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'''simple docstring'''
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class A_ ( lowerCAmelCase_ ):
_lowerCamelCase : Optional[Any] = ["""image_processor""", """tokenizer"""]
_lowerCamelCase : Dict = """BlipImageProcessor"""
_lowerCamelCase : int = ("""BertTokenizer""", """BertTokenizerFast""")
def __init__( self : str , snake_case_ : Dict , snake_case_ : Any ):
_UpperCAmelCase = False
super().__init__(snake_case_ , snake_case_ )
_UpperCAmelCase = self.image_processor
def __call__( self : Tuple , snake_case_ : ImageInput = None , snake_case_ : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None , snake_case_ : bool = True , snake_case_ : Union[bool, str, PaddingStrategy] = False , snake_case_ : Union[bool, str, TruncationStrategy] = None , snake_case_ : Optional[int] = None , snake_case_ : int = 0 , snake_case_ : Optional[int] = None , snake_case_ : Optional[bool] = None , snake_case_ : bool = False , snake_case_ : bool = False , snake_case_ : bool = False , snake_case_ : bool = False , snake_case_ : bool = False , snake_case_ : bool = True , snake_case_ : Optional[Union[str, TensorType]] = None , **snake_case_ : Optional[Any] , ):
if images is None and text is None:
raise ValueError("You have to specify either images or text." )
# Get only text
if images is None:
_UpperCAmelCase = self.tokenizer
_UpperCAmelCase = self.tokenizer(
text=snake_case_ , add_special_tokens=snake_case_ , padding=snake_case_ , truncation=snake_case_ , max_length=snake_case_ , stride=snake_case_ , pad_to_multiple_of=snake_case_ , return_attention_mask=snake_case_ , return_overflowing_tokens=snake_case_ , return_special_tokens_mask=snake_case_ , return_offsets_mapping=snake_case_ , return_token_type_ids=snake_case_ , return_length=snake_case_ , verbose=snake_case_ , return_tensors=snake_case_ , **snake_case_ , )
return text_encoding
# add pixel_values
_UpperCAmelCase = self.image_processor(snake_case_ , return_tensors=snake_case_ )
if text is not None:
_UpperCAmelCase = self.tokenizer(
text=snake_case_ , add_special_tokens=snake_case_ , padding=snake_case_ , truncation=snake_case_ , max_length=snake_case_ , stride=snake_case_ , pad_to_multiple_of=snake_case_ , return_attention_mask=snake_case_ , return_overflowing_tokens=snake_case_ , return_special_tokens_mask=snake_case_ , return_offsets_mapping=snake_case_ , return_token_type_ids=snake_case_ , return_length=snake_case_ , verbose=snake_case_ , return_tensors=snake_case_ , **snake_case_ , )
else:
_UpperCAmelCase = None
if text_encoding is not None:
encoding_image_processor.update(snake_case_ )
return encoding_image_processor
def lowercase ( self : Optional[int] , *snake_case_ : Union[str, Any] , **snake_case_ : List[str] ):
return self.tokenizer.batch_decode(*snake_case_ , **snake_case_ )
def lowercase ( self : Union[str, Any] , *snake_case_ : int , **snake_case_ : Optional[int] ):
return self.tokenizer.decode(*snake_case_ , **snake_case_ )
@property
def lowercase ( self : Any ):
_UpperCAmelCase = self.tokenizer.model_input_names
_UpperCAmelCase = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
| 22 |
'''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_albert import AlbertTokenizer
else:
__SCREAMING_SNAKE_CASE :List[Any] = None
__SCREAMING_SNAKE_CASE :Union[str, Any] = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE :List[str] = {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}
__SCREAMING_SNAKE_CASE :List[Any] = {
'''vocab_file''': {
'''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/spiece.model''',
'''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/spiece.model''',
'''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model''',
'''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model''',
'''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/spiece.model''',
'''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/spiece.model''',
'''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model''',
'''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model''',
},
'''tokenizer_file''': {
'''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json''',
'''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json''',
'''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json''',
'''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json''',
'''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json''',
'''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json''',
'''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json''',
'''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json''',
},
}
__SCREAMING_SNAKE_CASE :Optional[Any] = {
'''albert-base-v1''': 512,
'''albert-large-v1''': 512,
'''albert-xlarge-v1''': 512,
'''albert-xxlarge-v1''': 512,
'''albert-base-v2''': 512,
'''albert-large-v2''': 512,
'''albert-xlarge-v2''': 512,
'''albert-xxlarge-v2''': 512,
}
__SCREAMING_SNAKE_CASE :Optional[int] = '''▁'''
class A_ ( lowerCAmelCase_ ):
_lowerCamelCase : Optional[int] = VOCAB_FILES_NAMES
_lowerCamelCase : Any = PRETRAINED_VOCAB_FILES_MAP
_lowerCamelCase : Tuple = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_lowerCamelCase : int = AlbertTokenizer
def __init__( self : Optional[Any] , snake_case_ : Optional[Any]=None , snake_case_ : Optional[Any]=None , snake_case_ : Optional[Any]=True , snake_case_ : str=True , snake_case_ : Tuple=False , snake_case_ : List[Any]="[CLS]" , snake_case_ : Union[str, Any]="[SEP]" , snake_case_ : str="<unk>" , snake_case_ : Union[str, Any]="[SEP]" , snake_case_ : List[Any]="<pad>" , snake_case_ : List[str]="[CLS]" , snake_case_ : int="[MASK]" , **snake_case_ : Any , ):
# Mask token behave like a normal word, i.e. include the space before it and
# is included in the raw text, there should be a match in a non-normalized sentence.
_UpperCAmelCase = (
AddedToken(snake_case_ , lstrip=snake_case_ , rstrip=snake_case_ , normalized=snake_case_ )
if isinstance(snake_case_ , snake_case_ )
else mask_token
)
super().__init__(
snake_case_ , tokenizer_file=snake_case_ , do_lower_case=snake_case_ , remove_space=snake_case_ , keep_accents=snake_case_ , bos_token=snake_case_ , eos_token=snake_case_ , unk_token=snake_case_ , sep_token=snake_case_ , pad_token=snake_case_ , cls_token=snake_case_ , mask_token=snake_case_ , **snake_case_ , )
_UpperCAmelCase = do_lower_case
_UpperCAmelCase = remove_space
_UpperCAmelCase = keep_accents
_UpperCAmelCase = vocab_file
_UpperCAmelCase = False if not self.vocab_file else True
def lowercase ( self : Union[str, Any] , snake_case_ : List[int] , snake_case_ : Optional[List[int]] = None ):
_UpperCAmelCase = [self.sep_token_id]
_UpperCAmelCase = [self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def lowercase ( self : Dict , snake_case_ : List[int] , snake_case_ : Optional[List[int]] = None ):
_UpperCAmelCase = [self.sep_token_id]
_UpperCAmelCase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def lowercase ( self : Optional[Any] , snake_case_ : str , snake_case_ : Optional[str] = None ):
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(snake_case_ ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
return
_UpperCAmelCase = os.path.join(
snake_case_ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(snake_case_ ):
copyfile(self.vocab_file , snake_case_ )
return (out_vocab_file,)
| 22 | 1 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : int ) -> list:
'''simple docstring'''
_UpperCAmelCase = int(__lowercase )
if n_element < 1:
_UpperCAmelCase = ValueError("a should be a positive number" )
raise my_error
_UpperCAmelCase = [1]
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = (0, 0, 0)
_UpperCAmelCase = 1
while index < n_element:
while hamming_list[i] * 2 <= hamming_list[-1]:
i += 1
while hamming_list[j] * 3 <= hamming_list[-1]:
j += 1
while hamming_list[k] * 5 <= hamming_list[-1]:
k += 1
hamming_list.append(
min(hamming_list[i] * 2 , hamming_list[j] * 3 , hamming_list[k] * 5 ) )
index += 1
return hamming_list
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE :Any = input('''Enter the last number (nth term) of the Hamming Number Series: ''')
print('''Formula of Hamming Number Series => 2^i * 3^j * 5^k''')
__SCREAMING_SNAKE_CASE :Tuple = hamming(int(n))
print('''-----------------------------------------------------''')
print(F"The list with nth numbers is: {hamming_numbers}")
print('''-----------------------------------------------------''')
| 22 |
'''simple docstring'''
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
__SCREAMING_SNAKE_CASE :Dict = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE :int = {
'''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 A_ ( lowerCAmelCase_ ):
_lowerCamelCase : int = """perceiver"""
def __init__( self : Any , snake_case_ : List[Any]=2_5_6 , snake_case_ : str=1_2_8_0 , snake_case_ : Optional[int]=7_6_8 , snake_case_ : int=1 , snake_case_ : List[Any]=2_6 , snake_case_ : Dict=8 , snake_case_ : List[Any]=8 , snake_case_ : Tuple=None , snake_case_ : Tuple=None , snake_case_ : Any="kv" , snake_case_ : Any=1 , snake_case_ : List[str]=1 , snake_case_ : Optional[int]="gelu" , snake_case_ : List[Any]=0.1 , snake_case_ : Dict=0.0_2 , snake_case_ : int=1e-12 , snake_case_ : List[str]=True , snake_case_ : str=2_6_2 , snake_case_ : Optional[Any]=2_0_4_8 , snake_case_ : Union[str, Any]=5_6 , snake_case_ : Dict=[3_6_8, 4_9_6] , snake_case_ : Tuple=1_6 , snake_case_ : Union[str, Any]=1_9_2_0 , snake_case_ : List[Any]=1_6 , snake_case_ : Tuple=[1, 1_6, 2_2_4, 2_2_4] , **snake_case_ : List[Any] , ):
super().__init__(**snake_case_ )
_UpperCAmelCase = num_latents
_UpperCAmelCase = d_latents
_UpperCAmelCase = d_model
_UpperCAmelCase = num_blocks
_UpperCAmelCase = num_self_attends_per_block
_UpperCAmelCase = num_self_attention_heads
_UpperCAmelCase = num_cross_attention_heads
_UpperCAmelCase = qk_channels
_UpperCAmelCase = v_channels
_UpperCAmelCase = cross_attention_shape_for_attention
_UpperCAmelCase = self_attention_widening_factor
_UpperCAmelCase = cross_attention_widening_factor
_UpperCAmelCase = hidden_act
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = initializer_range
_UpperCAmelCase = layer_norm_eps
_UpperCAmelCase = use_query_residual
# masked language modeling attributes
_UpperCAmelCase = vocab_size
_UpperCAmelCase = max_position_embeddings
# image classification attributes
_UpperCAmelCase = image_size
# flow attributes
_UpperCAmelCase = train_size
# multimodal autoencoding attributes
_UpperCAmelCase = num_frames
_UpperCAmelCase = audio_samples_per_frame
_UpperCAmelCase = samples_per_patch
_UpperCAmelCase = output_shape
class A_ ( lowerCAmelCase_ ):
@property
def lowercase ( self : int ):
if self.task == "multiple-choice":
_UpperCAmelCase = {0: "batch", 1: "choice", 2: "sequence"}
else:
_UpperCAmelCase = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("inputs", dynamic_axis),
("attention_mask", dynamic_axis),
] )
@property
def lowercase ( self : Optional[Any] ):
return 1e-4
def lowercase ( self : List[str] , snake_case_ : Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"] , snake_case_ : int = -1 , snake_case_ : int = -1 , snake_case_ : int = -1 , snake_case_ : bool = False , snake_case_ : Optional[TensorType] = None , snake_case_ : int = 3 , snake_case_ : int = 4_0 , snake_case_ : int = 4_0 , ):
# copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified
if isinstance(snake_case_ , snake_case_ ):
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
_UpperCAmelCase = compute_effective_axis_dimension(
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 = preprocessor.num_special_tokens_to_add(snake_case_ )
_UpperCAmelCase = compute_effective_axis_dimension(
snake_case_ , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=snake_case_ )
# Generate dummy inputs according to compute batch and sequence
_UpperCAmelCase = [" ".join(["a"] ) * seq_length] * batch_size
_UpperCAmelCase = dict(preprocessor(snake_case_ , return_tensors=snake_case_ ) )
_UpperCAmelCase = inputs.pop("input_ids" )
return inputs
elif isinstance(snake_case_ , snake_case_ ) 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
_UpperCAmelCase = compute_effective_axis_dimension(snake_case_ , fixed_dimension=OnnxConfig.default_fixed_batch )
_UpperCAmelCase = self._generate_dummy_images(snake_case_ , snake_case_ , snake_case_ , snake_case_ )
_UpperCAmelCase = dict(preprocessor(images=snake_case_ , return_tensors=snake_case_ ) )
_UpperCAmelCase = inputs.pop("pixel_values" )
return inputs
else:
raise ValueError(
"Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor." )
| 22 | 1 |
'''simple docstring'''
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
def UpperCAmelCase_ ( __lowercase : str ) -> List[str]:
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase = image.size
_UpperCAmelCase , _UpperCAmelCase = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32
_UpperCAmelCase = image.resize((w, h) , resample=PIL_INTERPOLATION["lanczos"] )
_UpperCAmelCase = np.array(__lowercase ).astype(np.floataa ) / 255.0
_UpperCAmelCase = image[None].transpose(0 , 3 , 1 , 2 )
_UpperCAmelCase = torch.from_numpy(__lowercase )
return 2.0 * image - 1.0
class A_ ( lowerCAmelCase_ ):
def __init__( self : Optional[Any] , snake_case_ : VQModel , snake_case_ : UNetaDModel , snake_case_ : Union[
DDIMScheduler,
PNDMScheduler,
LMSDiscreteScheduler,
EulerDiscreteScheduler,
EulerAncestralDiscreteScheduler,
DPMSolverMultistepScheduler,
] , ):
super().__init__()
self.register_modules(vqvae=snake_case_ , unet=snake_case_ , scheduler=snake_case_ )
@torch.no_grad()
def __call__( self : Any , snake_case_ : Union[torch.Tensor, PIL.Image.Image] = None , snake_case_ : Optional[int] = 1 , snake_case_ : Optional[int] = 1_0_0 , snake_case_ : Optional[float] = 0.0 , snake_case_ : Optional[Union[torch.Generator, List[torch.Generator]]] = None , snake_case_ : Optional[str] = "pil" , snake_case_ : bool = True , ):
if isinstance(snake_case_ , PIL.Image.Image ):
_UpperCAmelCase = 1
elif isinstance(snake_case_ , torch.Tensor ):
_UpperCAmelCase = image.shape[0]
else:
raise ValueError(f'`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(snake_case_ )}' )
if isinstance(snake_case_ , PIL.Image.Image ):
_UpperCAmelCase = preprocess(snake_case_ )
_UpperCAmelCase , _UpperCAmelCase = image.shape[-2:]
# in_channels should be 6: 3 for latents, 3 for low resolution image
_UpperCAmelCase = (batch_size, self.unet.config.in_channels // 2, height, width)
_UpperCAmelCase = next(self.unet.parameters() ).dtype
_UpperCAmelCase = randn_tensor(snake_case_ , generator=snake_case_ , device=self.device , dtype=snake_case_ )
_UpperCAmelCase = image.to(device=self.device , dtype=snake_case_ )
# set timesteps and move to the correct device
self.scheduler.set_timesteps(snake_case_ , device=self.device )
_UpperCAmelCase = self.scheduler.timesteps
# scale the initial noise by the standard deviation required by the scheduler
_UpperCAmelCase = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
_UpperCAmelCase = "eta" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
_UpperCAmelCase = {}
if accepts_eta:
_UpperCAmelCase = eta
for t in self.progress_bar(snake_case_ ):
# concat latents and low resolution image in the channel dimension.
_UpperCAmelCase = torch.cat([latents, image] , dim=1 )
_UpperCAmelCase = self.scheduler.scale_model_input(snake_case_ , snake_case_ )
# predict the noise residual
_UpperCAmelCase = self.unet(snake_case_ , snake_case_ ).sample
# compute the previous noisy sample x_t -> x_t-1
_UpperCAmelCase = self.scheduler.step(snake_case_ , snake_case_ , snake_case_ , **snake_case_ ).prev_sample
# decode the image latents with the VQVAE
_UpperCAmelCase = self.vqvae.decode(snake_case_ ).sample
_UpperCAmelCase = torch.clamp(snake_case_ , -1.0 , 1.0 )
_UpperCAmelCase = image / 2 + 0.5
_UpperCAmelCase = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
_UpperCAmelCase = self.numpy_to_pil(snake_case_ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=snake_case_ )
| 22 |
'''simple docstring'''
import warnings
from ...utils import is_sklearn_available, requires_backends
if is_sklearn_available():
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
__SCREAMING_SNAKE_CASE :List[str] = (
'''This metric will be removed from the library soon, metrics should be handled with the 🤗 Evaluate '''
'''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'''
)
def UpperCAmelCase_ ( __lowercase : Any , __lowercase : Tuple ) -> int:
'''simple docstring'''
warnings.warn(__lowercase , __lowercase )
requires_backends(__lowercase , "sklearn" )
return (preds == labels).mean()
def UpperCAmelCase_ ( __lowercase : int , __lowercase : str ) -> Optional[Any]:
'''simple docstring'''
warnings.warn(__lowercase , __lowercase )
requires_backends(__lowercase , "sklearn" )
_UpperCAmelCase = simple_accuracy(__lowercase , __lowercase )
_UpperCAmelCase = fa_score(y_true=__lowercase , y_pred=__lowercase )
return {
"acc": acc,
"f1": fa,
"acc_and_f1": (acc + fa) / 2,
}
def UpperCAmelCase_ ( __lowercase : Optional[int] , __lowercase : List[str] ) -> List[Any]:
'''simple docstring'''
warnings.warn(__lowercase , __lowercase )
requires_backends(__lowercase , "sklearn" )
_UpperCAmelCase = pearsonr(__lowercase , __lowercase )[0]
_UpperCAmelCase = spearmanr(__lowercase , __lowercase )[0]
return {
"pearson": pearson_corr,
"spearmanr": spearman_corr,
"corr": (pearson_corr + spearman_corr) / 2,
}
def UpperCAmelCase_ ( __lowercase : Optional[Any] , __lowercase : str , __lowercase : str ) -> Tuple:
'''simple docstring'''
warnings.warn(__lowercase , __lowercase )
requires_backends(__lowercase , "sklearn" )
assert len(__lowercase ) == len(__lowercase ), f'Predictions and labels have mismatched lengths {len(__lowercase )} and {len(__lowercase )}'
if task_name == "cola":
return {"mcc": matthews_corrcoef(__lowercase , __lowercase )}
elif task_name == "sst-2":
return {"acc": simple_accuracy(__lowercase , __lowercase )}
elif task_name == "mrpc":
return acc_and_fa(__lowercase , __lowercase )
elif task_name == "sts-b":
return pearson_and_spearman(__lowercase , __lowercase )
elif task_name == "qqp":
return acc_and_fa(__lowercase , __lowercase )
elif task_name == "mnli":
return {"mnli/acc": simple_accuracy(__lowercase , __lowercase )}
elif task_name == "mnli-mm":
return {"mnli-mm/acc": simple_accuracy(__lowercase , __lowercase )}
elif task_name == "qnli":
return {"acc": simple_accuracy(__lowercase , __lowercase )}
elif task_name == "rte":
return {"acc": simple_accuracy(__lowercase , __lowercase )}
elif task_name == "wnli":
return {"acc": simple_accuracy(__lowercase , __lowercase )}
elif task_name == "hans":
return {"acc": simple_accuracy(__lowercase , __lowercase )}
else:
raise KeyError(__lowercase )
def UpperCAmelCase_ ( __lowercase : List[Any] , __lowercase : Dict , __lowercase : str ) -> Union[str, Any]:
'''simple docstring'''
warnings.warn(__lowercase , __lowercase )
requires_backends(__lowercase , "sklearn" )
if len(__lowercase ) != len(__lowercase ):
raise ValueError(f'Predictions and labels have mismatched lengths {len(__lowercase )} and {len(__lowercase )}' )
if task_name == "xnli":
return {"acc": simple_accuracy(__lowercase , __lowercase )}
else:
raise KeyError(__lowercase )
| 22 | 1 |
'''simple docstring'''
__SCREAMING_SNAKE_CASE :List[str] = '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
__SCREAMING_SNAKE_CASE :Tuple = [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
__SCREAMING_SNAKE_CASE :Optional[int] = {
'''{processor_class}''': '''FakeProcessorClass''',
'''{model_class}''': '''FakeModelClass''',
'''{object_class}''': '''FakeObjectClass''',
}
| 22 |
'''simple docstring'''
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCAmelCase_ ( __lowercase : int , __lowercase : Dict , __lowercase : str , __lowercase : Optional[Any] , __lowercase : str ) -> List[str]:
'''simple docstring'''
_UpperCAmelCase = TapasConfig.from_json_file(__lowercase )
# set absolute/relative position embeddings parameter
_UpperCAmelCase = reset_position_index_per_cell
# set remaining parameters of TapasConfig as well as the model based on the task
if task == "SQA":
_UpperCAmelCase = TapasForQuestionAnswering(config=__lowercase )
elif task == "WTQ":
# run_task_main.py hparams
_UpperCAmelCase = 4
_UpperCAmelCase = True
# hparam_utils.py hparams
_UpperCAmelCase = 0.66_4694
_UpperCAmelCase = 0.20_7951
_UpperCAmelCase = 0.12_1194
_UpperCAmelCase = True
_UpperCAmelCase = True
_UpperCAmelCase = False
_UpperCAmelCase = 0.035_2513
_UpperCAmelCase = TapasForQuestionAnswering(config=__lowercase )
elif task == "WIKISQL_SUPERVISED":
# run_task_main.py hparams
_UpperCAmelCase = 4
_UpperCAmelCase = False
# hparam_utils.py hparams
_UpperCAmelCase = 36.4519
_UpperCAmelCase = 0.90_3421
_UpperCAmelCase = 222.088
_UpperCAmelCase = True
_UpperCAmelCase = True
_UpperCAmelCase = True
_UpperCAmelCase = 0.76_3141
_UpperCAmelCase = TapasForQuestionAnswering(config=__lowercase )
elif task == "TABFACT":
_UpperCAmelCase = TapasForSequenceClassification(config=__lowercase )
elif task == "MLM":
_UpperCAmelCase = TapasForMaskedLM(config=__lowercase )
elif task == "INTERMEDIATE_PRETRAINING":
_UpperCAmelCase = TapasModel(config=__lowercase )
else:
raise ValueError(f'Task {task} not supported.' )
print(f'Building PyTorch model from configuration: {config}' )
# Load weights from tf checkpoint
load_tf_weights_in_tapas(__lowercase , __lowercase , __lowercase )
# Save pytorch-model (weights and configuration)
print(f'Save PyTorch model to {pytorch_dump_path}' )
model.save_pretrained(__lowercase )
# Save tokenizer files
print(f'Save tokenizer files to {pytorch_dump_path}' )
_UpperCAmelCase = TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + "vocab.txt" , model_max_length=512 )
tokenizer.save_pretrained(__lowercase )
print("Used relative position embeddings:" , model.config.reset_position_index_per_cell )
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE :List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--task''', default='''SQA''', type=str, help='''Model task for which to convert a checkpoint. Defaults to SQA.'''
)
parser.add_argument(
'''--reset_position_index_per_cell''',
default=False,
action='''store_true''',
help='''Whether to use relative position embeddings or not. Defaults to True.''',
)
parser.add_argument(
'''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--tapas_config_file''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained TAPAS model. \n'''
'''This specifies the model architecture.'''
),
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
__SCREAMING_SNAKE_CASE :List[str] = parser.parse_args()
convert_tf_checkpoint_to_pytorch(
args.task,
args.reset_position_index_per_cell,
args.tf_checkpoint_path,
args.tapas_config_file,
args.pytorch_dump_path,
)
| 22 | 1 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
Pipeline,
ZeroShotClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
from .test_pipelines_common import ANY
# These 2 model types require different inputs than those of the usual text models.
__SCREAMING_SNAKE_CASE :List[Any] = {'''LayoutLMv2Config''', '''LayoutLMv3Config'''}
@is_pipeline_test
class A_ ( unittest.TestCase ):
_lowerCamelCase : str = MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
_lowerCamelCase : Dict = TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if model_mapping is not None:
_lowerCamelCase : int = {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
if tf_model_mapping is not None:
_lowerCamelCase : Any = {
config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
}
def lowercase ( self : Tuple , snake_case_ : List[str] , snake_case_ : Optional[Any] , snake_case_ : Union[str, Any] ):
_UpperCAmelCase = ZeroShotClassificationPipeline(
model=snake_case_ , tokenizer=snake_case_ , candidate_labels=["polics", "health"] )
return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]
def lowercase ( self : int , snake_case_ : Tuple , snake_case_ : Optional[int] ):
_UpperCAmelCase = classifier("Who are you voting for in 2020?" , candidate_labels="politics" )
self.assertEqual(snake_case_ , {"sequence": ANY(snake_case_ ), "labels": [ANY(snake_case_ )], "scores": [ANY(snake_case_ )]} )
# No kwarg
_UpperCAmelCase = classifier("Who are you voting for in 2020?" , ["politics"] )
self.assertEqual(snake_case_ , {"sequence": ANY(snake_case_ ), "labels": [ANY(snake_case_ )], "scores": [ANY(snake_case_ )]} )
_UpperCAmelCase = classifier("Who are you voting for in 2020?" , candidate_labels=["politics"] )
self.assertEqual(snake_case_ , {"sequence": ANY(snake_case_ ), "labels": [ANY(snake_case_ )], "scores": [ANY(snake_case_ )]} )
_UpperCAmelCase = classifier("Who are you voting for in 2020?" , candidate_labels="politics, public health" )
self.assertEqual(
snake_case_ , {"sequence": ANY(snake_case_ ), "labels": [ANY(snake_case_ ), ANY(snake_case_ )], "scores": [ANY(snake_case_ ), ANY(snake_case_ )]} )
self.assertAlmostEqual(sum(nested_simplify(outputs["scores"] ) ) , 1.0 )
_UpperCAmelCase = classifier("Who are you voting for in 2020?" , candidate_labels=["politics", "public health"] )
self.assertEqual(
snake_case_ , {"sequence": ANY(snake_case_ ), "labels": [ANY(snake_case_ ), ANY(snake_case_ )], "scores": [ANY(snake_case_ ), ANY(snake_case_ )]} )
self.assertAlmostEqual(sum(nested_simplify(outputs["scores"] ) ) , 1.0 )
_UpperCAmelCase = classifier(
"Who are you voting for in 2020?" , candidate_labels="politics" , hypothesis_template="This text is about {}" )
self.assertEqual(snake_case_ , {"sequence": ANY(snake_case_ ), "labels": [ANY(snake_case_ )], "scores": [ANY(snake_case_ )]} )
# https://github.com/huggingface/transformers/issues/13846
_UpperCAmelCase = classifier(["I am happy"] , ["positive", "negative"] )
self.assertEqual(
snake_case_ , [
{"sequence": ANY(snake_case_ ), "labels": [ANY(snake_case_ ), ANY(snake_case_ )], "scores": [ANY(snake_case_ ), ANY(snake_case_ )]}
for i in range(1 )
] , )
_UpperCAmelCase = classifier(["I am happy", "I am sad"] , ["positive", "negative"] )
self.assertEqual(
snake_case_ , [
{"sequence": ANY(snake_case_ ), "labels": [ANY(snake_case_ ), ANY(snake_case_ )], "scores": [ANY(snake_case_ ), ANY(snake_case_ )]}
for i in range(2 )
] , )
with self.assertRaises(snake_case_ ):
classifier("" , candidate_labels="politics" )
with self.assertRaises(snake_case_ ):
classifier(snake_case_ , candidate_labels="politics" )
with self.assertRaises(snake_case_ ):
classifier("Who are you voting for in 2020?" , candidate_labels="" )
with self.assertRaises(snake_case_ ):
classifier("Who are you voting for in 2020?" , candidate_labels=snake_case_ )
with self.assertRaises(snake_case_ ):
classifier(
"Who are you voting for in 2020?" , candidate_labels="politics" , hypothesis_template="Not formatting template" , )
with self.assertRaises(snake_case_ ):
classifier(
"Who are you voting for in 2020?" , candidate_labels="politics" , hypothesis_template=snake_case_ , )
self.run_entailment_id(snake_case_ )
def lowercase ( self : str , snake_case_ : Pipeline ):
_UpperCAmelCase = zero_shot_classifier.model.config
_UpperCAmelCase = config.labelaid
_UpperCAmelCase = zero_shot_classifier.entailment_id
_UpperCAmelCase = {"LABEL_0": 0, "LABEL_1": 1, "LABEL_2": 2}
self.assertEqual(zero_shot_classifier.entailment_id , -1 )
_UpperCAmelCase = {"entailment": 0, "neutral": 1, "contradiction": 2}
self.assertEqual(zero_shot_classifier.entailment_id , 0 )
_UpperCAmelCase = {"ENTAIL": 0, "NON-ENTAIL": 1}
self.assertEqual(zero_shot_classifier.entailment_id , 0 )
_UpperCAmelCase = {"ENTAIL": 2, "NEUTRAL": 1, "CONTR": 0}
self.assertEqual(zero_shot_classifier.entailment_id , 2 )
_UpperCAmelCase = original_labelaid
self.assertEqual(snake_case_ , zero_shot_classifier.entailment_id )
@require_torch
def lowercase ( self : Optional[Any] ):
_UpperCAmelCase = pipeline(
"zero-shot-classification" , model="sshleifer/tiny-distilbert-base-cased-distilled-squad" , framework="pt" , )
# There was a regression in 4.10 for this
# Adding a test so we don't make the mistake again.
# https://github.com/huggingface/transformers/issues/13381#issuecomment-912343499
zero_shot_classifier(
"Who are you voting for in 2020?" * 1_0_0 , candidate_labels=["politics", "public health", "science"] )
@require_torch
def lowercase ( self : Dict ):
_UpperCAmelCase = pipeline(
"zero-shot-classification" , model="sshleifer/tiny-distilbert-base-cased-distilled-squad" , framework="pt" , )
_UpperCAmelCase = zero_shot_classifier(
"Who are you voting for in 2020?" , candidate_labels=["politics", "public health", "science"] )
self.assertEqual(
nested_simplify(snake_case_ ) , {
"sequence": "Who are you voting for in 2020?",
"labels": ["science", "public health", "politics"],
"scores": [0.3_3_3, 0.3_3_3, 0.3_3_3],
} , )
@require_tf
def lowercase ( self : str ):
_UpperCAmelCase = pipeline(
"zero-shot-classification" , model="sshleifer/tiny-distilbert-base-cased-distilled-squad" , framework="tf" , )
_UpperCAmelCase = zero_shot_classifier(
"Who are you voting for in 2020?" , candidate_labels=["politics", "public health", "science"] )
self.assertEqual(
nested_simplify(snake_case_ ) , {
"sequence": "Who are you voting for in 2020?",
"labels": ["science", "public health", "politics"],
"scores": [0.3_3_3, 0.3_3_3, 0.3_3_3],
} , )
@slow
@require_torch
def lowercase ( self : List[str] ):
_UpperCAmelCase = pipeline("zero-shot-classification" , model="roberta-large-mnli" , framework="pt" )
_UpperCAmelCase = zero_shot_classifier(
"Who are you voting for in 2020?" , candidate_labels=["politics", "public health", "science"] )
self.assertEqual(
nested_simplify(snake_case_ ) , {
"sequence": "Who are you voting for in 2020?",
"labels": ["politics", "public health", "science"],
"scores": [0.9_7_6, 0.0_1_5, 0.0_0_9],
} , )
_UpperCAmelCase = zero_shot_classifier(
"The dominant sequence transduction models are based on complex recurrent or convolutional neural networks"
" in an encoder-decoder configuration. The best performing models also connect the encoder and decoder"
" through an attention mechanism. We propose a new simple network architecture, the Transformer, based"
" solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two"
" machine translation tasks show these models to be superior in quality while being more parallelizable"
" and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014"
" English-to-German translation task, improving over the existing best results, including ensembles by"
" over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new"
" single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small"
" fraction of the training costs of the best models from the literature. We show that the Transformer"
" generalizes well to other tasks by applying it successfully to English constituency parsing both with"
" large and limited training data." , candidate_labels=["machine learning", "statistics", "translation", "vision"] , multi_label=snake_case_ , )
self.assertEqual(
nested_simplify(snake_case_ ) , {
"sequence": (
"The dominant sequence transduction models are based on complex recurrent or convolutional neural"
" networks in an encoder-decoder configuration. The best performing models also connect the"
" encoder and decoder through an attention mechanism. We propose a new simple network"
" architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence"
" and convolutions entirely. Experiments on two machine translation tasks show these models to be"
" superior in quality while being more parallelizable and requiring significantly less time to"
" train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,"
" improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014"
" English-to-French translation task, our model establishes a new single-model state-of-the-art"
" BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training"
" costs of the best models from the literature. We show that the Transformer generalizes well to"
" other tasks by applying it successfully to English constituency parsing both with large and"
" limited training data."
),
"labels": ["translation", "machine learning", "vision", "statistics"],
"scores": [0.8_1_7, 0.7_1_3, 0.0_1_8, 0.0_1_8],
} , )
@slow
@require_tf
def lowercase ( self : Tuple ):
_UpperCAmelCase = pipeline("zero-shot-classification" , model="roberta-large-mnli" , framework="tf" )
_UpperCAmelCase = zero_shot_classifier(
"Who are you voting for in 2020?" , candidate_labels=["politics", "public health", "science"] )
self.assertEqual(
nested_simplify(snake_case_ ) , {
"sequence": "Who are you voting for in 2020?",
"labels": ["politics", "public health", "science"],
"scores": [0.9_7_6, 0.0_1_5, 0.0_0_9],
} , )
_UpperCAmelCase = zero_shot_classifier(
"The dominant sequence transduction models are based on complex recurrent or convolutional neural networks"
" in an encoder-decoder configuration. The best performing models also connect the encoder and decoder"
" through an attention mechanism. We propose a new simple network architecture, the Transformer, based"
" solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two"
" machine translation tasks show these models to be superior in quality while being more parallelizable"
" and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014"
" English-to-German translation task, improving over the existing best results, including ensembles by"
" over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new"
" single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small"
" fraction of the training costs of the best models from the literature. We show that the Transformer"
" generalizes well to other tasks by applying it successfully to English constituency parsing both with"
" large and limited training data." , candidate_labels=["machine learning", "statistics", "translation", "vision"] , multi_label=snake_case_ , )
self.assertEqual(
nested_simplify(snake_case_ ) , {
"sequence": (
"The dominant sequence transduction models are based on complex recurrent or convolutional neural"
" networks in an encoder-decoder configuration. The best performing models also connect the"
" encoder and decoder through an attention mechanism. We propose a new simple network"
" architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence"
" and convolutions entirely. Experiments on two machine translation tasks show these models to be"
" superior in quality while being more parallelizable and requiring significantly less time to"
" train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,"
" improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014"
" English-to-French translation task, our model establishes a new single-model state-of-the-art"
" BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training"
" costs of the best models from the literature. We show that the Transformer generalizes well to"
" other tasks by applying it successfully to English constituency parsing both with large and"
" limited training data."
),
"labels": ["translation", "machine learning", "vision", "statistics"],
"scores": [0.8_1_7, 0.7_1_3, 0.0_1_8, 0.0_1_8],
} , )
| 22 |
'''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
__SCREAMING_SNAKE_CASE :str = [
'''good first issue''',
'''feature request''',
'''wip''',
]
def UpperCAmelCase_ ( ) -> Optional[Any]:
'''simple docstring'''
_UpperCAmelCase = Github(os.environ["GITHUB_TOKEN"] )
_UpperCAmelCase = g.get_repo("huggingface/accelerate" )
_UpperCAmelCase = repo.get_issues(state="open" )
for issue in open_issues:
_UpperCAmelCase = sorted([comment for comment in issue.get_comments()] , key=lambda __lowercase : i.created_at , reverse=__lowercase )
_UpperCAmelCase = comments[0] if len(__lowercase ) > 0 else None
_UpperCAmelCase = dt.utcnow()
_UpperCAmelCase = (current_time - issue.updated_at).days
_UpperCAmelCase = (current_time - issue.created_at).days
if (
last_comment is not None
and last_comment.user.login == "github-actions[bot]"
and days_since_updated > 7
and days_since_creation >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Close issue since it has been 7 days of inactivity since bot mention.
issue.edit(state="closed" )
elif (
days_since_updated > 23
and days_since_creation >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Add stale comment
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/accelerate/blob/main/CONTRIBUTING.md) "
"are likely to be ignored." )
if __name__ == "__main__":
main()
| 22 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__SCREAMING_SNAKE_CASE :Any = {
'''configuration_rembert''': ['''REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RemBertConfig''', '''RemBertOnnxConfig''']
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE :Union[str, Any] = ['''RemBertTokenizer''']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE :List[str] = ['''RemBertTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE :int = [
'''REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''RemBertForCausalLM''',
'''RemBertForMaskedLM''',
'''RemBertForMultipleChoice''',
'''RemBertForQuestionAnswering''',
'''RemBertForSequenceClassification''',
'''RemBertForTokenClassification''',
'''RemBertLayer''',
'''RemBertModel''',
'''RemBertPreTrainedModel''',
'''load_tf_weights_in_rembert''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE :Optional[Any] = [
'''TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFRemBertForCausalLM''',
'''TFRemBertForMaskedLM''',
'''TFRemBertForMultipleChoice''',
'''TFRemBertForQuestionAnswering''',
'''TFRemBertForSequenceClassification''',
'''TFRemBertForTokenClassification''',
'''TFRemBertLayer''',
'''TFRemBertModel''',
'''TFRemBertPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_rembert import RemBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_rembert_fast import RemBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_rembert import (
REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
RemBertForCausalLM,
RemBertForMaskedLM,
RemBertForMultipleChoice,
RemBertForQuestionAnswering,
RemBertForSequenceClassification,
RemBertForTokenClassification,
RemBertLayer,
RemBertModel,
RemBertPreTrainedModel,
load_tf_weights_in_rembert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_rembert import (
TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRemBertForCausalLM,
TFRemBertForMaskedLM,
TFRemBertForMultipleChoice,
TFRemBertForQuestionAnswering,
TFRemBertForSequenceClassification,
TFRemBertForTokenClassification,
TFRemBertLayer,
TFRemBertModel,
TFRemBertPreTrainedModel,
)
else:
import sys
__SCREAMING_SNAKE_CASE :Any = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 22 |
'''simple docstring'''
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 UpperCAmelCase_ ( __lowercase : Any , __lowercase : int ) -> int:
'''simple docstring'''
_UpperCAmelCase = 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}}" )
_UpperCAmelCase = DatasetInfosDict.from_directory(__lowercase )
assert dataset_infos
assert dataset_infos["default"].dataset_size == 42
@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=42 , ),
] , )
def UpperCAmelCase_ ( __lowercase : Tuple , __lowercase : DatasetInfo ) -> Any:
'''simple docstring'''
_UpperCAmelCase = str(__lowercase )
dataset_info.write_to_directory(__lowercase )
_UpperCAmelCase = DatasetInfo.from_directory(__lowercase )
assert dataset_info == reloaded
assert os.path.exists(os.path.join(__lowercase , "dataset_info.json" ) )
def UpperCAmelCase_ ( ) -> Optional[Any]:
'''simple docstring'''
_UpperCAmelCase = 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": 42}] , download_checksums={} , download_size=1337 , post_processing_size=442 , dataset_size=1234 , size_in_bytes=1337 + 442 + 1234 , )
_UpperCAmelCase = dataset_info._to_yaml_dict()
assert sorted(__lowercase ) == 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) )
_UpperCAmelCase = yaml.safe_dump(__lowercase )
_UpperCAmelCase = yaml.safe_load(__lowercase )
assert dataset_info_yaml_dict == reloaded
def UpperCAmelCase_ ( ) -> Optional[int]:
'''simple docstring'''
_UpperCAmelCase = DatasetInfo()
_UpperCAmelCase = 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=42 , )
} ),
DatasetInfosDict(
{
"v1": DatasetInfo(dataset_size=42 ),
"v2": DatasetInfo(dataset_size=1337 ),
} ),
] , )
def UpperCAmelCase_ ( __lowercase : int , __lowercase : DatasetInfosDict ) -> Dict:
'''simple docstring'''
_UpperCAmelCase = str(__lowercase )
dataset_infos_dict.write_to_directory(__lowercase )
_UpperCAmelCase = DatasetInfosDict.from_directory(__lowercase )
# the config_name of the dataset_infos_dict take over the attribute
for config_name, dataset_info in dataset_infos_dict.items():
_UpperCAmelCase = 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
_UpperCAmelCase = 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(__lowercase , "README.md" ) )
| 22 | 1 |
'''simple docstring'''
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 UpperCAmelCase_ ( __lowercase : Any , __lowercase : int ) -> int:
'''simple docstring'''
_UpperCAmelCase = 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}}" )
_UpperCAmelCase = DatasetInfosDict.from_directory(__lowercase )
assert dataset_infos
assert dataset_infos["default"].dataset_size == 42
@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=42 , ),
] , )
def UpperCAmelCase_ ( __lowercase : Tuple , __lowercase : DatasetInfo ) -> Any:
'''simple docstring'''
_UpperCAmelCase = str(__lowercase )
dataset_info.write_to_directory(__lowercase )
_UpperCAmelCase = DatasetInfo.from_directory(__lowercase )
assert dataset_info == reloaded
assert os.path.exists(os.path.join(__lowercase , "dataset_info.json" ) )
def UpperCAmelCase_ ( ) -> Optional[Any]:
'''simple docstring'''
_UpperCAmelCase = 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": 42}] , download_checksums={} , download_size=1337 , post_processing_size=442 , dataset_size=1234 , size_in_bytes=1337 + 442 + 1234 , )
_UpperCAmelCase = dataset_info._to_yaml_dict()
assert sorted(__lowercase ) == 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) )
_UpperCAmelCase = yaml.safe_dump(__lowercase )
_UpperCAmelCase = yaml.safe_load(__lowercase )
assert dataset_info_yaml_dict == reloaded
def UpperCAmelCase_ ( ) -> Optional[int]:
'''simple docstring'''
_UpperCAmelCase = DatasetInfo()
_UpperCAmelCase = 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=42 , )
} ),
DatasetInfosDict(
{
"v1": DatasetInfo(dataset_size=42 ),
"v2": DatasetInfo(dataset_size=1337 ),
} ),
] , )
def UpperCAmelCase_ ( __lowercase : int , __lowercase : DatasetInfosDict ) -> Dict:
'''simple docstring'''
_UpperCAmelCase = str(__lowercase )
dataset_infos_dict.write_to_directory(__lowercase )
_UpperCAmelCase = DatasetInfosDict.from_directory(__lowercase )
# the config_name of the dataset_infos_dict take over the attribute
for config_name, dataset_info in dataset_infos_dict.items():
_UpperCAmelCase = 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
_UpperCAmelCase = 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(__lowercase , "README.md" ) )
| 22 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : str ) -> str:
'''simple docstring'''
return " ".join(
"".join(word[::-1] ) if len(__lowercase ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(reverse_long_words('''Hey wollef sroirraw'''))
| 22 | 1 |
'''simple docstring'''
from __future__ import annotations
def UpperCAmelCase_ ( __lowercase : list[int | float] , __lowercase : int , __lowercase : int ) -> int | float:
'''simple docstring'''
if len(__lowercase ) == 0:
raise ValueError("find_max() arg is an empty sequence" )
if (
left >= len(__lowercase )
or left < -len(__lowercase )
or right >= len(__lowercase )
or right < -len(__lowercase )
):
raise IndexError("list index out of range" )
if left == right:
return nums[left]
_UpperCAmelCase = (left + right) >> 1 # the middle
_UpperCAmelCase = find_max(__lowercase , __lowercase , __lowercase ) # find max in range[left, mid]
_UpperCAmelCase = find_max(__lowercase , mid + 1 , __lowercase ) # find max in range[mid + 1, right]
return left_max if left_max >= right_max else right_max
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
| 22 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : str ) -> list:
'''simple docstring'''
if n_term == "":
return []
_UpperCAmelCase = []
for temp in range(int(__lowercase ) ):
series.append(f'1/{temp + 1}' if series else "1" )
return series
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE :str = input('''Enter the last number (nth term) of the Harmonic Series''')
print('''Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n''')
print(harmonic_series(nth_term))
| 22 | 1 |
'''simple docstring'''
import argparse
import json
import torch
from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def UpperCAmelCase_ ( __lowercase : Union[str, Any] , __lowercase : List[Any]=1 ) -> Union[str, Any]:
'''simple docstring'''
if n_shave_prefix_segments >= 0:
return ".".join(path.split("." )[n_shave_prefix_segments:] )
else:
return ".".join(path.split("." )[:n_shave_prefix_segments] )
def UpperCAmelCase_ ( __lowercase : List[Any] , __lowercase : Any=0 ) -> Dict:
'''simple docstring'''
_UpperCAmelCase = []
for old_item in old_list:
_UpperCAmelCase = old_item.replace("in_layers.0" , "norm1" )
_UpperCAmelCase = new_item.replace("in_layers.2" , "conv1" )
_UpperCAmelCase = new_item.replace("out_layers.0" , "norm2" )
_UpperCAmelCase = new_item.replace("out_layers.3" , "conv2" )
_UpperCAmelCase = new_item.replace("emb_layers.1" , "time_emb_proj" )
_UpperCAmelCase = new_item.replace("skip_connection" , "conv_shortcut" )
_UpperCAmelCase = shave_segments(__lowercase , n_shave_prefix_segments=__lowercase )
mapping.append({"old": old_item, "new": new_item} )
return mapping
def UpperCAmelCase_ ( __lowercase : Tuple , __lowercase : int=0 ) -> Tuple:
'''simple docstring'''
_UpperCAmelCase = []
for old_item in old_list:
_UpperCAmelCase = old_item
_UpperCAmelCase = new_item.replace("norm.weight" , "group_norm.weight" )
_UpperCAmelCase = new_item.replace("norm.bias" , "group_norm.bias" )
_UpperCAmelCase = new_item.replace("proj_out.weight" , "proj_attn.weight" )
_UpperCAmelCase = new_item.replace("proj_out.bias" , "proj_attn.bias" )
_UpperCAmelCase = shave_segments(__lowercase , n_shave_prefix_segments=__lowercase )
mapping.append({"old": old_item, "new": new_item} )
return mapping
def UpperCAmelCase_ ( __lowercase : Any , __lowercase : Tuple , __lowercase : Union[str, Any] , __lowercase : List[Any]=None , __lowercase : int=None , __lowercase : str=None ) -> List[str]:
'''simple docstring'''
assert isinstance(__lowercase , __lowercase ), "Paths should be a list of dicts containing 'old' and 'new' keys."
# Splits the attention layers into three variables.
if attention_paths_to_split is not None:
for path, path_map in attention_paths_to_split.items():
_UpperCAmelCase = old_checkpoint[path]
_UpperCAmelCase = old_tensor.shape[0] // 3
_UpperCAmelCase = (-1, channels) if len(old_tensor.shape ) == 3 else (-1)
_UpperCAmelCase = old_tensor.shape[0] // config["num_head_channels"] // 3
_UpperCAmelCase = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:] )
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = old_tensor.split(channels // num_heads , dim=1 )
_UpperCAmelCase = query.reshape(__lowercase )
_UpperCAmelCase = key.reshape(__lowercase )
_UpperCAmelCase = value.reshape(__lowercase )
for path in paths:
_UpperCAmelCase = path["new"]
# These have already been assigned
if attention_paths_to_split is not None and new_path in attention_paths_to_split:
continue
# Global renaming happens here
_UpperCAmelCase = new_path.replace("middle_block.0" , "mid_block.resnets.0" )
_UpperCAmelCase = new_path.replace("middle_block.1" , "mid_block.attentions.0" )
_UpperCAmelCase = new_path.replace("middle_block.2" , "mid_block.resnets.1" )
if additional_replacements is not None:
for replacement in additional_replacements:
_UpperCAmelCase = new_path.replace(replacement["old"] , replacement["new"] )
# proj_attn.weight has to be converted from conv 1D to linear
if "proj_attn.weight" in new_path:
_UpperCAmelCase = old_checkpoint[path["old"]][:, :, 0]
else:
_UpperCAmelCase = old_checkpoint[path["old"]]
def UpperCAmelCase_ ( __lowercase : Any , __lowercase : List[str] ) -> Tuple:
'''simple docstring'''
_UpperCAmelCase = {}
_UpperCAmelCase = checkpoint["time_embed.0.weight"]
_UpperCAmelCase = checkpoint["time_embed.0.bias"]
_UpperCAmelCase = checkpoint["time_embed.2.weight"]
_UpperCAmelCase = checkpoint["time_embed.2.bias"]
_UpperCAmelCase = checkpoint["input_blocks.0.0.weight"]
_UpperCAmelCase = checkpoint["input_blocks.0.0.bias"]
_UpperCAmelCase = checkpoint["out.0.weight"]
_UpperCAmelCase = checkpoint["out.0.bias"]
_UpperCAmelCase = checkpoint["out.2.weight"]
_UpperCAmelCase = checkpoint["out.2.bias"]
# Retrieves the keys for the input blocks only
_UpperCAmelCase = len({".".join(layer.split("." )[:2] ) for layer in checkpoint if "input_blocks" in layer} )
_UpperCAmelCase = {
layer_id: [key for key in checkpoint if f'input_blocks.{layer_id}' in key]
for layer_id in range(__lowercase )
}
# Retrieves the keys for the middle blocks only
_UpperCAmelCase = len({".".join(layer.split("." )[:2] ) for layer in checkpoint if "middle_block" in layer} )
_UpperCAmelCase = {
layer_id: [key for key in checkpoint if f'middle_block.{layer_id}' in key]
for layer_id in range(__lowercase )
}
# Retrieves the keys for the output blocks only
_UpperCAmelCase = len({".".join(layer.split("." )[:2] ) for layer in checkpoint if "output_blocks" in layer} )
_UpperCAmelCase = {
layer_id: [key for key in checkpoint if f'output_blocks.{layer_id}' in key]
for layer_id in range(__lowercase )
}
for i in range(1 , __lowercase ):
_UpperCAmelCase = (i - 1) // (config["num_res_blocks"] + 1)
_UpperCAmelCase = (i - 1) % (config["num_res_blocks"] + 1)
_UpperCAmelCase = [key for key in input_blocks[i] if f'input_blocks.{i}.0' in key]
_UpperCAmelCase = [key for key in input_blocks[i] if f'input_blocks.{i}.1' in key]
if f'input_blocks.{i}.0.op.weight' in checkpoint:
_UpperCAmelCase = checkpoint[
f'input_blocks.{i}.0.op.weight'
]
_UpperCAmelCase = checkpoint[
f'input_blocks.{i}.0.op.bias'
]
continue
_UpperCAmelCase = renew_resnet_paths(__lowercase )
_UpperCAmelCase = {"old": f'input_blocks.{i}.0', "new": f'down_blocks.{block_id}.resnets.{layer_in_block_id}'}
_UpperCAmelCase = {"old": "resnets.2.op", "new": "downsamplers.0.op"}
assign_to_checkpoint(
__lowercase , __lowercase , __lowercase , additional_replacements=[meta_path, resnet_op] , config=__lowercase )
if len(__lowercase ):
_UpperCAmelCase = renew_attention_paths(__lowercase )
_UpperCAmelCase = {
"old": f'input_blocks.{i}.1',
"new": f'down_blocks.{block_id}.attentions.{layer_in_block_id}',
}
_UpperCAmelCase = {
f'input_blocks.{i}.1.qkv.bias': {
"key": f'down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias',
"query": f'down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias',
"value": f'down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias',
},
f'input_blocks.{i}.1.qkv.weight': {
"key": f'down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight',
"query": f'down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight',
"value": f'down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight',
},
}
assign_to_checkpoint(
__lowercase , __lowercase , __lowercase , additional_replacements=[meta_path] , attention_paths_to_split=__lowercase , config=__lowercase , )
_UpperCAmelCase = middle_blocks[0]
_UpperCAmelCase = middle_blocks[1]
_UpperCAmelCase = middle_blocks[2]
_UpperCAmelCase = renew_resnet_paths(__lowercase )
assign_to_checkpoint(__lowercase , __lowercase , __lowercase , config=__lowercase )
_UpperCAmelCase = renew_resnet_paths(__lowercase )
assign_to_checkpoint(__lowercase , __lowercase , __lowercase , config=__lowercase )
_UpperCAmelCase = renew_attention_paths(__lowercase )
_UpperCAmelCase = {
"middle_block.1.qkv.bias": {
"key": "mid_block.attentions.0.key.bias",
"query": "mid_block.attentions.0.query.bias",
"value": "mid_block.attentions.0.value.bias",
},
"middle_block.1.qkv.weight": {
"key": "mid_block.attentions.0.key.weight",
"query": "mid_block.attentions.0.query.weight",
"value": "mid_block.attentions.0.value.weight",
},
}
assign_to_checkpoint(
__lowercase , __lowercase , __lowercase , attention_paths_to_split=__lowercase , config=__lowercase )
for i in range(__lowercase ):
_UpperCAmelCase = i // (config["num_res_blocks"] + 1)
_UpperCAmelCase = i % (config["num_res_blocks"] + 1)
_UpperCAmelCase = [shave_segments(__lowercase , 2 ) for name in output_blocks[i]]
_UpperCAmelCase = {}
for layer in output_block_layers:
_UpperCAmelCase , _UpperCAmelCase = layer.split("." )[0], shave_segments(__lowercase , 1 )
if layer_id in output_block_list:
output_block_list[layer_id].append(__lowercase )
else:
_UpperCAmelCase = [layer_name]
if len(__lowercase ) > 1:
_UpperCAmelCase = [key for key in output_blocks[i] if f'output_blocks.{i}.0' in key]
_UpperCAmelCase = [key for key in output_blocks[i] if f'output_blocks.{i}.1' in key]
_UpperCAmelCase = renew_resnet_paths(__lowercase )
_UpperCAmelCase = renew_resnet_paths(__lowercase )
_UpperCAmelCase = {"old": f'output_blocks.{i}.0', "new": f'up_blocks.{block_id}.resnets.{layer_in_block_id}'}
assign_to_checkpoint(__lowercase , __lowercase , __lowercase , additional_replacements=[meta_path] , config=__lowercase )
if ["conv.weight", "conv.bias"] in output_block_list.values():
_UpperCAmelCase = list(output_block_list.values() ).index(["conv.weight", "conv.bias"] )
_UpperCAmelCase = checkpoint[
f'output_blocks.{i}.{index}.conv.weight'
]
_UpperCAmelCase = checkpoint[
f'output_blocks.{i}.{index}.conv.bias'
]
# Clear attentions as they have been attributed above.
if len(__lowercase ) == 2:
_UpperCAmelCase = []
if len(__lowercase ):
_UpperCAmelCase = renew_attention_paths(__lowercase )
_UpperCAmelCase = {
"old": f'output_blocks.{i}.1',
"new": f'up_blocks.{block_id}.attentions.{layer_in_block_id}',
}
_UpperCAmelCase = {
f'output_blocks.{i}.1.qkv.bias': {
"key": f'up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias',
"query": f'up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias',
"value": f'up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias',
},
f'output_blocks.{i}.1.qkv.weight': {
"key": f'up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight',
"query": f'up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight',
"value": f'up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight',
},
}
assign_to_checkpoint(
__lowercase , __lowercase , __lowercase , additional_replacements=[meta_path] , attention_paths_to_split=to_split if any("qkv" in key for key in attentions ) else None , config=__lowercase , )
else:
_UpperCAmelCase = renew_resnet_paths(__lowercase , n_shave_prefix_segments=1 )
for path in resnet_0_paths:
_UpperCAmelCase = ".".join(["output_blocks", str(__lowercase ), path["old"]] )
_UpperCAmelCase = ".".join(["up_blocks", str(__lowercase ), "resnets", str(__lowercase ), path["new"]] )
_UpperCAmelCase = checkpoint[old_path]
return new_checkpoint
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE :Optional[Any] = argparse.ArgumentParser()
parser.add_argument(
'''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
)
parser.add_argument(
'''--config_file''',
default=None,
type=str,
required=True,
help='''The config json file corresponding to the architecture.''',
)
parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
__SCREAMING_SNAKE_CASE :Tuple = parser.parse_args()
__SCREAMING_SNAKE_CASE :Optional[int] = torch.load(args.checkpoint_path)
with open(args.config_file) as f:
__SCREAMING_SNAKE_CASE :List[Any] = json.loads(f.read())
__SCREAMING_SNAKE_CASE :Union[str, Any] = convert_ldm_checkpoint(checkpoint, config)
if "ldm" in config:
del config["ldm"]
__SCREAMING_SNAKE_CASE :Optional[int] = UNetaDModel(**config)
model.load_state_dict(converted_checkpoint)
try:
__SCREAMING_SNAKE_CASE :Optional[Any] = DDPMScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))
__SCREAMING_SNAKE_CASE :List[str] = VQModel.from_pretrained('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))
__SCREAMING_SNAKE_CASE :List[str] = LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
pipe.save_pretrained(args.dump_path)
except: # noqa: E722
model.save_pretrained(args.dump_path)
| 22 |
'''simple docstring'''
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
__SCREAMING_SNAKE_CASE :int = get_tests_dir('''fixtures/test_sentencepiece_no_bos.model''')
@require_sentencepiece
@require_tokenizers
class A_ ( lowerCAmelCase_ , unittest.TestCase ):
_lowerCamelCase : List[str] = PegasusTokenizer
_lowerCamelCase : int = PegasusTokenizerFast
_lowerCamelCase : Union[str, Any] = True
_lowerCamelCase : List[str] = True
def lowercase ( self : Optional[int] ):
super().setUp()
# We have a SentencePiece fixture for testing
_UpperCAmelCase = PegasusTokenizer(snake_case_ )
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def lowercase ( self : Tuple ):
return PegasusTokenizer.from_pretrained("google/pegasus-large" )
def lowercase ( self : Union[str, Any] , **snake_case_ : Union[str, Any] ):
return PegasusTokenizer.from_pretrained(self.tmpdirname , **snake_case_ )
def lowercase ( self : Tuple , snake_case_ : Any ):
return ("This is a test", "This is a test")
def lowercase ( self : Optional[int] ):
_UpperCAmelCase = "</s>"
_UpperCAmelCase = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case_ ) , snake_case_ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case_ ) , snake_case_ )
def lowercase ( self : Union[str, Any] ):
_UpperCAmelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , "<pad>" )
self.assertEqual(vocab_keys[1] , "</s>" )
self.assertEqual(vocab_keys[-1] , "v" )
self.assertEqual(len(snake_case_ ) , 1_1_0_3 )
def lowercase ( self : Any ):
self.assertEqual(self.get_tokenizer().vocab_size , 1_1_0_3 )
def lowercase ( self : List[Any] ):
_UpperCAmelCase = self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
_UpperCAmelCase = self.tokenizer_class.from_pretrained(self.tmpdirname )
_UpperCAmelCase = (
"Let's see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important"
" </s> <pad> <pad> <pad>"
)
_UpperCAmelCase = rust_tokenizer([raw_input_str] , return_tensors=snake_case_ , add_special_tokens=snake_case_ ).input_ids[0]
_UpperCAmelCase = py_tokenizer([raw_input_str] , return_tensors=snake_case_ , add_special_tokens=snake_case_ ).input_ids[0]
self.assertListEqual(snake_case_ , snake_case_ )
def lowercase ( self : Tuple ):
_UpperCAmelCase = self._large_tokenizer
# <mask_1> masks whole sentence while <mask_2> masks single word
_UpperCAmelCase = "<mask_1> To ensure a <mask_2> flow of bank resolutions."
_UpperCAmelCase = [2, 4_1_3, 6_1_5, 1_1_4, 3, 1_9_7_1, 1_1_3, 1_6_7_9, 1_0_7_1_0, 1_0_7, 1]
_UpperCAmelCase = tokenizer([raw_input_str] , return_tensors=snake_case_ ).input_ids[0]
self.assertListEqual(snake_case_ , snake_case_ )
def lowercase ( self : Union[str, Any] ):
_UpperCAmelCase = self._large_tokenizer
# The tracebacks for the following asserts are **better** without messages or self.assertEqual
assert tokenizer.vocab_size == 9_6_1_0_3
assert tokenizer.pad_token_id == 0
assert tokenizer.eos_token_id == 1
assert tokenizer.offset == 1_0_3
assert tokenizer.unk_token_id == tokenizer.offset + 2 == 1_0_5
assert tokenizer.unk_token == "<unk>"
assert tokenizer.model_max_length == 1_0_2_4
_UpperCAmelCase = "To ensure a smooth flow of bank resolutions."
_UpperCAmelCase = [4_1_3, 6_1_5, 1_1_4, 2_2_9_1, 1_9_7_1, 1_1_3, 1_6_7_9, 1_0_7_1_0, 1_0_7, 1]
_UpperCAmelCase = tokenizer([raw_input_str] , return_tensors=snake_case_ ).input_ids[0]
self.assertListEqual(snake_case_ , snake_case_ )
assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3] ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]
@require_torch
def lowercase ( self : int ):
_UpperCAmelCase = ["This is going to be way too long." * 1_5_0, "short example"]
_UpperCAmelCase = ["not super long but more than 5 tokens", "tiny"]
_UpperCAmelCase = self._large_tokenizer(snake_case_ , padding=snake_case_ , truncation=snake_case_ , return_tensors="pt" )
_UpperCAmelCase = self._large_tokenizer(
text_target=snake_case_ , max_length=5 , padding=snake_case_ , truncation=snake_case_ , return_tensors="pt" )
assert batch.input_ids.shape == (2, 1_0_2_4)
assert batch.attention_mask.shape == (2, 1_0_2_4)
assert targets["input_ids"].shape == (2, 5)
assert len(snake_case_ ) == 2 # input_ids, attention_mask.
@slow
def lowercase ( self : Dict ):
# fmt: off
_UpperCAmelCase = {"input_ids": [[3_8_9_7_9, 1_4_3, 1_8_4_8_5, 6_0_6, 1_3_0, 2_6_6_6_9, 8_7_6_8_6, 1_2_1, 5_4_1_8_9, 1_1_2_9, 1_1_1, 2_6_6_6_9, 8_7_6_8_6, 1_2_1, 9_1_1_4, 1_4_7_8_7, 1_2_1, 1_3_2_4_9, 1_5_8, 5_9_2, 9_5_6, 1_2_1, 1_4_6_2_1, 3_1_5_7_6, 1_4_3, 6_2_6_1_3, 1_0_8, 9_6_8_8, 9_3_0, 4_3_4_3_0, 1_1_5_6_2, 6_2_6_1_3, 3_0_4, 1_0_8, 1_1_4_4_3, 8_9_7, 1_0_8, 9_3_1_4, 1_7_4_1_5, 6_3_3_9_9, 1_0_8, 1_1_4_4_3, 7_6_1_4, 1_8_3_1_6, 1_1_8, 4_2_8_4, 7_1_4_8, 1_2_4_3_0, 1_4_3, 1_4_0_0, 2_5_7_0_3, 1_5_8, 1_1_1, 4_2_8_4, 7_1_4_8, 1_1_7_7_2, 1_4_3, 2_1_2_9_7, 1_0_6_4, 1_5_8, 1_2_2, 2_0_4, 3_5_0_6, 1_7_5_4, 1_1_3_3, 1_4_7_8_7, 1_5_8_1, 1_1_5, 3_3_2_2_4, 4_4_8_2, 1_1_1, 1_3_5_5, 1_1_0, 2_9_1_7_3, 3_1_7, 5_0_8_3_3, 1_0_8, 2_0_1_4_7, 9_4_6_6_5, 1_1_1, 7_7_1_9_8, 1_0_7, 1], [1_1_0, 6_2_6_1_3, 1_1_7, 6_3_8, 1_1_2, 1_1_3_3, 1_2_1, 2_0_0_9_8, 1_3_5_5, 7_9_0_5_0, 1_3_8_7_2, 1_3_5, 1_5_9_6, 5_3_5_4_1, 1_3_5_2, 1_4_1, 1_3_0_3_9, 5_5_4_2, 1_2_4, 3_0_2, 5_1_8, 1_1_1, 2_6_8, 2_9_5_6, 1_1_5, 1_4_9, 4_4_2_7, 1_0_7, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1_3_9, 1_2_3_5, 2_7_9_9, 1_8_2_8_9, 1_7_7_8_0, 2_0_4, 1_0_9, 9_4_7_4, 1_2_9_6, 1_0_7, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=snake_case_ , model_name="google/bigbird-pegasus-large-arxiv" , revision="ba85d0851d708441f91440d509690f1ab6353415" , )
@require_sentencepiece
@require_tokenizers
class A_ ( lowerCAmelCase_ , unittest.TestCase ):
_lowerCamelCase : List[str] = PegasusTokenizer
_lowerCamelCase : List[Any] = PegasusTokenizerFast
_lowerCamelCase : int = True
_lowerCamelCase : Union[str, Any] = True
def lowercase ( self : Any ):
super().setUp()
# We have a SentencePiece fixture for testing
_UpperCAmelCase = PegasusTokenizer(snake_case_ , offset=0 , mask_token_sent=snake_case_ , mask_token="[MASK]" )
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def lowercase ( self : Tuple ):
return PegasusTokenizer.from_pretrained("google/bigbird-pegasus-large-arxiv" )
def lowercase ( self : Optional[Any] , **snake_case_ : Dict ):
return PegasusTokenizer.from_pretrained(self.tmpdirname , **snake_case_ )
def lowercase ( self : Union[str, Any] , snake_case_ : str ):
return ("This is a test", "This is a test")
def lowercase ( self : List[str] ):
_UpperCAmelCase = self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
_UpperCAmelCase = self.tokenizer_class.from_pretrained(self.tmpdirname )
_UpperCAmelCase = (
"Let's see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"
" <pad> <pad> <pad>"
)
_UpperCAmelCase = rust_tokenizer([raw_input_str] , return_tensors=snake_case_ , add_special_tokens=snake_case_ ).input_ids[0]
_UpperCAmelCase = py_tokenizer([raw_input_str] , return_tensors=snake_case_ , add_special_tokens=snake_case_ ).input_ids[0]
self.assertListEqual(snake_case_ , snake_case_ )
@require_torch
def lowercase ( self : Tuple ):
_UpperCAmelCase = ["This is going to be way too long." * 1_0_0_0, "short example"]
_UpperCAmelCase = ["not super long but more than 5 tokens", "tiny"]
_UpperCAmelCase = self._large_tokenizer(snake_case_ , padding=snake_case_ , truncation=snake_case_ , return_tensors="pt" )
_UpperCAmelCase = self._large_tokenizer(
text_target=snake_case_ , max_length=5 , padding=snake_case_ , truncation=snake_case_ , return_tensors="pt" )
assert batch.input_ids.shape == (2, 4_0_9_6)
assert batch.attention_mask.shape == (2, 4_0_9_6)
assert targets["input_ids"].shape == (2, 5)
assert len(snake_case_ ) == 2 # input_ids, attention_mask.
def lowercase ( self : Union[str, Any] ):
_UpperCAmelCase = (
"This is an example string that is used to test the original TF implementation against the HF"
" implementation"
)
_UpperCAmelCase = self._large_tokenizer(snake_case_ ).input_ids
self.assertListEqual(
snake_case_ , [1_8_2, 1_1_7, 1_4_2, 5_8_7, 4_2_1_1, 1_2_0, 1_1_7, 2_6_3, 1_1_2, 8_0_4, 1_0_9, 8_5_6, 2_5_0_1_6, 3_1_3_7, 4_6_4, 1_0_9, 2_6_9_5_5, 3_1_3_7, 1] , )
| 22 | 1 |
'''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_albert import AlbertTokenizer
else:
__SCREAMING_SNAKE_CASE :List[Any] = None
__SCREAMING_SNAKE_CASE :Union[str, Any] = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE :List[str] = {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}
__SCREAMING_SNAKE_CASE :List[Any] = {
'''vocab_file''': {
'''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/spiece.model''',
'''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/spiece.model''',
'''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model''',
'''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model''',
'''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/spiece.model''',
'''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/spiece.model''',
'''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model''',
'''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model''',
},
'''tokenizer_file''': {
'''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json''',
'''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json''',
'''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json''',
'''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json''',
'''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json''',
'''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json''',
'''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json''',
'''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json''',
},
}
__SCREAMING_SNAKE_CASE :Optional[Any] = {
'''albert-base-v1''': 512,
'''albert-large-v1''': 512,
'''albert-xlarge-v1''': 512,
'''albert-xxlarge-v1''': 512,
'''albert-base-v2''': 512,
'''albert-large-v2''': 512,
'''albert-xlarge-v2''': 512,
'''albert-xxlarge-v2''': 512,
}
__SCREAMING_SNAKE_CASE :Optional[int] = '''▁'''
class A_ ( lowerCAmelCase_ ):
_lowerCamelCase : Optional[int] = VOCAB_FILES_NAMES
_lowerCamelCase : Any = PRETRAINED_VOCAB_FILES_MAP
_lowerCamelCase : Tuple = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_lowerCamelCase : int = AlbertTokenizer
def __init__( self : Optional[Any] , snake_case_ : Optional[Any]=None , snake_case_ : Optional[Any]=None , snake_case_ : Optional[Any]=True , snake_case_ : str=True , snake_case_ : Tuple=False , snake_case_ : List[Any]="[CLS]" , snake_case_ : Union[str, Any]="[SEP]" , snake_case_ : str="<unk>" , snake_case_ : Union[str, Any]="[SEP]" , snake_case_ : List[Any]="<pad>" , snake_case_ : List[str]="[CLS]" , snake_case_ : int="[MASK]" , **snake_case_ : Any , ):
# Mask token behave like a normal word, i.e. include the space before it and
# is included in the raw text, there should be a match in a non-normalized sentence.
_UpperCAmelCase = (
AddedToken(snake_case_ , lstrip=snake_case_ , rstrip=snake_case_ , normalized=snake_case_ )
if isinstance(snake_case_ , snake_case_ )
else mask_token
)
super().__init__(
snake_case_ , tokenizer_file=snake_case_ , do_lower_case=snake_case_ , remove_space=snake_case_ , keep_accents=snake_case_ , bos_token=snake_case_ , eos_token=snake_case_ , unk_token=snake_case_ , sep_token=snake_case_ , pad_token=snake_case_ , cls_token=snake_case_ , mask_token=snake_case_ , **snake_case_ , )
_UpperCAmelCase = do_lower_case
_UpperCAmelCase = remove_space
_UpperCAmelCase = keep_accents
_UpperCAmelCase = vocab_file
_UpperCAmelCase = False if not self.vocab_file else True
def lowercase ( self : Union[str, Any] , snake_case_ : List[int] , snake_case_ : Optional[List[int]] = None ):
_UpperCAmelCase = [self.sep_token_id]
_UpperCAmelCase = [self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def lowercase ( self : Dict , snake_case_ : List[int] , snake_case_ : Optional[List[int]] = None ):
_UpperCAmelCase = [self.sep_token_id]
_UpperCAmelCase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def lowercase ( self : Optional[Any] , snake_case_ : str , snake_case_ : Optional[str] = None ):
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(snake_case_ ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
return
_UpperCAmelCase = os.path.join(
snake_case_ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(snake_case_ ):
copyfile(self.vocab_file , snake_case_ )
return (out_vocab_file,)
| 22 |
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast
@require_vision
class A_ ( unittest.TestCase ):
def lowercase ( self : int ):
_UpperCAmelCase = tempfile.mkdtemp()
_UpperCAmelCase = BlipImageProcessor()
_UpperCAmelCase = BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-BertModel" )
_UpperCAmelCase = BlipProcessor(snake_case_ , snake_case_ )
processor.save_pretrained(self.tmpdirname )
def lowercase ( self : Tuple , **snake_case_ : int ):
return AutoProcessor.from_pretrained(self.tmpdirname , **snake_case_ ).tokenizer
def lowercase ( self : Dict , **snake_case_ : Any ):
return AutoProcessor.from_pretrained(self.tmpdirname , **snake_case_ ).image_processor
def lowercase ( self : int ):
shutil.rmtree(self.tmpdirname )
def lowercase ( self : Optional[Any] ):
_UpperCAmelCase = [np.random.randint(2_5_5 , size=(3, 3_0, 4_0_0) , dtype=np.uinta )]
_UpperCAmelCase = [Image.fromarray(np.moveaxis(snake_case_ , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def lowercase ( self : int ):
_UpperCAmelCase = BlipProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
_UpperCAmelCase = self.get_tokenizer(bos_token="(BOS)" , eos_token="(EOS)" )
_UpperCAmelCase = self.get_image_processor(do_normalize=snake_case_ , padding_value=1.0 )
_UpperCAmelCase = BlipProcessor.from_pretrained(
self.tmpdirname , bos_token="(BOS)" , eos_token="(EOS)" , do_normalize=snake_case_ , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , snake_case_ )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , snake_case_ )
def lowercase ( self : Any ):
_UpperCAmelCase = self.get_image_processor()
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = BlipProcessor(tokenizer=snake_case_ , image_processor=snake_case_ )
_UpperCAmelCase = self.prepare_image_inputs()
_UpperCAmelCase = image_processor(snake_case_ , return_tensors="np" )
_UpperCAmelCase = processor(images=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 lowercase ( self : Optional[int] ):
_UpperCAmelCase = self.get_image_processor()
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = BlipProcessor(tokenizer=snake_case_ , image_processor=snake_case_ )
_UpperCAmelCase = "lower newer"
_UpperCAmelCase = processor(text=snake_case_ )
_UpperCAmelCase = tokenizer(snake_case_ , return_token_type_ids=snake_case_ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def lowercase ( self : Optional[Any] ):
_UpperCAmelCase = self.get_image_processor()
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = BlipProcessor(tokenizer=snake_case_ , image_processor=snake_case_ )
_UpperCAmelCase = "lower newer"
_UpperCAmelCase = self.prepare_image_inputs()
_UpperCAmelCase = processor(text=snake_case_ , images=snake_case_ )
self.assertListEqual(list(inputs.keys() ) , ["pixel_values", "input_ids", "attention_mask"] )
# test if it raises when no input is passed
with pytest.raises(snake_case_ ):
processor()
def lowercase ( self : Union[str, Any] ):
_UpperCAmelCase = self.get_image_processor()
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = BlipProcessor(tokenizer=snake_case_ , image_processor=snake_case_ )
_UpperCAmelCase = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
_UpperCAmelCase = processor.batch_decode(snake_case_ )
_UpperCAmelCase = tokenizer.batch_decode(snake_case_ )
self.assertListEqual(snake_case_ , snake_case_ )
def lowercase ( self : str ):
_UpperCAmelCase = self.get_image_processor()
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = BlipProcessor(tokenizer=snake_case_ , image_processor=snake_case_ )
_UpperCAmelCase = "lower newer"
_UpperCAmelCase = self.prepare_image_inputs()
_UpperCAmelCase = processor(text=snake_case_ , images=snake_case_ )
# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
self.assertListEqual(list(inputs.keys() ) , ["pixel_values", "input_ids", "attention_mask"] )
| 22 | 1 |
'''simple docstring'''
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
import sqlitea
import sqlalchemy
class A_ ( lowerCAmelCase_ ):
def __init__( self : List[Any] , snake_case_ : Union[str, "sqlalchemy.sql.Selectable"] , snake_case_ : Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"] , snake_case_ : Optional[Features] = None , snake_case_ : str = None , snake_case_ : bool = False , **snake_case_ : int , ):
super().__init__(features=snake_case_ , cache_dir=snake_case_ , keep_in_memory=snake_case_ , **snake_case_ )
_UpperCAmelCase = Sql(
cache_dir=snake_case_ , features=snake_case_ , sql=snake_case_ , con=snake_case_ , **snake_case_ , )
def lowercase ( self : Tuple ):
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
self.builder.download_and_prepare(
download_config=snake_case_ , download_mode=snake_case_ , verification_mode=snake_case_ , base_path=snake_case_ , )
# Build dataset for splits
_UpperCAmelCase = self.builder.as_dataset(
split="train" , verification_mode=snake_case_ , in_memory=self.keep_in_memory )
return dataset
class A_ :
def __init__( self : List[Any] , snake_case_ : Dataset , snake_case_ : str , snake_case_ : Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"] , snake_case_ : Optional[int] = None , snake_case_ : Optional[int] = None , **snake_case_ : Union[str, Any] , ):
if num_proc is not None and num_proc <= 0:
raise ValueError(f'num_proc {num_proc} must be an integer > 0.' )
_UpperCAmelCase = dataset
_UpperCAmelCase = name
_UpperCAmelCase = con
_UpperCAmelCase = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
_UpperCAmelCase = num_proc
_UpperCAmelCase = to_sql_kwargs
def lowercase ( self : Dict ):
_UpperCAmelCase = self.to_sql_kwargs.pop("sql" , snake_case_ )
_UpperCAmelCase = self.to_sql_kwargs.pop("con" , snake_case_ )
_UpperCAmelCase = self.to_sql_kwargs.pop("index" , snake_case_ )
_UpperCAmelCase = self._write(index=snake_case_ , **self.to_sql_kwargs )
return written
def lowercase ( self : Tuple , snake_case_ : Any ):
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = args
_UpperCAmelCase = {**to_sql_kwargs, "if_exists": "append"} if offset > 0 else to_sql_kwargs
_UpperCAmelCase = query_table(
table=self.dataset.data , key=slice(snake_case_ , offset + self.batch_size ) , indices=self.dataset._indices , )
_UpperCAmelCase = batch.to_pandas()
_UpperCAmelCase = df.to_sql(self.name , self.con , index=snake_case_ , **snake_case_ )
return num_rows or len(snake_case_ )
def lowercase ( self : Optional[int] , snake_case_ : Optional[Any] , **snake_case_ : str ):
_UpperCAmelCase = 0
if self.num_proc is None or self.num_proc == 1:
for offset in logging.tqdm(
range(0 , len(self.dataset ) , self.batch_size ) , unit="ba" , disable=not logging.is_progress_bar_enabled() , desc="Creating SQL from Arrow format" , ):
written += self._batch_sql((offset, index, to_sql_kwargs) )
else:
_UpperCAmelCase , _UpperCAmelCase = len(self.dataset ), self.batch_size
with multiprocessing.Pool(self.num_proc ) as pool:
for num_rows in logging.tqdm(
pool.imap(
self._batch_sql , [(offset, index, to_sql_kwargs) for offset in range(0 , snake_case_ , snake_case_ )] , ) , total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size , unit="ba" , disable=not logging.is_progress_bar_enabled() , desc="Creating SQL from Arrow format" , ):
written += num_rows
return written
| 22 |
'''simple docstring'''
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
def UpperCAmelCase_ ( __lowercase : str ) -> List[str]:
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase = image.size
_UpperCAmelCase , _UpperCAmelCase = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32
_UpperCAmelCase = image.resize((w, h) , resample=PIL_INTERPOLATION["lanczos"] )
_UpperCAmelCase = np.array(__lowercase ).astype(np.floataa ) / 255.0
_UpperCAmelCase = image[None].transpose(0 , 3 , 1 , 2 )
_UpperCAmelCase = torch.from_numpy(__lowercase )
return 2.0 * image - 1.0
class A_ ( lowerCAmelCase_ ):
def __init__( self : Optional[Any] , snake_case_ : VQModel , snake_case_ : UNetaDModel , snake_case_ : Union[
DDIMScheduler,
PNDMScheduler,
LMSDiscreteScheduler,
EulerDiscreteScheduler,
EulerAncestralDiscreteScheduler,
DPMSolverMultistepScheduler,
] , ):
super().__init__()
self.register_modules(vqvae=snake_case_ , unet=snake_case_ , scheduler=snake_case_ )
@torch.no_grad()
def __call__( self : Any , snake_case_ : Union[torch.Tensor, PIL.Image.Image] = None , snake_case_ : Optional[int] = 1 , snake_case_ : Optional[int] = 1_0_0 , snake_case_ : Optional[float] = 0.0 , snake_case_ : Optional[Union[torch.Generator, List[torch.Generator]]] = None , snake_case_ : Optional[str] = "pil" , snake_case_ : bool = True , ):
if isinstance(snake_case_ , PIL.Image.Image ):
_UpperCAmelCase = 1
elif isinstance(snake_case_ , torch.Tensor ):
_UpperCAmelCase = image.shape[0]
else:
raise ValueError(f'`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(snake_case_ )}' )
if isinstance(snake_case_ , PIL.Image.Image ):
_UpperCAmelCase = preprocess(snake_case_ )
_UpperCAmelCase , _UpperCAmelCase = image.shape[-2:]
# in_channels should be 6: 3 for latents, 3 for low resolution image
_UpperCAmelCase = (batch_size, self.unet.config.in_channels // 2, height, width)
_UpperCAmelCase = next(self.unet.parameters() ).dtype
_UpperCAmelCase = randn_tensor(snake_case_ , generator=snake_case_ , device=self.device , dtype=snake_case_ )
_UpperCAmelCase = image.to(device=self.device , dtype=snake_case_ )
# set timesteps and move to the correct device
self.scheduler.set_timesteps(snake_case_ , device=self.device )
_UpperCAmelCase = self.scheduler.timesteps
# scale the initial noise by the standard deviation required by the scheduler
_UpperCAmelCase = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
_UpperCAmelCase = "eta" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
_UpperCAmelCase = {}
if accepts_eta:
_UpperCAmelCase = eta
for t in self.progress_bar(snake_case_ ):
