_new) )
return rename_keys
@torch.no_grad()
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> Optional[Any]:
lowercase__ : Dict = SwiftFormerConfig()
# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
lowercase__ : List[str] = 10_00
lowercase__ : Tuple = '''huggingface/label-files'''
lowercase__ : Dict = '''imagenet-1k-id2label.json'''
lowercase__ : Dict = json.load(open(hf_hub_download(snake_case_ , snake_case_ , repo_type='''dataset''' ) , '''r''' ) )
lowercase__ : Optional[Any] = {int(snake_case_ ): v for k, v in idalabel.items()}
lowercase__ : Dict = idalabel
lowercase__ : Optional[Any] = {v: k for k, v in idalabel.items()}
# size of the architecture
if swiftformer_name == "swiftformer_xs":
lowercase__ : int = [3, 3, 6, 4]
lowercase__ : List[str] = [48, 56, 1_12, 2_20]
elif swiftformer_name == "swiftformer_s":
lowercase__ : List[str] = [3, 3, 9, 6]
lowercase__ : Dict = [48, 64, 1_68, 2_24]
elif swiftformer_name == "swiftformer_l1":
lowercase__ : Union[str, Any] = [4, 3, 10, 5]
lowercase__ : str = [48, 96, 1_92, 3_84]
elif swiftformer_name == "swiftformer_l3":
lowercase__ : Optional[int] = [4, 4, 12, 6]
lowercase__ : Any = [64, 1_28, 3_20, 5_12]
# load state_dict of original model, remove and rename some keys
if original_ckpt:
if original_ckpt.startswith('''https''' ):
lowercase__ : List[Any] = torch.hub.load_state_dict_from_url(snake_case_ , map_location='''cpu''' , check_hash=snake_case_ )
else:
lowercase__ : Optional[Any] = torch.load(snake_case_ , map_location='''cpu''' )
lowercase__ : int = checkpoint
lowercase__ : List[str] = create_rename_keys(snake_case_ )
for rename_key_src, rename_key_dest in rename_keys:
rename_key(snake_case_ , snake_case_ , snake_case_ )
# load HuggingFace model
lowercase__ : Any = SwiftFormerForImageClassification(snake_case_ ).eval()
hf_model.load_state_dict(snake_case_ )
# prepare test inputs
lowercase__ : Union[str, Any] = prepare_img()
lowercase__ : List[Any] = ViTImageProcessor.from_pretrained('''preprocessor_config''' )
lowercase__ : str = processor(images=snake_case_ , return_tensors='''pt''' )
# compare outputs from both models
lowercase__ : Optional[int] = get_expected_output(snake_case_ )
lowercase__ : Dict = hf_model(inputs['''pixel_values'''] ).logits
assert hf_logits.shape == torch.Size([1, 10_00] )
assert torch.allclose(hf_logits[0, 0:5] , snake_case_ , atol=1E-3 )
Path(snake_case_ ).mkdir(exist_ok=snake_case_ )
print(f"""Saving model {swiftformer_name} to {pytorch_dump_folder_path}""" )
hf_model.save_pretrained(snake_case_ )
if __name__ == "__main__":
lowerCAmelCase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--swiftformer_name',
default='swiftformer_xs',
choices=['swiftformer_xs', 'swiftformer_s', 'swiftformer_l1', 'swiftformer_l3'],
type=str,
help='Name of the SwiftFormer model you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path',
default='./converted_outputs/',
type=str,
help='Path to the output PyTorch model directory.',
)
parser.add_argument('--original_ckpt', default=None, type=str, help='Path to the original model checkpoint.')
lowerCAmelCase_ = parser.parse_args()
convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)
| 368 |
"""simple docstring"""
import copy
from typing import Dict, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig
lowerCAmelCase_ = {
'facebook/maskformer-swin-base-ade': (
'https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json'
)
# See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}
lowerCAmelCase_ = logging.get_logger(__name__)
class __A ( A_ ):
'''simple docstring'''
lowerCAmelCase : Optional[int] = "maskformer"
lowerCAmelCase : Any = {"hidden_size": "mask_feature_size"}
lowerCAmelCase : Optional[int] = ["resnet", "swin"]
lowerCAmelCase : str = ["detr"]
def __init__( self : int ,_snake_case : int = 256 ,_snake_case : int = 256 ,_snake_case : float = 0.1 ,_snake_case : bool = False ,_snake_case : Optional[Dict] = None ,_snake_case : Optional[Dict] = None ,_snake_case : float = 0.02 ,_snake_case : float = 1.0 ,_snake_case : float = 1.0 ,_snake_case : float = 1.0 ,_snake_case : float = 20.0 ,_snake_case : Optional[bool] = None ,**_snake_case : Optional[Any] ,) -> Dict:
"""simple docstring"""
if backbone_config is None:
# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
lowercase__ : Any = SwinConfig(
image_size=384 ,in_channels=3 ,patch_size=4 ,embed_dim=128 ,depths=[2, 2, 18, 2] ,num_heads=[4, 8, 16, 32] ,window_size=12 ,drop_path_rate=0.3 ,out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4'''] ,)
if isinstance(_snake_case ,_snake_case ):
lowercase__ : List[str] = backbone_config.pop('''model_type''' )
lowercase__ : List[Any] = CONFIG_MAPPING[backbone_model_type]
lowercase__ : str = config_class.from_dict(_snake_case )
# verify that the backbone is supported
if backbone_config.model_type not in self.backbones_supported:
logger.warning_once(
f"""Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. """
f"""Supported model types: {",".join(self.backbones_supported )}""" )
if decoder_config is None:
# fall back to https://huggingface.co/facebook/detr-resnet-50
lowercase__ : Union[str, Any] = DetrConfig()
else:
# verify that the decoder is supported
lowercase__ : Tuple = (
decoder_config.pop('''model_type''' ) if isinstance(_snake_case ,_snake_case ) else decoder_config.model_type
)
if decoder_type not in self.decoders_supported:
raise ValueError(
f"""Transformer Decoder {decoder_type} not supported, please use one of"""
f""" {",".join(self.decoders_supported )}""" )
if isinstance(_snake_case ,_snake_case ):
lowercase__ : Optional[int] = CONFIG_MAPPING[decoder_type]
lowercase__ : Optional[Any] = config_class.from_dict(_snake_case )
lowercase__ : List[Any] = backbone_config
lowercase__ : List[Any] = decoder_config
# main feature dimension for the model
lowercase__ : List[str] = fpn_feature_size
lowercase__ : int = mask_feature_size
# initializer
lowercase__ : str = init_std
lowercase__ : str = init_xavier_std
# Hungarian matcher && loss
lowercase__ : Optional[int] = cross_entropy_weight
lowercase__ : List[Any] = dice_weight
lowercase__ : List[str] = mask_weight
lowercase__ : str = use_auxiliary_loss
lowercase__ : Optional[int] = no_object_weight
lowercase__ : Optional[Any] = output_auxiliary_logits
lowercase__ : Optional[Any] = self.decoder_config.encoder_attention_heads
lowercase__ : Optional[Any] = self.decoder_config.num_hidden_layers
super().__init__(**_snake_case )
@classmethod
def UpperCAmelCase ( cls : Any ,_snake_case : PretrainedConfig ,_snake_case : PretrainedConfig ,**_snake_case : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
return cls(
backbone_config=_snake_case ,decoder_config=_snake_case ,**_snake_case ,)
def UpperCAmelCase ( self : str ) -> Dict[str, any]:
"""simple docstring"""
lowercase__ : Optional[Any] = copy.deepcopy(self.__dict__ )
lowercase__ : int = self.backbone_config.to_dict()
lowercase__ : List[Any] = self.decoder_config.to_dict()
lowercase__ : List[str] = self.__class__.model_type
return output
| 302 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case : Any = logging.get_logger(__name__)
__snake_case : Tuple = {
"""unc-nlp/lxmert-base-uncased""": """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json""",
}
class A__(a_ ):
"""simple docstring"""
_A : Union[str, Any] = '''lxmert'''
_A : Optional[Any] = {}
def __init__( self , _lowercase=30_522 , _lowercase=768 , _lowercase=12 , _lowercase=9_500 , _lowercase=1_600 , _lowercase=400 , _lowercase=3_072 , _lowercase="gelu" , _lowercase=0.1 , _lowercase=0.1 , _lowercase=512 , _lowercase=2 , _lowercase=0.0_2 , _lowercase=1e-12 , _lowercase=9 , _lowercase=5 , _lowercase=5 , _lowercase=2_048 , _lowercase=4 , _lowercase=6.6_7 , _lowercase=True , _lowercase=True , _lowercase=True , _lowercase=True , _lowercase=True , _lowercase=True , _lowercase=True , **_lowercase , ) -> int:
a_ : List[Any] = vocab_size
a_ : Union[str, Any] = hidden_size
a_ : Optional[int] = num_attention_heads
a_ : List[str] = hidden_act
a_ : Any = intermediate_size
a_ : Union[str, Any] = hidden_dropout_prob
a_ : Optional[Any] = attention_probs_dropout_prob
a_ : List[Any] = max_position_embeddings
a_ : Optional[Any] = type_vocab_size
a_ : Optional[int] = initializer_range
a_ : List[str] = layer_norm_eps
a_ : str = num_qa_labels
a_ : int = num_object_labels
a_ : List[str] = num_attr_labels
a_ : int = l_layers
a_ : List[str] = x_layers
a_ : Any = r_layers
a_ : int = visual_feat_dim
a_ : Union[str, Any] = visual_pos_dim
a_ : List[str] = visual_loss_normalizer
a_ : Dict = task_matched
a_ : Union[str, Any] = task_mask_lm
a_ : Any = task_obj_predict
a_ : int = task_qa
a_ : Dict = visual_obj_loss
a_ : Union[str, Any] = visual_attr_loss
a_ : int = visual_feat_loss
a_ : Optional[int] = {"""vision""": r_layers, """cross_encoder""": x_layers, """language""": l_layers}
super().__init__(**_lowercase )
| 248 |
import argparse
import logging
import sys
from unittest.mock import patch
import run_glue_deebert
from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
logging.basicConfig(level=logging.DEBUG)
__snake_case : Tuple = logging.getLogger()
def _UpperCAmelCase ( ):
'''simple docstring'''
a_ : int = argparse.ArgumentParser()
parser.add_argument("""-f""")
a_ : Any = parser.parse_args()
return args.f
class A__(a_ ):
"""simple docstring"""
def UpperCamelCase__ ( self ) -> None:
a_ : List[str] = logging.StreamHandler(sys.stdout )
logger.addHandler(_lowercase )
def UpperCamelCase__ ( self , _lowercase ) -> Dict:
a_ : List[str] = get_gpu_count()
if n_gpu > 1:
pass
# XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
# script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
# distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
# cmd = [sys.executable] + distributed_args + args
# execute_subprocess_async(cmd, env=self.get_env())
# XXX: test the results - need to save them first into .json file
else:
args.insert(0 , """run_glue_deebert.py""" )
with patch.object(_lowercase , """argv""" , _lowercase ):
a_ : Optional[int] = run_glue_deebert.main()
for value in result.values():
self.assertGreaterEqual(_lowercase , 0.6_6_6 )
@slow
@require_torch_non_multi_gpu
def UpperCamelCase__ ( self ) -> List[str]:
a_ : Tuple = """
--model_type roberta
--model_name_or_path roberta-base
--task_name MRPC
--do_train
--do_eval
--do_lower_case
--data_dir ./tests/fixtures/tests_samples/MRPC/
--max_seq_length 128
--per_gpu_eval_batch_size=1
--per_gpu_train_batch_size=8
--learning_rate 2e-4
--num_train_epochs 3
--overwrite_output_dir
--seed 42
--output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
--plot_data_dir ./examples/deebert/results/
--save_steps 0
--overwrite_cache
--eval_after_first_stage
""".split()
self.run_and_check(_lowercase )
a_ : Tuple = """
--model_type roberta
--model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
--task_name MRPC
--do_eval
--do_lower_case
--data_dir ./tests/fixtures/tests_samples/MRPC/
--output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
--plot_data_dir ./examples/deebert/results/
--max_seq_length 128
--eval_each_highway
--eval_highway
--overwrite_cache
--per_gpu_eval_batch_size=1
""".split()
self.run_and_check(_lowercase )
a_ : Optional[Any] = """
--model_type roberta
--model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
--task_name MRPC
--do_eval
--do_lower_case
--data_dir ./tests/fixtures/tests_samples/MRPC/
--output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
--plot_data_dir ./examples/deebert/results/
--max_seq_length 128
--early_exit_entropy 0.1
--eval_highway
--overwrite_cache
--per_gpu_eval_batch_size=1
""".split()
self.run_and_check(_lowercase )
| 248 | 1 |
"""simple docstring"""
def lowercase__ ( lowercase_ ) -> list:
"""simple docstring"""
def merge(lowercase_ ,lowercase_ ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
yield from left
yield from right
return list(_merge() )
if len(lowercase_ ) <= 1:
return collection
_UpperCamelCase : Optional[int] = len(lowercase_ ) // 2
return merge(merge_sort(collection[:mid] ) ,merge_sort(collection[mid:] ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
lowerCamelCase__ = input("Enter numbers separated by a comma:\n").strip()
lowerCamelCase__ = [int(item) for item in user_input.split(",")]
print(*merge_sort(unsorted), sep=",")
| 371 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
lowerCamelCase__ = R"\n [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and\n can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.\n\n Args:\n title_sep (`str`, *optional*, defaults to `\" / \"`):\n Separator inserted between the title and the text of the retrieved document when calling [`RagRetriever`].\n doc_sep (`str`, *optional*, defaults to `\" // \"`):\n Separator inserted between the text of the retrieved document and the original input when calling\n [`RagRetriever`].\n n_docs (`int`, *optional*, defaults to 5):\n Number of documents to retrieve.\n max_combined_length (`int`, *optional*, defaults to 300):\n Max length of contextualized input returned by [`~RagRetriever.__call__`].\n retrieval_vector_size (`int`, *optional*, defaults to 768):\n Dimensionality of the document embeddings indexed by [`RagRetriever`].\n retrieval_batch_size (`int`, *optional*, defaults to 8):\n Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated\n [`RagRetriever`].\n dataset (`str`, *optional*, defaults to `\"wiki_dpr\"`):\n A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids\n using `datasets.list_datasets()`).\n dataset_split (`str`, *optional*, defaults to `\"train\"`)\n Which split of the `dataset` to load.\n index_name (`str`, *optional*, defaults to `\"compressed\"`)\n The index name of the index associated with the `dataset`. One can choose between `\"legacy\"`, `\"exact\"` and\n `\"compressed\"`.\n index_path (`str`, *optional*)\n The path to the serialized faiss index on disk.\n passages_path (`str`, *optional*):\n A path to text passages compatible with the faiss index. Required if using\n [`~models.rag.retrieval_rag.LegacyIndex`]\n use_dummy_dataset (`bool`, *optional*, defaults to `False`)\n Whether to load a \"dummy\" variant of the dataset specified by `dataset`.\n label_smoothing (`float`, *optional*, defaults to 0.0):\n Only relevant if `return_loss` is set to `True`. Controls the `epsilon` parameter value for label smoothing\n in the loss calculation. If set to 0, no label smoothing is performed.\n do_marginalize (`bool`, *optional*, defaults to `False`):\n If `True`, the logits are marginalized over all documents by making use of\n `torch.nn.functional.log_softmax`.\n reduce_loss (`bool`, *optional*, defaults to `False`):\n Whether or not to reduce the NLL loss using the `torch.Tensor.sum` operation.\n do_deduplication (`bool`, *optional*, defaults to `True`):\n Whether or not to deduplicate the generations from different context documents for a given input. Has to be\n set to `False` if used while training with distributed backend.\n exclude_bos_score (`bool`, *optional*, defaults to `False`):\n Whether or not to disregard the BOS token when computing the loss.\n output_retrieved(`bool`, *optional*, defaults to `False`):\n If set to `True`, `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and\n `context_attention_mask` are returned. See returned tensors for more detail.\n use_cache (`bool`, *optional*, defaults to `True`):\n Whether or not the model should return the last key/values attentions (not used by all models).\n forced_eos_token_id (`int`, *optional*):\n The id of the token to force as the last generated token when `max_length` is reached. Usually set to\n `eos_token_id`.\n"
@add_start_docstrings(_UpperCamelCase )
class __SCREAMING_SNAKE_CASE ( _UpperCamelCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ :int = "rag"
SCREAMING_SNAKE_CASE__ :List[str] = True
def __init__( self : List[Any] , __a : Optional[Any]=None , __a : str=True , __a : Tuple=None , __a : Dict=None , __a : Optional[int]=None , __a : Optional[int]=None , __a : List[Any]=None , __a : Dict=" / " , __a : int=" // " , __a : Optional[Any]=5 , __a : Dict=300 , __a : Optional[int]=768 , __a : Tuple=8 , __a : Union[str, Any]="wiki_dpr" , __a : Dict="train" , __a : List[Any]="compressed" , __a : str=None , __a : Tuple=None , __a : int=False , __a : str=False , __a : Optional[int]=0.0 , __a : Dict=True , __a : Tuple=False , __a : Dict=False , __a : str=False , __a : str=True , __a : Optional[Any]=None , **__a : Tuple , ) -> Any:
super().__init__(
bos_token_id=__a , pad_token_id=__a , eos_token_id=__a , decoder_start_token_id=__a , forced_eos_token_id=__a , is_encoder_decoder=__a , prefix=__a , vocab_size=__a , **__a , )
assert (
"question_encoder" in kwargs and "generator" in kwargs
), "Config has to be initialized with question_encoder and generator config"
_UpperCamelCase : Optional[int] = kwargs.pop("question_encoder" )
_UpperCamelCase : str = question_encoder_config.pop("model_type" )
_UpperCamelCase : Tuple = kwargs.pop("generator" )
_UpperCamelCase : str = decoder_config.pop("model_type" )
from ..auto.configuration_auto import AutoConfig
_UpperCamelCase : Union[str, Any] = AutoConfig.for_model(__a , **__a )
_UpperCamelCase : str = AutoConfig.for_model(__a , **__a )
_UpperCamelCase : Optional[int] = reduce_loss
_UpperCamelCase : str = label_smoothing
_UpperCamelCase : int = exclude_bos_score
_UpperCamelCase : List[str] = do_marginalize
_UpperCamelCase : Optional[int] = title_sep
_UpperCamelCase : Optional[int] = doc_sep
_UpperCamelCase : Union[str, Any] = n_docs
_UpperCamelCase : Tuple = max_combined_length
_UpperCamelCase : Union[str, Any] = dataset
_UpperCamelCase : Any = dataset_split
_UpperCamelCase : List[str] = index_name
_UpperCamelCase : int = retrieval_vector_size
_UpperCamelCase : str = retrieval_batch_size
_UpperCamelCase : Dict = passages_path
_UpperCamelCase : str = index_path
_UpperCamelCase : Tuple = use_dummy_dataset
_UpperCamelCase : Union[str, Any] = output_retrieved
_UpperCamelCase : Optional[Any] = do_deduplication
_UpperCamelCase : str = use_cache
if self.forced_eos_token_id is None:
_UpperCamelCase : List[str] = getattr(self.generator , "forced_eos_token_id" , __a )
@classmethod
def __SCREAMING_SNAKE_CASE ( cls : Union[str, Any] , __a : PretrainedConfig , __a : PretrainedConfig , **__a : Optional[int] ) -> PretrainedConfig:
return cls(question_encoder=question_encoder_config.to_dict() , generator=generator_config.to_dict() , **__a )
def __SCREAMING_SNAKE_CASE ( self : Dict ) -> int:
_UpperCamelCase : Dict = copy.deepcopy(self.__dict__ )
_UpperCamelCase : List[Any] = self.question_encoder.to_dict()
_UpperCamelCase : Tuple = self.generator.to_dict()
_UpperCamelCase : Any = self.__class__.model_type
return output
| 310 | 0 |
def A_ ( A__ , A__ ) -> str:
a__ : list[list[str]] = [[] for _ in range(A__ )]
a__ : Optional[int] = key - 1
if key <= 0:
raise ValueError('Height of grid can\'t be 0 or negative' )
if key == 1 or len(A__ ) <= key:
return input_string
for position, character in enumerate(A__ ):
a__ : Union[str, Any] = position % (lowest * 2) # puts it in bounds
a__ : List[Any] = min(A__ , lowest * 2 - num ) # creates zigzag pattern
temp_grid[num].append(A__ )
a__ : Any = [''.join(A__ ) for row in temp_grid]
a__ : Optional[Any] = ''.join(A__ )
return output_string
def A_ ( A__ , A__ ) -> str:
a__ : Optional[int] = []
a__ : str = key - 1
if key <= 0:
raise ValueError('Height of grid can\'t be 0 or negative' )
if key == 1:
return input_string
a__ : list[list[str]] = [[] for _ in range(A__ )] # generates template
for position in range(len(A__ ) ):
a__ : List[Any] = position % (lowest * 2) # puts it in bounds
a__ : Tuple = min(A__ , lowest * 2 - num ) # creates zigzag pattern
temp_grid[num].append('*' )
a__ : List[Any] = 0
for row in temp_grid: # fills in the characters
a__ : Optional[int] = input_string[counter : counter + len(A__ )]
grid.append(list(A__ ) )
counter += len(A__ )
a__ : Any = '' # reads as zigzag
for position in range(len(A__ ) ):
a__ : List[Any] = position % (lowest * 2) # puts it in bounds
a__ : Optional[Any] = min(A__ , lowest * 2 - num ) # creates zigzag pattern
output_string += grid[num][0]
grid[num].pop(0 )
return output_string
def A_ ( A__ ) -> dict[int, str]:
a__ : Dict = {}
for key_guess in range(1 , len(A__ ) ): # tries every key
a__ : Tuple = decrypt(A__ , A__ )
return results
if __name__ == "__main__":
import doctest
doctest.testmod()
| 99 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from pathlib import Path
import torch
from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter
lowercase_ = 'Create a default config file for Accelerate with only a few flags set.'