# concat latents and low resolution image in the channel dimension.
_UpperCAmelCase = torch.cat([latents, image] , dim=1 )
_UpperCAmelCase = self.scheduler.scale_model_input(snake_case_ , snake_case_ )
# predict the noise residual
_UpperCAmelCase = self.unet(snake_case_ , snake_case_ ).sample
# compute the previous noisy sample x_t -> x_t-1
_UpperCAmelCase = self.scheduler.step(snake_case_ , snake_case_ , snake_case_ , **snake_case_ ).prev_sample
# decode the image latents with the VQVAE
_UpperCAmelCase = self.vqvae.decode(snake_case_ ).sample
_UpperCAmelCase = torch.clamp(snake_case_ , -1.0 , 1.0 )
_UpperCAmelCase = image / 2 + 0.5
_UpperCAmelCase = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
_UpperCAmelCase = self.numpy_to_pil(snake_case_ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=snake_case_ )
| 22 | 1 |
'''simple docstring'''
from __future__ import annotations
def UpperCAmelCase_ ( __lowercase : str , __lowercase : list[str] | None = None ) -> list[list[str]]:
'''simple docstring'''
_UpperCAmelCase = word_bank or []
# create a table
_UpperCAmelCase = len(__lowercase ) + 1
_UpperCAmelCase = []
for _ in range(__lowercase ):
table.append([] )
# seed value
_UpperCAmelCase = [[]] # because empty string has empty combination
# iterate through the indices
for i in range(__lowercase ):
# condition
if table[i] != []:
for word in word_bank:
# slice condition
if target[i : i + len(__lowercase )] == word:
_UpperCAmelCase = [
[word, *way] for way in table[i]
]
# adds the word to every combination the current position holds
# now,push that combination to the table[i+len(word)]
table[i + len(__lowercase )] += new_combinations
# combinations are in reverse order so reverse for better output
for combination in table[len(__lowercase )]:
combination.reverse()
return table[len(__lowercase )]
if __name__ == "__main__":
print(all_construct('''jwajalapa''', ['''jwa''', '''j''', '''w''', '''a''', '''la''', '''lapa''']))
print(all_construct('''rajamati''', ['''s''', '''raj''', '''amat''', '''raja''', '''ma''', '''i''', '''t''']))
print(
all_construct(
'''hexagonosaurus''',
['''h''', '''ex''', '''hex''', '''ag''', '''ago''', '''ru''', '''auru''', '''rus''', '''go''', '''no''', '''o''', '''s'''],
)
)
| 22 |
'''simple docstring'''
import string
from math import logaa
def UpperCAmelCase_ ( __lowercase : str , __lowercase : str ) -> int:
'''simple docstring'''
_UpperCAmelCase = document.translate(
str.maketrans("" , "" , string.punctuation ) ).replace("\n" , "" )
_UpperCAmelCase = document_without_punctuation.split(" " ) # word tokenization
return len([word for word in tokenize_document if word.lower() == term.lower()] )
def UpperCAmelCase_ ( __lowercase : str , __lowercase : str ) -> tuple[int, int]:
'''simple docstring'''
_UpperCAmelCase = corpus.lower().translate(
str.maketrans("" , "" , string.punctuation ) ) # strip all punctuation and replace it with ''
_UpperCAmelCase = corpus_without_punctuation.split("\n" )
_UpperCAmelCase = term.lower()
return (len([doc for doc in docs if term in doc] ), len(__lowercase ))
def UpperCAmelCase_ ( __lowercase : int , __lowercase : int , __lowercase : Union[str, Any]=False ) -> float:
'''simple docstring'''
if smoothing:
if n == 0:
raise ValueError("log10(0) is undefined." )
return round(1 + logaa(n / (1 + df) ) , 3 )
if df == 0:
raise ZeroDivisionError("df must be > 0" )
elif n == 0:
raise ValueError("log10(0) is undefined." )
return round(logaa(n / df ) , 3 )
def UpperCAmelCase_ ( __lowercase : int , __lowercase : int ) -> float:
'''simple docstring'''
return round(tf * idf , 3 )
| 22 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
__SCREAMING_SNAKE_CASE :Dict = {
'''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/config.json''',
'''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/config.json''',
'''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/config.json''',
'''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json''',
'''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/config.json''',
'''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/config.json''',
'''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/config.json''',
'''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json''',
}
class A_ ( lowerCAmelCase_ ):
_lowerCamelCase : Optional[Any] = """albert"""
def __init__( self : Any , snake_case_ : Dict=3_0_0_0_0 , snake_case_ : Optional[int]=1_2_8 , snake_case_ : str=4_0_9_6 , snake_case_ : List[Any]=1_2 , snake_case_ : str=1 , snake_case_ : Optional[Any]=6_4 , snake_case_ : Tuple=1_6_3_8_4 , snake_case_ : Optional[Any]=1 , snake_case_ : Optional[Any]="gelu_new" , snake_case_ : Dict=0 , snake_case_ : str=0 , snake_case_ : Union[str, Any]=5_1_2 , snake_case_ : Any=2 , snake_case_ : Optional[int]=0.0_2 , snake_case_ : Tuple=1e-12 , snake_case_ : Optional[int]=0.1 , snake_case_ : Union[str, Any]="absolute" , snake_case_ : List[str]=0 , snake_case_ : List[Any]=2 , snake_case_ : Any=3 , **snake_case_ : List[Any] , ):
super().__init__(pad_token_id=snake_case_ , bos_token_id=snake_case_ , eos_token_id=snake_case_ , **snake_case_ )
_UpperCAmelCase = vocab_size
_UpperCAmelCase = embedding_size
_UpperCAmelCase = hidden_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_hidden_groups
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = inner_group_num
_UpperCAmelCase = hidden_act
_UpperCAmelCase = intermediate_size
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = max_position_embeddings
_UpperCAmelCase = type_vocab_size
_UpperCAmelCase = initializer_range
_UpperCAmelCase = layer_norm_eps
_UpperCAmelCase = classifier_dropout_prob
_UpperCAmelCase = position_embedding_type
class A_ ( lowerCAmelCase_ ):
@property
def lowercase ( self : Dict ):
if self.task == "multiple-choice":
_UpperCAmelCase = {0: "batch", 1: "choice", 2: "sequence"}
else:
_UpperCAmelCase = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
("token_type_ids", dynamic_axis),
] )
| 22 |
'''simple docstring'''
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_pt_objects import * # noqa F403
else:
from .scheduling_consistency_models import CMStochasticIterativeScheduler
from .scheduling_ddim import DDIMScheduler
from .scheduling_ddim_inverse import DDIMInverseScheduler
from .scheduling_ddim_parallel import DDIMParallelScheduler
from .scheduling_ddpm import DDPMScheduler
from .scheduling_ddpm_parallel import DDPMParallelScheduler
from .scheduling_deis_multistep import DEISMultistepScheduler
from .scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler
from .scheduling_dpmsolver_multistep_inverse import DPMSolverMultistepInverseScheduler
from .scheduling_dpmsolver_singlestep import DPMSolverSinglestepScheduler
from .scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler
from .scheduling_euler_discrete import EulerDiscreteScheduler
from .scheduling_heun_discrete import HeunDiscreteScheduler
from .scheduling_ipndm import IPNDMScheduler
from .scheduling_k_dpm_2_ancestral_discrete import KDPMaAncestralDiscreteScheduler
from .scheduling_k_dpm_2_discrete import KDPMaDiscreteScheduler
from .scheduling_karras_ve import KarrasVeScheduler
from .scheduling_pndm import PNDMScheduler
from .scheduling_repaint import RePaintScheduler
from .scheduling_sde_ve import ScoreSdeVeScheduler
from .scheduling_sde_vp import ScoreSdeVpScheduler
from .scheduling_unclip import UnCLIPScheduler
from .scheduling_unipc_multistep import UniPCMultistepScheduler
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
from .scheduling_vq_diffusion import VQDiffusionScheduler
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_flax_objects import * # noqa F403
else:
from .scheduling_ddim_flax import FlaxDDIMScheduler
from .scheduling_ddpm_flax import FlaxDDPMScheduler
from .scheduling_dpmsolver_multistep_flax import FlaxDPMSolverMultistepScheduler
from .scheduling_karras_ve_flax import FlaxKarrasVeScheduler
from .scheduling_lms_discrete_flax import FlaxLMSDiscreteScheduler
from .scheduling_pndm_flax import FlaxPNDMScheduler
from .scheduling_sde_ve_flax import FlaxScoreSdeVeScheduler
from .scheduling_utils_flax import (
FlaxKarrasDiffusionSchedulers,
FlaxSchedulerMixin,
FlaxSchedulerOutput,
broadcast_to_shape_from_left,
)
try:
if not (is_torch_available() and is_scipy_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_torch_and_scipy_objects import * # noqa F403
else:
from .scheduling_lms_discrete import LMSDiscreteScheduler
try:
if not (is_torch_available() and is_torchsde_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_torch_and_torchsde_objects import * # noqa F403
else:
from .scheduling_dpmsolver_sde import DPMSolverSDEScheduler
| 22 | 1 |
'''simple docstring'''
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datasets.features import ArrayaD, ClassLabel, Features, Image, Value
from datasets.features.features import ArrayaDExtensionType, cast_to_python_objects
from datasets.keyhash import DuplicatedKeysError, InvalidKeyError
from .utils import require_pil
class A_ ( lowerCAmelCase_ ):
def lowercase ( self : List[Any] ):
_UpperCAmelCase = pa.array(TypedSequence([1, 2, 3] ) )
self.assertEqual(arr.type , pa.intaa() )
def lowercase ( self : Optional[Any] ):
with self.assertRaises(snake_case_ ):
_UpperCAmelCase = pa.array(TypedSequence([1, 2, 3] ) , type=pa.intaa() )
def lowercase ( self : Dict ):
with self.assertRaises(snake_case_ ):
_UpperCAmelCase = pa.array(TypedSequence([1, 2, 3] , try_type=Value("bool" ) , type=Value("int64" ) ) )
def lowercase ( self : str ):
_UpperCAmelCase = pa.array(TypedSequence([1, 2, 3] , type=Value("int32" ) ) )
self.assertEqual(arr.type , pa.intaa() )
def lowercase ( self : Union[str, Any] ):
with self.assertRaises((TypeError, pa.lib.ArrowInvalid) ):
_UpperCAmelCase = pa.array(TypedSequence(["foo", "bar"] , type=Value("int64" ) ) )
def lowercase ( self : int ):
_UpperCAmelCase = pa.array(TypedSequence([1, 2, 3] , try_type=Value("int32" ) ) )
self.assertEqual(arr.type , pa.intaa() )
def lowercase ( self : Dict ):
_UpperCAmelCase = pa.array(TypedSequence(["foo", "bar"] , try_type=Value("int64" ) ) )
self.assertEqual(arr.type , pa.string() )
def lowercase ( self : int ):
_UpperCAmelCase = pa.array(TypedSequence([[[1, 2, 3]]] , type=ArrayaD((1, 3) , "int64" ) ) )
self.assertEqual(arr.type , ArrayaDExtensionType((1, 3) , "int64" ) )
def lowercase ( self : Dict ):
with self.assertRaises((TypeError, pa.lib.ArrowInvalid) ):
_UpperCAmelCase = pa.array(TypedSequence(["foo", "bar"] , type=ArrayaD((1, 3) , "int64" ) ) )
def lowercase ( self : str ):
_UpperCAmelCase = pa.array(TypedSequence([[[1, 2, 3]]] , try_type=ArrayaD((1, 3) , "int64" ) ) )
self.assertEqual(arr.type , ArrayaDExtensionType((1, 3) , "int64" ) )
def lowercase ( self : Tuple ):
_UpperCAmelCase = pa.array(TypedSequence(["foo", "bar"] , try_type=ArrayaD((1, 3) , "int64" ) ) )
self.assertEqual(arr.type , pa.string() )
@require_pil
def lowercase ( self : Optional[int] ):
import PIL.Image
_UpperCAmelCase = PIL.Image.fromarray(np.arange(1_0 , dtype=np.uinta ).reshape(2 , 5 ) )
with patch(
"datasets.arrow_writer.cast_to_python_objects" , side_effect=snake_case_ ) as mock_cast_to_python_objects:
_UpperCAmelCase = pa.array(TypedSequence([{"path": None, "bytes": b"image_bytes"}, pil_image] , type=Image() ) )
_UpperCAmelCase , _UpperCAmelCase = mock_cast_to_python_objects.call_args_list[-1]
self.assertIn("optimize_list_casting" , snake_case_ )
self.assertFalse(kwargs["optimize_list_casting"] )
def UpperCAmelCase_ ( __lowercase : List[str] , __lowercase : int ) -> Tuple:
'''simple docstring'''
_UpperCAmelCase = pa.BufferReader(__lowercase ) if isinstance(__lowercase , pa.Buffer ) else pa.memory_map(__lowercase )
_UpperCAmelCase = pa.ipc.open_stream(__lowercase )
_UpperCAmelCase = f.read_all()
assert len(pa_table.to_batches() ) == expected_num_chunks
assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
del pa_table
@pytest.mark.parametrize("writer_batch_size" , [None, 1, 10] )
@pytest.mark.parametrize(
"fields" , [None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}] )
def UpperCAmelCase_ ( __lowercase : Tuple , __lowercase : int ) -> Union[str, Any]:
'''simple docstring'''
_UpperCAmelCase = pa.BufferOutputStream()
_UpperCAmelCase = pa.schema(__lowercase ) if fields else None
with ArrowWriter(stream=__lowercase , schema=__lowercase , writer_batch_size=__lowercase ) as writer:
writer.write({"col_1": "foo", "col_2": 1} )
writer.write({"col_1": "bar", "col_2": 2} )
_UpperCAmelCase , _UpperCAmelCase = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
if not fields:
_UpperCAmelCase = {"col_1": pa.string(), "col_2": pa.intaa()}
assert writer._schema == pa.schema(__lowercase , metadata=writer._schema.metadata )
_check_output(output.getvalue() , expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
def UpperCAmelCase_ ( ) -> Any:
'''simple docstring'''
_UpperCAmelCase = pa.BufferOutputStream()
_UpperCAmelCase = Features({"labels": ClassLabel(names=["neg", "pos"] )} )
with ArrowWriter(stream=__lowercase , features=__lowercase ) as writer:
writer.write({"labels": 0} )
writer.write({"labels": 1} )
_UpperCAmelCase , _UpperCAmelCase = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
assert writer._schema == features.arrow_schema
assert writer._schema.metadata == features.arrow_schema.metadata
_UpperCAmelCase = pa.BufferReader(output.getvalue() )
_UpperCAmelCase = pa.ipc.open_stream(__lowercase )
_UpperCAmelCase = f.read_all()
_UpperCAmelCase = pa_table.schema
assert pa_table.num_rows == 2
assert schema == features.arrow_schema
assert schema.metadata == features.arrow_schema.metadata
assert features == Features.from_arrow_schema(__lowercase )
@pytest.mark.parametrize("writer_batch_size" , [None, 1, 10] )
def UpperCAmelCase_ ( __lowercase : List[Any] ) -> str:
'''simple docstring'''
_UpperCAmelCase = pa.BufferOutputStream()
with ArrowWriter(
stream=__lowercase , writer_batch_size=__lowercase , hash_salt="split_name" , check_duplicates=__lowercase , ) as writer:
with pytest.raises(__lowercase ):
writer.write({"col_1": "foo", "col_2": 1} , key=[1, 2] )
_UpperCAmelCase , _UpperCAmelCase = writer.finalize()
@pytest.mark.parametrize("writer_batch_size" , [None, 2, 10] )
def UpperCAmelCase_ ( __lowercase : Optional[Any] ) -> Dict:
'''simple docstring'''
_UpperCAmelCase = pa.BufferOutputStream()
with ArrowWriter(
stream=__lowercase , writer_batch_size=__lowercase , hash_salt="split_name" , check_duplicates=__lowercase , ) as writer:
with pytest.raises(__lowercase ):
writer.write({"col_1": "foo", "col_2": 1} , key=10 )
writer.write({"col_1": "bar", "col_2": 2} , key=10 )
_UpperCAmelCase , _UpperCAmelCase = writer.finalize()
@pytest.mark.parametrize("writer_batch_size" , [None, 2, 10] )
def UpperCAmelCase_ ( __lowercase : Union[str, Any] ) -> int:
'''simple docstring'''
_UpperCAmelCase = pa.BufferOutputStream()
with ArrowWriter(
stream=__lowercase , writer_batch_size=__lowercase , hash_salt="split_name" , check_duplicates=__lowercase , ) as writer:
writer.write({"col_1": "foo", "col_2": 1} , key=1 )
writer.write({"col_1": "bar", "col_2": 2} , key=2 )
_UpperCAmelCase , _UpperCAmelCase = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
_check_output(output.getvalue() , expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
@pytest.mark.parametrize("writer_batch_size" , [None, 1, 10] )
@pytest.mark.parametrize(
"fields" , [None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}] )
def UpperCAmelCase_ ( __lowercase : str , __lowercase : Optional[int] ) -> Optional[int]:
'''simple docstring'''
_UpperCAmelCase = pa.BufferOutputStream()
_UpperCAmelCase = pa.schema(__lowercase ) if fields else None
with ArrowWriter(stream=__lowercase , schema=__lowercase , writer_batch_size=__lowercase ) as writer:
writer.write_batch({"col_1": ["foo", "bar"], "col_2": [1, 2]} )
writer.write_batch({"col_1": [], "col_2": []} )
_UpperCAmelCase , _UpperCAmelCase = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
if not fields:
_UpperCAmelCase = {"col_1": pa.string(), "col_2": pa.intaa()}
assert writer._schema == pa.schema(__lowercase , metadata=writer._schema.metadata )
_check_output(output.getvalue() , expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
@pytest.mark.parametrize("writer_batch_size" , [None, 1, 10] )
@pytest.mark.parametrize(
"fields" , [None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}] )
def UpperCAmelCase_ ( __lowercase : str , __lowercase : Any ) -> Optional[int]:
'''simple docstring'''
_UpperCAmelCase = pa.BufferOutputStream()
_UpperCAmelCase = pa.schema(__lowercase ) if fields else None
with ArrowWriter(stream=__lowercase , schema=__lowercase , writer_batch_size=__lowercase ) as writer:
writer.write_table(pa.Table.from_pydict({"col_1": ["foo", "bar"], "col_2": [1, 2]} ) )
_UpperCAmelCase , _UpperCAmelCase = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
if not fields:
_UpperCAmelCase = {"col_1": pa.string(), "col_2": pa.intaa()}
assert writer._schema == pa.schema(__lowercase , metadata=writer._schema.metadata )
_check_output(output.getvalue() , expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
@pytest.mark.parametrize("writer_batch_size" , [None, 1, 10] )
@pytest.mark.parametrize(
"fields" , [None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}] )
def UpperCAmelCase_ ( __lowercase : Union[str, Any] , __lowercase : Tuple ) -> Tuple:
'''simple docstring'''
_UpperCAmelCase = pa.BufferOutputStream()
_UpperCAmelCase = pa.schema(__lowercase ) if fields else None
with ArrowWriter(stream=__lowercase , schema=__lowercase , writer_batch_size=__lowercase ) as writer:
writer.write_row(pa.Table.from_pydict({"col_1": ["foo"], "col_2": [1]} ) )
writer.write_row(pa.Table.from_pydict({"col_1": ["bar"], "col_2": [2]} ) )
_UpperCAmelCase , _UpperCAmelCase = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
if not fields:
_UpperCAmelCase = {"col_1": pa.string(), "col_2": pa.intaa()}
assert writer._schema == pa.schema(__lowercase , metadata=writer._schema.metadata )
_check_output(output.getvalue() , expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
def UpperCAmelCase_ ( ) -> Dict:
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmp_dir:
_UpperCAmelCase = {"col_1": pa.string(), "col_2": pa.intaa()}
_UpperCAmelCase = os.path.join(__lowercase , "test.arrow" )
with ArrowWriter(path=__lowercase , schema=pa.schema(__lowercase ) ) as writer:
writer.write_batch({"col_1": ["foo", "bar"], "col_2": [1, 2]} )
_UpperCAmelCase , _UpperCAmelCase = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
assert writer._schema == pa.schema(__lowercase , metadata=writer._schema.metadata )
_check_output(__lowercase , 1 )
def UpperCAmelCase_ ( __lowercase : str ) -> int:
'''simple docstring'''
if pa.types.is_list(__lowercase ):
return get_base_dtype(arr_type.value_type )
else:
return arr_type
def UpperCAmelCase_ ( __lowercase : List[Any] , __lowercase : Optional[Any] ) -> Tuple:
'''simple docstring'''
if isinstance(lst[0] , __lowercase ):
change_first_primitive_element_in_list(lst[0] , __lowercase )
else:
_UpperCAmelCase = value
@pytest.mark.parametrize("optimized_int_type, expected_dtype" , [(None, pa.intaa()), (Value("int32" ), pa.intaa())] )
@pytest.mark.parametrize("sequence" , [[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]] )
def UpperCAmelCase_ ( __lowercase : Union[str, Any] , __lowercase : str , __lowercase : Dict ) -> Optional[int]:
'''simple docstring'''
_UpperCAmelCase = pa.array(TypedSequence(__lowercase , optimized_int_type=__lowercase ) )
assert get_base_dtype(arr.type ) == expected_dtype
@pytest.mark.parametrize(
"col, expected_dtype" , [
("attention_mask", pa.inta()),
("special_tokens_mask", pa.inta()),
("token_type_ids", pa.inta()),
("input_ids", pa.intaa()),
("other", pa.intaa()),
] , )
@pytest.mark.parametrize("sequence" , [[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]] )
def UpperCAmelCase_ ( __lowercase : Dict , __lowercase : List[Any] , __lowercase : List[Any] ) -> Tuple:
'''simple docstring'''
_UpperCAmelCase = pa.array(OptimizedTypedSequence(__lowercase , col=__lowercase ) )
assert get_base_dtype(arr.type ) == expected_dtype
# not in range
if col != "other":
# avoids errors due to in-place modifications
_UpperCAmelCase = copy.deepcopy(__lowercase )
_UpperCAmelCase = np.iinfo(expected_dtype.to_pandas_dtype() ).max + 1
change_first_primitive_element_in_list(__lowercase , __lowercase )
_UpperCAmelCase = pa.array(OptimizedTypedSequence(__lowercase , col=__lowercase ) )
assert get_base_dtype(arr.type ) == pa.intaa()
@pytest.mark.parametrize("raise_exception" , [False, True] )
def UpperCAmelCase_ ( __lowercase : List[Any] , __lowercase : Tuple ) -> str:
'''simple docstring'''
_UpperCAmelCase = str(tmp_path / "dataset-train.arrow" )
try:
with ArrowWriter(path=__lowercase ) as writer:
if raise_exception:
raise pa.lib.ArrowInvalid()
else:
writer.stream.close()
except pa.lib.ArrowInvalid:
pass
finally:
assert writer.stream.closed
def UpperCAmelCase_ ( __lowercase : str ) -> Union[str, Any]:
'''simple docstring'''
_UpperCAmelCase = "mock://dataset-train.arrow"
with ArrowWriter(path=__lowercase , storage_options=mockfs.storage_options ) as writer:
assert isinstance(writer._fs , type(__lowercase ) )
assert writer._fs.storage_options == mockfs.storage_options
writer.write({"col_1": "foo", "col_2": 1} )
writer.write({"col_1": "bar", "col_2": 2} )
_UpperCAmelCase , _UpperCAmelCase = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
assert mockfs.exists(__lowercase )
def UpperCAmelCase_ ( ) -> str:
'''simple docstring'''
_UpperCAmelCase = pa.BufferOutputStream()
with ParquetWriter(stream=__lowercase ) as writer:
writer.write({"col_1": "foo", "col_2": 1} )
writer.write({"col_1": "bar", "col_2": 2} )
_UpperCAmelCase , _UpperCAmelCase = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
_UpperCAmelCase = pa.BufferReader(output.getvalue() )
_UpperCAmelCase = pq.read_table(__lowercase )
assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
@require_pil
@pytest.mark.parametrize("embed_local_files" , [False, True] )
def UpperCAmelCase_ ( __lowercase : str , __lowercase : str ) -> Tuple:
'''simple docstring'''
import PIL.Image
_UpperCAmelCase = str(tmp_path / "test_image_rgb.jpg" )
PIL.Image.fromarray(np.zeros((5, 5) , dtype=np.uinta ) ).save(__lowercase , format="png" )
_UpperCAmelCase = pa.BufferOutputStream()
with ParquetWriter(
stream=__lowercase , features=Features({"image": Image()} ) , embed_local_files=__lowercase ) as writer:
writer.write({"image": image_path} )
writer.finalize()
_UpperCAmelCase = pa.BufferReader(output.getvalue() )
_UpperCAmelCase = pq.read_table(__lowercase )
_UpperCAmelCase = pa_table.to_pydict()
if embed_local_files:
assert isinstance(out["image"][0]["path"] , __lowercase )
with open(__lowercase , "rb" ) as f:
assert out["image"][0]["bytes"] == f.read()
else:
assert out["image"][0]["path"] == image_path
assert out["image"][0]["bytes"] is None
def UpperCAmelCase_ ( ) -> str:
'''simple docstring'''
_UpperCAmelCase = pa.schema([pa.field("col_1" , pa.string() , nullable=__lowercase )] )
_UpperCAmelCase = pa.BufferOutputStream()
with ArrowWriter(stream=__lowercase ) as writer:
writer._build_writer(inferred_schema=__lowercase )
assert writer._schema == pa.schema([pa.field("col_1" , pa.string() )] )
| 22 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : int ) -> int:
'''simple docstring'''
if not isinstance(__lowercase , __lowercase ) or number < 0:
raise ValueError("Input must be a non-negative integer" )
_UpperCAmelCase = 0
while number:
# This way we arrive at next set bit (next 1) instead of looping
# through each bit and checking for 1s hence the
# loop won't run 32 times it will only run the number of `1` times
number &= number - 1
count += 1
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 22 | 1 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__SCREAMING_SNAKE_CASE :List[Any] = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE :Dict = {
'''ut/deta''': '''https://huggingface.co/ut/deta/resolve/main/config.json''',
}
class A_ ( lowerCAmelCase_ ):
_lowerCamelCase : str = """deta"""
_lowerCamelCase : List[str] = {
"""hidden_size""": """d_model""",
"""num_attention_heads""": """encoder_attention_heads""",
}
def __init__( self : Optional[Any] , snake_case_ : Dict=None , snake_case_ : int=9_0_0 , snake_case_ : Tuple=2_0_4_8 , snake_case_ : Union[str, Any]=6 , snake_case_ : Tuple=2_0_4_8 , snake_case_ : int=8 , snake_case_ : str=6 , snake_case_ : int=1_0_2_4 , snake_case_ : str=8 , snake_case_ : Tuple=0.0 , snake_case_ : int=True , snake_case_ : str="relu" , snake_case_ : Optional[int]=2_5_6 , snake_case_ : int=0.1 , snake_case_ : int=0.0 , snake_case_ : Dict=0.0 , snake_case_ : List[Any]=0.0_2 , snake_case_ : Optional[int]=1.0 , snake_case_ : Tuple=True , snake_case_ : Dict=False , snake_case_ : Any="sine" , snake_case_ : int=5 , snake_case_ : Union[str, Any]=4 , snake_case_ : List[Any]=4 , snake_case_ : Optional[int]=True , snake_case_ : Optional[Any]=3_0_0 , snake_case_ : str=True , snake_case_ : Optional[Any]=True , snake_case_ : Dict=1 , snake_case_ : Optional[int]=5 , snake_case_ : str=2 , snake_case_ : int=1 , snake_case_ : Union[str, Any]=1 , snake_case_ : Dict=5 , snake_case_ : Union[str, Any]=2 , snake_case_ : Optional[Any]=0.1 , snake_case_ : Optional[int]=0.2_5 , **snake_case_ : Optional[Any] , ):
if backbone_config is None:
logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone." )
_UpperCAmelCase = CONFIG_MAPPING["resnet"](out_features=["stage2", "stage3", "stage4"] )
else:
if isinstance(snake_case_ , snake_case_ ):
_UpperCAmelCase = backbone_config.pop("model_type" )
_UpperCAmelCase = CONFIG_MAPPING[backbone_model_type]
_UpperCAmelCase = config_class.from_dict(snake_case_ )
_UpperCAmelCase = backbone_config
_UpperCAmelCase = num_queries
_UpperCAmelCase = max_position_embeddings
_UpperCAmelCase = d_model
_UpperCAmelCase = encoder_ffn_dim
_UpperCAmelCase = encoder_layers
_UpperCAmelCase = encoder_attention_heads
_UpperCAmelCase = decoder_ffn_dim
_UpperCAmelCase = decoder_layers
_UpperCAmelCase = decoder_attention_heads
_UpperCAmelCase = dropout
_UpperCAmelCase = attention_dropout
_UpperCAmelCase = activation_dropout
_UpperCAmelCase = activation_function
_UpperCAmelCase = init_std
_UpperCAmelCase = init_xavier_std
_UpperCAmelCase = encoder_layerdrop
_UpperCAmelCase = auxiliary_loss
_UpperCAmelCase = position_embedding_type
# deformable attributes
_UpperCAmelCase = num_feature_levels
_UpperCAmelCase = encoder_n_points
_UpperCAmelCase = decoder_n_points
_UpperCAmelCase = two_stage
_UpperCAmelCase = two_stage_num_proposals
_UpperCAmelCase = with_box_refine
_UpperCAmelCase = assign_first_stage
if two_stage is True and with_box_refine is False:
raise ValueError("If two_stage is True, with_box_refine must be True." )
# Hungarian matcher
_UpperCAmelCase = class_cost
_UpperCAmelCase = bbox_cost
_UpperCAmelCase = giou_cost
# Loss coefficients
_UpperCAmelCase = mask_loss_coefficient
_UpperCAmelCase = dice_loss_coefficient
_UpperCAmelCase = bbox_loss_coefficient
_UpperCAmelCase = giou_loss_coefficient
_UpperCAmelCase = eos_coefficient
_UpperCAmelCase = focal_alpha
super().__init__(is_encoder_decoder=snake_case_ , **snake_case_ )
@property
def lowercase ( self : List[Any] ):
return self.encoder_attention_heads
@property
def lowercase ( self : Dict ):
return self.d_model
def lowercase ( self : List[Any] ):
_UpperCAmelCase = copy.deepcopy(self.__dict__ )
_UpperCAmelCase = self.backbone_config.to_dict()
_UpperCAmelCase = self.__class__.model_type
return output
| 22 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
from typing import Any, Generic, TypeVar
__SCREAMING_SNAKE_CASE :Optional[int] = TypeVar('''T''')
class A_ ( Generic[T] ):
def __init__( self : List[Any] , snake_case_ : list[T] , snake_case_ : Callable[[T, T], T] ):
_UpperCAmelCase = None
_UpperCAmelCase = len(snake_case_ )
_UpperCAmelCase = [any_type for _ in range(self.N )] + arr
_UpperCAmelCase = fnc
self.build()
def lowercase ( self : List[Any] ):
for p in range(self.N - 1 , 0 , -1 ):
_UpperCAmelCase = self.fn(self.st[p * 2] , self.st[p * 2 + 1] )
def lowercase ( self : Optional[Any] , snake_case_ : int , snake_case_ : T ):
p += self.N
_UpperCAmelCase = v
while p > 1:
_UpperCAmelCase = p // 2
_UpperCAmelCase = self.fn(self.st[p * 2] , self.st[p * 2 + 1] )
def lowercase ( self : Any , snake_case_ : int , snake_case_ : int ): # noqa: E741
_UpperCAmelCase , _UpperCAmelCase = l + self.N, r + self.N
_UpperCAmelCase = None
while l <= r:
if l % 2 == 1:
_UpperCAmelCase = self.st[l] if res is None else self.fn(snake_case_ , self.st[l] )
if r % 2 == 0:
_UpperCAmelCase = self.st[r] if res is None else self.fn(snake_case_ , self.st[r] )
_UpperCAmelCase , _UpperCAmelCase = (l + 1) // 2, (r - 1) // 2
return res
if __name__ == "__main__":
from functools import reduce
__SCREAMING_SNAKE_CASE :Union[str, Any] = [1, 10, -2, 9, -3, 8, 4, -7, 5, 6, 11, -12]
__SCREAMING_SNAKE_CASE :List[str] = {
0: 7,
1: 2,
2: 6,
3: -14,
4: 5,
5: 4,
6: 7,
7: -10,
8: 9,
9: 10,
10: 12,
11: 1,
}
__SCREAMING_SNAKE_CASE :Any = SegmentTree(test_array, min)
__SCREAMING_SNAKE_CASE :Any = SegmentTree(test_array, max)
__SCREAMING_SNAKE_CASE :Any = SegmentTree(test_array, lambda a, b: a + b)
def UpperCAmelCase_ ( ) -> None:
'''simple docstring'''
for i in range(len(__lowercase ) ):
for j in range(__lowercase , len(__lowercase ) ):
_UpperCAmelCase = reduce(__lowercase , test_array[i : j + 1] )
_UpperCAmelCase = reduce(__lowercase , test_array[i : j + 1] )
_UpperCAmelCase = reduce(lambda __lowercase , __lowercase : a + b , test_array[i : j + 1] )
assert min_range == min_segment_tree.query(__lowercase , __lowercase )
assert max_range == max_segment_tree.query(__lowercase , __lowercase )
assert sum_range == sum_segment_tree.query(__lowercase , __lowercase )
test_all_segments()
for index, value in test_updates.items():
__SCREAMING_SNAKE_CASE :str = value
min_segment_tree.update(index, value)
max_segment_tree.update(index, value)
sum_segment_tree.update(index, value)
test_all_segments()
| 22 | 1 |
'''simple docstring'''
from __future__ import annotations
def UpperCAmelCase_ ( __lowercase : list[float] , __lowercase : list[float] ) -> float:
'''simple docstring'''
_UpperCAmelCase = sorted(numsa + numsa )
_UpperCAmelCase , _UpperCAmelCase = divmod(len(__lowercase ) , 2 )
if mod == 1:
return all_numbers[div]
else:
return (all_numbers[div] + all_numbers[div - 1]) / 2
if __name__ == "__main__":
import doctest
doctest.testmod()
__SCREAMING_SNAKE_CASE :Tuple = [float(x) for x in input('''Enter the elements of first array: ''').split()]
__SCREAMING_SNAKE_CASE :Any = [float(x) for x in input('''Enter the elements of second array: ''').split()]
print(F"The median of two arrays is: {median_of_two_arrays(array_a, array_a)}")
| 22 |
'''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 UpperCAmelCase_ ( __lowercase : int , __lowercase : Dict ) -> List[Any]:
'''simple docstring'''
_UpperCAmelCase = _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 UpperCAmelCase_ ( __lowercase : Dict , __lowercase : Optional[Any] , __lowercase : int ) -> str:
'''simple docstring'''
_UpperCAmelCase = _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 UpperCAmelCase_ ( __lowercase : Optional[Any] , __lowercase : List[Any] ) -> List[Any]:
'''simple docstring'''
if expected is RuntimeError:
with pytest.raises(__lowercase ):
_number_of_shards_in_gen_kwargs(__lowercase )
else:
_UpperCAmelCase = _number_of_shards_in_gen_kwargs(__lowercase )
assert out == expected
| 22 | 1 |
'''simple docstring'''
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
__SCREAMING_SNAKE_CASE :Dict = logging.get_logger(__name__)
def UpperCAmelCase_ ( __lowercase : str ) -> Dict:
'''simple docstring'''
_UpperCAmelCase = r"\w+[.]\d+"
_UpperCAmelCase = re.findall(__lowercase , __lowercase )
for pat in pats:
_UpperCAmelCase = key.replace(__lowercase , "_".join(pat.split("." ) ) )
return key
def UpperCAmelCase_ ( __lowercase : int , __lowercase : Optional[Any] , __lowercase : List[Any] ) -> List[Any]:
'''simple docstring'''
_UpperCAmelCase = pt_tuple_key[:-1] + ("scale",)
if (
any("norm" in str_ for str_ in pt_tuple_key )
and (pt_tuple_key[-1] == "bias")
and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
):
_UpperCAmelCase = pt_tuple_key[:-1] + ("scale",)
return renamed_pt_tuple_key, pt_tensor
elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
_UpperCAmelCase = pt_tuple_key[:-1] + ("scale",)
return renamed_pt_tuple_key, pt_tensor
# embedding
if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
_UpperCAmelCase = pt_tuple_key[:-1] + ("embedding",)
return renamed_pt_tuple_key, pt_tensor
# conv layer
_UpperCAmelCase = pt_tuple_key[:-1] + ("kernel",)
if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
_UpperCAmelCase = pt_tensor.transpose(2 , 3 , 1 , 0 )
return renamed_pt_tuple_key, pt_tensor
# linear layer
_UpperCAmelCase = pt_tuple_key[:-1] + ("kernel",)
if pt_tuple_key[-1] == "weight":
_UpperCAmelCase = pt_tensor.T
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm weight
_UpperCAmelCase = pt_tuple_key[:-1] + ("weight",)
if pt_tuple_key[-1] == "gamma":
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm bias
_UpperCAmelCase = pt_tuple_key[:-1] + ("bias",)
if pt_tuple_key[-1] == "beta":
return renamed_pt_tuple_key, pt_tensor
return pt_tuple_key, pt_tensor
def UpperCAmelCase_ ( __lowercase : Tuple , __lowercase : Dict , __lowercase : List[str]=42 ) -> Dict:
'''simple docstring'''
_UpperCAmelCase = {k: v.numpy() for k, v in pt_state_dict.items()}
# Step 2: Since the model is stateless, get random Flax params
_UpperCAmelCase = flax_model.init_weights(PRNGKey(__lowercase ) )
_UpperCAmelCase = flatten_dict(__lowercase )
_UpperCAmelCase = {}
# Need to change some parameters name to match Flax names
for pt_key, pt_tensor in pt_state_dict.items():
_UpperCAmelCase = rename_key(__lowercase )
_UpperCAmelCase = tuple(renamed_pt_key.split("." ) )
# Correctly rename weight parameters
_UpperCAmelCase , _UpperCAmelCase = rename_key_and_reshape_tensor(__lowercase , __lowercase , __lowercase )
if flax_key in random_flax_state_dict:
if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
raise ValueError(
f'PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '
f'{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.' )
# also add unexpected weight so that warning is thrown
_UpperCAmelCase = jnp.asarray(__lowercase )
return unflatten_dict(__lowercase )
| 22 |
'''simple docstring'''
import math
def UpperCAmelCase_ ( __lowercase : int ) -> bool:
'''simple docstring'''
return math.sqrt(__lowercase ) * math.sqrt(__lowercase ) == num
def UpperCAmelCase_ ( __lowercase : int ) -> bool:
'''simple docstring'''
_UpperCAmelCase = 0
_UpperCAmelCase = n
while left <= right:
_UpperCAmelCase = (left + right) // 2
if mid**2 == n:
return True
elif mid**2 > n:
_UpperCAmelCase = mid - 1
else:
_UpperCAmelCase = mid + 1
return False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 22 | 1 |
'''simple docstring'''
import unittest
from typing import Tuple
import torch
from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class A_ :
@property
def lowercase ( self : Optional[int] ):
return self.get_dummy_input()
@property
def lowercase ( self : List[str] ):
if self.block_type == "down":
return (4, 3_2, 1_6, 1_6)
elif self.block_type == "mid":
return (4, 3_2, 3_2, 3_2)
elif self.block_type == "up":
return (4, 3_2, 6_4, 6_4)
raise ValueError(f'\'{self.block_type}\' is not a supported block_type. Set it to \'up\', \'mid\', or \'down\'.' )
def lowercase ( self : str , snake_case_ : Dict=True , snake_case_ : int=False , snake_case_ : Any=False , snake_case_ : Union[str, Any]=False , ):
_UpperCAmelCase = 4
_UpperCAmelCase = 3_2
_UpperCAmelCase = (3_2, 3_2)
_UpperCAmelCase = torch.manual_seed(0 )
_UpperCAmelCase = torch.device(snake_case_ )
_UpperCAmelCase = (batch_size, num_channels) + sizes
_UpperCAmelCase = randn_tensor(snake_case_ , generator=snake_case_ , device=snake_case_ )
_UpperCAmelCase = {"hidden_states": hidden_states}
if include_temb:
_UpperCAmelCase = 1_2_8
_UpperCAmelCase = randn_tensor((batch_size, temb_channels) , generator=snake_case_ , device=snake_case_ )
if include_res_hidden_states_tuple:
_UpperCAmelCase = torch.manual_seed(1 )
_UpperCAmelCase = (randn_tensor(snake_case_ , generator=snake_case_ , device=snake_case_ ),)
if include_encoder_hidden_states:
_UpperCAmelCase = floats_tensor((batch_size, 3_2, 3_2) ).to(snake_case_ )
if include_skip_sample:
_UpperCAmelCase = randn_tensor(((batch_size, 3) + sizes) , generator=snake_case_ , device=snake_case_ )
return dummy_input
def lowercase ( self : List[str] ):
_UpperCAmelCase = {
"in_channels": 3_2,
"out_channels": 3_2,
"temb_channels": 1_2_8,
}
if self.block_type == "up":
_UpperCAmelCase = 3_2
if self.block_type == "mid":
init_dict.pop("out_channels" )
_UpperCAmelCase = self.dummy_input
return init_dict, inputs_dict
def lowercase ( self : int , snake_case_ : List[str] ):
_UpperCAmelCase , _UpperCAmelCase = self.prepare_init_args_and_inputs_for_common()
_UpperCAmelCase = self.block_class(**snake_case_ )
unet_block.to(snake_case_ )
unet_block.eval()
with torch.no_grad():
_UpperCAmelCase = unet_block(**snake_case_ )
if isinstance(snake_case_ , snake_case_ ):
_UpperCAmelCase = output[0]
self.assertEqual(output.shape , self.output_shape )
_UpperCAmelCase = output[0, -1, -3:, -3:]
_UpperCAmelCase = torch.tensor(snake_case_ ).to(snake_case_ )
assert torch_all_close(output_slice.flatten() , snake_case_ , atol=5e-3 )
@unittest.skipIf(torch_device == "mps" , "Training is not supported in mps" )
def lowercase ( self : str ):
_UpperCAmelCase , _UpperCAmelCase = self.prepare_init_args_and_inputs_for_common()
_UpperCAmelCase = self.block_class(**snake_case_ )
model.to(snake_case_ )
model.train()
_UpperCAmelCase = model(**snake_case_ )
if isinstance(snake_case_ , snake_case_ ):
_UpperCAmelCase = output[0]
_UpperCAmelCase = torch.device(snake_case_ )
_UpperCAmelCase = randn_tensor(output.shape , device=snake_case_ )
_UpperCAmelCase = torch.nn.functional.mse_loss(snake_case_ , snake_case_ )
loss.backward()
| 22 |
'''simple docstring'''
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
__SCREAMING_SNAKE_CASE :Dict = 1e-4
if is_torch_available():
import torch
from transformers import AutoformerConfig, AutoformerForPrediction, AutoformerModel
from transformers.models.autoformer.modeling_autoformer import AutoformerDecoder, AutoformerEncoder
@require_torch
class A_ :
def __init__( self : List[Any] , snake_case_ : int , snake_case_ : Dict=1_6 , snake_case_ : Dict=1_3 , snake_case_ : int=7 , snake_case_ : Any=1_4 , snake_case_ : int=1_0 , snake_case_ : Any=1_9 , snake_case_ : int=5 , snake_case_ : Any=4 , snake_case_ : Tuple=True , snake_case_ : Optional[int]=1_6 , snake_case_ : List[str]=2 , snake_case_ : Any=4 , snake_case_ : List[Any]=4 , snake_case_ : Optional[Any]="gelu" , snake_case_ : Optional[int]=0.1 , snake_case_ : Union[str, Any]=0.1 , snake_case_ : Tuple=[1, 2, 3, 4, 5] , snake_case_ : str=2_5 , snake_case_ : Any=5 , ):
_UpperCAmelCase = d_model
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = prediction_length
_UpperCAmelCase = context_length
_UpperCAmelCase = cardinality
_UpperCAmelCase = num_time_features
_UpperCAmelCase = lags_sequence
_UpperCAmelCase = embedding_dimension
_UpperCAmelCase = is_training
_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 = context_length
_UpperCAmelCase = prediction_length + label_length
_UpperCAmelCase = label_length
_UpperCAmelCase = moving_average
_UpperCAmelCase = autocorrelation_factor
def lowercase ( self : Union[str, Any] ):
return AutoformerConfig(
d_model=self.d_model , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , prediction_length=self.prediction_length , context_length=self.context_length , label_length=self.label_length , lags_sequence=self.lags_sequence , num_time_features=self.num_time_features , num_static_categorical_features=1 , cardinality=[self.cardinality] , embedding_dimension=[self.embedding_dimension] , moving_average=self.moving_average , )
def lowercase ( self : int , snake_case_ : Optional[Any] ):
_UpperCAmelCase = config.context_length + max(config.lags_sequence )
_UpperCAmelCase = ids_tensor([self.batch_size, 1] , config.cardinality[0] )
_UpperCAmelCase = floats_tensor([self.batch_size, _past_length, config.num_time_features] )
_UpperCAmelCase = floats_tensor([self.batch_size, _past_length] )
_UpperCAmelCase = floats_tensor([self.batch_size, _past_length] ) > 0.5
# decoder inputs
_UpperCAmelCase = floats_tensor([self.batch_size, config.prediction_length, config.num_time_features] )
_UpperCAmelCase = floats_tensor([self.batch_size, config.prediction_length] )
_UpperCAmelCase = {
"past_values": past_values,
"static_categorical_features": static_categorical_features,
"past_time_features": past_time_features,
"past_observed_mask": past_observed_mask,
"future_time_features": future_time_features,
"future_values": future_values,
}
return inputs_dict
def lowercase ( self : List[Any] ):
_UpperCAmelCase = self.get_config()
_UpperCAmelCase = self.prepare_autoformer_inputs_dict(snake_case_ )
return config, inputs_dict
def lowercase ( self : List[str] ):
_UpperCAmelCase , _UpperCAmelCase = self.prepare_config_and_inputs()
return config, inputs_dict
def lowercase ( self : Optional[Any] , snake_case_ : int , snake_case_ : Optional[int] ):
_UpperCAmelCase = AutoformerModel(config=snake_case_ ).to(snake_case_ ).eval()
_UpperCAmelCase = model(**snake_case_ )
_UpperCAmelCase = outputs.encoder_last_hidden_state
_UpperCAmelCase = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
_UpperCAmelCase = model.get_encoder()
encoder.save_pretrained(snake_case_ )
_UpperCAmelCase = AutoformerEncoder.from_pretrained(snake_case_ ).to(snake_case_ )
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = model.create_network_inputs(**snake_case_ )
_UpperCAmelCase , _UpperCAmelCase = model.decomposition_layer(transformer_inputs[:, : config.context_length, ...] )
_UpperCAmelCase = torch.cat(
(transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...]) , dim=-1 , )
_UpperCAmelCase = encoder(inputs_embeds=snake_case_ )[0]
self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1e-3 )
_UpperCAmelCase = (
torch.mean(transformer_inputs[:, : config.context_length, ...] , dim=1 )
.unsqueeze(1 )
.repeat(1 , config.prediction_length , 1 )
)
_UpperCAmelCase = torch.zeros(
[transformer_inputs.shape[0], config.prediction_length, transformer_inputs.shape[2]] , device=enc_input.device , )
_UpperCAmelCase = torch.cat(
(
torch.cat((seasonal_input[:, -config.label_length :, ...], zeros) , dim=1 ),
feature[:, config.context_length - config.label_length :, ...],
) , dim=-1 , )
_UpperCAmelCase = torch.cat(
(
torch.cat((trend_input[:, -config.label_length :, ...], mean) , dim=1 ),
feature[:, config.context_length - config.label_length :, ...],
) , dim=-1 , )
with tempfile.TemporaryDirectory() as tmpdirname:
_UpperCAmelCase = model.get_decoder()
decoder.save_pretrained(snake_case_ )
_UpperCAmelCase = AutoformerDecoder.from_pretrained(snake_case_ ).to(snake_case_ )
_UpperCAmelCase = decoder(
trend=snake_case_ , inputs_embeds=snake_case_ , encoder_hidden_states=snake_case_ , )[0]
self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1e-3 )
@require_torch
class A_ ( lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
_lowerCamelCase : List[Any] = (AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
_lowerCamelCase : Tuple = (AutoformerForPrediction,) if is_torch_available() else ()
_lowerCamelCase : List[Any] = {"""feature-extraction""": AutoformerModel} if is_torch_available() else {}
_lowerCamelCase : Optional[Any] = False
_lowerCamelCase : Tuple = False
_lowerCamelCase : int = False
_lowerCamelCase : Optional[Any] = False
_lowerCamelCase : Optional[Any] = False
_lowerCamelCase : List[Any] = False
def lowercase ( self : Tuple ):
_UpperCAmelCase = AutoformerModelTester(self )
_UpperCAmelCase = ConfigTester(self , config_class=snake_case_ , has_text_modality=snake_case_ )
def lowercase ( self : Optional[Any] ):
self.config_tester.run_common_tests()
def lowercase ( self : Union[str, Any] ):
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
_UpperCAmelCase = model_class(snake_case_ )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(snake_case_ )
_UpperCAmelCase , _UpperCAmelCase = model_class.from_pretrained(snake_case_ , output_loading_info=snake_case_ )
self.assertEqual(info["missing_keys"] , [] )
def lowercase ( self : Optional[int] ):
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*snake_case_ )
@unittest.skip(reason="Model has no tokens embeddings" )
def lowercase ( self : Optional[int] ):
pass
def lowercase ( self : Optional[int] ):
_UpperCAmelCase = inspect.signature(getattr(snake_case_ , "forward" ) )
# The main input is the name of the argument after `self`
_UpperCAmelCase = list(model_signature.parameters.keys() )[1]
self.assertEqual(AutoformerModel.main_input_name , snake_case_ )
def lowercase ( self : List[str] ):
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase = model_class(snake_case_ )
_UpperCAmelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_UpperCAmelCase = [*signature.parameters.keys()]
_UpperCAmelCase = [
"past_values",
"past_time_features",
"past_observed_mask",
"static_categorical_features",
"static_real_features",
"future_values",
"future_time_features",
]
if model.__class__.__name__ in ["AutoformerForPrediction"]:
expected_arg_names.append("future_observed_mask" )
expected_arg_names.extend(
[
"decoder_attention_mask",
"head_mask",
"decoder_head_mask",
"cross_attn_head_mask",
"encoder_outputs",
"past_key_values",
"output_hidden_states",
"output_attentions",
"use_cache",
"return_dict",
] )
self.assertListEqual(arg_names[: len(snake_case_ )] , snake_case_ )
def lowercase ( self : Optional[int] ):
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
_UpperCAmelCase = True
_UpperCAmelCase = getattr(self.model_tester , "seq_length" , snake_case_ )
_UpperCAmelCase = getattr(self.model_tester , "decoder_seq_length" , snake_case_ )
_UpperCAmelCase = getattr(self.model_tester , "encoder_seq_length" , snake_case_ )
_UpperCAmelCase = getattr(self.model_tester , "d_model" , snake_case_ )
_UpperCAmelCase = getattr(self.model_tester , "num_attention_heads" , snake_case_ )
_UpperCAmelCase = d_model // num_attention_heads
for model_class in self.all_model_classes:
_UpperCAmelCase = True
_UpperCAmelCase = False
_UpperCAmelCase = True
_UpperCAmelCase = model_class(snake_case_ )
model.to(snake_case_ )
model.eval()
with torch.no_grad():
_UpperCAmelCase = model(**self._prepare_for_class(snake_case_ , snake_case_ ) )
_UpperCAmelCase = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(snake_case_ ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
_UpperCAmelCase = True
_UpperCAmelCase = model_class(snake_case_ )
model.to(snake_case_ )
model.eval()
with torch.no_grad():
_UpperCAmelCase = model(**self._prepare_for_class(snake_case_ , snake_case_ ) )
_UpperCAmelCase = outputs.encoder_attentions
self.assertEqual(len(snake_case_ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, dim] , )
_UpperCAmelCase = len(snake_case_ )
_UpperCAmelCase = 7
if "last_hidden_state" in outputs:
correct_outlen += 1
if "trend" in outputs:
correct_outlen += 1
if "past_key_values" in outputs:
correct_outlen += 1 # past_key_values have been returned
if "loss" in outputs:
correct_outlen += 1
if "params" in outputs:
correct_outlen += 1
self.assertEqual(snake_case_ , snake_case_ )
# decoder attentions
_UpperCAmelCase = outputs.decoder_attentions
self.assertIsInstance(snake_case_ , (list, tuple) )
self.assertEqual(len(snake_case_ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, decoder_seq_length, dim] , )
# cross attentions
_UpperCAmelCase = outputs.cross_attentions
self.assertIsInstance(snake_case_ , (list, tuple) )
self.assertEqual(len(snake_case_ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(cross_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, decoder_seq_length, dim] , )
# Check attention is always last and order is fine
_UpperCAmelCase = True
_UpperCAmelCase = True
_UpperCAmelCase = model_class(snake_case_ )
model.to(snake_case_ )
model.eval()
with torch.no_grad():
_UpperCAmelCase = model(**self._prepare_for_class(snake_case_ , snake_case_ ) )
self.assertEqual(out_len + 2 , len(snake_case_ ) )
_UpperCAmelCase = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(snake_case_ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, dim] , )
@is_flaky()
def lowercase ( self : Dict ):
super().test_retain_grad_hidden_states_attentions()
def UpperCAmelCase_ ( __lowercase : str="train-batch.pt" ) -> List[str]:
'''simple docstring'''
_UpperCAmelCase = hf_hub_download(repo_id="hf-internal-testing/tourism-monthly-batch" , filename=__lowercase , repo_type="dataset" )
_UpperCAmelCase = torch.load(__lowercase , map_location=__lowercase )
return batch
@require_torch
@slow
class A_ ( unittest.TestCase ):
def lowercase ( self : Optional[int] ):
_UpperCAmelCase = AutoformerModel.from_pretrained("huggingface/autoformer-tourism-monthly" ).to(snake_case_ )
_UpperCAmelCase = prepare_batch()
with torch.no_grad():
_UpperCAmelCase = model(
past_values=batch["past_values"] , past_time_features=batch["past_time_features"] , past_observed_mask=batch["past_observed_mask"] , static_categorical_features=batch["static_categorical_features"] , future_values=batch["future_values"] , future_time_features=batch["future_time_features"] , )[0]
_UpperCAmelCase = torch.Size(
(6_4, model.config.prediction_length + model.config.label_length, model.config.feature_size) )
self.assertEqual(output.shape , snake_case_ )
_UpperCAmelCase = torch.tensor(
[[0.3_5_9_3, -1.3_3_9_8, 0.6_3_3_0], [0.2_2_7_9, 1.5_3_9_6, -0.1_7_9_2], [0.0_4_5_0, 1.3_2_2_5, -0.2_3_3_5]] , device=snake_case_ )
self.assertTrue(torch.allclose(output[0, :3, :3] , snake_case_ , atol=snake_case_ ) )
def lowercase ( self : Optional[Any] ):
_UpperCAmelCase = AutoformerForPrediction.from_pretrained("huggingface/autoformer-tourism-monthly" ).to(snake_case_ )
_UpperCAmelCase = prepare_batch("val-batch.pt" )
with torch.no_grad():
_UpperCAmelCase = model(
past_values=batch["past_values"] , past_time_features=batch["past_time_features"] , past_observed_mask=batch["past_observed_mask"] , static_categorical_features=batch["static_categorical_features"] , ).encoder_last_hidden_state
_UpperCAmelCase = torch.Size((6_4, model.config.context_length, model.config.d_model) )
self.assertEqual(output.shape , snake_case_ )
_UpperCAmelCase = torch.tensor(
[[-0.0_7_3_4, -0.9_0_3_6, 0.8_3_5_8], [4.7_1_8_6, 2.4_1_1_3, 1.9_5_8_1], [1.7_9_5_3, 2.3_5_5_8, 1.2_9_7_0]] , device=snake_case_ )
self.assertTrue(torch.allclose(output[0, :3, :3] , snake_case_ , atol=snake_case_ ) )
def lowercase ( self : Tuple ):
_UpperCAmelCase = AutoformerForPrediction.from_pretrained("huggingface/autoformer-tourism-monthly" ).to(snake_case_ )
_UpperCAmelCase = prepare_batch("val-batch.pt" )
with torch.no_grad():
_UpperCAmelCase = model.generate(
static_categorical_features=batch["static_categorical_features"] , past_time_features=batch["past_time_features"] , past_values=batch["past_values"] , future_time_features=batch["future_time_features"] , past_observed_mask=batch["past_observed_mask"] , )
_UpperCAmelCase = torch.Size((6_4, model.config.num_parallel_samples, model.config.prediction_length) )
self.assertEqual(outputs.sequences.shape , snake_case_ )
_UpperCAmelCase = torch.tensor([3_1_3_0.6_7_6_3, 4_0_5_6.5_2_9_3, 7_0_5_3.0_7_8_6] , device=snake_case_ )
_UpperCAmelCase = outputs.sequences.mean(dim=1 )
self.assertTrue(torch.allclose(mean_prediction[0, -3:] , snake_case_ , rtol=1e-1 ) )
| 22 | 1 |
'''simple docstring'''
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_space_optuna,
default_hp_space_ray,
default_hp_space_sigopt,
default_hp_space_wandb,
)
from .utils import logging
__SCREAMING_SNAKE_CASE :int = logging.get_logger(__name__)
class A_ :
_lowerCamelCase : str
_lowerCamelCase : str = None
@staticmethod
def lowercase ( ):
raise NotImplementedError
def lowercase ( self : Union[str, Any] , snake_case_ : Optional[int] , snake_case_ : int , snake_case_ : str , **snake_case_ : List[Any] ):
raise NotImplementedError
def lowercase ( self : Any , snake_case_ : int ):
raise NotImplementedError
def lowercase ( self : List[str] ):
if not self.is_available():
raise RuntimeError(
f'You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}.' )
@classmethod
def lowercase ( cls : List[Any] ):
return f'`pip install {cls.pip_package or cls.name}`'
class A_ ( lowerCAmelCase_ ):
_lowerCamelCase : int = """optuna"""
@staticmethod
def lowercase ( ):
return is_optuna_available()
def lowercase ( self : List[str] , snake_case_ : Any , snake_case_ : int , snake_case_ : str , **snake_case_ : Tuple ):
return run_hp_search_optuna(snake_case_ , snake_case_ , snake_case_ , **snake_case_ )
def lowercase ( self : int , snake_case_ : Optional[int] ):
return default_hp_space_optuna(snake_case_ )
class A_ ( lowerCAmelCase_ ):
_lowerCamelCase : Any = """ray"""
_lowerCamelCase : Tuple = """'ray[tune]'"""
@staticmethod
def lowercase ( ):
return is_ray_available()
def lowercase ( self : Optional[Any] , snake_case_ : Any , snake_case_ : int , snake_case_ : str , **snake_case_ : List[str] ):
return run_hp_search_ray(snake_case_ , snake_case_ , snake_case_ , **snake_case_ )
def lowercase ( self : Any , snake_case_ : str ):
return default_hp_space_ray(snake_case_ )
class A_ ( lowerCAmelCase_ ):
_lowerCamelCase : int = """sigopt"""
@staticmethod
def lowercase ( ):
return is_sigopt_available()
def lowercase ( self : Any , snake_case_ : int , snake_case_ : int , snake_case_ : str , **snake_case_ : Dict ):
return run_hp_search_sigopt(snake_case_ , snake_case_ , snake_case_ , **snake_case_ )
def lowercase ( self : Dict , snake_case_ : Optional[Any] ):
return default_hp_space_sigopt(snake_case_ )
class A_ ( lowerCAmelCase_ ):
_lowerCamelCase : Optional[int] = """wandb"""
@staticmethod
def lowercase ( ):
return is_wandb_available()
def lowercase ( self : Optional[Any] , snake_case_ : Optional[Any] , snake_case_ : int , snake_case_ : str , **snake_case_ : Optional[Any] ):
return run_hp_search_wandb(snake_case_ , snake_case_ , snake_case_ , **snake_case_ )
def lowercase ( self : Any , snake_case_ : Union[str, Any] ):
return default_hp_space_wandb(snake_case_ )
__SCREAMING_SNAKE_CASE :Dict = {
HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}
def UpperCAmelCase_ ( ) -> str:
'''simple docstring'''
_UpperCAmelCase = [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
if len(__lowercase ) > 0:
_UpperCAmelCase = available_backends[0].name
if len(__lowercase ) > 1:
logger.info(
f'{len(__lowercase )} hyperparameter search backends available. Using {name} as the default.' )
return name
raise RuntimeError(
"No hyperparameter search backend available.\n"
+ "\n".join(
f' - To install {backend.name} run {backend.pip_install()}'
for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() ) )
| 22 |
'''simple docstring'''
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_space_optuna,
default_hp_space_ray,
default_hp_space_sigopt,
default_hp_space_wandb,
)
from .utils import logging
__SCREAMING_SNAKE_CASE :int = logging.get_logger(__name__)
class A_ :
_lowerCamelCase : str
_lowerCamelCase : str = None
@staticmethod
def lowercase ( ):
raise NotImplementedError
def lowercase ( self : Union[str, Any] , snake_case_ : Optional[int] , snake_case_ : int , snake_case_ : str , **snake_case_ : List[Any] ):
raise NotImplementedError
def lowercase ( self : Any , snake_case_ : int ):
raise NotImplementedError
def lowercase ( self : List[str] ):
if not self.is_available():
raise RuntimeError(
f'You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}.' )
@classmethod
def lowercase ( cls : List[Any] ):
return f'`pip install {cls.pip_package or cls.name}`'
class A_ ( lowerCAmelCase_ ):
_lowerCamelCase : int = """optuna"""
@staticmethod
def lowercase ( ):
return is_optuna_available()
def lowercase ( self : List[str] , snake_case_ : Any , snake_case_ : int , snake_case_ : str , **snake_case_ : Tuple ):
return run_hp_search_optuna(snake_case_ , snake_case_ , snake_case_ , **snake_case_ )
def lowercase ( self : int , snake_case_ : Optional[int] ):
return default_hp_space_optuna(snake_case_ )
class A_ ( lowerCAmelCase_ ):
_lowerCamelCase : Any = """ray"""
_lowerCamelCase : Tuple = """'ray[tune]'"""
@staticmethod
def lowercase ( ):
return is_ray_available()
def lowercase ( self : Optional[Any] , snake_case_ : Any , snake_case_ : int , snake_case_ : str , **snake_case_ : List[str] ):
return run_hp_search_ray(snake_case_ , snake_case_ , snake_case_ , **snake_case_ )
def lowercase ( self : Any , snake_case_ : str ):
return default_hp_space_ray(snake_case_ )
class A_ ( lowerCAmelCase_ ):
_lowerCamelCase : int = """sigopt"""
@staticmethod
def lowercase ( ):
return is_sigopt_available()
def lowercase ( self : Any , snake_case_ : int , snake_case_ : int , snake_case_ : str , **snake_case_ : Dict ):
return run_hp_search_sigopt(snake_case_ , snake_case_ , snake_case_ , **snake_case_ )
def lowercase ( self : Dict , snake_case_ : Optional[Any] ):
return default_hp_space_sigopt(snake_case_ )
class A_ ( lowerCAmelCase_ ):
_lowerCamelCase : Optional[int] = """wandb"""
@staticmethod
def lowercase ( ):
return is_wandb_available()
def lowercase ( self : Optional[Any] , snake_case_ : Optional[Any] , snake_case_ : int , snake_case_ : str , **snake_case_ : Optional[Any] ):
return run_hp_search_wandb(snake_case_ , snake_case_ , snake_case_ , **snake_case_ )
def lowercase ( self : Any , snake_case_ : Union[str, Any] ):
return default_hp_space_wandb(snake_case_ )
__SCREAMING_SNAKE_CASE :Dict = {
HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}
def UpperCAmelCase_ ( ) -> str:
'''simple docstring'''
_UpperCAmelCase = [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
if len(__lowercase ) > 0:
_UpperCAmelCase = available_backends[0].name
if len(__lowercase ) > 1:
logger.info(
f'{len(__lowercase )} hyperparameter search backends available. Using {name} as the default.' )
return name
raise RuntimeError(
"No hyperparameter search backend available.\n"
+ "\n".join(
f' - To install {backend.name} run {backend.pip_install()}'
for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() ) )
| 22 | 1 |
'''simple docstring'''
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vision_available():
from transformers import AutoImageProcessor
@require_torch
@require_vision
class A_ ( unittest.TestCase ):
@slow
def lowercase ( self : Optional[int] ):
_UpperCAmelCase = AutoImageProcessor.from_pretrained("microsoft/dit-base-finetuned-rvlcdip" )
_UpperCAmelCase = AutoModelForImageClassification.from_pretrained("microsoft/dit-base-finetuned-rvlcdip" )
model.to(snake_case_ )
from datasets import load_dataset
_UpperCAmelCase = load_dataset("nielsr/rvlcdip-demo" )
_UpperCAmelCase = dataset["train"][0]["image"].convert("RGB" )
_UpperCAmelCase = image_processor(snake_case_ , return_tensors="pt" ).to(snake_case_ )
# forward pass
with torch.no_grad():
_UpperCAmelCase = model(**snake_case_ )
_UpperCAmelCase = outputs.logits
_UpperCAmelCase = torch.Size((1, 1_6) )
self.assertEqual(logits.shape , snake_case_ )
_UpperCAmelCase = torch.tensor(
[-0.4_1_5_8, -0.4_0_9_2, -0.4_3_4_7] , device=snake_case_ , dtype=torch.float , )
self.assertTrue(torch.allclose(logits[0, :3] , snake_case_ , atol=1e-4 ) )
| 22 |
'''simple docstring'''
__SCREAMING_SNAKE_CASE :List[str] = '''0.18.2'''
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_librosa_available,
is_note_seq_available,
is_onnx_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
is_transformers_available,
is_transformers_version,
is_unidecode_available,
logging,
)
try:
if not is_onnx_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_onnx_objects import * # noqa F403
else:
from .pipelines import OnnxRuntimeModel
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_pt_objects import * # noqa F403
else:
from .models import (
AutoencoderKL,
ControlNetModel,
ModelMixin,
PriorTransformer,
TaFilmDecoder,
TransformeraDModel,
UNetaDModel,
UNetaDConditionModel,
UNetaDModel,
UNetaDConditionModel,
VQModel,
)
from .optimization import (
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
get_scheduler,
)
from .pipelines import (
AudioPipelineOutput,
ConsistencyModelPipeline,
DanceDiffusionPipeline,
DDIMPipeline,
DDPMPipeline,
DiffusionPipeline,
DiTPipeline,
ImagePipelineOutput,
KarrasVePipeline,
LDMPipeline,
LDMSuperResolutionPipeline,
PNDMPipeline,
RePaintPipeline,
ScoreSdeVePipeline,
)
from .schedulers import (
CMStochasticIterativeScheduler,
DDIMInverseScheduler,
DDIMParallelScheduler,
DDIMScheduler,
DDPMParallelScheduler,
DDPMScheduler,
DEISMultistepScheduler,
DPMSolverMultistepInverseScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
HeunDiscreteScheduler,
IPNDMScheduler,
KarrasVeScheduler,
KDPMaAncestralDiscreteScheduler,
KDPMaDiscreteScheduler,
PNDMScheduler,
RePaintScheduler,
SchedulerMixin,
ScoreSdeVeScheduler,
UnCLIPScheduler,
UniPCMultistepScheduler,
VQDiffusionScheduler,
)
from .training_utils import EMAModel
try:
if not (is_torch_available() and is_scipy_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_scipy_objects import * # noqa F403
else:
from .schedulers import LMSDiscreteScheduler
try:
if not (is_torch_available() and is_torchsde_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_torchsde_objects import * # noqa F403
else:
from .schedulers import DPMSolverSDEScheduler
try:
if not (is_torch_available() and is_transformers_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .pipelines import (
AltDiffusionImgaImgPipeline,
AltDiffusionPipeline,
AudioLDMPipeline,
CycleDiffusionPipeline,
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
ImageTextPipelineOutput,
KandinskyImgaImgPipeline,
KandinskyInpaintPipeline,
KandinskyPipeline,
KandinskyPriorPipeline,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaControlnetPipeline,
KandinskyVaaImgaImgPipeline,
KandinskyVaaInpaintPipeline,
KandinskyVaaPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
KandinskyVaaPriorPipeline,
LDMTextToImagePipeline,
PaintByExamplePipeline,
SemanticStableDiffusionPipeline,
ShapEImgaImgPipeline,
ShapEPipeline,
StableDiffusionAttendAndExcitePipeline,
StableDiffusionControlNetImgaImgPipeline,
StableDiffusionControlNetInpaintPipeline,
StableDiffusionControlNetPipeline,
StableDiffusionDepthaImgPipeline,
StableDiffusionDiffEditPipeline,
StableDiffusionImageVariationPipeline,
StableDiffusionImgaImgPipeline,
StableDiffusionInpaintPipeline,
StableDiffusionInpaintPipelineLegacy,
StableDiffusionInstructPixaPixPipeline,
StableDiffusionLatentUpscalePipeline,
StableDiffusionLDMaDPipeline,
StableDiffusionModelEditingPipeline,
StableDiffusionPanoramaPipeline,
StableDiffusionParadigmsPipeline,
StableDiffusionPipeline,
StableDiffusionPipelineSafe,
StableDiffusionPixaPixZeroPipeline,
StableDiffusionSAGPipeline,
StableDiffusionUpscalePipeline,
StableUnCLIPImgaImgPipeline,
StableUnCLIPPipeline,
TextToVideoSDPipeline,
TextToVideoZeroPipeline,
UnCLIPImageVariationPipeline,
UnCLIPPipeline,
UniDiffuserModel,
UniDiffuserPipeline,
UniDiffuserTextDecoder,
VersatileDiffusionDualGuidedPipeline,
VersatileDiffusionImageVariationPipeline,
VersatileDiffusionPipeline,
VersatileDiffusionTextToImagePipeline,
VideoToVideoSDPipeline,
VQDiffusionPipeline,
)
try:
if not (is_torch_available() and is_transformers_available() and is_invisible_watermark_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_transformers_and_invisible_watermark_objects import * # noqa F403
else:
from .pipelines import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline
try:
if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_transformers_and_k_diffusion_objects import * # noqa F403
else:
from .pipelines import StableDiffusionKDiffusionPipeline
try:
if not (is_torch_available() and is_transformers_available() and is_onnx_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_transformers_and_onnx_objects import * # noqa F403
else:
from .pipelines import (
OnnxStableDiffusionImgaImgPipeline,
OnnxStableDiffusionInpaintPipeline,
OnnxStableDiffusionInpaintPipelineLegacy,
OnnxStableDiffusionPipeline,
OnnxStableDiffusionUpscalePipeline,
StableDiffusionOnnxPipeline,
)
try:
if not (is_torch_available() and is_librosa_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_librosa_objects import * # noqa F403
else:
from .pipelines import AudioDiffusionPipeline, Mel
try:
if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_transformers_and_torch_and_note_seq_objects import * # noqa F403
else:
from .pipelines import SpectrogramDiffusionPipeline
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_flax_objects import * # noqa F403
else:
from .models.controlnet_flax import FlaxControlNetModel
from .models.modeling_flax_utils import FlaxModelMixin
from .models.unet_ad_condition_flax import FlaxUNetaDConditionModel
from .models.vae_flax import FlaxAutoencoderKL
from .pipelines import FlaxDiffusionPipeline
from .schedulers import (
FlaxDDIMScheduler,
FlaxDDPMScheduler,
FlaxDPMSolverMultistepScheduler,
FlaxKarrasVeScheduler,
FlaxLMSDiscreteScheduler,
FlaxPNDMScheduler,
FlaxSchedulerMixin,
FlaxScoreSdeVeScheduler,
)
try:
if not (is_flax_available() and is_transformers_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_flax_and_transformers_objects import * # noqa F403
else:
from .pipelines import (
FlaxStableDiffusionControlNetPipeline,
FlaxStableDiffusionImgaImgPipeline,
FlaxStableDiffusionInpaintPipeline,
FlaxStableDiffusionPipeline,
)
try:
if not (is_note_seq_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_note_seq_objects import * # noqa F403
else:
from .pipelines import MidiProcessor
| 22 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
__SCREAMING_SNAKE_CASE :Optional[int] = {'''tokenization_byt5''': ['''ByT5Tokenizer''']}
if TYPE_CHECKING:
from .tokenization_byta import ByTaTokenizer
else:
import sys
__SCREAMING_SNAKE_CASE :List[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 22 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
__SCREAMING_SNAKE_CASE :Optional[int] = True
except (ImportError, ModuleNotFoundError):
__SCREAMING_SNAKE_CASE :str = False
if NLTK_AVAILABLE:
with FileLock('''.lock''') as lock:
nltk.download('''punkt''', quiet=True)
def UpperCAmelCase_ ( __lowercase : str ) -> str:
'''simple docstring'''
re.sub("<n>" , "" , __lowercase ) # remove pegasus newline char
assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
return "\n".join(nltk.sent_tokenize(__lowercase ) )
| 22 | 1 |
'''simple docstring'''
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
__SCREAMING_SNAKE_CASE :int = TypeVar('''KEY''')
__SCREAMING_SNAKE_CASE :List[Any] = TypeVar('''VAL''')
@dataclass(frozen=lowerCAmelCase_ , slots=lowerCAmelCase_ )
class A_ ( Generic[KEY, VAL] ):
_lowerCamelCase : KEY
_lowerCamelCase : VAL
class A_ ( _Item ):
def __init__( self : List[Any] ):
super().__init__(snake_case_ , snake_case_ )
def __bool__( self : List[Any] ):
return False
__SCREAMING_SNAKE_CASE :Dict = _DeletedItem()
class A_ ( MutableMapping[KEY, VAL] ):
def __init__( self : str , snake_case_ : int = 8 , snake_case_ : float = 0.7_5 ):
_UpperCAmelCase = initial_block_size
_UpperCAmelCase = [None] * initial_block_size
assert 0.0 < capacity_factor < 1.0
_UpperCAmelCase = capacity_factor
_UpperCAmelCase = 0
def lowercase ( self : Any , snake_case_ : KEY ):
return hash(snake_case_ ) % len(self._buckets )
def lowercase ( self : List[Any] , snake_case_ : int ):
return (ind + 1) % len(self._buckets )
def lowercase ( self : Any , snake_case_ : int , snake_case_ : KEY , snake_case_ : VAL ):
_UpperCAmelCase = self._buckets[ind]
if not stored:
_UpperCAmelCase = _Item(snake_case_ , snake_case_ )
self._len += 1
return True
elif stored.key == key:
_UpperCAmelCase = _Item(snake_case_ , snake_case_ )
return True
else:
return False
def lowercase ( self : Optional[Any] ):
_UpperCAmelCase = len(self._buckets ) * self._capacity_factor
return len(self ) >= int(snake_case_ )
def lowercase ( self : Tuple ):
if len(self._buckets ) <= self._initial_block_size:
return False
_UpperCAmelCase = len(self._buckets ) * self._capacity_factor / 2
return len(self ) < limit
def lowercase ( self : str , snake_case_ : int ):
_UpperCAmelCase = self._buckets
_UpperCAmelCase = [None] * new_size
_UpperCAmelCase = 0
for item in old_buckets:
if item:
self._add_item(item.key , item.val )
def lowercase ( self : Dict ):
self._resize(len(self._buckets ) * 2 )
def lowercase ( self : Optional[Any] ):
self._resize(len(self._buckets ) // 2 )
def lowercase ( self : List[Any] , snake_case_ : KEY ):
_UpperCAmelCase = self._get_bucket_index(snake_case_ )
for _ in range(len(self._buckets ) ):
yield ind
_UpperCAmelCase = self._get_next_ind(snake_case_ )
def lowercase ( self : Optional[int] , snake_case_ : KEY , snake_case_ : VAL ):
for ind in self._iterate_buckets(snake_case_ ):
if self._try_set(snake_case_ , snake_case_ , snake_case_ ):
break
def __setitem__( self : Tuple , snake_case_ : KEY , snake_case_ : VAL ):
if self._is_full():
self._size_up()
self._add_item(snake_case_ , snake_case_ )
def __delitem__( self : Optional[int] , snake_case_ : KEY ):
for ind in self._iterate_buckets(snake_case_ ):
_UpperCAmelCase = self._buckets[ind]
if item is None:
raise KeyError(snake_case_ )
if item is _deleted:
continue
if item.key == key:
_UpperCAmelCase = _deleted
self._len -= 1
break
if self._is_sparse():
self._size_down()
def __getitem__( self : Dict , snake_case_ : KEY ):
for ind in self._iterate_buckets(snake_case_ ):
_UpperCAmelCase = self._buckets[ind]
if item is None:
break
if item is _deleted:
continue
if item.key == key:
return item.val
raise KeyError(snake_case_ )
def __len__( self : List[Any] ):
return self._len
def __iter__( self : Union[str, Any] ):
yield from (item.key for item in self._buckets if item)
def __repr__( self : Optional[int] ):
_UpperCAmelCase = " ,".join(
f'{item.key}: {item.val}' for item in self._buckets if item )
return f'HashMap({val_string})'
| 22 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel
if is_vision_available():
from transformers import MaskFormerImageProcessor
if is_vision_available():
from PIL import Image
class A_ :
def __init__( self : str , snake_case_ : int , snake_case_ : Union[str, Any]=2 , snake_case_ : List[Any]=True , snake_case_ : str=False , snake_case_ : str=1_0 , snake_case_ : str=3 , snake_case_ : Dict=3_2 * 4 , snake_case_ : Any=3_2 * 6 , snake_case_ : Optional[Any]=4 , snake_case_ : Optional[int]=3_2 , ):
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = is_training
_UpperCAmelCase = use_auxiliary_loss
_UpperCAmelCase = num_queries
_UpperCAmelCase = num_channels
_UpperCAmelCase = min_size
_UpperCAmelCase = max_size
_UpperCAmelCase = num_labels
_UpperCAmelCase = mask_feature_size
def lowercase ( self : Union[str, Any] ):
_UpperCAmelCase = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
snake_case_ )
_UpperCAmelCase = torch.ones([self.batch_size, self.min_size, self.max_size] , device=snake_case_ )
_UpperCAmelCase = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=snake_case_ ) > 0.5
).float()
_UpperCAmelCase = (torch.rand((self.batch_size, self.num_labels) , device=snake_case_ ) > 0.5).long()
_UpperCAmelCase = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def lowercase ( self : List[Any] ):
return MaskFormerConfig.from_backbone_and_decoder_configs(
backbone_config=SwinConfig(
depths=[1, 1, 1, 1] , ) , decoder_config=DetrConfig(
decoder_ffn_dim=1_2_8 , num_queries=self.num_queries , decoder_attention_heads=2 , d_model=self.mask_feature_size , ) , mask_feature_size=self.mask_feature_size , fpn_feature_size=self.mask_feature_size , num_channels=self.num_channels , num_labels=self.num_labels , )
def lowercase ( self : Optional[Any] ):
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = self.prepare_config_and_inputs()
_UpperCAmelCase = {"pixel_values": pixel_values, "pixel_mask": pixel_mask}
return config, inputs_dict
def lowercase ( self : List[Any] , snake_case_ : Optional[Any] , snake_case_ : Optional[Any] ):
_UpperCAmelCase = output.encoder_hidden_states
_UpperCAmelCase = output.pixel_decoder_hidden_states
_UpperCAmelCase = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(snake_case_ ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(snake_case_ ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(snake_case_ ) , config.decoder_config.decoder_layers )
def lowercase ( self : Tuple , snake_case_ : str , snake_case_ : Optional[int] , snake_case_ : Any , snake_case_ : Optional[Any]=False ):
with torch.no_grad():
_UpperCAmelCase = MaskFormerModel(config=snake_case_ )
model.to(snake_case_ )
model.eval()
_UpperCAmelCase = model(pixel_values=snake_case_ , pixel_mask=snake_case_ )
_UpperCAmelCase = model(snake_case_ , output_hidden_states=snake_case_ )
# the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
# encoder and pixel decoder
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.mask_feature_size) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(snake_case_ , snake_case_ )
def lowercase ( self : Any , snake_case_ : List[str] , snake_case_ : List[Any] , snake_case_ : int , snake_case_ : str , snake_case_ : List[Any] ):
_UpperCAmelCase = MaskFormerForInstanceSegmentation(config=snake_case_ )
model.to(snake_case_ )
model.eval()
def comm_check_on_output(snake_case_ : int ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
_UpperCAmelCase = model(pixel_values=snake_case_ , pixel_mask=snake_case_ )
_UpperCAmelCase = model(snake_case_ )
comm_check_on_output(snake_case_ )
_UpperCAmelCase = model(
pixel_values=snake_case_ , pixel_mask=snake_case_ , mask_labels=snake_case_ , class_labels=snake_case_ )
comm_check_on_output(snake_case_ )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class A_ ( lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
_lowerCamelCase : Dict = (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
_lowerCamelCase : Tuple = (
{"""feature-extraction""": MaskFormerModel, """image-segmentation""": MaskFormerForInstanceSegmentation}
if is_torch_available()
else {}
)
_lowerCamelCase : Optional[Any] = False
_lowerCamelCase : Dict = False
_lowerCamelCase : Any = False
_lowerCamelCase : List[Any] = False
def lowercase ( self : Optional[int] ):
_UpperCAmelCase = MaskFormerModelTester(self )
_UpperCAmelCase = ConfigTester(self , config_class=snake_case_ , has_text_modality=snake_case_ )
def lowercase ( self : Optional[Any] ):
self.config_tester.run_common_tests()
def lowercase ( self : Union[str, Any] ):
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(snake_case_ , **snake_case_ , output_hidden_states=snake_case_ )
def lowercase ( self : int ):
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*snake_case_ )
@unittest.skip(reason="MaskFormer does not use inputs_embeds" )
def lowercase ( self : Any ):
pass
@unittest.skip(reason="MaskFormer does not have a get_input_embeddings method" )
def lowercase ( self : List[str] ):
pass
@unittest.skip(reason="MaskFormer is not a generative model" )
def lowercase ( self : List[str] ):
pass
@unittest.skip(reason="MaskFormer does not use token embeddings" )
def lowercase ( self : List[Any] ):
pass
@require_torch_multi_gpu
@unittest.skip(
reason="MaskFormer has some layers using `add_module` which doesn't work well with `nn.DataParallel`" )
def lowercase ( self : Any ):
pass
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
def lowercase ( self : Union[str, Any] ):
pass
def lowercase ( self : List[str] ):
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase = model_class(snake_case_ )
_UpperCAmelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_UpperCAmelCase = [*signature.parameters.keys()]
_UpperCAmelCase = ["pixel_values"]
self.assertListEqual(arg_names[:1] , snake_case_ )
@slow
def lowercase ( self : Optional[int] ):
for model_name in ["facebook/maskformer-swin-small-coco"]:
_UpperCAmelCase = MaskFormerModel.from_pretrained(snake_case_ )
self.assertIsNotNone(snake_case_ )
def lowercase ( self : Optional[int] ):
_UpperCAmelCase = (self.model_tester.min_size,) * 2
_UpperCAmelCase = {
"pixel_values": torch.randn((2, 3, *size) , device=snake_case_ ),
"mask_labels": torch.randn((2, 1_0, *size) , device=snake_case_ ),
"class_labels": torch.zeros(2 , 1_0 , device=snake_case_ ).long(),
}
_UpperCAmelCase = MaskFormerForInstanceSegmentation(MaskFormerConfig() ).to(snake_case_ )
_UpperCAmelCase = model(**snake_case_ )
self.assertTrue(outputs.loss is not None )
def lowercase ( self : Dict ):
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(snake_case_ , **snake_case_ , output_hidden_states=snake_case_ )
def lowercase ( self : Any ):
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase = model_class(snake_case_ ).to(snake_case_ )
_UpperCAmelCase = model(**snake_case_ , output_attentions=snake_case_ )
self.assertTrue(outputs.attentions is not None )
def lowercase ( self : int ):
if not self.model_tester.is_training:
return
# only MaskFormerForInstanceSegmentation has the loss
_UpperCAmelCase = self.all_model_classes[1]
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
_UpperCAmelCase = model_class(snake_case_ )
model.to(snake_case_ )
model.train()
_UpperCAmelCase = model(snake_case_ , mask_labels=snake_case_ , class_labels=snake_case_ ).loss
loss.backward()
def lowercase ( self : int ):
# only MaskFormerForInstanceSegmentation has the loss
_UpperCAmelCase = self.all_model_classes[1]
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
_UpperCAmelCase = True
_UpperCAmelCase = True
_UpperCAmelCase = model_class(snake_case_ )
model.to(snake_case_ )
model.train()
_UpperCAmelCase = model(snake_case_ , mask_labels=snake_case_ , class_labels=snake_case_ )
_UpperCAmelCase = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
_UpperCAmelCase = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
# we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
_UpperCAmelCase = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
_UpperCAmelCase = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=snake_case_ )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
__SCREAMING_SNAKE_CASE :Dict = 1e-4
def UpperCAmelCase_ ( ) -> List[str]:
'''simple docstring'''
_UpperCAmelCase = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_vision
@slow
class A_ ( unittest.TestCase ):
@cached_property
def lowercase ( self : Dict ):
return (
MaskFormerImageProcessor.from_pretrained("facebook/maskformer-swin-small-coco" )
if is_vision_available()
else None
)
def lowercase ( self : List[Any] ):
_UpperCAmelCase = MaskFormerModel.from_pretrained("facebook/maskformer-swin-small-coco" ).to(snake_case_ )
_UpperCAmelCase = self.default_image_processor
_UpperCAmelCase = prepare_img()
_UpperCAmelCase = image_processor(snake_case_ , return_tensors="pt" ).to(snake_case_ )
_UpperCAmelCase = inputs["pixel_values"].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0 )
# check size
self.assertEqual(snake_case_ , (1, 3, 8_0_0, 1_0_8_8) )
with torch.no_grad():
_UpperCAmelCase = model(**snake_case_ )
_UpperCAmelCase = torch.tensor(
[[-0.0_4_8_2, 0.9_2_2_8, 0.4_9_5_1], [-0.2_5_4_7, 0.8_0_1_7, 0.8_5_2_7], [-0.0_0_6_9, 0.3_3_8_5, -0.0_0_8_9]] ).to(snake_case_ )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , snake_case_ , atol=snake_case_ ) )
_UpperCAmelCase = torch.tensor(
[[-0.8_4_2_2, -0.8_4_3_4, -0.9_7_1_8], [-1.0_1_4_4, -0.5_5_6_5, -0.4_1_9_5], [-1.0_0_3_8, -0.4_4_8_4, -0.1_9_6_1]] ).to(snake_case_ )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , snake_case_ , atol=snake_case_ ) )
_UpperCAmelCase = torch.tensor(
[[0.2_8_5_2, -0.0_1_5_9, 0.9_7_3_5], [0.6_2_5_4, 0.1_8_5_8, 0.8_5_2_9], [-0.0_6_8_0, -0.4_1_1_6, 1.8_4_1_3]] ).to(snake_case_ )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , snake_case_ , atol=snake_case_ ) )
def lowercase ( self : Tuple ):
_UpperCAmelCase = (
MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-small-coco" )
.to(snake_case_ )
.eval()
)
_UpperCAmelCase = self.default_image_processor
_UpperCAmelCase = prepare_img()
_UpperCAmelCase = image_processor(snake_case_ , return_tensors="pt" ).to(snake_case_ )
_UpperCAmelCase = inputs["pixel_values"].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0 )
# check size
self.assertEqual(snake_case_ , (1, 3, 8_0_0, 1_0_8_8) )
with torch.no_grad():
_UpperCAmelCase = model(**snake_case_ )
# masks_queries_logits
_UpperCAmelCase = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
_UpperCAmelCase = [
[-1.3_7_3_7_1_2_4, -1.7_7_2_4_9_3_7, -1.9_3_6_4_2_3_3],
[-1.5_9_7_7_2_8_1, -1.9_8_6_7_9_3_9, -2.1_5_2_3_6_9_5],
[-1.5_7_9_5_3_9_8, -1.9_2_6_9_8_3_2, -2.0_9_3_9_4_2],
]
_UpperCAmelCase = torch.tensor(snake_case_ ).to(snake_case_ )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , snake_case_ , atol=snake_case_ ) )
# class_queries_logits
_UpperCAmelCase = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
_UpperCAmelCase = torch.tensor(
[
[1.6_512e00, -5.2_572e00, -3.3_519e00],
[3.6_169e-02, -5.9_025e00, -2.9_313e00],
[1.0_766e-04, -7.7_630e00, -5.1_263e00],
] ).to(snake_case_ )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , snake_case_ , atol=snake_case_ ) )
def lowercase ( self : int ):
_UpperCAmelCase = (
MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-resnet101-coco-stuff" )
.to(snake_case_ )
.eval()
)
_UpperCAmelCase = self.default_image_processor
_UpperCAmelCase = prepare_img()
_UpperCAmelCase = image_processor(snake_case_ , return_tensors="pt" ).to(snake_case_ )
_UpperCAmelCase = inputs["pixel_values"].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0 )
# check size
self.assertEqual(snake_case_ , (1, 3, 8_0_0, 1_0_8_8) )
with torch.no_grad():
_UpperCAmelCase = model(**snake_case_ )
# masks_queries_logits
_UpperCAmelCase = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
_UpperCAmelCase = [[-0.9_0_4_6, -2.6_3_6_6, -4.6_0_6_2], [-3.4_1_7_9, -5.7_8_9_0, -8.8_0_5_7], [-4.9_1_7_9, -7.6_5_6_0, -1_0.7_7_1_1]]
_UpperCAmelCase = torch.tensor(snake_case_ ).to(snake_case_ )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , snake_case_ , atol=snake_case_ ) )
# class_queries_logits
_UpperCAmelCase = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
_UpperCAmelCase = torch.tensor(
[[4.7_1_8_8, -3.2_5_8_5, -2.8_8_5_7], [6.6_8_7_1, -2.9_1_8_1, -1.2_4_8_7], [7.2_4_4_9, -2.2_7_6_4, -2.1_8_7_4]] ).to(snake_case_ )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , snake_case_ , atol=snake_case_ ) )
def lowercase ( self : List[Any] ):
_UpperCAmelCase = (
MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-small-coco" )
.to(snake_case_ )
.eval()
)
_UpperCAmelCase = self.default_image_processor
_UpperCAmelCase = image_processor(
[np.zeros((3, 8_0_0, 1_3_3_3) ), np.zeros((3, 8_0_0, 1_3_3_3) )] , segmentation_maps=[np.zeros((3_8_4, 3_8_4) ).astype(np.floataa ), np.zeros((3_8_4, 3_8_4) ).astype(np.floataa )] , return_tensors="pt" , )
_UpperCAmelCase = inputs["pixel_values"].to(snake_case_ )
_UpperCAmelCase = [el.to(snake_case_ ) for el in inputs["mask_labels"]]
_UpperCAmelCase = [el.to(snake_case_ ) for el in inputs["class_labels"]]
with torch.no_grad():
_UpperCAmelCase = model(**snake_case_ )
self.assertTrue(outputs.loss is not None )
| 22 | 1 |
'''simple docstring'''
import warnings
from ...utils import is_sklearn_available, requires_backends
if is_sklearn_available():
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
__SCREAMING_SNAKE_CASE :List[str] = (
'''This metric will be removed from the library soon, metrics should be handled with the 🤗 Evaluate '''
'''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'''
)
def UpperCAmelCase_ ( __lowercase : Any , __lowercase : Tuple ) -> int:
'''simple docstring'''
warnings.warn(__lowercase , __lowercase )
requires_backends(__lowercase , "sklearn" )
return (preds == labels).mean()
def UpperCAmelCase_ ( __lowercase : int , __lowercase : str ) -> Optional[Any]:
'''simple docstring'''
warnings.warn(__lowercase , __lowercase )
requires_backends(__lowercase , "sklearn" )
_UpperCAmelCase = simple_accuracy(__lowercase , __lowercase )
_UpperCAmelCase = fa_score(y_true=__lowercase , y_pred=__lowercase )
return {
"acc": acc,
"f1": fa,
"acc_and_f1": (acc + fa) / 2,
}
def UpperCAmelCase_ ( __lowercase : Optional[int] , __lowercase : List[str] ) -> List[Any]:
'''simple docstring'''
warnings.warn(__lowercase , __lowercase )
requires_backends(__lowercase , "sklearn" )
_UpperCAmelCase = pearsonr(__lowercase , __lowercase )[0]
_UpperCAmelCase = spearmanr(__lowercase , __lowercase )[0]
return {
"pearson": pearson_corr,
"spearmanr": spearman_corr,
"corr": (pearson_corr + spearman_corr) / 2,
}
def UpperCAmelCase_ ( __lowercase : Optional[Any] , __lowercase : str , __lowercase : str ) -> Tuple:
'''simple docstring'''
warnings.warn(__lowercase , __lowercase )
requires_backends(__lowercase , "sklearn" )
assert len(__lowercase ) == len(__lowercase ), f'Predictions and labels have mismatched lengths {len(__lowercase )} and {len(__lowercase )}'
if task_name == "cola":
return {"mcc": matthews_corrcoef(__lowercase , __lowercase )}
elif task_name == "sst-2":
return {"acc": simple_accuracy(__lowercase , __lowercase )}
elif task_name == "mrpc":
return acc_and_fa(__lowercase , __lowercase )
elif task_name == "sts-b":
return pearson_and_spearman(__lowercase , __lowercase )
elif task_name == "qqp":
return acc_and_fa(__lowercase , __lowercase )
elif task_name == "mnli":
return {"mnli/acc": simple_accuracy(__lowercase , __lowercase )}
elif task_name == "mnli-mm":
return {"mnli-mm/acc": simple_accuracy(__lowercase , __lowercase )}
elif task_name == "qnli":
return {"acc": simple_accuracy(__lowercase , __lowercase )}
elif task_name == "rte":
return {"acc": simple_accuracy(__lowercase , __lowercase )}
elif task_name == "wnli":
return {"acc": simple_accuracy(__lowercase , __lowercase )}
elif task_name == "hans":
return {"acc": simple_accuracy(__lowercase , __lowercase )}
else:
raise KeyError(__lowercase )
def UpperCAmelCase_ ( __lowercase : List[Any] , __lowercase : Dict , __lowercase : str ) -> Union[str, Any]:
'''simple docstring'''
warnings.warn(__lowercase , __lowercase )
requires_backends(__lowercase , "sklearn" )
if len(__lowercase ) != len(__lowercase ):
raise ValueError(f'Predictions and labels have mismatched lengths {len(__lowercase )} and {len(__lowercase )}' )
if task_name == "xnli":
return {"acc": simple_accuracy(__lowercase , __lowercase )}
else:
raise KeyError(__lowercase )
| 22 |
'''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_albert import AlbertTokenizer
else:
__SCREAMING_SNAKE_CASE :List[Any] = None
__SCREAMING_SNAKE_CASE :Union[str, Any] = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE :List[str] = {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}
__SCREAMING_SNAKE_CASE :List[Any] = {
'''vocab_file''': {
'''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/spiece.model''',
'''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/spiece.model''',
'''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model''',
'''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model''',
'''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/spiece.model''',
'''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/spiece.model''',
'''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model''',
'''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model''',
},
'''tokenizer_file''': {
'''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json''',
'''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json''',
'''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json''',
'''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json''',
'''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json''',
'''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json''',
'''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json''',
'''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json''',
},
}
__SCREAMING_SNAKE_CASE :Optional[Any] = {
'''albert-base-v1''': 512,
'''albert-large-v1''': 512,
'''albert-xlarge-v1''': 512,
'''albert-xxlarge-v1''': 512,
'''albert-base-v2''': 512,
'''albert-large-v2''': 512,
'''albert-xlarge-v2''': 512,
'''albert-xxlarge-v2''': 512,
}
__SCREAMING_SNAKE_CASE :Optional[int] = '''▁'''
class A_ ( lowerCAmelCase_ ):
_lowerCamelCase : Optional[int] = VOCAB_FILES_NAMES
_lowerCamelCase : Any = PRETRAINED_VOCAB_FILES_MAP
_lowerCamelCase : Tuple = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_lowerCamelCase : int = AlbertTokenizer
def __init__( self : Optional[Any] , snake_case_ : Optional[Any]=None , snake_case_ : Optional[Any]=None , snake_case_ : Optional[Any]=True , snake_case_ : str=True , snake_case_ : Tuple=False , snake_case_ : List[Any]="[CLS]" , snake_case_ : Union[str, Any]="[SEP]" , snake_case_ : str="<unk>" , snake_case_ : Union[str, Any]="[SEP]" , snake_case_ : List[Any]="<pad>" , snake_case_ : List[str]="[CLS]" , snake_case_ : int="[MASK]" , **snake_case_ : Any , ):