def a ( A__ : Optional[Any]="no" , A__ : str = default_json_config_file , A__ : bool = False ) -> Optional[int]:
"""simple docstring"""
_lowercase =Path(A__ )
path.parent.mkdir(parents=A__ , exist_ok=A__ )
if path.exists():
print(
F'''Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`.''' )
return False
_lowercase =mixed_precision.lower()
if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
raise ValueError(
F'''`mixed_precision` should be one of \'no\', \'fp16\', \'bf16\', or \'fp8\'. Received {mixed_precision}''' )
_lowercase ={
'compute_environment': 'LOCAL_MACHINE',
'mixed_precision': mixed_precision,
}
if torch.cuda.is_available():
_lowercase =torch.cuda.device_count()
_lowercase =num_gpus
_lowercase =False
if num_gpus > 1:
_lowercase ='MULTI_GPU'
else:
_lowercase ='NO'
elif is_xpu_available() and use_xpu:
_lowercase =torch.xpu.device_count()
_lowercase =num_xpus
_lowercase =False
if num_xpus > 1:
_lowercase ='MULTI_XPU'
else:
_lowercase ='NO'
elif is_npu_available():
_lowercase =torch.npu.device_count()
_lowercase =num_npus
_lowercase =False
if num_npus > 1:
_lowercase ='MULTI_NPU'
else:
_lowercase ='NO'
else:
_lowercase =0
_lowercase =True
_lowercase =1
_lowercase ='NO'
_lowercase =ClusterConfig(**A__ )
config.to_json_file(A__ )
return path
def a ( A__ : Dict , A__ : Optional[Any] ) -> List[Any]:
"""simple docstring"""
_lowercase =parser.add_parser('default' , parents=A__ , help=A__ , formatter_class=A__ )
parser.add_argument(
'--config_file' , default=A__ , help=(
'The path to use to store the config file. Will default to a file named default_config.yaml in the cache '
'location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '
'such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '
'with \'huggingface\'.'
) , dest='save_location' , )
parser.add_argument(
'--mixed_precision' , choices=['no', 'fp16', 'bf16'] , type=A__ , help='Whether or not to use mixed precision training. '
'Choose between FP16 and BF16 (bfloat16) training. '
'BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.' , default='no' , )
parser.set_defaults(func=A__ )
return parser
def a ( A__ : List[str] ) -> Any:
"""simple docstring"""
_lowercase =write_basic_config(args.mixed_precision , args.save_location )
if config_file:
print(F'''accelerate configuration saved at {config_file}''' )
| 205 | 0 |
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version
if is_deepspeed_available():
from deepspeed import DeepSpeedEngine
if is_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
def A_ ( A__ ) -> Optional[int]:
if is_torch_version('<' , '2.0.0' ) or not hasattr(A__ , '_dynamo' ):
return False
return isinstance(A__ , torch._dynamo.eval_frame.OptimizedModule )
def A_ ( A__ , A__ = True ) -> int:
a__ : Optional[Any] = (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)
a__ : Union[str, Any] = is_compiled_module(A__ )
if is_compiled:
a__ : List[str] = model
a__ : Dict = model._orig_mod
if is_deepspeed_available():
options += (DeepSpeedEngine,)
while isinstance(A__ , A__ ):
a__ : str = model.module
if not keep_fpaa_wrapper:
a__ : Union[str, Any] = getattr(A__ , 'forward' )
a__ : List[Any] = model.__dict__.pop('_original_forward' , A__ )
if original_forward is not None:
while hasattr(A__ , '__wrapped__' ):
a__ : int = forward.__wrapped__
if forward == original_forward:
break
a__ : List[Any] = forward
if getattr(A__ , '_converted_to_transformer_engine' , A__ ):
convert_model(A__ , to_transformer_engine=A__ )
if is_compiled:
a__ : List[str] = model
a__ : Any = compiled_model
return model
def A_ ( ) -> int:
PartialState().wait_for_everyone()
def A_ ( A__ , A__ ) -> Dict:
if PartialState().distributed_type == DistributedType.TPU:
xm.save(A__ , A__ )
elif PartialState().local_process_index == 0:
torch.save(A__ , A__ )
@contextmanager
def A_ ( **A__ ) -> Any:
for key, value in kwargs.items():
a__ : Optional[int] = str(A__ )
yield
for key in kwargs:
if key.upper() in os.environ:
del os.environ[key.upper()]
def A_ ( A__ ) -> List[str]:
if not hasattr(A__ , '__qualname__' ) and not hasattr(A__ , '__name__' ):
a__ : Dict = getattr(A__ , '__class__' , A__ )
if hasattr(A__ , '__qualname__' ):
return obj.__qualname__
if hasattr(A__ , '__name__' ):
return obj.__name__
return str(A__ )
def A_ ( A__ , A__ ) -> Dict:
for key, value in source.items():
if isinstance(A__ , A__ ):
a__ : Optional[Any] = destination.setdefault(A__ , {} )
merge_dicts(A__ , A__ )
else:
a__ : Optional[int] = value
return destination
def A_ ( A__ = None ) -> bool:
if port is None:
a__ : List[Any] = 2_9500
with socket.socket(socket.AF_INET , socket.SOCK_STREAM ) as s:
return s.connect_ex(('localhost', port) ) == 0
| 358 |
import unittest
import numpy as np
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transformers.models.bert.modeling_flax_bert import (
FlaxBertForMaskedLM,
FlaxBertForMultipleChoice,
FlaxBertForNextSentencePrediction,
FlaxBertForPreTraining,
FlaxBertForQuestionAnswering,
FlaxBertForSequenceClassification,
FlaxBertForTokenClassification,
FlaxBertModel,
)
class A__ ( unittest.TestCase ):
"""simple docstring"""
def __init__( self , lowercase , lowercase=13 , lowercase=7 , lowercase=True , lowercase=True , lowercase=True , lowercase=True , lowercase=99 , lowercase=32 , lowercase=5 , lowercase=4 , lowercase=37 , lowercase="gelu" , lowercase=0.1 , lowercase=0.1 , lowercase=512 , lowercase=16 , lowercase=2 , lowercase=0.02 , lowercase=4 , ) -> Tuple:
'''simple docstring'''
a__ : str = parent
a__ : Optional[Any] = batch_size
a__ : str = seq_length
a__ : int = is_training
a__ : str = use_attention_mask
a__ : List[str] = use_token_type_ids
a__ : Optional[Any] = use_labels
a__ : List[Any] = vocab_size
a__ : Tuple = hidden_size
a__ : Dict = num_hidden_layers
a__ : List[str] = num_attention_heads
a__ : int = intermediate_size
a__ : Any = hidden_act
a__ : Optional[int] = hidden_dropout_prob
a__ : Tuple = attention_probs_dropout_prob
a__ : Tuple = max_position_embeddings
a__ : Optional[int] = type_vocab_size
a__ : List[Any] = type_sequence_label_size
a__ : Union[str, Any] = initializer_range
a__ : str = num_choices
def __lowercase ( self) -> Optional[Any]:
'''simple docstring'''
a__ : str = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
a__ : Dict = None
if self.use_attention_mask:
a__ : Optional[int] = random_attention_mask([self.batch_size, self.seq_length])
a__ : Dict = None
if self.use_token_type_ids:
a__ : int = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size)
a__ : Dict = BertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=lowercase , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def __lowercase ( self) -> Optional[Any]:
'''simple docstring'''
a__ : Tuple = self.prepare_config_and_inputs()
a__ , a__ , a__ , a__ : Optional[int] = config_and_inputs
a__ : str = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
return config, inputs_dict
def __lowercase ( self) -> Optional[int]:
'''simple docstring'''
a__ : Union[str, Any] = self.prepare_config_and_inputs()
a__ , a__ , a__ , a__ : int = config_and_inputs
a__ : str = True
a__ : Any = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
a__ : List[str] = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2)
return (
config,
input_ids,
attention_mask,
encoder_hidden_states,
encoder_attention_mask,
)
@require_flax
class A__ ( __UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
__A : Optional[Any] = True
__A : Tuple = (
(
FlaxBertModel,
FlaxBertForPreTraining,
FlaxBertForMaskedLM,
FlaxBertForMultipleChoice,
FlaxBertForQuestionAnswering,
FlaxBertForNextSentencePrediction,
FlaxBertForSequenceClassification,
FlaxBertForTokenClassification,
FlaxBertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def __lowercase ( self) -> Tuple:
'''simple docstring'''
a__ : Optional[Any] = FlaxBertModelTester(self)
@slow
def __lowercase ( self) -> Optional[Any]:
'''simple docstring'''
a__ : Union[str, Any] = FlaxBertModel.from_pretrained('bert-base-cased')
a__ : Optional[Any] = model(np.ones((1, 1)))
self.assertIsNotNone(lowercase)
| 225 | 0 |
"""simple docstring"""
import importlib
import os
import sys
# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append('.')
def lowercase__ ( _UpperCAmelCase ) -> str:
'''simple docstring'''
lowercase : int = test_file.split(os.path.sep )
if components[0:2] != ["tests", "models"]:
raise ValueError(
'`test_file` should start with `tests/models/` (with `/` being the OS specific path separator). Got '
f'''{test_file} instead.''' )
lowercase : Optional[int] = components[-1]
if not test_fn.endswith('py' ):
raise ValueError(f'''`test_file` should be a python file. Got {test_fn} instead.''' )
if not test_fn.startswith('test_modeling_' ):
raise ValueError(
f'''`test_file` should point to a file name of the form `test_modeling_*.py`. Got {test_fn} instead.''' )
lowercase : Union[str, Any] = components[:-1] + [test_fn.replace('.py' , '' )]
lowercase : Dict = '.'.join(UpperCAmelCase_ )
return test_module_path
def lowercase__ ( _UpperCAmelCase ) -> Dict:
'''simple docstring'''
lowercase : Optional[Any] = get_module_path(UpperCAmelCase_ )
lowercase : str = importlib.import_module(UpperCAmelCase_ )
return test_module
def lowercase__ ( _UpperCAmelCase ) -> List[Any]:
'''simple docstring'''
lowercase : Optional[int] = []
lowercase : Union[str, Any] = get_test_module(UpperCAmelCase_ )
for attr in dir(UpperCAmelCase_ ):
if attr.endswith('ModelTester' ):
tester_classes.append(getattr(UpperCAmelCase_ , UpperCAmelCase_ ) )
# sort with class names
return sorted(UpperCAmelCase_ , key=lambda _UpperCAmelCase : x.__name__ )
def lowercase__ ( _UpperCAmelCase ) -> Optional[int]:
'''simple docstring'''
lowercase : List[Any] = []
lowercase : int = get_test_module(UpperCAmelCase_ )
for attr in dir(UpperCAmelCase_ ):
lowercase : str = getattr(UpperCAmelCase_ , UpperCAmelCase_ )
# (TF/Flax)ModelTesterMixin is also an attribute in specific model test module. Let's exclude them by checking
# `all_model_classes` is not empty (which also excludes other special classes).
lowercase : List[Any] = getattr(UpperCAmelCase_ , 'all_model_classes' , [] )
if len(UpperCAmelCase_ ) > 0:
test_classes.append(UpperCAmelCase_ )
# sort with class names
return sorted(UpperCAmelCase_ , key=lambda _UpperCAmelCase : x.__name__ )
def lowercase__ ( _UpperCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
lowercase : Dict = get_test_classes(UpperCAmelCase_ )
lowercase : List[str] = set()
for test_class in test_classes:
model_classes.update(test_class.all_model_classes )
# sort with class names
return sorted(UpperCAmelCase_ , key=lambda _UpperCAmelCase : x.__name__ )
def lowercase__ ( _UpperCAmelCase ) -> Tuple:
'''simple docstring'''
lowercase : List[str] = test_class()
if hasattr(UpperCAmelCase_ , 'setUp' ):
test.setUp()
lowercase : Tuple = None
if hasattr(UpperCAmelCase_ , 'model_tester' ):