# Mask token behave like a normal word, i.e. include the space before it and
# is included in the raw text, there should be a match in a non-normalized sentence.
_UpperCAmelCase = (
AddedToken(snake_case_ , lstrip=snake_case_ , rstrip=snake_case_ , normalized=snake_case_ )
if isinstance(snake_case_ , snake_case_ )
else mask_token
)
super().__init__(
snake_case_ , tokenizer_file=snake_case_ , do_lower_case=snake_case_ , remove_space=snake_case_ , keep_accents=snake_case_ , bos_token=snake_case_ , eos_token=snake_case_ , unk_token=snake_case_ , sep_token=snake_case_ , pad_token=snake_case_ , cls_token=snake_case_ , mask_token=snake_case_ , **snake_case_ , )
_UpperCAmelCase = do_lower_case
_UpperCAmelCase = remove_space
_UpperCAmelCase = keep_accents
_UpperCAmelCase = vocab_file
_UpperCAmelCase = False if not self.vocab_file else True
def lowercase ( self : Union[str, Any] , snake_case_ : List[int] , snake_case_ : Optional[List[int]] = None ):
_UpperCAmelCase = [self.sep_token_id]
_UpperCAmelCase = [self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def lowercase ( self : Dict , snake_case_ : List[int] , snake_case_ : Optional[List[int]] = None ):
_UpperCAmelCase = [self.sep_token_id]
_UpperCAmelCase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def lowercase ( self : Optional[Any] , snake_case_ : str , snake_case_ : Optional[str] = None ):
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(snake_case_ ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
return
_UpperCAmelCase = os.path.join(
snake_case_ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(snake_case_ ):
copyfile(self.vocab_file , snake_case_ )
return (out_vocab_file,)
| 22 | 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
__SCREAMING_SNAKE_CASE :Optional[Any] = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE :Dict = {
'''google/mobilenet_v2_1.4_224''': '''https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json''',
'''google/mobilenet_v2_1.0_224''': '''https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json''',
'''google/mobilenet_v2_0.75_160''': '''https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json''',
'''google/mobilenet_v2_0.35_96''': '''https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json''',
# See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}
class A_ ( lowerCAmelCase_ ):
_lowerCamelCase : List[Any] = """mobilenet_v2"""
def __init__( self : str , snake_case_ : List[str]=3 , snake_case_ : Any=2_2_4 , snake_case_ : Union[str, Any]=1.0 , snake_case_ : int=8 , snake_case_ : List[str]=8 , snake_case_ : Dict=6 , snake_case_ : Union[str, Any]=3_2 , snake_case_ : Optional[int]=True , snake_case_ : Optional[Any]=True , snake_case_ : Optional[Any]="relu6" , snake_case_ : int=True , snake_case_ : Any=0.8 , snake_case_ : List[str]=0.0_2 , snake_case_ : Optional[int]=0.0_0_1 , snake_case_ : Dict=2_5_5 , **snake_case_ : Dict , ):
super().__init__(**snake_case_ )
if depth_multiplier <= 0:
raise ValueError("depth_multiplier must be greater than zero." )
_UpperCAmelCase = num_channels
_UpperCAmelCase = image_size
_UpperCAmelCase = depth_multiplier
_UpperCAmelCase = depth_divisible_by
_UpperCAmelCase = min_depth
_UpperCAmelCase = expand_ratio
_UpperCAmelCase = output_stride
_UpperCAmelCase = first_layer_is_expansion
_UpperCAmelCase = finegrained_output
_UpperCAmelCase = hidden_act
_UpperCAmelCase = tf_padding
_UpperCAmelCase = classifier_dropout_prob
_UpperCAmelCase = initializer_range
_UpperCAmelCase = layer_norm_eps
_UpperCAmelCase = semantic_loss_ignore_index
class A_ ( lowerCAmelCase_ ):
_lowerCamelCase : Optional[int] = version.parse("""1.11""" )
@property
def lowercase ( self : Optional[int] ):
return OrderedDict([("pixel_values", {0: "batch"})] )
@property
def lowercase ( self : Union[str, Any] ):
if self.task == "image-classification":
return OrderedDict([("logits", {0: "batch"})] )
else:
return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})] )
@property
def lowercase ( self : List[Any] ):
return 1e-4
| 22 |
'''simple docstring'''
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
__SCREAMING_SNAKE_CASE :Dict = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE :int = {
'''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 A_ ( lowerCAmelCase_ ):
_lowerCamelCase : int = """perceiver"""
def __init__( self : Any , snake_case_ : List[Any]=2_5_6 , snake_case_ : str=1_2_8_0 , snake_case_ : Optional[int]=7_6_8 , snake_case_ : int=1 , snake_case_ : List[Any]=2_6 , snake_case_ : Dict=8 , snake_case_ : List[Any]=8 , snake_case_ : Tuple=None , snake_case_ : Tuple=None , snake_case_ : Any="kv" , snake_case_ : Any=1 , snake_case_ : List[str]=1 , snake_case_ : Optional[int]="gelu" , snake_case_ : List[Any]=0.1 , snake_case_ : Dict=0.0_2 , snake_case_ : int=1e-12 , snake_case_ : List[str]=True , snake_case_ : str=2_6_2 , snake_case_ : Optional[Any]=2_0_4_8 , snake_case_ : Union[str, Any]=5_6 , snake_case_ : Dict=[3_6_8, 4_9_6] , snake_case_ : Tuple=1_6 , snake_case_ : Union[str, Any]=1_9_2_0 , snake_case_ : List[Any]=1_6 , snake_case_ : Tuple=[1, 1_6, 2_2_4, 2_2_4] , **snake_case_ : List[Any] , ):
super().__init__(**snake_case_ )
_UpperCAmelCase = num_latents
_UpperCAmelCase = d_latents
_UpperCAmelCase = d_model
_UpperCAmelCase = num_blocks
_UpperCAmelCase = num_self_attends_per_block
_UpperCAmelCase = num_self_attention_heads
_UpperCAmelCase = num_cross_attention_heads
_UpperCAmelCase = qk_channels
_UpperCAmelCase = v_channels
_UpperCAmelCase = cross_attention_shape_for_attention
_UpperCAmelCase = self_attention_widening_factor
_UpperCAmelCase = cross_attention_widening_factor
_UpperCAmelCase = hidden_act
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = initializer_range
_UpperCAmelCase = layer_norm_eps
_UpperCAmelCase = use_query_residual
# masked language modeling attributes
_UpperCAmelCase = vocab_size
_UpperCAmelCase = max_position_embeddings
# image classification attributes
_UpperCAmelCase = image_size
# flow attributes
_UpperCAmelCase = train_size
# multimodal autoencoding attributes
_UpperCAmelCase = num_frames
_UpperCAmelCase = audio_samples_per_frame
_UpperCAmelCase = samples_per_patch
_UpperCAmelCase = output_shape
class A_ ( lowerCAmelCase_ ):
@property
def lowercase ( self : int ):
if self.task == "multiple-choice":
_UpperCAmelCase = {0: "batch", 1: "choice", 2: "sequence"}
else:
_UpperCAmelCase = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("inputs", dynamic_axis),
("attention_mask", dynamic_axis),
] )
@property
def lowercase ( self : Optional[Any] ):
return 1e-4
def lowercase ( self : List[str] , snake_case_ : Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"] , snake_case_ : int = -1 , snake_case_ : int = -1 , snake_case_ : int = -1 , snake_case_ : bool = False , snake_case_ : Optional[TensorType] = None , snake_case_ : int = 3 , snake_case_ : int = 4_0 , snake_case_ : int = 4_0 , ):
# copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified
if isinstance(snake_case_ , snake_case_ ):
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
_UpperCAmelCase = compute_effective_axis_dimension(
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 = preprocessor.num_special_tokens_to_add(snake_case_ )
_UpperCAmelCase = compute_effective_axis_dimension(
snake_case_ , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=snake_case_ )
# Generate dummy inputs according to compute batch and sequence
_UpperCAmelCase = [" ".join(["a"] ) * seq_length] * batch_size
_UpperCAmelCase = dict(preprocessor(snake_case_ , return_tensors=snake_case_ ) )
_UpperCAmelCase = inputs.pop("input_ids" )
return inputs
elif isinstance(snake_case_ , snake_case_ ) 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
_UpperCAmelCase = compute_effective_axis_dimension(snake_case_ , fixed_dimension=OnnxConfig.default_fixed_batch )
_UpperCAmelCase = self._generate_dummy_images(snake_case_ , snake_case_ , snake_case_ , snake_case_ )
_UpperCAmelCase = dict(preprocessor(images=snake_case_ , return_tensors=snake_case_ ) )
_UpperCAmelCase = inputs.pop("pixel_values" )
return inputs
else:
raise ValueError(
"Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor." )
| 22 | 1 |
'''simple docstring'''
import argparse
import os
from pathlib import Path
import fairseq
import torch
from packaging import version
from torch import nn
from transformers import (
BartConfig,
BartForConditionalGeneration,
BartForSequenceClassification,
BartModel,
BartTokenizer,
)
from transformers.utils import logging
__SCREAMING_SNAKE_CASE :List[Any] = ['''bart.large''', '''bart.large.mnli''', '''bart.large.cnn''', '''bart_xsum/model.pt''']
__SCREAMING_SNAKE_CASE :int = {'''bart.large''': BartModel, '''bart.large.mnli''': BartForSequenceClassification}
if version.parse(fairseq.__version__) < version.parse('''0.9.0'''):
raise Exception('''requires fairseq >= 0.9.0''')
logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE :str = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE :Dict = ''' Hello world! cécé herlolip'''
__SCREAMING_SNAKE_CASE :Tuple = [
('''model.classification_heads.mnli.dense.weight''', '''classification_head.dense.weight'''),
('''model.classification_heads.mnli.dense.bias''', '''classification_head.dense.bias'''),
('''model.classification_heads.mnli.out_proj.weight''', '''classification_head.out_proj.weight'''),
('''model.classification_heads.mnli.out_proj.bias''', '''classification_head.out_proj.bias'''),
]
def UpperCAmelCase_ ( __lowercase : Optional[int] ) -> int:
'''simple docstring'''
_UpperCAmelCase = [
"encoder.version",
"decoder.version",
"model.encoder.version",
"model.decoder.version",
"_float_tensor",
]
for k in ignore_keys:
state_dict.pop(__lowercase , __lowercase )
def UpperCAmelCase_ ( __lowercase : Optional[int] , __lowercase : Optional[int] , __lowercase : Dict ) -> Any:
'''simple docstring'''
_UpperCAmelCase = dct.pop(__lowercase )
_UpperCAmelCase = val
def UpperCAmelCase_ ( __lowercase : Optional[Any] ) -> Tuple:
'''simple docstring'''
_UpperCAmelCase = torch.load(__lowercase , map_location="cpu" )
_UpperCAmelCase = torch.hub.load("pytorch/fairseq" , "bart.large.cnn" ).eval()
hub_interface.model.load_state_dict(sd["model"] )
return hub_interface
def UpperCAmelCase_ ( __lowercase : Tuple ) -> Union[str, Any]:
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase = emb.weight.shape
_UpperCAmelCase = nn.Linear(__lowercase , __lowercase , bias=__lowercase )
_UpperCAmelCase = emb.weight.data
return lin_layer
@torch.no_grad()
def UpperCAmelCase_ ( __lowercase : List[Any] , __lowercase : int , __lowercase : Tuple=None ) -> Tuple:
'''simple docstring'''
if not os.path.exists(__lowercase ):
_UpperCAmelCase = torch.hub.load("pytorch/fairseq" , __lowercase ).eval()
else:
_UpperCAmelCase = load_xsum_checkpoint(__lowercase )
bart.model.upgrade_state_dict(bart.model.state_dict() )
if hf_checkpoint_name is None:
_UpperCAmelCase = checkpoint_path.replace("." , "-" )
_UpperCAmelCase = BartConfig.from_pretrained(__lowercase )
_UpperCAmelCase = bart.encode(__lowercase ).unsqueeze(0 )
_UpperCAmelCase = BartTokenizer.from_pretrained(__lowercase ).encode(__lowercase , return_tensors="pt" ).unsqueeze(0 )
if not torch.eq(__lowercase , __lowercase ).all():
raise ValueError(
f'converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokensa}' )
if checkpoint_path == "bart.large.mnli":
_UpperCAmelCase = bart.state_dict()
remove_ignore_keys_(__lowercase )
_UpperCAmelCase = state_dict["model.decoder.embed_tokens.weight"]
for src, dest in mnli_rename_keys:
rename_key(__lowercase , __lowercase , __lowercase )
_UpperCAmelCase = BartForSequenceClassification(__lowercase ).eval()
model.load_state_dict(__lowercase )
_UpperCAmelCase = bart.predict("mnli" , __lowercase , return_logits=__lowercase )
_UpperCAmelCase = model(__lowercase )[0] # logits
else: # no classification heads to worry about
_UpperCAmelCase = bart.model.state_dict()
remove_ignore_keys_(__lowercase )
_UpperCAmelCase = state_dict["decoder.embed_tokens.weight"]
_UpperCAmelCase = bart.extract_features(__lowercase )
if hf_checkpoint_name == "facebook/bart-large":
_UpperCAmelCase = BartModel(__lowercase ).eval()
model.load_state_dict(__lowercase )
_UpperCAmelCase = model(__lowercase ).model[0]
else:
_UpperCAmelCase = BartForConditionalGeneration(__lowercase ).eval() # an existing summarization ckpt
model.model.load_state_dict(__lowercase )
if hasattr(__lowercase , "lm_head" ):
_UpperCAmelCase = make_linear_from_emb(model.model.shared )
_UpperCAmelCase = model.model(__lowercase )[0]
# Check results
if fairseq_output.shape != new_model_outputs.shape:
raise ValueError(
f'`fairseq_output` shape and `new_model_output` shape are different: {fairseq_output.shape=}, {new_model_outputs.shape}' )
if (fairseq_output != new_model_outputs).any().item():
raise ValueError("Some values in `fairseq_output` are different from `new_model_outputs`" )
Path(__lowercase ).mkdir(exist_ok=__lowercase )
model.save_pretrained(__lowercase )
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE :Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''fairseq_path''', type=str, help='''bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'''
)
parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument(
'''--hf_config''', default=None, type=str, help='''Which huggingface architecture to use: bart-large-xsum'''
)
__SCREAMING_SNAKE_CASE :Union[str, Any] = parser.parse_args()
convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)
| 22 |
'''simple docstring'''
import warnings
from ...utils import is_sklearn_available, requires_backends
if is_sklearn_available():
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
__SCREAMING_SNAKE_CASE :List[str] = (
'''This metric will be removed from the library soon, metrics should be handled with the 🤗 Evaluate '''
'''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'''
)
def UpperCAmelCase_ ( __lowercase : Any , __lowercase : Tuple ) -> int:
'''simple docstring'''
warnings.warn(__lowercase , __lowercase )
requires_backends(__lowercase , "sklearn" )
return (preds == labels).mean()
def UpperCAmelCase_ ( __lowercase : int , __lowercase : str ) -> Optional[Any]:
'''simple docstring'''
warnings.warn(__lowercase , __lowercase )
requires_backends(__lowercase , "sklearn" )
_UpperCAmelCase = simple_accuracy(__lowercase , __lowercase )
_UpperCAmelCase = fa_score(y_true=__lowercase , y_pred=__lowercase )
return {
"acc": acc,
"f1": fa,
"acc_and_f1": (acc + fa) / 2,
}
def UpperCAmelCase_ ( __lowercase : Optional[int] , __lowercase : List[str] ) -> List[Any]:
'''simple docstring'''
warnings.warn(__lowercase , __lowercase )
requires_backends(__lowercase , "sklearn" )
_UpperCAmelCase = pearsonr(__lowercase , __lowercase )[0]
_UpperCAmelCase = spearmanr(__lowercase , __lowercase )[0]
return {
"pearson": pearson_corr,
"spearmanr": spearman_corr,
"corr": (pearson_corr + spearman_corr) / 2,
}
def UpperCAmelCase_ ( __lowercase : Optional[Any] , __lowercase : str , __lowercase : str ) -> Tuple:
'''simple docstring'''
warnings.warn(__lowercase , __lowercase )
requires_backends(__lowercase , "sklearn" )
assert len(__lowercase ) == len(__lowercase ), f'Predictions and labels have mismatched lengths {len(__lowercase )} and {len(__lowercase )}'
if task_name == "cola":
return {"mcc": matthews_corrcoef(__lowercase , __lowercase )}
elif task_name == "sst-2":
return {"acc": simple_accuracy(__lowercase , __lowercase )}
elif task_name == "mrpc":
return acc_and_fa(__lowercase , __lowercase )
elif task_name == "sts-b":
return pearson_and_spearman(__lowercase , __lowercase )
elif task_name == "qqp":
return acc_and_fa(__lowercase , __lowercase )
elif task_name == "mnli":
return {"mnli/acc": simple_accuracy(__lowercase , __lowercase )}
elif task_name == "mnli-mm":
return {"mnli-mm/acc": simple_accuracy(__lowercase , __lowercase )}
elif task_name == "qnli":
return {"acc": simple_accuracy(__lowercase , __lowercase )}
elif task_name == "rte":
return {"acc": simple_accuracy(__lowercase , __lowercase )}
elif task_name == "wnli":
return {"acc": simple_accuracy(__lowercase , __lowercase )}
elif task_name == "hans":
return {"acc": simple_accuracy(__lowercase , __lowercase )}
else:
raise KeyError(__lowercase )
def UpperCAmelCase_ ( __lowercase : List[Any] , __lowercase : Dict , __lowercase : str ) -> Union[str, Any]:
'''simple docstring'''
warnings.warn(__lowercase , __lowercase )
requires_backends(__lowercase , "sklearn" )
if len(__lowercase ) != len(__lowercase ):
raise ValueError(f'Predictions and labels have mismatched lengths {len(__lowercase )} and {len(__lowercase )}' )
if task_name == "xnli":
return {"acc": simple_accuracy(__lowercase , __lowercase )}
else:
raise KeyError(__lowercase )
| 22 | 1 |
'''simple docstring'''
import numpy as np
import torch
from imwatermark import WatermarkEncoder
# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
__SCREAMING_SNAKE_CASE :List[Any] = 0b1_0_1_1_0_0_1_1_1_1_1_0_1_1_0_0_1_0_0_1_0_0_0_0_0_1_1_1_1_0_1_1_1_0_1_1_0_0_0_1_1_0_0_1_1_1_1_0
# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1
__SCREAMING_SNAKE_CASE :Optional[Any] = [int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]
class A_ :
def __init__( self : List[Any] ):
_UpperCAmelCase = WATERMARK_BITS
_UpperCAmelCase = WatermarkEncoder()
self.encoder.set_watermark("bits" , self.watermark )
def lowercase ( self : int , snake_case_ : torch.FloatTensor ):
# can't encode images that are smaller than 256
if images.shape[-1] < 2_5_6:
return images
_UpperCAmelCase = (2_5_5 * (images / 2 + 0.5)).cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
_UpperCAmelCase = [self.encoder.encode(snake_case_ , "dwtDct" ) for image in images]
_UpperCAmelCase = torch.from_numpy(np.array(snake_case_ ) ).permute(0 , 3 , 1 , 2 )
_UpperCAmelCase = torch.clamp(2 * (images / 2_5_5 - 0.5) , min=-1.0 , max=1.0 )
return images
| 22 |
'''simple docstring'''
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCAmelCase_ ( __lowercase : int , __lowercase : Dict , __lowercase : str , __lowercase : Optional[Any] , __lowercase : str ) -> List[str]:
'''simple docstring'''
_UpperCAmelCase = TapasConfig.from_json_file(__lowercase )
# set absolute/relative position embeddings parameter
_UpperCAmelCase = reset_position_index_per_cell
# set remaining parameters of TapasConfig as well as the model based on the task
if task == "SQA":
_UpperCAmelCase = TapasForQuestionAnswering(config=__lowercase )
elif task == "WTQ":
# run_task_main.py hparams
_UpperCAmelCase = 4
_UpperCAmelCase = True
# hparam_utils.py hparams
_UpperCAmelCase = 0.66_4694
_UpperCAmelCase = 0.20_7951
_UpperCAmelCase = 0.12_1194
_UpperCAmelCase = True
_UpperCAmelCase = True
_UpperCAmelCase = False
_UpperCAmelCase = 0.035_2513
_UpperCAmelCase = TapasForQuestionAnswering(config=__lowercase )
elif task == "WIKISQL_SUPERVISED":
# run_task_main.py hparams
_UpperCAmelCase = 4
_UpperCAmelCase = False
# hparam_utils.py hparams
_UpperCAmelCase = 36.4519
_UpperCAmelCase = 0.90_3421
_UpperCAmelCase = 222.088
_UpperCAmelCase = True
_UpperCAmelCase = True
_UpperCAmelCase = True
_UpperCAmelCase = 0.76_3141
_UpperCAmelCase = TapasForQuestionAnswering(config=__lowercase )
elif task == "TABFACT":
_UpperCAmelCase = TapasForSequenceClassification(config=__lowercase )
elif task == "MLM":
_UpperCAmelCase = TapasForMaskedLM(config=__lowercase )
elif task == "INTERMEDIATE_PRETRAINING":
_UpperCAmelCase = TapasModel(config=__lowercase )
else:
raise ValueError(f'Task {task} not supported.' )
print(f'Building PyTorch model from configuration: {config}' )
# Load weights from tf checkpoint
load_tf_weights_in_tapas(__lowercase , __lowercase , __lowercase )
# Save pytorch-model (weights and configuration)
print(f'Save PyTorch model to {pytorch_dump_path}' )
model.save_pretrained(__lowercase )
# Save tokenizer files
print(f'Save tokenizer files to {pytorch_dump_path}' )
_UpperCAmelCase = TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + "vocab.txt" , model_max_length=512 )
tokenizer.save_pretrained(__lowercase )
print("Used relative position embeddings:" , model.config.reset_position_index_per_cell )
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE :List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--task''', default='''SQA''', type=str, help='''Model task for which to convert a checkpoint. Defaults to SQA.'''
)
parser.add_argument(
'''--reset_position_index_per_cell''',
default=False,
action='''store_true''',
help='''Whether to use relative position embeddings or not. Defaults to True.''',
)
parser.add_argument(
'''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--tapas_config_file''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained TAPAS model. \n'''
'''This specifies the model architecture.'''
),
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
__SCREAMING_SNAKE_CASE :List[str] = parser.parse_args()
convert_tf_checkpoint_to_pytorch(
args.task,
args.reset_position_index_per_cell,
args.tf_checkpoint_path,
args.tapas_config_file,
args.pytorch_dump_path,
)
| 22 | 1 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : int = 100 ) -> int:
'''simple docstring'''
_UpperCAmelCase = set()
_UpperCAmelCase = 0
_UpperCAmelCase = n + 1 # maximum limit
for a in range(2 , __lowercase ):
for b in range(2 , __lowercase ):
_UpperCAmelCase = a**b # calculates the current power
collect_powers.add(__lowercase ) # adds the result to the set
return len(__lowercase )
if __name__ == "__main__":
print('''Number of terms ''', solution(int(str(input()).strip())))
| 22 |
'''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
__SCREAMING_SNAKE_CASE :str = [
'''good first issue''',
'''feature request''',
'''wip''',
]
def UpperCAmelCase_ ( ) -> Optional[Any]:
'''simple docstring'''
_UpperCAmelCase = Github(os.environ["GITHUB_TOKEN"] )
_UpperCAmelCase = g.get_repo("huggingface/accelerate" )
_UpperCAmelCase = repo.get_issues(state="open" )
for issue in open_issues:
_UpperCAmelCase = sorted([comment for comment in issue.get_comments()] , key=lambda __lowercase : i.created_at , reverse=__lowercase )
_UpperCAmelCase = comments[0] if len(__lowercase ) > 0 else None
_UpperCAmelCase = dt.utcnow()
_UpperCAmelCase = (current_time - issue.updated_at).days
_UpperCAmelCase = (current_time - issue.created_at).days
if (
last_comment is not None
and last_comment.user.login == "github-actions[bot]"
and days_since_updated > 7
and days_since_creation >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Close issue since it has been 7 days of inactivity since bot mention.
issue.edit(state="closed" )
elif (
days_since_updated > 23
and days_since_creation >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Add stale comment
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/accelerate/blob/main/CONTRIBUTING.md) "
"are likely to be ignored." )
if __name__ == "__main__":
main()
| 22 | 1 |
'''simple docstring'''
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class A_ ( lowerCAmelCase_ ):
_lowerCamelCase : Tuple = """Speech2TextFeatureExtractor"""
_lowerCamelCase : Optional[Any] = """Speech2TextTokenizer"""
def __init__( self : Dict , snake_case_ : Optional[Any] , snake_case_ : str ):
super().__init__(snake_case_ , snake_case_ )
_UpperCAmelCase = self.feature_extractor
_UpperCAmelCase = False
def __call__( self : Optional[int] , *snake_case_ : Union[str, Any] , **snake_case_ : str ):
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*snake_case_ , **snake_case_ )
if "raw_speech" in kwargs:
warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead." )
_UpperCAmelCase = kwargs.pop("raw_speech" )
else:
_UpperCAmelCase = kwargs.pop("audio" , snake_case_ )
_UpperCAmelCase = kwargs.pop("sampling_rate" , snake_case_ )
_UpperCAmelCase = kwargs.pop("text" , snake_case_ )
if len(snake_case_ ) > 0:
_UpperCAmelCase = args[0]
_UpperCAmelCase = args[1:]
if audio is None and text is None:
raise ValueError("You need to specify either an `audio` or `text` input to process." )
if audio is not None:
_UpperCAmelCase = self.feature_extractor(snake_case_ , *snake_case_ , sampling_rate=snake_case_ , **snake_case_ )
if text is not None:
_UpperCAmelCase = self.tokenizer(snake_case_ , **snake_case_ )
if text is None:
return inputs
elif audio is None:
return encodings
else:
_UpperCAmelCase = encodings["input_ids"]
return inputs
def lowercase ( self : List[str] , *snake_case_ : List[str] , **snake_case_ : int ):
return self.tokenizer.batch_decode(*snake_case_ , **snake_case_ )
def lowercase ( self : Optional[Any] , *snake_case_ : Dict , **snake_case_ : List[str] ):
return self.tokenizer.decode(*snake_case_ , **snake_case_ )
@contextmanager
def lowercase ( self : List[str] ):
warnings.warn(
"`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
"labels by using the argument `text` of the regular `__call__` method (either in the same call as "
"your audio inputs, or in a separate call." )
_UpperCAmelCase = True
_UpperCAmelCase = self.tokenizer
yield
_UpperCAmelCase = self.feature_extractor
_UpperCAmelCase = False
| 22 |
'''simple docstring'''
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 UpperCAmelCase_ ( __lowercase : Any , __lowercase : int ) -> int:
'''simple docstring'''
_UpperCAmelCase = 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}}" )
_UpperCAmelCase = DatasetInfosDict.from_directory(__lowercase )
assert dataset_infos
assert dataset_infos["default"].dataset_size == 42
@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=42 , ),
] , )
def UpperCAmelCase_ ( __lowercase : Tuple , __lowercase : DatasetInfo ) -> Any:
'''simple docstring'''
_UpperCAmelCase = str(__lowercase )
dataset_info.write_to_directory(__lowercase )
_UpperCAmelCase = DatasetInfo.from_directory(__lowercase )
assert dataset_info == reloaded
assert os.path.exists(os.path.join(__lowercase , "dataset_info.json" ) )
def UpperCAmelCase_ ( ) -> Optional[Any]:
'''simple docstring'''
_UpperCAmelCase = 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": 42}] , download_checksums={} , download_size=1337 , post_processing_size=442 , dataset_size=1234 , size_in_bytes=1337 + 442 + 1234 , )
_UpperCAmelCase = dataset_info._to_yaml_dict()
assert sorted(__lowercase ) == 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) )
_UpperCAmelCase = yaml.safe_dump(__lowercase )
_UpperCAmelCase = yaml.safe_load(__lowercase )
assert dataset_info_yaml_dict == reloaded
def UpperCAmelCase_ ( ) -> Optional[int]:
'''simple docstring'''
_UpperCAmelCase = DatasetInfo()
_UpperCAmelCase = 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=42 , )
} ),
DatasetInfosDict(
{
"v1": DatasetInfo(dataset_size=42 ),
"v2": DatasetInfo(dataset_size=1337 ),
} ),
] , )
def UpperCAmelCase_ ( __lowercase : int , __lowercase : DatasetInfosDict ) -> Dict:
'''simple docstring'''
_UpperCAmelCase = str(__lowercase )
dataset_infos_dict.write_to_directory(__lowercase )
_UpperCAmelCase = DatasetInfosDict.from_directory(__lowercase )
# the config_name of the dataset_infos_dict take over the attribute
for config_name, dataset_info in dataset_infos_dict.items():
_UpperCAmelCase = 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
_UpperCAmelCase = 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(__lowercase , "README.md" ) )
| 22 | 1 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : int ) -> int:
'''simple docstring'''
if not isinstance(__lowercase , __lowercase ):
raise ValueError("multiplicative_persistence() only accepts integral values" )
if num < 0:
raise ValueError("multiplicative_persistence() does not accept negative values" )
_UpperCAmelCase = 0
_UpperCAmelCase = str(__lowercase )
while len(__lowercase ) != 1:
_UpperCAmelCase = [int(__lowercase ) for i in num_string]
_UpperCAmelCase = 1
for i in range(0 , len(__lowercase ) ):
total *= numbers[i]
_UpperCAmelCase = str(__lowercase )
steps += 1
return steps
def UpperCAmelCase_ ( __lowercase : int ) -> int:
'''simple docstring'''
if not isinstance(__lowercase , __lowercase ):
raise ValueError("additive_persistence() only accepts integral values" )
if num < 0:
raise ValueError("additive_persistence() does not accept negative values" )
_UpperCAmelCase = 0
_UpperCAmelCase = str(__lowercase )
while len(__lowercase ) != 1:
_UpperCAmelCase = [int(__lowercase ) for i in num_string]
_UpperCAmelCase = 0
for i in range(0 , len(__lowercase ) ):
total += numbers[i]
_UpperCAmelCase = str(__lowercase )
steps += 1
return steps
if __name__ == "__main__":
import doctest
doctest.testmod()
| 22 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : str ) -> str:
'''simple docstring'''
return " ".join(
"".join(word[::-1] ) if len(__lowercase ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(reverse_long_words('''Hey wollef sroirraw'''))
| 22 | 1 |
'''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
__SCREAMING_SNAKE_CASE :str = [
'''good first issue''',
'''feature request''',
'''wip''',
]
def UpperCAmelCase_ ( ) -> Optional[Any]:
'''simple docstring'''
_UpperCAmelCase = Github(os.environ["GITHUB_TOKEN"] )
_UpperCAmelCase = g.get_repo("huggingface/accelerate" )
_UpperCAmelCase = repo.get_issues(state="open" )
for issue in open_issues:
_UpperCAmelCase = sorted([comment for comment in issue.get_comments()] , key=lambda __lowercase : i.created_at , reverse=__lowercase )
_UpperCAmelCase = comments[0] if len(__lowercase ) > 0 else None
_UpperCAmelCase = dt.utcnow()
_UpperCAmelCase = (current_time - issue.updated_at).days
_UpperCAmelCase = (current_time - issue.created_at).days
if (
last_comment is not None
and last_comment.user.login == "github-actions[bot]"
and days_since_updated > 7
and days_since_creation >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Close issue since it has been 7 days of inactivity since bot mention.
issue.edit(state="closed" )
elif (
days_since_updated > 23
and days_since_creation >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Add stale comment
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/accelerate/blob/main/CONTRIBUTING.md) "
"are likely to be ignored." )
if __name__ == "__main__":
main()
| 22 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : str ) -> list:
'''simple docstring'''
if n_term == "":
return []
_UpperCAmelCase = []
for temp in range(int(__lowercase ) ):
series.append(f'1/{temp + 1}' if series else "1" )
return series
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE :str = input('''Enter the last number (nth term) of the Harmonic Series''')
print('''Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n''')
print(harmonic_series(nth_term))
| 22 | 1 |
'''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_ ( lowerCAmelCase_ ):
_lowerCamelCase : Optional[Any] = DistilBertTokenizer
_lowerCamelCase : List[Any] = DistilBertTokenizerFast
_lowerCamelCase : Union[str, Any] = True
@slow
def lowercase ( self : List[Any] ):
_UpperCAmelCase = DistilBertTokenizer.from_pretrained("distilbert-base-uncased" )
_UpperCAmelCase = tokenizer.encode("sequence builders" , add_special_tokens=snake_case_ )
_UpperCAmelCase = tokenizer.encode("multi-sequence build" , add_special_tokens=snake_case_ )
_UpperCAmelCase = tokenizer.build_inputs_with_special_tokens(snake_case_ )
_UpperCAmelCase = tokenizer.build_inputs_with_special_tokens(snake_case_ , snake_case_ )
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
]
| 22 |
'''simple docstring'''
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
__SCREAMING_SNAKE_CASE :int = get_tests_dir('''fixtures/test_sentencepiece_no_bos.model''')
@require_sentencepiece
@require_tokenizers
class A_ ( lowerCAmelCase_ , unittest.TestCase ):
_lowerCamelCase : List[str] = PegasusTokenizer
_lowerCamelCase : int = PegasusTokenizerFast
_lowerCamelCase : Union[str, Any] = True
_lowerCamelCase : List[str] = True
def lowercase ( self : Optional[int] ):
super().setUp()
# We have a SentencePiece fixture for testing
_UpperCAmelCase = PegasusTokenizer(snake_case_ )
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def lowercase ( self : Tuple ):
return PegasusTokenizer.from_pretrained("google/pegasus-large" )
def lowercase ( self : Union[str, Any] , **snake_case_ : Union[str, Any] ):
return PegasusTokenizer.from_pretrained(self.tmpdirname , **snake_case_ )
def lowercase ( self : Tuple , snake_case_ : Any ):
return ("This is a test", "This is a test")
def lowercase ( self : Optional[int] ):
_UpperCAmelCase = "</s>"
_UpperCAmelCase = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case_ ) , snake_case_ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case_ ) , snake_case_ )
def lowercase ( self : Union[str, Any] ):
_UpperCAmelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , "<pad>" )
self.assertEqual(vocab_keys[1] , "</s>" )
self.assertEqual(vocab_keys[-1] , "v" )
self.assertEqual(len(snake_case_ ) , 1_1_0_3 )
def lowercase ( self : Any ):
self.assertEqual(self.get_tokenizer().vocab_size , 1_1_0_3 )
def lowercase ( self : List[Any] ):
_UpperCAmelCase = self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
_UpperCAmelCase = self.tokenizer_class.from_pretrained(self.tmpdirname )
_UpperCAmelCase = (
"Let's see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important"
" </s> <pad> <pad> <pad>"
)
_UpperCAmelCase = rust_tokenizer([raw_input_str] , return_tensors=snake_case_ , add_special_tokens=snake_case_ ).input_ids[0]
_UpperCAmelCase = py_tokenizer([raw_input_str] , return_tensors=snake_case_ , add_special_tokens=snake_case_ ).input_ids[0]
self.assertListEqual(snake_case_ , snake_case_ )
def lowercase ( self : Tuple ):
_UpperCAmelCase = self._large_tokenizer
# <mask_1> masks whole sentence while <mask_2> masks single word
_UpperCAmelCase = "<mask_1> To ensure a <mask_2> flow of bank resolutions."
_UpperCAmelCase = [2, 4_1_3, 6_1_5, 1_1_4, 3, 1_9_7_1, 1_1_3, 1_6_7_9, 1_0_7_1_0, 1_0_7, 1]
_UpperCAmelCase = tokenizer([raw_input_str] , return_tensors=snake_case_ ).input_ids[0]
self.assertListEqual(snake_case_ , snake_case_ )
def lowercase ( self : Union[str, Any] ):
_UpperCAmelCase = self._large_tokenizer
# The tracebacks for the following asserts are **better** without messages or self.assertEqual
assert tokenizer.vocab_size == 9_6_1_0_3
assert tokenizer.pad_token_id == 0
assert tokenizer.eos_token_id == 1
assert tokenizer.offset == 1_0_3
assert tokenizer.unk_token_id == tokenizer.offset + 2 == 1_0_5
assert tokenizer.unk_token == "<unk>"
assert tokenizer.model_max_length == 1_0_2_4
_UpperCAmelCase = "To ensure a smooth flow of bank resolutions."
_UpperCAmelCase = [4_1_3, 6_1_5, 1_1_4, 2_2_9_1, 1_9_7_1, 1_1_3, 1_6_7_9, 1_0_7_1_0, 1_0_7, 1]
_UpperCAmelCase = tokenizer([raw_input_str] , return_tensors=snake_case_ ).input_ids[0]
self.assertListEqual(snake_case_ , snake_case_ )
assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3] ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]
@require_torch
def lowercase ( self : int ):
_UpperCAmelCase = ["This is going to be way too long." * 1_5_0, "short example"]
_UpperCAmelCase = ["not super long but more than 5 tokens", "tiny"]
_UpperCAmelCase = self._large_tokenizer(snake_case_ , padding=snake_case_ , truncation=snake_case_ , return_tensors="pt" )
_UpperCAmelCase = self._large_tokenizer(
text_target=snake_case_ , max_length=5 , padding=snake_case_ , truncation=snake_case_ , return_tensors="pt" )
assert batch.input_ids.shape == (2, 1_0_2_4)
assert batch.attention_mask.shape == (2, 1_0_2_4)
assert targets["input_ids"].shape == (2, 5)
assert len(snake_case_ ) == 2 # input_ids, attention_mask.
@slow
def lowercase ( self : Dict ):
# fmt: off
_UpperCAmelCase = {"input_ids": [[3_8_9_7_9, 1_4_3, 1_8_4_8_5, 6_0_6, 1_3_0, 2_6_6_6_9, 8_7_6_8_6, 1_2_1, 5_4_1_8_9, 1_1_2_9, 1_1_1, 2_6_6_6_9, 8_7_6_8_6, 1_2_1, 9_1_1_4, 1_4_7_8_7, 1_2_1, 1_3_2_4_9, 1_5_8, 5_9_2, 9_5_6, 1_2_1, 1_4_6_2_1, 3_1_5_7_6, 1_4_3, 6_2_6_1_3, 1_0_8, 9_6_8_8, 9_3_0, 4_3_4_3_0, 1_1_5_6_2, 6_2_6_1_3, 3_0_4, 1_0_8, 1_1_4_4_3, 8_9_7, 1_0_8, 9_3_1_4, 1_7_4_1_5, 6_3_3_9_9, 1_0_8, 1_1_4_4_3, 7_6_1_4, 1_8_3_1_6, 1_1_8, 4_2_8_4, 7_1_4_8, 1_2_4_3_0, 1_4_3, 1_4_0_0, 2_5_7_0_3, 1_5_8, 1_1_1, 4_2_8_4, 7_1_4_8, 1_1_7_7_2, 1_4_3, 2_1_2_9_7, 1_0_6_4, 1_5_8, 1_2_2, 2_0_4, 3_5_0_6, 1_7_5_4, 1_1_3_3, 1_4_7_8_7, 1_5_8_1, 1_1_5, 3_3_2_2_4, 4_4_8_2, 1_1_1, 1_3_5_5, 1_1_0, 2_9_1_7_3, 3_1_7, 5_0_8_3_3, 1_0_8, 2_0_1_4_7, 9_4_6_6_5, 1_1_1, 7_7_1_9_8, 1_0_7, 1], [1_1_0, 6_2_6_1_3, 1_1_7, 6_3_8, 1_1_2, 1_1_3_3, 1_2_1, 2_0_0_9_8, 1_3_5_5, 7_9_0_5_0, 1_3_8_7_2, 1_3_5, 1_5_9_6, 5_3_5_4_1, 1_3_5_2, 1_4_1, 1_3_0_3_9, 5_5_4_2, 1_2_4, 3_0_2, 5_1_8, 1_1_1, 2_6_8, 2_9_5_6, 1_1_5, 1_4_9, 4_4_2_7, 1_0_7, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1_3_9, 1_2_3_5, 2_7_9_9, 1_8_2_8_9, 1_7_7_8_0, 2_0_4, 1_0_9, 9_4_7_4, 1_2_9_6, 1_0_7, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=snake_case_ , model_name="google/bigbird-pegasus-large-arxiv" , revision="ba85d0851d708441f91440d509690f1ab6353415" , )
@require_sentencepiece
@require_tokenizers
class A_ ( lowerCAmelCase_ , unittest.TestCase ):
_lowerCamelCase : List[str] = PegasusTokenizer
_lowerCamelCase : List[Any] = PegasusTokenizerFast
_lowerCamelCase : int = True
_lowerCamelCase : Union[str, Any] = True
def lowercase ( self : Any ):
super().setUp()
# We have a SentencePiece fixture for testing
_UpperCAmelCase = PegasusTokenizer(snake_case_ , offset=0 , mask_token_sent=snake_case_ , mask_token="[MASK]" )
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def lowercase ( self : Tuple ):
return PegasusTokenizer.from_pretrained("google/bigbird-pegasus-large-arxiv" )
def lowercase ( self : Optional[Any] , **snake_case_ : Dict ):
return PegasusTokenizer.from_pretrained(self.tmpdirname , **snake_case_ )
def lowercase ( self : Union[str, Any] , snake_case_ : str ):
return ("This is a test", "This is a test")
def lowercase ( self : List[str] ):
_UpperCAmelCase = self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
_UpperCAmelCase = self.tokenizer_class.from_pretrained(self.tmpdirname )
_UpperCAmelCase = (
"Let's see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"
" <pad> <pad> <pad>"
)
_UpperCAmelCase = rust_tokenizer([raw_input_str] , return_tensors=snake_case_ , add_special_tokens=snake_case_ ).input_ids[0]
_UpperCAmelCase = py_tokenizer([raw_input_str] , return_tensors=snake_case_ , add_special_tokens=snake_case_ ).input_ids[0]
self.assertListEqual(snake_case_ , snake_case_ )
@require_torch
def lowercase ( self : Tuple ):
_UpperCAmelCase = ["This is going to be way too long." * 1_0_0_0, "short example"]
_UpperCAmelCase = ["not super long but more than 5 tokens", "tiny"]
_UpperCAmelCase = self._large_tokenizer(snake_case_ , padding=snake_case_ , truncation=snake_case_ , return_tensors="pt" )
_UpperCAmelCase = self._large_tokenizer(
text_target=snake_case_ , max_length=5 , padding=snake_case_ , truncation=snake_case_ , return_tensors="pt" )
assert batch.input_ids.shape == (2, 4_0_9_6)
assert batch.attention_mask.shape == (2, 4_0_9_6)
assert targets["input_ids"].shape == (2, 5)
assert len(snake_case_ ) == 2 # input_ids, attention_mask.
def lowercase ( self : Union[str, Any] ):
_UpperCAmelCase = (
"This is an example string that is used to test the original TF implementation against the HF"
" implementation"
)
_UpperCAmelCase = self._large_tokenizer(snake_case_ ).input_ids
self.assertListEqual(
snake_case_ , [1_8_2, 1_1_7, 1_4_2, 5_8_7, 4_2_1_1, 1_2_0, 1_1_7, 2_6_3, 1_1_2, 8_0_4, 1_0_9, 8_5_6, 2_5_0_1_6, 3_1_3_7, 4_6_4, 1_0_9, 2_6_9_5_5, 3_1_3_7, 1] , )
| 22 | 1 |
'''simple docstring'''
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 UpperCAmelCase_ ( __lowercase : Dict[str, torch.Tensor] ) -> Dict[str, torch.Tensor]:
'''simple docstring'''
_UpperCAmelCase = []
_UpperCAmelCase = []
_UpperCAmelCase = []
for rt in rc.restypes:
_UpperCAmelCase = 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 = {name: i for i, name in enumerate(__lowercase )}
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 = torch.tensor(
__lowercase , dtype=torch.intaa , device=protein["aatype"].device , )
_UpperCAmelCase = torch.tensor(
__lowercase , dtype=torch.intaa , device=protein["aatype"].device , )
_UpperCAmelCase = torch.tensor(
__lowercase , dtype=torch.floataa , device=protein["aatype"].device , )
_UpperCAmelCase = 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 = restype_atomaa_to_atomaa[protein_aatype]
_UpperCAmelCase = restype_atomaa_mask[protein_aatype]
_UpperCAmelCase = residx_atomaa_mask
_UpperCAmelCase = residx_atomaa_to_atomaa.long()
# create the gather indices for mapping back
_UpperCAmelCase = restype_atomaa_to_atomaa[protein_aatype]
_UpperCAmelCase = residx_atomaa_to_atomaa.long()
# create the corresponding mask
_UpperCAmelCase = torch.zeros([21, 37] , dtype=torch.floataa , device=protein["aatype"].device )
for restype, restype_letter in enumerate(rc.restypes ):
_UpperCAmelCase = rc.restype_atoa[restype_letter]
_UpperCAmelCase = rc.residue_atoms[restype_name]
for atom_name in atom_names:
_UpperCAmelCase = rc.atom_order[atom_name]
_UpperCAmelCase = 1
_UpperCAmelCase = restype_atomaa_mask[protein_aatype]
_UpperCAmelCase = residx_atomaa_mask
return protein
def UpperCAmelCase_ ( __lowercase : Dict[str, torch.Tensor] ) -> Dict[str, np.ndarray]:
'''simple docstring'''
_UpperCAmelCase = tree_map(lambda __lowercase : torch.tensor(__lowercase , device=batch["aatype"].device ) , __lowercase , np.ndarray )
_UpperCAmelCase = tensor_tree_map(lambda __lowercase : np.array(__lowercase ) , make_atomaa_masks(__lowercase ) )
return out
| 22 |
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast
@require_vision
class A_ ( unittest.TestCase ):
def lowercase ( self : int ):
_UpperCAmelCase = tempfile.mkdtemp()
_UpperCAmelCase = BlipImageProcessor()
_UpperCAmelCase = BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-BertModel" )
_UpperCAmelCase = BlipProcessor(snake_case_ , snake_case_ )
processor.save_pretrained(self.tmpdirname )
def lowercase ( self : Tuple , **snake_case_ : int ):
return AutoProcessor.from_pretrained(self.tmpdirname , **snake_case_ ).tokenizer
def lowercase ( self : Dict , **snake_case_ : Any ):
return AutoProcessor.from_pretrained(self.tmpdirname , **snake_case_ ).image_processor
def lowercase ( self : int ):
shutil.rmtree(self.tmpdirname )
def lowercase ( self : Optional[Any] ):
_UpperCAmelCase = [np.random.randint(2_5_5 , size=(3, 3_0, 4_0_0) , dtype=np.uinta )]
_UpperCAmelCase = [Image.fromarray(np.moveaxis(snake_case_ , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def lowercase ( self : int ):
_UpperCAmelCase = BlipProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
_UpperCAmelCase = self.get_tokenizer(bos_token="(BOS)" , eos_token="(EOS)" )
_UpperCAmelCase = self.get_image_processor(do_normalize=snake_case_ , padding_value=1.0 )
_UpperCAmelCase = BlipProcessor.from_pretrained(
self.tmpdirname , bos_token="(BOS)" , eos_token="(EOS)" , do_normalize=snake_case_ , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , snake_case_ )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , snake_case_ )
def lowercase ( self : Any ):
_UpperCAmelCase = self.get_image_processor()
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = BlipProcessor(tokenizer=snake_case_ , image_processor=snake_case_ )
_UpperCAmelCase = self.prepare_image_inputs()
_UpperCAmelCase = image_processor(snake_case_ , return_tensors="np" )
_UpperCAmelCase = processor(images=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 lowercase ( self : Optional[int] ):
_UpperCAmelCase = self.get_image_processor()
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = BlipProcessor(tokenizer=snake_case_ , image_processor=snake_case_ )
_UpperCAmelCase = "lower newer"
_UpperCAmelCase = processor(text=snake_case_ )
_UpperCAmelCase = tokenizer(snake_case_ , return_token_type_ids=snake_case_ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def lowercase ( self : Optional[Any] ):
_UpperCAmelCase = self.get_image_processor()
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = BlipProcessor(tokenizer=snake_case_ , image_processor=snake_case_ )
_UpperCAmelCase = "lower newer"
_UpperCAmelCase = self.prepare_image_inputs()
_UpperCAmelCase = processor(text=snake_case_ , images=snake_case_ )
self.assertListEqual(list(inputs.keys() ) , ["pixel_values", "input_ids", "attention_mask"] )
# test if it raises when no input is passed
with pytest.raises(snake_case_ ):
processor()
def lowercase ( self : Union[str, Any] ):
_UpperCAmelCase = self.get_image_processor()
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = BlipProcessor(tokenizer=snake_case_ , image_processor=snake_case_ )
_UpperCAmelCase = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
_UpperCAmelCase = processor.batch_decode(snake_case_ )
_UpperCAmelCase = tokenizer.batch_decode(snake_case_ )
self.assertListEqual(snake_case_ , snake_case_ )
def lowercase ( self : str ):
_UpperCAmelCase = self.get_image_processor()
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = BlipProcessor(tokenizer=snake_case_ , image_processor=snake_case_ )
_UpperCAmelCase = "lower newer"
_UpperCAmelCase = self.prepare_image_inputs()
_UpperCAmelCase = processor(text=snake_case_ , images=snake_case_ )
# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
self.assertListEqual(list(inputs.keys() ) , ["pixel_values", "input_ids", "attention_mask"] )
| 22 | 1 |
'''simple docstring'''
from __future__ import annotations
def UpperCAmelCase_ ( __lowercase : list[int] ) -> bool:
'''simple docstring'''
return len(set(__lowercase ) ) == len(__lowercase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 22 |
'''simple docstring'''
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
def UpperCAmelCase_ ( __lowercase : str ) -> List[str]:
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase = image.size
_UpperCAmelCase , _UpperCAmelCase = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32
_UpperCAmelCase = image.resize((w, h) , resample=PIL_INTERPOLATION["lanczos"] )
_UpperCAmelCase = np.array(__lowercase ).astype(np.floataa ) / 255.0
_UpperCAmelCase = image[None].transpose(0 , 3 , 1 , 2 )
_UpperCAmelCase = torch.from_numpy(__lowercase )
return 2.0 * image - 1.0
class A_ ( lowerCAmelCase_ ):
def __init__( self : Optional[Any] , snake_case_ : VQModel , snake_case_ : UNetaDModel , snake_case_ : Union[
DDIMScheduler,
PNDMScheduler,
LMSDiscreteScheduler,
EulerDiscreteScheduler,
EulerAncestralDiscreteScheduler,
DPMSolverMultistepScheduler,
] , ):
super().__init__()
self.register_modules(vqvae=snake_case_ , unet=snake_case_ , scheduler=snake_case_ )
@torch.no_grad()
def __call__( self : Any , snake_case_ : Union[torch.Tensor, PIL.Image.Image] = None , snake_case_ : Optional[int] = 1 , snake_case_ : Optional[int] = 1_0_0 , snake_case_ : Optional[float] = 0.0 , snake_case_ : Optional[Union[torch.Generator, List[torch.Generator]]] = None , snake_case_ : Optional[str] = "pil" , snake_case_ : bool = True , ):
if isinstance(snake_case_ , PIL.Image.Image ):
_UpperCAmelCase = 1
elif isinstance(snake_case_ , torch.Tensor ):
_UpperCAmelCase = image.shape[0]
else:
raise ValueError(f'`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(snake_case_ )}' )
if isinstance(snake_case_ , PIL.Image.Image ):
_UpperCAmelCase = preprocess(snake_case_ )
_UpperCAmelCase , _UpperCAmelCase = image.shape[-2:]
# in_channels should be 6: 3 for latents, 3 for low resolution image
_UpperCAmelCase = (batch_size, self.unet.config.in_channels // 2, height, width)
_UpperCAmelCase = next(self.unet.parameters() ).dtype
_UpperCAmelCase = randn_tensor(snake_case_ , generator=snake_case_ , device=self.device , dtype=snake_case_ )
_UpperCAmelCase = image.to(device=self.device , dtype=snake_case_ )
# set timesteps and move to the correct device
self.scheduler.set_timesteps(snake_case_ , device=self.device )
_UpperCAmelCase = self.scheduler.timesteps
# scale the initial noise by the standard deviation required by the scheduler
_UpperCAmelCase = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
_UpperCAmelCase = "eta" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
_UpperCAmelCase = {}
if accepts_eta:
_UpperCAmelCase = eta
for t in self.progress_bar(snake_case_ ):