# `(TF/Flax)ModelTesterMixin` has this attribute default to `None`. Let's skip this case.
if test.model_tester is not None:
lowercase : Tuple = test.model_tester.__class__
return model_tester
def lowercase__ ( _UpperCAmelCase , _UpperCAmelCase ) -> Optional[int]:
'''simple docstring'''
lowercase : Tuple = get_test_classes(UpperCAmelCase_ )
lowercase : str = []
for test_class in test_classes:
if model_class in test_class.all_model_classes:
target_test_classes.append(UpperCAmelCase_ )
# sort with class names
return sorted(UpperCAmelCase_ , key=lambda _UpperCAmelCase : x.__name__ )
def lowercase__ ( _UpperCAmelCase , _UpperCAmelCase ) -> int:
'''simple docstring'''
lowercase : Dict = get_test_classes_for_model(UpperCAmelCase_ , UpperCAmelCase_ )
lowercase : Union[str, Any] = []
for test_class in test_classes:
lowercase : str = get_model_tester_from_test_class(UpperCAmelCase_ )
if tester_class is not None:
tester_classes.append(UpperCAmelCase_ )
# sort with class names
return sorted(UpperCAmelCase_ , key=lambda _UpperCAmelCase : x.__name__ )
def lowercase__ ( _UpperCAmelCase ) -> Tuple:
'''simple docstring'''
lowercase : List[Any] = get_test_classes(UpperCAmelCase_ )
lowercase : Tuple = {test_class: get_model_tester_from_test_class(UpperCAmelCase_ ) for test_class in test_classes}
return test_tester_mapping
def lowercase__ ( _UpperCAmelCase ) -> str:
'''simple docstring'''
lowercase : Optional[Any] = get_model_classes(UpperCAmelCase_ )
lowercase : str = {
model_class: get_test_classes_for_model(UpperCAmelCase_ , UpperCAmelCase_ ) for model_class in model_classes
}
return model_test_mapping
def lowercase__ ( _UpperCAmelCase ) -> Optional[Any]:
'''simple docstring'''
lowercase : Any = get_model_classes(UpperCAmelCase_ )
lowercase : Union[str, Any] = {
model_class: get_tester_classes_for_model(UpperCAmelCase_ , UpperCAmelCase_ ) for model_class in model_classes
}
return model_to_tester_mapping
def lowercase__ ( _UpperCAmelCase ) -> Optional[int]:
'''simple docstring'''
if isinstance(UpperCAmelCase_ , UpperCAmelCase_ ):
return o
elif isinstance(UpperCAmelCase_ , UpperCAmelCase_ ):
return o.__name__
elif isinstance(UpperCAmelCase_ , (list, tuple) ):
return [to_json(UpperCAmelCase_ ) for x in o]
elif isinstance(UpperCAmelCase_ , UpperCAmelCase_ ):
return {to_json(UpperCAmelCase_ ): to_json(UpperCAmelCase_ ) for k, v in o.items()}
else:
return o
| 255 | """simple docstring"""
import os
import pytest
from attr import dataclass
_a : Optional[int]= "us-east-1" # defaults region
@dataclass
class UpperCamelCase :
UpperCAmelCase : str
UpperCAmelCase : Any = """arn:aws:iam::558105141721:role/sagemaker_execution_role"""
UpperCAmelCase : Optional[Any] = {
"""task_name""": """mnli""",
"""per_device_train_batch_size""": 16,
"""per_device_eval_batch_size""": 16,
"""do_train""": True,
"""do_eval""": True,
"""do_predict""": True,
"""output_dir""": """/opt/ml/model""",
"""overwrite_output_dir""": True,
"""max_steps""": 500,
"""save_steps""": 5500,
}
UpperCAmelCase : List[Any] = {**hyperparameters, """max_steps""": 1000}
@property
def _lowercase (self : Dict) -> 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 _lowercase (self : Tuple) -> str:
return f"{self.framework}-transfromers-test"
@property
def _lowercase (self : Any) -> str:
return f"./tests/sagemaker/scripts/{self.framework}"
@property
def _lowercase (self : List[str]) -> 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 __UpperCAmelCase ( UpperCAmelCase_ : Optional[Any] ) -> int:
'''simple docstring'''
__snake_case : List[Any] = SageMakerTestEnvironment(framework=request.cls.framework )
| 172 | 0 |
'''simple docstring'''
from timeit import timeit
def _A ( snake_case ) -> int:
if number < 0:
raise ValueError("the value of input must not be negative" )
_lowercase : Union[str, Any] = 0
while number:
number &= number - 1
result += 1
return result
def _A ( snake_case ) -> int:
if number < 0:
raise ValueError("the value of input must not be negative" )
_lowercase : int = 0
while number:
if number % 2 == 1:
result += 1
number >>= 1
return result
def _A ( ) -> None:
def do_benchmark(snake_case ) -> None:
_lowercase : Optional[int] = "import __main__ as z"
print(F'''Benchmark when {number = }:''' )
print(F'''{get_set_bits_count_using_modulo_operator(snake_case ) = }''' )
_lowercase : int = timeit("z.get_set_bits_count_using_modulo_operator(25)" , setup=snake_case )
print(F'''timeit() runs in {timing} seconds''' )
print(F'''{get_set_bits_count_using_brian_kernighans_algorithm(snake_case ) = }''' )
_lowercase : Optional[int] = timeit(
"z.get_set_bits_count_using_brian_kernighans_algorithm(25)" , setup=snake_case , )
print(F'''timeit() runs in {timing} seconds''' )
for number in (25, 37, 58, 0):
do_benchmark(snake_case )
print()
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 199 |
'''simple docstring'''
def _A ( snake_case , snake_case ) -> int:
return int((input_a, input_a).count(0 ) != 0 )
def _A ( ) -> None:
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))
| 199 | 1 |
'''simple docstring'''
import json
import os
import torch
from diffusers import UNetaDModel
os.makedirs('hub/hopper-medium-v2/unet/hor32', exist_ok=True)
os.makedirs('hub/hopper-medium-v2/unet/hor128', exist_ok=True)
os.makedirs('hub/hopper-medium-v2/value_function', exist_ok=True)
def _UpperCamelCase ( UpperCamelCase__ ):
if hor == 1_2_8:
UpperCAmelCase__ : int = ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
UpperCAmelCase__ : Tuple = (3_2, 1_2_8, 2_5_6)
UpperCAmelCase__ : Union[str, Any] = ("""UpResnetBlock1D""", """UpResnetBlock1D""")
elif hor == 3_2:
UpperCAmelCase__ : Dict = ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
UpperCAmelCase__ : Union[str, Any] = (3_2, 6_4, 1_2_8, 2_5_6)
UpperCAmelCase__ : str = ("""UpResnetBlock1D""", """UpResnetBlock1D""", """UpResnetBlock1D""")
UpperCAmelCase__ : Any = torch.load(f'''/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch''' )
UpperCAmelCase__ : Tuple = model.state_dict()
UpperCAmelCase__ : Union[str, Any] = {
"""down_block_types""": down_block_types,
"""block_out_channels""": block_out_channels,
"""up_block_types""": up_block_types,
"""layers_per_block""": 1,
"""use_timestep_embedding""": True,
"""out_block_type""": """OutConv1DBlock""",
"""norm_num_groups""": 8,
"""downsample_each_block""": False,
"""in_channels""": 1_4,
"""out_channels""": 1_4,
"""extra_in_channels""": 0,
"""time_embedding_type""": """positional""",
"""flip_sin_to_cos""": False,
"""freq_shift""": 1,
"""sample_size""": 6_5_5_3_6,
"""mid_block_type""": """MidResTemporalBlock1D""",
"""act_fn""": """mish""",
}
UpperCAmelCase__ : List[Any] = UNetaDModel(**UpperCamelCase__ )
print(f'''length of state dict: {len(state_dict.keys() )}''' )
print(f'''length of value function dict: {len(hf_value_function.state_dict().keys() )}''' )
UpperCAmelCase__ : Optional[Any] = dict(zip(model.state_dict().keys() , hf_value_function.state_dict().keys() ) )
for k, v in mapping.items():
UpperCAmelCase__ : Union[str, Any] = state_dict.pop(UpperCamelCase__ )
hf_value_function.load_state_dict(UpperCamelCase__ )
torch.save(hf_value_function.state_dict() , f'''hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin''' )
with open(f'''hub/hopper-medium-v2/unet/hor{hor}/config.json''' , """w""" ) as f:
json.dump(UpperCamelCase__ , UpperCamelCase__ )
def _UpperCamelCase ( ):
UpperCAmelCase__ : Any = {
"""in_channels""": 1_4,
"""down_block_types""": ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D"""),
"""up_block_types""": (),
"""out_block_type""": """ValueFunction""",
"""mid_block_type""": """ValueFunctionMidBlock1D""",
"""block_out_channels""": (3_2, 6_4, 1_2_8, 2_5_6),
"""layers_per_block""": 1,
"""downsample_each_block""": True,
"""sample_size""": 6_5_5_3_6,
"""out_channels""": 1_4,
"""extra_in_channels""": 0,
"""time_embedding_type""": """positional""",
"""use_timestep_embedding""": True,
"""flip_sin_to_cos""": False,
"""freq_shift""": 1,
"""norm_num_groups""": 8,
"""act_fn""": """mish""",
}
UpperCAmelCase__ : Tuple = torch.load("""/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch""" )
UpperCAmelCase__ : Optional[Any] = model
UpperCAmelCase__ : Dict = UNetaDModel(**UpperCamelCase__ )
print(f'''length of state dict: {len(state_dict.keys() )}''' )
print(f'''length of value function dict: {len(hf_value_function.state_dict().keys() )}''' )
UpperCAmelCase__ : Dict = dict(zip(state_dict.keys() , hf_value_function.state_dict().keys() ) )
for k, v in mapping.items():
UpperCAmelCase__ : str = state_dict.pop(UpperCamelCase__ )
hf_value_function.load_state_dict(UpperCamelCase__ )
torch.save(hf_value_function.state_dict() , """hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin""" )
with open("""hub/hopper-medium-v2/value_function/config.json""" , """w""" ) as f:
json.dump(UpperCamelCase__ , UpperCamelCase__ )
if __name__ == "__main__":
unet(32)
# unet(128)
value_function() | 163 |
'''simple docstring'''
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
__A =datasets.utils.logging.get_logger(__name__)
class _snake_case ( folder_based_builder.FolderBasedBuilderConfig ):
lowerCAmelCase :bool = None
lowerCAmelCase :bool = None
class _snake_case ( folder_based_builder.FolderBasedBuilder ):
lowerCAmelCase :Optional[Any] = datasets.Audio()
lowerCAmelCase :Tuple = '''audio'''
lowerCAmelCase :Optional[Any] = AudioFolderConfig
lowerCAmelCase :List[str] # definition at the bottom of the script
lowerCAmelCase :Union[str, Any] = AudioClassification(audio_column='''audio''' , label_column='''label''' )
__A =[
'.aiff',
'.au',
'.avr',
'.caf',
'.flac',
'.htk',
'.svx',
'.mat4',
'.mat5',
'.mpc2k',
'.ogg',
'.paf',
'.pvf',
'.raw',
'.rf64',
'.sd2',
'.sds',
'.ircam',
'.voc',
'.w64',
'.wav',
'.nist',
'.wavex',
'.wve',
'.xi',
'.mp3',
'.opus',
]
__A =AUDIO_EXTENSIONS | 163 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__a: Tuple = {
"""configuration_conditional_detr""": [
"""CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""ConditionalDetrConfig""",
"""ConditionalDetrOnnxConfig""",
]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a: Union[str, Any] = ["""ConditionalDetrFeatureExtractor"""]
__a: Union[str, Any] = ["""ConditionalDetrImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a: List[Any] = [
"""CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""ConditionalDetrForObjectDetection""",
"""ConditionalDetrForSegmentation""",
"""ConditionalDetrModel""",
"""ConditionalDetrPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_conditional_detr import (
CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
ConditionalDetrConfig,
ConditionalDetrOnnxConfig,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
from .image_processing_conditional_detr import ConditionalDetrImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_conditional_detr import (
CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
ConditionalDetrForObjectDetection,
ConditionalDetrForSegmentation,
ConditionalDetrModel,
ConditionalDetrPreTrainedModel,
)
else:
import sys
__a: str = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 214 | '''simple docstring'''
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class UpperCAmelCase ( a__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = None
def __UpperCamelCase ( UpperCAmelCase , UpperCAmelCase=0.9_9_9 , UpperCAmelCase="cosine" , ):
if alpha_transform_type == "cosine":
def alpha_bar_fn(UpperCAmelCase ):
return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(UpperCAmelCase ):
return math.exp(t * -1_2.0 )
else:
raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}""" )
lowercase__ : str = []
for i in range(UpperCAmelCase ):
lowercase__ : int = i / num_diffusion_timesteps
lowercase__ : Tuple = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(UpperCAmelCase ) / alpha_bar_fn(UpperCAmelCase ) , UpperCAmelCase ) )
return torch.tensor(UpperCAmelCase , dtype=torch.floataa )
class UpperCAmelCase ( a__ , a__ ):
'''simple docstring'''
@register_to_config
def __init__( self , __lowerCAmelCase = 1000 , __lowerCAmelCase = "fixed_small_log" , __lowerCAmelCase = True , __lowerCAmelCase = 1.0 , __lowerCAmelCase = "epsilon" , __lowerCAmelCase = "squaredcos_cap_v2" , ) -> Optional[int]:
if beta_schedule != "squaredcos_cap_v2":
raise ValueError('''UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\'''' )
lowercase__ : Union[str, Any] = betas_for_alpha_bar(__lowerCAmelCase )
lowercase__ : List[Any] = 1.0 - self.betas
lowercase__ : int = torch.cumprod(self.alphas , dim=0 )
lowercase__ : str = torch.tensor(1.0 )
# standard deviation of the initial noise distribution
lowercase__ : Optional[Any] = 1.0
# setable values
lowercase__ : Optional[Any] = None
lowercase__ : List[Any] = torch.from_numpy(np.arange(0 , __lowerCAmelCase )[::-1].copy() )
lowercase__ : Tuple = variance_type
def _lowerCAmelCase( self , __lowerCAmelCase , __lowerCAmelCase = None ) -> torch.FloatTensor:
return sample
def _lowerCAmelCase( self , __lowerCAmelCase , __lowerCAmelCase = None ) -> Optional[int]:
lowercase__ : List[str] = num_inference_steps
lowercase__ : List[str] = (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
lowercase__ : List[str] = (np.arange(0 , __lowerCAmelCase ) * step_ratio).round()[::-1].copy().astype(np.intaa )
lowercase__ : str = torch.from_numpy(__lowerCAmelCase ).to(__lowerCAmelCase )
def _lowerCAmelCase( self , __lowerCAmelCase , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None ) -> Tuple:
if prev_timestep is None:
lowercase__ : Any = t - 1
lowercase__ : Any = self.alphas_cumprod[t]
lowercase__ : List[Any] = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
lowercase__ : str = 1 - alpha_prod_t
lowercase__ : int = 1 - alpha_prod_t_prev
if prev_timestep == t - 1:
lowercase__ : Tuple = self.betas[t]
else:
lowercase__ : Dict = 1 - alpha_prod_t / alpha_prod_t_prev
# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
# and sample from it to get previous sample
# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
lowercase__ : Any = beta_prod_t_prev / beta_prod_t * beta
if variance_type is None:
lowercase__ : Union[str, Any] = self.config.variance_type
# hacks - were probably added for training stability
if variance_type == "fixed_small_log":
lowercase__ : int = torch.log(torch.clamp(__lowerCAmelCase , min=1E-20 ) )
lowercase__ : Dict = torch.exp(0.5 * variance )
elif variance_type == "learned_range":
# NOTE difference with DDPM scheduler
lowercase__ : Union[str, Any] = variance.log()
lowercase__ : Optional[int] = beta.log()
lowercase__ : Tuple = (predicted_variance + 1) / 2
lowercase__ : Dict = frac * max_log + (1 - frac) * min_log
return variance
def _lowerCAmelCase( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = None , __lowerCAmelCase=None , __lowerCAmelCase = True , ) -> Union[UnCLIPSchedulerOutput, Tuple]:
lowercase__ : Tuple = timestep
if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
lowercase__ , lowercase__ : str = torch.split(__lowerCAmelCase , sample.shape[1] , dim=1 )
else:
lowercase__ : Dict = None
# 1. compute alphas, betas
if prev_timestep is None:
lowercase__ : int = t - 1
lowercase__ : Optional[int] = self.alphas_cumprod[t]
lowercase__ : Optional[int] = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
lowercase__ : Optional[int] = 1 - alpha_prod_t
lowercase__ : List[Any] = 1 - alpha_prod_t_prev
if prev_timestep == t - 1:
lowercase__ : Optional[int] = self.betas[t]
lowercase__ : Optional[Any] = self.alphas[t]
else:
lowercase__ : Any = 1 - alpha_prod_t / alpha_prod_t_prev
lowercase__ : Optional[int] = 1 - beta
# 2. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
if self.config.prediction_type == "epsilon":
lowercase__ : int = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
elif self.config.prediction_type == "sample":
lowercase__ : Dict = model_output
else:
raise ValueError(
F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"""
''' for the UnCLIPScheduler.''' )
# 3. Clip "predicted x_0"
if self.config.clip_sample:
lowercase__ : List[Any] = torch.clamp(
__lowerCAmelCase , -self.config.clip_sample_range , self.config.clip_sample_range )
# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
lowercase__ : Optional[Any] = (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
lowercase__ : Tuple = alpha ** 0.5 * beta_prod_t_prev / beta_prod_t
# 5. Compute predicted previous sample µ_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
lowercase__ : List[Any] = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
# 6. Add noise
lowercase__ : List[Any] = 0
if t > 0:
lowercase__ : Dict = randn_tensor(
model_output.shape , dtype=model_output.dtype , generator=__lowerCAmelCase , device=model_output.device )
lowercase__ : Union[str, Any] = self._get_variance(
__lowerCAmelCase , predicted_variance=__lowerCAmelCase , prev_timestep=__lowerCAmelCase , )
if self.variance_type == "fixed_small_log":
lowercase__ : List[Any] = variance
elif self.variance_type == "learned_range":
lowercase__ : int = (0.5 * variance).exp()
else:
raise ValueError(
F"""variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"""
''' for the UnCLIPScheduler.''' )
lowercase__ : List[str] = variance * variance_noise
lowercase__ : Union[str, Any] = pred_prev_sample + variance
if not return_dict:
return (pred_prev_sample,)
return UnCLIPSchedulerOutput(prev_sample=__lowerCAmelCase , pred_original_sample=__lowerCAmelCase )
def _lowerCAmelCase( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , ) -> torch.FloatTensor:
# Make sure alphas_cumprod and timestep have same device and dtype as original_samples
lowercase__ : Tuple = self.alphas_cumprod.to(device=original_samples.device , dtype=original_samples.dtype )
lowercase__ : str = timesteps.to(original_samples.device )
lowercase__ : Union[str, Any] = alphas_cumprod[timesteps] ** 0.5
lowercase__ : List[str] = sqrt_alpha_prod.flatten()
while len(sqrt_alpha_prod.shape ) < len(original_samples.shape ):
lowercase__ : List[str] = sqrt_alpha_prod.unsqueeze(-1 )
lowercase__ : int = (1 - alphas_cumprod[timesteps]) ** 0.5
lowercase__ : List[Any] = sqrt_one_minus_alpha_prod.flatten()
while len(sqrt_one_minus_alpha_prod.shape ) < len(original_samples.shape ):
lowercase__ : int = sqrt_one_minus_alpha_prod.unsqueeze(-1 )
lowercase__ : Optional[int] = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
return noisy_samples
| 214 | 1 |
"""simple docstring"""
import heapq as hq
import math
from collections.abc import Iterator
class UpperCamelCase__ :
"""simple docstring"""
def __init__( self : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Optional[int] ):
lowerCAmelCase_ : Any = str(id_ )
lowerCAmelCase_ : List[str] = None
lowerCAmelCase_ : List[Any] = None
lowerCAmelCase_ : int = []
lowerCAmelCase_ : Any = {} # {vertex:distance}
def __lt__( self : Optional[int] , SCREAMING_SNAKE_CASE_ : Dict ):
return self.key < other.key
def __repr__( self : Union[str, Any] ):
return self.id
def SCREAMING_SNAKE_CASE__ ( self : int , SCREAMING_SNAKE_CASE_ : List[str] ):
self.neighbors.append(SCREAMING_SNAKE_CASE_ )
def SCREAMING_SNAKE_CASE__ ( self : str , SCREAMING_SNAKE_CASE_ : List[Any] , SCREAMING_SNAKE_CASE_ : Tuple ):
lowerCAmelCase_ : str = weight
def UpperCamelCase_ ( lowerCAmelCase__ : Tuple , lowerCAmelCase__ : Union[str, Any] , lowerCAmelCase__ : Tuple , lowerCAmelCase__ : Dict ) -> int:
"""simple docstring"""
graph[a - 1].add_neighbor(graph[b - 1] )
graph[b - 1].add_neighbor(graph[a - 1] )
# add the edges:
graph[a - 1].add_edge(graph[b - 1] , lowerCAmelCase__ )
graph[b - 1].add_edge(graph[a - 1] , lowerCAmelCase__ )
def UpperCamelCase_ ( lowerCAmelCase__ : list , lowerCAmelCase__ : Vertex ) -> list:
"""simple docstring"""
lowerCAmelCase_ : Tuple = []
for u in graph:
lowerCAmelCase_ : int = math.inf
lowerCAmelCase_ : str = None
lowerCAmelCase_ : int = 0
lowerCAmelCase_ : Tuple = graph[:]
while q:
lowerCAmelCase_ : List[str] = min(lowerCAmelCase__ )
q.remove(lowerCAmelCase__ )
for v in u.neighbors:
if (v in q) and (u.edges[v.id] < v.key):
lowerCAmelCase_ : Dict = u
lowerCAmelCase_ : int = u.edges[v.id]
for i in range(1 , len(lowerCAmelCase__ ) ):
a.append((int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1) )
return a
def UpperCamelCase_ ( lowerCAmelCase__ : list , lowerCAmelCase__ : Vertex ) -> Iterator[tuple]:
"""simple docstring"""
for u in graph:
lowerCAmelCase_ : Optional[int] = math.inf
lowerCAmelCase_ : str = None
lowerCAmelCase_ : Any = 0
lowerCAmelCase_ : str = list(lowerCAmelCase__ )
hq.heapify(lowerCAmelCase__ )
while h:
lowerCAmelCase_ : List[Any] = hq.heappop(lowerCAmelCase__ )
for v in u.neighbors:
if (v in h) and (u.edges[v.id] < v.key):
lowerCAmelCase_ : List[str] = u
lowerCAmelCase_ : int = u.edges[v.id]
hq.heapify(lowerCAmelCase__ )
for i in range(1 , len(lowerCAmelCase__ ) ):
yield (int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1)
def UpperCamelCase_ ( ) -> None:
"""simple docstring"""
if __name__ == "__main__":
import doctest
doctest.testmod()
| 224 |
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
from typing import List, Tuple
from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class UpperCamelCase__ :
"""simple docstring"""
def __init__( self : Dict , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : Optional[Any]=3 , SCREAMING_SNAKE_CASE_ : Any=3_2 , SCREAMING_SNAKE_CASE_ : int=3 , SCREAMING_SNAKE_CASE_ : Optional[Any]=1_0 , SCREAMING_SNAKE_CASE_ : Dict=[1_0, 2_0, 3_0, 4_0] , SCREAMING_SNAKE_CASE_ : int=[1, 1, 2, 1] , SCREAMING_SNAKE_CASE_ : int=True , SCREAMING_SNAKE_CASE_ : Optional[Any]=True , SCREAMING_SNAKE_CASE_ : int="relu" , SCREAMING_SNAKE_CASE_ : Union[str, Any]=3 , SCREAMING_SNAKE_CASE_ : List[str]=None , ):
lowerCAmelCase_ : Optional[int] = parent
lowerCAmelCase_ : Union[str, Any] = batch_size
lowerCAmelCase_ : Optional[int] = image_size
lowerCAmelCase_ : Optional[int] = num_channels
lowerCAmelCase_ : Any = embeddings_size
lowerCAmelCase_ : Dict = hidden_sizes
lowerCAmelCase_ : Any = depths
lowerCAmelCase_ : Optional[int] = is_training
lowerCAmelCase_ : int = use_labels
lowerCAmelCase_ : List[Any] = hidden_act
lowerCAmelCase_ : Dict = num_labels
lowerCAmelCase_ : Optional[int] = scope
lowerCAmelCase_ : Tuple = len(SCREAMING_SNAKE_CASE_ )
def SCREAMING_SNAKE_CASE__ ( self : Any ):
lowerCAmelCase_ : List[str] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowerCAmelCase_ : Union[str, Any] = None
if self.use_labels:
lowerCAmelCase_ : int = ids_tensor([self.batch_size] , self.num_labels )
lowerCAmelCase_ : List[str] = self.get_config()
return config, pixel_values, labels
def SCREAMING_SNAKE_CASE__ ( self : str ):
return RegNetConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , )
def SCREAMING_SNAKE_CASE__ ( self : Any , SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : List[Any] , SCREAMING_SNAKE_CASE_ : int ):
lowerCAmelCase_ : List[Any] = TFRegNetModel(config=SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : List[Any] = model(SCREAMING_SNAKE_CASE_ , training=SCREAMING_SNAKE_CASE_ )
# 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 // 3_2, self.image_size // 3_2) , )
def SCREAMING_SNAKE_CASE__ ( self : List[str] , SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : Optional[Any] ):
lowerCAmelCase_ : int = self.num_labels
lowerCAmelCase_ : int = TFRegNetForImageClassification(SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : Dict = model(SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ , training=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def SCREAMING_SNAKE_CASE__ ( self : List[str] ):
lowerCAmelCase_ : str = self.prepare_config_and_inputs()
lowerCAmelCase_ ,lowerCAmelCase_ ,lowerCAmelCase_ : Tuple = config_and_inputs
lowerCAmelCase_ : Optional[Any] = {'pixel_values': pixel_values}
return config, inputs_dict
@require_tf
class UpperCamelCase__ ( lowercase_, lowercase_, unittest.TestCase ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = (TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
_SCREAMING_SNAKE_CASE = (
{"""feature-extraction""": TFRegNetModel, """image-classification""": TFRegNetForImageClassification}
if is_tf_available()
else {}
)
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = False
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ):
lowerCAmelCase_ : List[Any] = TFRegNetModelTester(self )
lowerCAmelCase_ : Tuple = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE_ , has_text_modality=SCREAMING_SNAKE_CASE_ )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ):
return
@unittest.skip(reason='RegNet does not use inputs_embeds' )
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ):
pass
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices('GPU' ) ) == 0 , reason='TF does not support backprop for grouped convolutions on CPU.' , )
@slow
def SCREAMING_SNAKE_CASE__ ( self : Tuple ):
super().test_keras_fit()
@unittest.skip(reason='RegNet does not support input and output embeddings' )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ):
pass
def SCREAMING_SNAKE_CASE__ ( self : int ):
lowerCAmelCase_ ,lowerCAmelCase_ : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase_ : int = model_class(SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : Dict = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCAmelCase_ : List[Any] = [*signature.parameters.keys()]
lowerCAmelCase_ : Any = ['pixel_values']
self.assertListEqual(arg_names[:1] , SCREAMING_SNAKE_CASE_ )
def SCREAMING_SNAKE_CASE__ ( self : Any ):
lowerCAmelCase_ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_ )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ):
def check_hidden_states_output(SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : Tuple ):
lowerCAmelCase_ : List[Any] = model_class(SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : Union[str, Any] = model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) , training=SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : List[str] = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
lowerCAmelCase_ : Union[str, Any] = self.model_tester.num_stages
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , expected_num_stages + 1 )
# RegNet'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 // 2, self.model_tester.image_size // 2] , )
lowerCAmelCase_ ,lowerCAmelCase_ : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
lowerCAmelCase_ : Optional[int] = ['basic', 'bottleneck']
for model_class in self.all_model_classes:
for layer_type in layers_type:
lowerCAmelCase_ : str = layer_type
lowerCAmelCase_ : List[str] = True
check_hidden_states_output(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowerCAmelCase_ : List[str] = True
check_hidden_states_output(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ):
lowerCAmelCase_ ,lowerCAmelCase_ : int = self.model_tester.prepare_config_and_inputs_for_common()
def check_equivalence(SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : str={} ):
lowerCAmelCase_ : Optional[Any] = model(SCREAMING_SNAKE_CASE_ , return_dict=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : List[Any] = model(SCREAMING_SNAKE_CASE_ , return_dict=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ).to_tuple()
def recursive_check(SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int ):
if isinstance(SCREAMING_SNAKE_CASE_ , (List, Tuple) ):
for tuple_iterable_value, dict_iterable_value in zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
recursive_check(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
elif tuple_object is None:
return
else:
self.assertTrue(
all(tf.equal(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) ) , msg=(
'Tuple and dict output are not equal. Difference:'
F" {tf.math.reduce_max(tf.abs(tuple_object - dict_object ) )}"
) , )
recursive_check(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
for model_class in self.all_model_classes:
lowerCAmelCase_ : Union[str, Any] = model_class(SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : List[Any] = self._prepare_for_class(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : Union[str, Any] = self._prepare_for_class(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
check_equivalence(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : Optional[int] = self._prepare_for_class(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , return_labels=SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : str = self._prepare_for_class(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , return_labels=SCREAMING_SNAKE_CASE_ )
check_equivalence(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : Tuple = self._prepare_for_class(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : int = self._prepare_for_class(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
check_equivalence(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , {'output_hidden_states': True} )
lowerCAmelCase_ : List[Any] = self._prepare_for_class(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , return_labels=SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : Any = self._prepare_for_class(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , return_labels=SCREAMING_SNAKE_CASE_ )
check_equivalence(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , {'output_hidden_states': True} )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ):
lowerCAmelCase_ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE_ )
@slow
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ):
for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase_ : int = TFRegNetModel.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
def UpperCamelCase_ ( ) -> List[str]:
"""simple docstring"""
lowerCAmelCase_ : Optional[Any] = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_tf
@require_vision
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ):
return (
AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ):
lowerCAmelCase_ : int = TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
lowerCAmelCase_ : Tuple = self.default_image_processor
lowerCAmelCase_ : Dict = prepare_img()
lowerCAmelCase_ : Union[str, Any] = image_processor(images=SCREAMING_SNAKE_CASE_ , return_tensors='tf' )
# forward pass
lowerCAmelCase_ : List[str] = model(**SCREAMING_SNAKE_CASE_ , training=SCREAMING_SNAKE_CASE_ )
# verify the logits
lowerCAmelCase_ : Optional[Any] = tf.TensorShape((1, 1_0_0_0) )
self.assertEqual(outputs.logits.shape , SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : List[Any] = tf.constant([-0.41_80, -1.50_51, -3.48_36] )
tf.debugging.assert_near(outputs.logits[0, :3] , SCREAMING_SNAKE_CASE_ , atol=1E-4 )
| 224 | 1 |
"""simple docstring"""
import sys
from typing import Tuple
import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class lowercase_ :
'''simple docstring'''
def __init__( self : Any , _UpperCAmelCase : Tuple , _UpperCAmelCase : str=sys.maxsize ):
_A = 'bilinear'
_A = max_size
_A = short_edge_length
def __call__( self : List[Any] , _UpperCAmelCase : Dict ):
_A = []
for img in imgs:
_A , _A = img.shape[:2]
# later: provide list and randomly choose index for resize
_A = np.random.randint(self.short_edge_length[0] , self.short_edge_length[1] + 1 )
if size == 0:
return img
_A = size * 1.0 / min(_UpperCAmelCase , _UpperCAmelCase )
if h < w:
_A , _A = size, scale * w
else:
_A , _A = scale * h, size
if max(_UpperCAmelCase , _UpperCAmelCase ) > self.max_size:
_A = self.max_size * 1.0 / max(_UpperCAmelCase , _UpperCAmelCase )
_A = newh * scale
_A = neww * scale
_A = int(neww + 0.5 )
_A = int(newh + 0.5 )
if img.dtype == np.uinta:
_A = Image.fromarray(_UpperCAmelCase )
_A = pil_image.resize((neww, newh) , PILImageResampling.BILINEAR )
_A = np.asarray(_UpperCAmelCase )
else:
_A = img.permute(2 , 0 , 1 ).unsqueeze(0 ) # 3, 0, 1) # hw(c) -> nchw
_A = nn.functional.interpolate(
_UpperCAmelCase , (newh, neww) , mode=self.interp_method , align_corners=_UpperCAmelCase ).squeeze(0 )
img_augs.append(_UpperCAmelCase )
return img_augs
class lowercase_ :
'''simple docstring'''
def __init__( self : int , _UpperCAmelCase : Dict ):
_A = ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST] , cfg.INPUT.MAX_SIZE_TEST )
_A = cfg.INPUT.FORMAT
_A = cfg.SIZE_DIVISIBILITY
_A = cfg.PAD_VALUE
_A = cfg.INPUT.MAX_SIZE_TEST
_A = cfg.MODEL.DEVICE
_A = torch.tensor(cfg.MODEL.PIXEL_STD ).to(self.device ).view(len(cfg.MODEL.PIXEL_STD ) , 1 , 1 )
_A = torch.tensor(cfg.MODEL.PIXEL_MEAN ).to(self.device ).view(len(cfg.MODEL.PIXEL_STD ) , 1 , 1 )
_A = lambda _UpperCAmelCase : (x - self.pixel_mean) / self.pixel_std
def lowerCAmelCase_ ( self : Optional[Any] , _UpperCAmelCase : Optional[Any] ):
_A = tuple(max(_UpperCAmelCase ) for s in zip(*[img.shape for img in images] ) )
_A = [im.shape[-2:] for im in images]
_A = [
nn.functional.pad(
_UpperCAmelCase , [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]] , value=self.pad_value , )
for size, im in zip(_UpperCAmelCase , _UpperCAmelCase )
]
return torch.stack(_UpperCAmelCase ), torch.tensor(_UpperCAmelCase )
def __call__( self : Optional[int] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : int=False ):
with torch.no_grad():
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
_A = [images]
if single_image:
assert len(_UpperCAmelCase ) == 1
for i in range(len(_UpperCAmelCase ) ):
if isinstance(images[i] , torch.Tensor ):
images.insert(_UpperCAmelCase , images.pop(_UpperCAmelCase ).to(self.device ).float() )
elif not isinstance(images[i] , torch.Tensor ):
images.insert(
_UpperCAmelCase , torch.as_tensor(img_tensorize(images.pop(_UpperCAmelCase ) , input_format=self.input_format ) )
.to(self.device )
.float() , )
# resize smallest edge
_A = torch.tensor([im.shape[:2] for im in images] )
_A = self.aug(_UpperCAmelCase )
# transpose images and convert to torch tensors
# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
# now normalize before pad to avoid useless arithmetic
_A = [self.normalizer(_UpperCAmelCase ) for x in images]
# now pad them to do the following operations
_A , _A = self.pad(_UpperCAmelCase )
# Normalize
if self.size_divisibility > 0:
raise NotImplementedError()
# pad
_A = torch.true_divide(_UpperCAmelCase , _UpperCAmelCase )
if single_image:
return images[0], sizes[0], scales_yx[0]
else:
return images, sizes, scales_yx
def _snake_case ( _snake_case : int , _snake_case : List[str] ) -> Any:
'''simple docstring'''
boxes[:, 0::2] *= scale_yx[:, 1]
boxes[:, 1::2] *= scale_yx[:, 0]
return boxes
def _snake_case ( _snake_case : Optional[Any] , _snake_case : Tuple[int, int] ) -> Union[str, Any]:
'''simple docstring'''
assert torch.isfinite(_snake_case ).all(), "Box tensor contains infinite or NaN!"
_A , _A = box_size
tensor[:, 0].clamp_(min=0 , max=_snake_case )
tensor[:, 1].clamp_(min=0 , max=_snake_case )
tensor[:, 2].clamp_(min=0 , max=_snake_case )
tensor[:, 3].clamp_(min=0 , max=_snake_case )
| 271 |
"""simple docstring"""
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase : Union[str, Any] = (IPNDMScheduler,)
UpperCAmelCase : Optional[Any] = (('''num_inference_steps''', 50),)
def lowerCAmelCase_ ( self : Union[str, Any] , **_UpperCAmelCase : List[Any] ):
_A = {'num_train_timesteps': 1_000}
config.update(**_UpperCAmelCase )
return config
def lowerCAmelCase_ ( self : Tuple , _UpperCAmelCase : Optional[int]=0 , **_UpperCAmelCase : Union[str, Any] ):
_A = dict(self.forward_default_kwargs )
_A = kwargs.pop('num_inference_steps' , _UpperCAmelCase )
_A = self.dummy_sample
_A = 0.1 * sample
_A = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
for scheduler_class in self.scheduler_classes:
_A = self.get_scheduler_config(**_UpperCAmelCase )
_A = scheduler_class(**_UpperCAmelCase )
scheduler.set_timesteps(_UpperCAmelCase )
# copy over dummy past residuals
_A = dummy_past_residuals[:]
if time_step is None:
_A = scheduler.timesteps[len(scheduler.timesteps ) // 2]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(_UpperCAmelCase )
_A = scheduler_class.from_pretrained(_UpperCAmelCase )
new_scheduler.set_timesteps(_UpperCAmelCase )
# copy over dummy past residuals
_A = dummy_past_residuals[:]
_A = scheduler.step(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample
_A = new_scheduler.step(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
_A = scheduler.step(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample
_A = new_scheduler.step(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def lowerCAmelCase_ ( self : str ):
pass
def lowerCAmelCase_ ( self : Optional[Any] , _UpperCAmelCase : Any=0 , **_UpperCAmelCase : Any ):
_A = dict(self.forward_default_kwargs )
_A = kwargs.pop('num_inference_steps' , _UpperCAmelCase )
_A = self.dummy_sample
_A = 0.1 * sample
_A = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
for scheduler_class in self.scheduler_classes:
_A = self.get_scheduler_config()
_A = scheduler_class(**_UpperCAmelCase )
scheduler.set_timesteps(_UpperCAmelCase )
# copy over dummy past residuals (must be after setting timesteps)
_A = dummy_past_residuals[:]
if time_step is None:
_A = scheduler.timesteps[len(scheduler.timesteps ) // 2]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(_UpperCAmelCase )
_A = scheduler_class.from_pretrained(_UpperCAmelCase )
# copy over dummy past residuals
new_scheduler.set_timesteps(_UpperCAmelCase )
# copy over dummy past residual (must be after setting timesteps)
_A = dummy_past_residuals[:]
_A = scheduler.step(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample
_A = new_scheduler.step(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
_A = scheduler.step(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample
_A = new_scheduler.step(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def lowerCAmelCase_ ( self : List[str] , **_UpperCAmelCase : Optional[int] ):
_A = self.scheduler_classes[0]
_A = self.get_scheduler_config(**_UpperCAmelCase )
_A = scheduler_class(**_UpperCAmelCase )
_A = 10
_A = self.dummy_model()
_A = self.dummy_sample_deter
scheduler.set_timesteps(_UpperCAmelCase )
for i, t in enumerate(scheduler.timesteps ):
_A = model(_UpperCAmelCase , _UpperCAmelCase )
_A = scheduler.step(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ).prev_sample
for i, t in enumerate(scheduler.timesteps ):
_A = model(_UpperCAmelCase , _UpperCAmelCase )
_A = scheduler.step(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ).prev_sample
return sample
def lowerCAmelCase_ ( self : Union[str, Any] ):
_A = dict(self.forward_default_kwargs )
_A = kwargs.pop('num_inference_steps' , _UpperCAmelCase )
for scheduler_class in self.scheduler_classes:
_A = self.get_scheduler_config()
_A = scheduler_class(**_UpperCAmelCase )
_A = self.dummy_sample
_A = 0.1 * sample
if num_inference_steps is not None and hasattr(_UpperCAmelCase , 'set_timesteps' ):
scheduler.set_timesteps(_UpperCAmelCase )
elif num_inference_steps is not None and not hasattr(_UpperCAmelCase , 'set_timesteps' ):
_A = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
_A = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
_A = dummy_past_residuals[:]
_A = scheduler.timesteps[5]
_A = scheduler.timesteps[6]
_A = scheduler.step(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample
_A = scheduler.step(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
_A = scheduler.step(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample
_A = scheduler.step(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def lowerCAmelCase_ ( self : Tuple ):
for timesteps in [100, 1_000]:
self.check_over_configs(num_train_timesteps=_UpperCAmelCase , time_step=_UpperCAmelCase )
def lowerCAmelCase_ ( self : List[Any] ):
for t, num_inference_steps in zip([1, 5, 10] , [10, 50, 100] ):
self.check_over_forward(num_inference_steps=_UpperCAmelCase , time_step=_UpperCAmelCase )
def lowerCAmelCase_ ( self : Optional[int] ):
_A = self.full_loop()
_A = torch.mean(torch.abs(_UpperCAmelCase ) )
assert abs(result_mean.item() - 2_540_529 ) < 10
| 271 | 1 |
def UpperCamelCase__ ( A__ , A__ ) -> str:
snake_case__ : List[Any] = ''
for word_or_phrase in separated:
if not isinstance(A__ , A__ ):
raise Exception('join() accepts only strings to be joined' )
joined += word_or_phrase + separator
return joined.strip(A__ )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 143 | import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import HuggingFace
@pytest.mark.skipif(
literal_eval(os.getenv("""TEST_SAGEMAKER""" ,"""False""" ) ) is not True ,reason="""Skipping test because should only be run when releasing minor transformers version""" ,)
@pytest.mark.usefixtures("""sm_env""" )
@parameterized_class(
[
{
"""framework""": """pytorch""",
"""script""": """run_glue_model_parallelism.py""",
"""model_name_or_path""": """roberta-large""",
"""instance_type""": """ml.p3dn.24xlarge""",
"""results""": {"""train_runtime""": 1600, """eval_accuracy""": 0.3, """eval_loss""": 1.2},
},
{
"""framework""": """pytorch""",
"""script""": """run_glue.py""",
"""model_name_or_path""": """roberta-large""",
"""instance_type""": """ml.p3dn.24xlarge""",
"""results""": {"""train_runtime""": 1600, """eval_accuracy""": 0.3, """eval_loss""": 1.2},
},
] )
class __snake_case ( unittest.TestCase ):
def __a ( self ) -> Tuple:
'''simple docstring'''
if self.framework == "pytorch":
subprocess.run(
F"""cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py""".split() , encoding='utf-8' , check=__UpperCamelCase , )
assert hasattr(self , 'env' )
def __a ( self , __UpperCamelCase ) -> Optional[int]:
'''simple docstring'''
snake_case__ : Tuple = {
'enabled': True,
'processes_per_host': 8,
}
snake_case__ : Any = {
'enabled': True,
'parameters': {
'microbatches': 4,
'placement_strategy': 'spread',
'pipeline': 'interleaved',
'optimize': 'speed',
'partitions': 4,
'ddp': True,
},
}
snake_case__ : Optional[int] = {'smdistributed': {'modelparallel': smp_options}, 'mpi': mpi_options}
snake_case__ : int = 'trainer' if self.script == 'run_glue.py' else 'smtrainer'
# creates estimator
return HuggingFace(
entry_point=self.script , source_dir=self.env.test_path , role=self.env.role , image_uri=self.env.image_uri , base_job_name=F"""{self.env.base_job_name}-{instance_count}-smp-{name_extension}""" , instance_count=__UpperCamelCase , instance_type=self.instance_type , debugger_hook_config=__UpperCamelCase , hyperparameters={
**self.env.hyperparameters,
'model_name_or_path': self.model_name_or_path,
'max_steps': 500,
} , metric_definitions=self.env.metric_definitions , distribution=__UpperCamelCase , py_version='py36' , )
def __a ( self , __UpperCamelCase ) -> List[Any]:
'''simple docstring'''
TrainingJobAnalytics(__UpperCamelCase ).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv""" )
@parameterized.expand([(1,)] )
def __a ( self , __UpperCamelCase ) -> List[Any]:
'''simple docstring'''
snake_case__ : str = self.create_estimator(__UpperCamelCase )
# run training
estimator.fit()
# result dataframe
snake_case__ : Dict = TrainingJobAnalytics(estimator.latest_training_job.name ).dataframe()
# extract kpis
snake_case__ : Any = list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value'] )
snake_case__ : List[str] = list(result_metrics_df[result_metrics_df.metric_name == 'eval_loss']['value'] )
# get train time from SageMaker job, this includes starting, preprocessing, stopping
snake_case__ : Any = (
Session().describe_training_job(estimator.latest_training_job.name ).get('TrainingTimeInSeconds' , 999999 )
)
# assert kpis
assert train_runtime <= self.results["train_runtime"]
assert all(t >= self.results['eval_accuracy'] for t in eval_accuracy )
assert all(t <= self.results['eval_loss'] for t in eval_loss )
# dump tests result into json file to share in PR
with open(F"""{estimator.latest_training_job.name}.json""" , 'w' ) as outfile:
json.dump({'train_time': train_runtime, 'eval_accuracy': eval_accuracy, 'eval_loss': eval_loss} , __UpperCamelCase )
| 143 | 1 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@require_torchaudio
@require_sentencepiece
class lowerCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
def UpperCAmelCase_ ( self ) -> Tuple:
lowerCAmelCase__ : List[Any] = "laion/clap-htsat-unfused"
lowerCAmelCase__ : Tuple = tempfile.mkdtemp()
def UpperCAmelCase_ ( self ,**__UpperCAmelCase ) -> int:
return RobertaTokenizer.from_pretrained(self.checkpoint ,**_SCREAMING_SNAKE_CASE )
def UpperCAmelCase_ ( self ,**__UpperCAmelCase ) -> Optional[int]:
return ClapFeatureExtractor.from_pretrained(self.checkpoint ,**_SCREAMING_SNAKE_CASE )
def UpperCAmelCase_ ( self ) -> Optional[Any]:
shutil.rmtree(self.tmpdirname )
def UpperCAmelCase_ ( self ) -> List[str]:
lowerCAmelCase__ : List[Any] = self.get_tokenizer()
lowerCAmelCase__ : List[Any] = self.get_feature_extractor()
lowerCAmelCase__ : Dict = ClapProcessor(tokenizer=_SCREAMING_SNAKE_CASE ,feature_extractor=_SCREAMING_SNAKE_CASE )
processor.save_pretrained(self.tmpdirname )
lowerCAmelCase__ : List[Any] = ClapProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() ,tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer ,_SCREAMING_SNAKE_CASE )
self.assertEqual(processor.feature_extractor.to_json_string() ,feature_extractor.to_json_string() )
self.assertIsInstance(processor.feature_extractor ,_SCREAMING_SNAKE_CASE )
def UpperCAmelCase_ ( self ) -> Union[str, Any]:
lowerCAmelCase__ : str = ClapProcessor(tokenizer=self.get_tokenizer() ,feature_extractor=self.get_feature_extractor() )
processor.save_pretrained(self.tmpdirname )
lowerCAmelCase__ : Optional[int] = self.get_tokenizer(bos_token="""(BOS)""" ,eos_token="""(EOS)""" )
lowerCAmelCase__ : Optional[Any] = self.get_feature_extractor(do_normalize=_SCREAMING_SNAKE_CASE ,padding_value=1.0 )
lowerCAmelCase__ : Dict = ClapProcessor.from_pretrained(
self.tmpdirname ,bos_token="""(BOS)""" ,eos_token="""(EOS)""" ,do_normalize=_SCREAMING_SNAKE_CASE ,padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() ,tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer ,_SCREAMING_SNAKE_CASE )
self.assertEqual(processor.feature_extractor.to_json_string() ,feature_extractor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.feature_extractor ,_SCREAMING_SNAKE_CASE )
def UpperCAmelCase_ ( self ) -> str:
lowerCAmelCase__ : str = self.get_feature_extractor()
lowerCAmelCase__ : Tuple = self.get_tokenizer()
lowerCAmelCase__ : Any = ClapProcessor(tokenizer=_SCREAMING_SNAKE_CASE ,feature_extractor=_SCREAMING_SNAKE_CASE )
lowerCAmelCase__ : List[Any] = floats_list((3, 1000) )
lowerCAmelCase__ : Optional[Any] = feature_extractor(_SCREAMING_SNAKE_CASE ,return_tensors="""np""" )
lowerCAmelCase__ : List[str] = processor(audios=_SCREAMING_SNAKE_CASE ,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 ) -> Optional[int]:
lowerCAmelCase__ : int = self.get_feature_extractor()
lowerCAmelCase__ : str = self.get_tokenizer()
lowerCAmelCase__ : str = ClapProcessor(tokenizer=_SCREAMING_SNAKE_CASE ,feature_extractor=_SCREAMING_SNAKE_CASE )
lowerCAmelCase__ : Tuple = "This is a test string"
lowerCAmelCase__ : str = processor(text=_SCREAMING_SNAKE_CASE )
lowerCAmelCase__ : Tuple = tokenizer(_SCREAMING_SNAKE_CASE )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] ,encoded_processor[key] )
def UpperCAmelCase_ ( self ) -> str:
lowerCAmelCase__ : Optional[int] = self.get_feature_extractor()
lowerCAmelCase__ : Union[str, Any] = self.get_tokenizer()
lowerCAmelCase__ : Any = ClapProcessor(tokenizer=_SCREAMING_SNAKE_CASE ,feature_extractor=_SCREAMING_SNAKE_CASE )
lowerCAmelCase__ : Any = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
lowerCAmelCase__ : Tuple = processor.batch_decode(_SCREAMING_SNAKE_CASE )
lowerCAmelCase__ : int = tokenizer.batch_decode(_SCREAMING_SNAKE_CASE )
self.assertListEqual(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
def UpperCAmelCase_ ( self ) -> Union[str, Any]:
lowerCAmelCase__ : Tuple = self.get_feature_extractor()
lowerCAmelCase__ : str = self.get_tokenizer()
lowerCAmelCase__ : Any = ClapProcessor(tokenizer=_SCREAMING_SNAKE_CASE ,feature_extractor=_SCREAMING_SNAKE_CASE )
self.assertListEqual(
processor.model_input_names[2:] ,feature_extractor.model_input_names ,msg="""`processor` and `feature_extractor` model input names do not match""" ,)
| 364 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( UpperCamelCase , UpperCamelCase , UpperCamelCase ):
"""simple docstring"""
def count_of_possible_combinations(UpperCamelCase ) -> int:
if target < 0:
return 0
if target == 0:
return 1
return sum(count_of_possible_combinations(target - item ) for item in array )
return count_of_possible_combinations(UpperCamelCase )
def _SCREAMING_SNAKE_CASE ( UpperCamelCase , UpperCamelCase , UpperCamelCase ):
"""simple docstring"""
def count_of_possible_combinations_with_dp_array(
UpperCamelCase , UpperCamelCase ) -> int:
if target < 0:
return 0
if target == 0:
return 1
if dp_array[target] != -1:
return dp_array[target]
lowerCAmelCase__ : Any = sum(
count_of_possible_combinations_with_dp_array(target - item , UpperCamelCase )
for item in array )
lowerCAmelCase__ : Tuple = answer
return answer
lowerCAmelCase__ : Optional[int] = [-1] * (target + 1)
return count_of_possible_combinations_with_dp_array(UpperCamelCase , UpperCamelCase )
def _SCREAMING_SNAKE_CASE ( UpperCamelCase , UpperCamelCase , UpperCamelCase ):
"""simple docstring"""
lowerCAmelCase__ : str = [0] * (target + 1)
lowerCAmelCase__ : List[Any] = 1
for i in range(1 , target + 1 ):
for j in range(UpperCamelCase ):
if i - array[j] >= 0:
dp_array[i] += dp_array[i - array[j]]
return dp_array[target]
if __name__ == "__main__":
import doctest
doctest.testmod()
_lowerCAmelCase = 3
_lowerCAmelCase = 5
_lowerCAmelCase = [1, 2, 5]
print(combination_sum_iv(n, array, target))
| 184 | 0 |
import inspect
import unittest
from transformers import MobileNetVaConfig
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 MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation, MobileNetVaModel
from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import MobileNetVaImageProcessor
class __a ( A__ ):
def __lowercase ( self : Optional[int] ):
'''simple docstring'''
UpperCamelCase__ : Optional[Any] = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(SCREAMING_SNAKE_CASE , "tf_padding" ) )
self.parent.assertTrue(hasattr(SCREAMING_SNAKE_CASE , "depth_multiplier" ) )
class __a :
def __init__( self : List[Any] , SCREAMING_SNAKE_CASE : Optional[int] , SCREAMING_SNAKE_CASE : Dict=13 , SCREAMING_SNAKE_CASE : Dict=3 , SCREAMING_SNAKE_CASE : Any=32 , SCREAMING_SNAKE_CASE : Optional[int]=0.2_5 , SCREAMING_SNAKE_CASE : Union[str, Any]=8 , SCREAMING_SNAKE_CASE : List[str]=8 , SCREAMING_SNAKE_CASE : Dict=6 , SCREAMING_SNAKE_CASE : Tuple=32 , SCREAMING_SNAKE_CASE : Tuple=True , SCREAMING_SNAKE_CASE : List[str]=True , SCREAMING_SNAKE_CASE : Any=True , SCREAMING_SNAKE_CASE : str="relu6" , SCREAMING_SNAKE_CASE : Optional[Any]=12_80 , SCREAMING_SNAKE_CASE : List[Any]=0.1 , SCREAMING_SNAKE_CASE : Tuple=0.0_2 , SCREAMING_SNAKE_CASE : Optional[Any]=True , SCREAMING_SNAKE_CASE : str=True , SCREAMING_SNAKE_CASE : List[Any]=10 , SCREAMING_SNAKE_CASE : Union[str, Any]=None , ):
'''simple docstring'''
UpperCamelCase__ : Dict = parent
UpperCamelCase__ : int = batch_size
UpperCamelCase__ : Union[str, Any] = num_channels
UpperCamelCase__ : List[str] = image_size
UpperCamelCase__ : int = depth_multiplier
UpperCamelCase__ : Optional[Any] = depth_divisible_by
UpperCamelCase__ : List[str] = min_depth
UpperCamelCase__ : Tuple = expand_ratio
UpperCamelCase__ : Optional[Any] = tf_padding
UpperCamelCase__ : Dict = output_stride
UpperCamelCase__ : Any = first_layer_is_expansion
UpperCamelCase__ : List[Any] = finegrained_output
UpperCamelCase__ : Optional[Any] = hidden_act
UpperCamelCase__ : List[str] = last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier )
UpperCamelCase__ : str = classifier_dropout_prob
UpperCamelCase__ : str = use_labels
UpperCamelCase__ : Optional[Any] = is_training
UpperCamelCase__ : List[Any] = num_labels
UpperCamelCase__ : Any = initializer_range
UpperCamelCase__ : List[Any] = scope
def __lowercase ( self : Optional[Any] ):
'''simple docstring'''
UpperCamelCase__ : Dict = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCamelCase__ : Any = None
UpperCamelCase__ : Optional[int] = None
if self.use_labels:
UpperCamelCase__ : Union[str, Any] = ids_tensor([self.batch_size] , self.num_labels )
UpperCamelCase__ : str = ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels )
UpperCamelCase__ : Union[str, Any] = self.get_config()
return config, pixel_values, labels, pixel_labels
def __lowercase ( self : Optional[int] ):
'''simple docstring'''
return MobileNetVaConfig(
num_channels=self.num_channels , image_size=self.image_size , depth_multiplier=self.depth_multiplier , depth_divisible_by=self.depth_divisible_by , min_depth=self.min_depth , expand_ratio=self.expand_ratio , output_stride=self.output_stride , first_layer_is_expansion=self.first_layer_is_expansion , finegrained_output=self.finegrained_output , hidden_act=self.hidden_act , tf_padding=self.tf_padding , classifier_dropout_prob=self.classifier_dropout_prob , initializer_range=self.initializer_range , )
def __lowercase ( self : List[str] , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : Any , SCREAMING_SNAKE_CASE : List[str] , SCREAMING_SNAKE_CASE : List[Any] ):
'''simple docstring'''
UpperCamelCase__ : Tuple = MobileNetVaModel(config=SCREAMING_SNAKE_CASE )
model.to(SCREAMING_SNAKE_CASE )
model.eval()
UpperCamelCase__ : Dict = model(SCREAMING_SNAKE_CASE )
self.parent.assertEqual(
result.last_hidden_state.shape , (
self.batch_size,
self.last_hidden_size,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
self.parent.assertEqual(
result.pooler_output.shape , (self.batch_size, self.last_hidden_size) , )
def __lowercase ( self : Tuple , SCREAMING_SNAKE_CASE : Optional[Any] , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : Any , SCREAMING_SNAKE_CASE : List[Any] ):
'''simple docstring'''
UpperCamelCase__ : List[str] = self.num_labels
UpperCamelCase__ : Optional[int] = MobileNetVaForImageClassification(SCREAMING_SNAKE_CASE )
model.to(SCREAMING_SNAKE_CASE )
model.eval()
UpperCamelCase__ : List[Any] = model(SCREAMING_SNAKE_CASE , labels=SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __lowercase ( self : Any , SCREAMING_SNAKE_CASE : Optional[Any] , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : Optional[Any] , SCREAMING_SNAKE_CASE : List[Any] ):
'''simple docstring'''
UpperCamelCase__ : Optional[Any] = self.num_labels
UpperCamelCase__ : str = MobileNetVaForSemanticSegmentation(SCREAMING_SNAKE_CASE )
model.to(SCREAMING_SNAKE_CASE )
model.eval()
UpperCamelCase__ : List[Any] = model(SCREAMING_SNAKE_CASE )
self.parent.assertEqual(
result.logits.shape , (
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
UpperCamelCase__ : Union[str, Any] = model(SCREAMING_SNAKE_CASE , labels=SCREAMING_SNAKE_CASE )
self.parent.assertEqual(
result.logits.shape , (
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
def __lowercase ( self : Any ):
'''simple docstring'''
UpperCamelCase__ : Optional[int] = self.prepare_config_and_inputs()
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ : Union[str, Any] = config_and_inputs
UpperCamelCase__ : List[str] = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class __a ( A__ , A__ , unittest.TestCase ):
_lowerCAmelCase : Tuple = (
(MobileNetVaModel, MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation)
if is_torch_available()
else ()
)
_lowerCAmelCase : Optional[int] = (
{
'''feature-extraction''': MobileNetVaModel,
'''image-classification''': MobileNetVaForImageClassification,
'''image-segmentation''': MobileNetVaForSemanticSegmentation,
}
if is_torch_available()
else {}
)
_lowerCAmelCase : Tuple = False
_lowerCAmelCase : Union[str, Any] = False
_lowerCAmelCase : List[Any] = False
_lowerCAmelCase : List[str] = False
def __lowercase ( self : str ):
'''simple docstring'''
UpperCamelCase__ : Tuple = MobileNetVaModelTester(self )
UpperCamelCase__ : str = MobileNetVaConfigTester(self , config_class=SCREAMING_SNAKE_CASE , has_text_modality=SCREAMING_SNAKE_CASE )
def __lowercase ( self : Optional[Any] ):
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason="MobileNetV2 does not use inputs_embeds" )
def __lowercase ( self : int ):
'''simple docstring'''
pass
@unittest.skip(reason="MobileNetV2 does not support input and output embeddings" )
def __lowercase ( self : Dict ):
'''simple docstring'''
pass
@unittest.skip(reason="MobileNetV2 does not output attentions" )
def __lowercase ( self : Optional[int] ):
'''simple docstring'''
pass
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:
UpperCamelCase__ : str = model_class(SCREAMING_SNAKE_CASE )
UpperCamelCase__ : List[str] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCamelCase__ : int = [*signature.parameters.keys()]
UpperCamelCase__ : Any = ["pixel_values"]
self.assertListEqual(arg_names[:1] , SCREAMING_SNAKE_CASE )
def __lowercase ( self : List[Any] ):
'''simple docstring'''
UpperCamelCase__ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE )
def __lowercase ( self : Any ):
'''simple docstring'''
def check_hidden_states_output(SCREAMING_SNAKE_CASE : List[str] , SCREAMING_SNAKE_CASE : List[str] , SCREAMING_SNAKE_CASE : Optional[Any] ):
UpperCamelCase__ : Any = model_class(SCREAMING_SNAKE_CASE )
model.to(SCREAMING_SNAKE_CASE )
model.eval()
with torch.no_grad():
UpperCamelCase__ : Tuple = model(**self._prepare_for_class(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) )
UpperCamelCase__ : List[Any] = outputs.hidden_states
UpperCamelCase__ : Union[str, Any] = 16
self.assertEqual(len(SCREAMING_SNAKE_CASE ) , SCREAMING_SNAKE_CASE )
UpperCamelCase__ , UpperCamelCase__ : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCamelCase__ : Union[str, Any] = True
check_hidden_states_output(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
UpperCamelCase__ : str = True
check_hidden_states_output(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
def __lowercase ( self : List[str] ):
'''simple docstring'''
UpperCamelCase__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE )
def __lowercase ( self : List[str] ):
'''simple docstring'''
UpperCamelCase__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*SCREAMING_SNAKE_CASE )
@slow
def __lowercase ( self : List[str] ):
'''simple docstring'''
for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCamelCase__ : Optional[int] = MobileNetVaModel.from_pretrained(SCREAMING_SNAKE_CASE )
self.assertIsNotNone(SCREAMING_SNAKE_CASE )
def SCREAMING_SNAKE_CASE ( ) -> Optional[Any]:
UpperCamelCase__ : str = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class __a ( unittest.TestCase ):
@cached_property
def __lowercase ( self : str ):
'''simple docstring'''
return (
MobileNetVaImageProcessor.from_pretrained("google/mobilenet_v2_1.0_224" ) if is_vision_available() else None
)
@slow
def __lowercase ( self : Any ):
'''simple docstring'''
UpperCamelCase__ : List[Any] = MobileNetVaForImageClassification.from_pretrained("google/mobilenet_v2_1.0_224" ).to(SCREAMING_SNAKE_CASE )
UpperCamelCase__ : Tuple = self.default_image_processor