# concat latents and low resolution image in the channel dimension.
_UpperCAmelCase = torch.cat([latents, image] , dim=1 )
_UpperCAmelCase = self.scheduler.scale_model_input(snake_case_ , snake_case_ )
# predict the noise residual
_UpperCAmelCase = self.unet(snake_case_ , snake_case_ ).sample
# compute the previous noisy sample x_t -> x_t-1
_UpperCAmelCase = self.scheduler.step(snake_case_ , snake_case_ , snake_case_ , **snake_case_ ).prev_sample
# decode the image latents with the VQVAE
_UpperCAmelCase = self.vqvae.decode(snake_case_ ).sample
_UpperCAmelCase = torch.clamp(snake_case_ , -1.0 , 1.0 )
_UpperCAmelCase = image / 2 + 0.5
_UpperCAmelCase = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
_UpperCAmelCase = self.numpy_to_pil(snake_case_ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=snake_case_ )
| 22 | 1 |
'''simple docstring'''
from io import BytesIO
from typing import List, Union
import requests
from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_decord_available():
import numpy as np
from decord import VideoReader
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING
__SCREAMING_SNAKE_CASE :Union[str, Any] = logging.get_logger(__name__)
@add_end_docstrings(lowerCAmelCase_ )
class A_ ( lowerCAmelCase_ ):
def __init__( self : Tuple , *snake_case_ : Optional[int] , **snake_case_ : Optional[int] ):
super().__init__(*snake_case_ , **snake_case_ )
requires_backends(self , "decord" )
self.check_model_type(snake_case_ )
def lowercase ( self : Optional[int] , snake_case_ : List[str]=None , snake_case_ : Union[str, Any]=None , snake_case_ : Union[str, Any]=None ):
_UpperCAmelCase = {}
if frame_sampling_rate is not None:
_UpperCAmelCase = frame_sampling_rate
if num_frames is not None:
_UpperCAmelCase = num_frames
_UpperCAmelCase = {}
if top_k is not None:
_UpperCAmelCase = top_k
return preprocess_params, {}, postprocess_params
def __call__( self : Any , snake_case_ : Union[str, List[str]] , **snake_case_ : int ):
return super().__call__(snake_case_ , **snake_case_ )
def lowercase ( self : List[str] , snake_case_ : int , snake_case_ : str=None , snake_case_ : List[Any]=1 ):
if num_frames is None:
_UpperCAmelCase = self.model.config.num_frames
if video.startswith("http://" ) or video.startswith("https://" ):
_UpperCAmelCase = BytesIO(requests.get(snake_case_ ).content )
_UpperCAmelCase = VideoReader(snake_case_ )
videoreader.seek(0 )
_UpperCAmelCase = 0
_UpperCAmelCase = num_frames * frame_sampling_rate - 1
_UpperCAmelCase = np.linspace(snake_case_ , snake_case_ , num=snake_case_ , dtype=np.intaa )
_UpperCAmelCase = videoreader.get_batch(snake_case_ ).asnumpy()
_UpperCAmelCase = list(snake_case_ )
_UpperCAmelCase = self.image_processor(snake_case_ , return_tensors=self.framework )
return model_inputs
def lowercase ( self : str , snake_case_ : Dict ):
_UpperCAmelCase = self.model(**snake_case_ )
return model_outputs
def lowercase ( self : List[str] , snake_case_ : Dict , snake_case_ : Dict=5 ):
if top_k > self.model.config.num_labels:
_UpperCAmelCase = self.model.config.num_labels
if self.framework == "pt":
_UpperCAmelCase = model_outputs.logits.softmax(-1 )[0]
_UpperCAmelCase , _UpperCAmelCase = probs.topk(snake_case_ )
else:
raise ValueError(f'Unsupported framework: {self.framework}' )
_UpperCAmelCase = scores.tolist()
_UpperCAmelCase = ids.tolist()
return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(snake_case_ , snake_case_ )]
| 22 |
'''simple docstring'''
import string
from math import logaa
def UpperCAmelCase_ ( __lowercase : str , __lowercase : str ) -> int:
'''simple docstring'''
_UpperCAmelCase = document.translate(
str.maketrans("" , "" , string.punctuation ) ).replace("\n" , "" )
_UpperCAmelCase = document_without_punctuation.split(" " ) # word tokenization
return len([word for word in tokenize_document if word.lower() == term.lower()] )
def UpperCAmelCase_ ( __lowercase : str , __lowercase : str ) -> tuple[int, int]:
'''simple docstring'''
_UpperCAmelCase = corpus.lower().translate(
str.maketrans("" , "" , string.punctuation ) ) # strip all punctuation and replace it with ''
_UpperCAmelCase = corpus_without_punctuation.split("\n" )
_UpperCAmelCase = term.lower()
return (len([doc for doc in docs if term in doc] ), len(__lowercase ))
def UpperCAmelCase_ ( __lowercase : int , __lowercase : int , __lowercase : Union[str, Any]=False ) -> float:
'''simple docstring'''
if smoothing:
if n == 0:
raise ValueError("log10(0) is undefined." )
return round(1 + logaa(n / (1 + df) ) , 3 )
if df == 0:
raise ZeroDivisionError("df must be > 0" )
elif n == 0:
raise ValueError("log10(0) is undefined." )
return round(logaa(n / df ) , 3 )
def UpperCAmelCase_ ( __lowercase : int , __lowercase : int ) -> float:
'''simple docstring'''
return round(tf * idf , 3 )
| 22 | 1 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_pytesseract,
require_tf,
require_timm,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class A_ :
@staticmethod
def lowercase ( *snake_case_ : Dict , **snake_case_ : Optional[Any] ):
pass
@is_pipeline_test
@require_vision
@require_timm
@require_torch
class A_ ( unittest.TestCase ):
_lowerCamelCase : List[str] = MODEL_FOR_OBJECT_DETECTION_MAPPING
def lowercase ( self : List[Any] , snake_case_ : List[str] , snake_case_ : str , snake_case_ : str ):
_UpperCAmelCase = ObjectDetectionPipeline(model=snake_case_ , image_processor=snake_case_ )
return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]
def lowercase ( self : List[str] , snake_case_ : Tuple , snake_case_ : List[str] ):
_UpperCAmelCase = object_detector("./tests/fixtures/tests_samples/COCO/000000039769.png" , threshold=0.0 )
self.assertGreater(len(snake_case_ ) , 0 )
for detected_object in outputs:
self.assertEqual(
snake_case_ , {
"score": ANY(snake_case_ ),
"label": ANY(snake_case_ ),
"box": {"xmin": ANY(snake_case_ ), "ymin": ANY(snake_case_ ), "xmax": ANY(snake_case_ ), "ymax": ANY(snake_case_ )},
} , )
import datasets
_UpperCAmelCase = datasets.load_dataset("hf-internal-testing/fixtures_image_utils" , "image" , split="test" )
_UpperCAmelCase = [
Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" ),
"http://images.cocodataset.org/val2017/000000039769.jpg",
# RGBA
dataset[0]["file"],
# LA
dataset[1]["file"],
# L
dataset[2]["file"],
]
_UpperCAmelCase = object_detector(snake_case_ , threshold=0.0 )
self.assertEqual(len(snake_case_ ) , len(snake_case_ ) )
for outputs in batch_outputs:
self.assertGreater(len(snake_case_ ) , 0 )
for detected_object in outputs:
self.assertEqual(
snake_case_ , {
"score": ANY(snake_case_ ),
"label": ANY(snake_case_ ),
"box": {"xmin": ANY(snake_case_ ), "ymin": ANY(snake_case_ ), "xmax": ANY(snake_case_ ), "ymax": ANY(snake_case_ )},
} , )
@require_tf
@unittest.skip("Object detection not implemented in TF" )
def lowercase ( self : List[str] ):
pass
@require_torch
def lowercase ( self : List[Any] ):
_UpperCAmelCase = "hf-internal-testing/tiny-detr-mobilenetsv3"
_UpperCAmelCase = AutoModelForObjectDetection.from_pretrained(snake_case_ )
_UpperCAmelCase = AutoFeatureExtractor.from_pretrained(snake_case_ )
_UpperCAmelCase = ObjectDetectionPipeline(model=snake_case_ , feature_extractor=snake_case_ )
_UpperCAmelCase = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg" , threshold=0.0 )
self.assertEqual(
nested_simplify(snake_case_ , decimals=4 ) , [
{"score": 0.3_3_7_6, "label": "LABEL_0", "box": {"xmin": 1_5_9, "ymin": 1_2_0, "xmax": 4_8_0, "ymax": 3_5_9}},
{"score": 0.3_3_7_6, "label": "LABEL_0", "box": {"xmin": 1_5_9, "ymin": 1_2_0, "xmax": 4_8_0, "ymax": 3_5_9}},
] , )
_UpperCAmelCase = object_detector(
[
"http://images.cocodataset.org/val2017/000000039769.jpg",
"http://images.cocodataset.org/val2017/000000039769.jpg",
] , threshold=0.0 , )
self.assertEqual(
nested_simplify(snake_case_ , decimals=4 ) , [
[
{"score": 0.3_3_7_6, "label": "LABEL_0", "box": {"xmin": 1_5_9, "ymin": 1_2_0, "xmax": 4_8_0, "ymax": 3_5_9}},
{"score": 0.3_3_7_6, "label": "LABEL_0", "box": {"xmin": 1_5_9, "ymin": 1_2_0, "xmax": 4_8_0, "ymax": 3_5_9}},
],
[
{"score": 0.3_3_7_6, "label": "LABEL_0", "box": {"xmin": 1_5_9, "ymin": 1_2_0, "xmax": 4_8_0, "ymax": 3_5_9}},
{"score": 0.3_3_7_6, "label": "LABEL_0", "box": {"xmin": 1_5_9, "ymin": 1_2_0, "xmax": 4_8_0, "ymax": 3_5_9}},
],
] , )
@require_torch
@slow
def lowercase ( self : List[str] ):
_UpperCAmelCase = "facebook/detr-resnet-50"
_UpperCAmelCase = AutoModelForObjectDetection.from_pretrained(snake_case_ )
_UpperCAmelCase = AutoFeatureExtractor.from_pretrained(snake_case_ )
_UpperCAmelCase = ObjectDetectionPipeline(model=snake_case_ , feature_extractor=snake_case_ )
_UpperCAmelCase = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg" )
self.assertEqual(
nested_simplify(snake_case_ , decimals=4 ) , [
{"score": 0.9_9_8_2, "label": "remote", "box": {"xmin": 4_0, "ymin": 7_0, "xmax": 1_7_5, "ymax": 1_1_7}},
{"score": 0.9_9_6_0, "label": "remote", "box": {"xmin": 3_3_3, "ymin": 7_2, "xmax": 3_6_8, "ymax": 1_8_7}},
{"score": 0.9_9_5_5, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 6_3_9, "ymax": 4_7_3}},
{"score": 0.9_9_8_8, "label": "cat", "box": {"xmin": 1_3, "ymin": 5_2, "xmax": 3_1_4, "ymax": 4_7_0}},
{"score": 0.9_9_8_7, "label": "cat", "box": {"xmin": 3_4_5, "ymin": 2_3, "xmax": 6_4_0, "ymax": 3_6_8}},
] , )
_UpperCAmelCase = object_detector(
[
"http://images.cocodataset.org/val2017/000000039769.jpg",
"http://images.cocodataset.org/val2017/000000039769.jpg",
] )
self.assertEqual(
nested_simplify(snake_case_ , decimals=4 ) , [
[
{"score": 0.9_9_8_2, "label": "remote", "box": {"xmin": 4_0, "ymin": 7_0, "xmax": 1_7_5, "ymax": 1_1_7}},
{"score": 0.9_9_6_0, "label": "remote", "box": {"xmin": 3_3_3, "ymin": 7_2, "xmax": 3_6_8, "ymax": 1_8_7}},
{"score": 0.9_9_5_5, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 6_3_9, "ymax": 4_7_3}},
{"score": 0.9_9_8_8, "label": "cat", "box": {"xmin": 1_3, "ymin": 5_2, "xmax": 3_1_4, "ymax": 4_7_0}},
{"score": 0.9_9_8_7, "label": "cat", "box": {"xmin": 3_4_5, "ymin": 2_3, "xmax": 6_4_0, "ymax": 3_6_8}},
],
[
{"score": 0.9_9_8_2, "label": "remote", "box": {"xmin": 4_0, "ymin": 7_0, "xmax": 1_7_5, "ymax": 1_1_7}},
{"score": 0.9_9_6_0, "label": "remote", "box": {"xmin": 3_3_3, "ymin": 7_2, "xmax": 3_6_8, "ymax": 1_8_7}},
{"score": 0.9_9_5_5, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 6_3_9, "ymax": 4_7_3}},
{"score": 0.9_9_8_8, "label": "cat", "box": {"xmin": 1_3, "ymin": 5_2, "xmax": 3_1_4, "ymax": 4_7_0}},
{"score": 0.9_9_8_7, "label": "cat", "box": {"xmin": 3_4_5, "ymin": 2_3, "xmax": 6_4_0, "ymax": 3_6_8}},
],
] , )
@require_torch
@slow
def lowercase ( self : str ):
_UpperCAmelCase = "facebook/detr-resnet-50"
_UpperCAmelCase = pipeline("object-detection" , model=snake_case_ )
_UpperCAmelCase = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg" )
self.assertEqual(
nested_simplify(snake_case_ , decimals=4 ) , [
{"score": 0.9_9_8_2, "label": "remote", "box": {"xmin": 4_0, "ymin": 7_0, "xmax": 1_7_5, "ymax": 1_1_7}},
{"score": 0.9_9_6_0, "label": "remote", "box": {"xmin": 3_3_3, "ymin": 7_2, "xmax": 3_6_8, "ymax": 1_8_7}},
{"score": 0.9_9_5_5, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 6_3_9, "ymax": 4_7_3}},
{"score": 0.9_9_8_8, "label": "cat", "box": {"xmin": 1_3, "ymin": 5_2, "xmax": 3_1_4, "ymax": 4_7_0}},
{"score": 0.9_9_8_7, "label": "cat", "box": {"xmin": 3_4_5, "ymin": 2_3, "xmax": 6_4_0, "ymax": 3_6_8}},
] , )
_UpperCAmelCase = object_detector(
[
"http://images.cocodataset.org/val2017/000000039769.jpg",
"http://images.cocodataset.org/val2017/000000039769.jpg",
] )
self.assertEqual(
nested_simplify(snake_case_ , decimals=4 ) , [
[
{"score": 0.9_9_8_2, "label": "remote", "box": {"xmin": 4_0, "ymin": 7_0, "xmax": 1_7_5, "ymax": 1_1_7}},
{"score": 0.9_9_6_0, "label": "remote", "box": {"xmin": 3_3_3, "ymin": 7_2, "xmax": 3_6_8, "ymax": 1_8_7}},
{"score": 0.9_9_5_5, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 6_3_9, "ymax": 4_7_3}},
{"score": 0.9_9_8_8, "label": "cat", "box": {"xmin": 1_3, "ymin": 5_2, "xmax": 3_1_4, "ymax": 4_7_0}},
{"score": 0.9_9_8_7, "label": "cat", "box": {"xmin": 3_4_5, "ymin": 2_3, "xmax": 6_4_0, "ymax": 3_6_8}},
],
[
{"score": 0.9_9_8_2, "label": "remote", "box": {"xmin": 4_0, "ymin": 7_0, "xmax": 1_7_5, "ymax": 1_1_7}},
{"score": 0.9_9_6_0, "label": "remote", "box": {"xmin": 3_3_3, "ymin": 7_2, "xmax": 3_6_8, "ymax": 1_8_7}},
{"score": 0.9_9_5_5, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 6_3_9, "ymax": 4_7_3}},
{"score": 0.9_9_8_8, "label": "cat", "box": {"xmin": 1_3, "ymin": 5_2, "xmax": 3_1_4, "ymax": 4_7_0}},
{"score": 0.9_9_8_7, "label": "cat", "box": {"xmin": 3_4_5, "ymin": 2_3, "xmax": 6_4_0, "ymax": 3_6_8}},
],
] , )
@require_torch
@slow
def lowercase ( self : Union[str, Any] ):
_UpperCAmelCase = 0.9_9_8_5
_UpperCAmelCase = "facebook/detr-resnet-50"
_UpperCAmelCase = pipeline("object-detection" , model=snake_case_ )
_UpperCAmelCase = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg" , threshold=snake_case_ )
self.assertEqual(
nested_simplify(snake_case_ , decimals=4 ) , [
{"score": 0.9_9_8_8, "label": "cat", "box": {"xmin": 1_3, "ymin": 5_2, "xmax": 3_1_4, "ymax": 4_7_0}},
{"score": 0.9_9_8_7, "label": "cat", "box": {"xmin": 3_4_5, "ymin": 2_3, "xmax": 6_4_0, "ymax": 3_6_8}},
] , )
@require_torch
@require_pytesseract
@slow
def lowercase ( self : int ):
_UpperCAmelCase = "Narsil/layoutlmv3-finetuned-funsd"
_UpperCAmelCase = 0.9_9_9_3
_UpperCAmelCase = pipeline("object-detection" , model=snake_case_ , threshold=snake_case_ )
_UpperCAmelCase = object_detector(
"https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png" )
self.assertEqual(
nested_simplify(snake_case_ , decimals=4 ) , [
{"score": 0.9_9_9_3, "label": "I-ANSWER", "box": {"xmin": 2_9_4, "ymin": 2_5_4, "xmax": 3_4_3, "ymax": 2_6_4}},
{"score": 0.9_9_9_3, "label": "I-ANSWER", "box": {"xmin": 2_9_4, "ymin": 2_5_4, "xmax": 3_4_3, "ymax": 2_6_4}},
] , )
| 22 |
'''simple docstring'''
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_pt_objects import * # noqa F403
else:
from .scheduling_consistency_models import CMStochasticIterativeScheduler
from .scheduling_ddim import DDIMScheduler
from .scheduling_ddim_inverse import DDIMInverseScheduler
from .scheduling_ddim_parallel import DDIMParallelScheduler
from .scheduling_ddpm import DDPMScheduler
from .scheduling_ddpm_parallel import DDPMParallelScheduler
from .scheduling_deis_multistep import DEISMultistepScheduler
from .scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler
from .scheduling_dpmsolver_multistep_inverse import DPMSolverMultistepInverseScheduler
from .scheduling_dpmsolver_singlestep import DPMSolverSinglestepScheduler
from .scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler
from .scheduling_euler_discrete import EulerDiscreteScheduler
from .scheduling_heun_discrete import HeunDiscreteScheduler
from .scheduling_ipndm import IPNDMScheduler
from .scheduling_k_dpm_2_ancestral_discrete import KDPMaAncestralDiscreteScheduler
from .scheduling_k_dpm_2_discrete import KDPMaDiscreteScheduler
from .scheduling_karras_ve import KarrasVeScheduler
from .scheduling_pndm import PNDMScheduler
from .scheduling_repaint import RePaintScheduler
from .scheduling_sde_ve import ScoreSdeVeScheduler
from .scheduling_sde_vp import ScoreSdeVpScheduler
from .scheduling_unclip import UnCLIPScheduler
from .scheduling_unipc_multistep import UniPCMultistepScheduler
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
from .scheduling_vq_diffusion import VQDiffusionScheduler
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_flax_objects import * # noqa F403
else:
from .scheduling_ddim_flax import FlaxDDIMScheduler
from .scheduling_ddpm_flax import FlaxDDPMScheduler
from .scheduling_dpmsolver_multistep_flax import FlaxDPMSolverMultistepScheduler
from .scheduling_karras_ve_flax import FlaxKarrasVeScheduler
from .scheduling_lms_discrete_flax import FlaxLMSDiscreteScheduler
from .scheduling_pndm_flax import FlaxPNDMScheduler
from .scheduling_sde_ve_flax import FlaxScoreSdeVeScheduler
from .scheduling_utils_flax import (
FlaxKarrasDiffusionSchedulers,
FlaxSchedulerMixin,
FlaxSchedulerOutput,
broadcast_to_shape_from_left,
)
try:
if not (is_torch_available() and is_scipy_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_torch_and_scipy_objects import * # noqa F403
else:
from .scheduling_lms_discrete import LMSDiscreteScheduler
try:
if not (is_torch_available() and is_torchsde_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_torch_and_torchsde_objects import * # noqa F403
else:
from .scheduling_dpmsolver_sde import DPMSolverSDEScheduler
| 22 | 1 |
'''simple docstring'''
from collections.abc import Callable
def UpperCAmelCase_ ( __lowercase : Callable[[float], float] , __lowercase : float , __lowercase : float ) -> float:
'''simple docstring'''
_UpperCAmelCase = a
_UpperCAmelCase = b
if function(__lowercase ) == 0: # one of the a or b is a root for the function
return a
elif function(__lowercase ) == 0:
return b
elif (
function(__lowercase ) * function(__lowercase ) > 0
): # if none of these are root and they are both positive or negative,
# then this algorithm can't find the root
raise ValueError("could not find root in given interval." )
else:
_UpperCAmelCase = start + (end - start) / 2.0
while abs(start - mid ) > 10**-7: # until precisely equals to 10^-7
if function(__lowercase ) == 0:
return mid
elif function(__lowercase ) * function(__lowercase ) < 0:
_UpperCAmelCase = mid
else:
_UpperCAmelCase = mid
_UpperCAmelCase = start + (end - start) / 2.0
return mid
def UpperCAmelCase_ ( __lowercase : float ) -> float:
'''simple docstring'''
return x**3 - 2 * x - 5
if __name__ == "__main__":
print(bisection(f, 1, 1000))
import doctest
doctest.testmod()
| 22 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : int ) -> int:
'''simple docstring'''
if not isinstance(__lowercase , __lowercase ) or number < 0:
raise ValueError("Input must be a non-negative integer" )
_UpperCAmelCase = 0
while number:
# This way we arrive at next set bit (next 1) instead of looping
# through each bit and checking for 1s hence the
# loop won't run 32 times it will only run the number of `1` times
number &= number - 1
count += 1
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 22 | 1 |
'''simple docstring'''
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging
__SCREAMING_SNAKE_CASE :Optional[Any] = logging.get_logger(__name__)
class A_ ( lowerCAmelCase_ ):
_lowerCamelCase : Optional[int] = ["""input_features"""]
def __init__( self : Union[str, Any] , snake_case_ : List[Any]=8_0 , snake_case_ : Any=1_6_0_0_0 , snake_case_ : Union[str, Any]=1_6_0 , snake_case_ : Optional[int]=3_0 , snake_case_ : str=4_0_0 , snake_case_ : Optional[Any]=0.0 , snake_case_ : int=False , **snake_case_ : Optional[int] , ):
super().__init__(
feature_size=snake_case_ , sampling_rate=snake_case_ , padding_value=snake_case_ , return_attention_mask=snake_case_ , **snake_case_ , )
_UpperCAmelCase = n_fft
_UpperCAmelCase = hop_length
_UpperCAmelCase = chunk_length
_UpperCAmelCase = chunk_length * sampling_rate
_UpperCAmelCase = self.n_samples // hop_length
_UpperCAmelCase = sampling_rate
_UpperCAmelCase = mel_filter_bank(
num_frequency_bins=1 + n_fft // 2 , num_mel_filters=snake_case_ , min_frequency=0.0 , max_frequency=8_0_0_0.0 , sampling_rate=snake_case_ , norm="slaney" , mel_scale="slaney" , )
def lowercase ( self : Dict , snake_case_ : np.array ):
_UpperCAmelCase = spectrogram(
snake_case_ , window_function(self.n_fft , "hann" ) , frame_length=self.n_fft , hop_length=self.hop_length , power=2.0 , mel_filters=self.mel_filters , log_mel="log10" , )
_UpperCAmelCase = log_spec[:, :-1]
_UpperCAmelCase = np.maximum(snake_case_ , log_spec.max() - 8.0 )
_UpperCAmelCase = (log_spec + 4.0) / 4.0
return log_spec
@staticmethod
# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
def lowercase ( snake_case_ : List[np.ndarray] , snake_case_ : List[np.ndarray] , snake_case_ : float = 0.0 ):
if attention_mask is not None:
_UpperCAmelCase = np.array(snake_case_ , np.intaa )
_UpperCAmelCase = []
for vector, length in zip(snake_case_ , attention_mask.sum(-1 ) ):
_UpperCAmelCase = (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7 )
if length < normed_slice.shape[0]:
_UpperCAmelCase = padding_value
normed_input_values.append(snake_case_ )
else:
_UpperCAmelCase = [(x - x.mean()) / np.sqrt(x.var() + 1e-7 ) for x in input_values]
return normed_input_values
def __call__( self : Optional[int] , snake_case_ : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , snake_case_ : bool = True , snake_case_ : Optional[int] = None , snake_case_ : Optional[Union[str, TensorType]] = None , snake_case_ : Optional[bool] = None , snake_case_ : Optional[str] = "max_length" , snake_case_ : Optional[int] = None , snake_case_ : Optional[int] = None , snake_case_ : Optional[bool] = None , **snake_case_ : Union[str, Any] , ):
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
f'The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'
f' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'
f' was sampled with {self.sampling_rate} and not {sampling_rate}.' )
else:
logger.warning(
"It is strongly recommended to pass the `sampling_rate` argument to this function. "
"Failing to do so can result in silent errors that might be hard to debug." )
_UpperCAmelCase = isinstance(snake_case_ , np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(f'Only mono-channel audio is supported for input to {self}' )
_UpperCAmelCase = is_batched_numpy or (
isinstance(snake_case_ , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
_UpperCAmelCase = [np.asarray([speech] , dtype=np.floataa ).T for speech in raw_speech]
elif not is_batched and not isinstance(snake_case_ , np.ndarray ):
_UpperCAmelCase = np.asarray(snake_case_ , dtype=np.floataa )
elif isinstance(snake_case_ , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
_UpperCAmelCase = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
_UpperCAmelCase = [np.asarray([raw_speech] ).T]
_UpperCAmelCase = BatchFeature({"input_features": raw_speech} )
# convert into correct format for padding
_UpperCAmelCase = self.pad(
snake_case_ , padding=snake_case_ , max_length=max_length if max_length else self.n_samples , truncation=snake_case_ , pad_to_multiple_of=snake_case_ , return_attention_mask=return_attention_mask or do_normalize , )
# zero-mean and unit-variance normalization
if do_normalize:
_UpperCAmelCase = self.zero_mean_unit_var_norm(
padded_inputs["input_features"] , attention_mask=padded_inputs["attention_mask"] , padding_value=self.padding_value , )
_UpperCAmelCase = np.stack(padded_inputs["input_features"] , axis=0 )
# make sure list is in array format
_UpperCAmelCase = padded_inputs.get("input_features" ).transpose(2 , 0 , 1 )
_UpperCAmelCase = [self._np_extract_fbank_features(snake_case_ ) for waveform in input_features[0]]
if isinstance(input_features[0] , snake_case_ ):
_UpperCAmelCase = [np.asarray(snake_case_ , dtype=np.floataa ) for feature in input_features]
else:
_UpperCAmelCase = input_features
if return_attention_mask:
# rescale from sample (48000) to feature (3000)
_UpperCAmelCase = padded_inputs["attention_mask"][:, :: self.hop_length]
if return_tensors is not None:
_UpperCAmelCase = padded_inputs.convert_to_tensors(snake_case_ )
return padded_inputs
def lowercase ( self : Optional[Any] ):
_UpperCAmelCase = copy.deepcopy(self.__dict__ )
_UpperCAmelCase = self.__class__.__name__
if "mel_filters" in output:
del output["mel_filters"]
return output
| 22 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
from typing import Any, Generic, TypeVar
__SCREAMING_SNAKE_CASE :Optional[int] = TypeVar('''T''')
class A_ ( Generic[T] ):
def __init__( self : List[Any] , snake_case_ : list[T] , snake_case_ : Callable[[T, T], T] ):
_UpperCAmelCase = None
_UpperCAmelCase = len(snake_case_ )
_UpperCAmelCase = [any_type for _ in range(self.N )] + arr
_UpperCAmelCase = fnc
self.build()
def lowercase ( self : List[Any] ):
for p in range(self.N - 1 , 0 , -1 ):
_UpperCAmelCase = self.fn(self.st[p * 2] , self.st[p * 2 + 1] )
def lowercase ( self : Optional[Any] , snake_case_ : int , snake_case_ : T ):
p += self.N
_UpperCAmelCase = v
while p > 1:
_UpperCAmelCase = p // 2
_UpperCAmelCase = self.fn(self.st[p * 2] , self.st[p * 2 + 1] )
def lowercase ( self : Any , snake_case_ : int , snake_case_ : int ): # noqa: E741
_UpperCAmelCase , _UpperCAmelCase = l + self.N, r + self.N
_UpperCAmelCase = None
while l <= r:
if l % 2 == 1:
_UpperCAmelCase = self.st[l] if res is None else self.fn(snake_case_ , self.st[l] )
if r % 2 == 0:
_UpperCAmelCase = self.st[r] if res is None else self.fn(snake_case_ , self.st[r] )
_UpperCAmelCase , _UpperCAmelCase = (l + 1) // 2, (r - 1) // 2
return res
if __name__ == "__main__":
from functools import reduce
__SCREAMING_SNAKE_CASE :Union[str, Any] = [1, 10, -2, 9, -3, 8, 4, -7, 5, 6, 11, -12]
__SCREAMING_SNAKE_CASE :List[str] = {
0: 7,
1: 2,
2: 6,
3: -14,
4: 5,
5: 4,
6: 7,
7: -10,
8: 9,
9: 10,
10: 12,
11: 1,
}
__SCREAMING_SNAKE_CASE :Any = SegmentTree(test_array, min)
__SCREAMING_SNAKE_CASE :Any = SegmentTree(test_array, max)
__SCREAMING_SNAKE_CASE :Any = SegmentTree(test_array, lambda a, b: a + b)
def UpperCAmelCase_ ( ) -> None:
'''simple docstring'''
for i in range(len(__lowercase ) ):
for j in range(__lowercase , len(__lowercase ) ):
_UpperCAmelCase = reduce(__lowercase , test_array[i : j + 1] )
_UpperCAmelCase = reduce(__lowercase , test_array[i : j + 1] )
_UpperCAmelCase = reduce(lambda __lowercase , __lowercase : a + b , test_array[i : j + 1] )
assert min_range == min_segment_tree.query(__lowercase , __lowercase )
assert max_range == max_segment_tree.query(__lowercase , __lowercase )
assert sum_range == sum_segment_tree.query(__lowercase , __lowercase )
test_all_segments()
for index, value in test_updates.items():
__SCREAMING_SNAKE_CASE :str = value
min_segment_tree.update(index, value)
max_segment_tree.update(index, value)
sum_segment_tree.update(index, value)
test_all_segments()
| 22 | 1 |
'''simple docstring'''
from __future__ import annotations
def UpperCAmelCase_ ( __lowercase : dict , __lowercase : str ) -> set[str]:
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase = set(__lowercase ), [start]
while stack:
_UpperCAmelCase = stack.pop()
explored.add(__lowercase )
# Differences from BFS:
# 1) pop last element instead of first one
# 2) add adjacent elements to stack without exploring them
for adj in reversed(graph[v] ):
if adj not in explored:
stack.append(__lowercase )
return explored
__SCREAMING_SNAKE_CASE :List[Any] = {
'''A''': ['''B''', '''C''', '''D'''],
'''B''': ['''A''', '''D''', '''E'''],
'''C''': ['''A''', '''F'''],
'''D''': ['''B''', '''D'''],
'''E''': ['''B''', '''F'''],
'''F''': ['''C''', '''E''', '''G'''],
'''G''': ['''F'''],
}
if __name__ == "__main__":
import doctest
doctest.testmod()
print(depth_first_search(G, '''A'''))
| 22 |
'''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 UpperCAmelCase_ ( __lowercase : int , __lowercase : Dict ) -> List[Any]:
'''simple docstring'''
_UpperCAmelCase = _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 UpperCAmelCase_ ( __lowercase : Dict , __lowercase : Optional[Any] , __lowercase : int ) -> str:
'''simple docstring'''
_UpperCAmelCase = _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 UpperCAmelCase_ ( __lowercase : Optional[Any] , __lowercase : List[Any] ) -> List[Any]:
'''simple docstring'''
if expected is RuntimeError:
with pytest.raises(__lowercase ):
_number_of_shards_in_gen_kwargs(__lowercase )
else:
_UpperCAmelCase = _number_of_shards_in_gen_kwargs(__lowercase )
assert out == expected
| 22 | 1 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : int ) -> bool:
'''simple docstring'''
if not isinstance(__lowercase , __lowercase ):
_UpperCAmelCase = f'Input value of [number={number}] must be an integer'
raise TypeError(__lowercase )
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()
| 22 |
'''simple docstring'''
import math
def UpperCAmelCase_ ( __lowercase : int ) -> bool:
'''simple docstring'''
return math.sqrt(__lowercase ) * math.sqrt(__lowercase ) == num
def UpperCAmelCase_ ( __lowercase : int ) -> bool:
'''simple docstring'''
_UpperCAmelCase = 0
_UpperCAmelCase = n
while left <= right:
_UpperCAmelCase = (left + right) // 2
if mid**2 == n:
return True
elif mid**2 > n:
_UpperCAmelCase = mid - 1
else:
_UpperCAmelCase = mid + 1
return False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 22 | 1 |
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast
@require_vision
class A_ ( unittest.TestCase ):
def lowercase ( self : int ):
_UpperCAmelCase = tempfile.mkdtemp()
_UpperCAmelCase = BlipImageProcessor()
_UpperCAmelCase = BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-BertModel" )
_UpperCAmelCase = BlipProcessor(snake_case_ , snake_case_ )
processor.save_pretrained(self.tmpdirname )
def lowercase ( self : Tuple , **snake_case_ : int ):
return AutoProcessor.from_pretrained(self.tmpdirname , **snake_case_ ).tokenizer
def lowercase ( self : Dict , **snake_case_ : Any ):
return AutoProcessor.from_pretrained(self.tmpdirname , **snake_case_ ).image_processor
def lowercase ( self : int ):
shutil.rmtree(self.tmpdirname )
def lowercase ( self : Optional[Any] ):
_UpperCAmelCase = [np.random.randint(2_5_5 , size=(3, 3_0, 4_0_0) , dtype=np.uinta )]
_UpperCAmelCase = [Image.fromarray(np.moveaxis(snake_case_ , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def lowercase ( self : int ):
_UpperCAmelCase = BlipProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
_UpperCAmelCase = self.get_tokenizer(bos_token="(BOS)" , eos_token="(EOS)" )
_UpperCAmelCase = self.get_image_processor(do_normalize=snake_case_ , padding_value=1.0 )
_UpperCAmelCase = BlipProcessor.from_pretrained(
self.tmpdirname , bos_token="(BOS)" , eos_token="(EOS)" , do_normalize=snake_case_ , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , snake_case_ )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , snake_case_ )
def lowercase ( self : Any ):
_UpperCAmelCase = self.get_image_processor()
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = BlipProcessor(tokenizer=snake_case_ , image_processor=snake_case_ )
_UpperCAmelCase = self.prepare_image_inputs()
_UpperCAmelCase = image_processor(snake_case_ , return_tensors="np" )
_UpperCAmelCase = processor(images=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 lowercase ( self : Optional[int] ):
_UpperCAmelCase = self.get_image_processor()
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = BlipProcessor(tokenizer=snake_case_ , image_processor=snake_case_ )
_UpperCAmelCase = "lower newer"
_UpperCAmelCase = processor(text=snake_case_ )
_UpperCAmelCase = tokenizer(snake_case_ , return_token_type_ids=snake_case_ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def lowercase ( self : Optional[Any] ):
_UpperCAmelCase = self.get_image_processor()
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = BlipProcessor(tokenizer=snake_case_ , image_processor=snake_case_ )
_UpperCAmelCase = "lower newer"
_UpperCAmelCase = self.prepare_image_inputs()
_UpperCAmelCase = processor(text=snake_case_ , images=snake_case_ )
self.assertListEqual(list(inputs.keys() ) , ["pixel_values", "input_ids", "attention_mask"] )
# test if it raises when no input is passed
with pytest.raises(snake_case_ ):
processor()
def lowercase ( self : Union[str, Any] ):
_UpperCAmelCase = self.get_image_processor()
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = BlipProcessor(tokenizer=snake_case_ , image_processor=snake_case_ )
_UpperCAmelCase = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
_UpperCAmelCase = processor.batch_decode(snake_case_ )
_UpperCAmelCase = tokenizer.batch_decode(snake_case_ )
self.assertListEqual(snake_case_ , snake_case_ )
def lowercase ( self : str ):
_UpperCAmelCase = self.get_image_processor()
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = BlipProcessor(tokenizer=snake_case_ , image_processor=snake_case_ )
_UpperCAmelCase = "lower newer"
_UpperCAmelCase = self.prepare_image_inputs()
_UpperCAmelCase = processor(text=snake_case_ , images=snake_case_ )
# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
self.assertListEqual(list(inputs.keys() ) , ["pixel_values", "input_ids", "attention_mask"] )
| 22 |
'''simple docstring'''
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
__SCREAMING_SNAKE_CASE :Dict = 1e-4
if is_torch_available():
import torch
from transformers import AutoformerConfig, AutoformerForPrediction, AutoformerModel
from transformers.models.autoformer.modeling_autoformer import AutoformerDecoder, AutoformerEncoder
@require_torch
class A_ :
def __init__( self : List[Any] , snake_case_ : int , snake_case_ : Dict=1_6 , snake_case_ : Dict=1_3 , snake_case_ : int=7 , snake_case_ : Any=1_4 , snake_case_ : int=1_0 , snake_case_ : Any=1_9 , snake_case_ : int=5 , snake_case_ : Any=4 , snake_case_ : Tuple=True , snake_case_ : Optional[int]=1_6 , snake_case_ : List[str]=2 , snake_case_ : Any=4 , snake_case_ : List[Any]=4 , snake_case_ : Optional[Any]="gelu" , snake_case_ : Optional[int]=0.1 , snake_case_ : Union[str, Any]=0.1 , snake_case_ : Tuple=[1, 2, 3, 4, 5] , snake_case_ : str=2_5 , snake_case_ : Any=5 , ):
_UpperCAmelCase = d_model
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = prediction_length
_UpperCAmelCase = context_length
_UpperCAmelCase = cardinality
_UpperCAmelCase = num_time_features
_UpperCAmelCase = lags_sequence
_UpperCAmelCase = embedding_dimension
_UpperCAmelCase = is_training
_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 = context_length
_UpperCAmelCase = prediction_length + label_length
_UpperCAmelCase = label_length
_UpperCAmelCase = moving_average
_UpperCAmelCase = autocorrelation_factor
def lowercase ( self : Union[str, Any] ):
return AutoformerConfig(
d_model=self.d_model , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , prediction_length=self.prediction_length , context_length=self.context_length , label_length=self.label_length , lags_sequence=self.lags_sequence , num_time_features=self.num_time_features , num_static_categorical_features=1 , cardinality=[self.cardinality] , embedding_dimension=[self.embedding_dimension] , moving_average=self.moving_average , )
def lowercase ( self : int , snake_case_ : Optional[Any] ):
_UpperCAmelCase = config.context_length + max(config.lags_sequence )
_UpperCAmelCase = ids_tensor([self.batch_size, 1] , config.cardinality[0] )
_UpperCAmelCase = floats_tensor([self.batch_size, _past_length, config.num_time_features] )
_UpperCAmelCase = floats_tensor([self.batch_size, _past_length] )
_UpperCAmelCase = floats_tensor([self.batch_size, _past_length] ) > 0.5
# decoder inputs
_UpperCAmelCase = floats_tensor([self.batch_size, config.prediction_length, config.num_time_features] )
_UpperCAmelCase = floats_tensor([self.batch_size, config.prediction_length] )
_UpperCAmelCase = {
"past_values": past_values,
"static_categorical_features": static_categorical_features,
"past_time_features": past_time_features,
"past_observed_mask": past_observed_mask,
"future_time_features": future_time_features,
"future_values": future_values,
}
return inputs_dict
def lowercase ( self : List[Any] ):
_UpperCAmelCase = self.get_config()
_UpperCAmelCase = self.prepare_autoformer_inputs_dict(snake_case_ )
return config, inputs_dict
def lowercase ( self : List[str] ):
_UpperCAmelCase , _UpperCAmelCase = self.prepare_config_and_inputs()
return config, inputs_dict
def lowercase ( self : Optional[Any] , snake_case_ : int , snake_case_ : Optional[int] ):
_UpperCAmelCase = AutoformerModel(config=snake_case_ ).to(snake_case_ ).eval()
_UpperCAmelCase = model(**snake_case_ )
_UpperCAmelCase = outputs.encoder_last_hidden_state
_UpperCAmelCase = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
_UpperCAmelCase = model.get_encoder()
encoder.save_pretrained(snake_case_ )
_UpperCAmelCase = AutoformerEncoder.from_pretrained(snake_case_ ).to(snake_case_ )
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = model.create_network_inputs(**snake_case_ )
_UpperCAmelCase , _UpperCAmelCase = model.decomposition_layer(transformer_inputs[:, : config.context_length, ...] )
_UpperCAmelCase = torch.cat(
(transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...]) , dim=-1 , )
_UpperCAmelCase = encoder(inputs_embeds=snake_case_ )[0]
self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1e-3 )
_UpperCAmelCase = (
torch.mean(transformer_inputs[:, : config.context_length, ...] , dim=1 )
.unsqueeze(1 )
.repeat(1 , config.prediction_length , 1 )
)
_UpperCAmelCase = torch.zeros(
[transformer_inputs.shape[0], config.prediction_length, transformer_inputs.shape[2]] , device=enc_input.device , )
_UpperCAmelCase = torch.cat(
(
torch.cat((seasonal_input[:, -config.label_length :, ...], zeros) , dim=1 ),
feature[:, config.context_length - config.label_length :, ...],
) , dim=-1 , )
_UpperCAmelCase = torch.cat(
(
torch.cat((trend_input[:, -config.label_length :, ...], mean) , dim=1 ),
feature[:, config.context_length - config.label_length :, ...],
) , dim=-1 , )
with tempfile.TemporaryDirectory() as tmpdirname:
_UpperCAmelCase = model.get_decoder()
decoder.save_pretrained(snake_case_ )
_UpperCAmelCase = AutoformerDecoder.from_pretrained(snake_case_ ).to(snake_case_ )
_UpperCAmelCase = decoder(
trend=snake_case_ , inputs_embeds=snake_case_ , encoder_hidden_states=snake_case_ , )[0]
self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1e-3 )
@require_torch
class A_ ( lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
_lowerCamelCase : List[Any] = (AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
_lowerCamelCase : Tuple = (AutoformerForPrediction,) if is_torch_available() else ()
_lowerCamelCase : List[Any] = {"""feature-extraction""": AutoformerModel} if is_torch_available() else {}
_lowerCamelCase : Optional[Any] = False
_lowerCamelCase : Tuple = False
_lowerCamelCase : int = False
_lowerCamelCase : Optional[Any] = False
_lowerCamelCase : Optional[Any] = False
_lowerCamelCase : List[Any] = False
def lowercase ( self : Tuple ):
_UpperCAmelCase = AutoformerModelTester(self )
_UpperCAmelCase = ConfigTester(self , config_class=snake_case_ , has_text_modality=snake_case_ )
def lowercase ( self : Optional[Any] ):
self.config_tester.run_common_tests()
def lowercase ( self : Union[str, Any] ):
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
_UpperCAmelCase = model_class(snake_case_ )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(snake_case_ )
_UpperCAmelCase , _UpperCAmelCase = model_class.from_pretrained(snake_case_ , output_loading_info=snake_case_ )
self.assertEqual(info["missing_keys"] , [] )
def lowercase ( self : Optional[int] ):
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*snake_case_ )
@unittest.skip(reason="Model has no tokens embeddings" )
def lowercase ( self : Optional[int] ):
pass
def lowercase ( self : Optional[int] ):
_UpperCAmelCase = inspect.signature(getattr(snake_case_ , "forward" ) )
# The main input is the name of the argument after `self`
_UpperCAmelCase = list(model_signature.parameters.keys() )[1]
self.assertEqual(AutoformerModel.main_input_name , snake_case_ )
def lowercase ( self : List[str] ):
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase = model_class(snake_case_ )
_UpperCAmelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_UpperCAmelCase = [*signature.parameters.keys()]
_UpperCAmelCase = [
"past_values",
"past_time_features",
"past_observed_mask",
"static_categorical_features",
"static_real_features",
"future_values",
"future_time_features",
]
if model.__class__.__name__ in ["AutoformerForPrediction"]:
expected_arg_names.append("future_observed_mask" )
expected_arg_names.extend(
[
"decoder_attention_mask",
"head_mask",
"decoder_head_mask",
"cross_attn_head_mask",
"encoder_outputs",
"past_key_values",
"output_hidden_states",
"output_attentions",
"use_cache",
"return_dict",
] )
self.assertListEqual(arg_names[: len(snake_case_ )] , snake_case_ )
def lowercase ( self : Optional[int] ):
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
_UpperCAmelCase = True
_UpperCAmelCase = getattr(self.model_tester , "seq_length" , snake_case_ )
_UpperCAmelCase = getattr(self.model_tester , "decoder_seq_length" , snake_case_ )
_UpperCAmelCase = getattr(self.model_tester , "encoder_seq_length" , snake_case_ )
_UpperCAmelCase = getattr(self.model_tester , "d_model" , snake_case_ )
_UpperCAmelCase = getattr(self.model_tester , "num_attention_heads" , snake_case_ )
_UpperCAmelCase = d_model // num_attention_heads
for model_class in self.all_model_classes:
_UpperCAmelCase = True
_UpperCAmelCase = False
_UpperCAmelCase = True
_UpperCAmelCase = model_class(snake_case_ )
model.to(snake_case_ )
model.eval()
with torch.no_grad():
_UpperCAmelCase = model(**self._prepare_for_class(snake_case_ , snake_case_ ) )
_UpperCAmelCase = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(snake_case_ ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
_UpperCAmelCase = True
_UpperCAmelCase = model_class(snake_case_ )
model.to(snake_case_ )
model.eval()
with torch.no_grad():
_UpperCAmelCase = model(**self._prepare_for_class(snake_case_ , snake_case_ ) )
_UpperCAmelCase = outputs.encoder_attentions
self.assertEqual(len(snake_case_ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, dim] , )
_UpperCAmelCase = len(snake_case_ )
_UpperCAmelCase = 7
if "last_hidden_state" in outputs:
correct_outlen += 1
if "trend" in outputs:
correct_outlen += 1
if "past_key_values" in outputs:
correct_outlen += 1 # past_key_values have been returned
if "loss" in outputs:
correct_outlen += 1
if "params" in outputs:
correct_outlen += 1
self.assertEqual(snake_case_ , snake_case_ )
# decoder attentions
_UpperCAmelCase = outputs.decoder_attentions
self.assertIsInstance(snake_case_ , (list, tuple) )
self.assertEqual(len(snake_case_ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, decoder_seq_length, dim] , )
# cross attentions
_UpperCAmelCase = outputs.cross_attentions
self.assertIsInstance(snake_case_ , (list, tuple) )
self.assertEqual(len(snake_case_ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(cross_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, decoder_seq_length, dim] , )
# Check attention is always last and order is fine
_UpperCAmelCase = True
_UpperCAmelCase = True
_UpperCAmelCase = model_class(snake_case_ )
model.to(snake_case_ )
model.eval()
with torch.no_grad():
_UpperCAmelCase = model(**self._prepare_for_class(snake_case_ , snake_case_ ) )
self.assertEqual(out_len + 2 , len(snake_case_ ) )
_UpperCAmelCase = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(snake_case_ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, dim] , )
@is_flaky()
def lowercase ( self : Dict ):
super().test_retain_grad_hidden_states_attentions()
def UpperCAmelCase_ ( __lowercase : str="train-batch.pt" ) -> List[str]:
'''simple docstring'''
_UpperCAmelCase = hf_hub_download(repo_id="hf-internal-testing/tourism-monthly-batch" , filename=__lowercase , repo_type="dataset" )
_UpperCAmelCase = torch.load(__lowercase , map_location=__lowercase )
return batch
@require_torch
@slow
class A_ ( unittest.TestCase ):
def lowercase ( self : Optional[int] ):
_UpperCAmelCase = AutoformerModel.from_pretrained("huggingface/autoformer-tourism-monthly" ).to(snake_case_ )
_UpperCAmelCase = prepare_batch()
with torch.no_grad():
_UpperCAmelCase = model(
past_values=batch["past_values"] , past_time_features=batch["past_time_features"] , past_observed_mask=batch["past_observed_mask"] , static_categorical_features=batch["static_categorical_features"] , future_values=batch["future_values"] , future_time_features=batch["future_time_features"] , )[0]
_UpperCAmelCase = torch.Size(
(6_4, model.config.prediction_length + model.config.label_length, model.config.feature_size) )
self.assertEqual(output.shape , snake_case_ )
_UpperCAmelCase = torch.tensor(
[[0.3_5_9_3, -1.3_3_9_8, 0.6_3_3_0], [0.2_2_7_9, 1.5_3_9_6, -0.1_7_9_2], [0.0_4_5_0, 1.3_2_2_5, -0.2_3_3_5]] , device=snake_case_ )
self.assertTrue(torch.allclose(output[0, :3, :3] , snake_case_ , atol=snake_case_ ) )
def lowercase ( self : Optional[Any] ):
_UpperCAmelCase = AutoformerForPrediction.from_pretrained("huggingface/autoformer-tourism-monthly" ).to(snake_case_ )
_UpperCAmelCase = prepare_batch("val-batch.pt" )
with torch.no_grad():
_UpperCAmelCase = model(
past_values=batch["past_values"] , past_time_features=batch["past_time_features"] , past_observed_mask=batch["past_observed_mask"] , static_categorical_features=batch["static_categorical_features"] , ).encoder_last_hidden_state
_UpperCAmelCase = torch.Size((6_4, model.config.context_length, model.config.d_model) )
self.assertEqual(output.shape , snake_case_ )
_UpperCAmelCase = torch.tensor(
[[-0.0_7_3_4, -0.9_0_3_6, 0.8_3_5_8], [4.7_1_8_6, 2.4_1_1_3, 1.9_5_8_1], [1.7_9_5_3, 2.3_5_5_8, 1.2_9_7_0]] , device=snake_case_ )
self.assertTrue(torch.allclose(output[0, :3, :3] , snake_case_ , atol=snake_case_ ) )
def lowercase ( self : Tuple ):
_UpperCAmelCase = AutoformerForPrediction.from_pretrained("huggingface/autoformer-tourism-monthly" ).to(snake_case_ )
_UpperCAmelCase = prepare_batch("val-batch.pt" )
with torch.no_grad():
_UpperCAmelCase = model.generate(
static_categorical_features=batch["static_categorical_features"] , past_time_features=batch["past_time_features"] , past_values=batch["past_values"] , future_time_features=batch["future_time_features"] , past_observed_mask=batch["past_observed_mask"] , )
_UpperCAmelCase = torch.Size((6_4, model.config.num_parallel_samples, model.config.prediction_length) )
self.assertEqual(outputs.sequences.shape , snake_case_ )
_UpperCAmelCase = torch.tensor([3_1_3_0.6_7_6_3, 4_0_5_6.5_2_9_3, 7_0_5_3.0_7_8_6] , device=snake_case_ )
_UpperCAmelCase = outputs.sequences.mean(dim=1 )
self.assertTrue(torch.allclose(mean_prediction[0, -3:] , snake_case_ , rtol=1e-1 ) )
| 22 | 1 |
'''simple docstring'''
from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_tf_bert import TFBertModelTester
from ..clip.test_modeling_tf_clip import TFCLIPVisionModelTester
from ..deit.test_modeling_tf_deit import TFDeiTModelTester
from ..roberta.test_modeling_tf_roberta import TFRobertaModelTester
from ..vit.test_modeling_tf_vit import TFViTModelTester
if is_tf_available():
from transformers import (
TFBertModel,
TFCLIPVisionModel,
TFDeiTModel,
TFRobertaModel,
TFVisionTextDualEncoderModel,
TFViTModel,
VisionTextDualEncoderConfig,
)
if is_vision_available():
from PIL import Image
from transformers import VisionTextDualEncoderProcessor
def UpperCAmelCase_ ( __lowercase : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
if isinstance(__lowercase , collections.abc.Iterable ):
return x
return (x, x)
@require_tf
class A_ :
def lowercase ( self : str , snake_case_ : int , snake_case_ : int ):
pass
def lowercase ( self : Optional[Any] ):
pass
def lowercase ( self : int ):
pass
def lowercase ( self : List[Any] , snake_case_ : int , snake_case_ : Dict , snake_case_ : Any , snake_case_ : int , snake_case_ : List[str]=None , **snake_case_ : int ):
_UpperCAmelCase = VisionTextDualEncoderConfig.from_vision_text_configs(snake_case_ , snake_case_ )
_UpperCAmelCase = TFVisionTextDualEncoderModel(snake_case_ )
_UpperCAmelCase = model(input_ids=snake_case_ , pixel_values=snake_case_ , attention_mask=snake_case_ )
self.assertEqual(output["text_embeds"].shape , (input_ids.shape[0], config.projection_dim) )
self.assertEqual(output["image_embeds"].shape , (pixel_values.shape[0], config.projection_dim) )
def lowercase ( self : List[str] , snake_case_ : int , snake_case_ : Tuple , snake_case_ : Union[str, Any] , snake_case_ : Optional[int] , snake_case_ : Tuple=None , **snake_case_ : Optional[int] ):
_UpperCAmelCase , _UpperCAmelCase = self.get_vision_text_model(snake_case_ , snake_case_ )
_UpperCAmelCase = TFVisionTextDualEncoderModel(vision_model=snake_case_ , text_model=snake_case_ )
_UpperCAmelCase = model(input_ids=snake_case_ , pixel_values=snake_case_ , attention_mask=snake_case_ )
self.assertEqual(output["text_embeds"].shape , (input_ids.shape[0], model.config.projection_dim) )
self.assertEqual(output["image_embeds"].shape , (pixel_values.shape[0], model.config.projection_dim) )
def lowercase ( self : Union[str, Any] , snake_case_ : Union[str, Any] , snake_case_ : int , snake_case_ : Tuple , snake_case_ : int , snake_case_ : Union[str, Any]=None , **snake_case_ : Any ):
_UpperCAmelCase , _UpperCAmelCase = self.get_vision_text_model(snake_case_ , snake_case_ )
_UpperCAmelCase = {"vision_model": vision_model, "text_model": text_model}
_UpperCAmelCase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(**snake_case_ )
_UpperCAmelCase = model(input_ids=snake_case_ , pixel_values=snake_case_ , attention_mask=snake_case_ )
self.assertEqual(output["text_embeds"].shape , (input_ids.shape[0], model.config.projection_dim) )
self.assertEqual(output["image_embeds"].shape , (pixel_values.shape[0], model.config.projection_dim) )
def lowercase ( self : Optional[int] , snake_case_ : List[Any] , snake_case_ : Optional[Any] , snake_case_ : Optional[Any] , snake_case_ : Tuple , snake_case_ : Optional[Any]=None , **snake_case_ : Union[str, Any] ):
_UpperCAmelCase , _UpperCAmelCase = self.get_vision_text_model(snake_case_ , snake_case_ )
_UpperCAmelCase = TFVisionTextDualEncoderModel(vision_model=snake_case_ , text_model=snake_case_ )
_UpperCAmelCase = model(input_ids=snake_case_ , pixel_values=snake_case_ , attention_mask=snake_case_ )
_UpperCAmelCase = output[0].numpy()
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(snake_case_ )
_UpperCAmelCase = TFVisionTextDualEncoderModel.from_pretrained(snake_case_ )
_UpperCAmelCase = model(input_ids=snake_case_ , pixel_values=snake_case_ , attention_mask=snake_case_ )
_UpperCAmelCase = after_output[0].numpy()
_UpperCAmelCase = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(snake_case_ , 1e-5 )
def lowercase ( self : int , snake_case_ : Optional[Any] , snake_case_ : Any , snake_case_ : List[Any] , snake_case_ : Any , snake_case_ : int=None , **snake_case_ : str ):
_UpperCAmelCase , _UpperCAmelCase = self.get_vision_text_model(snake_case_ , snake_case_ )
_UpperCAmelCase = TFVisionTextDualEncoderModel(vision_model=snake_case_ , text_model=snake_case_ )
_UpperCAmelCase = model(
input_ids=snake_case_ , pixel_values=snake_case_ , attention_mask=snake_case_ , output_attentions=snake_case_ )
_UpperCAmelCase = output.vision_model_output.attentions
self.assertEqual(len(snake_case_ ) , vision_config.num_hidden_layers )
# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
_UpperCAmelCase = to_atuple(vision_model.config.image_size )
_UpperCAmelCase = to_atuple(vision_model.config.patch_size )
_UpperCAmelCase = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
_UpperCAmelCase = num_patches + 1
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len) )
_UpperCAmelCase = output.text_model_output.attentions
self.assertEqual(len(snake_case_ ) , text_config.num_hidden_layers )
self.assertEqual(
text_attentions[0].shape[-3:] , (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) , )
def lowercase ( self : Optional[int] , snake_case_ : np.ndarray , snake_case_ : np.ndarray , snake_case_ : float ):
_UpperCAmelCase = np.abs((a - b) ).max()
self.assertLessEqual(snake_case_ , snake_case_ , f'Difference between torch and flax is {diff} (>= {tol}).' )
def lowercase ( self : Tuple ):
_UpperCAmelCase = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_model(**snake_case_ )
def lowercase ( self : Any ):
_UpperCAmelCase = self.prepare_config_and_inputs()
self.check_model_from_pretrained_configs(**snake_case_ )
def lowercase ( self : Any ):
_UpperCAmelCase = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_from_pretrained(**snake_case_ )
def lowercase ( self : Union[str, Any] ):
_UpperCAmelCase = self.prepare_config_and_inputs()
self.check_save_load(**snake_case_ )
def lowercase ( self : Union[str, Any] ):
_UpperCAmelCase = self.prepare_config_and_inputs()
self.check_vision_text_output_attention(**snake_case_ )
@slow
def lowercase ( self : List[str] ):
_UpperCAmelCase , _UpperCAmelCase = self.get_pretrained_model_and_inputs()
_UpperCAmelCase = model_a(**snake_case_ )
_UpperCAmelCase = outputs[0].numpy()
with tempfile.TemporaryDirectory() as tmp_dirname:
model_a.save_pretrained(snake_case_ )
_UpperCAmelCase = TFVisionTextDualEncoderModel.from_pretrained(snake_case_ )
_UpperCAmelCase = model_a(**snake_case_ )
_UpperCAmelCase = after_outputs[0].numpy()
_UpperCAmelCase = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(snake_case_ , 1e-5 )
@require_tf
class A_ ( lowerCAmelCase_ , unittest.TestCase ):
def lowercase ( self : Optional[int] ):
_UpperCAmelCase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"hf-internal-testing/tiny-random-vit" , "hf-internal-testing/tiny-random-bert" )
_UpperCAmelCase = 1_3
_UpperCAmelCase = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
_UpperCAmelCase = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
_UpperCAmelCase = random_attention_mask([batch_size, 4] )
_UpperCAmelCase = {"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}
return model, inputs
def lowercase ( self : str , snake_case_ : Optional[int] , snake_case_ : Union[str, Any] ):
_UpperCAmelCase = TFViTModel(snake_case_ , name="vision_model" )
_UpperCAmelCase = TFBertModel(snake_case_ , name="text_model" )
return vision_model, text_model
def lowercase ( self : int ):
_UpperCAmelCase = TFViTModelTester(self )
_UpperCAmelCase = TFBertModelTester(self )
_UpperCAmelCase = vit_model_tester.prepare_config_and_inputs()
_UpperCAmelCase = bert_model_tester.prepare_config_and_inputs()
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = vision_config_and_inputs
(
(
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) ,
) = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_tf
class A_ ( lowerCAmelCase_ , unittest.TestCase ):
def lowercase ( self : List[Any] ):
# DeiT repo doesn't have TF weights, but we don't actually use the weights at all so let's
# just reinitialize it.
_UpperCAmelCase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"Rocketknight1/tiny-random-deit-tf" , "hf-internal-testing/tiny-random-roberta" )
_UpperCAmelCase = 1_3
_UpperCAmelCase = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
_UpperCAmelCase = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
_UpperCAmelCase = random_attention_mask([batch_size, 4] )
_UpperCAmelCase = {"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}
return model, inputs
def lowercase ( self : int , snake_case_ : int , snake_case_ : Dict , snake_case_ : str , snake_case_ : Tuple , snake_case_ : str=None , **snake_case_ : Tuple ):
_UpperCAmelCase , _UpperCAmelCase = self.get_vision_text_model(snake_case_ , snake_case_ )
_UpperCAmelCase = TFVisionTextDualEncoderModel(vision_model=snake_case_ , text_model=snake_case_ )
_UpperCAmelCase = model(
input_ids=snake_case_ , pixel_values=snake_case_ , attention_mask=snake_case_ , output_attentions=snake_case_ )
_UpperCAmelCase = output.vision_model_output.attentions
self.assertEqual(len(snake_case_ ) , vision_config.num_hidden_layers )
# in DEiT, the seq_len equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
_UpperCAmelCase = to_atuple(vision_model.config.image_size )
_UpperCAmelCase = to_atuple(vision_model.config.patch_size )
_UpperCAmelCase = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
_UpperCAmelCase = num_patches + 2
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len) )
_UpperCAmelCase = output.text_model_output.attentions
self.assertEqual(len(snake_case_ ) , text_config.num_hidden_layers )
self.assertEqual(
text_attentions[0].shape[-3:] , (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) , )
def lowercase ( self : List[Any] , snake_case_ : Dict , snake_case_ : Optional[Any] ):
_UpperCAmelCase = TFDeiTModel(snake_case_ , name="vision_model" )
_UpperCAmelCase = TFRobertaModel(snake_case_ , name="text_model" )
return vision_model, text_model
def lowercase ( self : Any ):
_UpperCAmelCase = TFDeiTModelTester(self )
_UpperCAmelCase = TFRobertaModelTester(self )
_UpperCAmelCase = vit_model_tester.prepare_config_and_inputs()
_UpperCAmelCase = bert_model_tester.prepare_config_and_inputs()
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = vision_config_and_inputs
(
(
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) ,
) = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_tf
class A_ ( lowerCAmelCase_ , unittest.TestCase ):
def lowercase ( self : Union[str, Any] ):
_UpperCAmelCase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"Rocketknight1/tiny-random-clip-tf" , "hf-internal-testing/tiny-random-bert" )
_UpperCAmelCase = 1_3
_UpperCAmelCase = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
_UpperCAmelCase = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
_UpperCAmelCase = random_attention_mask([batch_size, 4] )
_UpperCAmelCase = {"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}
return model, inputs
def lowercase ( self : Union[str, Any] , snake_case_ : str , snake_case_ : List[Any] ):
_UpperCAmelCase = TFCLIPVisionModel(snake_case_ , name="vision_model" )
_UpperCAmelCase = TFBertModel(snake_case_ , name="text_model" )
return vision_model, text_model
def lowercase ( self : Optional[int] ):
_UpperCAmelCase = TFCLIPVisionModelTester(self )
_UpperCAmelCase = TFBertModelTester(self )
_UpperCAmelCase = clip_model_tester.prepare_config_and_inputs()
_UpperCAmelCase = bert_model_tester.prepare_config_and_inputs()
_UpperCAmelCase , _UpperCAmelCase = vision_config_and_inputs
(
(
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) ,
) = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_vision
@require_tf
class A_ ( unittest.TestCase ):
@slow
def lowercase ( self : int ):
_UpperCAmelCase = TFVisionTextDualEncoderModel.from_pretrained(
"clip-italian/clip-italian" , logit_scale_init_value=1.0 , from_pt=snake_case_ )
_UpperCAmelCase = VisionTextDualEncoderProcessor.from_pretrained("clip-italian/clip-italian" )
_UpperCAmelCase = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
_UpperCAmelCase = processor(
text=["una foto di un gatto", "una foto di un cane"] , images=snake_case_ , padding=snake_case_ , return_tensors="np" )
_UpperCAmelCase = model(**snake_case_ )
# verify the logits
self.assertEqual(outputs.logits_per_image.shape , (inputs.pixel_values.shape[0], inputs.input_ids.shape[0]) )
self.assertEqual(
outputs.logits_per_text.shape , (inputs.input_ids.shape[0], inputs.pixel_values.shape[0]) , )
_UpperCAmelCase = np.array([[1.2_2_8_4_7_2_7, 0.3_1_0_4_1_2_2]] )
self.assertTrue(np.allclose(outputs.logits_per_image.numpy() , snake_case_ , atol=1e-3 ) )
| 22 |
'''simple docstring'''
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_space_optuna,
default_hp_space_ray,
default_hp_space_sigopt,
default_hp_space_wandb,
)
from .utils import logging
__SCREAMING_SNAKE_CASE :int = logging.get_logger(__name__)
class A_ :
_lowerCamelCase : str
_lowerCamelCase : str = None
@staticmethod
def lowercase ( ):
raise NotImplementedError
def lowercase ( self : Union[str, Any] , snake_case_ : Optional[int] , snake_case_ : int , snake_case_ : str , **snake_case_ : List[Any] ):
raise NotImplementedError
def lowercase ( self : Any , snake_case_ : int ):
raise NotImplementedError
def lowercase ( self : List[str] ):
if not self.is_available():
raise RuntimeError(
f'You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}.' )
@classmethod
def lowercase ( cls : List[Any] ):
return f'`pip install {cls.pip_package or cls.name}`'
class A_ ( lowerCAmelCase_ ):
_lowerCamelCase : int = """optuna"""
@staticmethod
def lowercase ( ):
return is_optuna_available()
def lowercase ( self : List[str] , snake_case_ : Any , snake_case_ : int , snake_case_ : str , **snake_case_ : Tuple ):
return run_hp_search_optuna(snake_case_ , snake_case_ , snake_case_ , **snake_case_ )
def lowercase ( self : int , snake_case_ : Optional[int] ):
return default_hp_space_optuna(snake_case_ )
class A_ ( lowerCAmelCase_ ):
_lowerCamelCase : Any = """ray"""
_lowerCamelCase : Tuple = """'ray[tune]'"""
@staticmethod
def lowercase ( ):
return is_ray_available()
def lowercase ( self : Optional[Any] , snake_case_ : Any , snake_case_ : int , snake_case_ : str , **snake_case_ : List[str] ):
return run_hp_search_ray(snake_case_ , snake_case_ , snake_case_ , **snake_case_ )
def lowercase ( self : Any , snake_case_ : str ):
return default_hp_space_ray(snake_case_ )
class A_ ( lowerCAmelCase_ ):
_lowerCamelCase : int = """sigopt"""
@staticmethod
def lowercase ( ):
return is_sigopt_available()
def lowercase ( self : Any , snake_case_ : int , snake_case_ : int , snake_case_ : str , **snake_case_ : Dict ):
return run_hp_search_sigopt(snake_case_ , snake_case_ , snake_case_ , **snake_case_ )
def lowercase ( self : Dict , snake_case_ : Optional[Any] ):
return default_hp_space_sigopt(snake_case_ )
class A_ ( lowerCAmelCase_ ):
_lowerCamelCase : Optional[int] = """wandb"""
@staticmethod
def lowercase ( ):
return is_wandb_available()
def lowercase ( self : Optional[Any] , snake_case_ : Optional[Any] , snake_case_ : int , snake_case_ : str , **snake_case_ : Optional[Any] ):
return run_hp_search_wandb(snake_case_ , snake_case_ , snake_case_ , **snake_case_ )
def lowercase ( self : Any , snake_case_ : Union[str, Any] ):
return default_hp_space_wandb(snake_case_ )
__SCREAMING_SNAKE_CASE :Dict = {
HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}
def UpperCAmelCase_ ( ) -> str:
'''simple docstring'''
_UpperCAmelCase = [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
if len(__lowercase ) > 0:
_UpperCAmelCase = available_backends[0].name
if len(__lowercase ) > 1:
logger.info(
f'{len(__lowercase )} hyperparameter search backends available. Using {name} as the default.' )
return name
raise RuntimeError(
"No hyperparameter search backend available.\n"
+ "\n".join(
f' - To install {backend.name} run {backend.pip_install()}'
for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() ) )
| 22 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE :Any = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE :int = {'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/config.json'''}
class A_ ( lowerCAmelCase_ ):
_lowerCamelCase : str = """openai-gpt"""
_lowerCamelCase : Union[str, Any] = {
"""max_position_embeddings""": """n_positions""",
"""hidden_size""": """n_embd""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self : Optional[Any] , snake_case_ : Optional[Any]=4_0_4_7_8 , snake_case_ : int=5_1_2 , snake_case_ : Optional[Any]=7_6_8 , snake_case_ : Any=1_2 , snake_case_ : str=1_2 , snake_case_ : Optional[Any]="gelu" , snake_case_ : Optional[Any]=0.1 , snake_case_ : Optional[int]=0.1 , snake_case_ : str=0.1 , snake_case_ : Optional[Any]=1e-5 , snake_case_ : int=0.0_2 , snake_case_ : Optional[int]="cls_index" , snake_case_ : Union[str, Any]=True , snake_case_ : Any=None , snake_case_ : Optional[int]=True , snake_case_ : List[Any]=0.1 , **snake_case_ : List[Any] , ):
_UpperCAmelCase = vocab_size
_UpperCAmelCase = n_positions
_UpperCAmelCase = n_embd
_UpperCAmelCase = n_layer
_UpperCAmelCase = n_head
_UpperCAmelCase = afn
_UpperCAmelCase = resid_pdrop
_UpperCAmelCase = embd_pdrop
_UpperCAmelCase = attn_pdrop
_UpperCAmelCase = layer_norm_epsilon
_UpperCAmelCase = initializer_range
_UpperCAmelCase = summary_type
_UpperCAmelCase = summary_use_proj
_UpperCAmelCase = summary_activation
_UpperCAmelCase = summary_first_dropout
_UpperCAmelCase = summary_proj_to_labels
super().__init__(**snake_case_ )
| 22 |
'''simple docstring'''
__SCREAMING_SNAKE_CASE :List[str] = '''0.18.2'''
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_librosa_available,
is_note_seq_available,
is_onnx_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
is_transformers_available,
is_transformers_version,
is_unidecode_available,
logging,
)
try:
if not is_onnx_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_onnx_objects import * # noqa F403
else:
from .pipelines import OnnxRuntimeModel
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_pt_objects import * # noqa F403
else:
from .models import (
AutoencoderKL,
ControlNetModel,
ModelMixin,
PriorTransformer,
TaFilmDecoder,
TransformeraDModel,
UNetaDModel,
UNetaDConditionModel,
UNetaDModel,
UNetaDConditionModel,
VQModel,
)
from .optimization import (
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
get_scheduler,
)
from .pipelines import (
AudioPipelineOutput,
ConsistencyModelPipeline,
DanceDiffusionPipeline,
DDIMPipeline,
DDPMPipeline,
DiffusionPipeline,
DiTPipeline,
ImagePipelineOutput,
KarrasVePipeline,
LDMPipeline,
LDMSuperResolutionPipeline,
PNDMPipeline,
RePaintPipeline,
ScoreSdeVePipeline,
)
from .schedulers import (
CMStochasticIterativeScheduler,
DDIMInverseScheduler,
DDIMParallelScheduler,
DDIMScheduler,
DDPMParallelScheduler,
DDPMScheduler,
DEISMultistepScheduler,
DPMSolverMultistepInverseScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
HeunDiscreteScheduler,
IPNDMScheduler,
KarrasVeScheduler,
KDPMaAncestralDiscreteScheduler,
KDPMaDiscreteScheduler,
PNDMScheduler,
RePaintScheduler,
SchedulerMixin,
ScoreSdeVeScheduler,
UnCLIPScheduler,
UniPCMultistepScheduler,
VQDiffusionScheduler,
)
from .training_utils import EMAModel
try:
if not (is_torch_available() and is_scipy_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_scipy_objects import * # noqa F403
else:
from .schedulers import LMSDiscreteScheduler
try:
if not (is_torch_available() and is_torchsde_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_torchsde_objects import * # noqa F403
else:
from .schedulers import DPMSolverSDEScheduler
try:
if not (is_torch_available() and is_transformers_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .pipelines import (
AltDiffusionImgaImgPipeline,
AltDiffusionPipeline,
AudioLDMPipeline,
CycleDiffusionPipeline,
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
ImageTextPipelineOutput,
KandinskyImgaImgPipeline,
KandinskyInpaintPipeline,
KandinskyPipeline,
KandinskyPriorPipeline,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaControlnetPipeline,
KandinskyVaaImgaImgPipeline,
KandinskyVaaInpaintPipeline,
KandinskyVaaPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
KandinskyVaaPriorPipeline,
LDMTextToImagePipeline,
PaintByExamplePipeline,
SemanticStableDiffusionPipeline,
ShapEImgaImgPipeline,
ShapEPipeline,
StableDiffusionAttendAndExcitePipeline,
StableDiffusionControlNetImgaImgPipeline,
StableDiffusionControlNetInpaintPipeline,
StableDiffusionControlNetPipeline,
StableDiffusionDepthaImgPipeline,
StableDiffusionDiffEditPipeline,
StableDiffusionImageVariationPipeline,
StableDiffusionImgaImgPipeline,
StableDiffusionInpaintPipeline,
StableDiffusionInpaintPipelineLegacy,
StableDiffusionInstructPixaPixPipeline,
StableDiffusionLatentUpscalePipeline,
StableDiffusionLDMaDPipeline,
StableDiffusionModelEditingPipeline,
StableDiffusionPanoramaPipeline,
StableDiffusionParadigmsPipeline,
StableDiffusionPipeline,
StableDiffusionPipelineSafe,
StableDiffusionPixaPixZeroPipeline,
StableDiffusionSAGPipeline,
StableDiffusionUpscalePipeline,
StableUnCLIPImgaImgPipeline,
StableUnCLIPPipeline,
TextToVideoSDPipeline,
TextToVideoZeroPipeline,
UnCLIPImageVariationPipeline,
UnCLIPPipeline,
UniDiffuserModel,
UniDiffuserPipeline,
UniDiffuserTextDecoder,
VersatileDiffusionDualGuidedPipeline,
VersatileDiffusionImageVariationPipeline,
VersatileDiffusionPipeline,
VersatileDiffusionTextToImagePipeline,
VideoToVideoSDPipeline,
VQDiffusionPipeline,
)
try:
if not (is_torch_available() and is_transformers_available() and is_invisible_watermark_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_transformers_and_invisible_watermark_objects import * # noqa F403
else:
from .pipelines import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline
try:
if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_transformers_and_k_diffusion_objects import * # noqa F403
else:
from .pipelines import StableDiffusionKDiffusionPipeline
try:
if not (is_torch_available() and is_transformers_available() and is_onnx_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_transformers_and_onnx_objects import * # noqa F403
else:
from .pipelines import (
OnnxStableDiffusionImgaImgPipeline,
OnnxStableDiffusionInpaintPipeline,
OnnxStableDiffusionInpaintPipelineLegacy,
OnnxStableDiffusionPipeline,
OnnxStableDiffusionUpscalePipeline,
StableDiffusionOnnxPipeline,
)
try:
if not (is_torch_available() and is_librosa_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_librosa_objects import * # noqa F403
else:
from .pipelines import AudioDiffusionPipeline, Mel
try:
if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_transformers_and_torch_and_note_seq_objects import * # noqa F403
else:
from .pipelines import SpectrogramDiffusionPipeline
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_flax_objects import * # noqa F403
else:
from .models.controlnet_flax import FlaxControlNetModel
from .models.modeling_flax_utils import FlaxModelMixin
from .models.unet_ad_condition_flax import FlaxUNetaDConditionModel
from .models.vae_flax import FlaxAutoencoderKL
from .pipelines import FlaxDiffusionPipeline
from .schedulers import (
FlaxDDIMScheduler,
FlaxDDPMScheduler,
FlaxDPMSolverMultistepScheduler,
FlaxKarrasVeScheduler,
FlaxLMSDiscreteScheduler,
FlaxPNDMScheduler,
FlaxSchedulerMixin,
FlaxScoreSdeVeScheduler,
)
try:
if not (is_flax_available() and is_transformers_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_flax_and_transformers_objects import * # noqa F403
else:
from .pipelines import (
FlaxStableDiffusionControlNetPipeline,
FlaxStableDiffusionImgaImgPipeline,
FlaxStableDiffusionInpaintPipeline,
FlaxStableDiffusionPipeline,
)
try:
if not (is_note_seq_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_note_seq_objects import * # noqa F403
else:
from .pipelines import MidiProcessor
| 22 | 1 |
'''simple docstring'''
__SCREAMING_SNAKE_CASE :Union[str, Any] = range(2, 20 + 1)
__SCREAMING_SNAKE_CASE :Optional[Any] = [10**k for k in range(ks[-1] + 1)]
__SCREAMING_SNAKE_CASE :dict[int, dict[int, list[list[int]]]] = {}
def UpperCAmelCase_ ( __lowercase : Dict , __lowercase : int , __lowercase : Optional[int] , __lowercase : Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
_UpperCAmelCase = sum(a_i[j] for j in range(__lowercase , len(__lowercase ) ) )
_UpperCAmelCase = sum(a_i[j] * base[j] for j in range(min(len(__lowercase ) , __lowercase ) ) )
_UpperCAmelCase , _UpperCAmelCase = 0, 0
_UpperCAmelCase = n - i
_UpperCAmelCase = memo.get(__lowercase )
if sub_memo is not None:
_UpperCAmelCase = sub_memo.get(__lowercase )
if jumps is not None and len(__lowercase ) > 0:
# find and make the largest jump without going over
_UpperCAmelCase = -1
for _k in range(len(__lowercase ) - 1 , -1 , -1 ):
if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
_UpperCAmelCase = _k
break
if max_jump >= 0:
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = jumps[max_jump]
# since the difference between jumps is cached, add c
_UpperCAmelCase = diff + c
for j in range(min(__lowercase , len(__lowercase ) ) ):
_UpperCAmelCase , _UpperCAmelCase = divmod(__lowercase , 10 )
if new_c > 0:
add(__lowercase , __lowercase , __lowercase )
else:
_UpperCAmelCase = []
else:
_UpperCAmelCase = {c: []}
_UpperCAmelCase = sub_memo
if dn >= max_dn or c + diff >= base[k]:
return diff, dn
if k > ks[0]:
while True:
# keep doing smaller jumps
_UpperCAmelCase , _UpperCAmelCase = next_term(__lowercase , k - 1 , i + dn , __lowercase )
diff += _diff
dn += terms_jumped
if dn >= max_dn or c + diff >= base[k]:
break
else:
# would be too small a jump, just compute sequential terms instead
_UpperCAmelCase , _UpperCAmelCase = compute(__lowercase , __lowercase , i + dn , __lowercase )
diff += _diff
dn += terms_jumped
_UpperCAmelCase = sub_memo[c]
# keep jumps sorted by # of terms skipped
_UpperCAmelCase = 0
while j < len(__lowercase ):
if jumps[j][1] > dn:
break
j += 1
# cache the jump for this value digitsum(b) and c
sub_memo[c].insert(__lowercase , (diff, dn, k) )
return (diff, dn)
def UpperCAmelCase_ ( __lowercase : Any , __lowercase : Optional[Any] , __lowercase : int , __lowercase : Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
if i >= n:
return 0, i
if k > len(__lowercase ):
a_i.extend([0 for _ in range(k - len(__lowercase ) )] )
# note: a_i -> b * 10^k + c
# ds_b -> digitsum(b)
# ds_c -> digitsum(c)
_UpperCAmelCase = i
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = 0, 0, 0
for j in range(len(__lowercase ) ):
if j >= k:
ds_b += a_i[j]
else:
ds_c += a_i[j]
while i < n:
i += 1
_UpperCAmelCase = ds_c + ds_b
diff += addend
_UpperCAmelCase = 0
for j in range(__lowercase ):
_UpperCAmelCase = a_i[j] + addend
_UpperCAmelCase , _UpperCAmelCase = divmod(__lowercase , 10 )
ds_c += a_i[j]
if addend > 0:
break
if addend > 0:
add(__lowercase , __lowercase , __lowercase )
return diff, i - start_i
def UpperCAmelCase_ ( __lowercase : List[str] , __lowercase : Tuple , __lowercase : int ) -> str:
'''simple docstring'''
for j in range(__lowercase , len(__lowercase ) ):
_UpperCAmelCase = digits[j] + addend
if s >= 10:
_UpperCAmelCase , _UpperCAmelCase = divmod(__lowercase , 10 )
_UpperCAmelCase = addend // 10 + quotient
else:
_UpperCAmelCase = s
_UpperCAmelCase = addend // 10
if addend == 0:
break
while addend > 0:
_UpperCAmelCase , _UpperCAmelCase = divmod(__lowercase , 10 )
digits.append(__lowercase )
def UpperCAmelCase_ ( __lowercase : int = 10**15 ) -> int:
'''simple docstring'''
_UpperCAmelCase = [1]
_UpperCAmelCase = 1
_UpperCAmelCase = 0
while True:
_UpperCAmelCase , _UpperCAmelCase = next_term(__lowercase , 20 , i + dn , __lowercase )
dn += terms_jumped
if dn == n - i:
break
_UpperCAmelCase = 0
for j in range(len(__lowercase ) ):
a_n += digits[j] * 10**j
return a_n
if __name__ == "__main__":
print(F"{solution() = }")
| 22 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
__SCREAMING_SNAKE_CASE :Optional[int] = True
except (ImportError, ModuleNotFoundError):
__SCREAMING_SNAKE_CASE :str = False
if NLTK_AVAILABLE:
with FileLock('''.lock''') as lock:
nltk.download('''punkt''', quiet=True)
def UpperCAmelCase_ ( __lowercase : str ) -> str:
'''simple docstring'''
re.sub("<n>" , "" , __lowercase ) # remove pegasus newline char
assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
return "\n".join(nltk.sent_tokenize(__lowercase ) )
| 22 | 1 |
'''simple docstring'''
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
__SCREAMING_SNAKE_CASE :int = '''\
'''
__SCREAMING_SNAKE_CASE :List[Any] = '''
Perplexity (PPL) is one of the most common metrics for evaluating language models.
It is defined as the exponentiated average negative log-likelihood of a sequence.
For more information, see https://huggingface.co/docs/transformers/perplexity
'''
__SCREAMING_SNAKE_CASE :Tuple = '''
Args:
model_id (str): model used for calculating Perplexity
NOTE: Perplexity can only be calculated for causal language models.
This includes models such as gpt2, causal variations of bert,
causal versions of t5, and more (the full list can be found
in the AutoModelForCausalLM documentation here:
https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )
input_texts (list of str): input text, each separate text snippet
is one list entry.
batch_size (int): the batch size to run texts through the model. Defaults to 16.
add_start_token (bool): whether to add the start token to the texts,
so the perplexity can include the probability of the first word. Defaults to True.
device (str): device to run on, defaults to \'cuda\' when available
Returns:
perplexity: dictionary containing the perplexity scores for the texts
in the input list, as well as the mean perplexity. If one of the input texts is
longer than the max input length of the model, then it is truncated to the
max length for the perplexity computation.
Examples:
Example 1:
>>> perplexity = datasets.load_metric("perplexity")
>>> input_texts = ["lorem ipsum", "Happy Birthday!", "Bienvenue"]
>>> results = perplexity.compute(model_id=\'gpt2\',
... add_start_token=False,
... input_texts=input_texts) # doctest:+ELLIPSIS
>>> print(list(results.keys()))
[\'perplexities\', \'mean_perplexity\']
>>> print(round(results["mean_perplexity"], 2))
78.22
>>> print(round(results["perplexities"][0], 2))
11.11
Example 2:
>>> perplexity = datasets.load_metric("perplexity")
>>> input_texts = datasets.load_dataset("wikitext",
... "wikitext-2-raw-v1",
... split="test")["text"][:50] # doctest:+ELLIPSIS
[...]
>>> input_texts = [s for s in input_texts if s!=\'\']
>>> results = perplexity.compute(model_id=\'gpt2\',
... input_texts=input_texts) # doctest:+ELLIPSIS
>>> print(list(results.keys()))
[\'perplexities\', \'mean_perplexity\']
>>> print(round(results["mean_perplexity"], 2))
60.35
>>> print(round(results["perplexities"][0], 2))
81.12
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class A_ ( datasets.Metric ):
def lowercase ( self : int ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"input_texts": datasets.Value("string" ),
} ) , reference_urls=["https://huggingface.co/docs/transformers/perplexity"] , )
def lowercase ( self : Union[str, Any] , snake_case_ : int , snake_case_ : Optional[Any] , snake_case_ : int = 1_6 , snake_case_ : bool = True , snake_case_ : int=None ):
if device is not None:
assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
if device == "gpu":
_UpperCAmelCase = "cuda"
else:
_UpperCAmelCase = "cuda" if torch.cuda.is_available() else "cpu"
_UpperCAmelCase = AutoModelForCausalLM.from_pretrained(snake_case_ )
_UpperCAmelCase = model.to(snake_case_ )
_UpperCAmelCase = AutoTokenizer.from_pretrained(snake_case_ )
# if batch_size > 1 (which generally leads to padding being required), and
# if there is not an already assigned pad_token, assign an existing
# special token to also be the padding token
if tokenizer.pad_token is None and batch_size > 1:
_UpperCAmelCase = list(tokenizer.special_tokens_map_extended.values() )
# check that the model already has at least one special token defined
assert (
len(snake_case_ ) > 0
), "If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1."
# assign one of the special tokens to also be the pad token
tokenizer.add_special_tokens({"pad_token": existing_special_tokens[0]} )
if add_start_token:
# leave room for <BOS> token to be added:
assert (
tokenizer.bos_token is not None
), "Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False"
_UpperCAmelCase = model.config.max_length - 1
else:
_UpperCAmelCase = model.config.max_length
_UpperCAmelCase = tokenizer(
snake_case_ , add_special_tokens=snake_case_ , padding=snake_case_ , truncation=snake_case_ , max_length=snake_case_ , return_tensors="pt" , return_attention_mask=snake_case_ , ).to(snake_case_ )
_UpperCAmelCase = encodings["input_ids"]
_UpperCAmelCase = encodings["attention_mask"]
# check that each input is long enough:
if add_start_token:
assert torch.all(torch.ge(attn_masks.sum(1 ) , 1 ) ), "Each input text must be at least one token long."
else:
assert torch.all(
torch.ge(attn_masks.sum(1 ) , 2 ) ), "When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings."
_UpperCAmelCase = []
_UpperCAmelCase = CrossEntropyLoss(reduction="none" )
for start_index in logging.tqdm(range(0 , len(snake_case_ ) , snake_case_ ) ):
_UpperCAmelCase = min(start_index + batch_size , len(snake_case_ ) )
_UpperCAmelCase = encoded_texts[start_index:end_index]
_UpperCAmelCase = attn_masks[start_index:end_index]
if add_start_token:
_UpperCAmelCase = torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0 ) ).to(snake_case_ )
_UpperCAmelCase = torch.cat([bos_tokens_tensor, encoded_batch] , dim=1 )
_UpperCAmelCase = torch.cat(
[torch.ones(bos_tokens_tensor.size() , dtype=torch.intaa ).to(snake_case_ ), attn_mask] , dim=1 )
_UpperCAmelCase = encoded_batch
with torch.no_grad():
_UpperCAmelCase = model(snake_case_ , attention_mask=snake_case_ ).logits
_UpperCAmelCase = out_logits[..., :-1, :].contiguous()
_UpperCAmelCase = labels[..., 1:].contiguous()
_UpperCAmelCase = attn_mask[..., 1:].contiguous()
_UpperCAmelCase = torch.expa(
(loss_fct(shift_logits.transpose(1 , 2 ) , snake_case_ ) * shift_attention_mask_batch).sum(1 )
/ shift_attention_mask_batch.sum(1 ) )
ppls += perplexity_batch.tolist()
return {"perplexities": ppls, "mean_perplexity": np.mean(snake_case_ )}
| 22 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel
if is_vision_available():
from transformers import MaskFormerImageProcessor
if is_vision_available():
from PIL import Image
class A_ :
def __init__( self : str , snake_case_ : int , snake_case_ : Union[str, Any]=2 , snake_case_ : List[Any]=True , snake_case_ : str=False , snake_case_ : str=1_0 , snake_case_ : str=3 , snake_case_ : Dict=3_2 * 4 , snake_case_ : Any=3_2 * 6 , snake_case_ : Optional[Any]=4 , snake_case_ : Optional[int]=3_2 , ):
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = is_training
_UpperCAmelCase = use_auxiliary_loss
_UpperCAmelCase = num_queries
_UpperCAmelCase = num_channels
_UpperCAmelCase = min_size
_UpperCAmelCase = max_size
_UpperCAmelCase = num_labels
_UpperCAmelCase = mask_feature_size
def lowercase ( self : Union[str, Any] ):
_UpperCAmelCase = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
snake_case_ )
_UpperCAmelCase = torch.ones([self.batch_size, self.min_size, self.max_size] , device=snake_case_ )
_UpperCAmelCase = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=snake_case_ ) > 0.5
).float()
_UpperCAmelCase = (torch.rand((self.batch_size, self.num_labels) , device=snake_case_ ) > 0.5).long()
_UpperCAmelCase = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def lowercase ( self : List[Any] ):
return MaskFormerConfig.from_backbone_and_decoder_configs(
backbone_config=SwinConfig(
depths=[1, 1, 1, 1] , ) , decoder_config=DetrConfig(
decoder_ffn_dim=1_2_8 , num_queries=self.num_queries , decoder_attention_heads=2 , d_model=self.mask_feature_size , ) , mask_feature_size=self.mask_feature_size , fpn_feature_size=self.mask_feature_size , num_channels=self.num_channels , num_labels=self.num_labels , )
def lowercase ( self : Optional[Any] ):
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = self.prepare_config_and_inputs()
_UpperCAmelCase = {"pixel_values": pixel_values, "pixel_mask": pixel_mask}
return config, inputs_dict
def lowercase ( self : List[Any] , snake_case_ : Optional[Any] , snake_case_ : Optional[Any] ):
_UpperCAmelCase = output.encoder_hidden_states
_UpperCAmelCase = output.pixel_decoder_hidden_states
_UpperCAmelCase = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(snake_case_ ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(snake_case_ ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(snake_case_ ) , config.decoder_config.decoder_layers )
def lowercase ( self : Tuple , snake_case_ : str , snake_case_ : Optional[int] , snake_case_ : Any , snake_case_ : Optional[Any]=False ):
with torch.no_grad():
_UpperCAmelCase = MaskFormerModel(config=snake_case_ )
model.to(snake_case_ )
model.eval()
_UpperCAmelCase = model(pixel_values=snake_case_ , pixel_mask=snake_case_ )
_UpperCAmelCase = model(snake_case_ , output_hidden_states=snake_case_ )
# the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
# encoder and pixel decoder
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.mask_feature_size) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(snake_case_ , snake_case_ )
def lowercase ( self : Any , snake_case_ : List[str] , snake_case_ : List[Any] , snake_case_ : int , snake_case_ : str , snake_case_ : List[Any] ):
_UpperCAmelCase = MaskFormerForInstanceSegmentation(config=snake_case_ )
model.to(snake_case_ )
model.eval()
def comm_check_on_output(snake_case_ : int ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
_UpperCAmelCase = model(pixel_values=snake_case_ , pixel_mask=snake_case_ )
_UpperCAmelCase = model(snake_case_ )
comm_check_on_output(snake_case_ )
_UpperCAmelCase = model(
pixel_values=snake_case_ , pixel_mask=snake_case_ , mask_labels=snake_case_ , class_labels=snake_case_ )
comm_check_on_output(snake_case_ )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class A_ ( lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
_lowerCamelCase : Dict = (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
_lowerCamelCase : Tuple = (
{"""feature-extraction""": MaskFormerModel, """image-segmentation""": MaskFormerForInstanceSegmentation}
if is_torch_available()
else {}
)
_lowerCamelCase : Optional[Any] = False
_lowerCamelCase : Dict = False
_lowerCamelCase : Any = False
_lowerCamelCase : List[Any] = False
def lowercase ( self : Optional[int] ):
_UpperCAmelCase = MaskFormerModelTester(self )
_UpperCAmelCase = ConfigTester(self , config_class=snake_case_ , has_text_modality=snake_case_ )
def lowercase ( self : Optional[Any] ):
self.config_tester.run_common_tests()
def lowercase ( self : Union[str, Any] ):
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(snake_case_ , **snake_case_ , output_hidden_states=snake_case_ )
def lowercase ( self : int ):
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*snake_case_ )
@unittest.skip(reason="MaskFormer does not use inputs_embeds" )
def lowercase ( self : Any ):
pass
@unittest.skip(reason="MaskFormer does not have a get_input_embeddings method" )
def lowercase ( self : List[str] ):
pass
@unittest.skip(reason="MaskFormer is not a generative model" )
def lowercase ( self : List[str] ):
pass
@unittest.skip(reason="MaskFormer does not use token embeddings" )
def lowercase ( self : List[Any] ):
pass
@require_torch_multi_gpu
@unittest.skip(
reason="MaskFormer has some layers using `add_module` which doesn't work well with `nn.DataParallel`" )
def lowercase ( self : Any ):
pass
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
def lowercase ( self : Union[str, Any] ):
pass
def lowercase ( self : List[str] ):
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase = model_class(snake_case_ )
_UpperCAmelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_UpperCAmelCase = [*signature.parameters.keys()]
_UpperCAmelCase = ["pixel_values"]
self.assertListEqual(arg_names[:1] , snake_case_ )
@slow
def lowercase ( self : Optional[int] ):
for model_name in ["facebook/maskformer-swin-small-coco"]:
_UpperCAmelCase = MaskFormerModel.from_pretrained(snake_case_ )
self.assertIsNotNone(snake_case_ )
def lowercase ( self : Optional[int] ):
_UpperCAmelCase = (self.model_tester.min_size,) * 2
_UpperCAmelCase = {
"pixel_values": torch.randn((2, 3, *size) , device=snake_case_ ),
"mask_labels": torch.randn((2, 1_0, *size) , device=snake_case_ ),
"class_labels": torch.zeros(2 , 1_0 , device=snake_case_ ).long(),
}
_UpperCAmelCase = MaskFormerForInstanceSegmentation(MaskFormerConfig() ).to(snake_case_ )
_UpperCAmelCase = model(**snake_case_ )
self.assertTrue(outputs.loss is not None )
def lowercase ( self : Dict ):
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(snake_case_ , **snake_case_ , output_hidden_states=snake_case_ )
def lowercase ( self : Any ):
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase = model_class(snake_case_ ).to(snake_case_ )
_UpperCAmelCase = model(**snake_case_ , output_attentions=snake_case_ )
self.assertTrue(outputs.attentions is not None )
def lowercase ( self : int ):
if not self.model_tester.is_training:
return
# only MaskFormerForInstanceSegmentation has the loss
_UpperCAmelCase = self.all_model_classes[1]
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
_UpperCAmelCase = model_class(snake_case_ )
model.to(snake_case_ )
model.train()
_UpperCAmelCase = model(snake_case_ , mask_labels=snake_case_ , class_labels=snake_case_ ).loss
loss.backward()
def lowercase ( self : int ):
# only MaskFormerForInstanceSegmentation has the loss
_UpperCAmelCase = self.all_model_classes[1]
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
_UpperCAmelCase = True
_UpperCAmelCase = True
_UpperCAmelCase = model_class(snake_case_ )
model.to(snake_case_ )
model.train()
_UpperCAmelCase = model(snake_case_ , mask_labels=snake_case_ , class_labels=snake_case_ )
_UpperCAmelCase = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
_UpperCAmelCase = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
# we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
_UpperCAmelCase = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
_UpperCAmelCase = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=snake_case_ )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
__SCREAMING_SNAKE_CASE :Dict = 1e-4
def UpperCAmelCase_ ( ) -> List[str]:
'''simple docstring'''
_UpperCAmelCase = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_vision
@slow
class A_ ( unittest.TestCase ):
@cached_property
def lowercase ( self : Dict ):
return (
MaskFormerImageProcessor.from_pretrained("facebook/maskformer-swin-small-coco" )
if is_vision_available()
else None
)
def lowercase ( self : List[Any] ):
_UpperCAmelCase = MaskFormerModel.from_pretrained("facebook/maskformer-swin-small-coco" ).to(snake_case_ )
_UpperCAmelCase = self.default_image_processor
_UpperCAmelCase = prepare_img()
_UpperCAmelCase = image_processor(snake_case_ , return_tensors="pt" ).to(snake_case_ )
_UpperCAmelCase = inputs["pixel_values"].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0 )
# check size
self.assertEqual(snake_case_ , (1, 3, 8_0_0, 1_0_8_8) )
with torch.no_grad():
_UpperCAmelCase = model(**snake_case_ )
_UpperCAmelCase = torch.tensor(
[[-0.0_4_8_2, 0.9_2_2_8, 0.4_9_5_1], [-0.2_5_4_7, 0.8_0_1_7, 0.8_5_2_7], [-0.0_0_6_9, 0.3_3_8_5, -0.0_0_8_9]] ).to(snake_case_ )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , snake_case_ , atol=snake_case_ ) )
_UpperCAmelCase = torch.tensor(
[[-0.8_4_2_2, -0.8_4_3_4, -0.9_7_1_8], [-1.0_1_4_4, -0.5_5_6_5, -0.4_1_9_5], [-1.0_0_3_8, -0.4_4_8_4, -0.1_9_6_1]] ).to(snake_case_ )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , snake_case_ , atol=snake_case_ ) )
_UpperCAmelCase = torch.tensor(
[[0.2_8_5_2, -0.0_1_5_9, 0.9_7_3_5], [0.6_2_5_4, 0.1_8_5_8, 0.8_5_2_9], [-0.0_6_8_0, -0.4_1_1_6, 1.8_4_1_3]] ).to(snake_case_ )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , snake_case_ , atol=snake_case_ ) )
def lowercase ( self : Tuple ):
_UpperCAmelCase = (
MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-small-coco" )
.to(snake_case_ )
.eval()
)
_UpperCAmelCase = self.default_image_processor
_UpperCAmelCase = prepare_img()
_UpperCAmelCase = image_processor(snake_case_ , return_tensors="pt" ).to(snake_case_ )
_UpperCAmelCase = inputs["pixel_values"].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0 )
# check size
self.assertEqual(snake_case_ , (1, 3, 8_0_0, 1_0_8_8) )
with torch.no_grad():
_UpperCAmelCase = model(**snake_case_ )
# masks_queries_logits
_UpperCAmelCase = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
_UpperCAmelCase = [
[-1.3_7_3_7_1_2_4, -1.7_7_2_4_9_3_7, -1.9_3_6_4_2_3_3],
[-1.5_9_7_7_2_8_1, -1.9_8_6_7_9_3_9, -2.1_5_2_3_6_9_5],
[-1.5_7_9_5_3_9_8, -1.9_2_6_9_8_3_2, -2.0_9_3_9_4_2],
]
_UpperCAmelCase = torch.tensor(snake_case_ ).to(snake_case_ )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , snake_case_ , atol=snake_case_ ) )
# class_queries_logits
_UpperCAmelCase = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
_UpperCAmelCase = torch.tensor(
[
[1.6_512e00, -5.2_572e00, -3.3_519e00],
[3.6_169e-02, -5.9_025e00, -2.9_313e00],
[1.0_766e-04, -7.7_630e00, -5.1_263e00],
] ).to(snake_case_ )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , snake_case_ , atol=snake_case_ ) )
def lowercase ( self : int ):
_UpperCAmelCase = (
MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-resnet101-coco-stuff" )
.to(snake_case_ )
.eval()
)
_UpperCAmelCase = self.default_image_processor
_UpperCAmelCase = prepare_img()
_UpperCAmelCase = image_processor(snake_case_ , return_tensors="pt" ).to(snake_case_ )
_UpperCAmelCase = inputs["pixel_values"].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0 )
# check size
self.assertEqual(snake_case_ , (1, 3, 8_0_0, 1_0_8_8) )
with torch.no_grad():
_UpperCAmelCase = model(**snake_case_ )
# masks_queries_logits
_UpperCAmelCase = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
_UpperCAmelCase = [[-0.9_0_4_6, -2.6_3_6_6, -4.6_0_6_2], [-3.4_1_7_9, -5.7_8_9_0, -8.8_0_5_7], [-4.9_1_7_9, -7.6_5_6_0, -1_0.7_7_1_1]]
_UpperCAmelCase = torch.tensor(snake_case_ ).to(snake_case_ )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , snake_case_ , atol=snake_case_ ) )
# class_queries_logits
_UpperCAmelCase = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
_UpperCAmelCase = torch.tensor(
[[4.7_1_8_8, -3.2_5_8_5, -2.8_8_5_7], [6.6_8_7_1, -2.9_1_8_1, -1.2_4_8_7], [7.2_4_4_9, -2.2_7_6_4, -2.1_8_7_4]] ).to(snake_case_ )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , snake_case_ , atol=snake_case_ ) )
def lowercase ( self : List[Any] ):
_UpperCAmelCase = (
MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-small-coco" )
.to(snake_case_ )
.eval()
)
_UpperCAmelCase = self.default_image_processor
_UpperCAmelCase = image_processor(
[np.zeros((3, 8_0_0, 1_3_3_3) ), np.zeros((3, 8_0_0, 1_3_3_3) )] , segmentation_maps=[np.zeros((3_8_4, 3_8_4) ).astype(np.floataa ), np.zeros((3_8_4, 3_8_4) ).astype(np.floataa )] , return_tensors="pt" , )
_UpperCAmelCase = inputs["pixel_values"].to(snake_case_ )
_UpperCAmelCase = [el.to(snake_case_ ) for el in inputs["mask_labels"]]
_UpperCAmelCase = [el.to(snake_case_ ) for el in inputs["class_labels"]]
with torch.no_grad():
_UpperCAmelCase = model(**snake_case_ )
self.assertTrue(outputs.loss is not None )
| 22 | 1 |
'''simple docstring'''
from __future__ import annotations
import time
from collections.abc import Sequence
from random import randint
from matplotlib import pyplot as plt
def UpperCAmelCase_ ( __lowercase : Sequence[float] , __lowercase : int , __lowercase : int ) -> tuple[int | None, int | None, float]:
'''simple docstring'''
if not arr:
return None, None, 0
if low == high:
return low, high, arr[low]
_UpperCAmelCase = (low + high) // 2
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = max_subarray(__lowercase , __lowercase , __lowercase )
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = max_subarray(__lowercase , mid + 1 , __lowercase )
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = max_cross_sum(__lowercase , __lowercase , __lowercase , __lowercase )
if left_sum >= right_sum and left_sum >= cross_sum:
return left_low, left_high, left_sum
elif right_sum >= left_sum and right_sum >= cross_sum:
return right_low, right_high, right_sum
return cross_left, cross_right, cross_sum
def UpperCAmelCase_ ( __lowercase : Sequence[float] , __lowercase : int , __lowercase : int , __lowercase : int ) -> tuple[int, int, float]:
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase = float("-inf" ), -1
_UpperCAmelCase , _UpperCAmelCase = float("-inf" ), -1
_UpperCAmelCase = 0
for i in range(__lowercase , low - 1 , -1 ):
summ += arr[i]
if summ > left_sum:
_UpperCAmelCase = summ
_UpperCAmelCase = i
_UpperCAmelCase = 0
for i in range(mid + 1 , high + 1 ):
summ += arr[i]
if summ > right_sum:
_UpperCAmelCase = summ
_UpperCAmelCase = i
return max_left, max_right, (left_sum + right_sum)
def UpperCAmelCase_ ( __lowercase : int ) -> float:
'''simple docstring'''
_UpperCAmelCase = [randint(1 , __lowercase ) for _ in range(__lowercase )]
_UpperCAmelCase = time.time()
max_subarray(__lowercase , 0 , input_size - 1 )
_UpperCAmelCase = time.time()
return end - start
def UpperCAmelCase_ ( ) -> None:
'''simple docstring'''
_UpperCAmelCase = [10, 100, 1000, 1_0000, 5_0000, 10_0000, 20_0000, 30_0000, 40_0000, 50_0000]
_UpperCAmelCase = [time_max_subarray(__lowercase ) for input_size in input_sizes]
print("No of Inputs\t\tTime Taken" )
for input_size, runtime in zip(__lowercase , __lowercase ):
print(__lowercase , "\t\t" , __lowercase )
plt.plot(__lowercase , __lowercase )
plt.xlabel("Number of Inputs" )
plt.ylabel("Time taken in seconds" )
plt.show()
if __name__ == "__main__":
from doctest import testmod
testmod()
| 22 |
'''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_albert import AlbertTokenizer
else:
__SCREAMING_SNAKE_CASE :List[Any] = None
__SCREAMING_SNAKE_CASE :Union[str, Any] = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE :List[str] = {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}
__SCREAMING_SNAKE_CASE :List[Any] = {
'''vocab_file''': {
'''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/spiece.model''',
'''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/spiece.model''',
'''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model''',
'''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model''',
'''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/spiece.model''',
'''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/spiece.model''',
'''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model''',
'''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model''',
},
'''tokenizer_file''': {
'''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json''',
'''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json''',
'''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json''',
'''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json''',
'''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json''',
'''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json''',
'''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json''',
'''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json''',
},
}
__SCREAMING_SNAKE_CASE :Optional[Any] = {
'''albert-base-v1''': 512,
'''albert-large-v1''': 512,
'''albert-xlarge-v1''': 512,
'''albert-xxlarge-v1''': 512,
'''albert-base-v2''': 512,
'''albert-large-v2''': 512,
'''albert-xlarge-v2''': 512,
'''albert-xxlarge-v2''': 512,
}
__SCREAMING_SNAKE_CASE :Optional[int] = '''▁'''
class A_ ( lowerCAmelCase_ ):
_lowerCamelCase : Optional[int] = VOCAB_FILES_NAMES
_lowerCamelCase : Any = PRETRAINED_VOCAB_FILES_MAP
_lowerCamelCase : Tuple = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_lowerCamelCase : int = AlbertTokenizer
def __init__( self : Optional[Any] , snake_case_ : Optional[Any]=None , snake_case_ : Optional[Any]=None , snake_case_ : Optional[Any]=True , snake_case_ : str=True , snake_case_ : Tuple=False , snake_case_ : List[Any]="[CLS]" , snake_case_ : Union[str, Any]="[SEP]" , snake_case_ : str="<unk>" , snake_case_ : Union[str, Any]="[SEP]" , snake_case_ : List[Any]="<pad>" , snake_case_ : List[str]="[CLS]" , snake_case_ : int="[MASK]" , **snake_case_ : Any , ):
# Mask token behave like a normal word, i.e. include the space before it and
# is included in the raw text, there should be a match in a non-normalized sentence.
_UpperCAmelCase = (
AddedToken(snake_case_ , lstrip=snake_case_ , rstrip=snake_case_ , normalized=snake_case_ )
if isinstance(snake_case_ , snake_case_ )
else mask_token
)
super().__init__(
snake_case_ , tokenizer_file=snake_case_ , do_lower_case=snake_case_ , remove_space=snake_case_ , keep_accents=snake_case_ , bos_token=snake_case_ , eos_token=snake_case_ , unk_token=snake_case_ , sep_token=snake_case_ , pad_token=snake_case_ , cls_token=snake_case_ , mask_token=snake_case_ , **snake_case_ , )
_UpperCAmelCase = do_lower_case
_UpperCAmelCase = remove_space
_UpperCAmelCase = keep_accents
_UpperCAmelCase = vocab_file
_UpperCAmelCase = False if not self.vocab_file else True
def lowercase ( self : Union[str, Any] , snake_case_ : List[int] , snake_case_ : Optional[List[int]] = None ):
_UpperCAmelCase = [self.sep_token_id]
_UpperCAmelCase = [self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def lowercase ( self : Dict , snake_case_ : List[int] , snake_case_ : Optional[List[int]] = None ):
_UpperCAmelCase = [self.sep_token_id]
_UpperCAmelCase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def lowercase ( self : Optional[Any] , snake_case_ : str , snake_case_ : Optional[str] = None ):
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(snake_case_ ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
return
_UpperCAmelCase = os.path.join(
snake_case_ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(snake_case_ ):
copyfile(self.vocab_file , snake_case_ )
return (out_vocab_file,)
| 22 | 1 |
'''simple docstring'''
import unittest
from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available
from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
if is_bsa_available():
from transformers import MarkupLMFeatureExtractor
class A_ ( unittest.TestCase ):
def __init__( self : Optional[Any] , snake_case_ : Optional[Any] ):
_UpperCAmelCase = parent
def lowercase ( self : str ):
return {}
def UpperCAmelCase_ ( ) -> Optional[Any]:
'''simple docstring'''
_UpperCAmelCase = "<HTML>\n\n <HEAD>\n <TITLE>sample document</TITLE>\n </HEAD>\n\n <BODY BGCOLOR=\"FFFFFF\">\n <HR>\n <a href=\"http://google.com\">Goog</a>\n <H1>This is one header</H1>\n <H2>This is a another Header</H2>\n <P>Travel from\n <P>\n <B>SFO to JFK</B>\n <BR>\n <B><I>on May 2, 2015 at 2:00 pm. For details go to confirm.com </I></B>\n <HR>\n <div style=\"color:#0000FF\">\n <h3>Traveler <b> name </b> is\n <p> John Doe </p>\n </div>"
_UpperCAmelCase = "\n <!DOCTYPE html>\n <html>\n <body>\n\n <h1>My First Heading</h1>\n <p>My first paragraph.</p>\n\n </body>\n </html>\n "
return [html_string_a, html_string_a]
@require_bsa
class A_ ( lowerCAmelCase_ , unittest.TestCase ):
_lowerCamelCase : Dict = MarkupLMFeatureExtractor if is_bsa_available() else None
def lowercase ( self : List[str] ):
_UpperCAmelCase = MarkupLMFeatureExtractionTester(self )
@property
def lowercase ( self : int ):
return self.feature_extract_tester.prepare_feat_extract_dict()
def lowercase ( self : List[Any] ):
# Initialize feature_extractor
_UpperCAmelCase = self.feature_extraction_class()
# Test not batched input
_UpperCAmelCase = get_html_strings()[0]
_UpperCAmelCase = feature_extractor(snake_case_ )
# fmt: off
_UpperCAmelCase = [["sample document", "Goog", "This is one header", "This is a another Header", "Travel from", "SFO to JFK", "on May 2, 2015 at 2:00 pm. For details go to confirm.com", "Traveler", "name", "is", "John Doe"]]
_UpperCAmelCase = [["/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 , snake_case_ )
self.assertEqual(encoding.xpaths , snake_case_ )
# Test batched
_UpperCAmelCase = get_html_strings()
_UpperCAmelCase = feature_extractor(snake_case_ )
# fmt: off
_UpperCAmelCase = expected_nodes + [["My First Heading", "My first paragraph."]]