UpperCamelCase__ : Union[str, Any] = prepare_img()
UpperCamelCase__ : str = image_processor(images=SCREAMING_SNAKE_CASE , return_tensors="pt" ).to(SCREAMING_SNAKE_CASE )
# forward pass
with torch.no_grad():
UpperCamelCase__ : List[str] = model(**SCREAMING_SNAKE_CASE )
# verify the logits
UpperCamelCase__ : List[str] = torch.Size((1, 10_01) )
self.assertEqual(outputs.logits.shape , SCREAMING_SNAKE_CASE )
UpperCamelCase__ : Optional[Any] = torch.tensor([0.2_4_4_5, -1.1_9_9_3, 0.1_9_0_5] ).to(SCREAMING_SNAKE_CASE )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , SCREAMING_SNAKE_CASE , atol=1e-4 ) )
@slow
def __lowercase ( self : List[str] ):
'''simple docstring'''
UpperCamelCase__ : str = MobileNetVaForSemanticSegmentation.from_pretrained("google/deeplabv3_mobilenet_v2_1.0_513" )
UpperCamelCase__ : Dict = model.to(SCREAMING_SNAKE_CASE )
UpperCamelCase__ : Tuple = MobileNetVaImageProcessor.from_pretrained("google/deeplabv3_mobilenet_v2_1.0_513" )
UpperCamelCase__ : Union[str, Any] = prepare_img()
UpperCamelCase__ : List[str] = image_processor(images=SCREAMING_SNAKE_CASE , return_tensors="pt" ).to(SCREAMING_SNAKE_CASE )
# forward pass
with torch.no_grad():
UpperCamelCase__ : Optional[int] = model(**SCREAMING_SNAKE_CASE )
UpperCamelCase__ : Optional[int] = outputs.logits
# verify the logits
UpperCamelCase__ : List[Any] = torch.Size((1, 21, 65, 65) )
self.assertEqual(logits.shape , SCREAMING_SNAKE_CASE )
UpperCamelCase__ : Union[str, Any] = torch.tensor(
[
[[1_7.5_7_9_0, 1_7.7_5_8_1, 1_8.3_3_5_5], [1_8.3_2_5_7, 1_8.4_2_3_0, 1_8.8_9_7_3], [1_8.6_1_6_9, 1_8.8_6_5_0, 1_9.2_1_8_7]],
[[-2.1_5_9_5, -2.0_9_7_7, -2.3_7_4_1], [-2.4_2_2_6, -2.3_0_2_8, -2.6_8_3_5], [-2.7_8_1_9, -2.5_9_9_1, -2.7_7_0_6]],
[[4.2_0_5_8, 4.8_3_1_7, 4.7_6_3_8], [4.4_1_3_6, 5.0_3_6_1, 4.9_3_8_3], [4.5_0_2_8, 4.9_6_4_4, 4.8_7_3_4]],
] , device=SCREAMING_SNAKE_CASE , )
self.assertTrue(torch.allclose(logits[0, :3, :3, :3] , SCREAMING_SNAKE_CASE , atol=1e-4 ) ) | 189 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase : Optional[int] =logging.get_logger(__name__)
lowerCamelCase : Dict ={
'''facebook/s2t-wav2vec2-large-en-de''': (
'''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json'''
),
# See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}
class __a ( A__ ):
_lowerCAmelCase : Tuple = '''speech_to_text_2'''
_lowerCAmelCase : Dict = ['''past_key_values''']
_lowerCAmelCase : Any = {'''num_attention_heads''': '''decoder_attention_heads''', '''hidden_size''': '''d_model'''}
def __init__( self : List[str] , SCREAMING_SNAKE_CASE : Union[str, Any]=1_00_00 , SCREAMING_SNAKE_CASE : List[Any]=6 , SCREAMING_SNAKE_CASE : List[Any]=20_48 , SCREAMING_SNAKE_CASE : Dict=4 , SCREAMING_SNAKE_CASE : List[Any]=0.0 , SCREAMING_SNAKE_CASE : Optional[int]=True , SCREAMING_SNAKE_CASE : List[Any]="relu" , SCREAMING_SNAKE_CASE : Tuple=2_56 , SCREAMING_SNAKE_CASE : List[str]=0.1 , SCREAMING_SNAKE_CASE : Tuple=0.0 , SCREAMING_SNAKE_CASE : Any=0.0 , SCREAMING_SNAKE_CASE : int=0.0_2 , SCREAMING_SNAKE_CASE : Dict=2 , SCREAMING_SNAKE_CASE : List[str]=True , SCREAMING_SNAKE_CASE : Any=1 , SCREAMING_SNAKE_CASE : List[Any]=0 , SCREAMING_SNAKE_CASE : Any=2 , SCREAMING_SNAKE_CASE : str=10_24 , **SCREAMING_SNAKE_CASE : int , ):
'''simple docstring'''
UpperCamelCase__ : int = vocab_size
UpperCamelCase__ : Optional[Any] = d_model
UpperCamelCase__ : Optional[Any] = decoder_ffn_dim
UpperCamelCase__ : str = decoder_layers
UpperCamelCase__ : Any = decoder_attention_heads
UpperCamelCase__ : List[str] = dropout
UpperCamelCase__ : int = attention_dropout
UpperCamelCase__ : Optional[int] = activation_dropout
UpperCamelCase__ : Union[str, Any] = activation_function
UpperCamelCase__ : Tuple = init_std
UpperCamelCase__ : Optional[int] = decoder_layerdrop
UpperCamelCase__ : Dict = use_cache
UpperCamelCase__ : str = decoder_layers
UpperCamelCase__ : Union[str, Any] = scale_embedding # scale factor will be sqrt(d_model) if True
UpperCamelCase__ : Optional[Any] = max_target_positions
super().__init__(
pad_token_id=SCREAMING_SNAKE_CASE , bos_token_id=SCREAMING_SNAKE_CASE , eos_token_id=SCREAMING_SNAKE_CASE , decoder_start_token_id=SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE , ) | 189 | 1 |
'''simple docstring'''
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def _a ( _lowerCamelCase , _lowerCamelCase ) -> Any:
"""simple docstring"""
__snake_case : Optional[Any] = torch.load(_lowerCamelCase , map_location="""cpu""" )
__snake_case : Optional[Any] = chkpt["""model"""]
# We have the base model one level deeper than the original XLM repository
__snake_case : Optional[Any] = {}
for k, v in state_dict.items():
if "pred_layer" in k:
__snake_case : int = v
else:
__snake_case : Any = v
__snake_case : Dict = chkpt["""params"""]
__snake_case : str = {n: v for n, v in config.items() if not isinstance(_lowerCamelCase , (torch.FloatTensor, numpy.ndarray) )}
__snake_case : List[str] = chkpt["""dico_word2id"""]
__snake_case : Union[str, Any] = {s + """</w>""" if s.find("""@@""" ) == -1 and i > 13 else s.replace("""@@""" , """""" ): i for s, i in vocab.items()}
# Save pytorch-model
__snake_case : Tuple = pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
__snake_case : Optional[int] = pytorch_dump_folder_path + """/""" + CONFIG_NAME
__snake_case : List[Any] = pytorch_dump_folder_path + """/""" + VOCAB_FILES_NAMES["""vocab_file"""]
print(F'''Save PyTorch model to {pytorch_weights_dump_path}''' )
torch.save(_lowerCamelCase , _lowerCamelCase )
print(F'''Save configuration file to {pytorch_config_dump_path}''' )
with open(_lowerCamelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(_lowerCamelCase , indent=2 ) + """\n""" )
print(F'''Save vocab file to {pytorch_config_dump_path}''' )
with open(_lowerCamelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(_lowerCamelCase , indent=2 ) + """\n""" )
if __name__ == "__main__":
__UpperCamelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--xlm_checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump."
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
__UpperCamelCase = parser.parse_args()
convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)
| 13 |
'''simple docstring'''
__UpperCamelCase = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
def _a ( ) -> None:
"""simple docstring"""
__snake_case : Dict = input("""Enter message: """ )
__snake_case : Optional[int] = input("""Enter key [alphanumeric]: """ )
__snake_case : Tuple = input("""Encrypt/Decrypt [e/d]: """ )
if mode.lower().startswith("""e""" ):
__snake_case : Any = """encrypt"""
__snake_case : Optional[Any] = encrypt_message(_lowerCamelCase , _lowerCamelCase )
elif mode.lower().startswith("""d""" ):
__snake_case : Optional[int] = """decrypt"""
__snake_case : Any = decrypt_message(_lowerCamelCase , _lowerCamelCase )
print(F'''\n{mode.title()}ed message:''' )
print(_lowerCamelCase )
def _a ( _lowerCamelCase , _lowerCamelCase ) -> str:
"""simple docstring"""
return translate_message(_lowerCamelCase , _lowerCamelCase , """encrypt""" )
def _a ( _lowerCamelCase , _lowerCamelCase ) -> str:
"""simple docstring"""
return translate_message(_lowerCamelCase , _lowerCamelCase , """decrypt""" )
def _a ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) -> str:
"""simple docstring"""
__snake_case : str = []
__snake_case : Dict = 0
__snake_case : Optional[int] = key.upper()
for symbol in message:
__snake_case : Any = LETTERS.find(symbol.upper() )
if num != -1:
if mode == "encrypt":
num += LETTERS.find(key[key_index] )
elif mode == "decrypt":
num -= LETTERS.find(key[key_index] )
num %= len(_lowerCamelCase )
if symbol.isupper():
translated.append(LETTERS[num] )
elif symbol.islower():
translated.append(LETTERS[num].lower() )
key_index += 1
if key_index == len(_lowerCamelCase ):
__snake_case : Tuple = 0
else:
translated.append(_lowerCamelCase )
return "".join(_lowerCamelCase )
if __name__ == "__main__":
main()
| 13 | 1 |
"""simple docstring"""
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaTokenizerFast,
)
def A__ ( UpperCamelCase ):
A = model.config
A = DonutSwinConfig(
image_size=original_config.input_size , patch_size=4 , depths=original_config.encoder_layer , num_heads=[4, 8, 16, 32] , window_size=original_config.window_size , embed_dim=128 , )
A = MBartConfig(
is_decoder=UpperCamelCase , is_encoder_decoder=UpperCamelCase , add_cross_attention=UpperCamelCase , decoder_layers=original_config.decoder_layer , max_position_embeddings=original_config.max_position_embeddings , vocab_size=len(
model.decoder.tokenizer ) , scale_embedding=UpperCamelCase , add_final_layer_norm=UpperCamelCase , )
return encoder_config, decoder_config
def A__ ( UpperCamelCase ):
if "encoder.model" in name:
A = name.replace("encoder.model" , "encoder" )
if "decoder.model" in name:
A = name.replace("decoder.model" , "decoder" )
if "patch_embed.proj" in name:
A = name.replace("patch_embed.proj" , "embeddings.patch_embeddings.projection" )
if "patch_embed.norm" in name:
A = name.replace("patch_embed.norm" , "embeddings.norm" )
if name.startswith("encoder" ):
if "layers" in name:
A = "encoder." + name
if "attn.proj" in name:
A = name.replace("attn.proj" , "attention.output.dense" )
if "attn" in name and "mask" not in name:
A = name.replace("attn" , "attention.self" )
if "norm1" in name:
A = name.replace("norm1" , "layernorm_before" )
if "norm2" in name:
A = name.replace("norm2" , "layernorm_after" )
if "mlp.fc1" in name:
A = name.replace("mlp.fc1" , "intermediate.dense" )
if "mlp.fc2" in name:
A = name.replace("mlp.fc2" , "output.dense" )
if name == "encoder.norm.weight":
A = "encoder.layernorm.weight"
if name == "encoder.norm.bias":
A = "encoder.layernorm.bias"
return name
def A__ ( UpperCamelCase , UpperCamelCase ):
for key in orig_state_dict.copy().keys():
A = orig_state_dict.pop(UpperCamelCase )
if "qkv" in key:
A = key.split("." )
A = int(key_split[3] )
A = int(key_split[5] )
A = model.encoder.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
A = val[:dim, :]
A = val[dim : dim * 2, :]
A = val[-dim:, :]
else:
A = val[:dim]
A = val[dim : dim * 2]
A = val[-dim:]
elif "attn_mask" in key or key in ["encoder.model.norm.weight", "encoder.model.norm.bias"]:
# HuggingFace implementation doesn't use attn_mask buffer
# and model doesn't use final LayerNorms for the encoder
pass
else:
A = val
return orig_state_dict
def A__ ( UpperCamelCase , UpperCamelCase=None , UpperCamelCase=False ):
# load original model
A = DonutModel.from_pretrained(UpperCamelCase ).eval()
# load HuggingFace model
A, A = get_configs(UpperCamelCase )
A = DonutSwinModel(UpperCamelCase )
A = MBartForCausalLM(UpperCamelCase )
A = VisionEncoderDecoderModel(encoder=UpperCamelCase , decoder=UpperCamelCase )
model.eval()
A = original_model.state_dict()
A = convert_state_dict(UpperCamelCase , UpperCamelCase )
model.load_state_dict(UpperCamelCase )
# verify results on scanned document
A = load_dataset("hf-internal-testing/example-documents" )
A = dataset["test"][0]["image"].convert("RGB" )
A = XLMRobertaTokenizerFast.from_pretrained(UpperCamelCase , from_slow=UpperCamelCase )
A = DonutImageProcessor(
do_align_long_axis=original_model.config.align_long_axis , size=original_model.config.input_size[::-1] )
A = DonutProcessor(UpperCamelCase , UpperCamelCase )
A = processor(UpperCamelCase , return_tensors="pt" ).pixel_values
if model_name == "naver-clova-ix/donut-base-finetuned-docvqa":
A = "<s_docvqa><s_question>{user_input}</s_question><s_answer>"
A = "When is the coffee break?"
A = task_prompt.replace("{user_input}" , UpperCamelCase )
elif model_name == "naver-clova-ix/donut-base-finetuned-rvlcdip":
A = "<s_rvlcdip>"
elif model_name in [
"naver-clova-ix/donut-base-finetuned-cord-v1",
"naver-clova-ix/donut-base-finetuned-cord-v1-2560",
]:
A = "<s_cord>"
elif model_name == "naver-clova-ix/donut-base-finetuned-cord-v2":
A = "s_cord-v2>"
elif model_name == "naver-clova-ix/donut-base-finetuned-zhtrainticket":
A = "<s_zhtrainticket>"
elif model_name in ["naver-clova-ix/donut-proto", "naver-clova-ix/donut-base"]:
# use a random prompt
A = "hello world"
else:
raise ValueError("Model name not supported" )
A = original_model.decoder.tokenizer(UpperCamelCase , add_special_tokens=UpperCamelCase , return_tensors="pt" )[
"input_ids"
]
A = original_model.encoder.model.patch_embed(UpperCamelCase )
A, A = model.encoder.embeddings(UpperCamelCase )
assert torch.allclose(UpperCamelCase , UpperCamelCase , atol=1E-3 )
# verify encoder hidden states
A = original_model.encoder(UpperCamelCase )
A = model.encoder(UpperCamelCase ).last_hidden_state
assert torch.allclose(UpperCamelCase , UpperCamelCase , atol=1E-2 )
# verify decoder hidden states
A = original_model(UpperCamelCase , UpperCamelCase , UpperCamelCase ).logits
A = model(UpperCamelCase , decoder_input_ids=UpperCamelCase ).logits
assert torch.allclose(UpperCamelCase , UpperCamelCase , atol=1E-3 )
print("Looks ok!" )
if pytorch_dump_folder_path is not None:
print(F"Saving model and processor to {pytorch_dump_folder_path}" )
model.save_pretrained(UpperCamelCase )
processor.save_pretrained(UpperCamelCase )
if push_to_hub:
model.push_to_hub("nielsr/" + model_name.split("/" )[-1] , commit_message="Update model" )
processor.push_to_hub("nielsr/" + model_name.split("/" )[-1] , commit_message="Update model" )
if __name__ == "__main__":
_snake_case : Optional[int] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='naver-clova-ix/donut-base-finetuned-docvqa',
required=False,
type=str,
help='Name of the original model you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path',
default=None,
required=False,
type=str,
help='Path to the output PyTorch model directory.',
)
parser.add_argument(
'--push_to_hub',
action='store_true',
help='Whether or not to push the converted model and processor to the 🤗 hub.',
)
_snake_case : List[Any] = parser.parse_args()
convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 292 |
"""simple docstring"""
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
UpperCamelCase : Union[str, Any] = version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def A ( snake_case :str , snake_case :tuple , snake_case :Path , snake_case :Dict , snake_case :int , snake_case :List[str] , snake_case :Union[str, Any] , snake_case :Union[str, Any]=False , ) -> str:
output_path.parent.mkdir(parents=snake_case , exist_ok=snake_case )
# PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
# so we check the torch version for backwards compatibility
if is_torch_less_than_1_11:
export(
snake_case , snake_case , f=output_path.as_posix() , input_names=snake_case , output_names=snake_case , dynamic_axes=snake_case , do_constant_folding=snake_case , use_external_data_format=snake_case , enable_onnx_checker=snake_case , opset_version=snake_case , )
else:
export(
snake_case , snake_case , f=output_path.as_posix() , input_names=snake_case , output_names=snake_case , dynamic_axes=snake_case , do_constant_folding=snake_case , opset_version=snake_case , )
@torch.no_grad()
def A ( snake_case :str , snake_case :str , snake_case :int , snake_case :bool = False ) -> List[str]:
__UpperCamelCase = torch.floataa if fpaa else torch.floataa
if fpaa and torch.cuda.is_available():
__UpperCamelCase = 'cuda'
elif fpaa and not torch.cuda.is_available():
raise ValueError('`float16` model export is only supported on GPUs with CUDA' )
else:
__UpperCamelCase = 'cpu'
__UpperCamelCase = Path(snake_case )
# VAE DECODER
__UpperCamelCase = AutoencoderKL.from_pretrained(model_path + '/vae' )
__UpperCamelCase = vae_decoder.config.latent_channels
# forward only through the decoder part
__UpperCamelCase = vae_decoder.decode
onnx_export(
snake_case , model_args=(
torch.randn(1 , snake_case , 2_5 , 2_5 ).to(device=snake_case , dtype=snake_case ),
False,
) , output_path=output_path / 'vae_decoder' / 'model.onnx' , ordered_input_names=['latent_sample', 'return_dict'] , output_names=['sample'] , dynamic_axes={
'latent_sample': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
} , opset=snake_case , )
del vae_decoder
if __name__ == "__main__":
UpperCamelCase : Dict = argparse.ArgumentParser()
parser.add_argument(
"--model_path",
type=str,
required=True,
help="Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).",
)
parser.add_argument("--output_path", type=str, required=True, help="Path to the output model.")
parser.add_argument(
"--opset",
default=1_4,
type=int,
help="The version of the ONNX operator set to use.",
)
parser.add_argument("--fp16", action="store_true", default=False, help="Export the models in `float16` mode")
UpperCamelCase : List[Any] = parser.parse_args()
print(args.output_path)
convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
print("SD: Done: ONNX")
| 316 | 0 |
import requests
_UpperCAmelCase : Union[str, Any] = """https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="""
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
snake_case_ = requests.get(_NEWS_API + bbc_news_api_key ).json()
# each article in the list is a dict
for i, article in enumerate(bbc_news_page['articles'] , 1 ):
print(F'''{i}.) {article["title"]}''' )
if __name__ == "__main__":
fetch_bbc_news(bbc_news_api_key="""<Your BBC News API key goes here>""")
| 350 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_UpperCAmelCase : int = logging.get_logger(__name__)
_UpperCAmelCase : Optional[Any] = {
"""junnyu/roformer_chinese_small""": """https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json""",
"""junnyu/roformer_chinese_base""": """https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json""",
"""junnyu/roformer_chinese_char_small""": (
"""https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json"""
),
"""junnyu/roformer_chinese_char_base""": (
"""https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json"""
),
"""junnyu/roformer_small_discriminator""": (
"""https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json"""
),
"""junnyu/roformer_small_generator""": (
"""https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json"""
),
# See all RoFormer models at https://huggingface.co/models?filter=roformer
}
class lowercase ( lowercase_ ):
__SCREAMING_SNAKE_CASE : List[Any] = '''roformer'''
def __init__( self , snake_case=5_0000 , snake_case=None , snake_case=768 , snake_case=12 , snake_case=12 , snake_case=3072 , snake_case="gelu" , snake_case=0.1 , snake_case=0.1 , snake_case=1536 , snake_case=2 , snake_case=0.02 , snake_case=1e-1_2 , snake_case=0 , snake_case=False , snake_case=True , **snake_case , ):
super().__init__(pad_token_id=snake_case , **snake_case )
snake_case_ = vocab_size
snake_case_ = hidden_size if embedding_size is None else embedding_size
snake_case_ = hidden_size
snake_case_ = num_hidden_layers
snake_case_ = num_attention_heads
snake_case_ = hidden_act
snake_case_ = intermediate_size
snake_case_ = hidden_dropout_prob
snake_case_ = attention_probs_dropout_prob
snake_case_ = max_position_embeddings
snake_case_ = type_vocab_size
snake_case_ = initializer_range
snake_case_ = layer_norm_eps
snake_case_ = rotary_value
snake_case_ = use_cache
class lowercase ( lowercase_ ):
@property
def a ( self ):
if self.task == "multiple-choice":
snake_case_ = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
snake_case_ = {0: 'batch', 1: 'sequence'}
snake_case_ = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
('token_type_ids', dynamic_axis),
] )
| 200 | 0 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool
class UpperCAmelCase__ ( A_ ):
"""simple docstring"""
UpperCAmelCase__ : Tuple = "philschmid/bart-large-cnn-samsum"
UpperCAmelCase__ : Optional[Any] = (
"This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, "
"and returns a summary of the text."