_UpperCAmelCase = expected_xpaths + [["/html/body/h1", "/html/body/p"]]
self.assertEqual(len(encoding.nodes ) , 2 )
self.assertEqual(len(encoding.xpaths ) , 2 )
self.assertEqual(encoding.nodes , snake_case_ )
self.assertEqual(encoding.xpaths , snake_case_ )
| 22 |
'''simple docstring'''
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
__SCREAMING_SNAKE_CASE :Dict = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE :int = {
'''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 A_ ( lowerCAmelCase_ ):
_lowerCamelCase : int = """perceiver"""
def __init__( self : Any , snake_case_ : List[Any]=2_5_6 , snake_case_ : str=1_2_8_0 , snake_case_ : Optional[int]=7_6_8 , snake_case_ : int=1 , snake_case_ : List[Any]=2_6 , snake_case_ : Dict=8 , snake_case_ : List[Any]=8 , snake_case_ : Tuple=None , snake_case_ : Tuple=None , snake_case_ : Any="kv" , snake_case_ : Any=1 , snake_case_ : List[str]=1 , snake_case_ : Optional[int]="gelu" , snake_case_ : List[Any]=0.1 , snake_case_ : Dict=0.0_2 , snake_case_ : int=1e-12 , snake_case_ : List[str]=True , snake_case_ : str=2_6_2 , snake_case_ : Optional[Any]=2_0_4_8 , snake_case_ : Union[str, Any]=5_6 , snake_case_ : Dict=[3_6_8, 4_9_6] , snake_case_ : Tuple=1_6 , snake_case_ : Union[str, Any]=1_9_2_0 , snake_case_ : List[Any]=1_6 , snake_case_ : Tuple=[1, 1_6, 2_2_4, 2_2_4] , **snake_case_ : List[Any] , ):
super().__init__(**snake_case_ )
_UpperCAmelCase = num_latents
_UpperCAmelCase = d_latents
_UpperCAmelCase = d_model
_UpperCAmelCase = num_blocks
_UpperCAmelCase = num_self_attends_per_block
_UpperCAmelCase = num_self_attention_heads
_UpperCAmelCase = num_cross_attention_heads
_UpperCAmelCase = qk_channels
_UpperCAmelCase = v_channels
_UpperCAmelCase = cross_attention_shape_for_attention
_UpperCAmelCase = self_attention_widening_factor
_UpperCAmelCase = cross_attention_widening_factor
_UpperCAmelCase = hidden_act
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = initializer_range
_UpperCAmelCase = layer_norm_eps
_UpperCAmelCase = use_query_residual
# masked language modeling attributes
_UpperCAmelCase = vocab_size
_UpperCAmelCase = max_position_embeddings
# image classification attributes
_UpperCAmelCase = image_size
# flow attributes
_UpperCAmelCase = train_size
# multimodal autoencoding attributes
_UpperCAmelCase = num_frames
_UpperCAmelCase = audio_samples_per_frame
_UpperCAmelCase = samples_per_patch
_UpperCAmelCase = output_shape
class A_ ( lowerCAmelCase_ ):
@property
def lowercase ( self : int ):
if self.task == "multiple-choice":
_UpperCAmelCase = {0: "batch", 1: "choice", 2: "sequence"}
else:
_UpperCAmelCase = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("inputs", dynamic_axis),
("attention_mask", dynamic_axis),
] )
@property
def lowercase ( self : Optional[Any] ):
return 1e-4
def lowercase ( self : List[str] , snake_case_ : Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"] , snake_case_ : int = -1 , snake_case_ : int = -1 , snake_case_ : int = -1 , snake_case_ : bool = False , snake_case_ : Optional[TensorType] = None , snake_case_ : int = 3 , snake_case_ : int = 4_0 , snake_case_ : int = 4_0 , ):
# copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified
if isinstance(snake_case_ , snake_case_ ):
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
_UpperCAmelCase = compute_effective_axis_dimension(
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 = preprocessor.num_special_tokens_to_add(snake_case_ )
_UpperCAmelCase = compute_effective_axis_dimension(
snake_case_ , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=snake_case_ )
# Generate dummy inputs according to compute batch and sequence
_UpperCAmelCase = [" ".join(["a"] ) * seq_length] * batch_size
_UpperCAmelCase = dict(preprocessor(snake_case_ , return_tensors=snake_case_ ) )
_UpperCAmelCase = inputs.pop("input_ids" )
return inputs
elif isinstance(snake_case_ , snake_case_ ) 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
_UpperCAmelCase = compute_effective_axis_dimension(snake_case_ , fixed_dimension=OnnxConfig.default_fixed_batch )
_UpperCAmelCase = self._generate_dummy_images(snake_case_ , snake_case_ , snake_case_ , snake_case_ )
_UpperCAmelCase = dict(preprocessor(images=snake_case_ , return_tensors=snake_case_ ) )
_UpperCAmelCase = inputs.pop("pixel_values" )
return inputs
else:
raise ValueError(
"Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor." )
| 22 | 1 |
'''simple docstring'''
import argparse
import io
import requests
import torch
from omegaconf import OmegaConf
from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
assign_to_checkpoint,
conv_attn_to_linear,
create_vae_diffusers_config,
renew_vae_attention_paths,
renew_vae_resnet_paths,
)
def UpperCAmelCase_ ( __lowercase : str , __lowercase : List[Any] ) -> List[Any]:
'''simple docstring'''
_UpperCAmelCase = checkpoint
_UpperCAmelCase = {}
_UpperCAmelCase = vae_state_dict["encoder.conv_in.weight"]
_UpperCAmelCase = vae_state_dict["encoder.conv_in.bias"]
_UpperCAmelCase = vae_state_dict["encoder.conv_out.weight"]
_UpperCAmelCase = vae_state_dict["encoder.conv_out.bias"]
_UpperCAmelCase = vae_state_dict["encoder.norm_out.weight"]
_UpperCAmelCase = vae_state_dict["encoder.norm_out.bias"]
_UpperCAmelCase = vae_state_dict["decoder.conv_in.weight"]
_UpperCAmelCase = vae_state_dict["decoder.conv_in.bias"]
_UpperCAmelCase = vae_state_dict["decoder.conv_out.weight"]
_UpperCAmelCase = vae_state_dict["decoder.conv_out.bias"]
_UpperCAmelCase = vae_state_dict["decoder.norm_out.weight"]
_UpperCAmelCase = vae_state_dict["decoder.norm_out.bias"]
_UpperCAmelCase = vae_state_dict["quant_conv.weight"]
_UpperCAmelCase = vae_state_dict["quant_conv.bias"]
_UpperCAmelCase = vae_state_dict["post_quant_conv.weight"]
_UpperCAmelCase = vae_state_dict["post_quant_conv.bias"]
# Retrieves the keys for the encoder down blocks only
_UpperCAmelCase = len({".".join(layer.split("." )[:3] ) for layer in vae_state_dict if "encoder.down" in layer} )
_UpperCAmelCase = {
layer_id: [key for key in vae_state_dict if f'down.{layer_id}' in key] for layer_id in range(__lowercase )
}
# Retrieves the keys for the decoder up blocks only
_UpperCAmelCase = len({".".join(layer.split("." )[:3] ) for layer in vae_state_dict if "decoder.up" in layer} )
_UpperCAmelCase = {
layer_id: [key for key in vae_state_dict if f'up.{layer_id}' in key] for layer_id in range(__lowercase )
}
for i in range(__lowercase ):
_UpperCAmelCase = [key for key in down_blocks[i] if f'down.{i}' in key and f'down.{i}.downsample' not in key]
if f'encoder.down.{i}.downsample.conv.weight' in vae_state_dict:
_UpperCAmelCase = vae_state_dict.pop(
f'encoder.down.{i}.downsample.conv.weight' )
_UpperCAmelCase = vae_state_dict.pop(
f'encoder.down.{i}.downsample.conv.bias' )
_UpperCAmelCase = renew_vae_resnet_paths(__lowercase )
_UpperCAmelCase = {"old": f'down.{i}.block', "new": f'down_blocks.{i}.resnets'}
assign_to_checkpoint(__lowercase , __lowercase , __lowercase , additional_replacements=[meta_path] , config=__lowercase )
_UpperCAmelCase = [key for key in vae_state_dict if "encoder.mid.block" in key]
_UpperCAmelCase = 2
for i in range(1 , num_mid_res_blocks + 1 ):
_UpperCAmelCase = [key for key in mid_resnets if f'encoder.mid.block_{i}' in key]
_UpperCAmelCase = renew_vae_resnet_paths(__lowercase )
_UpperCAmelCase = {"old": f'mid.block_{i}', "new": f'mid_block.resnets.{i - 1}'}
assign_to_checkpoint(__lowercase , __lowercase , __lowercase , additional_replacements=[meta_path] , config=__lowercase )
_UpperCAmelCase = [key for key in vae_state_dict if "encoder.mid.attn" in key]
_UpperCAmelCase = renew_vae_attention_paths(__lowercase )
_UpperCAmelCase = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
assign_to_checkpoint(__lowercase , __lowercase , __lowercase , additional_replacements=[meta_path] , config=__lowercase )
conv_attn_to_linear(__lowercase )
for i in range(__lowercase ):
_UpperCAmelCase = num_up_blocks - 1 - i
_UpperCAmelCase = [
key for key in up_blocks[block_id] if f'up.{block_id}' in key and f'up.{block_id}.upsample' not in key
]
if f'decoder.up.{block_id}.upsample.conv.weight' in vae_state_dict:
_UpperCAmelCase = vae_state_dict[
f'decoder.up.{block_id}.upsample.conv.weight'
]
_UpperCAmelCase = vae_state_dict[
f'decoder.up.{block_id}.upsample.conv.bias'
]
_UpperCAmelCase = renew_vae_resnet_paths(__lowercase )
_UpperCAmelCase = {"old": f'up.{block_id}.block', "new": f'up_blocks.{i}.resnets'}
assign_to_checkpoint(__lowercase , __lowercase , __lowercase , additional_replacements=[meta_path] , config=__lowercase )
_UpperCAmelCase = [key for key in vae_state_dict if "decoder.mid.block" in key]
_UpperCAmelCase = 2
for i in range(1 , num_mid_res_blocks + 1 ):
_UpperCAmelCase = [key for key in mid_resnets if f'decoder.mid.block_{i}' in key]
_UpperCAmelCase = renew_vae_resnet_paths(__lowercase )
_UpperCAmelCase = {"old": f'mid.block_{i}', "new": f'mid_block.resnets.{i - 1}'}
assign_to_checkpoint(__lowercase , __lowercase , __lowercase , additional_replacements=[meta_path] , config=__lowercase )
_UpperCAmelCase = [key for key in vae_state_dict if "decoder.mid.attn" in key]
_UpperCAmelCase = renew_vae_attention_paths(__lowercase )
_UpperCAmelCase = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
assign_to_checkpoint(__lowercase , __lowercase , __lowercase , additional_replacements=[meta_path] , config=__lowercase )
conv_attn_to_linear(__lowercase )
return new_checkpoint
def UpperCAmelCase_ ( __lowercase : str , __lowercase : str , ) -> int:
'''simple docstring'''
_UpperCAmelCase = requests.get(
" https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml" )
_UpperCAmelCase = io.BytesIO(r.content )
_UpperCAmelCase = OmegaConf.load(__lowercase )
_UpperCAmelCase = 512
_UpperCAmelCase = "cuda" if torch.cuda.is_available() else "cpu"
if checkpoint_path.endswith("safetensors" ):
from safetensors import safe_open
_UpperCAmelCase = {}
with safe_open(__lowercase , framework="pt" , device="cpu" ) as f:
for key in f.keys():
_UpperCAmelCase = f.get_tensor(__lowercase )
else:
_UpperCAmelCase = torch.load(__lowercase , map_location=__lowercase )["state_dict"]
# Convert the VAE model.
_UpperCAmelCase = create_vae_diffusers_config(__lowercase , image_size=__lowercase )
_UpperCAmelCase = custom_convert_ldm_vae_checkpoint(__lowercase , __lowercase )
_UpperCAmelCase = AutoencoderKL(**__lowercase )
vae.load_state_dict(__lowercase )
vae.save_pretrained(__lowercase )
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE :Union[str, Any] = argparse.ArgumentParser()
parser.add_argument('''--vae_pt_path''', default=None, type=str, required=True, help='''Path to the VAE.pt to convert.''')
parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the VAE.pt to convert.''')
__SCREAMING_SNAKE_CASE :Any = parser.parse_args()
vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)
| 22 |
'''simple docstring'''
import warnings
from ...utils import is_sklearn_available, requires_backends
if is_sklearn_available():
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
__SCREAMING_SNAKE_CASE :List[str] = (
'''This metric will be removed from the library soon, metrics should be handled with the 🤗 Evaluate '''
'''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'''
)
def UpperCAmelCase_ ( __lowercase : Any , __lowercase : Tuple ) -> int:
'''simple docstring'''
warnings.warn(__lowercase , __lowercase )
requires_backends(__lowercase , "sklearn" )
return (preds == labels).mean()
def UpperCAmelCase_ ( __lowercase : int , __lowercase : str ) -> Optional[Any]:
'''simple docstring'''
warnings.warn(__lowercase , __lowercase )
requires_backends(__lowercase , "sklearn" )
_UpperCAmelCase = simple_accuracy(__lowercase , __lowercase )
_UpperCAmelCase = fa_score(y_true=__lowercase , y_pred=__lowercase )
return {
"acc": acc,
"f1": fa,
"acc_and_f1": (acc + fa) / 2,
}
def UpperCAmelCase_ ( __lowercase : Optional[int] , __lowercase : List[str] ) -> List[Any]:
'''simple docstring'''
warnings.warn(__lowercase , __lowercase )
requires_backends(__lowercase , "sklearn" )
_UpperCAmelCase = pearsonr(__lowercase , __lowercase )[0]
_UpperCAmelCase = spearmanr(__lowercase , __lowercase )[0]
return {
"pearson": pearson_corr,
"spearmanr": spearman_corr,
"corr": (pearson_corr + spearman_corr) / 2,
}
def UpperCAmelCase_ ( __lowercase : Optional[Any] , __lowercase : str , __lowercase : str ) -> Tuple:
'''simple docstring'''
warnings.warn(__lowercase , __lowercase )
requires_backends(__lowercase , "sklearn" )
assert len(__lowercase ) == len(__lowercase ), f'Predictions and labels have mismatched lengths {len(__lowercase )} and {len(__lowercase )}'
if task_name == "cola":
return {"mcc": matthews_corrcoef(__lowercase , __lowercase )}
elif task_name == "sst-2":
return {"acc": simple_accuracy(__lowercase , __lowercase )}
elif task_name == "mrpc":
return acc_and_fa(__lowercase , __lowercase )
elif task_name == "sts-b":
return pearson_and_spearman(__lowercase , __lowercase )
elif task_name == "qqp":
return acc_and_fa(__lowercase , __lowercase )
elif task_name == "mnli":
return {"mnli/acc": simple_accuracy(__lowercase , __lowercase )}
elif task_name == "mnli-mm":
return {"mnli-mm/acc": simple_accuracy(__lowercase , __lowercase )}
elif task_name == "qnli":
return {"acc": simple_accuracy(__lowercase , __lowercase )}
elif task_name == "rte":
return {"acc": simple_accuracy(__lowercase , __lowercase )}
elif task_name == "wnli":
return {"acc": simple_accuracy(__lowercase , __lowercase )}
elif task_name == "hans":
return {"acc": simple_accuracy(__lowercase , __lowercase )}
else:
raise KeyError(__lowercase )
def UpperCAmelCase_ ( __lowercase : List[Any] , __lowercase : Dict , __lowercase : str ) -> Union[str, Any]:
'''simple docstring'''
warnings.warn(__lowercase , __lowercase )
requires_backends(__lowercase , "sklearn" )
if len(__lowercase ) != len(__lowercase ):
raise ValueError(f'Predictions and labels have mismatched lengths {len(__lowercase )} and {len(__lowercase )}' )
if task_name == "xnli":
return {"acc": simple_accuracy(__lowercase , __lowercase )}
else:
raise KeyError(__lowercase )
| 22 | 1 |
'''simple docstring'''
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
__SCREAMING_SNAKE_CASE :Any = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE :str = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}
__SCREAMING_SNAKE_CASE :List[str] = {
'''vocab_file''': {
'''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'''
},
'''merges_file''': {
'''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'''
},
}
__SCREAMING_SNAKE_CASE :Optional[Any] = {'''allegro/herbert-base-cased''': 514}
__SCREAMING_SNAKE_CASE :Optional[int] = {}
class A_ ( lowerCAmelCase_ ):
_lowerCamelCase : Tuple = VOCAB_FILES_NAMES
_lowerCamelCase : List[str] = PRETRAINED_VOCAB_FILES_MAP
_lowerCamelCase : int = PRETRAINED_INIT_CONFIGURATION
_lowerCamelCase : Optional[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_lowerCamelCase : Tuple = HerbertTokenizer
def __init__( self : Dict , snake_case_ : Union[str, Any]=None , snake_case_ : Any=None , snake_case_ : Dict=None , snake_case_ : List[Any]="<s>" , snake_case_ : Tuple="<unk>" , snake_case_ : Dict="<pad>" , snake_case_ : List[str]="<mask>" , snake_case_ : int="</s>" , **snake_case_ : str , ):
super().__init__(
snake_case_ , snake_case_ , tokenizer_file=snake_case_ , cls_token=snake_case_ , unk_token=snake_case_ , pad_token=snake_case_ , mask_token=snake_case_ , sep_token=snake_case_ , **snake_case_ , )
def lowercase ( self : str , snake_case_ : List[int] , snake_case_ : Optional[List[int]] = None ):
_UpperCAmelCase = [self.cls_token_id]
_UpperCAmelCase = [self.sep_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def lowercase ( self : Union[str, Any] , snake_case_ : List[int] , snake_case_ : Optional[List[int]] = None , snake_case_ : bool = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=snake_case_ , token_ids_a=snake_case_ , already_has_special_tokens=snake_case_ )
if token_ids_a is None:
return [1] + ([0] * len(snake_case_ )) + [1]
return [1] + ([0] * len(snake_case_ )) + [1] + ([0] * len(snake_case_ )) + [1]
def lowercase ( self : Any , snake_case_ : List[int] , snake_case_ : Optional[List[int]] = None ):
_UpperCAmelCase = [self.sep_token_id]
_UpperCAmelCase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def lowercase ( self : List[Any] , snake_case_ : str , snake_case_ : Optional[str] = None ):
_UpperCAmelCase = self._tokenizer.model.save(snake_case_ , name=snake_case_ )
return tuple(snake_case_ )
| 22 |
'''simple docstring'''
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCAmelCase_ ( __lowercase : int , __lowercase : Dict , __lowercase : str , __lowercase : Optional[Any] , __lowercase : str ) -> List[str]:
'''simple docstring'''
_UpperCAmelCase = TapasConfig.from_json_file(__lowercase )
# set absolute/relative position embeddings parameter
_UpperCAmelCase = reset_position_index_per_cell
# set remaining parameters of TapasConfig as well as the model based on the task
if task == "SQA":
_UpperCAmelCase = TapasForQuestionAnswering(config=__lowercase )
elif task == "WTQ":
# run_task_main.py hparams
_UpperCAmelCase = 4
_UpperCAmelCase = True
# hparam_utils.py hparams
_UpperCAmelCase = 0.66_4694
_UpperCAmelCase = 0.20_7951
_UpperCAmelCase = 0.12_1194
_UpperCAmelCase = True
_UpperCAmelCase = True
_UpperCAmelCase = False
_UpperCAmelCase = 0.035_2513
_UpperCAmelCase = TapasForQuestionAnswering(config=__lowercase )
elif task == "WIKISQL_SUPERVISED":
# run_task_main.py hparams
_UpperCAmelCase = 4
_UpperCAmelCase = False
# hparam_utils.py hparams
_UpperCAmelCase = 36.4519
_UpperCAmelCase = 0.90_3421
_UpperCAmelCase = 222.088
_UpperCAmelCase = True
_UpperCAmelCase = True
_UpperCAmelCase = True
_UpperCAmelCase = 0.76_3141
_UpperCAmelCase = TapasForQuestionAnswering(config=__lowercase )
elif task == "TABFACT":
_UpperCAmelCase = TapasForSequenceClassification(config=__lowercase )
elif task == "MLM":
_UpperCAmelCase = TapasForMaskedLM(config=__lowercase )
elif task == "INTERMEDIATE_PRETRAINING":
_UpperCAmelCase = TapasModel(config=__lowercase )
else:
raise ValueError(f'Task {task} not supported.' )
print(f'Building PyTorch model from configuration: {config}' )
# Load weights from tf checkpoint
load_tf_weights_in_tapas(__lowercase , __lowercase , __lowercase )
# Save pytorch-model (weights and configuration)
print(f'Save PyTorch model to {pytorch_dump_path}' )
model.save_pretrained(__lowercase )
# Save tokenizer files
print(f'Save tokenizer files to {pytorch_dump_path}' )
_UpperCAmelCase = TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + "vocab.txt" , model_max_length=512 )
tokenizer.save_pretrained(__lowercase )
print("Used relative position embeddings:" , model.config.reset_position_index_per_cell )
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE :List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--task''', default='''SQA''', type=str, help='''Model task for which to convert a checkpoint. Defaults to SQA.'''
)
parser.add_argument(
'''--reset_position_index_per_cell''',
default=False,
action='''store_true''',
help='''Whether to use relative position embeddings or not. Defaults to True.''',
)
parser.add_argument(
'''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--tapas_config_file''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained TAPAS model. \n'''
'''This specifies the model architecture.'''
),
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
__SCREAMING_SNAKE_CASE :List[str] = parser.parse_args()
convert_tf_checkpoint_to_pytorch(
args.task,
args.reset_position_index_per_cell,
args.tf_checkpoint_path,
args.tapas_config_file,
args.pytorch_dump_path,
)
| 22 | 1 |
'''simple docstring'''
def UpperCAmelCase_ ( ) -> list[list[int]]:
'''simple docstring'''
return [list(range(1000 - i , -1000 - i , -1 ) ) for i in range(1000 )]
__SCREAMING_SNAKE_CASE :str = generate_large_matrix()
__SCREAMING_SNAKE_CASE :Any = (
[[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
[[3, 2], [1, 0]],
[[7, 7, 6]],
[[7, 7, 6], [-1, -2, -3]],
grid,
)
def UpperCAmelCase_ ( __lowercase : list[list[int]] ) -> None:
'''simple docstring'''
assert all(row == sorted(__lowercase , reverse=__lowercase ) for row in grid )
assert all(list(__lowercase ) == sorted(__lowercase , reverse=__lowercase ) for col in zip(*__lowercase ) )
def UpperCAmelCase_ ( __lowercase : list[int] ) -> int:
'''simple docstring'''
_UpperCAmelCase = 0
_UpperCAmelCase = len(__lowercase ) - 1
# Edge cases such as no values or all numbers are negative.
if not array or array[0] < 0:
return 0
while right + 1 > left:
_UpperCAmelCase = (left + right) // 2
_UpperCAmelCase = array[mid]
# Num must be negative and the index must be greater than or equal to 0.
if num < 0 and array[mid - 1] >= 0:
return mid
if num >= 0:
_UpperCAmelCase = mid + 1
else:
_UpperCAmelCase = mid - 1
# No negative numbers so return the last index of the array + 1 which is the length.
return len(__lowercase )
def UpperCAmelCase_ ( __lowercase : list[list[int]] ) -> int:
'''simple docstring'''
_UpperCAmelCase = 0
_UpperCAmelCase = len(grid[0] )
for i in range(len(__lowercase ) ):
_UpperCAmelCase = find_negative_index(grid[i][:bound] )
total += bound
return (len(__lowercase ) * len(grid[0] )) - total
def UpperCAmelCase_ ( __lowercase : list[list[int]] ) -> int:
'''simple docstring'''
return len([number for row in grid for number in row if number < 0] )
def UpperCAmelCase_ ( __lowercase : list[list[int]] ) -> int:
'''simple docstring'''
_UpperCAmelCase = 0
for row in grid:
for i, number in enumerate(__lowercase ):
if number < 0:
total += len(__lowercase ) - i
break
return total
def UpperCAmelCase_ ( ) -> None:
'''simple docstring'''
from timeit import timeit
print("Running benchmarks" )
_UpperCAmelCase = (
"from __main__ import count_negatives_binary_search, "
"count_negatives_brute_force, count_negatives_brute_force_with_break, grid"
)
for func in (
"count_negatives_binary_search", # took 0.7727 seconds
"count_negatives_brute_force_with_break", # took 4.6505 seconds
"count_negatives_brute_force", # took 12.8160 seconds
):
_UpperCAmelCase = timeit(f'{func}(grid=grid)' , setup=__lowercase , number=500 )
print(f'{func}() took {time:0.4f} seconds' )
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 22 |
'''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
__SCREAMING_SNAKE_CASE :str = [
'''good first issue''',
'''feature request''',
'''wip''',
]
def UpperCAmelCase_ ( ) -> Optional[Any]:
'''simple docstring'''
_UpperCAmelCase = Github(os.environ["GITHUB_TOKEN"] )
_UpperCAmelCase = g.get_repo("huggingface/accelerate" )
_UpperCAmelCase = repo.get_issues(state="open" )
for issue in open_issues:
_UpperCAmelCase = sorted([comment for comment in issue.get_comments()] , key=lambda __lowercase : i.created_at , reverse=__lowercase )
_UpperCAmelCase = comments[0] if len(__lowercase ) > 0 else None
_UpperCAmelCase = dt.utcnow()
_UpperCAmelCase = (current_time - issue.updated_at).days
_UpperCAmelCase = (current_time - issue.created_at).days
if (
last_comment is not None
and last_comment.user.login == "github-actions[bot]"
and days_since_updated > 7
and days_since_creation >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Close issue since it has been 7 days of inactivity since bot mention.
issue.edit(state="closed" )
elif (
days_since_updated > 23
and days_since_creation >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Add stale comment
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/accelerate/blob/main/CONTRIBUTING.md) "
"are likely to be ignored." )
if __name__ == "__main__":
main()
| 22 | 1 |
'''simple docstring'''
import json
import logging
import os
import socket
import git
import numpy as np
import torch
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s''',
datefmt='''%m/%d/%Y %H:%M:%S''',
level=logging.INFO,
)
__SCREAMING_SNAKE_CASE :Union[str, Any] = logging.getLogger(__name__)
def UpperCAmelCase_ ( __lowercase : str ) -> Tuple:
'''simple docstring'''
_UpperCAmelCase = git.Repo(search_parent_directories=__lowercase )
_UpperCAmelCase = {
"repo_id": str(__lowercase ),
"repo_sha": str(repo.head.object.hexsha ),
"repo_branch": str(repo.active_branch ),
}
with open(os.path.join(__lowercase , "git_log.json" ) , "w" ) as f:
json.dump(__lowercase , __lowercase , indent=4 )
def UpperCAmelCase_ ( __lowercase : Tuple ) -> Tuple:
'''simple docstring'''
if params.n_gpu <= 0:
_UpperCAmelCase = 0
_UpperCAmelCase = -1
_UpperCAmelCase = True
_UpperCAmelCase = False
return
assert torch.cuda.is_available()
logger.info("Initializing GPUs" )
if params.n_gpu > 1:
assert params.local_rank != -1
_UpperCAmelCase = int(os.environ["WORLD_SIZE"] )
_UpperCAmelCase = int(os.environ["N_GPU_NODE"] )
_UpperCAmelCase = int(os.environ["RANK"] )
# number of nodes / node ID
_UpperCAmelCase = params.world_size // params.n_gpu_per_node
_UpperCAmelCase = params.global_rank // params.n_gpu_per_node
_UpperCAmelCase = True
assert params.n_nodes == int(os.environ["N_NODES"] )
assert params.node_id == int(os.environ["NODE_RANK"] )
# local job (single GPU)
else:
assert params.local_rank == -1
_UpperCAmelCase = 1
_UpperCAmelCase = 0
_UpperCAmelCase = 0
_UpperCAmelCase = 0
_UpperCAmelCase = 1
_UpperCAmelCase = 1
_UpperCAmelCase = False
# sanity checks
assert params.n_nodes >= 1
assert 0 <= params.node_id < params.n_nodes
assert 0 <= params.local_rank <= params.global_rank < params.world_size
assert params.world_size == params.n_nodes * params.n_gpu_per_node
# define whether this is the master process / if we are in multi-node distributed mode
_UpperCAmelCase = params.node_id == 0 and params.local_rank == 0
_UpperCAmelCase = params.n_nodes > 1
# summary
_UpperCAmelCase = f'--- Global rank: {params.global_rank} - '
logger.info(PREFIX + "Number of nodes: %i" % params.n_nodes )
logger.info(PREFIX + "Node ID : %i" % params.node_id )
logger.info(PREFIX + "Local rank : %i" % params.local_rank )
logger.info(PREFIX + "World size : %i" % params.world_size )
logger.info(PREFIX + "GPUs per node : %i" % params.n_gpu_per_node )
logger.info(PREFIX + "Master : %s" % str(params.is_master ) )
logger.info(PREFIX + "Multi-node : %s" % str(params.multi_node ) )
logger.info(PREFIX + "Multi-GPU : %s" % str(params.multi_gpu ) )
logger.info(PREFIX + "Hostname : %s" % socket.gethostname() )
# set GPU device
torch.cuda.set_device(params.local_rank )
# initialize multi-GPU
if params.multi_gpu:
logger.info("Initializing PyTorch distributed" )
torch.distributed.init_process_group(
init_method="env://" , backend="nccl" , )
def UpperCAmelCase_ ( __lowercase : Union[str, Any] ) -> str:
'''simple docstring'''
np.random.seed(args.seed )
torch.manual_seed(args.seed )
if args.n_gpu > 0:
torch.cuda.manual_seed_all(args.seed )
| 22 |
'''simple docstring'''
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 UpperCAmelCase_ ( __lowercase : Any , __lowercase : int ) -> int:
'''simple docstring'''
_UpperCAmelCase = 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}}" )
_UpperCAmelCase = DatasetInfosDict.from_directory(__lowercase )
assert dataset_infos
assert dataset_infos["default"].dataset_size == 42
@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=42 , ),
] , )
def UpperCAmelCase_ ( __lowercase : Tuple , __lowercase : DatasetInfo ) -> Any:
'''simple docstring'''
_UpperCAmelCase = str(__lowercase )
dataset_info.write_to_directory(__lowercase )
_UpperCAmelCase = DatasetInfo.from_directory(__lowercase )
assert dataset_info == reloaded
assert os.path.exists(os.path.join(__lowercase , "dataset_info.json" ) )
def UpperCAmelCase_ ( ) -> Optional[Any]:
'''simple docstring'''
_UpperCAmelCase = 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": 42}] , download_checksums={} , download_size=1337 , post_processing_size=442 , dataset_size=1234 , size_in_bytes=1337 + 442 + 1234 , )
_UpperCAmelCase = dataset_info._to_yaml_dict()
assert sorted(__lowercase ) == 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) )
_UpperCAmelCase = yaml.safe_dump(__lowercase )
_UpperCAmelCase = yaml.safe_load(__lowercase )
assert dataset_info_yaml_dict == reloaded
def UpperCAmelCase_ ( ) -> Optional[int]:
'''simple docstring'''
_UpperCAmelCase = DatasetInfo()
_UpperCAmelCase = 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=42 , )
} ),
DatasetInfosDict(
{
"v1": DatasetInfo(dataset_size=42 ),
"v2": DatasetInfo(dataset_size=1337 ),
} ),
] , )
def UpperCAmelCase_ ( __lowercase : int , __lowercase : DatasetInfosDict ) -> Dict:
'''simple docstring'''
_UpperCAmelCase = str(__lowercase )
dataset_infos_dict.write_to_directory(__lowercase )
_UpperCAmelCase = DatasetInfosDict.from_directory(__lowercase )
# the config_name of the dataset_infos_dict take over the attribute
for config_name, dataset_info in dataset_infos_dict.items():
_UpperCAmelCase = 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
_UpperCAmelCase = 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(__lowercase , "README.md" ) )
| 22 | 1 |
'''simple docstring'''
import colorsys
from PIL import Image # type: ignore
def UpperCAmelCase_ ( __lowercase : float , __lowercase : float , __lowercase : int ) -> float:
'''simple docstring'''
_UpperCAmelCase = x
_UpperCAmelCase = y
for step in range(__lowercase ): # noqa: B007
_UpperCAmelCase = a * a - b * b + x
_UpperCAmelCase = 2 * a * b + y
_UpperCAmelCase = a_new
# divergence happens for all complex number with an absolute value
# greater than 4
if a * a + b * b > 4:
break
return step / (max_step - 1)
def UpperCAmelCase_ ( __lowercase : float ) -> tuple:
'''simple docstring'''
if distance == 1:
return (0, 0, 0)
else:
return (255, 255, 255)
def UpperCAmelCase_ ( __lowercase : float ) -> tuple:
'''simple docstring'''
if distance == 1:
return (0, 0, 0)
else:
return tuple(round(i * 255 ) for i in colorsys.hsv_to_rgb(__lowercase , 1 , 1 ) )
def UpperCAmelCase_ ( __lowercase : int = 800 , __lowercase : int = 600 , __lowercase : float = -0.6 , __lowercase : float = 0 , __lowercase : float = 3.2 , __lowercase : int = 50 , __lowercase : bool = True , ) -> Image.Image:
'''simple docstring'''
_UpperCAmelCase = Image.new("RGB" , (image_width, image_height) )
_UpperCAmelCase = img.load()
# loop through the image-coordinates
for image_x in range(__lowercase ):
for image_y in range(__lowercase ):
# determine the figure-coordinates based on the image-coordinates
_UpperCAmelCase = figure_width / image_width * image_height
_UpperCAmelCase = figure_center_x + (image_x / image_width - 0.5) * figure_width
_UpperCAmelCase = figure_center_y + (image_y / image_height - 0.5) * figure_height
_UpperCAmelCase = get_distance(__lowercase , __lowercase , __lowercase )
# color the corresponding pixel based on the selected coloring-function
if use_distance_color_coding:
_UpperCAmelCase = get_color_coded_rgb(__lowercase )
else:
_UpperCAmelCase = get_black_and_white_rgb(__lowercase )
return img
if __name__ == "__main__":
import doctest
doctest.testmod()
# colored version, full figure
__SCREAMING_SNAKE_CASE :Any = get_image()
# uncomment for colored version, different section, zoomed in
# img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
# figure_width = 0.8)
# uncomment for black and white version, full figure
# img = get_image(use_distance_color_coding = False)
# uncomment to save the image
# img.save("mandelbrot.png")
img.show()
| 22 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : str ) -> str:
'''simple docstring'''
return " ".join(
"".join(word[::-1] ) if len(__lowercase ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(reverse_long_words('''Hey wollef sroirraw'''))
| 22 | 1 |
'''simple docstring'''
from itertools import permutations
def UpperCAmelCase_ ( __lowercase : tuple ) -> bool:
'''simple docstring'''
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
_UpperCAmelCase = [7, 11, 13, 17]
for i, test in enumerate(__lowercase ):
if (num[i + 4] * 100 + num[i + 5] * 10 + num[i + 6]) % test != 0:
return False
return True
def UpperCAmelCase_ ( __lowercase : int = 10 ) -> int:
'''simple docstring'''
return sum(
int("".join(map(__lowercase , __lowercase ) ) )
for num in permutations(range(__lowercase ) )
if is_substring_divisible(__lowercase ) )
if __name__ == "__main__":
print(F"{solution() = }")
| 22 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : str ) -> list:
'''simple docstring'''
if n_term == "":
return []
_UpperCAmelCase = []
for temp in range(int(__lowercase ) ):
series.append(f'1/{temp + 1}' if series else "1" )
return series
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE :str = input('''Enter the last number (nth term) of the Harmonic Series''')
print('''Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n''')
print(harmonic_series(nth_term))
| 22 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE :str = logging.get_logger(__name__)
class A_ ( lowerCAmelCase_ ):
_lowerCamelCase : Tuple = """timm_backbone"""
def __init__( self : List[str] , snake_case_ : Union[str, Any]=None , snake_case_ : List[str]=3 , snake_case_ : str=True , snake_case_ : Dict=True , snake_case_ : Union[str, Any]=None , **snake_case_ : Dict , ):
super().__init__(**snake_case_ )
_UpperCAmelCase = backbone
_UpperCAmelCase = num_channels
_UpperCAmelCase = features_only
_UpperCAmelCase = use_pretrained_backbone
_UpperCAmelCase = True
_UpperCAmelCase = out_indices if out_indices is not None else (-1,)
| 22 |
'''simple docstring'''
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
__SCREAMING_SNAKE_CASE :int = get_tests_dir('''fixtures/test_sentencepiece_no_bos.model''')
@require_sentencepiece
@require_tokenizers
class A_ ( lowerCAmelCase_ , unittest.TestCase ):
_lowerCamelCase : List[str] = PegasusTokenizer
_lowerCamelCase : int = PegasusTokenizerFast
_lowerCamelCase : Union[str, Any] = True
_lowerCamelCase : List[str] = True
def lowercase ( self : Optional[int] ):
super().setUp()
# We have a SentencePiece fixture for testing
_UpperCAmelCase = PegasusTokenizer(snake_case_ )
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def lowercase ( self : Tuple ):
return PegasusTokenizer.from_pretrained("google/pegasus-large" )
def lowercase ( self : Union[str, Any] , **snake_case_ : Union[str, Any] ):
return PegasusTokenizer.from_pretrained(self.tmpdirname , **snake_case_ )
def lowercase ( self : Tuple , snake_case_ : Any ):
return ("This is a test", "This is a test")
def lowercase ( self : Optional[int] ):
_UpperCAmelCase = "</s>"
_UpperCAmelCase = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case_ ) , snake_case_ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case_ ) , snake_case_ )
def lowercase ( self : Union[str, Any] ):
_UpperCAmelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , "<pad>" )
self.assertEqual(vocab_keys[1] , "</s>" )
self.assertEqual(vocab_keys[-1] , "v" )
self.assertEqual(len(snake_case_ ) , 1_1_0_3 )
def lowercase ( self : Any ):
self.assertEqual(self.get_tokenizer().vocab_size , 1_1_0_3 )
def lowercase ( self : List[Any] ):
_UpperCAmelCase = self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
_UpperCAmelCase = self.tokenizer_class.from_pretrained(self.tmpdirname )
_UpperCAmelCase = (
"Let's see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important"
" </s> <pad> <pad> <pad>"
)
_UpperCAmelCase = rust_tokenizer([raw_input_str] , return_tensors=snake_case_ , add_special_tokens=snake_case_ ).input_ids[0]
_UpperCAmelCase = py_tokenizer([raw_input_str] , return_tensors=snake_case_ , add_special_tokens=snake_case_ ).input_ids[0]
self.assertListEqual(snake_case_ , snake_case_ )
def lowercase ( self : Tuple ):
_UpperCAmelCase = self._large_tokenizer
# <mask_1> masks whole sentence while <mask_2> masks single word
_UpperCAmelCase = "<mask_1> To ensure a <mask_2> flow of bank resolutions."
_UpperCAmelCase = [2, 4_1_3, 6_1_5, 1_1_4, 3, 1_9_7_1, 1_1_3, 1_6_7_9, 1_0_7_1_0, 1_0_7, 1]
_UpperCAmelCase = tokenizer([raw_input_str] , return_tensors=snake_case_ ).input_ids[0]
self.assertListEqual(snake_case_ , snake_case_ )
def lowercase ( self : Union[str, Any] ):
_UpperCAmelCase = self._large_tokenizer
# The tracebacks for the following asserts are **better** without messages or self.assertEqual
assert tokenizer.vocab_size == 9_6_1_0_3
assert tokenizer.pad_token_id == 0
assert tokenizer.eos_token_id == 1
assert tokenizer.offset == 1_0_3
assert tokenizer.unk_token_id == tokenizer.offset + 2 == 1_0_5
assert tokenizer.unk_token == "<unk>"
assert tokenizer.model_max_length == 1_0_2_4
_UpperCAmelCase = "To ensure a smooth flow of bank resolutions."
_UpperCAmelCase = [4_1_3, 6_1_5, 1_1_4, 2_2_9_1, 1_9_7_1, 1_1_3, 1_6_7_9, 1_0_7_1_0, 1_0_7, 1]
_UpperCAmelCase = tokenizer([raw_input_str] , return_tensors=snake_case_ ).input_ids[0]
self.assertListEqual(snake_case_ , snake_case_ )
assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3] ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]
@require_torch
def lowercase ( self : int ):
_UpperCAmelCase = ["This is going to be way too long." * 1_5_0, "short example"]
_UpperCAmelCase = ["not super long but more than 5 tokens", "tiny"]
_UpperCAmelCase = self._large_tokenizer(snake_case_ , padding=snake_case_ , truncation=snake_case_ , return_tensors="pt" )
_UpperCAmelCase = self._large_tokenizer(
text_target=snake_case_ , max_length=5 , padding=snake_case_ , truncation=snake_case_ , return_tensors="pt" )
assert batch.input_ids.shape == (2, 1_0_2_4)
assert batch.attention_mask.shape == (2, 1_0_2_4)
assert targets["input_ids"].shape == (2, 5)
assert len(snake_case_ ) == 2 # input_ids, attention_mask.
@slow
def lowercase ( self : Dict ):
# fmt: off
_UpperCAmelCase = {"input_ids": [[3_8_9_7_9, 1_4_3, 1_8_4_8_5, 6_0_6, 1_3_0, 2_6_6_6_9, 8_7_6_8_6, 1_2_1, 5_4_1_8_9, 1_1_2_9, 1_1_1, 2_6_6_6_9, 8_7_6_8_6, 1_2_1, 9_1_1_4, 1_4_7_8_7, 1_2_1, 1_3_2_4_9, 1_5_8, 5_9_2, 9_5_6, 1_2_1, 1_4_6_2_1, 3_1_5_7_6, 1_4_3, 6_2_6_1_3, 1_0_8, 9_6_8_8, 9_3_0, 4_3_4_3_0, 1_1_5_6_2, 6_2_6_1_3, 3_0_4, 1_0_8, 1_1_4_4_3, 8_9_7, 1_0_8, 9_3_1_4, 1_7_4_1_5, 6_3_3_9_9, 1_0_8, 1_1_4_4_3, 7_6_1_4, 1_8_3_1_6, 1_1_8, 4_2_8_4, 7_1_4_8, 1_2_4_3_0, 1_4_3, 1_4_0_0, 2_5_7_0_3, 1_5_8, 1_1_1, 4_2_8_4, 7_1_4_8, 1_1_7_7_2, 1_4_3, 2_1_2_9_7, 1_0_6_4, 1_5_8, 1_2_2, 2_0_4, 3_5_0_6, 1_7_5_4, 1_1_3_3, 1_4_7_8_7, 1_5_8_1, 1_1_5, 3_3_2_2_4, 4_4_8_2, 1_1_1, 1_3_5_5, 1_1_0, 2_9_1_7_3, 3_1_7, 5_0_8_3_3, 1_0_8, 2_0_1_4_7, 9_4_6_6_5, 1_1_1, 7_7_1_9_8, 1_0_7, 1], [1_1_0, 6_2_6_1_3, 1_1_7, 6_3_8, 1_1_2, 1_1_3_3, 1_2_1, 2_0_0_9_8, 1_3_5_5, 7_9_0_5_0, 1_3_8_7_2, 1_3_5, 1_5_9_6, 5_3_5_4_1, 1_3_5_2, 1_4_1, 1_3_0_3_9, 5_5_4_2, 1_2_4, 3_0_2, 5_1_8, 1_1_1, 2_6_8, 2_9_5_6, 1_1_5, 1_4_9, 4_4_2_7, 1_0_7, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1_3_9, 1_2_3_5, 2_7_9_9, 1_8_2_8_9, 1_7_7_8_0, 2_0_4, 1_0_9, 9_4_7_4, 1_2_9_6, 1_0_7, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=snake_case_ , model_name="google/bigbird-pegasus-large-arxiv" , revision="ba85d0851d708441f91440d509690f1ab6353415" , )
@require_sentencepiece
@require_tokenizers
class A_ ( lowerCAmelCase_ , unittest.TestCase ):
_lowerCamelCase : List[str] = PegasusTokenizer
_lowerCamelCase : List[Any] = PegasusTokenizerFast
_lowerCamelCase : int = True
_lowerCamelCase : Union[str, Any] = True
def lowercase ( self : Any ):
super().setUp()
# We have a SentencePiece fixture for testing
_UpperCAmelCase = PegasusTokenizer(snake_case_ , offset=0 , mask_token_sent=snake_case_ , mask_token="[MASK]" )
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def lowercase ( self : Tuple ):
return PegasusTokenizer.from_pretrained("google/bigbird-pegasus-large-arxiv" )
def lowercase ( self : Optional[Any] , **snake_case_ : Dict ):
return PegasusTokenizer.from_pretrained(self.tmpdirname , **snake_case_ )
def lowercase ( self : Union[str, Any] , snake_case_ : str ):
return ("This is a test", "This is a test")
def lowercase ( self : List[str] ):
_UpperCAmelCase = self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
_UpperCAmelCase = self.tokenizer_class.from_pretrained(self.tmpdirname )
_UpperCAmelCase = (
"Let's see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"
" <pad> <pad> <pad>"
)
_UpperCAmelCase = rust_tokenizer([raw_input_str] , return_tensors=snake_case_ , add_special_tokens=snake_case_ ).input_ids[0]
_UpperCAmelCase = py_tokenizer([raw_input_str] , return_tensors=snake_case_ , add_special_tokens=snake_case_ ).input_ids[0]
self.assertListEqual(snake_case_ , snake_case_ )
@require_torch
def lowercase ( self : Tuple ):
_UpperCAmelCase = ["This is going to be way too long." * 1_0_0_0, "short example"]
_UpperCAmelCase = ["not super long but more than 5 tokens", "tiny"]
_UpperCAmelCase = self._large_tokenizer(snake_case_ , padding=snake_case_ , truncation=snake_case_ , return_tensors="pt" )
_UpperCAmelCase = self._large_tokenizer(
text_target=snake_case_ , max_length=5 , padding=snake_case_ , truncation=snake_case_ , return_tensors="pt" )
assert batch.input_ids.shape == (2, 4_0_9_6)
assert batch.attention_mask.shape == (2, 4_0_9_6)
assert targets["input_ids"].shape == (2, 5)
assert len(snake_case_ ) == 2 # input_ids, attention_mask.
def lowercase ( self : Union[str, Any] ):
_UpperCAmelCase = (
"This is an example string that is used to test the original TF implementation against the HF"
" implementation"
)
_UpperCAmelCase = self._large_tokenizer(snake_case_ ).input_ids
self.assertListEqual(
snake_case_ , [1_8_2, 1_1_7, 1_4_2, 5_8_7, 4_2_1_1, 1_2_0, 1_1_7, 2_6_3, 1_1_2, 8_0_4, 1_0_9, 8_5_6, 2_5_0_1_6, 3_1_3_7, 4_6_4, 1_0_9, 2_6_9_5_5, 3_1_3_7, 1] , )
| 22 | 1 |
'''simple docstring'''
import tempfile
import unittest
import numpy as np
from diffusers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionPipeline,
PNDMScheduler,
)
from diffusers.utils.testing_utils import is_onnx_available, nightly, require_onnxruntime, require_torch_gpu
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class A_ ( lowerCAmelCase_ , unittest.TestCase ):
_lowerCamelCase : str = """hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"""
def lowercase ( self : str , snake_case_ : List[str]=0 ):
_UpperCAmelCase = np.random.RandomState(snake_case_ )
_UpperCAmelCase = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 7.5,
"output_type": "numpy",
}
return inputs
def lowercase ( self : Any ):
_UpperCAmelCase = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="CPUExecutionProvider" )
pipe.set_progress_bar_config(disable=snake_case_ )
_UpperCAmelCase = self.get_dummy_inputs()
_UpperCAmelCase = pipe(**snake_case_ ).images
_UpperCAmelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 1_2_8, 1_2_8, 3)
_UpperCAmelCase = np.array([0.6_5_0_7_2, 0.5_8_4_9_2, 0.4_8_2_1_9, 0.5_5_5_2_1, 0.5_3_1_8_0, 0.5_5_9_3_9, 0.5_0_6_9_7, 0.3_9_8_0_0, 0.4_6_4_5_5] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def lowercase ( self : int ):
_UpperCAmelCase = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="CPUExecutionProvider" )
_UpperCAmelCase = PNDMScheduler.from_config(pipe.scheduler.config , skip_prk_steps=snake_case_ )
pipe.set_progress_bar_config(disable=snake_case_ )
_UpperCAmelCase = self.get_dummy_inputs()
_UpperCAmelCase = pipe(**snake_case_ ).images
_UpperCAmelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 1_2_8, 1_2_8, 3)
_UpperCAmelCase = np.array([0.6_5_8_6_3, 0.5_9_4_2_5, 0.4_9_3_2_6, 0.5_6_3_1_3, 0.5_3_8_7_5, 0.5_6_6_2_7, 0.5_1_0_6_5, 0.3_9_7_7_7, 0.4_6_3_3_0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def lowercase ( self : Optional[int] ):
_UpperCAmelCase = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="CPUExecutionProvider" )
_UpperCAmelCase = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=snake_case_ )
_UpperCAmelCase = self.get_dummy_inputs()
_UpperCAmelCase = pipe(**snake_case_ ).images
_UpperCAmelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 1_2_8, 1_2_8, 3)
_UpperCAmelCase = np.array([0.5_3_7_5_5, 0.6_0_7_8_6, 0.4_7_4_0_2, 0.4_9_4_8_8, 0.5_1_8_6_9, 0.4_9_8_1_9, 0.4_7_9_8_5, 0.3_8_9_5_7, 0.4_4_2_7_9] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def lowercase ( self : List[Any] ):
_UpperCAmelCase = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="CPUExecutionProvider" )
_UpperCAmelCase = EulerDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=snake_case_ )
_UpperCAmelCase = self.get_dummy_inputs()
_UpperCAmelCase = pipe(**snake_case_ ).images
_UpperCAmelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 1_2_8, 1_2_8, 3)
_UpperCAmelCase = np.array([0.5_3_7_5_5, 0.6_0_7_8_6, 0.4_7_4_0_2, 0.4_9_4_8_8, 0.5_1_8_6_9, 0.4_9_8_1_9, 0.4_7_9_8_5, 0.3_8_9_5_7, 0.4_4_2_7_9] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def lowercase ( self : Any ):
_UpperCAmelCase = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="CPUExecutionProvider" )
_UpperCAmelCase = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=snake_case_ )
_UpperCAmelCase = self.get_dummy_inputs()
_UpperCAmelCase = pipe(**snake_case_ ).images
_UpperCAmelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 1_2_8, 1_2_8, 3)
_UpperCAmelCase = np.array([0.5_3_8_1_7, 0.6_0_8_1_2, 0.4_7_3_8_4, 0.4_9_5_3_0, 0.5_1_8_9_4, 0.4_9_8_1_4, 0.4_7_9_8_4, 0.3_8_9_5_8, 0.4_4_2_7_1] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def lowercase ( self : Optional[int] ):
_UpperCAmelCase = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="CPUExecutionProvider" )
_UpperCAmelCase = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=snake_case_ )
_UpperCAmelCase = self.get_dummy_inputs()
_UpperCAmelCase = pipe(**snake_case_ ).images
_UpperCAmelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 1_2_8, 1_2_8, 3)
_UpperCAmelCase = np.array([0.5_3_8_9_5, 0.6_0_8_0_8, 0.4_7_9_3_3, 0.4_9_6_0_8, 0.5_1_8_8_6, 0.4_9_9_5_0, 0.4_8_0_5_3, 0.3_8_9_5_7, 0.4_4_2_0_0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def lowercase ( self : Optional[int] ):
_UpperCAmelCase = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="CPUExecutionProvider" )
pipe.set_progress_bar_config(disable=snake_case_ )
_UpperCAmelCase = self.get_dummy_inputs()
_UpperCAmelCase = 3 * [inputs["prompt"]]
# forward
_UpperCAmelCase = pipe(**snake_case_ )
_UpperCAmelCase = output.images[0, -3:, -3:, -1]
_UpperCAmelCase = self.get_dummy_inputs()
_UpperCAmelCase = 3 * [inputs.pop("prompt" )]
_UpperCAmelCase = pipe.tokenizer(
snake_case_ , padding="max_length" , max_length=pipe.tokenizer.model_max_length , truncation=snake_case_ , return_tensors="np" , )
_UpperCAmelCase = text_inputs["input_ids"]
_UpperCAmelCase = pipe.text_encoder(input_ids=text_inputs.astype(np.intaa ) )[0]
_UpperCAmelCase = prompt_embeds
# forward
_UpperCAmelCase = pipe(**snake_case_ )
_UpperCAmelCase = output.images[0, -3:, -3:, -1]
assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1e-4
def lowercase ( self : Optional[Any] ):
_UpperCAmelCase = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="CPUExecutionProvider" )
pipe.set_progress_bar_config(disable=snake_case_ )
_UpperCAmelCase = self.get_dummy_inputs()
_UpperCAmelCase = 3 * ["this is a negative prompt"]
_UpperCAmelCase = negative_prompt
_UpperCAmelCase = 3 * [inputs["prompt"]]
# forward
_UpperCAmelCase = pipe(**snake_case_ )
_UpperCAmelCase = output.images[0, -3:, -3:, -1]
_UpperCAmelCase = self.get_dummy_inputs()
_UpperCAmelCase = 3 * [inputs.pop("prompt" )]
_UpperCAmelCase = []
for p in [prompt, negative_prompt]:
_UpperCAmelCase = pipe.tokenizer(
snake_case_ , padding="max_length" , max_length=pipe.tokenizer.model_max_length , truncation=snake_case_ , return_tensors="np" , )
_UpperCAmelCase = text_inputs["input_ids"]
embeds.append(pipe.text_encoder(input_ids=text_inputs.astype(np.intaa ) )[0] )
_UpperCAmelCase , _UpperCAmelCase = embeds
# forward
_UpperCAmelCase = pipe(**snake_case_ )
_UpperCAmelCase = output.images[0, -3:, -3:, -1]
assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1e-4
@nightly
@require_onnxruntime
@require_torch_gpu
class A_ ( unittest.TestCase ):
@property
def lowercase ( self : Tuple ):
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def lowercase ( self : int ):
_UpperCAmelCase = ort.SessionOptions()
_UpperCAmelCase = False
return options
def lowercase ( self : Any ):
# using the PNDM scheduler by default
_UpperCAmelCase = OnnxStableDiffusionPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4" , revision="onnx" , safety_checker=snake_case_ , feature_extractor=snake_case_ , provider=self.gpu_provider , sess_options=self.gpu_options , )
sd_pipe.set_progress_bar_config(disable=snake_case_ )
_UpperCAmelCase = "A painting of a squirrel eating a burger"
np.random.seed(0 )
_UpperCAmelCase = sd_pipe([prompt] , guidance_scale=6.0 , num_inference_steps=1_0 , output_type="np" )
_UpperCAmelCase = output.images
_UpperCAmelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 5_1_2, 5_1_2, 3)
_UpperCAmelCase = np.array([0.0_4_5_2, 0.0_3_9_0, 0.0_0_8_7, 0.0_3_5_0, 0.0_6_1_7, 0.0_3_6_4, 0.0_5_4_4, 0.0_5_2_3, 0.0_7_2_0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def lowercase ( self : List[str] ):
_UpperCAmelCase = DDIMScheduler.from_pretrained(
"runwayml/stable-diffusion-v1-5" , subfolder="scheduler" , revision="onnx" )
_UpperCAmelCase = OnnxStableDiffusionPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5" , revision="onnx" , scheduler=snake_case_ , safety_checker=snake_case_ , feature_extractor=snake_case_ , provider=self.gpu_provider , sess_options=self.gpu_options , )
sd_pipe.set_progress_bar_config(disable=snake_case_ )
_UpperCAmelCase = "open neural network exchange"
_UpperCAmelCase = np.random.RandomState(0 )
_UpperCAmelCase = sd_pipe([prompt] , guidance_scale=7.5 , num_inference_steps=1_0 , generator=snake_case_ , output_type="np" )
_UpperCAmelCase = output.images
_UpperCAmelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 5_1_2, 5_1_2, 3)
_UpperCAmelCase = np.array([0.2_8_6_7, 0.1_9_7_4, 0.1_4_8_1, 0.7_2_9_4, 0.7_2_5_1, 0.6_6_6_7, 0.4_1_9_4, 0.5_6_4_2, 0.6_4_8_6] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def lowercase ( self : List[str] ):
_UpperCAmelCase = LMSDiscreteScheduler.from_pretrained(
"runwayml/stable-diffusion-v1-5" , subfolder="scheduler" , revision="onnx" )
_UpperCAmelCase = OnnxStableDiffusionPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5" , revision="onnx" , scheduler=snake_case_ , safety_checker=snake_case_ , feature_extractor=snake_case_ , provider=self.gpu_provider , sess_options=self.gpu_options , )
sd_pipe.set_progress_bar_config(disable=snake_case_ )
_UpperCAmelCase = "open neural network exchange"
_UpperCAmelCase = np.random.RandomState(0 )
_UpperCAmelCase = sd_pipe([prompt] , guidance_scale=7.5 , num_inference_steps=1_0 , generator=snake_case_ , output_type="np" )
_UpperCAmelCase = output.images
_UpperCAmelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 5_1_2, 5_1_2, 3)
_UpperCAmelCase = np.array([0.2_3_0_6, 0.1_9_5_9, 0.1_5_9_3, 0.6_5_4_9, 0.6_3_9_4, 0.5_4_0_8, 0.5_0_6_5, 0.6_0_1_0, 0.6_1_6_1] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def lowercase ( self : Optional[int] ):
_UpperCAmelCase = 0
def test_callback_fn(snake_case_ : int , snake_case_ : int , snake_case_ : np.ndarray ) -> None:
_UpperCAmelCase = True
nonlocal number_of_steps
number_of_steps += 1
if step == 0:
assert latents.shape == (1, 4, 6_4, 6_4)
_UpperCAmelCase = latents[0, -3:, -3:, -1]
_UpperCAmelCase = np.array(
[-0.6_7_7_2, -0.3_8_3_5, -1.2_4_5_6, 0.1_9_0_5, -1.0_9_7_4, 0.6_9_6_7, -1.9_3_5_3, 0.0_1_7_8, 1.0_1_6_7] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 1e-3
elif step == 5:
assert latents.shape == (1, 4, 6_4, 6_4)
_UpperCAmelCase = latents[0, -3:, -3:, -1]
_UpperCAmelCase = np.array(
[-0.3_3_5_1, 0.2_2_4_1, -0.1_8_3_7, -0.2_3_2_5, -0.6_5_7_7, 0.3_3_9_3, -0.0_2_4_1, 0.5_8_9_9, 1.3_8_7_5] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 1e-3
_UpperCAmelCase = False
_UpperCAmelCase = OnnxStableDiffusionPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5" , revision="onnx" , safety_checker=snake_case_ , feature_extractor=snake_case_ , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=snake_case_ )
_UpperCAmelCase = "Andromeda galaxy in a bottle"
_UpperCAmelCase = np.random.RandomState(0 )
pipe(
prompt=snake_case_ , num_inference_steps=5 , guidance_scale=7.5 , generator=snake_case_ , callback=snake_case_ , callback_steps=1 , )
assert test_callback_fn.has_been_called
assert number_of_steps == 6
def lowercase ( self : Dict ):
_UpperCAmelCase = OnnxStableDiffusionPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5" , revision="onnx" , safety_checker=snake_case_ , feature_extractor=snake_case_ , provider=self.gpu_provider , sess_options=self.gpu_options , )
assert isinstance(snake_case_ , snake_case_ )
assert pipe.safety_checker is None
_UpperCAmelCase = pipe("example prompt" , num_inference_steps=2 ).images[0]
assert image is not None
# check that there's no error when saving a pipeline with one of the models being None
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(snake_case_ )
_UpperCAmelCase = OnnxStableDiffusionPipeline.from_pretrained(snake_case_ )
# sanity check that the pipeline still works
assert pipe.safety_checker is None
_UpperCAmelCase = pipe("example prompt" , num_inference_steps=2 ).images[0]
assert image is not None
| 22 |
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast
@require_vision
class A_ ( unittest.TestCase ):
def lowercase ( self : int ):
_UpperCAmelCase = tempfile.mkdtemp()
_UpperCAmelCase = BlipImageProcessor()
_UpperCAmelCase = BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-BertModel" )
_UpperCAmelCase = BlipProcessor(snake_case_ , snake_case_ )
processor.save_pretrained(self.tmpdirname )
def lowercase ( self : Tuple , **snake_case_ : int ):
return AutoProcessor.from_pretrained(self.tmpdirname , **snake_case_ ).tokenizer
def lowercase ( self : Dict , **snake_case_ : Any ):
return AutoProcessor.from_pretrained(self.tmpdirname , **snake_case_ ).image_processor
def lowercase ( self : int ):
shutil.rmtree(self.tmpdirname )
def lowercase ( self : Optional[Any] ):
_UpperCAmelCase = [np.random.randint(2_5_5 , size=(3, 3_0, 4_0_0) , dtype=np.uinta )]
_UpperCAmelCase = [Image.fromarray(np.moveaxis(snake_case_ , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def lowercase ( self : int ):
_UpperCAmelCase = BlipProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
_UpperCAmelCase = self.get_tokenizer(bos_token="(BOS)" , eos_token="(EOS)" )
_UpperCAmelCase = self.get_image_processor(do_normalize=snake_case_ , padding_value=1.0 )
_UpperCAmelCase = BlipProcessor.from_pretrained(
self.tmpdirname , bos_token="(BOS)" , eos_token="(EOS)" , do_normalize=snake_case_ , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , snake_case_ )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , snake_case_ )
def lowercase ( self : Any ):
_UpperCAmelCase = self.get_image_processor()
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = BlipProcessor(tokenizer=snake_case_ , image_processor=snake_case_ )
_UpperCAmelCase = self.prepare_image_inputs()
_UpperCAmelCase = image_processor(snake_case_ , return_tensors="np" )
_UpperCAmelCase = processor(images=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 lowercase ( self : Optional[int] ):
_UpperCAmelCase = self.get_image_processor()
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = BlipProcessor(tokenizer=snake_case_ , image_processor=snake_case_ )
_UpperCAmelCase = "lower newer"
_UpperCAmelCase = processor(text=snake_case_ )
_UpperCAmelCase = tokenizer(snake_case_ , return_token_type_ids=snake_case_ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def lowercase ( self : Optional[Any] ):
_UpperCAmelCase = self.get_image_processor()
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = BlipProcessor(tokenizer=snake_case_ , image_processor=snake_case_ )
_UpperCAmelCase = "lower newer"
_UpperCAmelCase = self.prepare_image_inputs()
_UpperCAmelCase = processor(text=snake_case_ , images=snake_case_ )
self.assertListEqual(list(inputs.keys() ) , ["pixel_values", "input_ids", "attention_mask"] )
# test if it raises when no input is passed
with pytest.raises(snake_case_ ):
processor()
def lowercase ( self : Union[str, Any] ):
_UpperCAmelCase = self.get_image_processor()
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = BlipProcessor(tokenizer=snake_case_ , image_processor=snake_case_ )
_UpperCAmelCase = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
_UpperCAmelCase = processor.batch_decode(snake_case_ )
_UpperCAmelCase = tokenizer.batch_decode(snake_case_ )
self.assertListEqual(snake_case_ , snake_case_ )
def lowercase ( self : str ):
_UpperCAmelCase = self.get_image_processor()
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = BlipProcessor(tokenizer=snake_case_ , image_processor=snake_case_ )
_UpperCAmelCase = "lower newer"
_UpperCAmelCase = self.prepare_image_inputs()
_UpperCAmelCase = processor(text=snake_case_ , images=snake_case_ )
# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
self.assertListEqual(list(inputs.keys() ) , ["pixel_values", "input_ids", "attention_mask"] )
| 22 | 1 |
'''simple docstring'''
import json
import os
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__SCREAMING_SNAKE_CASE :int = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE :Optional[Any] = {'''vocab_file''': '''vocab.json'''}
__SCREAMING_SNAKE_CASE :Tuple = {
'''vocab_file''': {
'''mgp-str''': '''https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json''',
}
}
__SCREAMING_SNAKE_CASE :List[str] = {'''mgp-str''': 27}
class A_ ( lowerCAmelCase_ ):
_lowerCamelCase : Any = VOCAB_FILES_NAMES
_lowerCamelCase : int = PRETRAINED_VOCAB_FILES_MAP
_lowerCamelCase : str = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__( self : int , snake_case_ : List[str] , snake_case_ : List[Any]="[GO]" , snake_case_ : Optional[Any]="[GO]" , snake_case_ : Union[str, Any]="[s]" , snake_case_ : Any="[GO]" , **snake_case_ : Dict ):
super().__init__(
unk_token=snake_case_ , bos_token=snake_case_ , eos_token=snake_case_ , pad_token=snake_case_ , **snake_case_ , )
with open(snake_case_ , encoding="utf-8" ) as vocab_handle:
_UpperCAmelCase = json.load(snake_case_ )
_UpperCAmelCase = {v: k for k, v in self.vocab.items()}
@property
def lowercase ( self : str ):
return len(self.vocab )
def lowercase ( self : int ):
return dict(self.vocab , **self.added_tokens_encoder )
def lowercase ( self : Optional[Any] , snake_case_ : int ):
_UpperCAmelCase = []
for s in text:
char_tokens.extend(snake_case_ )
return char_tokens
def lowercase ( self : Union[str, Any] , snake_case_ : Union[str, Any] ):
return self.vocab.get(snake_case_ , self.vocab.get(self.unk_token ) )
def lowercase ( self : int , snake_case_ : int ):
return self.decoder.get(snake_case_ )
def lowercase ( self : int , snake_case_ : str , snake_case_ : Optional[str] = None ):
if not os.path.isdir(snake_case_ ):
logger.error("Vocabulary path ({}) should be a directory".format(snake_case_ ) )
return
_UpperCAmelCase = os.path.join(
snake_case_ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
with open(snake_case_ , "w" , encoding="utf-8" ) as f:
f.write(json.dumps(self.vocab , indent=2 , sort_keys=snake_case_ , ensure_ascii=snake_case_ ) + "\n" )
return (vocab_file,)
| 22 |
'''simple docstring'''
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
def UpperCAmelCase_ ( __lowercase : str ) -> List[str]:
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase = image.size
_UpperCAmelCase , _UpperCAmelCase = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32
_UpperCAmelCase = image.resize((w, h) , resample=PIL_INTERPOLATION["lanczos"] )
_UpperCAmelCase = np.array(__lowercase ).astype(np.floataa ) / 255.0
_UpperCAmelCase = image[None].transpose(0 , 3 , 1 , 2 )
_UpperCAmelCase = torch.from_numpy(__lowercase )
return 2.0 * image - 1.0
class A_ ( lowerCAmelCase_ ):
def __init__( self : Optional[Any] , snake_case_ : VQModel , snake_case_ : UNetaDModel , snake_case_ : Union[
DDIMScheduler,
PNDMScheduler,
LMSDiscreteScheduler,
EulerDiscreteScheduler,
EulerAncestralDiscreteScheduler,
DPMSolverMultistepScheduler,
] , ):
super().__init__()
self.register_modules(vqvae=snake_case_ , unet=snake_case_ , scheduler=snake_case_ )
@torch.no_grad()
def __call__( self : Any , snake_case_ : Union[torch.Tensor, PIL.Image.Image] = None , snake_case_ : Optional[int] = 1 , snake_case_ : Optional[int] = 1_0_0 , snake_case_ : Optional[float] = 0.0 , snake_case_ : Optional[Union[torch.Generator, List[torch.Generator]]] = None , snake_case_ : Optional[str] = "pil" , snake_case_ : bool = True , ):
if isinstance(snake_case_ , PIL.Image.Image ):
_UpperCAmelCase = 1
elif isinstance(snake_case_ , torch.Tensor ):
_UpperCAmelCase = image.shape[0]
else:
raise ValueError(f'`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(snake_case_ )}' )
if isinstance(snake_case_ , PIL.Image.Image ):
_UpperCAmelCase = preprocess(snake_case_ )
_UpperCAmelCase , _UpperCAmelCase = image.shape[-2:]
# in_channels should be 6: 3 for latents, 3 for low resolution image
_UpperCAmelCase = (batch_size, self.unet.config.in_channels // 2, height, width)
_UpperCAmelCase = next(self.unet.parameters() ).dtype
_UpperCAmelCase = randn_tensor(snake_case_ , generator=snake_case_ , device=self.device , dtype=snake_case_ )
_UpperCAmelCase = image.to(device=self.device , dtype=snake_case_ )
# set timesteps and move to the correct device
self.scheduler.set_timesteps(snake_case_ , device=self.device )
_UpperCAmelCase = self.scheduler.timesteps
# scale the initial noise by the standard deviation required by the scheduler
_UpperCAmelCase = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
_UpperCAmelCase = "eta" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
_UpperCAmelCase = {}
if accepts_eta:
_UpperCAmelCase = eta
for t in self.progress_bar(snake_case_ ):
# concat latents and low resolution image in the channel dimension.
_UpperCAmelCase = torch.cat([latents, image] , dim=1 )
_UpperCAmelCase = self.scheduler.scale_model_input(snake_case_ , snake_case_ )
# predict the noise residual
_UpperCAmelCase = self.unet(snake_case_ , snake_case_ ).sample
# compute the previous noisy sample x_t -> x_t-1
_UpperCAmelCase = self.scheduler.step(snake_case_ , snake_case_ , snake_case_ , **snake_case_ ).prev_sample
# decode the image latents with the VQVAE
_UpperCAmelCase = self.vqvae.decode(snake_case_ ).sample
_UpperCAmelCase = torch.clamp(snake_case_ , -1.0 , 1.0 )
_UpperCAmelCase = image / 2 + 0.5
_UpperCAmelCase = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
_UpperCAmelCase = self.numpy_to_pil(snake_case_ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=snake_case_ )
| 22 | 1 |
'''simple docstring'''
import unittest
from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
__SCREAMING_SNAKE_CASE :str = get_tests_dir('''fixtures/test_sentencepiece.model''')
@require_sentencepiece
@require_tokenizers
class A_ ( lowerCAmelCase_ , unittest.TestCase ):
_lowerCamelCase : Any = ReformerTokenizer
_lowerCamelCase : str = ReformerTokenizerFast
_lowerCamelCase : Union[str, Any] = True
_lowerCamelCase : Union[str, Any] = False
_lowerCamelCase : Any = True
def lowercase ( self : List[str] ):
super().setUp()
_UpperCAmelCase = ReformerTokenizer(snake_case_ , keep_accents=snake_case_ )
tokenizer.save_pretrained(self.tmpdirname )
def lowercase ( self : Dict ):
_UpperCAmelCase = "<s>"
_UpperCAmelCase = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case_ ) , snake_case_ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case_ ) , snake_case_ )
def lowercase ( self : Optional[int] ):
_UpperCAmelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , "<unk>" )
self.assertEqual(vocab_keys[1] , "<s>" )
self.assertEqual(vocab_keys[-1] , "j" )
self.assertEqual(len(snake_case_ ) , 1_0_0_0 )
def lowercase ( self : Any ):
self.assertEqual(self.get_tokenizer().vocab_size , 1_0_0_0 )
def lowercase ( self : Tuple ):
if not self.test_rust_tokenizer:
return
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = self.get_rust_tokenizer()
_UpperCAmelCase = "I was born in 92000, and this is falsé."
_UpperCAmelCase = tokenizer.tokenize(snake_case_ )
_UpperCAmelCase = rust_tokenizer.tokenize(snake_case_ )
self.assertListEqual(snake_case_ , snake_case_ )
_UpperCAmelCase = tokenizer.encode(snake_case_ , add_special_tokens=snake_case_ )
_UpperCAmelCase = rust_tokenizer.encode(snake_case_ , add_special_tokens=snake_case_ )
self.assertListEqual(snake_case_ , snake_case_ )
_UpperCAmelCase = self.get_rust_tokenizer()
_UpperCAmelCase = tokenizer.encode(snake_case_ )
_UpperCAmelCase = rust_tokenizer.encode(snake_case_ )
self.assertListEqual(snake_case_ , snake_case_ )
def lowercase ( self : Optional[Any] , snake_case_ : Dict=1_5 ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})' ):
_UpperCAmelCase = self.rust_tokenizer_class.from_pretrained(snake_case_ , **snake_case_ )
# Simple input
_UpperCAmelCase = "This is a simple input"
_UpperCAmelCase = ["This is a simple input 1", "This is a simple input 2"]
_UpperCAmelCase = ("This is a simple input", "This is a pair")
_UpperCAmelCase = [
("This is a simple input 1", "This is a simple input 2"),
("This is a simple pair 1", "This is a simple pair 2"),
]
# Simple input tests
self.assertRaises(snake_case_ , tokenizer_r.encode , snake_case_ , max_length=snake_case_ , padding="max_length" )
# Simple input
self.assertRaises(snake_case_ , tokenizer_r.encode_plus , snake_case_ , max_length=snake_case_ , padding="max_length" )
# Simple input
self.assertRaises(
snake_case_ , tokenizer_r.batch_encode_plus , snake_case_ , max_length=snake_case_ , padding="max_length" , )
# Pair input
self.assertRaises(snake_case_ , tokenizer_r.encode , snake_case_ , max_length=snake_case_ , padding="max_length" )
# Pair input
self.assertRaises(snake_case_ , tokenizer_r.encode_plus , snake_case_ , max_length=snake_case_ , padding="max_length" )
# Pair input
self.assertRaises(
snake_case_ , tokenizer_r.batch_encode_plus , snake_case_ , max_length=snake_case_ , padding="max_length" , )
def lowercase ( self : List[str] ):
pass
def lowercase ( self : List[Any] ):
_UpperCAmelCase = ReformerTokenizer(snake_case_ , keep_accents=snake_case_ )
_UpperCAmelCase = tokenizer.tokenize("This is a test" )
self.assertListEqual(snake_case_ , ["▁This", "▁is", "▁a", "▁t", "est"] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(snake_case_ ) , [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2] , )
_UpperCAmelCase = tokenizer.tokenize("I was born in 92000, and this is falsé." )
self.assertListEqual(
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",
".",
] , )
_UpperCAmelCase = tokenizer.convert_tokens_to_ids(snake_case_ )
self.assertListEqual(
snake_case_ , [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 0, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 0, 4] , )
_UpperCAmelCase = tokenizer.convert_ids_to_tokens(snake_case_ )
self.assertListEqual(
snake_case_ , [
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"<unk>",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"<unk>",
".",
] , )
@cached_property
def lowercase ( self : int ):
return ReformerTokenizer.from_pretrained("google/reformer-crime-and-punishment" )
@slow
def lowercase ( self : List[Any] ):
_UpperCAmelCase = "Hello World!"
_UpperCAmelCase = [1_2_6, 3_2, 2_6_2, 1_5_2, 3_8, 7_2, 2_8_7]
self.assertListEqual(snake_case_ , self.big_tokenizer.encode(snake_case_ ) )
@slow
def lowercase ( self : Tuple ):
_UpperCAmelCase = (
"This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
" add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"
)
_UpperCAmelCase = [
1_0_8,
2_6_5,
2_4,
1_1_1,
4,
2_5_8,
1_5_6,
3_5,
2_8,
2_7_5,
3,
2_5_9,
2_9_7,
2_6_0,
8_4,
4,
3_5,
1_1_0,
4_4,
8,
2_5_9,
9_1,
2_6_8,
2_1,
1_1,
2_0_9,
2_7_4,
1_0_9,
2_6_6,
2_7_7,
1_1_7,
8_6,
9_3,
3_1_5,
2_5_8,
2_7_8,
2_5_8,
2_7_7,
2_5_8,
0,
2_5_8,
2_8_8,
2_5_8,
3_1_9,
2_5_8,
0,
2_5_8,
0,
2_5_8,
0,
2_5_8,
0,
2_5_8,
2_8_7,
2_5_8,
3_1_5,
2_5_8,
2_8_9,
2_5_8,
2_7_8,
9_9,
2_6_9,
2_6_6,
2_6_2,
8,
2_5_9,
2_4_1,
4,
2_1_7,
2_3_0,
2_6_8,
2_6_6,
5_5,
1_6_8,
1_0_6,
7_5,
1_9_3,
2_6_6,
2_2_3,
2_7,
4_9,
2_6,
2_8_2,
2_5,
2_6_4,
2_9_9,
1_9,
2_6,
0,
2_5_8,
2_7_7,
1_1_7,
8_6,
9_3,
1_7_6,
1_8_3,
2_7_0,
1_1,
2_6_2,
4_2,
6_1,
2_6_5,
]
self.assertListEqual(snake_case_ , self.big_tokenizer.encode(snake_case_ ) )
@require_torch
@slow
def lowercase ( self : str ):
import torch
from transformers import ReformerConfig, ReformerModel
# Build sequence
_UpperCAmelCase = list(self.big_tokenizer.get_vocab().keys() )[:1_0]
_UpperCAmelCase = " ".join(snake_case_ )
_UpperCAmelCase = self.big_tokenizer.encode_plus(snake_case_ , return_tensors="pt" )
_UpperCAmelCase = self.big_tokenizer.batch_encode_plus([sequence, sequence] , return_tensors="pt" )
_UpperCAmelCase = ReformerConfig()
# The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
_UpperCAmelCase = encoded_sequence["input_ids"].shape
_UpperCAmelCase = ReformerModel(snake_case_ )
# Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size
with torch.no_grad():
model(**snake_case_ )
model(**snake_case_ )
@slow
def lowercase ( self : Optional[int] ):
# fmt: off
_UpperCAmelCase = {"input_ids": [[1_0_8, 2_6_5, 2_4, 1_1_1, 4, 2_5_8, 1_5_6, 7, 5_1, 2_7_9, 5_8, 7, 7_6, 2_5, 6_9, 2_7_8], [1_4_0, 2_4_3, 2_6_4, 1_3_4, 1_7, 2_6_7, 7_7, 2_6_3, 2_2, 2_6_2, 2_9_7, 2_5_8, 3_0_4, 1_7_7, 2_7_9, 2_6_6, 1_4, 8_9, 1_3, 3_5, 2_6_1, 2_9_9, 2_7_2, 1_3_7, 2_7_5, 2_7_8]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
# This tokenizer does not know some characters like ")".
# That is the reason why we use very simple texts here.
# Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
_UpperCAmelCase = [
"This is a very simple sentence.",
"The quick brown fox jumps over the lazy dog.",
]
self.tokenizer_integration_test_util(
expected_encoding=snake_case_ , model_name="google/reformer-crime-and-punishment" , revision="0e6c3decb8211d49bf881013425dc8b0448b3f5a" , padding=snake_case_ , sequences=snake_case_ , )
| 22 |
'''simple docstring'''
import string
from math import logaa
def UpperCAmelCase_ ( __lowercase : str , __lowercase : str ) -> int:
'''simple docstring'''
_UpperCAmelCase = document.translate(
str.maketrans("" , "" , string.punctuation ) ).replace("\n" , "" )
_UpperCAmelCase = document_without_punctuation.split(" " ) # word tokenization
return len([word for word in tokenize_document if word.lower() == term.lower()] )
def UpperCAmelCase_ ( __lowercase : str , __lowercase : str ) -> tuple[int, int]:
'''simple docstring'''
_UpperCAmelCase = corpus.lower().translate(
str.maketrans("" , "" , string.punctuation ) ) # strip all punctuation and replace it with ''
_UpperCAmelCase = corpus_without_punctuation.split("\n" )
_UpperCAmelCase = term.lower()
return (len([doc for doc in docs if term in doc] ), len(__lowercase ))
def UpperCAmelCase_ ( __lowercase : int , __lowercase : int , __lowercase : Union[str, Any]=False ) -> float:
'''simple docstring'''
if smoothing:
if n == 0:
raise ValueError("log10(0) is undefined." )
return round(1 + logaa(n / (1 + df) ) , 3 )
if df == 0:
raise ZeroDivisionError("df must be > 0" )
elif n == 0:
raise ValueError("log10(0) is undefined." )
return round(logaa(n / df ) , 3 )
def UpperCAmelCase_ ( __lowercase : int , __lowercase : int ) -> float:
'''simple docstring'''
return round(tf * idf , 3 )
| 22 | 1 |
'''simple docstring'''
from math import factorial
def UpperCAmelCase_ ( __lowercase : int , __lowercase : int , __lowercase : float ) -> float:
'''simple docstring'''
if successes > trials:
raise ValueError("successes must be lower or equal to trials" )
if trials < 0 or successes < 0:
raise ValueError("the function is defined for non-negative integers" )
if not isinstance(__lowercase , __lowercase ) or not isinstance(__lowercase , __lowercase ):
raise ValueError("the function is defined for non-negative integers" )
if not 0 < prob < 1:
raise ValueError("prob has to be in range of 1 - 0" )
_UpperCAmelCase = (prob**successes) * ((1 - prob) ** (trials - successes))
# Calculate the binomial coefficient: n! / k!(n-k)!
_UpperCAmelCase = float(factorial(__lowercase ) )
coefficient /= factorial(__lowercase ) * factorial(trials - successes )
return probability * coefficient
if __name__ == "__main__":
from doctest import testmod
testmod()
print('''Probability of 2 successes out of 4 trails''')
print('''with probability of 0.75 is:''', end=''' ''')
print(binomial_distribution(2, 4, 0.75))
| 22 |
'''simple docstring'''
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_pt_objects import * # noqa F403
else:
from .scheduling_consistency_models import CMStochasticIterativeScheduler
from .scheduling_ddim import DDIMScheduler
from .scheduling_ddim_inverse import DDIMInverseScheduler
from .scheduling_ddim_parallel import DDIMParallelScheduler
from .scheduling_ddpm import DDPMScheduler
from .scheduling_ddpm_parallel import DDPMParallelScheduler
from .scheduling_deis_multistep import DEISMultistepScheduler
from .scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler
from .scheduling_dpmsolver_multistep_inverse import DPMSolverMultistepInverseScheduler
from .scheduling_dpmsolver_singlestep import DPMSolverSinglestepScheduler
from .scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler
from .scheduling_euler_discrete import EulerDiscreteScheduler
from .scheduling_heun_discrete import HeunDiscreteScheduler
from .scheduling_ipndm import IPNDMScheduler
from .scheduling_k_dpm_2_ancestral_discrete import KDPMaAncestralDiscreteScheduler
from .scheduling_k_dpm_2_discrete import KDPMaDiscreteScheduler
from .scheduling_karras_ve import KarrasVeScheduler
from .scheduling_pndm import PNDMScheduler
from .scheduling_repaint import RePaintScheduler
from .scheduling_sde_ve import ScoreSdeVeScheduler
from .scheduling_sde_vp import ScoreSdeVpScheduler
from .scheduling_unclip import UnCLIPScheduler
from .scheduling_unipc_multistep import UniPCMultistepScheduler
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
from .scheduling_vq_diffusion import VQDiffusionScheduler
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_flax_objects import * # noqa F403
else:
from .scheduling_ddim_flax import FlaxDDIMScheduler
from .scheduling_ddpm_flax import FlaxDDPMScheduler
from .scheduling_dpmsolver_multistep_flax import FlaxDPMSolverMultistepScheduler
from .scheduling_karras_ve_flax import FlaxKarrasVeScheduler
from .scheduling_lms_discrete_flax import FlaxLMSDiscreteScheduler
from .scheduling_pndm_flax import FlaxPNDMScheduler
from .scheduling_sde_ve_flax import FlaxScoreSdeVeScheduler
from .scheduling_utils_flax import (
FlaxKarrasDiffusionSchedulers,
FlaxSchedulerMixin,
FlaxSchedulerOutput,
broadcast_to_shape_from_left,
)
try:
if not (is_torch_available() and is_scipy_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_torch_and_scipy_objects import * # noqa F403
else:
from .scheduling_lms_discrete import LMSDiscreteScheduler
try:
if not (is_torch_available() and is_torchsde_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_torch_and_torchsde_objects import * # noqa F403
else:
from .scheduling_dpmsolver_sde import DPMSolverSDEScheduler
| 22 | 1 |
'''simple docstring'''
import flax.linen as nn
import jax
import jax.numpy as jnp
class A_ ( nn.Module ):
_lowerCamelCase : int
_lowerCamelCase : jnp.dtype = jnp.floataa
def lowercase ( self : int ):
_UpperCAmelCase = nn.Conv(
self.out_channels , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
def __call__( self : Tuple , snake_case_ : int ):
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = hidden_states.shape
_UpperCAmelCase = jax.image.resize(
snake_case_ , shape=(batch, height * 2, width * 2, channels) , method="nearest" , )
_UpperCAmelCase = self.conv(snake_case_ )
return hidden_states
class A_ ( nn.Module ):
_lowerCamelCase : int
_lowerCamelCase : jnp.dtype = jnp.floataa
def lowercase ( self : Dict ):
_UpperCAmelCase = nn.Conv(
self.out_channels , kernel_size=(3, 3) , strides=(2, 2) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
def __call__( self : Tuple , snake_case_ : Tuple ):
# pad = ((0, 0), (0, 1), (0, 1), (0, 0)) # pad height and width dim
# hidden_states = jnp.pad(hidden_states, pad_width=pad)
_UpperCAmelCase = self.conv(snake_case_ )
return hidden_states
class A_ ( nn.Module ):
_lowerCamelCase : int
_lowerCamelCase : int = None
_lowerCamelCase : float = 0.0
_lowerCamelCase : bool = None
_lowerCamelCase : jnp.dtype = jnp.floataa
def lowercase ( self : List[str] ):
_UpperCAmelCase = self.in_channels if self.out_channels is None else self.out_channels
_UpperCAmelCase = nn.GroupNorm(num_groups=3_2 , epsilon=1e-5 )
_UpperCAmelCase = nn.Conv(
snake_case_ , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
_UpperCAmelCase = nn.Dense(snake_case_ , dtype=self.dtype )
_UpperCAmelCase = nn.GroupNorm(num_groups=3_2 , epsilon=1e-5 )
_UpperCAmelCase = nn.Dropout(self.dropout_prob )
_UpperCAmelCase = nn.Conv(
snake_case_ , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
_UpperCAmelCase = self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut
_UpperCAmelCase = None
if use_nin_shortcut:
_UpperCAmelCase = nn.Conv(
snake_case_ , kernel_size=(1, 1) , strides=(1, 1) , padding="VALID" , dtype=self.dtype , )
def __call__( self : Union[str, Any] , snake_case_ : Optional[int] , snake_case_ : Optional[Any] , snake_case_ : List[Any]=True ):
_UpperCAmelCase = hidden_states
_UpperCAmelCase = self.norma(snake_case_ )
_UpperCAmelCase = nn.swish(snake_case_ )
_UpperCAmelCase = self.conva(snake_case_ )
_UpperCAmelCase = self.time_emb_proj(nn.swish(snake_case_ ) )
_UpperCAmelCase = jnp.expand_dims(jnp.expand_dims(snake_case_ , 1 ) , 1 )
_UpperCAmelCase = hidden_states + temb
_UpperCAmelCase = self.norma(snake_case_ )
_UpperCAmelCase = nn.swish(snake_case_ )
_UpperCAmelCase = self.dropout(snake_case_ , snake_case_ )
_UpperCAmelCase = self.conva(snake_case_ )
if self.conv_shortcut is not None:
_UpperCAmelCase = self.conv_shortcut(snake_case_ )
return hidden_states + residual
| 22 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : int ) -> int:
'''simple docstring'''
if not isinstance(__lowercase , __lowercase ) or number < 0:
raise ValueError("Input must be a non-negative integer" )
_UpperCAmelCase = 0
while number:
# This way we arrive at next set bit (next 1) instead of looping
# through each bit and checking for 1s hence the
# loop won't run 32 times it will only run the number of `1` times
number &= number - 1
count += 1
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 22 | 1 |
'''simple docstring'''
import argparse
import json
import logging
import os
import sys
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
__SCREAMING_SNAKE_CASE :Union[str, Any] = [
os.path.join(os.path.dirname(__file__), dirname)
for dirname in [
'''text-classification''',
'''language-modeling''',
'''summarization''',
'''token-classification''',
'''question-answering''',
]
]
sys.path.extend(SRC_DIRS)
if SRC_DIRS is not None:
import run_clm_flax
import run_flax_glue
import run_flax_ner
import run_mlm_flax
import run_qa
import run_summarization_flax
import run_ta_mlm_flax
logging.basicConfig(level=logging.DEBUG)
__SCREAMING_SNAKE_CASE :Union[str, Any] = logging.getLogger()
def UpperCAmelCase_ ( ) -> List[str]:
'''simple docstring'''
_UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument("-f" )
_UpperCAmelCase = parser.parse_args()
return args.f
def UpperCAmelCase_ ( __lowercase : Tuple , __lowercase : int="eval" ) -> Tuple:
'''simple docstring'''
_UpperCAmelCase = os.path.join(__lowercase , f'{split}_results.json' )
if os.path.exists(__lowercase ):
with open(__lowercase , "r" ) as f:
return json.load(__lowercase )
raise ValueError(f'can\'t find {path}' )
__SCREAMING_SNAKE_CASE :List[str] = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class A_ ( lowerCAmelCase_ ):
def lowercase ( self : List[str] ):
_UpperCAmelCase = self.get_auto_remove_tmp_dir()
_UpperCAmelCase = f'\n run_glue.py\n --model_name_or_path distilbert-base-uncased\n --output_dir {tmp_dir}\n --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=1\n --learning_rate=1e-4\n --eval_steps=2\n --warmup_steps=2\n --seed=42\n --max_seq_length=128\n '.split()
with patch.object(snake_case_ , "argv" , snake_case_ ):
run_flax_glue.main()
_UpperCAmelCase = get_results(snake_case_ )
self.assertGreaterEqual(result["eval_accuracy"] , 0.7_5 )
@slow
def lowercase ( self : int ):
_UpperCAmelCase = self.get_auto_remove_tmp_dir()
_UpperCAmelCase = f'\n run_clm_flax.py\n --model_name_or_path distilgpt2\n --train_file ./tests/fixtures/sample_text.txt\n --validation_file ./tests/fixtures/sample_text.txt\n --do_train\n --do_eval\n --block_size 128\n --per_device_train_batch_size 4\n --per_device_eval_batch_size 4\n --num_train_epochs 2\n --logging_steps 2 --eval_steps 2\n --output_dir {tmp_dir}\n --overwrite_output_dir\n '.split()
with patch.object(snake_case_ , "argv" , snake_case_ ):
run_clm_flax.main()
_UpperCAmelCase = get_results(snake_case_ )
self.assertLess(result["eval_perplexity"] , 1_0_0 )
@slow
def lowercase ( self : str ):
_UpperCAmelCase = self.get_auto_remove_tmp_dir()
_UpperCAmelCase = f'\n run_summarization.py\n --model_name_or_path t5-small\n --train_file tests/fixtures/tests_samples/xsum/sample.json\n --validation_file tests/fixtures/tests_samples/xsum/sample.json\n --test_file tests/fixtures/tests_samples/xsum/sample.json\n --output_dir {tmp_dir}\n --overwrite_output_dir\n --num_train_epochs=3\n --warmup_steps=8\n --do_train\n --do_eval\n --do_predict\n --learning_rate=2e-4\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=1\n --predict_with_generate\n '.split()
with patch.object(snake_case_ , "argv" , snake_case_ ):
run_summarization_flax.main()
_UpperCAmelCase = get_results(snake_case_ , split="test" )
self.assertGreaterEqual(result["test_rouge1"] , 1_0 )
self.assertGreaterEqual(result["test_rouge2"] , 2 )
self.assertGreaterEqual(result["test_rougeL"] , 7 )
self.assertGreaterEqual(result["test_rougeLsum"] , 7 )
@slow
def lowercase ( self : List[Any] ):
_UpperCAmelCase = self.get_auto_remove_tmp_dir()
_UpperCAmelCase = f'\n run_mlm.py\n --model_name_or_path distilroberta-base\n --train_file ./tests/fixtures/sample_text.txt\n --validation_file ./tests/fixtures/sample_text.txt\n --output_dir {tmp_dir}\n --overwrite_output_dir\n --max_seq_length 128\n --per_device_train_batch_size 4\n --per_device_eval_batch_size 4\n --logging_steps 2 --eval_steps 2\n --do_train\n --do_eval\n --num_train_epochs=1\n '.split()
with patch.object(snake_case_ , "argv" , snake_case_ ):
run_mlm_flax.main()
_UpperCAmelCase = get_results(snake_case_ )
self.assertLess(result["eval_perplexity"] , 4_2 )
@slow
def lowercase ( self : Any ):
_UpperCAmelCase = self.get_auto_remove_tmp_dir()
_UpperCAmelCase = f'\n run_t5_mlm_flax.py\n --model_name_or_path t5-small\n --train_file ./tests/fixtures/sample_text.txt\n --validation_file ./tests/fixtures/sample_text.txt\n --do_train\n --do_eval\n --max_seq_length 128\n --per_device_train_batch_size 4\n --per_device_eval_batch_size 4\n --num_train_epochs 2\n --logging_steps 2 --eval_steps 2\n --output_dir {tmp_dir}\n --overwrite_output_dir\n '.split()
with patch.object(snake_case_ , "argv" , snake_case_ ):
run_ta_mlm_flax.main()
_UpperCAmelCase = get_results(snake_case_ )
self.assertGreaterEqual(result["eval_accuracy"] , 0.4_2 )
@slow
def lowercase ( self : Tuple ):
# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
_UpperCAmelCase = 7 if get_gpu_count() > 1 else 2
_UpperCAmelCase = self.get_auto_remove_tmp_dir()
_UpperCAmelCase = f'\n run_flax_ner.py\n --model_name_or_path bert-base-uncased\n --train_file tests/fixtures/tests_samples/conll/sample.json\n --validation_file tests/fixtures/tests_samples/conll/sample.json\n --output_dir {tmp_dir}\n --overwrite_output_dir\n --do_train\n --do_eval\n --warmup_steps=2\n --learning_rate=2e-4\n --logging_steps 2 --eval_steps 2\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=2\n --num_train_epochs={epochs}\n --seed 7\n '.split()
with patch.object(snake_case_ , "argv" , snake_case_ ):
run_flax_ner.main()
_UpperCAmelCase = get_results(snake_case_ )
self.assertGreaterEqual(result["eval_accuracy"] , 0.7_5 )
self.assertGreaterEqual(result["eval_f1"] , 0.3 )
@slow
def lowercase ( self : Any ):
_UpperCAmelCase = self.get_auto_remove_tmp_dir()
_UpperCAmelCase = f'\n run_qa.py\n --model_name_or_path bert-base-uncased\n --version_2_with_negative\n --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n --output_dir {tmp_dir}\n --overwrite_output_dir\n --num_train_epochs=3\n --warmup_steps=2\n --do_train\n --do_eval\n --logging_steps 2 --eval_steps 2\n --learning_rate=2e-4\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=1\n '.split()
with patch.object(snake_case_ , "argv" , snake_case_ ):
run_qa.main()
_UpperCAmelCase = get_results(snake_case_ )
self.assertGreaterEqual(result["eval_f1"] , 3_0 )
self.assertGreaterEqual(result["eval_exact"] , 3_0 )
| 22 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
from typing import Any, Generic, TypeVar
__SCREAMING_SNAKE_CASE :Optional[int] = TypeVar('''T''')
class A_ ( Generic[T] ):
def __init__( self : List[Any] , snake_case_ : list[T] , snake_case_ : Callable[[T, T], T] ):
_UpperCAmelCase = None
_UpperCAmelCase = len(snake_case_ )
_UpperCAmelCase = [any_type for _ in range(self.N )] + arr
_UpperCAmelCase = fnc
self.build()
def lowercase ( self : List[Any] ):
for p in range(self.N - 1 , 0 , -1 ):
_UpperCAmelCase = self.fn(self.st[p * 2] , self.st[p * 2 + 1] )
def lowercase ( self : Optional[Any] , snake_case_ : int , snake_case_ : T ):
p += self.N
_UpperCAmelCase = v
while p > 1:
_UpperCAmelCase = p // 2
_UpperCAmelCase = self.fn(self.st[p * 2] , self.st[p * 2 + 1] )
def lowercase ( self : Any , snake_case_ : int , snake_case_ : int ): # noqa: E741
_UpperCAmelCase , _UpperCAmelCase = l + self.N, r + self.N
_UpperCAmelCase = None
while l <= r:
if l % 2 == 1:
_UpperCAmelCase = self.st[l] if res is None else self.fn(snake_case_ , self.st[l] )
if r % 2 == 0:
_UpperCAmelCase = self.st[r] if res is None else self.fn(snake_case_ , self.st[r] )
_UpperCAmelCase , _UpperCAmelCase = (l + 1) // 2, (r - 1) // 2
return res
if __name__ == "__main__":
from functools import reduce
__SCREAMING_SNAKE_CASE :Union[str, Any] = [1, 10, -2, 9, -3, 8, 4, -7, 5, 6, 11, -12]
__SCREAMING_SNAKE_CASE :List[str] = {
0: 7,
1: 2,
2: 6,
3: -14,
4: 5,
5: 4,
6: 7,
7: -10,
8: 9,
9: 10,
10: 12,
11: 1,
}
__SCREAMING_SNAKE_CASE :Any = SegmentTree(test_array, min)
__SCREAMING_SNAKE_CASE :Any = SegmentTree(test_array, max)
__SCREAMING_SNAKE_CASE :Any = SegmentTree(test_array, lambda a, b: a + b)
def UpperCAmelCase_ ( ) -> None:
'''simple docstring'''
for i in range(len(__lowercase ) ):
for j in range(__lowercase , len(__lowercase ) ):
_UpperCAmelCase = reduce(__lowercase , test_array[i : j + 1] )
_UpperCAmelCase = reduce(__lowercase , test_array[i : j + 1] )
_UpperCAmelCase = reduce(lambda __lowercase , __lowercase : a + b , test_array[i : j + 1] )
assert min_range == min_segment_tree.query(__lowercase , __lowercase )
assert max_range == max_segment_tree.query(__lowercase , __lowercase )
assert sum_range == sum_segment_tree.query(__lowercase , __lowercase )
test_all_segments()
for index, value in test_updates.items():
__SCREAMING_SNAKE_CASE :str = value
min_segment_tree.update(index, value)
max_segment_tree.update(index, value)
sum_segment_tree.update(index, value)
test_all_segments()
| 22 | 1 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : int , __lowercase : Optional[int] ) -> str:
'''simple docstring'''
_UpperCAmelCase = 0
_UpperCAmelCase = len(__lowercase ) - 1
while left <= right:
# avoid divided by 0 during interpolation
if sorted_collection[left] == sorted_collection[right]:
if sorted_collection[left] == item:
return left
else:
return None
_UpperCAmelCase = left + ((item - sorted_collection[left]) * (right - left)) // (
sorted_collection[right] - sorted_collection[left]
)
# out of range check
if point < 0 or point >= len(__lowercase ):
return None
_UpperCAmelCase = sorted_collection[point]
if current_item == item:
return point
else:
if point < left:
_UpperCAmelCase = left
_UpperCAmelCase = point
elif point > right:
_UpperCAmelCase = right
_UpperCAmelCase = point
else:
if item < current_item:
_UpperCAmelCase = point - 1
else:
_UpperCAmelCase = point + 1
return None
def UpperCAmelCase_ ( __lowercase : Any , __lowercase : List[Any] , __lowercase : Dict , __lowercase : List[str] ) -> Union[str, Any]:
'''simple docstring'''
if sorted_collection[left] == sorted_collection[right]:
if sorted_collection[left] == item:
return left
else:
return None
_UpperCAmelCase = left + ((item - sorted_collection[left]) * (right - left)) // (
sorted_collection[right] - sorted_collection[left]
)
# out of range check
if point < 0 or point >= len(__lowercase ):
return None
if sorted_collection[point] == item:
return point
elif point < left:
return interpolation_search_by_recursion(__lowercase , __lowercase , __lowercase , __lowercase )
elif point > right:
return interpolation_search_by_recursion(__lowercase , __lowercase , __lowercase , __lowercase )
else:
if sorted_collection[point] > item:
return interpolation_search_by_recursion(
__lowercase , __lowercase , __lowercase , point - 1 )
else:
return interpolation_search_by_recursion(
__lowercase , __lowercase , point + 1 , __lowercase )
def UpperCAmelCase_ ( __lowercase : int ) -> Tuple:
'''simple docstring'''
if collection != sorted(__lowercase ):
raise ValueError("Collection must be ascending sorted" )
return True
if __name__ == "__main__":
import sys
__SCREAMING_SNAKE_CASE :Optional[int] = 0
if debug == 1:
__SCREAMING_SNAKE_CASE :Union[str, Any] = [10, 30, 40, 45, 50, 66, 77, 93]
try:
__assert_sorted(collection)
except ValueError:
sys.exit('''Sequence must be ascending sorted to apply interpolation search''')
__SCREAMING_SNAKE_CASE :int = 67
__SCREAMING_SNAKE_CASE :Union[str, Any] = interpolation_search(collection, target)
if result is not None:
print(F"{target} found at positions: {result}")
else:
print('''Not found''')
| 22 |
'''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 UpperCAmelCase_ ( __lowercase : int , __lowercase : Dict ) -> List[Any]:
'''simple docstring'''
_UpperCAmelCase = _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 UpperCAmelCase_ ( __lowercase : Dict , __lowercase : Optional[Any] , __lowercase : int ) -> str:
'''simple docstring'''
_UpperCAmelCase = _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 UpperCAmelCase_ ( __lowercase : Optional[Any] , __lowercase : List[Any] ) -> List[Any]:
'''simple docstring'''
if expected is RuntimeError:
with pytest.raises(__lowercase ):
_number_of_shards_in_gen_kwargs(__lowercase )
else:
_UpperCAmelCase = _number_of_shards_in_gen_kwargs(__lowercase )
assert out == expected
| 22 | 1 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : int = 100_0000 ) -> int:
'''simple docstring'''
_UpperCAmelCase = [i - 1 for i in range(limit + 1 )]
for i in range(2 , limit + 1 ):
if phi[i] == i - 1:
for j in range(2 * i , limit + 1 , __lowercase ):
phi[j] -= phi[j] // i
return sum(phi[2 : limit + 1] )
if __name__ == "__main__":
print(solution())
| 22 |
'''simple docstring'''
import math
def UpperCAmelCase_ ( __lowercase : int ) -> bool:
'''simple docstring'''
return math.sqrt(__lowercase ) * math.sqrt(__lowercase ) == num
def UpperCAmelCase_ ( __lowercase : int ) -> bool:
'''simple docstring'''
_UpperCAmelCase = 0
_UpperCAmelCase = n
while left <= right:
_UpperCAmelCase = (left + right) // 2
if mid**2 == n:
return True
elif mid**2 > n:
_UpperCAmelCase = mid - 1
else:
_UpperCAmelCase = mid + 1
return False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 22 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def UpperCAmelCase_ ( __lowercase : str ) -> None:
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase = analyze_text(__lowercase )
_UpperCAmelCase = list(" " + ascii_lowercase )
# what is our total sum of probabilities.
_UpperCAmelCase = sum(single_char_strings.values() )
# one length string
_UpperCAmelCase = 0
# for each alpha we go in our dict and if it is in it we calculate entropy
for ch in my_alphas:
if ch in single_char_strings:
_UpperCAmelCase = single_char_strings[ch]
_UpperCAmelCase = my_str / all_sum
my_fir_sum += prob * math.loga(__lowercase ) # entropy formula.
# print entropy
print(f'{round(-1 * my_fir_sum ):.1f}' )
# two len string
_UpperCAmelCase = sum(two_char_strings.values() )
_UpperCAmelCase = 0
# for each alpha (two in size) calculate entropy.
for cha in my_alphas:
for cha in my_alphas:
_UpperCAmelCase = cha + cha
if sequence in two_char_strings:
_UpperCAmelCase = two_char_strings[sequence]
_UpperCAmelCase = int(__lowercase ) / all_sum
my_sec_sum += prob * math.loga(__lowercase )
# print second entropy
print(f'{round(-1 * my_sec_sum ):.1f}' )
# print the difference between them
print(f'{round((-1 * my_sec_sum) - (-1 * my_fir_sum) ):.1f}' )
def UpperCAmelCase_ ( __lowercase : str ) -> tuple[dict, dict]:
'''simple docstring'''
_UpperCAmelCase = Counter() # type: ignore
_UpperCAmelCase = Counter() # type: ignore
single_char_strings[text[-1]] += 1
# first case when we have space at start.
two_char_strings[" " + text[0]] += 1
for i in range(0 , len(__lowercase ) - 1 ):
single_char_strings[text[i]] += 1
two_char_strings[text[i : i + 2]] += 1
return single_char_strings, two_char_strings
def UpperCAmelCase_ ( ) -> Optional[Any]:
'''simple docstring'''
import doctest
doctest.testmod()
# text = (
# "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
# "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
# "jointure saw horrible. He private he on be imagine suppose. Fertile "
# "beloved evident through no service elderly is. Blind there if every no so "
# "at. Own neglected you preferred way sincerity delivered his attempted. To "
# "of message cottage windows do besides against uncivil. Delightful "
# "unreserved impossible few estimating men favourable see entreaties. She "
# "propriety immediate was improving. He or entrance humoured likewise "
# "moderate. Much nor game son say feel. Fat make met can must form into "
# "gate. Me we offending prevailed discovery. "
# )
# calculate_prob(text)
if __name__ == "__main__":
main()
| 22 |
'''simple docstring'''
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
__SCREAMING_SNAKE_CASE :Dict = 1e-4
if is_torch_available():
import torch
from transformers import AutoformerConfig, AutoformerForPrediction, AutoformerModel
from transformers.models.autoformer.modeling_autoformer import AutoformerDecoder, AutoformerEncoder
@require_torch
class A_ :
def __init__( self : List[Any] , snake_case_ : int , snake_case_ : Dict=1_6 , snake_case_ : Dict=1_3 , snake_case_ : int=7 , snake_case_ : Any=1_4 , snake_case_ : int=1_0 , snake_case_ : Any=1_9 , snake_case_ : int=5 , snake_case_ : Any=4 , snake_case_ : Tuple=True , snake_case_ : Optional[int]=1_6 , snake_case_ : List[str]=2 , snake_case_ : Any=4 , snake_case_ : List[Any]=4 , snake_case_ : Optional[Any]="gelu" , snake_case_ : Optional[int]=0.1 , snake_case_ : Union[str, Any]=0.1 , snake_case_ : Tuple=[1, 2, 3, 4, 5] , snake_case_ : str=2_5 , snake_case_ : Any=5 , ):
_UpperCAmelCase = d_model
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = prediction_length
_UpperCAmelCase = context_length
_UpperCAmelCase = cardinality
_UpperCAmelCase = num_time_features
_UpperCAmelCase = lags_sequence
_UpperCAmelCase = embedding_dimension
_UpperCAmelCase = is_training
_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 = context_length
_UpperCAmelCase = prediction_length + label_length
_UpperCAmelCase = label_length
_UpperCAmelCase = moving_average
_UpperCAmelCase = autocorrelation_factor
def lowercase ( self : Union[str, Any] ):
return AutoformerConfig(
d_model=self.d_model , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , prediction_length=self.prediction_length , context_length=self.context_length , label_length=self.label_length , lags_sequence=self.lags_sequence , num_time_features=self.num_time_features , num_static_categorical_features=1 , cardinality=[self.cardinality] , embedding_dimension=[self.embedding_dimension] , moving_average=self.moving_average , )
def lowercase ( self : int , snake_case_ : Optional[Any] ):
_UpperCAmelCase = config.context_length + max(config.lags_sequence )
_UpperCAmelCase = ids_tensor([self.batch_size, 1] , config.cardinality[0] )
_UpperCAmelCase = floats_tensor([self.batch_size, _past_length, config.num_time_features] )
_UpperCAmelCase = floats_tensor([self.batch_size, _past_length] )
_UpperCAmelCase = floats_tensor([self.batch_size, _past_length] ) > 0.5
# decoder inputs
_UpperCAmelCase = floats_tensor([self.batch_size, config.prediction_length, config.num_time_features] )
_UpperCAmelCase = floats_tensor([self.batch_size, config.prediction_length] )
_UpperCAmelCase = {
"past_values": past_values,
"static_categorical_features": static_categorical_features,
"past_time_features": past_time_features,
"past_observed_mask": past_observed_mask,
"future_time_features": future_time_features,
"future_values": future_values,
}
return inputs_dict
def lowercase ( self : List[Any] ):
_UpperCAmelCase = self.get_config()
_UpperCAmelCase = self.prepare_autoformer_inputs_dict(snake_case_ )
return config, inputs_dict
def lowercase ( self : List[str] ):
_UpperCAmelCase , _UpperCAmelCase = self.prepare_config_and_inputs()
return config, inputs_dict
def lowercase ( self : Optional[Any] , snake_case_ : int , snake_case_ : Optional[int] ):
_UpperCAmelCase = AutoformerModel(config=snake_case_ ).to(snake_case_ ).eval()
_UpperCAmelCase = model(**snake_case_ )
_UpperCAmelCase = outputs.encoder_last_hidden_state
_UpperCAmelCase = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
_UpperCAmelCase = model.get_encoder()
encoder.save_pretrained(snake_case_ )
_UpperCAmelCase = AutoformerEncoder.from_pretrained(snake_case_ ).to(snake_case_ )
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = model.create_network_inputs(**snake_case_ )
_UpperCAmelCase , _UpperCAmelCase = model.decomposition_layer(transformer_inputs[:, : config.context_length, ...] )
_UpperCAmelCase = torch.cat(
(transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...]) , dim=-1 , )
_UpperCAmelCase = encoder(inputs_embeds=snake_case_ )[0]
self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1e-3 )
_UpperCAmelCase = (
torch.mean(transformer_inputs[:, : config.context_length, ...] , dim=1 )
.unsqueeze(1 )
.repeat(1 , config.prediction_length , 1 )
)
_UpperCAmelCase = torch.zeros(
[transformer_inputs.shape[0], config.prediction_length, transformer_inputs.shape[2]] , device=enc_input.device , )
_UpperCAmelCase = torch.cat(
(
torch.cat((seasonal_input[:, -config.label_length :, ...], zeros) , dim=1 ),
feature[:, config.context_length - config.label_length :, ...],
) , dim=-1 , )
_UpperCAmelCase = torch.cat(
(
torch.cat((trend_input[:, -config.label_length :, ...], mean) , dim=1 ),
feature[:, config.context_length - config.label_length :, ...],
) , dim=-1 , )
with tempfile.TemporaryDirectory() as tmpdirname:
_UpperCAmelCase = model.get_decoder()
decoder.save_pretrained(snake_case_ )
_UpperCAmelCase = AutoformerDecoder.from_pretrained(snake_case_ ).to(snake_case_ )
_UpperCAmelCase = decoder(
trend=snake_case_ , inputs_embeds=snake_case_ , encoder_hidden_states=snake_case_ , )[0]
self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1e-3 )
@require_torch
class A_ ( lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
_lowerCamelCase : List[Any] = (AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
_lowerCamelCase : Tuple = (AutoformerForPrediction,) if is_torch_available() else ()
_lowerCamelCase : List[Any] = {"""feature-extraction""": AutoformerModel} if is_torch_available() else {}
_lowerCamelCase : Optional[Any] = False
_lowerCamelCase : Tuple = False
_lowerCamelCase : int = False
_lowerCamelCase : Optional[Any] = False
_lowerCamelCase : Optional[Any] = False
_lowerCamelCase : List[Any] = False
def lowercase ( self : Tuple ):
_UpperCAmelCase = AutoformerModelTester(self )
_UpperCAmelCase = ConfigTester(self , config_class=snake_case_ , has_text_modality=snake_case_ )
def lowercase ( self : Optional[Any] ):
self.config_tester.run_common_tests()
def lowercase ( self : Union[str, Any] ):
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
_UpperCAmelCase = model_class(snake_case_ )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(snake_case_ )
_UpperCAmelCase , _UpperCAmelCase = model_class.from_pretrained(snake_case_ , output_loading_info=snake_case_ )
self.assertEqual(info["missing_keys"] , [] )
def lowercase ( self : Optional[int] ):
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*snake_case_ )
@unittest.skip(reason="Model has no tokens embeddings" )
def lowercase ( self : Optional[int] ):
pass
def lowercase ( self : Optional[int] ):
_UpperCAmelCase = inspect.signature(getattr(snake_case_ , "forward" ) )
# The main input is the name of the argument after `self`
_UpperCAmelCase = list(model_signature.parameters.keys() )[1]
self.assertEqual(AutoformerModel.main_input_name , snake_case_ )
def lowercase ( self : List[str] ):
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase = model_class(snake_case_ )
_UpperCAmelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_UpperCAmelCase = [*signature.parameters.keys()]
_UpperCAmelCase = [
"past_values",
"past_time_features",
"past_observed_mask",
"static_categorical_features",
"static_real_features",
"future_values",
"future_time_features",
]
if model.__class__.__name__ in ["AutoformerForPrediction"]:
expected_arg_names.append("future_observed_mask" )
expected_arg_names.extend(
[
"decoder_attention_mask",
"head_mask",
"decoder_head_mask",
"cross_attn_head_mask",
"encoder_outputs",
"past_key_values",
"output_hidden_states",
"output_attentions",
"use_cache",
"return_dict",
] )
self.assertListEqual(arg_names[: len(snake_case_ )] , snake_case_ )
def lowercase ( self : Optional[int] ):
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
_UpperCAmelCase = True
_UpperCAmelCase = getattr(self.model_tester , "seq_length" , snake_case_ )
_UpperCAmelCase = getattr(self.model_tester , "decoder_seq_length" , snake_case_ )
_UpperCAmelCase = getattr(self.model_tester , "encoder_seq_length" , snake_case_ )
_UpperCAmelCase = getattr(self.model_tester , "d_model" , snake_case_ )
_UpperCAmelCase = getattr(self.model_tester , "num_attention_heads" , snake_case_ )
_UpperCAmelCase = d_model // num_attention_heads
for model_class in self.all_model_classes:
_UpperCAmelCase = True
_UpperCAmelCase = False
_UpperCAmelCase = True
_UpperCAmelCase = model_class(snake_case_ )
model.to(snake_case_ )
model.eval()
with torch.no_grad():
_UpperCAmelCase = model(**self._prepare_for_class(snake_case_ , snake_case_ ) )
_UpperCAmelCase = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(snake_case_ ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
_UpperCAmelCase = True
_UpperCAmelCase = model_class(snake_case_ )
model.to(snake_case_ )
model.eval()
with torch.no_grad():
_UpperCAmelCase = model(**self._prepare_for_class(snake_case_ , snake_case_ ) )
_UpperCAmelCase = outputs.encoder_attentions
self.assertEqual(len(snake_case_ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, dim] , )
_UpperCAmelCase = len(snake_case_ )
_UpperCAmelCase = 7
if "last_hidden_state" in outputs:
correct_outlen += 1
if "trend" in outputs:
correct_outlen += 1
if "past_key_values" in outputs:
correct_outlen += 1 # past_key_values have been returned
if "loss" in outputs:
correct_outlen += 1
if "params" in outputs:
correct_outlen += 1
self.assertEqual(snake_case_ , snake_case_ )
# decoder attentions
_UpperCAmelCase = outputs.decoder_attentions
self.assertIsInstance(snake_case_ , (list, tuple) )
self.assertEqual(len(snake_case_ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, decoder_seq_length, dim] , )
# cross attentions
_UpperCAmelCase = outputs.cross_attentions
self.assertIsInstance(snake_case_ , (list, tuple) )
self.assertEqual(len(snake_case_ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(cross_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, decoder_seq_length, dim] , )
# Check attention is always last and order is fine
_UpperCAmelCase = True
_UpperCAmelCase = True
_UpperCAmelCase = model_class(snake_case_ )
model.to(snake_case_ )
model.eval()
with torch.no_grad():
_UpperCAmelCase = model(**self._prepare_for_class(snake_case_ , snake_case_ ) )
self.assertEqual(out_len + 2 , len(snake_case_ ) )
_UpperCAmelCase = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(snake_case_ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, dim] , )
@is_flaky()
def lowercase ( self : Dict ):
super().test_retain_grad_hidden_states_attentions()
def UpperCAmelCase_ ( __lowercase : str="train-batch.pt" ) -> List[str]:
'''simple docstring'''
_UpperCAmelCase = hf_hub_download(repo_id="hf-internal-testing/tourism-monthly-batch" , filename=__lowercase , repo_type="dataset" )
_UpperCAmelCase = torch.load(__lowercase , map_location=__lowercase )
return batch
@require_torch
@slow
class A_ ( unittest.TestCase ):
def lowercase ( self : Optional[int] ):
_UpperCAmelCase = AutoformerModel.from_pretrained("huggingface/autoformer-tourism-monthly" ).to(snake_case_ )
_UpperCAmelCase = prepare_batch()
with torch.no_grad():
_UpperCAmelCase = model(
past_values=batch["past_values"] , past_time_features=batch["past_time_features"] , past_observed_mask=batch["past_observed_mask"] , static_categorical_features=batch["static_categorical_features"] , future_values=batch["future_values"] , future_time_features=batch["future_time_features"] , )[0]
_UpperCAmelCase = torch.Size(
(6_4, model.config.prediction_length + model.config.label_length, model.config.feature_size) )
self.assertEqual(output.shape , snake_case_ )
_UpperCAmelCase = torch.tensor(
[[0.3_5_9_3, -1.3_3_9_8, 0.6_3_3_0], [0.2_2_7_9, 1.5_3_9_6, -0.1_7_9_2], [0.0_4_5_0, 1.3_2_2_5, -0.2_3_3_5]] , device=snake_case_ )
self.assertTrue(torch.allclose(output[0, :3, :3] , snake_case_ , atol=snake_case_ ) )
def lowercase ( self : Optional[Any] ):
_UpperCAmelCase = AutoformerForPrediction.from_pretrained("huggingface/autoformer-tourism-monthly" ).to(snake_case_ )
_UpperCAmelCase = prepare_batch("val-batch.pt" )
with torch.no_grad():
_UpperCAmelCase = model(
past_values=batch["past_values"] , past_time_features=batch["past_time_features"] , past_observed_mask=batch["past_observed_mask"] , static_categorical_features=batch["static_categorical_features"] , ).encoder_last_hidden_state
_UpperCAmelCase = torch.Size((6_4, model.config.context_length, model.config.d_model) )
self.assertEqual(output.shape , snake_case_ )
_UpperCAmelCase = torch.tensor(
[[-0.0_7_3_4, -0.9_0_3_6, 0.8_3_5_8], [4.7_1_8_6, 2.4_1_1_3, 1.9_5_8_1], [1.7_9_5_3, 2.3_5_5_8, 1.2_9_7_0]] , device=snake_case_ )
self.assertTrue(torch.allclose(output[0, :3, :3] , snake_case_ , atol=snake_case_ ) )
def lowercase ( self : Tuple ):
_UpperCAmelCase = AutoformerForPrediction.from_pretrained("huggingface/autoformer-tourism-monthly" ).to(snake_case_ )
_UpperCAmelCase = prepare_batch("val-batch.pt" )
with torch.no_grad():
_UpperCAmelCase = model.generate(
static_categorical_features=batch["static_categorical_features"] , past_time_features=batch["past_time_features"] , past_values=batch["past_values"] , future_time_features=batch["future_time_features"] , past_observed_mask=batch["past_observed_mask"] , )
_UpperCAmelCase = torch.Size((6_4, model.config.num_parallel_samples, model.config.prediction_length) )
self.assertEqual(outputs.sequences.shape , snake_case_ )
_UpperCAmelCase = torch.tensor([3_1_3_0.6_7_6_3, 4_0_5_6.5_2_9_3, 7_0_5_3.0_7_8_6] , device=snake_case_ )
_UpperCAmelCase = outputs.sequences.mean(dim=1 )
self.assertTrue(torch.allclose(mean_prediction[0, -3:] , snake_case_ , rtol=1e-1 ) )
| 22 | 1 |
'''simple docstring'''
import logging
from transformers import PretrainedConfig
__SCREAMING_SNAKE_CASE :Tuple = logging.getLogger(__name__)
__SCREAMING_SNAKE_CASE :str = {
'''bertabs-finetuned-cnndm''': '''https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json''',
}
class A_ ( lowerCAmelCase_ ):
_lowerCamelCase : Optional[int] = """bertabs"""
def __init__( self : Union[str, Any] , snake_case_ : Any=3_0_5_2_2 , snake_case_ : Optional[int]=5_1_2 , snake_case_ : List[Any]=6 , snake_case_ : List[Any]=5_1_2 , snake_case_ : Union[str, Any]=8 , snake_case_ : Dict=5_1_2 , snake_case_ : Optional[Any]=0.2 , snake_case_ : Tuple=6 , snake_case_ : Tuple=7_6_8 , snake_case_ : Union[str, Any]=8 , snake_case_ : Union[str, Any]=2_0_4_8 , snake_case_ : Union[str, Any]=0.2 , **snake_case_ : List[str] , ):
super().__init__(**snake_case_ )
_UpperCAmelCase = vocab_size
_UpperCAmelCase = max_pos
_UpperCAmelCase = enc_layers
_UpperCAmelCase = enc_hidden_size
_UpperCAmelCase = enc_heads
_UpperCAmelCase = enc_ff_size
_UpperCAmelCase = enc_dropout
_UpperCAmelCase = dec_layers
_UpperCAmelCase = dec_hidden_size
_UpperCAmelCase = dec_heads
_UpperCAmelCase = dec_ff_size
_UpperCAmelCase = dec_dropout
| 22 |
'''simple docstring'''
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_space_optuna,
default_hp_space_ray,
default_hp_space_sigopt,
default_hp_space_wandb,
)
from .utils import logging
__SCREAMING_SNAKE_CASE :int = logging.get_logger(__name__)
class A_ :
_lowerCamelCase : str
_lowerCamelCase : str = None
@staticmethod
def lowercase ( ):
raise NotImplementedError
def lowercase ( self : Union[str, Any] , snake_case_ : Optional[int] , snake_case_ : int , snake_case_ : str , **snake_case_ : List[Any] ):
raise NotImplementedError
def lowercase ( self : Any , snake_case_ : int ):
raise NotImplementedError
def lowercase ( self : List[str] ):
if not self.is_available():
raise RuntimeError(
f'You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}.' )
@classmethod
def lowercase ( cls : List[Any] ):
return f'`pip install {cls.pip_package or cls.name}`'
class A_ ( lowerCAmelCase_ ):
_lowerCamelCase : int = """optuna"""
@staticmethod
def lowercase ( ):
return is_optuna_available()
def lowercase ( self : List[str] , snake_case_ : Any , snake_case_ : int , snake_case_ : str , **snake_case_ : Tuple ):
return run_hp_search_optuna(snake_case_ , snake_case_ , snake_case_ , **snake_case_ )
def lowercase ( self : int , snake_case_ : Optional[int] ):
return default_hp_space_optuna(snake_case_ )
class A_ ( lowerCAmelCase_ ):
_lowerCamelCase : Any = """ray"""
_lowerCamelCase : Tuple = """'ray[tune]'"""
@staticmethod
def lowercase ( ):
return is_ray_available()
def lowercase ( self : Optional[Any] , snake_case_ : Any , snake_case_ : int , snake_case_ : str , **snake_case_ : List[str] ):
return run_hp_search_ray(snake_case_ , snake_case_ , snake_case_ , **snake_case_ )
def lowercase ( self : Any , snake_case_ : str ):
return default_hp_space_ray(snake_case_ )
class A_ ( lowerCAmelCase_ ):
_lowerCamelCase : int = """sigopt"""
@staticmethod
def lowercase ( ):
return is_sigopt_available()
def lowercase ( self : Any , snake_case_ : int , snake_case_ : int , snake_case_ : str , **snake_case_ : Dict ):
return run_hp_search_sigopt(snake_case_ , snake_case_ , snake_case_ , **snake_case_ )
def lowercase ( self : Dict , snake_case_ : Optional[Any] ):
return default_hp_space_sigopt(snake_case_ )
class A_ ( lowerCAmelCase_ ):
_lowerCamelCase : Optional[int] = """wandb"""
@staticmethod
def lowercase ( ):
return is_wandb_available()
def lowercase ( self : Optional[Any] , snake_case_ : Optional[Any] , snake_case_ : int , snake_case_ : str , **snake_case_ : Optional[Any] ):
return run_hp_search_wandb(snake_case_ , snake_case_ , snake_case_ , **snake_case_ )
def lowercase ( self : Any , snake_case_ : Union[str, Any] ):
return default_hp_space_wandb(snake_case_ )
__SCREAMING_SNAKE_CASE :Dict = {
HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}
def UpperCAmelCase_ ( ) -> str:
'''simple docstring'''
_UpperCAmelCase = [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
if len(__lowercase ) > 0:
_UpperCAmelCase = available_backends[0].name
if len(__lowercase ) > 1:
logger.info(
f'{len(__lowercase )} hyperparameter search backends available. Using {name} as the default.' )
return name
raise RuntimeError(
"No hyperparameter search backend available.\n"
+ "\n".join(
f' - To install {backend.name} run {backend.pip_install()}'
for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() ) )
| 22 | 1 |
'''simple docstring'''
from itertools import product
def UpperCAmelCase_ ( __lowercase : int , __lowercase : int ) -> list[int]:
'''simple docstring'''
_UpperCAmelCase = sides_number
_UpperCAmelCase = max_face_number * dice_number
_UpperCAmelCase = [0] * (max_total + 1)
_UpperCAmelCase = 1
_UpperCAmelCase = range(__lowercase , max_face_number + 1 )
for dice_numbers in product(__lowercase , repeat=__lowercase ):
_UpperCAmelCase = sum(__lowercase )
totals_frequencies[total] += 1
return totals_frequencies
def UpperCAmelCase_ ( ) -> float:
'''simple docstring'''
_UpperCAmelCase = total_frequency_distribution(
sides_number=4 , dice_number=9 )
_UpperCAmelCase = total_frequency_distribution(
sides_number=6 , dice_number=6 )
_UpperCAmelCase = 0
_UpperCAmelCase = 9
_UpperCAmelCase = 4 * 9
_UpperCAmelCase = 6
for peter_total in range(__lowercase , max_peter_total + 1 ):
peter_wins_count += peter_totals_frequencies[peter_total] * sum(
colin_totals_frequencies[min_colin_total:peter_total] )
_UpperCAmelCase = (4**9) * (6**6)
_UpperCAmelCase = peter_wins_count / total_games_number
_UpperCAmelCase = round(__lowercase , ndigits=7 )
return rounded_peter_win_probability
if __name__ == "__main__":
print(F"{solution() = }")
| 22 |
'''simple docstring'''
__SCREAMING_SNAKE_CASE :List[str] = '''0.18.2'''
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_librosa_available,
is_note_seq_available,
is_onnx_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
is_transformers_available,
is_transformers_version,
is_unidecode_available,
logging,
)
try:
if not is_onnx_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_onnx_objects import * # noqa F403
else:
from .pipelines import OnnxRuntimeModel
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_pt_objects import * # noqa F403
else:
from .models import (
AutoencoderKL,
ControlNetModel,
ModelMixin,
PriorTransformer,
TaFilmDecoder,
TransformeraDModel,
UNetaDModel,
UNetaDConditionModel,
UNetaDModel,
UNetaDConditionModel,
VQModel,
)
from .optimization import (
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
get_scheduler,
)
from .pipelines import (
AudioPipelineOutput,
ConsistencyModelPipeline,
DanceDiffusionPipeline,
DDIMPipeline,
DDPMPipeline,
DiffusionPipeline,
DiTPipeline,
ImagePipelineOutput,
KarrasVePipeline,
LDMPipeline,
LDMSuperResolutionPipeline,
PNDMPipeline,
RePaintPipeline,
ScoreSdeVePipeline,
)
from .schedulers import (
CMStochasticIterativeScheduler,
DDIMInverseScheduler,
DDIMParallelScheduler,
DDIMScheduler,
DDPMParallelScheduler,
DDPMScheduler,
DEISMultistepScheduler,
DPMSolverMultistepInverseScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
HeunDiscreteScheduler,
IPNDMScheduler,
KarrasVeScheduler,
KDPMaAncestralDiscreteScheduler,
KDPMaDiscreteScheduler,
PNDMScheduler,
RePaintScheduler,
SchedulerMixin,
ScoreSdeVeScheduler,
UnCLIPScheduler,
UniPCMultistepScheduler,
VQDiffusionScheduler,
)
from .training_utils import EMAModel
try:
if not (is_torch_available() and is_scipy_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_scipy_objects import * # noqa F403
else:
from .schedulers import LMSDiscreteScheduler
try:
if not (is_torch_available() and is_torchsde_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_torchsde_objects import * # noqa F403
else:
from .schedulers import DPMSolverSDEScheduler
try:
if not (is_torch_available() and is_transformers_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .pipelines import (
AltDiffusionImgaImgPipeline,
AltDiffusionPipeline,
AudioLDMPipeline,
CycleDiffusionPipeline,
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
ImageTextPipelineOutput,
KandinskyImgaImgPipeline,
KandinskyInpaintPipeline,
KandinskyPipeline,
KandinskyPriorPipeline,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaControlnetPipeline,
KandinskyVaaImgaImgPipeline,
KandinskyVaaInpaintPipeline,
KandinskyVaaPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
KandinskyVaaPriorPipeline,
LDMTextToImagePipeline,
PaintByExamplePipeline,
SemanticStableDiffusionPipeline,
ShapEImgaImgPipeline,
ShapEPipeline,
StableDiffusionAttendAndExcitePipeline,
StableDiffusionControlNetImgaImgPipeline,
StableDiffusionControlNetInpaintPipeline,
StableDiffusionControlNetPipeline,
StableDiffusionDepthaImgPipeline,
StableDiffusionDiffEditPipeline,
StableDiffusionImageVariationPipeline,
StableDiffusionImgaImgPipeline,
StableDiffusionInpaintPipeline,
StableDiffusionInpaintPipelineLegacy,
StableDiffusionInstructPixaPixPipeline,
StableDiffusionLatentUpscalePipeline,
StableDiffusionLDMaDPipeline,
StableDiffusionModelEditingPipeline,
StableDiffusionPanoramaPipeline,
StableDiffusionParadigmsPipeline,
StableDiffusionPipeline,
StableDiffusionPipelineSafe,
StableDiffusionPixaPixZeroPipeline,
StableDiffusionSAGPipeline,
StableDiffusionUpscalePipeline,
StableUnCLIPImgaImgPipeline,
StableUnCLIPPipeline,
TextToVideoSDPipeline,
TextToVideoZeroPipeline,
UnCLIPImageVariationPipeline,
UnCLIPPipeline,
UniDiffuserModel,
UniDiffuserPipeline,
UniDiffuserTextDecoder,
VersatileDiffusionDualGuidedPipeline,
VersatileDiffusionImageVariationPipeline,
VersatileDiffusionPipeline,
VersatileDiffusionTextToImagePipeline,
VideoToVideoSDPipeline,
VQDiffusionPipeline,
)
try:
if not (is_torch_available() and is_transformers_available() and is_invisible_watermark_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_transformers_and_invisible_watermark_objects import * # noqa F403
else:
from .pipelines import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline
try:
if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_transformers_and_k_diffusion_objects import * # noqa F403
else:
from .pipelines import StableDiffusionKDiffusionPipeline
try:
if not (is_torch_available() and is_transformers_available() and is_onnx_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_transformers_and_onnx_objects import * # noqa F403
else:
from .pipelines import (
OnnxStableDiffusionImgaImgPipeline,
OnnxStableDiffusionInpaintPipeline,
OnnxStableDiffusionInpaintPipelineLegacy,
OnnxStableDiffusionPipeline,
OnnxStableDiffusionUpscalePipeline,
StableDiffusionOnnxPipeline,
)
try:
if not (is_torch_available() and is_librosa_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_librosa_objects import * # noqa F403
else:
from .pipelines import AudioDiffusionPipeline, Mel
try:
if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_transformers_and_torch_and_note_seq_objects import * # noqa F403
else:
from .pipelines import SpectrogramDiffusionPipeline
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_flax_objects import * # noqa F403
else:
from .models.controlnet_flax import FlaxControlNetModel
from .models.modeling_flax_utils import FlaxModelMixin
from .models.unet_ad_condition_flax import FlaxUNetaDConditionModel
from .models.vae_flax import FlaxAutoencoderKL
from .pipelines import FlaxDiffusionPipeline
from .schedulers import (
FlaxDDIMScheduler,
FlaxDDPMScheduler,
FlaxDPMSolverMultistepScheduler,
FlaxKarrasVeScheduler,
FlaxLMSDiscreteScheduler,
FlaxPNDMScheduler,
FlaxSchedulerMixin,
FlaxScoreSdeVeScheduler,
)
try:
if not (is_flax_available() and is_transformers_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_flax_and_transformers_objects import * # noqa F403
else:
from .pipelines import (
FlaxStableDiffusionControlNetPipeline,
FlaxStableDiffusionImgaImgPipeline,
FlaxStableDiffusionInpaintPipeline,
FlaxStableDiffusionPipeline,
)
try:
if not (is_note_seq_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_note_seq_objects import * # noqa F403
else:
from .pipelines import MidiProcessor
| 22 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class A_ :
_lowerCamelCase : int
_lowerCamelCase : TreeNode | None = None
_lowerCamelCase : TreeNode | None = None
__SCREAMING_SNAKE_CASE :Any = namedtuple('''CoinsDistribResult''', '''moves excess''')
def UpperCAmelCase_ ( __lowercase : TreeNode | None ) -> int:
'''simple docstring'''
if root is None:
return 0
# Validation
def count_nodes(__lowercase : TreeNode | None ) -> int:
if node is None:
return 0
return count_nodes(node.left ) + count_nodes(node.right ) + 1
def count_coins(__lowercase : TreeNode | None ) -> int:
if node is None:
return 0
return count_coins(node.left ) + count_coins(node.right ) + node.data
if count_nodes(__lowercase ) != count_coins(__lowercase ):
raise ValueError("The nodes number should be same as the number of coins" )
# Main calculation
def get_distrib(__lowercase : TreeNode | None ) -> CoinsDistribResult:
if node is None:
return CoinsDistribResult(0 , 1 )
_UpperCAmelCase , _UpperCAmelCase = get_distrib(node.left )
_UpperCAmelCase , _UpperCAmelCase = get_distrib(node.right )
_UpperCAmelCase = 1 - left_distrib_excess
_UpperCAmelCase = 1 - right_distrib_excess
_UpperCAmelCase = (
left_distrib_moves
+ right_distrib_moves
+ abs(__lowercase )
+ abs(__lowercase )
)
_UpperCAmelCase = node.data - coins_to_left - coins_to_right
return CoinsDistribResult(__lowercase , __lowercase )
return get_distrib(__lowercase )[0]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 22 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
__SCREAMING_SNAKE_CASE :Optional[int] = True
except (ImportError, ModuleNotFoundError):
__SCREAMING_SNAKE_CASE :str = False
if NLTK_AVAILABLE:
with FileLock('''.lock''') as lock:
nltk.download('''punkt''', quiet=True)
def UpperCAmelCase_ ( __lowercase : str ) -> str:
'''simple docstring'''
re.sub("<n>" , "" , __lowercase ) # remove pegasus newline char
assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
return "\n".join(nltk.sent_tokenize(__lowercase ) )
| 22 | 1 |
'''simple docstring'''
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the experiment tracking capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
__SCREAMING_SNAKE_CASE :str = 16
__SCREAMING_SNAKE_CASE :List[str] = 32
def UpperCAmelCase_ ( __lowercase : Accelerator , __lowercase : int = 16 ) -> Optional[int]:
'''simple docstring'''
_UpperCAmelCase = AutoTokenizer.from_pretrained("bert-base-cased" )
_UpperCAmelCase = load_dataset("glue" , "mrpc" )
def tokenize_function(__lowercase : Optional[int] ):
# max_length=None => use the model max length (it's actually the default)
_UpperCAmelCase = tokenizer(examples["sentence1"] , examples["sentence2"] , truncation=__lowercase , max_length=__lowercase )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
_UpperCAmelCase = datasets.map(
__lowercase , batched=__lowercase , remove_columns=["idx", "sentence1", "sentence2"] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
_UpperCAmelCase = tokenized_datasets.rename_column("label" , "labels" )
def collate_fn(__lowercase : Optional[Any] ):
# On TPU it's best to pad everything to the same length or training will be very slow.
_UpperCAmelCase = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
_UpperCAmelCase = 16
elif accelerator.mixed_precision != "no":
_UpperCAmelCase = 8
else:
_UpperCAmelCase = None
return tokenizer.pad(
__lowercase , padding="longest" , max_length=__lowercase , pad_to_multiple_of=__lowercase , return_tensors="pt" , )
# Instantiate dataloaders.
_UpperCAmelCase = DataLoader(
tokenized_datasets["train"] , shuffle=__lowercase , collate_fn=__lowercase , batch_size=__lowercase )
_UpperCAmelCase = DataLoader(
tokenized_datasets["validation"] , shuffle=__lowercase , collate_fn=__lowercase , batch_size=__lowercase )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
__SCREAMING_SNAKE_CASE :Optional[int] = mocked_dataloaders # noqa: F811
def UpperCAmelCase_ ( __lowercase : Union[str, Any] , __lowercase : Tuple ) -> int:
'''simple docstring'''
if os.environ.get("TESTING_MOCKED_DATALOADERS" , __lowercase ) == "1":
_UpperCAmelCase = 2
# Initialize Accelerator
# New Code #
# We pass in "all" to `log_with` to grab all available trackers in the environment
# Note: If using a custom `Tracker` class, should be passed in here such as:
# >>> log_with = ["all", MyCustomTrackerClassInstance()]
if args.with_tracking:
_UpperCAmelCase = Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , log_with="all" , project_dir=args.project_dir )
else:
_UpperCAmelCase = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
_UpperCAmelCase = config["lr"]
_UpperCAmelCase = int(config["num_epochs"] )
_UpperCAmelCase = int(config["seed"] )
_UpperCAmelCase = int(config["batch_size"] )
set_seed(__lowercase )
_UpperCAmelCase , _UpperCAmelCase = get_dataloaders(__lowercase , __lowercase )
_UpperCAmelCase = evaluate.load("glue" , "mrpc" )
# If the batch size is too big we use gradient accumulation
_UpperCAmelCase = 1
if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
_UpperCAmelCase = batch_size // MAX_GPU_BATCH_SIZE
_UpperCAmelCase = MAX_GPU_BATCH_SIZE
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
_UpperCAmelCase = AutoModelForSequenceClassification.from_pretrained("bert-base-cased" , return_dict=__lowercase )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
_UpperCAmelCase = model.to(accelerator.device )
# Instantiate optimizer
_UpperCAmelCase = AdamW(params=model.parameters() , lr=__lowercase )
# Instantiate scheduler
_UpperCAmelCase = get_linear_schedule_with_warmup(
optimizer=__lowercase , num_warmup_steps=100 , num_training_steps=(len(__lowercase ) * num_epochs) // gradient_accumulation_steps , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = accelerator.prepare(
__lowercase , __lowercase , __lowercase , __lowercase , __lowercase )
# New Code #
# We need to initialize the trackers we use. Overall configurations can also be stored
if args.with_tracking:
_UpperCAmelCase = os.path.split(__lowercase )[-1].split("." )[0]
accelerator.init_trackers(__lowercase , __lowercase )
# Now we train the model
for epoch in range(__lowercase ):
model.train()
# New Code #
# For our tracking example, we will log the total loss of each epoch
if args.with_tracking:
_UpperCAmelCase = 0
for step, batch in enumerate(__lowercase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
_UpperCAmelCase = model(**__lowercase )
_UpperCAmelCase = outputs.loss
# New Code #
if args.with_tracking:
total_loss += loss.detach().float()
_UpperCAmelCase = loss / gradient_accumulation_steps
accelerator.backward(__lowercase )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(__lowercase ):
# We could avoid this line since we set the accelerator with `device_placement=True` (the default).
batch.to(accelerator.device )
with torch.no_grad():
_UpperCAmelCase = model(**__lowercase )
_UpperCAmelCase = outputs.logits.argmax(dim=-1 )
_UpperCAmelCase , _UpperCAmelCase = accelerator.gather_for_metrics((predictions, batch["labels"]) )
metric.add_batch(
predictions=__lowercase , references=__lowercase , )
_UpperCAmelCase = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f'epoch {epoch}:' , __lowercase )
# New Code #
# To actually log, we call `Accelerator.log`
# The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
if args.with_tracking:
accelerator.log(
{
"accuracy": eval_metric["accuracy"],
"f1": eval_metric["f1"],
"train_loss": total_loss.item() / len(__lowercase ),
"epoch": epoch,
} , step=__lowercase , )
# New Code #
# When a run is finished, you should call `accelerator.end_training()`
# to close all of the open trackers
if args.with_tracking:
accelerator.end_training()
def UpperCAmelCase_ ( ) -> int:
'''simple docstring'''
_UpperCAmelCase = argparse.ArgumentParser(description="Simple example of training script." )
parser.add_argument(
"--mixed_precision" , type=__lowercase , default=__lowercase , choices=["no", "fp16", "bf16", "fp8"] , help="Whether to use mixed precision. Choose"
"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
"and an Nvidia Ampere GPU." , )
parser.add_argument("--cpu" , action="store_true" , help="If passed, will train on the CPU." )
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=__lowercase , default="logs" , help="Location on where to store experiment tracking logs` and relevent project information" , )
_UpperCAmelCase = parser.parse_args()
_UpperCAmelCase = {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
training_function(__lowercase , __lowercase )
if __name__ == "__main__":
main()
| 22 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel
if is_vision_available():
from transformers import MaskFormerImageProcessor
if is_vision_available():
from PIL import Image
class A_ :
def __init__( self : str , snake_case_ : int , snake_case_ : Union[str, Any]=2 , snake_case_ : List[Any]=True , snake_case_ : str=False , snake_case_ : str=1_0 , snake_case_ : str=3 , snake_case_ : Dict=3_2 * 4 , snake_case_ : Any=3_2 * 6 , snake_case_ : Optional[Any]=4 , snake_case_ : Optional[int]=3_2 , ):
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = is_training
_UpperCAmelCase = use_auxiliary_loss
_UpperCAmelCase = num_queries
_UpperCAmelCase = num_channels
_UpperCAmelCase = min_size
_UpperCAmelCase = max_size
_UpperCAmelCase = num_labels
_UpperCAmelCase = mask_feature_size
def lowercase ( self : Union[str, Any] ):
_UpperCAmelCase = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
snake_case_ )
_UpperCAmelCase = torch.ones([self.batch_size, self.min_size, self.max_size] , device=snake_case_ )
_UpperCAmelCase = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=snake_case_ ) > 0.5
).float()
_UpperCAmelCase = (torch.rand((self.batch_size, self.num_labels) , device=snake_case_ ) > 0.5).long()
_UpperCAmelCase = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def lowercase ( self : List[Any] ):
return MaskFormerConfig.from_backbone_and_decoder_configs(
backbone_config=SwinConfig(
depths=[1, 1, 1, 1] , ) , decoder_config=DetrConfig(
decoder_ffn_dim=1_2_8 , num_queries=self.num_queries , decoder_attention_heads=2 , d_model=self.mask_feature_size , ) , mask_feature_size=self.mask_feature_size , fpn_feature_size=self.mask_feature_size , num_channels=self.num_channels , num_labels=self.num_labels , )
def lowercase ( self : Optional[Any] ):
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = self.prepare_config_and_inputs()
_UpperCAmelCase = {"pixel_values": pixel_values, "pixel_mask": pixel_mask}
return config, inputs_dict
def lowercase ( self : List[Any] , snake_case_ : Optional[Any] , snake_case_ : Optional[Any] ):
_UpperCAmelCase = output.encoder_hidden_states
_UpperCAmelCase = output.pixel_decoder_hidden_states
_UpperCAmelCase = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(snake_case_ ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(snake_case_ ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(snake_case_ ) , config.decoder_config.decoder_layers )
def lowercase ( self : Tuple , snake_case_ : str , snake_case_ : Optional[int] , snake_case_ : Any , snake_case_ : Optional[Any]=False ):
with torch.no_grad():
_UpperCAmelCase = MaskFormerModel(config=snake_case_ )
model.to(snake_case_ )
model.eval()
_UpperCAmelCase = model(pixel_values=snake_case_ , pixel_mask=snake_case_ )
_UpperCAmelCase = model(snake_case_ , output_hidden_states=snake_case_ )
# the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
# encoder and pixel decoder
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.mask_feature_size) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(snake_case_ , snake_case_ )
def lowercase ( self : Any , snake_case_ : List[str] , snake_case_ : List[Any] , snake_case_ : int , snake_case_ : str , snake_case_ : List[Any] ):
_UpperCAmelCase = MaskFormerForInstanceSegmentation(config=snake_case_ )
model.to(snake_case_ )
model.eval()
def comm_check_on_output(snake_case_ : int ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
_UpperCAmelCase = model(pixel_values=snake_case_ , pixel_mask=snake_case_ )
_UpperCAmelCase = model(snake_case_ )
comm_check_on_output(snake_case_ )
_UpperCAmelCase = model(
pixel_values=snake_case_ , pixel_mask=snake_case_ , mask_labels=snake_case_ , class_labels=snake_case_ )
comm_check_on_output(snake_case_ )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class A_ ( lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
_lowerCamelCase : Dict = (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
_lowerCamelCase : Tuple = (
{"""feature-extraction""": MaskFormerModel, """image-segmentation""": MaskFormerForInstanceSegmentation}
if is_torch_available()
else {}
)
_lowerCamelCase : Optional[Any] = False
_lowerCamelCase : Dict = False
_lowerCamelCase : Any = False
_lowerCamelCase : List[Any] = False
def lowercase ( self : Optional[int] ):
_UpperCAmelCase = MaskFormerModelTester(self )
_UpperCAmelCase = ConfigTester(self , config_class=snake_case_ , has_text_modality=snake_case_ )
def lowercase ( self : Optional[Any] ):
self.config_tester.run_common_tests()
def lowercase ( self : Union[str, Any] ):
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(snake_case_ , **snake_case_ , output_hidden_states=snake_case_ )
def lowercase ( self : int ):
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*snake_case_ )
@unittest.skip(reason="MaskFormer does not use inputs_embeds" )
def lowercase ( self : Any ):
pass
@unittest.skip(reason="MaskFormer does not have a get_input_embeddings method" )
def lowercase ( self : List[str] ):
pass
@unittest.skip(reason="MaskFormer is not a generative model" )
def lowercase ( self : List[str] ):
pass
@unittest.skip(reason="MaskFormer does not use token embeddings" )
def lowercase ( self : List[Any] ):
pass
@require_torch_multi_gpu
@unittest.skip(
reason="MaskFormer has some layers using `add_module` which doesn't work well with `nn.DataParallel`" )
def lowercase ( self : Any ):
pass
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
def lowercase ( self : Union[str, Any] ):
pass
def lowercase ( self : List[str] ):
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase = model_class(snake_case_ )
_UpperCAmelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_UpperCAmelCase = [*signature.parameters.keys()]
_UpperCAmelCase = ["pixel_values"]
self.assertListEqual(arg_names[:1] , snake_case_ )
@slow
def lowercase ( self : Optional[int] ):
for model_name in ["facebook/maskformer-swin-small-coco"]:
_UpperCAmelCase = MaskFormerModel.from_pretrained(snake_case_ )
self.assertIsNotNone(snake_case_ )
def lowercase ( self : Optional[int] ):
_UpperCAmelCase = (self.model_tester.min_size,) * 2
_UpperCAmelCase = {
"pixel_values": torch.randn((2, 3, *size) , device=snake_case_ ),
"mask_labels": torch.randn((2, 1_0, *size) , device=snake_case_ ),
"class_labels": torch.zeros(2 , 1_0 , device=snake_case_ ).long(),
}
_UpperCAmelCase = MaskFormerForInstanceSegmentation(MaskFormerConfig() ).to(snake_case_ )
_UpperCAmelCase = model(**snake_case_ )
self.assertTrue(outputs.loss is not None )
def lowercase ( self : Dict ):
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(snake_case_ , **snake_case_ , output_hidden_states=snake_case_ )
def lowercase ( self : Any ):
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase = model_class(snake_case_ ).to(snake_case_ )
_UpperCAmelCase = model(**snake_case_ , output_attentions=snake_case_ )
self.assertTrue(outputs.attentions is not None )
def lowercase ( self : int ):
if not self.model_tester.is_training:
return
# only MaskFormerForInstanceSegmentation has the loss
_UpperCAmelCase = self.all_model_classes[1]
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
_UpperCAmelCase = model_class(snake_case_ )
model.to(snake_case_ )
model.train()
_UpperCAmelCase = model(snake_case_ , mask_labels=snake_case_ , class_labels=snake_case_ ).loss
loss.backward()
def lowercase ( self : int ):
# only MaskFormerForInstanceSegmentation has the loss
_UpperCAmelCase = self.all_model_classes[1]
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
_UpperCAmelCase = True
_UpperCAmelCase = True
_UpperCAmelCase = model_class(snake_case_ )
model.to(snake_case_ )
model.train()
_UpperCAmelCase = model(snake_case_ , mask_labels=snake_case_ , class_labels=snake_case_ )
_UpperCAmelCase = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
_UpperCAmelCase = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
# we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
_UpperCAmelCase = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
_UpperCAmelCase = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=snake_case_ )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
__SCREAMING_SNAKE_CASE :Dict = 1e-4
def UpperCAmelCase_ ( ) -> List[str]:
'''simple docstring'''
_UpperCAmelCase = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_vision
@slow
class A_ ( unittest.TestCase ):
@cached_property
def lowercase ( self : Dict ):
return (
MaskFormerImageProcessor.from_pretrained("facebook/maskformer-swin-small-coco" )
if is_vision_available()
else None
)
def lowercase ( self : List[Any] ):
_UpperCAmelCase = MaskFormerModel.from_pretrained("facebook/maskformer-swin-small-coco" ).to(snake_case_ )
_UpperCAmelCase = self.default_image_processor
_UpperCAmelCase = prepare_img()
_UpperCAmelCase = image_processor(snake_case_ , return_tensors="pt" ).to(snake_case_ )
_UpperCAmelCase = inputs["pixel_values"].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0 )
# check size
self.assertEqual(snake_case_ , (1, 3, 8_0_0, 1_0_8_8) )
with torch.no_grad():
_UpperCAmelCase = model(**snake_case_ )
_UpperCAmelCase = torch.tensor(
[[-0.0_4_8_2, 0.9_2_2_8, 0.4_9_5_1], [-0.2_5_4_7, 0.8_0_1_7, 0.8_5_2_7], [-0.0_0_6_9, 0.3_3_8_5, -0.0_0_8_9]] ).to(snake_case_ )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , snake_case_ , atol=snake_case_ ) )
_UpperCAmelCase = torch.tensor(
[[-0.8_4_2_2, -0.8_4_3_4, -0.9_7_1_8], [-1.0_1_4_4, -0.5_5_6_5, -0.4_1_9_5], [-1.0_0_3_8, -0.4_4_8_4, -0.1_9_6_1]] ).to(snake_case_ )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , snake_case_ , atol=snake_case_ ) )
_UpperCAmelCase = torch.tensor(
[[0.2_8_5_2, -0.0_1_5_9, 0.9_7_3_5], [0.6_2_5_4, 0.1_8_5_8, 0.8_5_2_9], [-0.0_6_8_0, -0.4_1_1_6, 1.8_4_1_3]] ).to(snake_case_ )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , snake_case_ , atol=snake_case_ ) )
def lowercase ( self : Tuple ):
_UpperCAmelCase = (
MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-small-coco" )
.to(snake_case_ )
.eval()
)
_UpperCAmelCase = self.default_image_processor
_UpperCAmelCase = prepare_img()
_UpperCAmelCase = image_processor(snake_case_ , return_tensors="pt" ).to(snake_case_ )
_UpperCAmelCase = inputs["pixel_values"].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0 )
# check size
self.assertEqual(snake_case_ , (1, 3, 8_0_0, 1_0_8_8) )
with torch.no_grad():
_UpperCAmelCase = model(**snake_case_ )
# masks_queries_logits
_UpperCAmelCase = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
_UpperCAmelCase = [
[-1.3_7_3_7_1_2_4, -1.7_7_2_4_9_3_7, -1.9_3_6_4_2_3_3],
[-1.5_9_7_7_2_8_1, -1.9_8_6_7_9_3_9, -2.1_5_2_3_6_9_5],
[-1.5_7_9_5_3_9_8, -1.9_2_6_9_8_3_2, -2.0_9_3_9_4_2],
]
_UpperCAmelCase = torch.tensor(snake_case_ ).to(snake_case_ )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , snake_case_ , atol=snake_case_ ) )
# class_queries_logits
_UpperCAmelCase = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
_UpperCAmelCase = torch.tensor(
[
[1.6_512e00, -5.2_572e00, -3.3_519e00],
[3.6_169e-02, -5.9_025e00, -2.9_313e00],
[1.0_766e-04, -7.7_630e00, -5.1_263e00],
] ).to(snake_case_ )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , snake_case_ , atol=snake_case_ ) )
def lowercase ( self : int ):
_UpperCAmelCase = (
MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-resnet101-coco-stuff" )
.to(snake_case_ )
.eval()
)
_UpperCAmelCase = self.default_image_processor
_UpperCAmelCase = prepare_img()
_UpperCAmelCase = image_processor(snake_case_ , return_tensors="pt" ).to(snake_case_ )
_UpperCAmelCase = inputs["pixel_values"].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0 )
# check size
self.assertEqual(snake_case_ , (1, 3, 8_0_0, 1_0_8_8) )
with torch.no_grad():
_UpperCAmelCase = model(**snake_case_ )
# masks_queries_logits
_UpperCAmelCase = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
_UpperCAmelCase = [[-0.9_0_4_6, -2.6_3_6_6, -4.6_0_6_2], [-3.4_1_7_9, -5.7_8_9_0, -8.8_0_5_7], [-4.9_1_7_9, -7.6_5_6_0, -1_0.7_7_1_1]]
_UpperCAmelCase = torch.tensor(snake_case_ ).to(snake_case_ )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , snake_case_ , atol=snake_case_ ) )
# class_queries_logits
_UpperCAmelCase = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
_UpperCAmelCase = torch.tensor(
[[4.7_1_8_8, -3.2_5_8_5, -2.8_8_5_7], [6.6_8_7_1, -2.9_1_8_1, -1.2_4_8_7], [7.2_4_4_9, -2.2_7_6_4, -2.1_8_7_4]] ).to(snake_case_ )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , snake_case_ , atol=snake_case_ ) )
def lowercase ( self : List[Any] ):
_UpperCAmelCase = (
MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-small-coco" )
.to(snake_case_ )
.eval()
)
_UpperCAmelCase = self.default_image_processor
_UpperCAmelCase = image_processor(
[np.zeros((3, 8_0_0, 1_3_3_3) ), np.zeros((3, 8_0_0, 1_3_3_3) )] , segmentation_maps=[np.zeros((3_8_4, 3_8_4) ).astype(np.floataa ), np.zeros((3_8_4, 3_8_4) ).astype(np.floataa )] , return_tensors="pt" , )
_UpperCAmelCase = inputs["pixel_values"].to(snake_case_ )
_UpperCAmelCase = [el.to(snake_case_ ) for el in inputs["mask_labels"]]
_UpperCAmelCase = [el.to(snake_case_ ) for el in inputs["class_labels"]]
with torch.no_grad():
_UpperCAmelCase = model(**snake_case_ )
self.assertTrue(outputs.loss is not None )
| 22 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE :Optional[Any] = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE :Dict = {
'''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 A_ ( lowerCAmelCase_ ):
_lowerCamelCase : Tuple = """cvt"""
def __init__( self : Dict , snake_case_ : Optional[int]=3 , snake_case_ : Dict=[7, 3, 3] , snake_case_ : str=[4, 2, 2] , snake_case_ : Optional[int]=[2, 1, 1] , snake_case_ : Any=[6_4, 1_9_2, 3_8_4] , snake_case_ : List[Any]=[1, 3, 6] , snake_case_ : Tuple=[1, 2, 1_0] , snake_case_ : List[Any]=[4.0, 4.0, 4.0] , snake_case_ : Union[str, Any]=[0.0, 0.0, 0.0] , snake_case_ : List[str]=[0.0, 0.0, 0.0] , snake_case_ : Union[str, Any]=[0.0, 0.0, 0.1] , snake_case_ : int=[True, True, True] , snake_case_ : Optional[int]=[False, False, True] , snake_case_ : Dict=["dw_bn", "dw_bn", "dw_bn"] , snake_case_ : Union[str, Any]=[3, 3, 3] , snake_case_ : Optional[int]=[1, 1, 1] , snake_case_ : Union[str, Any]=[2, 2, 2] , snake_case_ : Union[str, Any]=[1, 1, 1] , snake_case_ : Optional[int]=[1, 1, 1] , snake_case_ : Optional[int]=0.0_2 , snake_case_ : Optional[int]=1e-12 , **snake_case_ : List[str] , ):
super().__init__(**snake_case_ )
_UpperCAmelCase = num_channels
_UpperCAmelCase = patch_sizes
_UpperCAmelCase = patch_stride
_UpperCAmelCase = patch_padding
_UpperCAmelCase = embed_dim
_UpperCAmelCase = num_heads
_UpperCAmelCase = depth
_UpperCAmelCase = mlp_ratio
_UpperCAmelCase = attention_drop_rate
_UpperCAmelCase = drop_rate
_UpperCAmelCase = drop_path_rate
_UpperCAmelCase = qkv_bias
_UpperCAmelCase = cls_token
_UpperCAmelCase = qkv_projection_method
_UpperCAmelCase = kernel_qkv
_UpperCAmelCase = padding_kv
_UpperCAmelCase = stride_kv
_UpperCAmelCase = padding_q
_UpperCAmelCase = stride_q
_UpperCAmelCase = initializer_range
_UpperCAmelCase = layer_norm_eps
| 22 |
'''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_albert import AlbertTokenizer
else:
__SCREAMING_SNAKE_CASE :List[Any] = None
__SCREAMING_SNAKE_CASE :Union[str, Any] = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE :List[str] = {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}
__SCREAMING_SNAKE_CASE :List[Any] = {
'''vocab_file''': {
'''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/spiece.model''',
'''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/spiece.model''',
'''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model''',
'''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model''',
'''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/spiece.model''',
'''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/spiece.model''',
'''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model''',
'''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model''',
},
'''tokenizer_file''': {
'''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json''',
'''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json''',
'''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json''',
'''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json''',
'''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json''',
'''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json''',
'''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json''',
'''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json''',
},
}
__SCREAMING_SNAKE_CASE :Optional[Any] = {
'''albert-base-v1''': 512,
'''albert-large-v1''': 512,
'''albert-xlarge-v1''': 512,
'''albert-xxlarge-v1''': 512,
'''albert-base-v2''': 512,
'''albert-large-v2''': 512,
'''albert-xlarge-v2''': 512,
'''albert-xxlarge-v2''': 512,
}
__SCREAMING_SNAKE_CASE :Optional[int] = '''▁'''
class A_ ( lowerCAmelCase_ ):
_lowerCamelCase : Optional[int] = VOCAB_FILES_NAMES
_lowerCamelCase : Any = PRETRAINED_VOCAB_FILES_MAP
_lowerCamelCase : Tuple = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_lowerCamelCase : int = AlbertTokenizer
def __init__( self : Optional[Any] , snake_case_ : Optional[Any]=None , snake_case_ : Optional[Any]=None , snake_case_ : Optional[Any]=True , snake_case_ : str=True , snake_case_ : Tuple=False , snake_case_ : List[Any]="[CLS]" , snake_case_ : Union[str, Any]="[SEP]" , snake_case_ : str="<unk>" , snake_case_ : Union[str, Any]="[SEP]" , snake_case_ : List[Any]="<pad>" , snake_case_ : List[str]="[CLS]" , snake_case_ : int="[MASK]" , **snake_case_ : Any , ):
# Mask token behave like a normal word, i.e. include the space before it and
# is included in the raw text, there should be a match in a non-normalized sentence.
_UpperCAmelCase = (
AddedToken(snake_case_ , lstrip=snake_case_ , rstrip=snake_case_ , normalized=snake_case_ )
if isinstance(snake_case_ , snake_case_ )
else mask_token
)
super().__init__(
snake_case_ , tokenizer_file=snake_case_ , do_lower_case=snake_case_ , remove_space=snake_case_ , keep_accents=snake_case_ , bos_token=snake_case_ , eos_token=snake_case_ , unk_token=snake_case_ , sep_token=snake_case_ , pad_token=snake_case_ , cls_token=snake_case_ , mask_token=snake_case_ , **snake_case_ , )
_UpperCAmelCase = do_lower_case
_UpperCAmelCase = remove_space
_UpperCAmelCase = keep_accents
_UpperCAmelCase = vocab_file
_UpperCAmelCase = False if not self.vocab_file else True
def lowercase ( self : Union[str, Any] , snake_case_ : List[int] , snake_case_ : Optional[List[int]] = None ):
_UpperCAmelCase = [self.sep_token_id]
_UpperCAmelCase = [self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def lowercase ( self : Dict , snake_case_ : List[int] , snake_case_ : Optional[List[int]] = None ):
_UpperCAmelCase = [self.sep_token_id]
_UpperCAmelCase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def lowercase ( self : Optional[Any] , snake_case_ : str , snake_case_ : Optional[str] = None ):
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(snake_case_ ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
return
_UpperCAmelCase = os.path.join(
snake_case_ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(snake_case_ ):
copyfile(self.vocab_file , snake_case_ )
return (out_vocab_file,)
| 22 | 1 |
'''simple docstring'''
import argparse
import json
import os
import re
import shutil
import torch
from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging
logging.set_verbosity_warning()
__SCREAMING_SNAKE_CASE :int = 2
class A_ :
def __init__( self : List[str] , *, # begin keyword-only arguments
snake_case_ : List[str]="<s>" , snake_case_ : Optional[int]="<pad>" , snake_case_ : Dict="</s>" , snake_case_ : Any="<unk>" , snake_case_ : Optional[Any]=None , ):
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = bos, unk, pad, eos
_UpperCAmelCase = []
_UpperCAmelCase = []
_UpperCAmelCase = {}
_UpperCAmelCase = self.add_symbol(snake_case_ )
_UpperCAmelCase = self.add_symbol(snake_case_ )
_UpperCAmelCase = self.add_symbol(snake_case_ )
_UpperCAmelCase = self.add_symbol(snake_case_ )
if extra_special_symbols:
for s in extra_special_symbols:
self.add_symbol(snake_case_ )
_UpperCAmelCase = len(self.symbols )
def __eq__( self : Optional[Any] , snake_case_ : str ):
return self.indices == other.indices
def __getitem__( self : str , snake_case_ : List[str] ):
if idx < len(self.symbols ):
return self.symbols[idx]
return self.unk_word
def __len__( self : Any ):
return len(self.symbols )
def __contains__( self : Union[str, Any] , snake_case_ : Tuple ):
return sym in self.indices
@classmethod
def lowercase ( cls : Tuple , snake_case_ : Tuple ):
_UpperCAmelCase = cls()
d.add_from_file(snake_case_ )
return d
def lowercase ( self : Optional[Any] , snake_case_ : int , snake_case_ : Dict=1 , snake_case_ : List[str]=False ):
if word in self.indices and not overwrite:
_UpperCAmelCase = self.indices[word]
_UpperCAmelCase = self.count[idx] + n
return idx
else:
_UpperCAmelCase = len(self.symbols )
_UpperCAmelCase = idx
self.symbols.append(snake_case_ )
self.count.append(snake_case_ )
return idx
def lowercase ( self : Tuple , snake_case_ : Any ):
return 0
def lowercase ( self : List[str] , snake_case_ : str ):
if isinstance(snake_case_ , snake_case_ ):
try:
with open(snake_case_ , "r" , encoding="utf-8" ) as fd:
self.add_from_file(snake_case_ )
except FileNotFoundError as fnfe:
raise fnfe
except UnicodeError:
raise Exception("Incorrect encoding detected in {}, please rebuild the dataset".format(snake_case_ ) )
return
_UpperCAmelCase = f.readlines()
_UpperCAmelCase = self._load_meta(snake_case_ )
for line in lines[indices_start_line:]:
try:
_UpperCAmelCase , _UpperCAmelCase = line.rstrip().rsplit(" " , 1 )
if field == "#fairseq:overwrite":
_UpperCAmelCase = True
_UpperCAmelCase , _UpperCAmelCase = line.rsplit(" " , 1 )
else:
_UpperCAmelCase = False
_UpperCAmelCase = int(snake_case_ )
_UpperCAmelCase = line
if word in self and not overwrite:
raise RuntimeError(
"Duplicate word found when loading Dictionary: '{}'. "
"Duplicate words can overwrite earlier ones by adding the "
"#fairseq:overwrite flag at the end of the corresponding row "
"in the dictionary file. If using the Camembert model, please "
"download an updated copy of the model file.".format(snake_case_ ) )
self.add_symbol(snake_case_ , n=snake_case_ , overwrite=snake_case_ )
except ValueError:
raise ValueError("Incorrect dictionary format, expected '<token> <cnt> [flags]'" )
def UpperCAmelCase_ ( __lowercase : Optional[Any] ) -> str:
'''simple docstring'''
_UpperCAmelCase = dict((re.sub(r"@@$" , "" , __lowercase ), v) if k.endswith("@@" ) else (re.sub(r"$" , "</w>" , __lowercase ), v) for k, v in d.items() )
_UpperCAmelCase = "<s> <pad> </s> <unk>".split()
# restore the special tokens
for k in keep_keys:
del da[f'{k}</w>']
_UpperCAmelCase = d[k] # restore
return da
def UpperCAmelCase_ ( __lowercase : List[str] , __lowercase : List[str] ) -> Optional[int]:
'''simple docstring'''
if not os.path.exists(__lowercase ):
raise ValueError(f'path {biogpt_checkpoint_path} does not exist!' )
os.makedirs(__lowercase , exist_ok=__lowercase )
print(f'Writing results to {pytorch_dump_folder_path}' )
# handle various types of models
_UpperCAmelCase = os.path.join(__lowercase , "checkpoint.pt" )
if not os.path.isfile(__lowercase ):
raise ValueError(f'path to the file {checkpoint_file} does not exist!' )
_UpperCAmelCase = torch.load(__lowercase , map_location="cpu" )
_UpperCAmelCase = chkpt["cfg"]["model"]
# dicts
_UpperCAmelCase = os.path.join(__lowercase , "dict.txt" )
if not os.path.isfile(__lowercase ):
raise ValueError(f'path to the file {dict_file} does not exist!' )
_UpperCAmelCase = Dictionary.load(__lowercase )
_UpperCAmelCase = rewrite_dict_keys(src_dict.indices )
_UpperCAmelCase = len(__lowercase )
_UpperCAmelCase = os.path.join(__lowercase , VOCAB_FILES_NAMES["vocab_file"] )
print(f'Generating {src_vocab_file} of {src_vocab_size} records' )
with open(__lowercase , "w" , encoding="utf-8" ) as f:
f.write(json.dumps(__lowercase , ensure_ascii=__lowercase , indent=__lowercase ) )
# merges_file (bpecodes)
_UpperCAmelCase = os.path.join(__lowercase , "bpecodes" )
if not os.path.isfile(__lowercase ):
raise ValueError(f'path to the file {bpecodes_file} does not exist!' )
_UpperCAmelCase = os.path.join(__lowercase , VOCAB_FILES_NAMES["merges_file"] )
shutil.copyfile(__lowercase , __lowercase )
# model config
_UpperCAmelCase = os.path.join(__lowercase , "config.json" )
_UpperCAmelCase = {
"activation_dropout": args["activation_dropout"],
"architectures": ["BioGptForCausalLM"],
"attention_probs_dropout_prob": args["attention_dropout"],
"bos_token_id": 0,
"eos_token_id": 2,
"hidden_act": args["activation_fn"],
"hidden_dropout_prob": args["dropout"],
"hidden_size": args["decoder_embed_dim"],
"initializer_range": 0.02,
"intermediate_size": args["decoder_ffn_embed_dim"],
"layer_norm_eps": 1E-12,
"layerdrop": args["decoder_layerdrop"],
"max_position_embeddings": args["max_target_positions"],
"model_type": "biogpt",
"num_attention_heads": args["decoder_attention_heads"],
"num_hidden_layers": args["decoder_layers"],
"pad_token_id": 1,
"scale_embedding": not args["no_scale_embedding"],
"tie_word_embeddings": args["share_decoder_input_output_embed"],
"vocab_size": src_vocab_size,
}
# good hparam defaults to start with
print(f'Generating {biogpt_model_config_file}' )
with open(__lowercase , "w" , encoding="utf-8" ) as f:
f.write(json.dumps(__lowercase , ensure_ascii=__lowercase , indent=__lowercase ) )
# tokenizer config
_UpperCAmelCase = os.path.join(__lowercase , __lowercase )
_UpperCAmelCase = {
"bos_token": "<s>",
"eos_token": "</s>",
"model_max_length": 1024,
"pad_token": "<pad>",
"special_tokens_map_file": None,
"tokenizer_class": "BioGptTokenizer",
"unk_token": "<unk>",
}
print(f'Generating {biogpt_tokenizer_config_file}' )
with open(__lowercase , "w" , encoding="utf-8" ) as f:
f.write(json.dumps(__lowercase , ensure_ascii=__lowercase , indent=__lowercase ) )
# model
_UpperCAmelCase = chkpt["model"]
# remove unneeded keys
_UpperCAmelCase = [
"decoder.version",
]
for k in ignore_keys:
model_state_dict.pop(__lowercase , __lowercase )
_UpperCAmelCase = list(model_state_dict.keys() )
for layer_name in layer_names:
if layer_name.endswith("output_projection.weight" ):
_UpperCAmelCase = model_state_dict.pop(__lowercase )
else:
_UpperCAmelCase = model_state_dict.pop(__lowercase )
_UpperCAmelCase = BioGptConfig.from_pretrained(__lowercase )
_UpperCAmelCase = BioGptForCausalLM(__lowercase )
# check that it loads ok
model_new.load_state_dict(__lowercase )
# save
_UpperCAmelCase = os.path.join(__lowercase , __lowercase )
print(f'Generating {pytorch_weights_dump_path}' )
torch.save(__lowercase , __lowercase )
print("Conversion is done!" )
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE :Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--biogpt_checkpoint_path''',
default=None,
type=str,
required=True,
help=(
'''Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,'''
''' bpecodes, etc.'''