)
UpperCAmelCase__ : Optional[Any] = "summarizer"
UpperCAmelCase__ : Optional[int] = AutoTokenizer
UpperCAmelCase__ : str = AutoModelForSeqaSeqLM
UpperCAmelCase__ : Any = ["text"]
UpperCAmelCase__ : str = ["text"]
def _a ( self , A_ ) -> Optional[Any]:
return self.pre_processor(A_ , return_tensors='pt' , truncation=A_ )
def _a ( self , A_ ) -> Dict:
return self.model.generate(**A_ )[0]
def _a ( self , A_ ) -> List[str]:
return self.pre_processor.decode(A_ , skip_special_tokens=A_ , clean_up_tokenization_spaces=A_ )
| 62 |
'''simple docstring'''
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)
a_ : str = _symbol_database.Default()
a_ : Union[str, Any] = _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"
)
a_ : List[Any] = globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, "sentencepiece_model_pb2", _globals)
if _descriptor._USE_C_DESCRIPTORS is False:
a_ : List[str] = None
a_ : Tuple = 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"
a_ : Optional[int] = 4_5
a_ : Union[str, Any] = 1_5_8_1
a_ : List[Any] = 1_5_1_7
a_ : str = 1_5_7_0
a_ : List[Any] = 1_5_8_4
a_ : str = 1_7_9_3
a_ : List[str] = 1_7_9_5
a_ : Any = 1_9_1_6
a_ : List[str] = 1_8_6_4
a_ : Optional[Any] = 1_9_0_5
a_ : int = 1_9_1_9
a_ : int = 2_4_2_9
a_ : Dict = 2_2_0_8
a_ : Any = 2_4_1_8
a_ : Union[str, Any] = 2_3_2_3
a_ : str = 2_4_0_7
# @@protoc_insertion_point(module_scope)
| 168 | 0 |
"""simple docstring"""
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
lowercase__ = abspath(join(dirname(__file__), 'src'))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='ignore', category=FutureWarning)
def __a ( _SCREAMING_SNAKE_CASE ) ->Optional[int]:
config.addinivalue_line(
'markers' , 'is_pt_tf_cross_test: mark test to run only when PT and TF interactions are tested' )
config.addinivalue_line(
'markers' , 'is_pt_flax_cross_test: mark test to run only when PT and FLAX interactions are tested' )
config.addinivalue_line('markers' , 'is_pipeline_test: mark test to run only when pipelines are tested' )
config.addinivalue_line('markers' , 'is_staging_test: mark test to run only in the staging environment' )
config.addinivalue_line('markers' , 'accelerate_tests: mark test that require accelerate' )
config.addinivalue_line('markers' , 'tool_tests: mark the tool tests that are run on their specific schedule' )
def __a ( _SCREAMING_SNAKE_CASE ) ->Optional[int]:
from transformers.testing_utils import pytest_addoption_shared
pytest_addoption_shared(_SCREAMING_SNAKE_CASE )
def __a ( _SCREAMING_SNAKE_CASE ) ->Union[str, Any]:
from transformers.testing_utils import pytest_terminal_summary_main
a__: int = terminalreporter.config.getoption('--make-reports' )
if make_reports:
pytest_terminal_summary_main(_SCREAMING_SNAKE_CASE , id=_SCREAMING_SNAKE_CASE )
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->List[str]:
# If no tests are collected, pytest exists with code 5, which makes the CI fail.
if exitstatus == 5:
a__: Optional[Any] = 0
# Doctest custom flag to ignore output.
lowercase__ = doctest.register_optionflag('IGNORE_RESULT')
lowercase__ = doctest.OutputChecker
class __snake_case ( __lowerCAmelCase ):
def lowerCamelCase_ ( self , lowercase , lowercase , lowercase) -> Union[str, Any]:
'''simple docstring'''
if IGNORE_RESULT & optionflags:
return True
return OutputChecker.check_output(self , lowercase , lowercase , lowercase)
lowercase__ = CustomOutputChecker
lowercase__ = HfDoctestModule
lowercase__ = HfDocTestParser
| 203 | """simple docstring"""
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->Tuple:
_enforce_args(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if n == 0:
return 0
a__: List[Any] = float('-inf' )
for i in range(1 , n + 1 ):
a__: Optional[Any] = max(
_SCREAMING_SNAKE_CASE , prices[i - 1] + naive_cut_rod_recursive(n - i , _SCREAMING_SNAKE_CASE ) )
return max_revue
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->str:
_enforce_args(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
a__: str = [float('-inf' ) for _ in range(n + 1 )]
return _top_down_cut_rod_recursive(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->Optional[Any]:
if max_rev[n] >= 0:
return max_rev[n]
elif n == 0:
return 0
else:
a__: Dict = float('-inf' )
for i in range(1 , n + 1 ):
a__: Optional[Any] = max(
_SCREAMING_SNAKE_CASE , prices[i - 1] + _top_down_cut_rod_recursive(n - i , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) , )
a__: Optional[int] = max_revenue
return max_rev[n]
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->Dict:
_enforce_args(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
# length 0.
a__: str = [float('-inf' ) for _ in range(n + 1 )]
a__: Tuple = 0
for i in range(1 , n + 1 ):
a__: List[str] = max_rev[i]
for j in range(1 , i + 1 ):
a__: Tuple = max(_SCREAMING_SNAKE_CASE , prices[j - 1] + max_rev[i - j] )
a__: Union[str, Any] = max_revenue_i
return max_rev[n]
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->Any:
if n < 0:
a__: Optional[int] = F'n must be greater than or equal to 0. Got n = {n}'
raise ValueError(_SCREAMING_SNAKE_CASE )
if n > len(_SCREAMING_SNAKE_CASE ):
a__: List[str] = (
'Each integral piece of rod must have a corresponding price. '
F'Got n = {n} but length of prices = {len(_SCREAMING_SNAKE_CASE )}'
)
raise ValueError(_SCREAMING_SNAKE_CASE )
def __a ( ) ->str:
a__: int = [6, 10, 12, 15, 20, 23]
a__: Union[str, Any] = len(_SCREAMING_SNAKE_CASE )
# the best revenue comes from cutting the rod into 6 pieces, each
# of length 1 resulting in a revenue of 6 * 6 = 36.
a__: Any = 36
a__: Optional[int] = top_down_cut_rod(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
a__: List[Any] = bottom_up_cut_rod(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
a__: int = naive_cut_rod_recursive(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
assert expected_max_revenue == max_rev_top_down
assert max_rev_top_down == max_rev_bottom_up
assert max_rev_bottom_up == max_rev_naive
if __name__ == "__main__":
main()
| 203 | 1 |
import unittest
from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available
from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
if is_bsa_available():
from transformers import MarkupLMFeatureExtractor
class snake_case__ (unittest.TestCase ):
"""simple docstring"""
def __init__( self : str , __lowerCamelCase : Optional[int] ) -> List[str]:
a = parent
def __UpperCAmelCase ( self : Optional[int] ) -> List[str]:
return {}
def __magic_name__ ( ):
'''simple docstring'''
a = "<HTML>\n\n <HEAD>\n <TITLE>sample document</TITLE>\n </HEAD>\n\n <BODY BGCOLOR=\"FFFFFF\">\n <HR>\n <a href=\"http://google.com\">Goog</a>\n <H1>This is one header</H1>\n <H2>This is a another Header</H2>\n <P>Travel from\n <P>\n <B>SFO to JFK</B>\n <BR>\n <B><I>on May 2, 2015 at 2:00 pm. For details go to confirm.com </I></B>\n <HR>\n <div style=\"color:#0000FF\">\n <h3>Traveler <b> name </b> is\n <p> John Doe </p>\n </div>"
a = "\n <!DOCTYPE html>\n <html>\n <body>\n\n <h1>My First Heading</h1>\n <p>My first paragraph.</p>\n\n </body>\n </html>\n "
return [html_string_a, html_string_a]
@require_bsa
class snake_case__ (_UpperCamelCase , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Dict = MarkupLMFeatureExtractor if is_bsa_available() else None
def __UpperCAmelCase ( self : int ) -> Union[str, Any]:
a = MarkupLMFeatureExtractionTester(self )
@property
def __UpperCAmelCase ( self : Optional[int] ) -> Optional[Any]:
return self.feature_extract_tester.prepare_feat_extract_dict()
def __UpperCAmelCase ( self : int ) -> Tuple:
# Initialize feature_extractor
a = self.feature_extraction_class()
# Test not batched input
a = get_html_strings()[0]
a = feature_extractor(__lowerCamelCase )
# fmt: off
a = [["sample document", "Goog", "This is one header", "This is a another Header", "Travel from", "SFO to JFK", "on May 2, 2015 at 2:00 pm. For details go to confirm.com", "Traveler", "name", "is", "John Doe"]]
a = [["/html/head/title", "/html/body/a", "/html/body/h1", "/html/body/h2", "/html/body/p", "/html/body/p/p/b[1]", "/html/body/p/p/b[2]/i", "/html/body/p/p/div/h3", "/html/body/p/p/div/h3/b", "/html/body/p/p/div/h3", "/html/body/p/p/div/h3/p"]]
# fmt: on
self.assertEqual(encoding.nodes , __lowerCamelCase )
self.assertEqual(encoding.xpaths , __lowerCamelCase )
# Test batched
a = get_html_strings()
a = feature_extractor(__lowerCamelCase )
# fmt: off
a = expected_nodes + [["My First Heading", "My first paragraph."]]
a = expected_xpaths + [["/html/body/h1", "/html/body/p"]]
self.assertEqual(len(encoding.nodes ) , 2 )
self.assertEqual(len(encoding.xpaths ) , 2 )
self.assertEqual(encoding.nodes , __lowerCamelCase )
self.assertEqual(encoding.xpaths , __lowerCamelCase )
| 107 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCamelCase__ = {
"""configuration_efficientnet""": [
"""EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""EfficientNetConfig""",
"""EfficientNetOnnxConfig""",
]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase__ = ["""EfficientNetImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase__ = [
"""EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""EfficientNetForImageClassification""",
"""EfficientNetModel""",
"""EfficientNetPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_efficientnet import (
EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
EfficientNetConfig,
EfficientNetOnnxConfig,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_efficientnet import EfficientNetImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_efficientnet import (
EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST,
EfficientNetForImageClassification,
EfficientNetModel,
EfficientNetPreTrainedModel,
)
else:
import sys
lowerCamelCase__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure)
| 302 | 0 |
'''simple docstring'''
import os
import pickle
import unittest
from transformers import AutoTokenizer
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.models.bert_japanese.tokenization_bert_japanese import (
VOCAB_FILES_NAMES,
BertJapaneseTokenizer,
CharacterTokenizer,
JumanppTokenizer,
MecabTokenizer,
SudachiTokenizer,
WordpieceTokenizer,
)
from transformers.testing_utils import custom_tokenizers, require_jumanpp, require_sudachi
from ...test_tokenization_common import TokenizerTesterMixin
@custom_tokenizers
class A ( __UpperCamelCase , unittest.TestCase ):
__magic_name__ = BertJapaneseTokenizer
__magic_name__ = False
__magic_name__ = True
def __lowerCAmelCase ( self ) -> Dict:
"""simple docstring"""
super().setUp()
A : Tuple = [
'[UNK]',
'[CLS]',
'[SEP]',
'こんにちは',
'こん',
'にちは',
'ばんは',
'##こん',
'##にちは',
'##ばんは',
'世界',
'##世界',
'、',
'##、',
'。',
'##。',
]
A : str = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
def __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE ) -> Optional[Any]:
"""simple docstring"""
A : Optional[Any] = 'こんにちは、世界。 \nこんばんは、世界。'
A : Optional[int] = 'こんにちは 、 世界 。 こんばんは 、 世界 。'
return input_text, output_text
def __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
"""simple docstring"""
A : List[str] = self.get_input_output_texts(SCREAMING_SNAKE_CASE )
A : Any = tokenizer.encode(SCREAMING_SNAKE_CASE , add_special_tokens=SCREAMING_SNAKE_CASE )
A : Dict = tokenizer.decode(SCREAMING_SNAKE_CASE , clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE )
return text, ids
def __lowerCAmelCase ( self ) -> Any:
"""simple docstring"""
pass # TODO add if relevant
def __lowerCAmelCase ( self ) -> Union[str, Any]:
"""simple docstring"""
pass # TODO add if relevant
def __lowerCAmelCase ( self ) -> int:
"""simple docstring"""
pass # TODO add if relevant
def __lowerCAmelCase ( self ) -> List[str]:
"""simple docstring"""
A : Optional[int] = self.tokenizer_class(self.vocab_file )
A : Dict = tokenizer.tokenize('''こんにちは、世界。\nこんばんは、世界。''' )
self.assertListEqual(SCREAMING_SNAKE_CASE , ['''こんにちは''', '''、''', '''世界''', '''。''', '''こん''', '''##ばんは''', '''、''', '''世界''', '''。'''] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE ) , [3, 12, 10, 14, 4, 9, 12, 10, 14] )
def __lowerCAmelCase ( self ) -> List[Any]:
"""simple docstring"""
A : Any = self.tokenizer_class(self.vocab_file , word_tokenizer_type='''mecab''' )
self.assertIsNotNone(SCREAMING_SNAKE_CASE )
A : Tuple = 'こんにちは、世界。\nこんばんは、世界。'
A : Dict = tokenizer.tokenize(SCREAMING_SNAKE_CASE )
self.assertListEqual(SCREAMING_SNAKE_CASE , ['''こんにちは''', '''、''', '''世界''', '''。''', '''こん''', '''##ばんは''', '''、''', '''世界''', '''。'''] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE ) , [3, 12, 10, 14, 4, 9, 12, 10, 14] )
A : List[Any] = os.path.join(self.tmpdirname , '''tokenizer.bin''' )
with open(SCREAMING_SNAKE_CASE , '''wb''' ) as handle:
pickle.dump(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
with open(SCREAMING_SNAKE_CASE , '''rb''' ) as handle:
A : Tuple = pickle.load(SCREAMING_SNAKE_CASE )
A : str = tokenizer_new.tokenize(SCREAMING_SNAKE_CASE )
self.assertListEqual(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
def __lowerCAmelCase ( self ) -> Any:
"""simple docstring"""
A : Union[str, Any] = MecabTokenizer(mecab_dic='''ipadic''' )
self.assertListEqual(
tokenizer.tokenize(''' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ''' ) , ['''アップルストア''', '''で''', '''iPhone''', '''8''', '''が''', '''発売''', '''さ''', '''れ''', '''た''', '''。'''] , )
def __lowerCAmelCase ( self ) -> List[str]:
"""simple docstring"""
try:
A : Any = MecabTokenizer(mecab_dic='''unidic_lite''' )
except ModuleNotFoundError:
return
self.assertListEqual(
tokenizer.tokenize(''' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ''' ) , ['''アップル''', '''ストア''', '''で''', '''iPhone''', '''8''', '''が''', '''発売''', '''さ''', '''れ''', '''た''', '''。'''] , )
def __lowerCAmelCase ( self ) -> List[str]:
"""simple docstring"""
try:
A : List[str] = MecabTokenizer(mecab_dic='''unidic''' )
except ModuleNotFoundError:
return
self.assertListEqual(
tokenizer.tokenize(''' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ''' ) , ['''アップル''', '''ストア''', '''で''', '''iPhone''', '''8''', '''が''', '''発売''', '''さ''', '''れ''', '''た''', '''。'''] , )
def __lowerCAmelCase ( self ) -> Optional[int]:
"""simple docstring"""
A : Dict = MecabTokenizer(do_lower_case=SCREAMING_SNAKE_CASE , mecab_dic='''ipadic''' )
self.assertListEqual(
tokenizer.tokenize(''' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ''' ) , ['''アップルストア''', '''で''', '''iphone''', '''8''', '''が''', '''発売''', '''さ''', '''れ''', '''た''', '''。'''] , )
def __lowerCAmelCase ( self ) -> List[str]:
"""simple docstring"""
try:
A : Optional[Any] = MecabTokenizer(
do_lower_case=SCREAMING_SNAKE_CASE , normalize_text=SCREAMING_SNAKE_CASE , mecab_option='''-d /usr/local/lib/mecab/dic/jumandic''' )
except RuntimeError:
# if dict doesn't exist in the system, previous code raises this error.
return
self.assertListEqual(
tokenizer.tokenize(''' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ''' ) , ['''ｱｯﾌﾟﾙストア''', '''で''', '''iPhone''', '''８''', '''が''', '''発売''', '''さ''', '''れた''', '''\u3000''', '''。'''] , )
def __lowerCAmelCase ( self ) -> List[str]:
"""simple docstring"""
A : Any = MecabTokenizer(normalize_text=SCREAMING_SNAKE_CASE , mecab_dic='''ipadic''' )
self.assertListEqual(
tokenizer.tokenize(''' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ''' ) , ['''ｱｯﾌﾟﾙストア''', '''で''', '''iPhone''', '''８''', '''が''', '''発売''', '''さ''', '''れ''', '''た''', ''' ''', '''。'''] , )
@require_sudachi
def __lowerCAmelCase ( self ) -> List[Any]:
"""simple docstring"""
A : int = self.tokenizer_class(self.vocab_file , word_tokenizer_type='''sudachi''' )
self.assertIsNotNone(SCREAMING_SNAKE_CASE )
A : Optional[Any] = 'こんにちは、世界。\nこんばんは、世界。'
A : Tuple = tokenizer.tokenize(SCREAMING_SNAKE_CASE )
self.assertListEqual(SCREAMING_SNAKE_CASE , ['''こんにちは''', '''、''', '''世界''', '''。''', '''こん''', '''##ばんは''', '''、''', '''世界''', '''。'''] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE ) , [3, 12, 10, 14, 4, 9, 12, 10, 14] )
A : Union[str, Any] = os.path.join(self.tmpdirname , '''tokenizer.bin''' )
with open(SCREAMING_SNAKE_CASE , '''wb''' ) as handle:
pickle.dump(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
with open(SCREAMING_SNAKE_CASE , '''rb''' ) as handle:
A : int = pickle.load(SCREAMING_SNAKE_CASE )
A : List[Any] = tokenizer_new.tokenize(SCREAMING_SNAKE_CASE )
self.assertListEqual(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
@require_sudachi
def __lowerCAmelCase ( self ) -> List[str]:
"""simple docstring"""
A : Any = SudachiTokenizer(sudachi_dict_type='''core''' )
self.assertListEqual(
tokenizer.tokenize(''' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ''' ) , [''' ''', '''\t''', '''アップル''', '''ストア''', '''で''', '''iPhone''', '''8''', ''' ''', '''が''', ''' ''', ''' ''', '''\n ''', '''発売''', '''さ''', '''れ''', '''た''', ''' ''', '''。''', ''' ''', ''' '''] , )
@require_sudachi
def __lowerCAmelCase ( self ) -> List[Any]:
"""simple docstring"""
A : List[str] = SudachiTokenizer(sudachi_dict_type='''core''' , sudachi_split_mode='''A''' )
self.assertListEqual(tokenizer.tokenize('''外国人参政権''' ) , ['''外国''', '''人''', '''参政''', '''権'''] )
@require_sudachi
def __lowerCAmelCase ( self ) -> List[str]:
"""simple docstring"""
A : Tuple = SudachiTokenizer(sudachi_dict_type='''core''' , sudachi_split_mode='''B''' )
self.assertListEqual(tokenizer.tokenize('''外国人参政権''' ) , ['''外国人''', '''参政権'''] )
@require_sudachi
def __lowerCAmelCase ( self ) -> str:
"""simple docstring"""
A : List[Any] = SudachiTokenizer(sudachi_dict_type='''core''' , sudachi_split_mode='''C''' )
self.assertListEqual(tokenizer.tokenize('''外国人参政権''' ) , ['''外国人参政権'''] )
@require_sudachi
def __lowerCAmelCase ( self ) -> Union[str, Any]:
"""simple docstring"""
A : int = SudachiTokenizer(do_lower_case=SCREAMING_SNAKE_CASE , sudachi_dict_type='''core''' )
self.assertListEqual(
tokenizer.tokenize(''' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ''' ) , [''' ''', '''\t''', '''アップル''', '''ストア''', '''で''', '''iphone''', '''8''', ''' ''', '''が''', ''' ''', ''' ''', '''\n ''', '''発売''', '''さ''', '''れ''', '''た''', ''' ''', '''。''', ''' ''', ''' '''] , )
@require_sudachi
def __lowerCAmelCase ( self ) -> Any:
"""simple docstring"""
A : int = SudachiTokenizer(normalize_text=SCREAMING_SNAKE_CASE , sudachi_dict_type='''core''' )
self.assertListEqual(
tokenizer.tokenize(''' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ''' ) , [''' ''', '''\t''', '''ｱｯﾌﾟﾙ''', '''ストア''', '''で''', '''iPhone''', '''８''', ''' ''', '''が''', ''' ''', ''' ''', '''\n ''', '''発売''', '''さ''', '''れ''', '''た''', '''\u3000''', '''。''', ''' ''', ''' '''] , )
@require_sudachi
def __lowerCAmelCase ( self ) -> Dict:
"""simple docstring"""
A : Optional[Any] = SudachiTokenizer(trim_whitespace=SCREAMING_SNAKE_CASE , sudachi_dict_type='''core''' )
self.assertListEqual(
tokenizer.tokenize(''' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ''' ) , ['''アップル''', '''ストア''', '''で''', '''iPhone''', '''8''', '''が''', '''発売''', '''さ''', '''れ''', '''た''', '''。'''] , )
@require_jumanpp
def __lowerCAmelCase ( self ) -> List[str]:
"""simple docstring"""
A : Optional[Any] = self.tokenizer_class(self.vocab_file , word_tokenizer_type='''jumanpp''' )
self.assertIsNotNone(SCREAMING_SNAKE_CASE )
A : List[Any] = 'こんにちは、世界。\nこんばんは、世界。'
A : List[Any] = tokenizer.tokenize(SCREAMING_SNAKE_CASE )
self.assertListEqual(SCREAMING_SNAKE_CASE , ['''こんにちは''', '''、''', '''世界''', '''。''', '''こん''', '''##ばんは''', '''、''', '''世界''', '''。'''] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE ) , [3, 12, 10, 14, 4, 9, 12, 10, 14] )
A : Any = os.path.join(self.tmpdirname , '''tokenizer.bin''' )
with open(SCREAMING_SNAKE_CASE , '''wb''' ) as handle:
pickle.dump(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
with open(SCREAMING_SNAKE_CASE , '''rb''' ) as handle:
A : Dict = pickle.load(SCREAMING_SNAKE_CASE )
A : Union[str, Any] = tokenizer_new.tokenize(SCREAMING_SNAKE_CASE )
self.assertListEqual(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
@require_jumanpp
def __lowerCAmelCase ( self ) -> Tuple:
"""simple docstring"""
A : Any = JumanppTokenizer()
self.assertListEqual(
tokenizer.tokenize(''' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ''' ) , ['''アップル''', '''ストア''', '''で''', '''iPhone''', '''8''', '''\u3000''', '''が''', '''\u3000''', '''\u3000''', '''\u3000''', '''発売''', '''さ''', '''れた''', '''\u3000''', '''。'''] , )
@require_jumanpp
def __lowerCAmelCase ( self ) -> Tuple:
"""simple docstring"""
A : int = JumanppTokenizer(do_lower_case=SCREAMING_SNAKE_CASE )
self.assertListEqual(
tokenizer.tokenize(''' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ''' ) , ['''アップル''', '''ストア''', '''で''', '''iphone''', '''8''', '''\u3000''', '''が''', '''\u3000''', '''\u3000''', '''\u3000''', '''発売''', '''さ''', '''れた''', '''\u3000''', '''。'''] , )
@require_jumanpp
def __lowerCAmelCase ( self ) -> List[Any]:
"""simple docstring"""
A : List[str] = JumanppTokenizer(normalize_text=SCREAMING_SNAKE_CASE )
self.assertListEqual(
tokenizer.tokenize(''' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ''' ) , ['''ｱ''', '''ｯ''', '''ﾌ''', '''ﾟ''', '''ﾙ''', '''ストア''', '''で''', '''iPhone''', '''８''', '''\u3000''', '''が''', '''\u3000''', '''\u3000''', '''\u3000''', '''発売''', '''さ''', '''れた''', '''\u3000''', '''。'''] , )
@require_jumanpp
def __lowerCAmelCase ( self ) -> int:
"""simple docstring"""
A : str = JumanppTokenizer(trim_whitespace=SCREAMING_SNAKE_CASE )
self.assertListEqual(
tokenizer.tokenize(''' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ''' ) , ['''アップル''', '''ストア''', '''で''', '''iPhone''', '''8''', '''が''', '''発売''', '''さ''', '''れた''', '''。'''] , )
@require_jumanpp
def __lowerCAmelCase ( self ) -> Dict:
"""simple docstring"""
A : Optional[int] = JumanppTokenizer()
self.assertListEqual(
tokenizer.tokenize('''ありがとうございますm(_ _)ｍ見つけるのが大変です。''' ) , ['''ありがとう''', '''ございます''', '''m(_ _)m''', '''見つける''', '''の''', '''が''', '''大変です''', '''。'''] , )
def __lowerCAmelCase ( self ) -> Union[str, Any]:
"""simple docstring"""
A : List[str] = ['[UNK]', '[CLS]', '[SEP]', 'こんにちは', 'こん', 'にちは', 'ばんは', '##こん', '##にちは', '##ばんは']
A : Dict = {}
for i, token in enumerate(SCREAMING_SNAKE_CASE ):
A : int = i
A : str = WordpieceTokenizer(vocab=SCREAMING_SNAKE_CASE , unk_token='''[UNK]''' )
self.assertListEqual(tokenizer.tokenize('''''' ) , [] )
self.assertListEqual(tokenizer.tokenize('''こんにちは''' ) , ['''こんにちは'''] )
self.assertListEqual(tokenizer.tokenize('''こんばんは''' ) , ['''こん''', '''##ばんは'''] )
self.assertListEqual(tokenizer.tokenize('''こんばんは こんばんにちは こんにちは''' ) , ['''こん''', '''##ばんは''', '''[UNK]''', '''こんにちは'''] )
def __lowerCAmelCase ( self ) -> int:
"""simple docstring"""
A : str = BertJapaneseTokenizer.from_pretrained('''nlp-waseda/roberta-base-japanese-with-auto-jumanpp''' )
A : Dict = tokenizer.subword_tokenizer
A : str = subword_tokenizer.tokenize('''国境 の 長い トンネル を 抜ける と 雪国 であった 。''' )
self.assertListEqual(SCREAMING_SNAKE_CASE , ['''▁国境''', '''▁の''', '''▁長い''', '''▁トンネル''', '''▁を''', '''▁抜ける''', '''▁と''', '''▁雪''', '''国''', '''▁であった''', '''▁。'''] )
A : Optional[int] = subword_tokenizer.tokenize('''こんばんは こんばん にち は こんにちは''' )
self.assertListEqual(SCREAMING_SNAKE_CASE , ['''▁こん''', '''ばん''', '''は''', '''▁こん''', '''ばん''', '''▁に''', '''ち''', '''▁は''', '''▁こんにちは'''] )
def __lowerCAmelCase ( self ) -> List[str]:
"""simple docstring"""
A : Tuple = self.tokenizer_class.from_pretrained('''cl-tohoku/bert-base-japanese''' )
A : Optional[Any] = tokenizer.encode('''ありがとう。''' , add_special_tokens=SCREAMING_SNAKE_CASE )
A : List[str] = tokenizer.encode('''どういたしまして。''' , add_special_tokens=SCREAMING_SNAKE_CASE )
A : Union[str, Any] = tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE )
A : Any = tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
# 2 is for "[CLS]", 3 is for "[SEP]"
assert encoded_sentence == [2] + text + [3]
assert encoded_pair == [2] + text + [3] + text_a + [3]
@custom_tokenizers
class A ( __UpperCamelCase , unittest.TestCase ):
__magic_name__ = BertJapaneseTokenizer
__magic_name__ = False
def __lowerCAmelCase ( self ) -> Dict:
"""simple docstring"""
super().setUp()
A : Any = ['[UNK]', '[CLS]', '[SEP]', 'こ', 'ん', 'に', 'ち', 'は', 'ば', '世', '界', '、', '。']
A : str = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
def __lowerCAmelCase ( self , **SCREAMING_SNAKE_CASE ) -> Dict:
"""simple docstring"""
return BertJapaneseTokenizer.from_pretrained(self.tmpdirname , subword_tokenizer_type='''character''' , **SCREAMING_SNAKE_CASE )
def __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE ) -> Any:
"""simple docstring"""
A : List[Any] = 'こんにちは、世界。 \nこんばんは、世界。'
A : Union[str, Any] = 'こ ん に ち は 、 世 界 。 こ ん ば ん は 、 世 界 。'
return input_text, output_text
def __lowerCAmelCase ( self ) -> Union[str, Any]:
"""simple docstring"""
pass # TODO add if relevant
def __lowerCAmelCase ( self ) -> Optional[Any]:
"""simple docstring"""
pass # TODO add if relevant
def __lowerCAmelCase ( self ) -> Optional[int]:
"""simple docstring"""
pass # TODO add if relevant
def __lowerCAmelCase ( self ) -> str:
"""simple docstring"""
A : Tuple = self.tokenizer_class(self.vocab_file , subword_tokenizer_type='''character''' )
A : Any = tokenizer.tokenize('''こんにちは、世界。 \nこんばんは、世界。''' )
self.assertListEqual(
SCREAMING_SNAKE_CASE , ['''こ''', '''ん''', '''に''', '''ち''', '''は''', '''、''', '''世''', '''界''', '''。''', '''こ''', '''ん''', '''ば''', '''ん''', '''は''', '''、''', '''世''', '''界''', '''。'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE ) , [3, 4, 5, 6, 7, 11, 9, 10, 12, 3, 4, 8, 4, 7, 11, 9, 10, 12] )
def __lowerCAmelCase ( self ) -> Optional[int]:
"""simple docstring"""
A : List[str] = ['[UNK]', '[CLS]', '[SEP]', 'こ', 'ん', 'に', 'ち', 'は', 'ば', '世', '界', '、', '。']
A : int = {}
for i, token in enumerate(SCREAMING_SNAKE_CASE ):
A : Tuple = i
A : Union[str, Any] = CharacterTokenizer(vocab=SCREAMING_SNAKE_CASE , unk_token='''[UNK]''' )
self.assertListEqual(tokenizer.tokenize('''''' ) , [] )
self.assertListEqual(tokenizer.tokenize('''こんにちは''' ) , ['''こ''', '''ん''', '''に''', '''ち''', '''は'''] )
self.assertListEqual(tokenizer.tokenize('''こんにちほ''' ) , ['''こ''', '''ん''', '''に''', '''ち''', '''[UNK]'''] )
def __lowerCAmelCase ( self ) -> str:
"""simple docstring"""
A : List[Any] = self.tokenizer_class.from_pretrained('''cl-tohoku/bert-base-japanese-char''' )
A : Optional[Any] = tokenizer.encode('''ありがとう。''' , add_special_tokens=SCREAMING_SNAKE_CASE )
A : List[str] = tokenizer.encode('''どういたしまして。''' , add_special_tokens=SCREAMING_SNAKE_CASE )
A : int = tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE )
A : Dict = tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
# 2 is for "[CLS]", 3 is for "[SEP]"
assert encoded_sentence == [2] + text + [3]
assert encoded_pair == [2] + text + [3] + text_a + [3]
@custom_tokenizers
class A ( unittest.TestCase ):
def __lowerCAmelCase ( self ) -> List[Any]:
"""simple docstring"""
A : Any = 'cl-tohoku/bert-base-japanese'
A : Optional[Any] = AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE )
self.assertIsInstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
class A ( unittest.TestCase ):
def __lowerCAmelCase ( self ) -> Union[str, Any]:
"""simple docstring"""
A : Union[str, Any] = 'cl-tohoku/bert-base-japanese'
with self.assertLogs('''transformers''' , level='''WARNING''' ) as cm:
BertTokenizer.from_pretrained(SCREAMING_SNAKE_CASE )
self.assertTrue(
cm.records[0].message.startswith(
'''The tokenizer class you load from this checkpoint is not the same type as the class this function'''
''' is called from.''' ) )
A : int = 'bert-base-cased'
with self.assertLogs('''transformers''' , level='''WARNING''' ) as cm:
BertJapaneseTokenizer.from_pretrained(SCREAMING_SNAKE_CASE )
self.assertTrue(
cm.records[0].message.startswith(
'''The tokenizer class you load from this checkpoint is not the same type as the class this function'''
''' is called from.''' ) )
| 351 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : int = logging.get_logger(__name__)
lowercase : int = {
'asapp/sew-tiny-100k': 'https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json',
# See all SEW models at https://huggingface.co/models?filter=sew
}
class A ( __snake_case ):
__magic_name__ = '''sew'''
def __init__( self , SCREAMING_SNAKE_CASE=32 , SCREAMING_SNAKE_CASE=768 , SCREAMING_SNAKE_CASE=12 , SCREAMING_SNAKE_CASE=12 , SCREAMING_SNAKE_CASE=3072 , SCREAMING_SNAKE_CASE=2 , SCREAMING_SNAKE_CASE="gelu" , SCREAMING_SNAKE_CASE=0.1 , SCREAMING_SNAKE_CASE=0.1 , SCREAMING_SNAKE_CASE=0.1 , SCREAMING_SNAKE_CASE=0.0 , SCREAMING_SNAKE_CASE=0.1 , SCREAMING_SNAKE_CASE=0.1 , SCREAMING_SNAKE_CASE=0.02 , SCREAMING_SNAKE_CASE=1e-5 , SCREAMING_SNAKE_CASE="group" , SCREAMING_SNAKE_CASE="gelu" , SCREAMING_SNAKE_CASE=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512) , SCREAMING_SNAKE_CASE=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1) , SCREAMING_SNAKE_CASE=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1) , SCREAMING_SNAKE_CASE=False , SCREAMING_SNAKE_CASE=128 , SCREAMING_SNAKE_CASE=16 , SCREAMING_SNAKE_CASE=True , SCREAMING_SNAKE_CASE=0.05 , SCREAMING_SNAKE_CASE=10 , SCREAMING_SNAKE_CASE=2 , SCREAMING_SNAKE_CASE=0.0 , SCREAMING_SNAKE_CASE=10 , SCREAMING_SNAKE_CASE=0 , SCREAMING_SNAKE_CASE="mean" , SCREAMING_SNAKE_CASE=False , SCREAMING_SNAKE_CASE=False , SCREAMING_SNAKE_CASE=256 , SCREAMING_SNAKE_CASE=0 , SCREAMING_SNAKE_CASE=1 , SCREAMING_SNAKE_CASE=2 , **SCREAMING_SNAKE_CASE , ) -> Tuple:
"""simple docstring"""
super().__init__(**SCREAMING_SNAKE_CASE , pad_token_id=SCREAMING_SNAKE_CASE , bos_token_id=SCREAMING_SNAKE_CASE , eos_token_id=SCREAMING_SNAKE_CASE )
A : Optional[Any] = hidden_size
A : Any = feat_extract_norm
A : Optional[int] = feat_extract_activation
A : Tuple = list(SCREAMING_SNAKE_CASE )
A : List[str] = list(SCREAMING_SNAKE_CASE )
A : List[str] = list(SCREAMING_SNAKE_CASE )
A : int = conv_bias
A : List[Any] = num_conv_pos_embeddings
A : Tuple = num_conv_pos_embedding_groups
A : int = len(self.conv_dim )
A : Dict = num_hidden_layers
A : Optional[int] = intermediate_size
A : Any = squeeze_factor
A : int = hidden_act
A : str = num_attention_heads
A : Dict = hidden_dropout
A : Optional[Any] = attention_dropout
A : List[str] = activation_dropout
A : Union[str, Any] = feat_proj_dropout
A : Union[str, Any] = final_dropout
A : int = layerdrop
A : Optional[Any] = layer_norm_eps
A : Any = initializer_range
A : Tuple = vocab_size
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
'''Configuration for convolutional layers is incorrect.'''
'''It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'''
F'but is `len(config.conv_dim) = {len(self.conv_dim )}`, `len(config.conv_stride)'
F'= {len(self.conv_stride )}`, `len(config.conv_kernel) = {len(self.conv_kernel )}`.' )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
A : Optional[Any] = apply_spec_augment
A : Optional[Any] = mask_time_prob
A : Union[str, Any] = mask_time_length
A : Optional[Any] = mask_time_min_masks
A : str = mask_feature_prob
A : Tuple = mask_feature_length
A : Any = mask_feature_min_masks
# ctc loss
A : List[Any] = ctc_loss_reduction
A : Dict = ctc_zero_infinity
# sequence classification
A : int = use_weighted_layer_sum
A : Optional[int] = classifier_proj_size
@property
def __lowerCAmelCase ( self ) -> Optional[Any]:
"""simple docstring"""
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 311 | 0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.