),
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
__SCREAMING_SNAKE_CASE :List[str] = parser.parse_args()
convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)
| 22 |
'''simple docstring'''
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
__SCREAMING_SNAKE_CASE :Dict = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE :int = {
'''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 A_ ( lowerCAmelCase_ ):
_lowerCamelCase : int = """perceiver"""
def __init__( self : Any , snake_case_ : List[Any]=2_5_6 , snake_case_ : str=1_2_8_0 , snake_case_ : Optional[int]=7_6_8 , snake_case_ : int=1 , snake_case_ : List[Any]=2_6 , snake_case_ : Dict=8 , snake_case_ : List[Any]=8 , snake_case_ : Tuple=None , snake_case_ : Tuple=None , snake_case_ : Any="kv" , snake_case_ : Any=1 , snake_case_ : List[str]=1 , snake_case_ : Optional[int]="gelu" , snake_case_ : List[Any]=0.1 , snake_case_ : Dict=0.0_2 , snake_case_ : int=1e-12 , snake_case_ : List[str]=True , snake_case_ : str=2_6_2 , snake_case_ : Optional[Any]=2_0_4_8 , snake_case_ : Union[str, Any]=5_6 , snake_case_ : Dict=[3_6_8, 4_9_6] , snake_case_ : Tuple=1_6 , snake_case_ : Union[str, Any]=1_9_2_0 , snake_case_ : List[Any]=1_6 , snake_case_ : Tuple=[1, 1_6, 2_2_4, 2_2_4] , **snake_case_ : List[Any] , ):
super().__init__(**snake_case_ )
_UpperCAmelCase = num_latents
_UpperCAmelCase = d_latents
_UpperCAmelCase = d_model
_UpperCAmelCase = num_blocks
_UpperCAmelCase = num_self_attends_per_block
_UpperCAmelCase = num_self_attention_heads
_UpperCAmelCase = num_cross_attention_heads
_UpperCAmelCase = qk_channels
_UpperCAmelCase = v_channels
_UpperCAmelCase = cross_attention_shape_for_attention
_UpperCAmelCase = self_attention_widening_factor
_UpperCAmelCase = cross_attention_widening_factor
_UpperCAmelCase = hidden_act
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = initializer_range
_UpperCAmelCase = layer_norm_eps
_UpperCAmelCase = use_query_residual
# masked language modeling attributes
_UpperCAmelCase = vocab_size
_UpperCAmelCase = max_position_embeddings
# image classification attributes
_UpperCAmelCase = image_size
# flow attributes
_UpperCAmelCase = train_size
# multimodal autoencoding attributes
_UpperCAmelCase = num_frames
_UpperCAmelCase = audio_samples_per_frame
_UpperCAmelCase = samples_per_patch
_UpperCAmelCase = output_shape
class A_ ( lowerCAmelCase_ ):
@property
def lowercase ( self : int ):
if self.task == "multiple-choice":
_UpperCAmelCase = {0: "batch", 1: "choice", 2: "sequence"}
else:
_UpperCAmelCase = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("inputs", dynamic_axis),
("attention_mask", dynamic_axis),
] )
@property
def lowercase ( self : Optional[Any] ):
return 1e-4
def lowercase ( self : List[str] , snake_case_ : Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"] , snake_case_ : int = -1 , snake_case_ : int = -1 , snake_case_ : int = -1 , snake_case_ : bool = False , snake_case_ : Optional[TensorType] = None , snake_case_ : int = 3 , snake_case_ : int = 4_0 , snake_case_ : int = 4_0 , ):
# copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified
if isinstance(snake_case_ , snake_case_ ):
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
_UpperCAmelCase = compute_effective_axis_dimension(
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 = preprocessor.num_special_tokens_to_add(snake_case_ )
_UpperCAmelCase = compute_effective_axis_dimension(
snake_case_ , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=snake_case_ )
# Generate dummy inputs according to compute batch and sequence
_UpperCAmelCase = [" ".join(["a"] ) * seq_length] * batch_size
_UpperCAmelCase = dict(preprocessor(snake_case_ , return_tensors=snake_case_ ) )
_UpperCAmelCase = inputs.pop("input_ids" )
return inputs
elif isinstance(snake_case_ , snake_case_ ) 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
_UpperCAmelCase = compute_effective_axis_dimension(snake_case_ , fixed_dimension=OnnxConfig.default_fixed_batch )
_UpperCAmelCase = self._generate_dummy_images(snake_case_ , snake_case_ , snake_case_ , snake_case_ )
_UpperCAmelCase = dict(preprocessor(images=snake_case_ , return_tensors=snake_case_ ) )
_UpperCAmelCase = inputs.pop("pixel_values" )
return inputs
else:
raise ValueError(
"Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor." )
| 22 | 1 |
'''simple docstring'''
import os
import unittest
from transformers import MobileBertTokenizer, MobileBertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english
@require_tokenizers
class A_ ( lowerCAmelCase_ , unittest.TestCase ):
_lowerCamelCase : str = MobileBertTokenizer
_lowerCamelCase : Any = MobileBertTokenizerFast
_lowerCamelCase : Any = True
_lowerCamelCase : Optional[Any] = True
_lowerCamelCase : List[str] = filter_non_english
_lowerCamelCase : List[Any] = """google/mobilebert-uncased"""
def lowercase ( self : List[str] ):
super().setUp()
_UpperCAmelCase = [
"[UNK]",
"[CLS]",
"[SEP]",
"[PAD]",
"[MASK]",
"want",
"##want",
"##ed",
"wa",
"un",
"runn",
"##ing",
",",
"low",
"lowest",
]
_UpperCAmelCase = 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] ) )
_UpperCAmelCase = [
(tokenizer_def[0], self.pre_trained_model_path, tokenizer_def[2]) # else the 'google/' prefix is stripped
for tokenizer_def in self.tokenizers_list
]
def lowercase ( self : Union[str, Any] , snake_case_ : Any ):
_UpperCAmelCase = "UNwant\u00E9d,running"
_UpperCAmelCase = "unwanted, running"
return input_text, output_text
def lowercase ( self : Union[str, Any] ):
_UpperCAmelCase = self.tokenizer_class(self.vocab_file )
_UpperCAmelCase = tokenizer.tokenize("UNwant\u00E9d,running" )
self.assertListEqual(snake_case_ , ["un", "##want", "##ed", ",", "runn", "##ing"] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case_ ) , [9, 6, 7, 1_2, 1_0, 1_1] )
def lowercase ( self : str ):
if not self.test_rust_tokenizer:
return
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = self.get_rust_tokenizer()
_UpperCAmelCase = "UNwant\u00E9d,running"
_UpperCAmelCase = tokenizer.tokenize(snake_case_ )
_UpperCAmelCase = rust_tokenizer.tokenize(snake_case_ )
self.assertListEqual(snake_case_ , snake_case_ )
_UpperCAmelCase = tokenizer.encode(snake_case_ , add_special_tokens=snake_case_ )
_UpperCAmelCase = rust_tokenizer.encode(snake_case_ , add_special_tokens=snake_case_ )
self.assertListEqual(snake_case_ , snake_case_ )
_UpperCAmelCase = self.get_rust_tokenizer()
_UpperCAmelCase = tokenizer.encode(snake_case_ )
_UpperCAmelCase = rust_tokenizer.encode(snake_case_ )
self.assertListEqual(snake_case_ , snake_case_ )
# With lower casing
_UpperCAmelCase = self.get_tokenizer(do_lower_case=snake_case_ )
_UpperCAmelCase = self.get_rust_tokenizer(do_lower_case=snake_case_ )
_UpperCAmelCase = "UNwant\u00E9d,running"
_UpperCAmelCase = tokenizer.tokenize(snake_case_ )
_UpperCAmelCase = rust_tokenizer.tokenize(snake_case_ )
self.assertListEqual(snake_case_ , snake_case_ )
_UpperCAmelCase = tokenizer.encode(snake_case_ , add_special_tokens=snake_case_ )
_UpperCAmelCase = rust_tokenizer.encode(snake_case_ , add_special_tokens=snake_case_ )
self.assertListEqual(snake_case_ , snake_case_ )
_UpperCAmelCase = self.get_rust_tokenizer()
_UpperCAmelCase = tokenizer.encode(snake_case_ )
_UpperCAmelCase = rust_tokenizer.encode(snake_case_ )
self.assertListEqual(snake_case_ , snake_case_ )
def lowercase ( self : Any ):
_UpperCAmelCase = BasicTokenizer()
self.assertListEqual(tokenizer.tokenize("ah\u535A\u63A8zz" ) , ["ah", "\u535A", "\u63A8", "zz"] )
def lowercase ( self : Optional[int] ):
_UpperCAmelCase = BasicTokenizer(do_lower_case=snake_case_ )
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo!how \n Are yoU? " ) , ["hello", "!", "how", "are", "you", "?"] )
self.assertListEqual(tokenizer.tokenize("H\u00E9llo" ) , ["hello"] )
def lowercase ( self : List[str] ):
_UpperCAmelCase = BasicTokenizer(do_lower_case=snake_case_ , strip_accents=snake_case_ )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) , ["hällo", "!", "how", "are", "you", "?"] )
self.assertListEqual(tokenizer.tokenize("H\u00E9llo" ) , ["h\u00E9llo"] )
def lowercase ( self : Optional[int] ):
_UpperCAmelCase = BasicTokenizer(do_lower_case=snake_case_ , strip_accents=snake_case_ )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) , ["hallo", "!", "how", "are", "you", "?"] )
self.assertListEqual(tokenizer.tokenize("H\u00E9llo" ) , ["hello"] )
def lowercase ( self : str ):
_UpperCAmelCase = BasicTokenizer(do_lower_case=snake_case_ )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) , ["hallo", "!", "how", "are", "you", "?"] )
self.assertListEqual(tokenizer.tokenize("H\u00E9llo" ) , ["hello"] )
def lowercase ( self : Dict ):
_UpperCAmelCase = BasicTokenizer(do_lower_case=snake_case_ )
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo!how \n Are yoU? " ) , ["HeLLo", "!", "how", "Are", "yoU", "?"] )
def lowercase ( self : List[Any] ):
_UpperCAmelCase = BasicTokenizer(do_lower_case=snake_case_ , strip_accents=snake_case_ )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) , ["HäLLo", "!", "how", "Are", "yoU", "?"] )
def lowercase ( self : Optional[int] ):
_UpperCAmelCase = BasicTokenizer(do_lower_case=snake_case_ , strip_accents=snake_case_ )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) , ["HaLLo", "!", "how", "Are", "yoU", "?"] )
def lowercase ( self : Optional[Any] ):
_UpperCAmelCase = BasicTokenizer(do_lower_case=snake_case_ , never_split=["[UNK]"] )
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo!how \n Are yoU? [UNK]" ) , ["HeLLo", "!", "how", "Are", "yoU", "?", "[UNK]"] )
def lowercase ( self : List[str] ):
_UpperCAmelCase = ["[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn", "##ing"]
_UpperCAmelCase = {}
for i, token in enumerate(snake_case_ ):
_UpperCAmelCase = i
_UpperCAmelCase = WordpieceTokenizer(vocab=snake_case_ , 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"] )
def lowercase ( self : List[Any] ):
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 lowercase ( self : Optional[int] ):
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 lowercase ( self : str ):
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(" " ) )
def lowercase ( self : Any ):
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = self.get_rust_tokenizer()
# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
self.assertListEqual([tokenizer.tokenize(snake_case_ ) for t in ["Test", "\xad", "test"]] , [["[UNK]"], [], ["[UNK]"]] )
self.assertListEqual(
[rust_tokenizer.tokenize(snake_case_ ) for t in ["Test", "\xad", "test"]] , [["[UNK]"], [], ["[UNK]"]] )
@slow
def lowercase ( self : List[str] ):
_UpperCAmelCase = self.tokenizer_class.from_pretrained("google/mobilebert-uncased" )
_UpperCAmelCase = tokenizer.encode("sequence builders" , add_special_tokens=snake_case_ )
_UpperCAmelCase = tokenizer.encode("multi-sequence build" , add_special_tokens=snake_case_ )
_UpperCAmelCase = tokenizer.build_inputs_with_special_tokens(snake_case_ )
_UpperCAmelCase = tokenizer.build_inputs_with_special_tokens(snake_case_ , snake_case_ )
assert encoded_sentence == [1_0_1] + text + [1_0_2]
assert encoded_pair == [1_0_1] + text + [1_0_2] + text_a + [1_0_2]
def lowercase ( self : Optional[Any] ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})' ):
_UpperCAmelCase = self.rust_tokenizer_class.from_pretrained(snake_case_ , **snake_case_ )
_UpperCAmelCase = f'A, naïve {tokenizer_r.mask_token} AllenNLP sentence.'
_UpperCAmelCase = tokenizer_r.encode_plus(
snake_case_ , return_attention_mask=snake_case_ , return_token_type_ids=snake_case_ , return_offsets_mapping=snake_case_ , add_special_tokens=snake_case_ , )
_UpperCAmelCase = tokenizer_r.do_lower_case if hasattr(snake_case_ , "do_lower_case" ) else False
_UpperCAmelCase = (
[
((0, 0), tokenizer_r.cls_token),
((0, 1), "A"),
((1, 2), ","),
((3, 5), "na"),
((5, 6), "##ï"),
((6, 8), "##ve"),
((9, 1_5), tokenizer_r.mask_token),
((1_6, 2_1), "Allen"),
((2_1, 2_3), "##NL"),
((2_3, 2_4), "##P"),
((2_5, 3_3), "sentence"),
((3_3, 3_4), "."),
((0, 0), tokenizer_r.sep_token),
]
if not do_lower_case
else [
((0, 0), tokenizer_r.cls_token),
((0, 1), "a"),
((1, 2), ","),
((3, 8), "naive"),
((9, 1_5), tokenizer_r.mask_token),
((1_6, 2_1), "allen"),
((2_1, 2_3), "##nl"),
((2_3, 2_4), "##p"),
((2_5, 3_3), "sentence"),
((3_3, 3_4), "."),
((0, 0), tokenizer_r.sep_token),
]
)
self.assertEqual(
[e[1] for e in expected_results] , tokenizer_r.convert_ids_to_tokens(tokens["input_ids"] ) )
self.assertEqual([e[0] for e in expected_results] , tokens["offset_mapping"] )
def lowercase ( self : List[str] ):
_UpperCAmelCase = ["的", "人", "有"]
_UpperCAmelCase = "".join(snake_case_ )
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})' ):
_UpperCAmelCase = True
_UpperCAmelCase = self.tokenizer_class.from_pretrained(snake_case_ , **snake_case_ )
_UpperCAmelCase = self.rust_tokenizer_class.from_pretrained(snake_case_ , **snake_case_ )
_UpperCAmelCase = tokenizer_p.encode(snake_case_ , add_special_tokens=snake_case_ )
_UpperCAmelCase = tokenizer_r.encode(snake_case_ , add_special_tokens=snake_case_ )
_UpperCAmelCase = tokenizer_r.convert_ids_to_tokens(snake_case_ )
_UpperCAmelCase = tokenizer_p.convert_ids_to_tokens(snake_case_ )
# it is expected that each Chinese character is not preceded by "##"
self.assertListEqual(snake_case_ , snake_case_ )
self.assertListEqual(snake_case_ , snake_case_ )
_UpperCAmelCase = False
_UpperCAmelCase = self.rust_tokenizer_class.from_pretrained(snake_case_ , **snake_case_ )
_UpperCAmelCase = self.tokenizer_class.from_pretrained(snake_case_ , **snake_case_ )
_UpperCAmelCase = tokenizer_r.encode(snake_case_ , add_special_tokens=snake_case_ )
_UpperCAmelCase = tokenizer_p.encode(snake_case_ , add_special_tokens=snake_case_ )
_UpperCAmelCase = tokenizer_r.convert_ids_to_tokens(snake_case_ )
_UpperCAmelCase = tokenizer_p.convert_ids_to_tokens(snake_case_ )
# it is expected that only the first Chinese character is not preceded by "##".
_UpperCAmelCase = [
f'##{token}' if idx != 0 else token for idx, token in enumerate(snake_case_ )
]
self.assertListEqual(snake_case_ , snake_case_ )
self.assertListEqual(snake_case_ , snake_case_ )
| 22 |
'''simple docstring'''
import warnings
from ...utils import is_sklearn_available, requires_backends
if is_sklearn_available():
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
__SCREAMING_SNAKE_CASE :List[str] = (
'''This metric will be removed from the library soon, metrics should be handled with the 🤗 Evaluate '''
'''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'''
)
def UpperCAmelCase_ ( __lowercase : Any , __lowercase : Tuple ) -> int:
'''simple docstring'''
warnings.warn(__lowercase , __lowercase )
requires_backends(__lowercase , "sklearn" )
return (preds == labels).mean()
def UpperCAmelCase_ ( __lowercase : int , __lowercase : str ) -> Optional[Any]:
'''simple docstring'''
warnings.warn(__lowercase , __lowercase )
requires_backends(__lowercase , "sklearn" )
_UpperCAmelCase = simple_accuracy(__lowercase , __lowercase )
_UpperCAmelCase = fa_score(y_true=__lowercase , y_pred=__lowercase )
return {
"acc": acc,
"f1": fa,
"acc_and_f1": (acc + fa) / 2,
}
def UpperCAmelCase_ ( __lowercase : Optional[int] , __lowercase : List[str] ) -> List[Any]:
'''simple docstring'''
warnings.warn(__lowercase , __lowercase )
requires_backends(__lowercase , "sklearn" )
_UpperCAmelCase = pearsonr(__lowercase , __lowercase )[0]
_UpperCAmelCase = spearmanr(__lowercase , __lowercase )[0]
return {
"pearson": pearson_corr,
"spearmanr": spearman_corr,
"corr": (pearson_corr + spearman_corr) / 2,
}
def UpperCAmelCase_ ( __lowercase : Optional[Any] , __lowercase : str , __lowercase : str ) -> Tuple:
'''simple docstring'''
warnings.warn(__lowercase , __lowercase )
requires_backends(__lowercase , "sklearn" )
assert len(__lowercase ) == len(__lowercase ), f'Predictions and labels have mismatched lengths {len(__lowercase )} and {len(__lowercase )}'
if task_name == "cola":
return {"mcc": matthews_corrcoef(__lowercase , __lowercase )}
elif task_name == "sst-2":
return {"acc": simple_accuracy(__lowercase , __lowercase )}
elif task_name == "mrpc":
return acc_and_fa(__lowercase , __lowercase )
elif task_name == "sts-b":
return pearson_and_spearman(__lowercase , __lowercase )
elif task_name == "qqp":
return acc_and_fa(__lowercase , __lowercase )
elif task_name == "mnli":
return {"mnli/acc": simple_accuracy(__lowercase , __lowercase )}
elif task_name == "mnli-mm":
return {"mnli-mm/acc": simple_accuracy(__lowercase , __lowercase )}
elif task_name == "qnli":
return {"acc": simple_accuracy(__lowercase , __lowercase )}
elif task_name == "rte":
return {"acc": simple_accuracy(__lowercase , __lowercase )}
elif task_name == "wnli":
return {"acc": simple_accuracy(__lowercase , __lowercase )}
elif task_name == "hans":
return {"acc": simple_accuracy(__lowercase , __lowercase )}
else:
raise KeyError(__lowercase )
def UpperCAmelCase_ ( __lowercase : List[Any] , __lowercase : Dict , __lowercase : str ) -> Union[str, Any]:
'''simple docstring'''
warnings.warn(__lowercase , __lowercase )
requires_backends(__lowercase , "sklearn" )
if len(__lowercase ) != len(__lowercase ):
raise ValueError(f'Predictions and labels have mismatched lengths {len(__lowercase )} and {len(__lowercase )}' )
if task_name == "xnli":
return {"acc": simple_accuracy(__lowercase , __lowercase )}
else:
raise KeyError(__lowercase )
| 22 | 1 |
'''simple docstring'''
import functools
from typing import Any
def UpperCAmelCase_ ( __lowercase : str , __lowercase : list[str] ) -> bool:
'''simple docstring'''
if not isinstance(__lowercase , __lowercase ) or len(__lowercase ) == 0:
raise ValueError("the string should be not empty string" )
if not isinstance(__lowercase , __lowercase ) or not all(
isinstance(__lowercase , __lowercase ) and len(__lowercase ) > 0 for item in words ):
raise ValueError("the words should be a list of non-empty strings" )
# Build trie
_UpperCAmelCase = {}
_UpperCAmelCase = "WORD_KEEPER"
for word in words:
_UpperCAmelCase = trie
for c in word:
if c not in trie_node:
_UpperCAmelCase = {}
_UpperCAmelCase = trie_node[c]
_UpperCAmelCase = True
_UpperCAmelCase = len(__lowercase )
# Dynamic programming method
@functools.cache
def is_breakable(__lowercase : int ) -> bool:
if index == len_string:
return True
_UpperCAmelCase = trie
for i in range(__lowercase , __lowercase ):
_UpperCAmelCase = trie_node.get(string[i] , __lowercase )
if trie_node is None:
return False
if trie_node.get(__lowercase , __lowercase ) and is_breakable(i + 1 ):
return True
return False
return is_breakable(0 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 22 |
'''simple docstring'''
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCAmelCase_ ( __lowercase : int , __lowercase : Dict , __lowercase : str , __lowercase : Optional[Any] , __lowercase : str ) -> List[str]:
'''simple docstring'''
_UpperCAmelCase = TapasConfig.from_json_file(__lowercase )
# set absolute/relative position embeddings parameter
_UpperCAmelCase = reset_position_index_per_cell
# set remaining parameters of TapasConfig as well as the model based on the task
if task == "SQA":
_UpperCAmelCase = TapasForQuestionAnswering(config=__lowercase )
elif task == "WTQ":
# run_task_main.py hparams
_UpperCAmelCase = 4
_UpperCAmelCase = True
# hparam_utils.py hparams
_UpperCAmelCase = 0.66_4694
_UpperCAmelCase = 0.20_7951
_UpperCAmelCase = 0.12_1194
_UpperCAmelCase = True
_UpperCAmelCase = True
_UpperCAmelCase = False
_UpperCAmelCase = 0.035_2513
_UpperCAmelCase = TapasForQuestionAnswering(config=__lowercase )
elif task == "WIKISQL_SUPERVISED":
# run_task_main.py hparams
_UpperCAmelCase = 4
_UpperCAmelCase = False
# hparam_utils.py hparams
_UpperCAmelCase = 36.4519
_UpperCAmelCase = 0.90_3421
_UpperCAmelCase = 222.088
_UpperCAmelCase = True
_UpperCAmelCase = True
_UpperCAmelCase = True
_UpperCAmelCase = 0.76_3141
_UpperCAmelCase = TapasForQuestionAnswering(config=__lowercase )
elif task == "TABFACT":
_UpperCAmelCase = TapasForSequenceClassification(config=__lowercase )
elif task == "MLM":
_UpperCAmelCase = TapasForMaskedLM(config=__lowercase )
elif task == "INTERMEDIATE_PRETRAINING":
_UpperCAmelCase = TapasModel(config=__lowercase )
else:
raise ValueError(f'Task {task} not supported.' )
print(f'Building PyTorch model from configuration: {config}' )
# Load weights from tf checkpoint
load_tf_weights_in_tapas(__lowercase , __lowercase , __lowercase )
# Save pytorch-model (weights and configuration)
print(f'Save PyTorch model to {pytorch_dump_path}' )
model.save_pretrained(__lowercase )
# Save tokenizer files
print(f'Save tokenizer files to {pytorch_dump_path}' )
_UpperCAmelCase = TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + "vocab.txt" , model_max_length=512 )
tokenizer.save_pretrained(__lowercase )
print("Used relative position embeddings:" , model.config.reset_position_index_per_cell )
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE :List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--task''', default='''SQA''', type=str, help='''Model task for which to convert a checkpoint. Defaults to SQA.'''
)
parser.add_argument(
'''--reset_position_index_per_cell''',
default=False,
action='''store_true''',
help='''Whether to use relative position embeddings or not. Defaults to True.''',
)
parser.add_argument(
'''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--tapas_config_file''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained TAPAS model. \n'''
'''This specifies the model architecture.'''
),
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
__SCREAMING_SNAKE_CASE :List[str] = parser.parse_args()
convert_tf_checkpoint_to_pytorch(
args.task,
args.reset_position_index_per_cell,
args.tf_checkpoint_path,
args.tapas_config_file,
args.pytorch_dump_path,
)
| 22 | 1 |
'''simple docstring'''
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
__SCREAMING_SNAKE_CASE :Optional[Any] = '''src/transformers'''
# This is to make sure the transformers module imported is the one in the repo.
__SCREAMING_SNAKE_CASE :Tuple = direct_transformers_import(PATH_TO_TRANSFORMERS)
__SCREAMING_SNAKE_CASE :Tuple = transformers.models.auto.configuration_auto.CONFIG_MAPPING
# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
__SCREAMING_SNAKE_CASE :List[str] = re.compile(R'''\[(.+?)\]\((https://huggingface\.co/.+?)\)''')
__SCREAMING_SNAKE_CASE :Union[str, Any] = {
'''DecisionTransformerConfig''',
'''EncoderDecoderConfig''',
'''MusicgenConfig''',
'''RagConfig''',
'''SpeechEncoderDecoderConfig''',
'''TimmBackboneConfig''',
'''VisionEncoderDecoderConfig''',
'''VisionTextDualEncoderConfig''',
'''LlamaConfig''',
}
def UpperCAmelCase_ ( __lowercase : List[Any] ) -> Optional[int]:
'''simple docstring'''
_UpperCAmelCase = None
# source code of `config_class`
_UpperCAmelCase = inspect.getsource(__lowercase )
_UpperCAmelCase = _re_checkpoint.findall(__lowercase )
# Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
# For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
for ckpt_name, ckpt_link in checkpoints:
# allow the link to end with `/`
if ckpt_link.endswith("/" ):
_UpperCAmelCase = ckpt_link[:-1]
# verify the checkpoint name corresponds to the checkpoint link
_UpperCAmelCase = f'https://huggingface.co/{ckpt_name}'
if ckpt_link == ckpt_link_from_name:
_UpperCAmelCase = ckpt_name
break
return checkpoint
def UpperCAmelCase_ ( ) -> List[str]:
'''simple docstring'''
_UpperCAmelCase = []
for config_class in list(CONFIG_MAPPING.values() ):
# Skip deprecated models
if "models.deprecated" in config_class.__module__:
continue
_UpperCAmelCase = get_checkpoint_from_config_class(__lowercase )
_UpperCAmelCase = config_class.__name__
if checkpoint is None and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
configs_without_checkpoint.append(__lowercase )
if len(__lowercase ) > 0:
_UpperCAmelCase = "\n".join(sorted(__lowercase ) )
raise ValueError(f'The following configurations don\'t contain any valid checkpoint:\n{message}' )
if __name__ == "__main__":
check_config_docstrings_have_checkpoints()
| 22 |
'''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
__SCREAMING_SNAKE_CASE :str = [
'''good first issue''',
'''feature request''',
'''wip''',
]
def UpperCAmelCase_ ( ) -> Optional[Any]:
'''simple docstring'''
_UpperCAmelCase = Github(os.environ["GITHUB_TOKEN"] )
_UpperCAmelCase = g.get_repo("huggingface/accelerate" )
_UpperCAmelCase = repo.get_issues(state="open" )
for issue in open_issues:
_UpperCAmelCase = sorted([comment for comment in issue.get_comments()] , key=lambda __lowercase : i.created_at , reverse=__lowercase )
_UpperCAmelCase = comments[0] if len(__lowercase ) > 0 else None
_UpperCAmelCase = dt.utcnow()
_UpperCAmelCase = (current_time - issue.updated_at).days
_UpperCAmelCase = (current_time - issue.created_at).days
if (
last_comment is not None
and last_comment.user.login == "github-actions[bot]"
and days_since_updated > 7
and days_since_creation >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Close issue since it has been 7 days of inactivity since bot mention.
issue.edit(state="closed" )
elif (
days_since_updated > 23
and days_since_creation >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Add stale comment
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/accelerate/blob/main/CONTRIBUTING.md) "
"are likely to be ignored." )
if __name__ == "__main__":
main()
| 22 | 1 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE :Union[str, Any] = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE :str = {
'''post_extract_proj''': '''feature_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.upsample.0''': '''encoder.upsample.projection''',
'''encoder.layer_norm''': '''encoder.layer_norm''',
'''w2v_model.layer_norm''': '''layer_norm''',
'''w2v_encoder.proj''': '''lm_head''',
'''mask_emb''': '''masked_spec_embed''',
}
def UpperCAmelCase_ ( __lowercase : List[Any] , __lowercase : str , __lowercase : Union[str, Any] , __lowercase : Optional[int] , __lowercase : List[Any] ) -> int:
'''simple docstring'''
for attribute in key.split("." ):
_UpperCAmelCase = getattr(__lowercase , __lowercase )
if weight_type is not None:
_UpperCAmelCase = getattr(__lowercase , __lowercase ).shape
else:
_UpperCAmelCase = 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":
_UpperCAmelCase = value
elif weight_type == "weight_g":
_UpperCAmelCase = value
elif weight_type == "weight_v":
_UpperCAmelCase = value
elif weight_type == "bias":
_UpperCAmelCase = value
else:
_UpperCAmelCase = value
logger.info(f'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.' )
def UpperCAmelCase_ ( __lowercase : List[Any] , __lowercase : List[str] , __lowercase : int ) -> List[Any]:
'''simple docstring'''
_UpperCAmelCase = []
_UpperCAmelCase = fairseq_model.state_dict()
_UpperCAmelCase = hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor
for name, value in fairseq_dict.items():
_UpperCAmelCase = False
if "conv_layers" in name:
load_conv_layer(
__lowercase , __lowercase , __lowercase , __lowercase , hf_model.config.feat_extract_norm == "group" , )
_UpperCAmelCase = True
else:
for key, mapped_key in MAPPING.items():
_UpperCAmelCase = "sew." + mapped_key if (is_finetuned and mapped_key != "lm_head") else mapped_key
if key in name or key.split("w2v_model." )[-1] == name.split("." )[0]:
_UpperCAmelCase = True
if "*" in mapped_key:
_UpperCAmelCase = name.split(__lowercase )[0].split("." )[-2]
_UpperCAmelCase = mapped_key.replace("*" , __lowercase )
if "weight_g" in name:
_UpperCAmelCase = "weight_g"
elif "weight_v" in name:
_UpperCAmelCase = "weight_v"
elif "weight" in name:
_UpperCAmelCase = "weight"
elif "bias" in name:
_UpperCAmelCase = "bias"
else:
_UpperCAmelCase = None
set_recursively(__lowercase , __lowercase , __lowercase , __lowercase , __lowercase )
continue
if not is_used:
unused_weights.append(__lowercase )
logger.warning(f'Unused weights: {unused_weights}' )
def UpperCAmelCase_ ( __lowercase : Optional[int] , __lowercase : Tuple , __lowercase : Dict , __lowercase : Union[str, Any] , __lowercase : Union[str, Any] ) -> str:
'''simple docstring'''
_UpperCAmelCase = full_name.split("conv_layers." )[-1]
_UpperCAmelCase = name.split("." )
_UpperCAmelCase = int(items[0] )
_UpperCAmelCase = 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.'
)
_UpperCAmelCase = 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.'
)
_UpperCAmelCase = 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."
)
_UpperCAmelCase = 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.'
)
_UpperCAmelCase = value
logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
else:
unused_weights.append(__lowercase )
def UpperCAmelCase_ ( __lowercase : Dict , __lowercase : str ) -> Optional[Any]:
'''simple docstring'''
_UpperCAmelCase = SEWConfig()
if is_finetuned:
_UpperCAmelCase = model.wav_encoder.wav_model.cfg
else:
_UpperCAmelCase = model.cfg
_UpperCAmelCase = fs_config.conv_bias
_UpperCAmelCase = eval(fs_config.conv_feature_layers )
_UpperCAmelCase = [x[0] for x in conv_layers]
_UpperCAmelCase = [x[1] for x in conv_layers]
_UpperCAmelCase = [x[2] for x in conv_layers]
_UpperCAmelCase = "gelu"
_UpperCAmelCase = "layer" if fs_config.extractor_mode == "layer_norm" else "group"
_UpperCAmelCase = 0.0
_UpperCAmelCase = fs_config.activation_fn.name
_UpperCAmelCase = fs_config.encoder_embed_dim
_UpperCAmelCase = 0.02
_UpperCAmelCase = fs_config.encoder_ffn_embed_dim
_UpperCAmelCase = 1E-5
_UpperCAmelCase = fs_config.encoder_layerdrop
_UpperCAmelCase = fs_config.encoder_attention_heads
_UpperCAmelCase = fs_config.conv_pos_groups
_UpperCAmelCase = fs_config.conv_pos
_UpperCAmelCase = len(__lowercase )
_UpperCAmelCase = fs_config.encoder_layers
_UpperCAmelCase = fs_config.squeeze_factor
# take care of any params that are overridden by the Wav2VecCtc model
if is_finetuned:
_UpperCAmelCase = model.cfg
_UpperCAmelCase = fs_config.final_dropout
_UpperCAmelCase = fs_config.layerdrop
_UpperCAmelCase = fs_config.activation_dropout
_UpperCAmelCase = fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
_UpperCAmelCase = fs_config.attention_dropout
_UpperCAmelCase = fs_config.dropout_input
_UpperCAmelCase = fs_config.dropout
_UpperCAmelCase = fs_config.mask_channel_length
_UpperCAmelCase = fs_config.mask_channel_prob
_UpperCAmelCase = fs_config.mask_length
_UpperCAmelCase = fs_config.mask_prob
_UpperCAmelCase = "Wav2Vec2FeatureExtractor"
_UpperCAmelCase = "Wav2Vec2CTCTokenizer"
return config
@torch.no_grad()
def UpperCAmelCase_ ( __lowercase : Union[str, Any] , __lowercase : Tuple , __lowercase : Optional[int]=None , __lowercase : List[str]=None , __lowercase : List[str]=True ) -> List[Any]:
'''simple docstring'''
if is_finetuned:
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={"data": "/".join(dict_path.split("/" )[:-1] )} )
else:
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] )
if config_path is not None:
_UpperCAmelCase = SEWConfig.from_pretrained(__lowercase )
else:
_UpperCAmelCase = convert_config(model[0] , __lowercase )
_UpperCAmelCase = model[0].eval()
_UpperCAmelCase = True if config.feat_extract_norm == "layer" else False
_UpperCAmelCase = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=1_6000 , padding_value=0 , do_normalize=__lowercase , return_attention_mask=__lowercase , )
if is_finetuned:
if dict_path:
_UpperCAmelCase = Dictionary.load(__lowercase )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
_UpperCAmelCase = target_dict.pad_index
_UpperCAmelCase = target_dict.bos_index
_UpperCAmelCase = target_dict.pad_index
_UpperCAmelCase = target_dict.bos_index
_UpperCAmelCase = target_dict.eos_index
_UpperCAmelCase = len(target_dict.symbols )
_UpperCAmelCase = os.path.join(__lowercase , "vocab.json" )
if not os.path.isdir(__lowercase ):
logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(__lowercase ) )
return
os.makedirs(__lowercase , exist_ok=__lowercase )
with open(__lowercase , "w" , encoding="utf-8" ) as vocab_handle:
json.dump(target_dict.indices , __lowercase )
_UpperCAmelCase = WavaVecaCTCTokenizer(
__lowercase , 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=__lowercase , )
_UpperCAmelCase = WavaVecaProcessor(feature_extractor=__lowercase , tokenizer=__lowercase )
processor.save_pretrained(__lowercase )
_UpperCAmelCase = SEWForCTC(__lowercase )
else:
_UpperCAmelCase = SEWModel(__lowercase )
feature_extractor.save_pretrained(__lowercase )
recursively_load_weights(__lowercase , __lowercase , __lowercase )
hf_model.save_pretrained(__lowercase )
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE :Tuple = 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(
'''--is_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
)
__SCREAMING_SNAKE_CASE :Any = parser.parse_args()
convert_sew_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
)
| 22 |
'''simple docstring'''
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 UpperCAmelCase_ ( __lowercase : Any , __lowercase : int ) -> int:
'''simple docstring'''
_UpperCAmelCase = 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}}" )
_UpperCAmelCase = DatasetInfosDict.from_directory(__lowercase )
assert dataset_infos
assert dataset_infos["default"].dataset_size == 42
@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=42 , ),
] , )
def UpperCAmelCase_ ( __lowercase : Tuple , __lowercase : DatasetInfo ) -> Any:
'''simple docstring'''
_UpperCAmelCase = str(__lowercase )
dataset_info.write_to_directory(__lowercase )
_UpperCAmelCase = DatasetInfo.from_directory(__lowercase )
assert dataset_info == reloaded
assert os.path.exists(os.path.join(__lowercase , "dataset_info.json" ) )
def UpperCAmelCase_ ( ) -> Optional[Any]:
'''simple docstring'''
_UpperCAmelCase = 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": 42}] , download_checksums={} , download_size=1337 , post_processing_size=442 , dataset_size=1234 , size_in_bytes=1337 + 442 + 1234 , )
_UpperCAmelCase = dataset_info._to_yaml_dict()
assert sorted(__lowercase ) == 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) )
_UpperCAmelCase = yaml.safe_dump(__lowercase )
_UpperCAmelCase = yaml.safe_load(__lowercase )
assert dataset_info_yaml_dict == reloaded
def UpperCAmelCase_ ( ) -> Optional[int]:
'''simple docstring'''
_UpperCAmelCase = DatasetInfo()
_UpperCAmelCase = 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=42 , )
} ),
DatasetInfosDict(
{
"v1": DatasetInfo(dataset_size=42 ),
"v2": DatasetInfo(dataset_size=1337 ),
} ),
] , )
def UpperCAmelCase_ ( __lowercase : int , __lowercase : DatasetInfosDict ) -> Dict:
'''simple docstring'''
_UpperCAmelCase = str(__lowercase )
dataset_infos_dict.write_to_directory(__lowercase )
_UpperCAmelCase = DatasetInfosDict.from_directory(__lowercase )
# the config_name of the dataset_infos_dict take over the attribute
for config_name, dataset_info in dataset_infos_dict.items():
_UpperCAmelCase = 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
_UpperCAmelCase = 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(__lowercase , "README.md" ) )
| 22 | 1 |
'''simple docstring'''
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers
__SCREAMING_SNAKE_CASE :int = '''python tqdm regex requests packaging filelock numpy tokenizers'''.split()
if sys.version_info < (3, 7):
pkgs_to_check_at_runtime.append('''dataclasses''')
if sys.version_info < (3, 8):
pkgs_to_check_at_runtime.append('''importlib_metadata''')
for pkg in pkgs_to_check_at_runtime:
if pkg in deps:
if pkg == "tokenizers":
# must be loaded here, or else tqdm check may fail
from .utils import is_tokenizers_available
if not is_tokenizers_available():
continue # not required, check version only if installed
require_version_core(deps[pkg])
else:
raise ValueError(F"can't find {pkg} in {deps.keys()}, check dependency_versions_table.py")
def UpperCAmelCase_ ( __lowercase : Union[str, Any] , __lowercase : str=None ) -> int:
'''simple docstring'''
require_version(deps[pkg] , __lowercase )
| 22 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : str ) -> str:
'''simple docstring'''
return " ".join(
"".join(word[::-1] ) if len(__lowercase ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(reverse_long_words('''Hey wollef sroirraw'''))
| 22 | 1 |
'''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 lowercase ( self : List[Any] ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
_UpperCAmelCase = AutoConfig.from_pretrained(snake_case_ )
self.assertIsNotNone(snake_case_ )
self.assertIsInstance(snake_case_ , snake_case_ )
_UpperCAmelCase = TFAutoModel.from_pretrained(snake_case_ , from_pt=snake_case_ )
self.assertIsNotNone(snake_case_ )
self.assertIsInstance(snake_case_ , snake_case_ )
_UpperCAmelCase = AutoModel.from_pretrained(snake_case_ , from_tf=snake_case_ )
self.assertIsNotNone(snake_case_ )
self.assertIsInstance(snake_case_ , snake_case_ )
@slow
def lowercase ( self : str ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
_UpperCAmelCase = AutoConfig.from_pretrained(snake_case_ )
self.assertIsNotNone(snake_case_ )
self.assertIsInstance(snake_case_ , snake_case_ )
_UpperCAmelCase = TFAutoModelForPreTraining.from_pretrained(snake_case_ , from_pt=snake_case_ )
self.assertIsNotNone(snake_case_ )
self.assertIsInstance(snake_case_ , snake_case_ )
_UpperCAmelCase = AutoModelForPreTraining.from_pretrained(snake_case_ , from_tf=snake_case_ )
self.assertIsNotNone(snake_case_ )
self.assertIsInstance(snake_case_ , snake_case_ )
@slow
def lowercase ( self : List[str] ):
for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase = AutoConfig.from_pretrained(snake_case_ )
self.assertIsNotNone(snake_case_ )
self.assertIsInstance(snake_case_ , snake_case_ )
_UpperCAmelCase = TFAutoModelForCausalLM.from_pretrained(snake_case_ , from_pt=snake_case_ )
_UpperCAmelCase , _UpperCAmelCase = TFAutoModelForCausalLM.from_pretrained(
snake_case_ , output_loading_info=snake_case_ , from_pt=snake_case_ )
self.assertIsNotNone(snake_case_ )
self.assertIsInstance(snake_case_ , snake_case_ )
_UpperCAmelCase = AutoModelForCausalLM.from_pretrained(snake_case_ , from_tf=snake_case_ )
_UpperCAmelCase , _UpperCAmelCase = AutoModelForCausalLM.from_pretrained(
snake_case_ , output_loading_info=snake_case_ , from_tf=snake_case_ )
self.assertIsNotNone(snake_case_ )
self.assertIsInstance(snake_case_ , snake_case_ )
@slow
def lowercase ( self : Union[str, Any] ):
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase = AutoConfig.from_pretrained(snake_case_ )
self.assertIsNotNone(snake_case_ )
self.assertIsInstance(snake_case_ , snake_case_ )
_UpperCAmelCase = TFAutoModelWithLMHead.from_pretrained(snake_case_ , from_pt=snake_case_ )
self.assertIsNotNone(snake_case_ )
self.assertIsInstance(snake_case_ , snake_case_ )
_UpperCAmelCase = AutoModelWithLMHead.from_pretrained(snake_case_ , from_tf=snake_case_ )
self.assertIsNotNone(snake_case_ )
self.assertIsInstance(snake_case_ , snake_case_ )
@slow
def lowercase ( self : Any ):
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase = AutoConfig.from_pretrained(snake_case_ )
self.assertIsNotNone(snake_case_ )
self.assertIsInstance(snake_case_ , snake_case_ )
_UpperCAmelCase = TFAutoModelForMaskedLM.from_pretrained(snake_case_ , from_pt=snake_case_ )
_UpperCAmelCase , _UpperCAmelCase = TFAutoModelForMaskedLM.from_pretrained(
snake_case_ , output_loading_info=snake_case_ , from_pt=snake_case_ )
self.assertIsNotNone(snake_case_ )
self.assertIsInstance(snake_case_ , snake_case_ )
_UpperCAmelCase = AutoModelForMaskedLM.from_pretrained(snake_case_ , from_tf=snake_case_ )
_UpperCAmelCase , _UpperCAmelCase = AutoModelForMaskedLM.from_pretrained(
snake_case_ , output_loading_info=snake_case_ , from_tf=snake_case_ )
self.assertIsNotNone(snake_case_ )
self.assertIsInstance(snake_case_ , snake_case_ )
@slow
def lowercase ( self : int ):
for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase = AutoConfig.from_pretrained(snake_case_ )
self.assertIsNotNone(snake_case_ )
self.assertIsInstance(snake_case_ , snake_case_ )
_UpperCAmelCase = TFAutoModelForSeqaSeqLM.from_pretrained(snake_case_ , from_pt=snake_case_ )
_UpperCAmelCase , _UpperCAmelCase = TFAutoModelForSeqaSeqLM.from_pretrained(
snake_case_ , output_loading_info=snake_case_ , from_pt=snake_case_ )
self.assertIsNotNone(snake_case_ )
self.assertIsInstance(snake_case_ , snake_case_ )
_UpperCAmelCase = AutoModelForSeqaSeqLM.from_pretrained(snake_case_ , from_tf=snake_case_ )
_UpperCAmelCase , _UpperCAmelCase = AutoModelForSeqaSeqLM.from_pretrained(
snake_case_ , output_loading_info=snake_case_ , from_tf=snake_case_ )
self.assertIsNotNone(snake_case_ )
self.assertIsInstance(snake_case_ , snake_case_ )
@slow
def lowercase ( self : Tuple ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
_UpperCAmelCase = AutoConfig.from_pretrained(snake_case_ )
self.assertIsNotNone(snake_case_ )
self.assertIsInstance(snake_case_ , snake_case_ )
_UpperCAmelCase = TFAutoModelForSequenceClassification.from_pretrained(snake_case_ , from_pt=snake_case_ )
self.assertIsNotNone(snake_case_ )
self.assertIsInstance(snake_case_ , snake_case_ )
_UpperCAmelCase = AutoModelForSequenceClassification.from_pretrained(snake_case_ , from_tf=snake_case_ )
self.assertIsNotNone(snake_case_ )
self.assertIsInstance(snake_case_ , snake_case_ )
@slow
def lowercase ( self : Tuple ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
_UpperCAmelCase = AutoConfig.from_pretrained(snake_case_ )
self.assertIsNotNone(snake_case_ )
self.assertIsInstance(snake_case_ , snake_case_ )
_UpperCAmelCase = TFAutoModelForQuestionAnswering.from_pretrained(snake_case_ , from_pt=snake_case_ )
self.assertIsNotNone(snake_case_ )
self.assertIsInstance(snake_case_ , snake_case_ )
_UpperCAmelCase = AutoModelForQuestionAnswering.from_pretrained(snake_case_ , from_tf=snake_case_ )
self.assertIsNotNone(snake_case_ )
self.assertIsInstance(snake_case_ , snake_case_ )
def lowercase ( self : Union[str, Any] ):
_UpperCAmelCase = TFAutoModelWithLMHead.from_pretrained(snake_case_ , from_pt=snake_case_ )
self.assertIsInstance(snake_case_ , snake_case_ )
self.assertEqual(model.num_parameters() , 1_4_4_1_0 )
self.assertEqual(model.num_parameters(only_trainable=snake_case_ ) , 1_4_4_1_0 )
_UpperCAmelCase = AutoModelWithLMHead.from_pretrained(snake_case_ , from_tf=snake_case_ )
self.assertIsInstance(snake_case_ , snake_case_ )
self.assertEqual(model.num_parameters() , 1_4_4_1_0 )
self.assertEqual(model.num_parameters(only_trainable=snake_case_ ) , 1_4_4_1_0 )
def lowercase ( self : List[Any] ):
_UpperCAmelCase = TFAutoModelWithLMHead.from_pretrained(snake_case_ , from_pt=snake_case_ )
self.assertIsInstance(snake_case_ , snake_case_ )
self.assertEqual(model.num_parameters() , 1_4_4_1_0 )
self.assertEqual(model.num_parameters(only_trainable=snake_case_ ) , 1_4_4_1_0 )
_UpperCAmelCase = AutoModelWithLMHead.from_pretrained(snake_case_ , from_tf=snake_case_ )
self.assertIsInstance(snake_case_ , snake_case_ )
self.assertEqual(model.num_parameters() , 1_4_4_1_0 )
self.assertEqual(model.num_parameters(only_trainable=snake_case_ ) , 1_4_4_1_0 )
| 22 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : str ) -> list:
'''simple docstring'''
if n_term == "":
return []
_UpperCAmelCase = []
for temp in range(int(__lowercase ) ):
series.append(f'1/{temp + 1}' if series else "1" )
return series
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE :str = input('''Enter the last number (nth term) of the Harmonic Series''')
print('''Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n''')
print(harmonic_series(nth_term))
| 22 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
__SCREAMING_SNAKE_CASE :Optional[int] = logging.get_logger(__name__)
class A_ ( lowerCAmelCase_ ):
_lowerCamelCase : Tuple = ["""pixel_values"""]
def __init__( self : List[str] , snake_case_ : bool = True , snake_case_ : Dict[str, int] = None , snake_case_ : float = None , snake_case_ : PILImageResampling = PILImageResampling.BILINEAR , snake_case_ : bool = True , snake_case_ : Union[int, float] = 1 / 2_5_5 , snake_case_ : bool = True , snake_case_ : Optional[Union[float, List[float]]] = None , snake_case_ : Optional[Union[float, List[float]]] = None , **snake_case_ : Tuple , ):
super().__init__(**snake_case_ )
_UpperCAmelCase = size if size is not None else {"shortest_edge": 3_8_4}
_UpperCAmelCase = get_size_dict(snake_case_ , default_to_square=snake_case_ )
_UpperCAmelCase = do_resize
_UpperCAmelCase = size
# Default value set here for backwards compatibility where the value in config is None
_UpperCAmelCase = crop_pct if crop_pct is not None else 2_2_4 / 2_5_6
_UpperCAmelCase = resample
_UpperCAmelCase = do_rescale
_UpperCAmelCase = rescale_factor
_UpperCAmelCase = do_normalize
_UpperCAmelCase = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
_UpperCAmelCase = image_std if image_std is not None else IMAGENET_STANDARD_STD
def lowercase ( self : Any , snake_case_ : np.ndarray , snake_case_ : Dict[str, int] , snake_case_ : float , snake_case_ : PILImageResampling = PILImageResampling.BICUBIC , snake_case_ : Optional[Union[str, ChannelDimension]] = None , **snake_case_ : Optional[int] , ):
_UpperCAmelCase = get_size_dict(snake_case_ , default_to_square=snake_case_ )
if "shortest_edge" not in size:
raise ValueError(f'Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}' )
_UpperCAmelCase = size["shortest_edge"]
if shortest_edge < 3_8_4:
# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
_UpperCAmelCase = int(shortest_edge / crop_pct )
_UpperCAmelCase = get_resize_output_image_size(snake_case_ , size=snake_case_ , default_to_square=snake_case_ )
_UpperCAmelCase = resize(image=snake_case_ , size=snake_case_ , resample=snake_case_ , data_format=snake_case_ , **snake_case_ )
# then crop to (shortest_edge, shortest_edge)
return center_crop(image=snake_case_ , size=(shortest_edge, shortest_edge) , data_format=snake_case_ , **snake_case_ )
else:
# warping (no cropping) when evaluated at 384 or larger
return resize(
snake_case_ , size=(shortest_edge, shortest_edge) , resample=snake_case_ , data_format=snake_case_ , **snake_case_ )
def lowercase ( self : int , snake_case_ : np.ndarray , snake_case_ : Union[int, float] , snake_case_ : Optional[Union[str, ChannelDimension]] = None , **snake_case_ : Tuple , ):
return rescale(snake_case_ , scale=snake_case_ , data_format=snake_case_ , **snake_case_ )
def lowercase ( self : Optional[int] , snake_case_ : np.ndarray , snake_case_ : Union[float, List[float]] , snake_case_ : Union[float, List[float]] , snake_case_ : Optional[Union[str, ChannelDimension]] = None , **snake_case_ : Union[str, Any] , ):
return normalize(snake_case_ , mean=snake_case_ , std=snake_case_ , data_format=snake_case_ , **snake_case_ )
def lowercase ( self : Any , snake_case_ : ImageInput , snake_case_ : bool = None , snake_case_ : Dict[str, int] = None , snake_case_ : float = None , snake_case_ : PILImageResampling = None , snake_case_ : bool = None , snake_case_ : float = None , snake_case_ : bool = None , snake_case_ : Optional[Union[float, List[float]]] = None , snake_case_ : Optional[Union[float, List[float]]] = None , snake_case_ : Optional[Union[str, TensorType]] = None , snake_case_ : ChannelDimension = ChannelDimension.FIRST , **snake_case_ : str , ):
_UpperCAmelCase = do_resize if do_resize is not None else self.do_resize
_UpperCAmelCase = crop_pct if crop_pct is not None else self.crop_pct
_UpperCAmelCase = resample if resample is not None else self.resample
_UpperCAmelCase = do_rescale if do_rescale is not None else self.do_rescale
_UpperCAmelCase = rescale_factor if rescale_factor is not None else self.rescale_factor
_UpperCAmelCase = do_normalize if do_normalize is not None else self.do_normalize
_UpperCAmelCase = image_mean if image_mean is not None else self.image_mean
_UpperCAmelCase = image_std if image_std is not None else self.image_std
_UpperCAmelCase = size if size is not None else self.size
_UpperCAmelCase = get_size_dict(snake_case_ , default_to_square=snake_case_ )
_UpperCAmelCase = make_list_of_images(snake_case_ )
if not valid_images(snake_case_ ):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray." )
if do_resize and size is None or resample is None:
raise ValueError("Size and resample must be specified if do_resize is True." )
if do_resize and size["shortest_edge"] < 3_8_4 and crop_pct is None:
raise ValueError("crop_pct must be specified if size < 384." )
if do_rescale and rescale_factor is None:
raise ValueError("Rescale factor must be specified if do_rescale is True." )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("Image mean and std must be specified if do_normalize is True." )
# All transformations expect numpy arrays.
_UpperCAmelCase = [to_numpy_array(snake_case_ ) for image in images]
if do_resize:
_UpperCAmelCase = [self.resize(image=snake_case_ , size=snake_case_ , crop_pct=snake_case_ , resample=snake_case_ ) for image in images]
if do_rescale:
_UpperCAmelCase = [self.rescale(image=snake_case_ , scale=snake_case_ ) for image in images]
if do_normalize:
_UpperCAmelCase = [self.normalize(image=snake_case_ , mean=snake_case_ , std=snake_case_ ) for image in images]
_UpperCAmelCase = [to_channel_dimension_format(snake_case_ , snake_case_ ) for image in images]
_UpperCAmelCase = {"pixel_values": images}
return BatchFeature(data=snake_case_ , tensor_type=snake_case_ )
| 22 |
'''simple docstring'''
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
__SCREAMING_SNAKE_CASE :int = get_tests_dir('''fixtures/test_sentencepiece_no_bos.model''')
@require_sentencepiece
@require_tokenizers
class A_ ( lowerCAmelCase_ , unittest.TestCase ):
_lowerCamelCase : List[str] = PegasusTokenizer
_lowerCamelCase : int = PegasusTokenizerFast
_lowerCamelCase : Union[str, Any] = True
_lowerCamelCase : List[str] = True
def lowercase ( self : Optional[int] ):
super().setUp()
# We have a SentencePiece fixture for testing
_UpperCAmelCase = PegasusTokenizer(snake_case_ )
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def lowercase ( self : Tuple ):
return PegasusTokenizer.from_pretrained("google/pegasus-large" )
def lowercase ( self : Union[str, Any] , **snake_case_ : Union[str, Any] ):
return PegasusTokenizer.from_pretrained(self.tmpdirname , **snake_case_ )
def lowercase ( self : Tuple , snake_case_ : Any ):
return ("This is a test", "This is a test")
def lowercase ( self : Optional[int] ):
_UpperCAmelCase = "</s>"
_UpperCAmelCase = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case_ ) , snake_case_ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case_ ) , snake_case_ )
def lowercase ( self : Union[str, Any] ):
_UpperCAmelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , "<pad>" )
self.assertEqual(vocab_keys[1] , "</s>" )
self.assertEqual(vocab_keys[-1] , "v" )
self.assertEqual(len(snake_case_ ) , 1_1_0_3 )
def lowercase ( self : Any ):
self.assertEqual(self.get_tokenizer().vocab_size , 1_1_0_3 )
def lowercase ( self : List[Any] ):
_UpperCAmelCase = self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
_UpperCAmelCase = self.tokenizer_class.from_pretrained(self.tmpdirname )
_UpperCAmelCase = (
"Let's see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important"
" </s> <pad> <pad> <pad>"
)
_UpperCAmelCase = rust_tokenizer([raw_input_str] , return_tensors=snake_case_ , add_special_tokens=snake_case_ ).input_ids[0]
_UpperCAmelCase = py_tokenizer([raw_input_str] , return_tensors=snake_case_ , add_special_tokens=snake_case_ ).input_ids[0]
self.assertListEqual(snake_case_ , snake_case_ )
def lowercase ( self : Tuple ):
_UpperCAmelCase = self._large_tokenizer
# <mask_1> masks whole sentence while <mask_2> masks single word
_UpperCAmelCase = "<mask_1> To ensure a <mask_2> flow of bank resolutions."
_UpperCAmelCase = [2, 4_1_3, 6_1_5, 1_1_4, 3, 1_9_7_1, 1_1_3, 1_6_7_9, 1_0_7_1_0, 1_0_7, 1]
_UpperCAmelCase = tokenizer([raw_input_str] , return_tensors=snake_case_ ).input_ids[0]
self.assertListEqual(snake_case_ , snake_case_ )
def lowercase ( self : Union[str, Any] ):
_UpperCAmelCase = self._large_tokenizer
# The tracebacks for the following asserts are **better** without messages or self.assertEqual
assert tokenizer.vocab_size == 9_6_1_0_3
assert tokenizer.pad_token_id == 0
assert tokenizer.eos_token_id == 1
assert tokenizer.offset == 1_0_3
assert tokenizer.unk_token_id == tokenizer.offset + 2 == 1_0_5
assert tokenizer.unk_token == "<unk>"
assert tokenizer.model_max_length == 1_0_2_4
_UpperCAmelCase = "To ensure a smooth flow of bank resolutions."
_UpperCAmelCase = [4_1_3, 6_1_5, 1_1_4, 2_2_9_1, 1_9_7_1, 1_1_3, 1_6_7_9, 1_0_7_1_0, 1_0_7, 1]
_UpperCAmelCase = tokenizer([raw_input_str] , return_tensors=snake_case_ ).input_ids[0]
self.assertListEqual(snake_case_ , snake_case_ )
assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3] ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]
@require_torch
def lowercase ( self : int ):
_UpperCAmelCase = ["This is going to be way too long." * 1_5_0, "short example"]
_UpperCAmelCase = ["not super long but more than 5 tokens", "tiny"]
_UpperCAmelCase = self._large_tokenizer(snake_case_ , padding=snake_case_ , truncation=snake_case_ , return_tensors="pt" )
_UpperCAmelCase = self._large_tokenizer(
text_target=snake_case_ , max_length=5 , padding=snake_case_ , truncation=snake_case_ , return_tensors="pt" )
assert batch.input_ids.shape == (2, 1_0_2_4)
assert batch.attention_mask.shape == (2, 1_0_2_4)
assert targets["input_ids"].shape == (2, 5)
assert len(snake_case_ ) == 2 # input_ids, attention_mask.
@slow
def lowercase ( self : Dict ):
# fmt: off
_UpperCAmelCase = {"input_ids": [[3_8_9_7_9, 1_4_3, 1_8_4_8_5, 6_0_6, 1_3_0, 2_6_6_6_9, 8_7_6_8_6, 1_2_1, 5_4_1_8_9, 1_1_2_9, 1_1_1, 2_6_6_6_9, 8_7_6_8_6, 1_2_1, 9_1_1_4, 1_4_7_8_7, 1_2_1, 1_3_2_4_9, 1_5_8, 5_9_2, 9_5_6, 1_2_1, 1_4_6_2_1, 3_1_5_7_6, 1_4_3, 6_2_6_1_3, 1_0_8, 9_6_8_8, 9_3_0, 4_3_4_3_0, 1_1_5_6_2, 6_2_6_1_3, 3_0_4, 1_0_8, 1_1_4_4_3, 8_9_7, 1_0_8, 9_3_1_4, 1_7_4_1_5, 6_3_3_9_9, 1_0_8, 1_1_4_4_3, 7_6_1_4, 1_8_3_1_6, 1_1_8, 4_2_8_4, 7_1_4_8, 1_2_4_3_0, 1_4_3, 1_4_0_0, 2_5_7_0_3, 1_5_8, 1_1_1, 4_2_8_4, 7_1_4_8, 1_1_7_7_2, 1_4_3, 2_1_2_9_7, 1_0_6_4, 1_5_8, 1_2_2, 2_0_4, 3_5_0_6, 1_7_5_4, 1_1_3_3, 1_4_7_8_7, 1_5_8_1, 1_1_5, 3_3_2_2_4, 4_4_8_2, 1_1_1, 1_3_5_5, 1_1_0, 2_9_1_7_3, 3_1_7, 5_0_8_3_3, 1_0_8, 2_0_1_4_7, 9_4_6_6_5, 1_1_1, 7_7_1_9_8, 1_0_7, 1], [1_1_0, 6_2_6_1_3, 1_1_7, 6_3_8, 1_1_2, 1_1_3_3, 1_2_1, 2_0_0_9_8, 1_3_5_5, 7_9_0_5_0, 1_3_8_7_2, 1_3_5, 1_5_9_6, 5_3_5_4_1, 1_3_5_2, 1_4_1, 1_3_0_3_9, 5_5_4_2, 1_2_4, 3_0_2, 5_1_8, 1_1_1, 2_6_8, 2_9_5_6, 1_1_5, 1_4_9, 4_4_2_7, 1_0_7, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1_3_9, 1_2_3_5, 2_7_9_9, 1_8_2_8_9, 1_7_7_8_0, 2_0_4, 1_0_9, 9_4_7_4, 1_2_9_6, 1_0_7, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=snake_case_ , model_name="google/bigbird-pegasus-large-arxiv" , revision="ba85d0851d708441f91440d509690f1ab6353415" , )
@require_sentencepiece
@require_tokenizers
class A_ ( lowerCAmelCase_ , unittest.TestCase ):
_lowerCamelCase : List[str] = PegasusTokenizer
_lowerCamelCase : List[Any] = PegasusTokenizerFast
_lowerCamelCase : int = True
_lowerCamelCase : Union[str, Any] = True
def lowercase ( self : Any ):
super().setUp()
# We have a SentencePiece fixture for testing
_UpperCAmelCase = PegasusTokenizer(snake_case_ , offset=0 , mask_token_sent=snake_case_ , mask_token="[MASK]" )
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def lowercase ( self : Tuple ):
return PegasusTokenizer.from_pretrained("google/bigbird-pegasus-large-arxiv" )
def lowercase ( self : Optional[Any] , **snake_case_ : Dict ):
return PegasusTokenizer.from_pretrained(self.tmpdirname , **snake_case_ )
def lowercase ( self : Union[str, Any] , snake_case_ : str ):
return ("This is a test", "This is a test")
def lowercase ( self : List[str] ):
_UpperCAmelCase = self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
_UpperCAmelCase = self.tokenizer_class.from_pretrained(self.tmpdirname )
_UpperCAmelCase = (
"Let's see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"
" <pad> <pad> <pad>"
)
_UpperCAmelCase = rust_tokenizer([raw_input_str] , return_tensors=snake_case_ , add_special_tokens=snake_case_ ).input_ids[0]
_UpperCAmelCase = py_tokenizer([raw_input_str] , return_tensors=snake_case_ , add_special_tokens=snake_case_ ).input_ids[0]
self.assertListEqual(snake_case_ , snake_case_ )
@require_torch
def lowercase ( self : Tuple ):
_UpperCAmelCase = ["This is going to be way too long." * 1_0_0_0, "short example"]
_UpperCAmelCase = ["not super long but more than 5 tokens", "tiny"]
_UpperCAmelCase = self._large_tokenizer(snake_case_ , padding=snake_case_ , truncation=snake_case_ , return_tensors="pt" )
_UpperCAmelCase = self._large_tokenizer(
text_target=snake_case_ , max_length=5 , padding=snake_case_ , truncation=snake_case_ , return_tensors="pt" )
assert batch.input_ids.shape == (2, 4_0_9_6)
assert batch.attention_mask.shape == (2, 4_0_9_6)
assert targets["input_ids"].shape == (2, 5)
assert len(snake_case_ ) == 2 # input_ids, attention_mask.
def lowercase ( self : Union[str, Any] ):
_UpperCAmelCase = (
"This is an example string that is used to test the original TF implementation against the HF"
" implementation"
)
_UpperCAmelCase = self._large_tokenizer(snake_case_ ).input_ids
self.assertListEqual(
snake_case_ , [1_8_2, 1_1_7, 1_4_2, 5_8_7, 4_2_1_1, 1_2_0, 1_1_7, 2_6_3, 1_1_2, 8_0_4, 1_0_9, 8_5_6, 2_5_0_1_6, 3_1_3_7, 4_6_4, 1_0_9, 2_6_9_5_5, 3_1_3_7, 1] , )
| 22 | 1 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
__SCREAMING_SNAKE_CASE :Optional[int] = True
except (ImportError, ModuleNotFoundError):
__SCREAMING_SNAKE_CASE :str = False
if NLTK_AVAILABLE:
with FileLock('''.lock''') as lock:
nltk.download('''punkt''', quiet=True)
def UpperCAmelCase_ ( __lowercase : str ) -> str:
'''simple docstring'''
re.sub("<n>" , "" , __lowercase ) # remove pegasus newline char
assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
return "\n".join(nltk.sent_tokenize(__lowercase ) )
| 22 |
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast
@require_vision
class A_ ( unittest.TestCase ):
def lowercase ( self : int ):
_UpperCAmelCase = tempfile.mkdtemp()
_UpperCAmelCase = BlipImageProcessor()
_UpperCAmelCase = BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-BertModel" )
_UpperCAmelCase = BlipProcessor(snake_case_ , snake_case_ )
processor.save_pretrained(self.tmpdirname )
def lowercase ( self : Tuple , **snake_case_ : int ):
return AutoProcessor.from_pretrained(self.tmpdirname , **snake_case_ ).tokenizer
def lowercase ( self : Dict , **snake_case_ : Any ):
return AutoProcessor.from_pretrained(self.tmpdirname , **snake_case_ ).image_processor
def lowercase ( self : int ):
shutil.rmtree(self.tmpdirname )
def lowercase ( self : Optional[Any] ):
_UpperCAmelCase = [np.random.randint(2_5_5 , size=(3, 3_0, 4_0_0) , dtype=np.uinta )]
_UpperCAmelCase = [Image.fromarray(np.moveaxis(snake_case_ , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def lowercase ( self : int ):
_UpperCAmelCase = BlipProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
_UpperCAmelCase = self.get_tokenizer(bos_token="(BOS)" , eos_token="(EOS)" )
_UpperCAmelCase = self.get_image_processor(do_normalize=snake_case_ , padding_value=1.0 )
_UpperCAmelCase = BlipProcessor.from_pretrained(
self.tmpdirname , bos_token="(BOS)" , eos_token="(EOS)" , do_normalize=snake_case_ , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , snake_case_ )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , snake_case_ )
def lowercase ( self : Any ):
_UpperCAmelCase = self.get_image_processor()
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = BlipProcessor(tokenizer=snake_case_ , image_processor=snake_case_ )
_UpperCAmelCase = self.prepare_image_inputs()
_UpperCAmelCase = image_processor(snake_case_ , return_tensors="np" )
_UpperCAmelCase = processor(images=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 lowercase ( self : Optional[int] ):
_UpperCAmelCase = self.get_image_processor()
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = BlipProcessor(tokenizer=snake_case_ , image_processor=snake_case_ )
_UpperCAmelCase = "lower newer"
_UpperCAmelCase = processor(text=snake_case_ )
_UpperCAmelCase = tokenizer(snake_case_ , return_token_type_ids=snake_case_ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def lowercase ( self : Optional[Any] ):
_UpperCAmelCase = self.get_image_processor()
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = BlipProcessor(tokenizer=snake_case_ , image_processor=snake_case_ )
_UpperCAmelCase = "lower newer"
_UpperCAmelCase = self.prepare_image_inputs()
_UpperCAmelCase = processor(text=snake_case_ , images=snake_case_ )
self.assertListEqual(list(inputs.keys() ) , ["pixel_values", "input_ids", "attention_mask"] )
# test if it raises when no input is passed
with pytest.raises(snake_case_ ):
processor()
def lowercase ( self : Union[str, Any] ):
_UpperCAmelCase = self.get_image_processor()
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = BlipProcessor(tokenizer=snake_case_ , image_processor=snake_case_ )
_UpperCAmelCase = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
_UpperCAmelCase = processor.batch_decode(snake_case_ )
_UpperCAmelCase = tokenizer.batch_decode(snake_case_ )
self.assertListEqual(snake_case_ , snake_case_ )
def lowercase ( self : str ):
_UpperCAmelCase = self.get_image_processor()
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = BlipProcessor(tokenizer=snake_case_ , image_processor=snake_case_ )
_UpperCAmelCase = "lower newer"
_UpperCAmelCase = self.prepare_image_inputs()
_UpperCAmelCase = processor(text=snake_case_ , images=snake_case_ )
# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
self.assertListEqual(list(inputs.keys() ) , ["pixel_values", "input_ids", "attention_mask"] )
| 22 | 1 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : list ) -> list:
'''simple docstring'''
if len(__lowercase ) <= 1:
return lst
_UpperCAmelCase = 1
while i < len(__lowercase ):
if lst[i - 1] <= lst[i]:
i += 1
else:
_UpperCAmelCase , _UpperCAmelCase = lst[i], lst[i - 1]
i -= 1
if i == 0:
_UpperCAmelCase = 1
return lst
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE :Tuple = input('''Enter numbers separated by a comma:\n''').strip()
__SCREAMING_SNAKE_CASE :List[str] = [int(item) for item in user_input.split(''',''')]
print(gnome_sort(unsorted))
| 22 |
'''simple docstring'''
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
def UpperCAmelCase_ ( __lowercase : str ) -> List[str]:
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase = image.size
_UpperCAmelCase , _UpperCAmelCase = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32
_UpperCAmelCase = image.resize((w, h) , resample=PIL_INTERPOLATION["lanczos"] )
_UpperCAmelCase = np.array(__lowercase ).astype(np.floataa ) / 255.0
_UpperCAmelCase = image[None].transpose(0 , 3 , 1 , 2 )
_UpperCAmelCase = torch.from_numpy(__lowercase )
return 2.0 * image - 1.0
class A_ ( lowerCAmelCase_ ):
def __init__( self : Optional[Any] , snake_case_ : VQModel , snake_case_ : UNetaDModel , snake_case_ : Union[
DDIMScheduler,
PNDMScheduler,
LMSDiscreteScheduler,
EulerDiscreteScheduler,
EulerAncestralDiscreteScheduler,
DPMSolverMultistepScheduler,
] , ):
super().__init__()
self.register_modules(vqvae=snake_case_ , unet=snake_case_ , scheduler=snake_case_ )
@torch.no_grad()
def __call__( self : Any , snake_case_ : Union[torch.Tensor, PIL.Image.Image] = None , snake_case_ : Optional[int] = 1 , snake_case_ : Optional[int] = 1_0_0 , snake_case_ : Optional[float] = 0.0 , snake_case_ : Optional[Union[torch.Generator, List[torch.Generator]]] = None , snake_case_ : Optional[str] = "pil" , snake_case_ : bool = True , ):
if isinstance(snake_case_ , PIL.Image.Image ):
_UpperCAmelCase = 1
elif isinstance(snake_case_ , torch.Tensor ):
_UpperCAmelCase = image.shape[0]
else:
raise ValueError(f'`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(snake_case_ )}' )
if isinstance(snake_case_ , PIL.Image.Image ):
_UpperCAmelCase = preprocess(snake_case_ )
_UpperCAmelCase , _UpperCAmelCase = image.shape[-2:]
# in_channels should be 6: 3 for latents, 3 for low resolution image
_UpperCAmelCase = (batch_size, self.unet.config.in_channels // 2, height, width)
_UpperCAmelCase = next(self.unet.parameters() ).dtype
_UpperCAmelCase = randn_tensor(snake_case_ , generator=snake_case_ , device=self.device , dtype=snake_case_ )
_UpperCAmelCase = image.to(device=self.device , dtype=snake_case_ )
# set timesteps and move to the correct device
self.scheduler.set_timesteps(snake_case_ , device=self.device )
_UpperCAmelCase = self.scheduler.timesteps
# scale the initial noise by the standard deviation required by the scheduler
_UpperCAmelCase = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
_UpperCAmelCase = "eta" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
_UpperCAmelCase = {}
if accepts_eta:
_UpperCAmelCase = eta
for t in self.progress_bar(snake_case_ ):
# concat latents and low resolution image in the channel dimension.
_UpperCAmelCase = torch.cat([latents, image] , dim=1 )
_UpperCAmelCase = self.scheduler.scale_model_input(snake_case_ , snake_case_ )
# predict the noise residual
_UpperCAmelCase = self.unet(snake_case_ , snake_case_ ).sample
# compute the previous noisy sample x_t -> x_t-1
_UpperCAmelCase = self.scheduler.step(snake_case_ , snake_case_ , snake_case_ , **snake_case_ ).prev_sample
# decode the image latents with the VQVAE
_UpperCAmelCase = self.vqvae.decode(snake_case_ ).sample
_UpperCAmelCase = torch.clamp(snake_case_ , -1.0 , 1.0 )
_UpperCAmelCase = image / 2 + 0.5
_UpperCAmelCase = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
_UpperCAmelCase = self.numpy_to_pil(snake_case_ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=snake_case_ )
| 22 | 1 |
'''simple docstring'''
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class A_ ( lowerCAmelCase_ ):
@staticmethod
@abstractmethod
def lowercase ( snake_case_ : ArgumentParser ):
raise NotImplementedError()
@abstractmethod
def lowercase ( self : Optional[Any] ):
raise NotImplementedError()
| 22 |
'''simple docstring'''
import string
from math import logaa
def UpperCAmelCase_ ( __lowercase : str , __lowercase : str ) -> int:
'''simple docstring'''
_UpperCAmelCase = document.translate(
str.maketrans("" , "" , string.punctuation ) ).replace("\n" , "" )
_UpperCAmelCase = document_without_punctuation.split(" " ) # word tokenization
return len([word for word in tokenize_document if word.lower() == term.lower()] )
def UpperCAmelCase_ ( __lowercase : str , __lowercase : str ) -> tuple[int, int]:
'''simple docstring'''
_UpperCAmelCase = corpus.lower().translate(
str.maketrans("" , "" , string.punctuation ) ) # strip all punctuation and replace it with ''
_UpperCAmelCase = corpus_without_punctuation.split("\n" )
_UpperCAmelCase = term.lower()
return (len([doc for doc in docs if term in doc] ), len(__lowercase ))
def UpperCAmelCase_ ( __lowercase : int , __lowercase : int , __lowercase : Union[str, Any]=False ) -> float:
'''simple docstring'''
if smoothing:
if n == 0:
raise ValueError("log10(0) is undefined." )
return round(1 + logaa(n / (1 + df) ) , 3 )
if df == 0:
raise ZeroDivisionError("df must be > 0" )
elif n == 0:
raise ValueError("log10(0) is undefined." )
return round(logaa(n / df ) , 3 )
def UpperCAmelCase_ ( __lowercase : int , __lowercase : int ) -> float:
'''simple docstring'''
return round(tf * idf , 3 )
| 22 | 1 |
'''simple docstring'''
from __future__ import annotations
from statistics import mean
def UpperCAmelCase_ ( __lowercase : list[int] , __lowercase : list[int] , __lowercase : int ) -> list[int]:
'''simple docstring'''
_UpperCAmelCase = [0] * no_of_processes
_UpperCAmelCase = [0] * no_of_processes
# Initialize remaining_time to waiting_time.
for i in range(__lowercase ):
_UpperCAmelCase = burst_time[i]
_UpperCAmelCase = []
_UpperCAmelCase = 0
_UpperCAmelCase = 0
# When processes are not completed,
# A process whose arrival time has passed \
# and has remaining execution time is put into the ready_process.
# The shortest process in the ready_process, target_process is executed.
while completed != no_of_processes:
_UpperCAmelCase = []
_UpperCAmelCase = -1
for i in range(__lowercase ):
if (arrival_time[i] <= total_time) and (remaining_time[i] > 0):
ready_process.append(__lowercase )
if len(__lowercase ) > 0:
_UpperCAmelCase = ready_process[0]
for i in ready_process:
if remaining_time[i] < remaining_time[target_process]:
_UpperCAmelCase = i
total_time += burst_time[target_process]
completed += 1
_UpperCAmelCase = 0
_UpperCAmelCase = (
total_time - arrival_time[target_process] - burst_time[target_process]
)
else:
total_time += 1
return waiting_time
def UpperCAmelCase_ ( __lowercase : list[int] , __lowercase : int , __lowercase : list[int] ) -> list[int]:
'''simple docstring'''
_UpperCAmelCase = [0] * no_of_processes
for i in range(__lowercase ):
_UpperCAmelCase = burst_time[i] + waiting_time[i]
return turn_around_time
if __name__ == "__main__":
print('''[TEST CASE 01]''')
__SCREAMING_SNAKE_CASE :Dict = 4
__SCREAMING_SNAKE_CASE :int = [2, 5, 3, 7]
__SCREAMING_SNAKE_CASE :List[Any] = [0, 0, 0, 0]
__SCREAMING_SNAKE_CASE :Tuple = calculate_waitingtime(arrival_time, burst_time, no_of_processes)
__SCREAMING_SNAKE_CASE :Any = calculate_turnaroundtime(
burst_time, no_of_processes, waiting_time
)
# Printing the Result
print('''PID\tBurst Time\tArrival Time\tWaiting Time\tTurnaround Time''')
for i, process_id in enumerate(list(range(1, 5))):
print(
F"{process_id}\t{burst_time[i]}\t\t\t{arrival_time[i]}\t\t\t\t"
F"{waiting_time[i]}\t\t\t\t{turn_around_time[i]}"
)
print(F"\nAverage waiting time = {mean(waiting_time):.5f}")
print(F"Average turnaround time = {mean(turn_around_time):.5f}")
| 22 |
'''simple docstring'''
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_pt_objects import * # noqa F403
else:
from .scheduling_consistency_models import CMStochasticIterativeScheduler
from .scheduling_ddim import DDIMScheduler
from .scheduling_ddim_inverse import DDIMInverseScheduler
from .scheduling_ddim_parallel import DDIMParallelScheduler
from .scheduling_ddpm import DDPMScheduler
from .scheduling_ddpm_parallel import DDPMParallelScheduler
from .scheduling_deis_multistep import DEISMultistepScheduler
from .scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler
from .scheduling_dpmsolver_multistep_inverse import DPMSolverMultistepInverseScheduler
from .scheduling_dpmsolver_singlestep import DPMSolverSinglestepScheduler
from .scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler
from .scheduling_euler_discrete import EulerDiscreteScheduler
from .scheduling_heun_discrete import HeunDiscreteScheduler
from .scheduling_ipndm import IPNDMScheduler
from .scheduling_k_dpm_2_ancestral_discrete import KDPMaAncestralDiscreteScheduler
from .scheduling_k_dpm_2_discrete import KDPMaDiscreteScheduler
from .scheduling_karras_ve import KarrasVeScheduler
from .scheduling_pndm import PNDMScheduler
from .scheduling_repaint import RePaintScheduler
from .scheduling_sde_ve import ScoreSdeVeScheduler
from .scheduling_sde_vp import ScoreSdeVpScheduler
from .scheduling_unclip import UnCLIPScheduler
from .scheduling_unipc_multistep import UniPCMultistepScheduler
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
from .scheduling_vq_diffusion import VQDiffusionScheduler
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_flax_objects import * # noqa F403
else:
from .scheduling_ddim_flax import FlaxDDIMScheduler
from .scheduling_ddpm_flax import FlaxDDPMScheduler
from .scheduling_dpmsolver_multistep_flax import FlaxDPMSolverMultistepScheduler
from .scheduling_karras_ve_flax import FlaxKarrasVeScheduler
from .scheduling_lms_discrete_flax import FlaxLMSDiscreteScheduler
from .scheduling_pndm_flax import FlaxPNDMScheduler
from .scheduling_sde_ve_flax import FlaxScoreSdeVeScheduler
from .scheduling_utils_flax import (
FlaxKarrasDiffusionSchedulers,
FlaxSchedulerMixin,
FlaxSchedulerOutput,
broadcast_to_shape_from_left,
)
try:
if not (is_torch_available() and is_scipy_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_torch_and_scipy_objects import * # noqa F403
else:
from .scheduling_lms_discrete import LMSDiscreteScheduler
try:
if not (is_torch_available() and is_torchsde_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_torch_and_torchsde_objects import * # noqa F403
else:
from .scheduling_dpmsolver_sde import DPMSolverSDEScheduler
| 22 | 1 |
'''simple docstring'''
import itertools
import json
import os
import unittest
from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class A_ ( lowerCAmelCase_ , unittest.TestCase ):
_lowerCamelCase : Optional[Any] = RobertaTokenizer
_lowerCamelCase : List[str] = RobertaTokenizerFast
_lowerCamelCase : Any = True
_lowerCamelCase : int = {"""cls_token""": """<s>"""}
def lowercase ( self : List[Any] ):
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
_UpperCAmelCase = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"\u0120",
"\u0120l",
"\u0120n",
"\u0120lo",
"\u0120low",
"er",
"\u0120lowest",
"\u0120newer",
"\u0120wider",
"<unk>",
]
_UpperCAmelCase = dict(zip(snake_case_ , range(len(snake_case_ ) ) ) )
_UpperCAmelCase = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
_UpperCAmelCase = {"unk_token": "<unk>"}
_UpperCAmelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
_UpperCAmelCase = 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(snake_case_ ) + "\n" )
with open(self.merges_file , "w" , encoding="utf-8" ) as fp:
fp.write("\n".join(snake_case_ ) )
def lowercase ( self : Optional[Any] , **snake_case_ : Dict ):
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname , **snake_case_ )
def lowercase ( self : Optional[int] , **snake_case_ : Any ):
kwargs.update(self.special_tokens_map )
return RobertaTokenizerFast.from_pretrained(self.tmpdirname , **snake_case_ )
def lowercase ( self : int , snake_case_ : Dict ):
_UpperCAmelCase = "lower newer"
_UpperCAmelCase = "lower newer"
return input_text, output_text
def lowercase ( self : Dict ):
_UpperCAmelCase = self.tokenizer_class(self.vocab_file , self.merges_file , **self.special_tokens_map )
_UpperCAmelCase = "lower newer"
_UpperCAmelCase = ["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
_UpperCAmelCase = tokenizer.tokenize(snake_case_ ) # , add_prefix_space=True)
self.assertListEqual(snake_case_ , snake_case_ )
_UpperCAmelCase = tokens + [tokenizer.unk_token]
_UpperCAmelCase = [0, 1, 2, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case_ ) , snake_case_ )
def lowercase ( self : Tuple ):
_UpperCAmelCase = self.get_tokenizer()
self.assertListEqual(tokenizer.encode("Hello world!" , add_special_tokens=snake_case_ ) , [0, 3_1_4_1_4, 2_3_2, 3_2_8, 2] )
self.assertListEqual(
tokenizer.encode("Hello world! cécé herlolip 418" , add_special_tokens=snake_case_ ) , [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2] , )
@slow
def lowercase ( self : str ):
_UpperCAmelCase = self.tokenizer_class.from_pretrained("roberta-base" )
_UpperCAmelCase = tokenizer.encode("sequence builders" , add_special_tokens=snake_case_ )
_UpperCAmelCase = tokenizer.encode("multi-sequence build" , add_special_tokens=snake_case_ )
_UpperCAmelCase = tokenizer.encode(
"sequence builders" , add_special_tokens=snake_case_ , add_prefix_space=snake_case_ )
_UpperCAmelCase = tokenizer.encode(
"sequence builders" , "multi-sequence build" , add_special_tokens=snake_case_ , add_prefix_space=snake_case_ )
_UpperCAmelCase = tokenizer.build_inputs_with_special_tokens(snake_case_ )
_UpperCAmelCase = tokenizer.build_inputs_with_special_tokens(snake_case_ , snake_case_ )
assert encoded_sentence == encoded_text_from_decode
assert encoded_pair == encoded_pair_from_decode
def lowercase ( self : Optional[int] ):
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = "Encode this sequence."
_UpperCAmelCase = tokenizer.byte_encoder[" ".encode("utf-8" )[0]]
# Testing encoder arguments
_UpperCAmelCase = tokenizer.encode(snake_case_ , add_special_tokens=snake_case_ , add_prefix_space=snake_case_ )
_UpperCAmelCase = tokenizer.convert_ids_to_tokens(encoded[0] )[0]
self.assertNotEqual(snake_case_ , snake_case_ )
_UpperCAmelCase = tokenizer.encode(snake_case_ , add_special_tokens=snake_case_ , add_prefix_space=snake_case_ )
_UpperCAmelCase = tokenizer.convert_ids_to_tokens(encoded[0] )[0]
self.assertEqual(snake_case_ , snake_case_ )
tokenizer.add_special_tokens({"bos_token": "<s>"} )
_UpperCAmelCase = tokenizer.encode(snake_case_ , add_special_tokens=snake_case_ )
_UpperCAmelCase = tokenizer.convert_ids_to_tokens(encoded[1] )[0]
self.assertNotEqual(snake_case_ , snake_case_ )
# Testing spaces after special tokens
_UpperCAmelCase = "<mask>"
tokenizer.add_special_tokens(
{"mask_token": AddedToken(snake_case_ , lstrip=snake_case_ , rstrip=snake_case_ )} ) # mask token has a left space
_UpperCAmelCase = tokenizer.convert_tokens_to_ids(snake_case_ )
_UpperCAmelCase = "Encode <mask> sequence"
_UpperCAmelCase = "Encode <mask>sequence"
_UpperCAmelCase = tokenizer.encode(snake_case_ )
_UpperCAmelCase = encoded.index(snake_case_ )
_UpperCAmelCase = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
self.assertEqual(snake_case_ , snake_case_ )
_UpperCAmelCase = tokenizer.encode(snake_case_ )
_UpperCAmelCase = encoded.index(snake_case_ )
_UpperCAmelCase = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
self.assertNotEqual(snake_case_ , snake_case_ )
def lowercase ( self : Dict ):
pass
def lowercase ( self : Union[str, Any] ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})' ):
_UpperCAmelCase = self.rust_tokenizer_class.from_pretrained(snake_case_ , **snake_case_ )
_UpperCAmelCase = self.tokenizer_class.from_pretrained(snake_case_ , **snake_case_ )
_UpperCAmelCase = "A, <mask> AllenNLP sentence."
_UpperCAmelCase = tokenizer_r.encode_plus(snake_case_ , add_special_tokens=snake_case_ , return_token_type_ids=snake_case_ )
_UpperCAmelCase = tokenizer_p.encode_plus(snake_case_ , add_special_tokens=snake_case_ , return_token_type_ids=snake_case_ )
# token_type_ids should put 0 everywhere
self.assertEqual(sum(tokens_r["token_type_ids"] ) , sum(tokens_p["token_type_ids"] ) )
# attention_mask should put 1 everywhere, so sum over length should be 1
self.assertEqual(
sum(tokens_r["attention_mask"] ) / len(tokens_r["attention_mask"] ) , sum(tokens_p["attention_mask"] ) / len(tokens_p["attention_mask"] ) , )
_UpperCAmelCase = tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"] )
_UpperCAmelCase = tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"] )
# Rust correctly handles the space before the mask while python doesnt
self.assertSequenceEqual(tokens_p["input_ids"] , [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2] )
self.assertSequenceEqual(tokens_r["input_ids"] , [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2] )
self.assertSequenceEqual(
snake_case_ , ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"] )
self.assertSequenceEqual(
snake_case_ , ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"] )
def lowercase ( self : Dict ):
for trim_offsets, add_prefix_space in itertools.product([True, False] , repeat=2 ):
_UpperCAmelCase = self.rust_tokenizer_class.from_pretrained(
self.tmpdirname , use_fast=snake_case_ , add_prefix_space=snake_case_ , trim_offsets=snake_case_ )
_UpperCAmelCase = json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__() )
_UpperCAmelCase = json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__() )
self.assertEqual(pre_tokenizer_state["add_prefix_space"] , snake_case_ )
self.assertEqual(post_processor_state["add_prefix_space"] , snake_case_ )
self.assertEqual(post_processor_state["trim_offsets"] , snake_case_ )
def lowercase ( self : List[Any] ):
# Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
# `trim_offsets`
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})' ):
_UpperCAmelCase = "hello" # `hello` is a token in the vocabulary of `pretrained_name`
_UpperCAmelCase = f'{text_of_1_token} {text_of_1_token}'
_UpperCAmelCase = self.rust_tokenizer_class.from_pretrained(
snake_case_ , use_fast=snake_case_ , add_prefix_space=snake_case_ , trim_offsets=snake_case_ )
_UpperCAmelCase = tokenizer_r(snake_case_ , return_offsets_mapping=snake_case_ , add_special_tokens=snake_case_ )
self.assertEqual(encoding.offset_mapping[0] , (0, len(snake_case_ )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(snake_case_ ) + 1, len(snake_case_ ) + 1 + len(snake_case_ )) , )
_UpperCAmelCase = self.rust_tokenizer_class.from_pretrained(
snake_case_ , use_fast=snake_case_ , add_prefix_space=snake_case_ , trim_offsets=snake_case_ )
_UpperCAmelCase = tokenizer_r(snake_case_ , return_offsets_mapping=snake_case_ , add_special_tokens=snake_case_ )
self.assertEqual(encoding.offset_mapping[0] , (0, len(snake_case_ )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(snake_case_ ) + 1, len(snake_case_ ) + 1 + len(snake_case_ )) , )
_UpperCAmelCase = self.rust_tokenizer_class.from_pretrained(
snake_case_ , use_fast=snake_case_ , add_prefix_space=snake_case_ , trim_offsets=snake_case_ )
_UpperCAmelCase = tokenizer_r(snake_case_ , return_offsets_mapping=snake_case_ , add_special_tokens=snake_case_ )
self.assertEqual(encoding.offset_mapping[0] , (0, len(snake_case_ )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(snake_case_ ), len(snake_case_ ) + 1 + len(snake_case_ )) , )
_UpperCAmelCase = self.rust_tokenizer_class.from_pretrained(
snake_case_ , use_fast=snake_case_ , add_prefix_space=snake_case_ , trim_offsets=snake_case_ )
_UpperCAmelCase = tokenizer_r(snake_case_ , return_offsets_mapping=snake_case_ , add_special_tokens=snake_case_ )
self.assertEqual(encoding.offset_mapping[0] , (0, len(snake_case_ )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(snake_case_ ), len(snake_case_ ) + 1 + len(snake_case_ )) , )
_UpperCAmelCase = f' {text}'
# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
# pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
# )
# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
# self.assertEqual(
# encoding.offset_mapping[1],
# (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
# )
_UpperCAmelCase = self.rust_tokenizer_class.from_pretrained(
snake_case_ , use_fast=snake_case_ , add_prefix_space=snake_case_ , trim_offsets=snake_case_ )
_UpperCAmelCase = tokenizer_r(snake_case_ , return_offsets_mapping=snake_case_ , add_special_tokens=snake_case_ )
self.assertEqual(encoding.offset_mapping[0] , (1, 1 + len(snake_case_ )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(snake_case_ ) + 1, 1 + len(snake_case_ ) + 1 + len(snake_case_ )) , )
_UpperCAmelCase = self.rust_tokenizer_class.from_pretrained(
snake_case_ , use_fast=snake_case_ , add_prefix_space=snake_case_ , trim_offsets=snake_case_ )
_UpperCAmelCase = tokenizer_r(snake_case_ , return_offsets_mapping=snake_case_ , add_special_tokens=snake_case_ )
self.assertEqual(encoding.offset_mapping[0] , (0, 1 + len(snake_case_ )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(snake_case_ ), 1 + len(snake_case_ ) + 1 + len(snake_case_ )) , )
_UpperCAmelCase = self.rust_tokenizer_class.from_pretrained(
snake_case_ , use_fast=snake_case_ , add_prefix_space=snake_case_ , trim_offsets=snake_case_ )
_UpperCAmelCase = tokenizer_r(snake_case_ , return_offsets_mapping=snake_case_ , add_special_tokens=snake_case_ )
self.assertEqual(encoding.offset_mapping[0] , (0, 1 + len(snake_case_ )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(snake_case_ ), 1 + len(snake_case_ ) + 1 + len(snake_case_ )) , )
| 22 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : int ) -> int:
'''simple docstring'''
if not isinstance(__lowercase , __lowercase ) or number < 0:
raise ValueError("Input must be a non-negative integer" )
_UpperCAmelCase = 0
while number:
# This way we arrive at next set bit (next 1) instead of looping
# through each bit and checking for 1s hence the
# loop won't run 32 times it will only run the number of `1` times
number &= number - 1
count += 1
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 22 | 1 |
'''simple docstring'''
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results
import re
import subprocess
import sys
__SCREAMING_SNAKE_CASE :Union[str, Any] = subprocess.check_output('''git merge-base main HEAD'''.split()).decode('''utf-8''')
__SCREAMING_SNAKE_CASE :Tuple = subprocess.check_output(F"git diff --name-only {fork_point_sha}".split()).decode('''utf-8''').split()
__SCREAMING_SNAKE_CASE :Any = '''|'''.join(sys.argv[1:])
__SCREAMING_SNAKE_CASE :int = re.compile(RF"^({joined_dirs}).*?\.py$")
__SCREAMING_SNAKE_CASE :Tuple = [x for x in modified_files if regex.match(x)]
print(''' '''.join(relevant_modified_files), end='''''')
| 22 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
from typing import Any, Generic, TypeVar
__SCREAMING_SNAKE_CASE :Optional[int] = TypeVar('''T''')
class A_ ( Generic[T] ):
def __init__( self : List[Any] , snake_case_ : list[T] , snake_case_ : Callable[[T, T], T] ):
_UpperCAmelCase = None
_UpperCAmelCase = len(snake_case_ )
_UpperCAmelCase = [any_type for _ in range(self.N )] + arr
_UpperCAmelCase = fnc
self.build()
def lowercase ( self : List[Any] ):
for p in range(self.N - 1 , 0 , -1 ):
_UpperCAmelCase = self.fn(self.st[p * 2] , self.st[p * 2 + 1] )
def lowercase ( self : Optional[Any] , snake_case_ : int , snake_case_ : T ):
p += self.N
_UpperCAmelCase = v
while p > 1:
_UpperCAmelCase = p // 2
_UpperCAmelCase = self.fn(self.st[p * 2] , self.st[p * 2 + 1] )
def lowercase ( self : Any , snake_case_ : int , snake_case_ : int ): # noqa: E741
_UpperCAmelCase , _UpperCAmelCase = l + self.N, r + self.N
_UpperCAmelCase = None
while l <= r:
if l % 2 == 1:
_UpperCAmelCase = self.st[l] if res is None else self.fn(snake_case_ , self.st[l] )
if r % 2 == 0:
_UpperCAmelCase = self.st[r] if res is None else self.fn(snake_case_ , self.st[r] )
_UpperCAmelCase , _UpperCAmelCase = (l + 1) // 2, (r - 1) // 2
return res
if __name__ == "__main__":
from functools import reduce
__SCREAMING_SNAKE_CASE :Union[str, Any] = [1, 10, -2, 9, -3, 8, 4, -7, 5, 6, 11, -12]
__SCREAMING_SNAKE_CASE :List[str] = {
0: 7,
1: 2,
2: 6,
3: -14,
4: 5,
5: 4,
6: 7,
7: -10,
8: 9,
9: 10,
10: 12,
11: 1,
}
__SCREAMING_SNAKE_CASE :Any = SegmentTree(test_array, min)
__SCREAMING_SNAKE_CASE :Any = SegmentTree(test_array, max)
__SCREAMING_SNAKE_CASE :Any = SegmentTree(test_array, lambda a, b: a + b)
def UpperCAmelCase_ ( ) -> None:
'''simple docstring'''
for i in range(len(__lowercase ) ):
for j in range(__lowercase , len(__lowercase ) ):
_UpperCAmelCase = reduce(__lowercase , test_array[i : j + 1] )
_UpperCAmelCase = reduce(__lowercase , test_array[i : j + 1] )
_UpperCAmelCase = reduce(lambda __lowercase , __lowercase : a + b , test_array[i : j + 1] )
assert min_range == min_segment_tree.query(__lowercase , __lowercase )
assert max_range == max_segment_tree.query(__lowercase , __lowercase )
assert sum_range == sum_segment_tree.query(__lowercase , __lowercase )
test_all_segments()
for index, value in test_updates.items():
__SCREAMING_SNAKE_CASE :str = value
min_segment_tree.update(index, value)
max_segment_tree.update(index, value)
sum_segment_tree.update(index, value)
test_all_segments()
| 22 | 1 |
'''simple docstring'''
import torch
from transformers import AutoModel
class A_ ( torch.nn.Module ):
def __init__( self : Union[str, Any] , snake_case_ : Any="sayef/fsner-bert-base-uncased" ):
super(snake_case_ , self ).__init__()
_UpperCAmelCase = AutoModel.from_pretrained(snake_case_ , return_dict=snake_case_ )
_UpperCAmelCase = torch.nn.CosineSimilarity(3 , 1e-08 )
_UpperCAmelCase = torch.nn.Softmax(dim=1 )
def lowercase ( self : Any , **snake_case_ : Union[str, Any] ):
return self.bert(**snake_case_ ).last_hidden_state
def lowercase ( self : str , snake_case_ : Optional[int] ):
return token_embeddings.sum(2 , keepdim=snake_case_ )
def lowercase ( self : Union[str, Any] , snake_case_ : Tuple , snake_case_ : str , snake_case_ : Tuple=1 ):
return self.softmax(T * self.cos(snake_case_ , snake_case_ ) )
def lowercase ( self : List[Any] , snake_case_ : int , snake_case_ : str ):
_UpperCAmelCase = W_supports["sizes"].tolist()
_UpperCAmelCase = W_supports["start_token_id"].item()
_UpperCAmelCase = W_supports["end_token_id"].item()
del W_supports["sizes"]
del W_supports["start_token_id"]
del W_supports["end_token_id"]
_UpperCAmelCase = self.BERT(**snake_case_ )
_UpperCAmelCase = self.BERT(**snake_case_ )
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = W_supports["input_ids"] == start_token_id
_UpperCAmelCase = W_supports["input_ids"] == end_token_id
for i, size in enumerate(snake_case_ ):
if i == 0:
_UpperCAmelCase = 0
else:
_UpperCAmelCase = support_sizes[i - 1]
_UpperCAmelCase = S[s : s + size][start_token_masks[s : s + size]]
_UpperCAmelCase = S[s : s + size][end_token_masks[s : s + size]]
_UpperCAmelCase = torch.matmul(q[i] , s_start.T ).sum(1 ).softmax(0 )
_UpperCAmelCase = torch.matmul(q[i] , s_end.T ).sum(1 ).softmax(0 )
if p_starts is not None:
_UpperCAmelCase = torch.vstack((p_starts, p_start) )
_UpperCAmelCase = torch.vstack((p_ends, p_end) )
else:
_UpperCAmelCase = p_start
_UpperCAmelCase = p_end
return p_starts, p_ends
| 22 |
'''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 UpperCAmelCase_ ( __lowercase : int , __lowercase : Dict ) -> List[Any]:
'''simple docstring'''
_UpperCAmelCase = _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 UpperCAmelCase_ ( __lowercase : Dict , __lowercase : Optional[Any] , __lowercase : int ) -> str:
'''simple docstring'''
_UpperCAmelCase = _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 UpperCAmelCase_ ( __lowercase : Optional[Any] , __lowercase : List[Any] ) -> List[Any]:
'''simple docstring'''
if expected is RuntimeError:
with pytest.raises(__lowercase ):
_number_of_shards_in_gen_kwargs(__lowercase )
else:
_UpperCAmelCase = _number_of_shards_in_gen_kwargs(__lowercase )
assert out == expected
| 22 | 1 |
'''simple docstring'''
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class A_ ( lowerCAmelCase_ ):
_lowerCamelCase : List[str] = ["""image_processor""", """tokenizer"""]
_lowerCamelCase : Optional[int] = """LayoutLMv3ImageProcessor"""
_lowerCamelCase : Union[str, Any] = ("""LayoutLMv3Tokenizer""", """LayoutLMv3TokenizerFast""")
def __init__( self : Tuple , snake_case_ : Tuple=None , snake_case_ : Optional[Any]=None , **snake_case_ : List[str] ):
_UpperCAmelCase = None
if "feature_extractor" in kwargs:
warnings.warn(
"The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
" instead." , snake_case_ , )
_UpperCAmelCase = kwargs.pop("feature_extractor" )
_UpperCAmelCase = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("You need to specify an `image_processor`." )
if tokenizer is None:
raise ValueError("You need to specify a `tokenizer`." )
super().__init__(snake_case_ , snake_case_ )
def __call__( self : List[str] , snake_case_ : Tuple , snake_case_ : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None , snake_case_ : Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None , snake_case_ : Union[List[List[int]], List[List[List[int]]]] = None , snake_case_ : Optional[Union[List[int], List[List[int]]]] = None , snake_case_ : bool = True , snake_case_ : Union[bool, str, PaddingStrategy] = False , snake_case_ : Union[bool, str, TruncationStrategy] = None , snake_case_ : Optional[int] = None , snake_case_ : int = 0 , snake_case_ : Optional[int] = None , snake_case_ : Optional[bool] = None , snake_case_ : Optional[bool] = None , snake_case_ : bool = False , snake_case_ : bool = False , snake_case_ : bool = False , snake_case_ : bool = False , snake_case_ : bool = True , snake_case_ : Optional[Union[str, TensorType]] = None , **snake_case_ : Optional[Any] , ):
# verify input
if self.image_processor.apply_ocr and (boxes is not None):
raise ValueError(
"You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True." )
if self.image_processor.apply_ocr and (word_labels is not None):
raise ValueError(
"You cannot provide word labels if you initialized the image processor with apply_ocr set to True." )
# first, apply the image processor
_UpperCAmelCase = self.image_processor(images=snake_case_ , return_tensors=snake_case_ )
# second, apply the tokenizer
if text is not None and self.image_processor.apply_ocr and text_pair is None:
if isinstance(snake_case_ , snake_case_ ):
_UpperCAmelCase = [text] # add batch dimension (as the image processor always adds a batch dimension)
_UpperCAmelCase = features["words"]
_UpperCAmelCase = self.tokenizer(
text=text if text is not None else features["words"] , text_pair=text_pair if text_pair is not None else None , boxes=boxes if boxes is not None else features["boxes"] , word_labels=snake_case_ , add_special_tokens=snake_case_ , padding=snake_case_ , truncation=snake_case_ , max_length=snake_case_ , stride=snake_case_ , pad_to_multiple_of=snake_case_ , return_token_type_ids=snake_case_ , return_attention_mask=snake_case_ , return_overflowing_tokens=snake_case_ , return_special_tokens_mask=snake_case_ , return_offsets_mapping=snake_case_ , return_length=snake_case_ , verbose=snake_case_ , return_tensors=snake_case_ , **snake_case_ , )
# add pixel values
_UpperCAmelCase = features.pop("pixel_values" )
if return_overflowing_tokens is True:
_UpperCAmelCase = self.get_overflowing_images(snake_case_ , encoded_inputs["overflow_to_sample_mapping"] )
_UpperCAmelCase = images
return encoded_inputs
def lowercase ( self : Optional[Any] , snake_case_ : List[Any] , snake_case_ : Tuple ):
# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
_UpperCAmelCase = []
for sample_idx in overflow_to_sample_mapping:
images_with_overflow.append(images[sample_idx] )
if len(snake_case_ ) != len(snake_case_ ):
raise ValueError(
"Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
f' {len(snake_case_ )} and {len(snake_case_ )}' )
return images_with_overflow
def lowercase ( self : Tuple , *snake_case_ : Optional[int] , **snake_case_ : Any ):
return self.tokenizer.batch_decode(*snake_case_ , **snake_case_ )
def lowercase ( self : Optional[Any] , *snake_case_ : List[Any] , **snake_case_ : Any ):
return self.tokenizer.decode(*snake_case_ , **snake_case_ )
@property
def lowercase ( self : Optional[Any] ):
return ["input_ids", "bbox", "attention_mask", "pixel_values"]
@property
def lowercase ( self : Union[str, Any] ):
warnings.warn(
"`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead." , snake_case_ , )
return self.image_processor_class
@property
def lowercase ( self : Tuple ):
warnings.warn(
"`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead." , snake_case_ , )
return self.image_processor
| 22 |
'''simple docstring'''
import math
def UpperCAmelCase_ ( __lowercase : int ) -> bool:
'''simple docstring'''
return math.sqrt(__lowercase ) * math.sqrt(__lowercase ) == num
def UpperCAmelCase_ ( __lowercase : int ) -> bool:
'''simple docstring'''
_UpperCAmelCase = 0
_UpperCAmelCase = n
while left <= right:
_UpperCAmelCase = (left + right) // 2
if mid**2 == n:
return True
elif mid**2 > n:
_UpperCAmelCase = mid - 1
else:
_UpperCAmelCase = mid + 1
return False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 22 | 1 |
'''simple docstring'''
import unittest
from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
LiltForQuestionAnswering,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltModel,
)
from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST
class A_ :
def __init__( self : int , snake_case_ : str , snake_case_ : Any=1_3 , snake_case_ : Dict=7 , snake_case_ : str=True , snake_case_ : Optional[int]=True , snake_case_ : Union[str, Any]=True , snake_case_ : Union[str, Any]=True , snake_case_ : Union[str, Any]=9_9 , snake_case_ : List[str]=2_4 , snake_case_ : Dict=2 , snake_case_ : int=6 , snake_case_ : Optional[int]=3_7 , snake_case_ : Any="gelu" , snake_case_ : str=0.1 , snake_case_ : Optional[int]=0.1 , snake_case_ : Union[str, Any]=5_1_2 , snake_case_ : Union[str, Any]=1_6 , snake_case_ : List[str]=2 , snake_case_ : str=0.0_2 , snake_case_ : int=3 , snake_case_ : List[str]=None , snake_case_ : Optional[Any]=1_0_0_0 , ):
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = seq_length
_UpperCAmelCase = is_training
_UpperCAmelCase = use_input_mask
_UpperCAmelCase = use_token_type_ids
_UpperCAmelCase = use_labels
_UpperCAmelCase = vocab_size
_UpperCAmelCase = hidden_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = intermediate_size
_UpperCAmelCase = hidden_act
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = max_position_embeddings
_UpperCAmelCase = type_vocab_size
_UpperCAmelCase = type_sequence_label_size
_UpperCAmelCase = initializer_range
_UpperCAmelCase = num_labels
_UpperCAmelCase = scope
_UpperCAmelCase = range_bbox
def lowercase ( self : Dict ):
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length, 4] , self.range_bbox )
# Ensure that bbox is legal
for i in range(bbox.shape[0] ):
for j in range(bbox.shape[1] ):
if bbox[i, j, 3] < bbox[i, j, 1]:
_UpperCAmelCase = bbox[i, j, 3]
_UpperCAmelCase = bbox[i, j, 1]
_UpperCAmelCase = t
if bbox[i, j, 2] < bbox[i, j, 0]:
_UpperCAmelCase = bbox[i, j, 2]
_UpperCAmelCase = bbox[i, j, 0]
_UpperCAmelCase = t
_UpperCAmelCase = None
if self.use_input_mask:
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
_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
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 = self.get_config()
return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels
def lowercase ( self : int ):
return LiltConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , )
def lowercase ( self : Optional[int] , snake_case_ : Any , snake_case_ : Dict , snake_case_ : Tuple , snake_case_ : Optional[Any] , snake_case_ : Optional[int] , snake_case_ : Dict , snake_case_ : Dict , ):
_UpperCAmelCase = LiltModel(config=snake_case_ )
model.to(snake_case_ )
model.eval()
_UpperCAmelCase = model(snake_case_ , bbox=snake_case_ , attention_mask=snake_case_ , token_type_ids=snake_case_ )
_UpperCAmelCase = model(snake_case_ , bbox=snake_case_ , token_type_ids=snake_case_ )
_UpperCAmelCase = model(snake_case_ , bbox=snake_case_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def lowercase ( self : int , snake_case_ : List[Any] , snake_case_ : int , snake_case_ : Tuple , snake_case_ : Tuple , snake_case_ : Dict , snake_case_ : Optional[int] , snake_case_ : List[str] , ):
_UpperCAmelCase = self.num_labels
_UpperCAmelCase = LiltForTokenClassification(config=snake_case_ )
model.to(snake_case_ )
model.eval()
_UpperCAmelCase = model(
snake_case_ , bbox=snake_case_ , attention_mask=snake_case_ , token_type_ids=snake_case_ , labels=snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def lowercase ( self : List[Any] , snake_case_ : Tuple , snake_case_ : str , snake_case_ : Dict , snake_case_ : Optional[Any] , snake_case_ : Union[str, Any] , snake_case_ : Any , snake_case_ : str , ):
_UpperCAmelCase = LiltForQuestionAnswering(config=snake_case_ )
model.to(snake_case_ )
model.eval()
_UpperCAmelCase = model(
snake_case_ , bbox=snake_case_ , attention_mask=snake_case_ , token_type_ids=snake_case_ , start_positions=snake_case_ , end_positions=snake_case_ , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def lowercase ( self : int ):
_UpperCAmelCase = self.prepare_config_and_inputs()
(
(
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) ,
) = config_and_inputs
_UpperCAmelCase = {
"input_ids": input_ids,
"bbox": bbox,
"token_type_ids": token_type_ids,
"attention_mask": input_mask,
}
return config, inputs_dict
@require_torch
class A_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
_lowerCamelCase : str = (
(
LiltModel,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltForQuestionAnswering,
)
if is_torch_available()
else ()
)
_lowerCamelCase : Dict = (
{
"""feature-extraction""": LiltModel,
"""question-answering""": LiltForQuestionAnswering,
"""text-classification""": LiltForSequenceClassification,
"""token-classification""": LiltForTokenClassification,
"""zero-shot""": LiltForSequenceClassification,
}
if is_torch_available()
else {}
)
_lowerCamelCase : Union[str, Any] = False
_lowerCamelCase : Optional[Any] = False
def lowercase ( self : List[Any] , snake_case_ : str , snake_case_ : str , snake_case_ : Dict , snake_case_ : Any , snake_case_ : Any ):
return True
def lowercase ( self : Tuple ):
_UpperCAmelCase = LiltModelTester(self )
_UpperCAmelCase = ConfigTester(self , config_class=snake_case_ , hidden_size=3_7 )
def lowercase ( self : Optional[Any] ):
self.config_tester.run_common_tests()
def lowercase ( self : Any ):
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case_ )
def lowercase ( self : Union[str, Any] ):
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
_UpperCAmelCase = type
self.model_tester.create_and_check_model(*snake_case_ )
def lowercase ( self : Union[str, Any] ):
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*snake_case_ )
def lowercase ( self : Tuple ):
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*snake_case_ )
@slow
def lowercase ( self : Tuple ):
for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase = LiltModel.from_pretrained(snake_case_ )
self.assertIsNotNone(snake_case_ )
@require_torch
@slow
class A_ ( unittest.TestCase ):
def lowercase ( self : Tuple ):
_UpperCAmelCase = LiltModel.from_pretrained("SCUT-DLVCLab/lilt-roberta-en-base" ).to(snake_case_ )
_UpperCAmelCase = torch.tensor([[1, 2]] , device=snake_case_ )
_UpperCAmelCase = torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]] , device=snake_case_ )
# forward pass
with torch.no_grad():
_UpperCAmelCase = model(input_ids=snake_case_ , bbox=snake_case_ )
_UpperCAmelCase = torch.Size([1, 2, 7_6_8] )
_UpperCAmelCase = torch.tensor(
[[-0.0_6_5_3, 0.0_9_5_0, -0.0_0_6_1], [-0.0_5_4_5, 0.0_9_2_6, -0.0_3_2_4]] , device=snake_case_ , )
self.assertTrue(outputs.last_hidden_state.shape , snake_case_ )
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3] , snake_case_ , atol=1e-3 ) )
| 22 |
'''simple docstring'''
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
__SCREAMING_SNAKE_CASE :Dict = 1e-4
if is_torch_available():
import torch
from transformers import AutoformerConfig, AutoformerForPrediction, AutoformerModel
from transformers.models.autoformer.modeling_autoformer import AutoformerDecoder, AutoformerEncoder
@require_torch
class A_ :
def __init__( self : List[Any] , snake_case_ : int , snake_case_ : Dict=1_6 , snake_case_ : Dict=1_3 , snake_case_ : int=7 , snake_case_ : Any=1_4 , snake_case_ : int=1_0 , snake_case_ : Any=1_9 , snake_case_ : int=5 , snake_case_ : Any=4 , snake_case_ : Tuple=True , snake_case_ : Optional[int]=1_6 , snake_case_ : List[str]=2 , snake_case_ : Any=4 , snake_case_ : List[Any]=4 , snake_case_ : Optional[Any]="gelu" , snake_case_ : Optional[int]=0.1 , snake_case_ : Union[str, Any]=0.1 , snake_case_ : Tuple=[1, 2, 3, 4, 5] , snake_case_ : str=2_5 , snake_case_ : Any=5 , ):
_UpperCAmelCase = d_model
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = prediction_length
_UpperCAmelCase = context_length
_UpperCAmelCase = cardinality
_UpperCAmelCase = num_time_features
_UpperCAmelCase = lags_sequence
_UpperCAmelCase = embedding_dimension
_UpperCAmelCase = is_training
_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 = context_length
_UpperCAmelCase = prediction_length + label_length
_UpperCAmelCase = label_length
_UpperCAmelCase = moving_average
_UpperCAmelCase = autocorrelation_factor
def lowercase ( self : Union[str, Any] ):
return AutoformerConfig(
d_model=self.d_model , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , prediction_length=self.prediction_length , context_length=self.context_length , label_length=self.label_length , lags_sequence=self.lags_sequence , num_time_features=self.num_time_features , num_static_categorical_features=1 , cardinality=[self.cardinality] , embedding_dimension=[self.embedding_dimension] , moving_average=self.moving_average , )
def lowercase ( self : int , snake_case_ : Optional[Any] ):
_UpperCAmelCase = config.context_length + max(config.lags_sequence )
_UpperCAmelCase = ids_tensor([self.batch_size, 1] , config.cardinality[0] )
_UpperCAmelCase = floats_tensor([self.batch_size, _past_length, config.num_time_features] )
_UpperCAmelCase = floats_tensor([self.batch_size, _past_length] )
_UpperCAmelCase = floats_tensor([self.batch_size, _past_length] ) > 0.5
# decoder inputs
_UpperCAmelCase = floats_tensor([self.batch_size, config.prediction_length, config.num_time_features] )
_UpperCAmelCase = floats_tensor([self.batch_size, config.prediction_length] )
_UpperCAmelCase = {
"past_values": past_values,
"static_categorical_features": static_categorical_features,
"past_time_features": past_time_features,
"past_observed_mask": past_observed_mask,
"future_time_features": future_time_features,
"future_values": future_values,
}
return inputs_dict
def lowercase ( self : List[Any] ):
_UpperCAmelCase = self.get_config()
_UpperCAmelCase = self.prepare_autoformer_inputs_dict(snake_case_ )
return config, inputs_dict
def lowercase ( self : List[str] ):
_UpperCAmelCase , _UpperCAmelCase = self.prepare_config_and_inputs()
return config, inputs_dict
def lowercase ( self : Optional[Any] , snake_case_ : int , snake_case_ : Optional[int] ):
_UpperCAmelCase = AutoformerModel(config=snake_case_ ).to(snake_case_ ).eval()
_UpperCAmelCase = model(**snake_case_ )
_UpperCAmelCase = outputs.encoder_last_hidden_state
_UpperCAmelCase = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
_UpperCAmelCase = model.get_encoder()
encoder.save_pretrained(snake_case_ )
_UpperCAmelCase = AutoformerEncoder.from_pretrained(snake_case_ ).to(snake_case_ )
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = model.create_network_inputs(**snake_case_ )
_UpperCAmelCase , _UpperCAmelCase = model.decomposition_layer(transformer_inputs[:, : config.context_length, ...] )
_UpperCAmelCase = torch.cat(
(transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...]) , dim=-1 , )
_UpperCAmelCase = encoder(inputs_embeds=snake_case_ )[0]
self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1e-3 )
_UpperCAmelCase = (
torch.mean(transformer_inputs[:, : config.context_length, ...] , dim=1 )
.unsqueeze(1 )
.repeat(1 , config.prediction_length , 1 )
)
_UpperCAmelCase = torch.zeros(
[transformer_inputs.shape[0], config.prediction_length, transformer_inputs.shape[2]] , device=enc_input.device , )
_UpperCAmelCase = torch.cat(
(
torch.cat((seasonal_input[:, -config.label_length :, ...], zeros) , dim=1 ),
feature[:, config.context_length - config.label_length :, ...],
) , dim=-1 , )
_UpperCAmelCase = torch.cat(
(
torch.cat((trend_input[:, -config.label_length :, ...], mean) , dim=1 ),
feature[:, config.context_length - config.label_length :, ...],
) , dim=-1 , )
with tempfile.TemporaryDirectory() as tmpdirname:
_UpperCAmelCase = model.get_decoder()
decoder.save_pretrained(snake_case_ )
_UpperCAmelCase = AutoformerDecoder.from_pretrained(snake_case_ ).to(snake_case_ )
_UpperCAmelCase = decoder(
trend=snake_case_ , inputs_embeds=snake_case_ , encoder_hidden_states=snake_case_ , )[0]
self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1e-3 )
@require_torch
class A_ ( lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
_lowerCamelCase : List[Any] = (AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
_lowerCamelCase : Tuple = (AutoformerForPrediction,) if is_torch_available() else ()
_lowerCamelCase : List[Any] = {"""feature-extraction""": AutoformerModel} if is_torch_available() else {}
_lowerCamelCase : Optional[Any] = False
_lowerCamelCase : Tuple = False
_lowerCamelCase : int = False
_lowerCamelCase : Optional[Any] = False
_lowerCamelCase : Optional[Any] = False
_lowerCamelCase : List[Any] = False
def lowercase ( self : Tuple ):
_UpperCAmelCase = AutoformerModelTester(self )
_UpperCAmelCase = ConfigTester(self , config_class=snake_case_ , has_text_modality=snake_case_ )
def lowercase ( self : Optional[Any] ):
self.config_tester.run_common_tests()
def lowercase ( self : Union[str, Any] ):
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
_UpperCAmelCase = model_class(snake_case_ )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(snake_case_ )
_UpperCAmelCase , _UpperCAmelCase = model_class.from_pretrained(snake_case_ , output_loading_info=snake_case_ )
self.assertEqual(info["missing_keys"] , [] )
def lowercase ( self : Optional[int] ):
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*snake_case_ )
@unittest.skip(reason="Model has no tokens embeddings" )
def lowercase ( self : Optional[int] ):
pass
def lowercase ( self : Optional[int] ):
_UpperCAmelCase = inspect.signature(getattr(snake_case_ , "forward" ) )
# The main input is the name of the argument after `self`
_UpperCAmelCase = list(model_signature.parameters.keys() )[1]
self.assertEqual(AutoformerModel.main_input_name , snake_case_ )
def lowercase ( self : List[str] ):
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase = model_class(snake_case_ )
_UpperCAmelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_UpperCAmelCase = [*signature.parameters.keys()]
_UpperCAmelCase = [
"past_values",
"past_time_features",
"past_observed_mask",
"static_categorical_features",
"static_real_features",
"future_values",
"future_time_features",
]
if model.__class__.__name__ in ["AutoformerForPrediction"]:
expected_arg_names.append("future_observed_mask" )
expected_arg_names.extend(
[
"decoder_attention_mask",
"head_mask",
"decoder_head_mask",
"cross_attn_head_mask",
"encoder_outputs",
"past_key_values",
"output_hidden_states",
"output_attentions",
"use_cache",
"return_dict",
] )
self.assertListEqual(arg_names[: len(snake_case_ )] , snake_case_ )
def lowercase ( self : Optional[int] ):
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
_UpperCAmelCase = True
_UpperCAmelCase = getattr(self.model_tester , "seq_length" , snake_case_ )
_UpperCAmelCase = getattr(self.model_tester , "decoder_seq_length" , snake_case_ )
_UpperCAmelCase = getattr(self.model_tester , "encoder_seq_length" , snake_case_ )
_UpperCAmelCase = getattr(self.model_tester , "d_model" , snake_case_ )
_UpperCAmelCase = getattr(self.model_tester , "num_attention_heads" , snake_case_ )
_UpperCAmelCase = d_model // num_attention_heads
for model_class in self.all_model_classes:
_UpperCAmelCase = True
_UpperCAmelCase = False
_UpperCAmelCase = True
_UpperCAmelCase = model_class(snake_case_ )
model.to(snake_case_ )
model.eval()
with torch.no_grad():
_UpperCAmelCase = model(**self._prepare_for_class(snake_case_ , snake_case_ ) )
_UpperCAmelCase = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(snake_case_ ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
_UpperCAmelCase = True
_UpperCAmelCase = model_class(snake_case_ )
model.to(snake_case_ )
model.eval()
with torch.no_grad():
_UpperCAmelCase = model(**self._prepare_for_class(snake_case_ , snake_case_ ) )
_UpperCAmelCase = outputs.encoder_attentions
self.assertEqual(len(snake_case_ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, dim] , )
_UpperCAmelCase = len(snake_case_ )
_UpperCAmelCase = 7
if "last_hidden_state" in outputs:
correct_outlen += 1
if "trend" in outputs:
correct_outlen += 1
if "past_key_values" in outputs:
correct_outlen += 1 # past_key_values have been returned
if "loss" in outputs:
correct_outlen += 1
if "params" in outputs:
correct_outlen += 1
self.assertEqual(snake_case_ , snake_case_ )
# decoder attentions
_UpperCAmelCase = outputs.decoder_attentions
self.assertIsInstance(snake_case_ , (list, tuple) )
self.assertEqual(len(snake_case_ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, decoder_seq_length, dim] , )
# cross attentions
_UpperCAmelCase = outputs.cross_attentions
self.assertIsInstance(snake_case_ , (list, tuple) )
self.assertEqual(len(snake_case_ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(cross_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, decoder_seq_length, dim] , )
# Check attention is always last and order is fine
_UpperCAmelCase = True
_UpperCAmelCase = True
_UpperCAmelCase = model_class(snake_case_ )
model.to(snake_case_ )
model.eval()
with torch.no_grad():
_UpperCAmelCase = model(**self._prepare_for_class(snake_case_ , snake_case_ ) )
self.assertEqual(out_len + 2 , len(snake_case_ ) )
_UpperCAmelCase = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(snake_case_ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, dim] , )
@is_flaky()
def lowercase ( self : Dict ):
super().test_retain_grad_hidden_states_attentions()
def UpperCAmelCase_ ( __lowercase : str="train-batch.pt" ) -> List[str]:
'''simple docstring'''
_UpperCAmelCase = hf_hub_download(repo_id="hf-internal-testing/tourism-monthly-batch" , filename=__lowercase , repo_type="dataset" )
_UpperCAmelCase = torch.load(__lowercase , map_location=__lowercase )
return batch
@require_torch
@slow
class A_ ( unittest.TestCase ):
def lowercase ( self : Optional[int] ):
_UpperCAmelCase = AutoformerModel.from_pretrained("huggingface/autoformer-tourism-monthly" ).to(snake_case_ )
_UpperCAmelCase = prepare_batch()
with torch.no_grad():
_UpperCAmelCase = model(
past_values=batch["past_values"] , past_time_features=batch["past_time_features"] , past_observed_mask=batch["past_observed_mask"] , static_categorical_features=batch["static_categorical_features"] , future_values=batch["future_values"] , future_time_features=batch["future_time_features"] , )[0]
_UpperCAmelCase = torch.Size(
(6_4, model.config.prediction_length + model.config.label_length, model.config.feature_size) )
self.assertEqual(output.shape , snake_case_ )
_UpperCAmelCase = torch.tensor(
[[0.3_5_9_3, -1.3_3_9_8, 0.6_3_3_0], [0.2_2_7_9, 1.5_3_9_6, -0.1_7_9_2], [0.0_4_5_0, 1.3_2_2_5, -0.2_3_3_5]] , device=snake_case_ )
self.assertTrue(torch.allclose(output[0, :3, :3] , snake_case_ , atol=snake_case_ ) )
def lowercase ( self : Optional[Any] ):
_UpperCAmelCase = AutoformerForPrediction.from_pretrained("huggingface/autoformer-tourism-monthly" ).to(snake_case_ )
_UpperCAmelCase = prepare_batch("val-batch.pt" )
with torch.no_grad():
_UpperCAmelCase = model(
past_values=batch["past_values"] , past_time_features=batch["past_time_features"] , past_observed_mask=batch["past_observed_mask"] , static_categorical_features=batch["static_categorical_features"] , ).encoder_last_hidden_state
_UpperCAmelCase = torch.Size((6_4, model.config.context_length, model.config.d_model) )
self.assertEqual(output.shape , snake_case_ )
_UpperCAmelCase = torch.tensor(
[[-0.0_7_3_4, -0.9_0_3_6, 0.8_3_5_8], [4.7_1_8_6, 2.4_1_1_3, 1.9_5_8_1], [1.7_9_5_3, 2.3_5_5_8, 1.2_9_7_0]] , device=snake_case_ )
self.assertTrue(torch.allclose(output[0, :3, :3] , snake_case_ , atol=snake_case_ ) )
def lowercase ( self : Tuple ):
_UpperCAmelCase = AutoformerForPrediction.from_pretrained("huggingface/autoformer-tourism-monthly" ).to(snake_case_ )
_UpperCAmelCase = prepare_batch("val-batch.pt" )
with torch.no_grad():
_UpperCAmelCase = model.generate(
static_categorical_features=batch["static_categorical_features"] , past_time_features=batch["past_time_features"] , past_values=batch["past_values"] , future_time_features=batch["future_time_features"] , past_observed_mask=batch["past_observed_mask"] , )
_UpperCAmelCase = torch.Size((6_4, model.config.num_parallel_samples, model.config.prediction_length) )
self.assertEqual(outputs.sequences.shape , snake_case_ )
_UpperCAmelCase = torch.tensor([3_1_3_0.6_7_6_3, 4_0_5_6.5_2_9_3, 7_0_5_3.0_7_8_6] , device=snake_case_ )
_UpperCAmelCase = outputs.sequences.mean(dim=1 )
self.assertTrue(torch.allclose(mean_prediction[0, -3:] , snake_case_ , rtol=1e-1 ) )
| 22 | 1 |
'''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 subprocess
from packaging.version import Version, parse
from accelerate.commands.config.config_args import default_config_file, load_config_from_file
__SCREAMING_SNAKE_CASE :Any = '''Run commands across TPU VMs for initial setup before running `accelerate launch`.'''
def UpperCAmelCase_ ( __lowercase : Optional[int]=None ) -> List[str]:
'''simple docstring'''
if subparsers is not None:
_UpperCAmelCase = subparsers.add_parser("tpu-config" , description=_description )
else:
_UpperCAmelCase = argparse.ArgumentParser("Accelerate tpu-config command" , description=_description )
# Core arguments
_UpperCAmelCase = parser.add_argument_group(
"Config Arguments" , "Arguments that can be configured through `accelerate config`." )
config_args.add_argument(
"--config_file" , type=__lowercase , default=__lowercase , help="Path to the config file to use for accelerate." , )
config_args.add_argument(
"--tpu_name" , default=__lowercase , help="The name of the TPU to use. If not specified, will use the TPU specified in the config file." , )
config_args.add_argument(
"--tpu_zone" , default=__lowercase , help="The zone of the TPU to use. If not specified, will use the zone specified in the config file." , )
_UpperCAmelCase = parser.add_argument_group("TPU Arguments" , "Arguments for options ran inside the TPU." )
pod_args.add_argument(
"--use_alpha" , action="store_true" , help="Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`." , )
pod_args.add_argument(
"--command_file" , default=__lowercase , help="The path to the file containing the commands to run on the pod on startup." , )
pod_args.add_argument(
"--command" , action="append" , nargs="+" , help="A command to run on the pod. Can be passed multiple times." , )
pod_args.add_argument(
"--install_accelerate" , action="store_true" , help="Whether to install accelerate on the pod. Defaults to False." , )
pod_args.add_argument(
"--accelerate_version" , default="latest" , help="The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify 'dev' to install from GitHub." , )
pod_args.add_argument(
"--debug" , action="store_true" , help="If set, will print the command that would be run instead of running it." )
if subparsers is not None:
parser.set_defaults(func=__lowercase )
return parser
def UpperCAmelCase_ ( __lowercase : str ) -> Union[str, Any]:
'''simple docstring'''
_UpperCAmelCase = None
# Get the default from the config file if it exists.
if args.config_file is not None or os.path.isfile(__lowercase ):
_UpperCAmelCase = load_config_from_file(args.config_file )
if not args.command_file and defaults.command_file is not None and not args.command:
_UpperCAmelCase = defaults.command_file
if not args.command and defaults.commands is not None:
_UpperCAmelCase = defaults.commands
if not args.tpu_name:
_UpperCAmelCase = defaults.tpu_name
if not args.tpu_zone:
_UpperCAmelCase = defaults.tpu_zone
if args.accelerate_version == "dev":
_UpperCAmelCase = "git+https://github.com/huggingface/accelerate.git"
elif args.accelerate_version == "latest":
_UpperCAmelCase = "accelerate -U"
elif isinstance(parse(args.accelerate_version ) , __lowercase ):
_UpperCAmelCase = f'accelerate=={args.accelerate_version}'
if not args.command_file and not args.command:
raise ValueError("You must specify either a command file or a command to run on the pod." )
if args.command_file:
with open(args.command_file , "r" ) as f:
_UpperCAmelCase = [f.read().splitlines()]
# To turn list of lists into list of strings
if isinstance(args.command[0] , __lowercase ):
_UpperCAmelCase = [line for cmd in args.command for line in cmd]
# Default to the shared folder and install accelerate
_UpperCAmelCase = ["cd /usr/share"]
if args.install_accelerate:
new_cmd += [f'pip install {args.accelerate_version}']
new_cmd += args.command
_UpperCAmelCase = "; ".join(__lowercase )
# Then send it to gcloud
# Eventually try to use google-api-core to do this instead of subprocess
_UpperCAmelCase = ["gcloud"]
if args.use_alpha:
cmd += ["alpha"]
cmd += [
"compute",
"tpus",
"tpu-vm",
"ssh",
args.tpu_name,
"--zone",
args.tpu_zone,
"--command",
args.command,
"--worker",
"all",
]
if args.debug:
print(f'Running {" ".join(__lowercase )}' )
return
subprocess.run(__lowercase )
print("Successfully setup pod." )
def UpperCAmelCase_ ( ) -> str:
'''simple docstring'''
_UpperCAmelCase = tpu_command_parser()
_UpperCAmelCase = parser.parse_args()
tpu_command_launcher(__lowercase )
| 22 |
'''simple docstring'''
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_space_optuna,
default_hp_space_ray,
default_hp_space_sigopt,
default_hp_space_wandb,
)
from .utils import logging
__SCREAMING_SNAKE_CASE :int = logging.get_logger(__name__)
class A_ :
_lowerCamelCase : str
_lowerCamelCase : str = None
@staticmethod
def lowercase ( ):
raise NotImplementedError
def lowercase ( self : Union[str, Any] , snake_case_ : Optional[int] , snake_case_ : int , snake_case_ : str , **snake_case_ : List[Any] ):
raise NotImplementedError
def lowercase ( self : Any , snake_case_ : int ):
raise NotImplementedError
def lowercase ( self : List[str] ):
if not self.is_available():
raise RuntimeError(
f'You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}.' )
@classmethod
def lowercase ( cls : List[Any] ):
return f'`pip install {cls.pip_package or cls.name}`'
class A_ ( lowerCAmelCase_ ):
_lowerCamelCase : int = """optuna"""
@staticmethod
def lowercase ( ):
return is_optuna_available()
def lowercase ( self : List[str] , snake_case_ : Any , snake_case_ : int , snake_case_ : str , **snake_case_ : Tuple ):
return run_hp_search_optuna(snake_case_ , snake_case_ , snake_case_ , **snake_case_ )
def lowercase ( self : int , snake_case_ : Optional[int] ):
return default_hp_space_optuna(snake_case_ )
class A_ ( lowerCAmelCase_ ):
_lowerCamelCase : Any = """ray"""
_lowerCamelCase : Tuple = """'ray[tune]'"""
@staticmethod
def lowercase ( ):
return is_ray_available()
def lowercase ( self : Optional[Any] , snake_case_ : Any , snake_case_ : int , snake_case_ : str , **snake_case_ : List[str] ):
return run_hp_search_ray(snake_case_ , snake_case_ , snake_case_ , **snake_case_ )
def lowercase ( self : Any , snake_case_ : str ):
return default_hp_space_ray(snake_case_ )
class A_ ( lowerCAmelCase_ ):
_lowerCamelCase : int = """sigopt"""
@staticmethod
def lowercase ( ):
return is_sigopt_available()
def lowercase ( self : Any , snake_case_ : int , snake_case_ : int , snake_case_ : str , **snake_case_ : Dict ):
return run_hp_search_sigopt(snake_case_ , snake_case_ , snake_case_ , **snake_case_ )
def lowercase ( self : Dict , snake_case_ : Optional[Any] ):
return default_hp_space_sigopt(snake_case_ )
class A_ ( lowerCAmelCase_ ):
_lowerCamelCase : Optional[int] = """wandb"""
@staticmethod
def lowercase ( ):
return is_wandb_available()
def lowercase ( self : Optional[Any] , snake_case_ : Optional[Any] , snake_case_ : int , snake_case_ : str , **snake_case_ : Optional[Any] ):
return run_hp_search_wandb(snake_case_ , snake_case_ , snake_case_ , **snake_case_ )
def lowercase ( self : Any , snake_case_ : Union[str, Any] ):
return default_hp_space_wandb(snake_case_ )
__SCREAMING_SNAKE_CASE :Dict = {
HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}
def UpperCAmelCase_ ( ) -> str:
'''simple docstring'''
_UpperCAmelCase = [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
if len(__lowercase ) > 0:
_UpperCAmelCase = available_backends[0].name
if len(__lowercase ) > 1:
logger.info(
f'{len(__lowercase )} hyperparameter search backends available. Using {name} as the default.' )
return name
raise RuntimeError(
"No hyperparameter search backend available.\n"
+ "\n".join(
f' - To install {backend.name} run {backend.pip_install()}'
for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() ) )
| 22 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tensorflow_text_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__SCREAMING_SNAKE_CASE :Union[str, Any] = {
'''configuration_bert''': ['''BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BertConfig''', '''BertOnnxConfig'''],
'''tokenization_bert''': ['''BasicTokenizer''', '''BertTokenizer''', '''WordpieceTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE :str = ['''BertTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE :Dict = [
'''BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''BertForMaskedLM''',
'''BertForMultipleChoice''',
'''BertForNextSentencePrediction''',
'''BertForPreTraining''',
'''BertForQuestionAnswering''',
'''BertForSequenceClassification''',
'''BertForTokenClassification''',
'''BertLayer''',
'''BertLMHeadModel''',
'''BertModel''',
'''BertPreTrainedModel''',
'''load_tf_weights_in_bert''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE :List[Any] = [
'''TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFBertEmbeddings''',
'''TFBertForMaskedLM''',
'''TFBertForMultipleChoice''',
'''TFBertForNextSentencePrediction''',
'''TFBertForPreTraining''',
'''TFBertForQuestionAnswering''',
'''TFBertForSequenceClassification''',
'''TFBertForTokenClassification''',
'''TFBertLMHeadModel''',
'''TFBertMainLayer''',
'''TFBertModel''',
'''TFBertPreTrainedModel''',
]
try:
if not is_tensorflow_text_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE :Any = ['''TFBertTokenizer''']
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE :Optional[int] = [
'''FlaxBertForCausalLM''',
'''FlaxBertForMaskedLM''',
'''FlaxBertForMultipleChoice''',
'''FlaxBertForNextSentencePrediction''',
'''FlaxBertForPreTraining''',
'''FlaxBertForQuestionAnswering''',
'''FlaxBertForSequenceClassification''',
'''FlaxBertForTokenClassification''',
'''FlaxBertModel''',
'''FlaxBertPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bert_fast import BertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_bert import (
BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
BertForMaskedLM,
BertForMultipleChoice,
BertForNextSentencePrediction,
BertForPreTraining,
BertForQuestionAnswering,
BertForSequenceClassification,
BertForTokenClassification,
BertLayer,
BertLMHeadModel,
BertModel,
BertPreTrainedModel,
load_tf_weights_in_bert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_bert import (
TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFBertEmbeddings,
TFBertForMaskedLM,
TFBertForMultipleChoice,
TFBertForNextSentencePrediction,
TFBertForPreTraining,
TFBertForQuestionAnswering,
TFBertForSequenceClassification,
TFBertForTokenClassification,
TFBertLMHeadModel,
TFBertMainLayer,
TFBertModel,
TFBertPreTrainedModel,
)
try:
if not is_tensorflow_text_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bert_tf import TFBertTokenizer
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_bert import (
FlaxBertForCausalLM,
FlaxBertForMaskedLM,
FlaxBertForMultipleChoice,
FlaxBertForNextSentencePrediction,
FlaxBertForPreTraining,
FlaxBertForQuestionAnswering,
FlaxBertForSequenceClassification,
FlaxBertForTokenClassification,
FlaxBertModel,
FlaxBertPreTrainedModel,
)
else:
import sys
__SCREAMING_SNAKE_CASE :Union[str, Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 22 |
'''simple docstring'''
__SCREAMING_SNAKE_CASE :List[str] = '''0.18.2'''
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_librosa_available,
is_note_seq_available,
is_onnx_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
is_transformers_available,
is_transformers_version,
is_unidecode_available,
logging,
)
try:
if not is_onnx_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_onnx_objects import * # noqa F403
else:
from .pipelines import OnnxRuntimeModel
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_pt_objects import * # noqa F403
else:
from .models import (
AutoencoderKL,
ControlNetModel,
ModelMixin,
PriorTransformer,
TaFilmDecoder,
TransformeraDModel,
UNetaDModel,
UNetaDConditionModel,
UNetaDModel,
UNetaDConditionModel,
VQModel,
)
from .optimization import (
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
get_scheduler,
)
from .pipelines import (
AudioPipelineOutput,
ConsistencyModelPipeline,
DanceDiffusionPipeline,
DDIMPipeline,
DDPMPipeline,
DiffusionPipeline,
DiTPipeline,
ImagePipelineOutput,
KarrasVePipeline,
LDMPipeline,
LDMSuperResolutionPipeline,
PNDMPipeline,
RePaintPipeline,
ScoreSdeVePipeline,
)
from .schedulers import (
CMStochasticIterativeScheduler,
DDIMInverseScheduler,
DDIMParallelScheduler,
DDIMScheduler,
DDPMParallelScheduler,
DDPMScheduler,
DEISMultistepScheduler,
DPMSolverMultistepInverseScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
HeunDiscreteScheduler,
IPNDMScheduler,
KarrasVeScheduler,
KDPMaAncestralDiscreteScheduler,
KDPMaDiscreteScheduler,
PNDMScheduler,
RePaintScheduler,
SchedulerMixin,
ScoreSdeVeScheduler,
UnCLIPScheduler,
UniPCMultistepScheduler,
VQDiffusionScheduler,
)
from .training_utils import EMAModel
try:
if not (is_torch_available() and is_scipy_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_scipy_objects import * # noqa F403
else:
from .schedulers import LMSDiscreteScheduler
try:
if not (is_torch_available() and is_torchsde_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_torchsde_objects import * # noqa F403
else:
from .schedulers import DPMSolverSDEScheduler
try:
if not (is_torch_available() and is_transformers_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .pipelines import (
AltDiffusionImgaImgPipeline,
AltDiffusionPipeline,
AudioLDMPipeline,
CycleDiffusionPipeline,
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
ImageTextPipelineOutput,
KandinskyImgaImgPipeline,
KandinskyInpaintPipeline,
KandinskyPipeline,
KandinskyPriorPipeline,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaControlnetPipeline,
KandinskyVaaImgaImgPipeline,
KandinskyVaaInpaintPipeline,
KandinskyVaaPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
KandinskyVaaPriorPipeline,
LDMTextToImagePipeline,
PaintByExamplePipeline,
SemanticStableDiffusionPipeline,
ShapEImgaImgPipeline,
ShapEPipeline,
StableDiffusionAttendAndExcitePipeline,
StableDiffusionControlNetImgaImgPipeline,
StableDiffusionControlNetInpaintPipeline,
StableDiffusionControlNetPipeline,
StableDiffusionDepthaImgPipeline,
StableDiffusionDiffEditPipeline,
StableDiffusionImageVariationPipeline,
StableDiffusionImgaImgPipeline,
StableDiffusionInpaintPipeline,
StableDiffusionInpaintPipelineLegacy,
StableDiffusionInstructPixaPixPipeline,
StableDiffusionLatentUpscalePipeline,
StableDiffusionLDMaDPipeline,
StableDiffusionModelEditingPipeline,
StableDiffusionPanoramaPipeline,
StableDiffusionParadigmsPipeline,
StableDiffusionPipeline,
StableDiffusionPipelineSafe,
StableDiffusionPixaPixZeroPipeline,
StableDiffusionSAGPipeline,
StableDiffusionUpscalePipeline,
StableUnCLIPImgaImgPipeline,
StableUnCLIPPipeline,
TextToVideoSDPipeline,
TextToVideoZeroPipeline,
UnCLIPImageVariationPipeline,
UnCLIPPipeline,
UniDiffuserModel,
UniDiffuserPipeline,
UniDiffuserTextDecoder,
VersatileDiffusionDualGuidedPipeline,
VersatileDiffusionImageVariationPipeline,
VersatileDiffusionPipeline,
VersatileDiffusionTextToImagePipeline,
VideoToVideoSDPipeline,
VQDiffusionPipeline,
)
try:
if not (is_torch_available() and is_transformers_available() and is_invisible_watermark_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_transformers_and_invisible_watermark_objects import * # noqa F403
else:
from .pipelines import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline
try:
if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_transformers_and_k_diffusion_objects import * # noqa F403
else:
from .pipelines import StableDiffusionKDiffusionPipeline
try:
if not (is_torch_available() and is_transformers_available() and is_onnx_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_transformers_and_onnx_objects import * # noqa F403
else:
from .pipelines import (
OnnxStableDiffusionImgaImgPipeline,
OnnxStableDiffusionInpaintPipeline,
OnnxStableDiffusionInpaintPipelineLegacy,
OnnxStableDiffusionPipeline,
OnnxStableDiffusionUpscalePipeline,
StableDiffusionOnnxPipeline,
)
try:
if not (is_torch_available() and is_librosa_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_librosa_objects import * # noqa F403
else:
from .pipelines import AudioDiffusionPipeline, Mel
try:
if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_transformers_and_torch_and_note_seq_objects import * # noqa F403
else:
from .pipelines import SpectrogramDiffusionPipeline
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_flax_objects import * # noqa F403
else:
from .models.controlnet_flax import FlaxControlNetModel
from .models.modeling_flax_utils import FlaxModelMixin
from .models.unet_ad_condition_flax import FlaxUNetaDConditionModel
from .models.vae_flax import FlaxAutoencoderKL
from .pipelines import FlaxDiffusionPipeline
from .schedulers import (
FlaxDDIMScheduler,
FlaxDDPMScheduler,
FlaxDPMSolverMultistepScheduler,
FlaxKarrasVeScheduler,
FlaxLMSDiscreteScheduler,
FlaxPNDMScheduler,
FlaxSchedulerMixin,
FlaxScoreSdeVeScheduler,
)
try:
if not (is_flax_available() and is_transformers_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_flax_and_transformers_objects import * # noqa F403
else:
from .pipelines import (
FlaxStableDiffusionControlNetPipeline,
FlaxStableDiffusionImgaImgPipeline,
FlaxStableDiffusionInpaintPipeline,
FlaxStableDiffusionPipeline,
)
try:
if not (is_note_seq_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_note_seq_objects import * # noqa F403
else:
from .pipelines import MidiProcessor
| 22 | 1 |
'''simple docstring'''
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs
__SCREAMING_SNAKE_CASE :Optional[int] = imread(R'''digital_image_processing/image_data/lena_small.jpg''')
__SCREAMING_SNAKE_CASE :Optional[int] = cvtColor(img, COLOR_BGR2GRAY)
def UpperCAmelCase_ ( ) -> int:
'''simple docstring'''
_UpperCAmelCase = cn.convert_to_negative(__lowercase )
# assert negative_img array for at least one True
assert negative_img.any()
def UpperCAmelCase_ ( ) -> Tuple:
'''simple docstring'''
with Image.open("digital_image_processing/image_data/lena_small.jpg" ) as img:
# Work around assertion for response
assert str(cc.change_contrast(__lowercase , 110 ) ).startswith(
"<PIL.Image.Image image mode=RGB size=100x100 at" )
def UpperCAmelCase_ ( ) -> Dict:
'''simple docstring'''
_UpperCAmelCase = canny.gen_gaussian_kernel(9 , sigma=1.4 )
# Assert ambiguous array
assert resp.all()
def UpperCAmelCase_ ( ) -> Any:
'''simple docstring'''
_UpperCAmelCase = imread("digital_image_processing/image_data/lena_small.jpg" , 0 )
# assert ambiguous array for all == True
assert canny_img.all()
_UpperCAmelCase = canny.canny(__lowercase )
# assert canny array for at least one True
assert canny_array.any()
def UpperCAmelCase_ ( ) -> str:
'''simple docstring'''
assert gg.gaussian_filter(__lowercase , 5 , sigma=0.9 ).all()
def UpperCAmelCase_ ( ) -> List[Any]:
'''simple docstring'''
_UpperCAmelCase = array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]] )
_UpperCAmelCase = conv.img_convolve(__lowercase , __lowercase ).astype(__lowercase )
assert res.any()
def UpperCAmelCase_ ( ) -> Union[str, Any]:
'''simple docstring'''
assert med.median_filter(__lowercase , 3 ).any()
def UpperCAmelCase_ ( ) -> List[Any]:
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase = sob.sobel_filter(__lowercase )
assert grad.any() and theta.any()
def UpperCAmelCase_ ( ) -> Any:
'''simple docstring'''
_UpperCAmelCase = sp.make_sepia(__lowercase , 20 )
assert sepia.all()
def UpperCAmelCase_ ( __lowercase : str = "digital_image_processing/image_data/lena_small.jpg" ) -> List[str]:
'''simple docstring'''
_UpperCAmelCase = bs.Burkes(imread(__lowercase , 1 ) , 120 )
burkes.process()
assert burkes.output_img.any()
def UpperCAmelCase_ ( __lowercase : str = "digital_image_processing/image_data/lena_small.jpg" , ) -> Dict:
'''simple docstring'''
_UpperCAmelCase = rs.NearestNeighbour(imread(__lowercase , 1 ) , 400 , 200 )
nn.process()
assert nn.output.any()
def UpperCAmelCase_ ( ) -> str:
'''simple docstring'''
_UpperCAmelCase = "digital_image_processing/image_data/lena.jpg"
# Reading the image and converting it to grayscale.
_UpperCAmelCase = imread(__lowercase , 0 )
# Test for get_neighbors_pixel function() return not None
_UpperCAmelCase = 0
_UpperCAmelCase = 0
_UpperCAmelCase = image[x_coordinate][y_coordinate]
_UpperCAmelCase = lbp.get_neighbors_pixel(
__lowercase , __lowercase , __lowercase , __lowercase )
assert neighbors_pixels is not None
# Test for local_binary_pattern function()
# Create a numpy array as the same height and width of read image
_UpperCAmelCase = np.zeros((image.shape[0], image.shape[1]) )
# Iterating through the image and calculating the local binary pattern value
# for each pixel.
for i in range(0 , image.shape[0] ):
for j in range(0 , image.shape[1] ):
_UpperCAmelCase = lbp.local_binary_value(__lowercase , __lowercase , __lowercase )
assert lbp_image.any()
| 22 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
__SCREAMING_SNAKE_CASE :Optional[int] = True
except (ImportError, ModuleNotFoundError):
__SCREAMING_SNAKE_CASE :str = False
if NLTK_AVAILABLE:
with FileLock('''.lock''') as lock:
nltk.download('''punkt''', quiet=True)
def UpperCAmelCase_ ( __lowercase : str ) -> str:
'''simple docstring'''
re.sub("<n>" , "" , __lowercase ) # remove pegasus newline char
assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
return "\n".join(nltk.sent_tokenize(__lowercase ) )
| 22 | 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,
)
__SCREAMING_SNAKE_CASE :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:
__SCREAMING_SNAKE_CASE :Optional[Any] = ['''RoFormerTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE :int = [
'''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:
__SCREAMING_SNAKE_CASE :List[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:
__SCREAMING_SNAKE_CASE :int = [
'''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
__SCREAMING_SNAKE_CASE :Tuple = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 22 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel
if is_vision_available():
from transformers import MaskFormerImageProcessor
if is_vision_available():
from PIL import Image
class A_ :
def __init__( self : str , snake_case_ : int , snake_case_ : Union[str, Any]=2 , snake_case_ : List[Any]=True , snake_case_ : str=False , snake_case_ : str=1_0 , snake_case_ : str=3 , snake_case_ : Dict=3_2 * 4 , snake_case_ : Any=3_2 * 6 , snake_case_ : Optional[Any]=4 , snake_case_ : Optional[int]=3_2 , ):
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = is_training
_UpperCAmelCase = use_auxiliary_loss
_UpperCAmelCase = num_queries
_UpperCAmelCase = num_channels
_UpperCAmelCase = min_size
_UpperCAmelCase = max_size
_UpperCAmelCase = num_labels
_UpperCAmelCase = mask_feature_size
def lowercase ( self : Union[str, Any] ):
_UpperCAmelCase = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
snake_case_ )
_UpperCAmelCase = torch.ones([self.batch_size, self.min_size, self.max_size] , device=snake_case_ )
_UpperCAmelCase = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=snake_case_ ) > 0.5
).float()
_UpperCAmelCase = (torch.rand((self.batch_size, self.num_labels) , device=snake_case_ ) > 0.5).long()
_UpperCAmelCase = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def lowercase ( self : List[Any] ):
return MaskFormerConfig.from_backbone_and_decoder_configs(
backbone_config=SwinConfig(
depths=[1, 1, 1, 1] , ) , decoder_config=DetrConfig(
decoder_ffn_dim=1_2_8 , num_queries=self.num_queries , decoder_attention_heads=2 , d_model=self.mask_feature_size , ) , mask_feature_size=self.mask_feature_size , fpn_feature_size=self.mask_feature_size , num_channels=self.num_channels , num_labels=self.num_labels , )
def lowercase ( self : Optional[Any] ):
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = self.prepare_config_and_inputs()
_UpperCAmelCase = {"pixel_values": pixel_values, "pixel_mask": pixel_mask}
return config, inputs_dict
def lowercase ( self : List[Any] , snake_case_ : Optional[Any] , snake_case_ : Optional[Any] ):
_UpperCAmelCase = output.encoder_hidden_states
_UpperCAmelCase = output.pixel_decoder_hidden_states
_UpperCAmelCase = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(snake_case_ ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(snake_case_ ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(snake_case_ ) , config.decoder_config.decoder_layers )
def lowercase ( self : Tuple , snake_case_ : str , snake_case_ : Optional[int] , snake_case_ : Any , snake_case_ : Optional[Any]=False ):
with torch.no_grad():
_UpperCAmelCase = MaskFormerModel(config=snake_case_ )
model.to(snake_case_ )
model.eval()
_UpperCAmelCase = model(pixel_values=snake_case_ , pixel_mask=snake_case_ )
_UpperCAmelCase = model(snake_case_ , output_hidden_states=snake_case_ )
# the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
# encoder and pixel decoder
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.mask_feature_size) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(snake_case_ , snake_case_ )
def lowercase ( self : Any , snake_case_ : List[str] , snake_case_ : List[Any] , snake_case_ : int , snake_case_ : str , snake_case_ : List[Any] ):
_UpperCAmelCase = MaskFormerForInstanceSegmentation(config=snake_case_ )
model.to(snake_case_ )
model.eval()
def comm_check_on_output(snake_case_ : int ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
_UpperCAmelCase = model(pixel_values=snake_case_ , pixel_mask=snake_case_ )
_UpperCAmelCase = model(snake_case_ )
comm_check_on_output(snake_case_ )
_UpperCAmelCase = model(
pixel_values=snake_case_ , pixel_mask=snake_case_ , mask_labels=snake_case_ , class_labels=snake_case_ )
comm_check_on_output(snake_case_ )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class A_ ( lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
_lowerCamelCase : Dict = (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
_lowerCamelCase : Tuple = (
{"""feature-extraction""": MaskFormerModel, """image-segmentation""": MaskFormerForInstanceSegmentation}
if is_torch_available()
else {}
)
_lowerCamelCase : Optional[Any] = False
_lowerCamelCase : Dict = False
_lowerCamelCase : Any = False
_lowerCamelCase : List[Any] = False
def lowercase ( self : Optional[int] ):
_UpperCAmelCase = MaskFormerModelTester(self )
_UpperCAmelCase = ConfigTester(self , config_class=snake_case_ , has_text_modality=snake_case_ )
def lowercase ( self : Optional[Any] ):
self.config_tester.run_common_tests()
def lowercase ( self : Union[str, Any] ):
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(snake_case_ , **snake_case_ , output_hidden_states=snake_case_ )
def lowercase ( self : int ):
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*snake_case_ )
@unittest.skip(reason="MaskFormer does not use inputs_embeds" )
def lowercase ( self : Any ):
pass
@unittest.skip(reason="MaskFormer does not have a get_input_embeddings method" )
def lowercase ( self : List[str] ):
pass
@unittest.skip(reason="MaskFormer is not a generative model" )
def lowercase ( self : List[str] ):
pass
@unittest.skip(reason="MaskFormer does not use token embeddings" )
def lowercase ( self : List[Any] ):
pass
@require_torch_multi_gpu
@unittest.skip(
reason="MaskFormer has some layers using `add_module` which doesn't work well with `nn.DataParallel`" )
def lowercase ( self : Any ):
pass
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
def lowercase ( self : Union[str, Any] ):
pass
def lowercase ( self : List[str] ):
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase = model_class(snake_case_ )
_UpperCAmelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_UpperCAmelCase = [*signature.parameters.keys()]
_UpperCAmelCase = ["pixel_values"]
self.assertListEqual(arg_names[:1] , snake_case_ )
@slow
def lowercase ( self : Optional[int] ):
for model_name in ["facebook/maskformer-swin-small-coco"]:
_UpperCAmelCase = MaskFormerModel.from_pretrained(snake_case_ )
self.assertIsNotNone(snake_case_ )
def lowercase ( self : Optional[int] ):
_UpperCAmelCase = (self.model_tester.min_size,) * 2
_UpperCAmelCase = {
"pixel_values": torch.randn((2, 3, *size) , device=snake_case_ ),
"mask_labels": torch.randn((2, 1_0, *size) , device=snake_case_ ),
"class_labels": torch.zeros(2 , 1_0 , device=snake_case_ ).long(),
}
_UpperCAmelCase = MaskFormerForInstanceSegmentation(MaskFormerConfig() ).to(snake_case_ )
_UpperCAmelCase = model(**snake_case_ )
self.assertTrue(outputs.loss is not None )
def lowercase ( self : Dict ):
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(snake_case_ , **snake_case_ , output_hidden_states=snake_case_ )
def lowercase ( self : Any ):
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase = model_class(snake_case_ ).to(snake_case_ )
_UpperCAmelCase = model(**snake_case_ , output_attentions=snake_case_ )
self.assertTrue(outputs.attentions is not None )
def lowercase ( self : int ):
if not self.model_tester.is_training:
return
# only MaskFormerForInstanceSegmentation has the loss
_UpperCAmelCase = self.all_model_classes[1]
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
_UpperCAmelCase = model_class(snake_case_ )
model.to(snake_case_ )
model.train()
_UpperCAmelCase = model(snake_case_ , mask_labels=snake_case_ , class_labels=snake_case_ ).loss
loss.backward()
def lowercase ( self : int ):
# only MaskFormerForInstanceSegmentation has the loss
_UpperCAmelCase = self.all_model_classes[1]
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
_UpperCAmelCase = True
_UpperCAmelCase = True
_UpperCAmelCase = model_class(snake_case_ )
model.to(snake_case_ )
model.train()
_UpperCAmelCase = model(snake_case_ , mask_labels=snake_case_ , class_labels=snake_case_ )
_UpperCAmelCase = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
_UpperCAmelCase = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
# we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
_UpperCAmelCase = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
_UpperCAmelCase = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=snake_case_ )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
__SCREAMING_SNAKE_CASE :Dict = 1e-4
def UpperCAmelCase_ ( ) -> List[str]:
'''simple docstring'''
_UpperCAmelCase = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_vision
@slow
class A_ ( unittest.TestCase ):
@cached_property
def lowercase ( self : Dict ):
return (
MaskFormerImageProcessor.from_pretrained("facebook/maskformer-swin-small-coco" )
if is_vision_available()
else None
)
def lowercase ( self : List[Any] ):
_UpperCAmelCase = MaskFormerModel.from_pretrained("facebook/maskformer-swin-small-coco" ).to(snake_case_ )
_UpperCAmelCase = self.default_image_processor
_UpperCAmelCase = prepare_img()
_UpperCAmelCase = image_processor(snake_case_ , return_tensors="pt" ).to(snake_case_ )
_UpperCAmelCase = inputs["pixel_values"].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0 )
# check size
self.assertEqual(snake_case_ , (1, 3, 8_0_0, 1_0_8_8) )
with torch.no_grad():
_UpperCAmelCase = model(**snake_case_ )
_UpperCAmelCase = torch.tensor(
[[-0.0_4_8_2, 0.9_2_2_8, 0.4_9_5_1], [-0.2_5_4_7, 0.8_0_1_7, 0.8_5_2_7], [-0.0_0_6_9, 0.3_3_8_5, -0.0_0_8_9]] ).to(snake_case_ )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , snake_case_ , atol=snake_case_ ) )
_UpperCAmelCase = torch.tensor(
[[-0.8_4_2_2, -0.8_4_3_4, -0.9_7_1_8], [-1.0_1_4_4, -0.5_5_6_5, -0.4_1_9_5], [-1.0_0_3_8, -0.4_4_8_4, -0.1_9_6_1]] ).to(snake_case_ )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , snake_case_ , atol=snake_case_ ) )
_UpperCAmelCase = torch.tensor(
[[0.2_8_5_2, -0.0_1_5_9, 0.9_7_3_5], [0.6_2_5_4, 0.1_8_5_8, 0.8_5_2_9], [-0.0_6_8_0, -0.4_1_1_6, 1.8_4_1_3]] ).to(snake_case_ )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , snake_case_ , atol=snake_case_ ) )
def lowercase ( self : Tuple ):
_UpperCAmelCase = (
MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-small-coco" )
.to(snake_case_ )
.eval()
)
_UpperCAmelCase = self.default_image_processor
_UpperCAmelCase = prepare_img()
_UpperCAmelCase = image_processor(snake_case_ , return_tensors="pt" ).to(snake_case_ )
_UpperCAmelCase = inputs["pixel_values"].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0 )
# check size
self.assertEqual(snake_case_ , (1, 3, 8_0_0, 1_0_8_8) )
with torch.no_grad():
_UpperCAmelCase = model(**snake_case_ )
# masks_queries_logits
_UpperCAmelCase = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
_UpperCAmelCase = [
[-1.3_7_3_7_1_2_4, -1.7_7_2_4_9_3_7, -1.9_3_6_4_2_3_3],
[-1.5_9_7_7_2_8_1, -1.9_8_6_7_9_3_9, -2.1_5_2_3_6_9_5],
[-1.5_7_9_5_3_9_8, -1.9_2_6_9_8_3_2, -2.0_9_3_9_4_2],
]
_UpperCAmelCase = torch.tensor(snake_case_ ).to(snake_case_ )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , snake_case_ , atol=snake_case_ ) )
# class_queries_logits
_UpperCAmelCase = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
_UpperCAmelCase = torch.tensor(
[
[1.6_512e00, -5.2_572e00, -3.3_519e00],
[3.6_169e-02, -5.9_025e00, -2.9_313e00],
[1.0_766e-04, -7.7_630e00, -5.1_263e00],
] ).to(snake_case_ )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , snake_case_ , atol=snake_case_ ) )
def lowercase ( self : int ):
_UpperCAmelCase = (
MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-resnet101-coco-stuff" )
.to(snake_case_ )
.eval()
)
_UpperCAmelCase = self.default_image_processor
_UpperCAmelCase = prepare_img()
_UpperCAmelCase = image_processor(snake_case_ , return_tensors="pt" ).to(snake_case_ )
_UpperCAmelCase = inputs["pixel_values"].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0 )
# check size
self.assertEqual(snake_case_ , (1, 3, 8_0_0, 1_0_8_8) )
with torch.no_grad():
_UpperCAmelCase = model(**snake_case_ )
# masks_queries_logits
_UpperCAmelCase = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
_UpperCAmelCase = [[-0.9_0_4_6, -2.6_3_6_6, -4.6_0_6_2], [-3.4_1_7_9, -5.7_8_9_0, -8.8_0_5_7], [-4.9_1_7_9, -7.6_5_6_0, -1_0.7_7_1_1]]
_UpperCAmelCase = torch.tensor(snake_case_ ).to(snake_case_ )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , snake_case_ , atol=snake_case_ ) )
# class_queries_logits
_UpperCAmelCase = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
_UpperCAmelCase = torch.tensor(
[[4.7_1_8_8, -3.2_5_8_5, -2.8_8_5_7], [6.6_8_7_1, -2.9_1_8_1, -1.2_4_8_7], [7.2_4_4_9, -2.2_7_6_4, -2.1_8_7_4]] ).to(snake_case_ )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , snake_case_ , atol=snake_case_ ) )
def lowercase ( self : List[Any] ):
_UpperCAmelCase = (
MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-small-coco" )
.to(snake_case_ )
.eval()
)
_UpperCAmelCase = self.default_image_processor
_UpperCAmelCase = image_processor(
[np.zeros((3, 8_0_0, 1_3_3_3) ), np.zeros((3, 8_0_0, 1_3_3_3) )] , segmentation_maps=[np.zeros((3_8_4, 3_8_4) ).astype(np.floataa ), np.zeros((3_8_4, 3_8_4) ).astype(np.floataa )] , return_tensors="pt" , )
_UpperCAmelCase = inputs["pixel_values"].to(snake_case_ )
_UpperCAmelCase = [el.to(snake_case_ ) for el in inputs["mask_labels"]]
_UpperCAmelCase = [el.to(snake_case_ ) for el in inputs["class_labels"]]
with torch.no_grad():
_UpperCAmelCase = model(**snake_case_ )
self.assertTrue(outputs.loss is not None )
| 22 | 1 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE :int = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE :List[Any] = {
'''microsoft/unispeech-large-1500h-cv''': (
'''https://huggingface.co/microsoft/unispeech-large-1500h-cv/resolve/main/config.json'''
),
# See all UniSpeech models at https://huggingface.co/models?filter=unispeech
}
class A_ ( lowerCAmelCase_ ):
_lowerCamelCase : Tuple = """unispeech"""
def __init__( self : Tuple , snake_case_ : Tuple=3_2 , snake_case_ : List[str]=7_6_8 , snake_case_ : Optional[int]=1_2 , snake_case_ : List[str]=1_2 , snake_case_ : Tuple=3_0_7_2 , snake_case_ : List[str]="gelu" , snake_case_ : List[str]=0.1 , snake_case_ : Dict=0.1 , snake_case_ : Tuple=0.1 , snake_case_ : Any=0.0 , snake_case_ : int=0.0 , snake_case_ : Tuple=0.1 , snake_case_ : Union[str, Any]=0.1 , snake_case_ : int=0.0_2 , snake_case_ : str=1e-5 , snake_case_ : Dict="group" , snake_case_ : Optional[Any]="gelu" , snake_case_ : Optional[Any]=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2) , snake_case_ : Dict=(5, 2, 2, 2, 2, 2, 2) , snake_case_ : Optional[Any]=(1_0, 3, 3, 3, 3, 2, 2) , snake_case_ : List[Any]=False , snake_case_ : int=1_2_8 , snake_case_ : List[Any]=1_6 , snake_case_ : Any=False , snake_case_ : Any=True , snake_case_ : int=0.0_5 , snake_case_ : Union[str, Any]=1_0 , snake_case_ : Union[str, Any]=2 , snake_case_ : List[str]=0.0 , snake_case_ : Union[str, Any]=1_0 , snake_case_ : Dict=0 , snake_case_ : Dict=3_2_0 , snake_case_ : Tuple=2 , snake_case_ : int=0.1 , snake_case_ : List[Any]=1_0_0 , snake_case_ : Tuple=2_5_6 , snake_case_ : Optional[int]=2_5_6 , snake_case_ : Dict=0.1 , snake_case_ : Dict="mean" , snake_case_ : Union[str, Any]=False , snake_case_ : List[Any]=False , snake_case_ : Dict=2_5_6 , snake_case_ : Dict=8_0 , snake_case_ : List[Any]=0 , snake_case_ : Optional[Any]=1 , snake_case_ : Optional[int]=2 , snake_case_ : Any=0.5 , **snake_case_ : int , ):
super().__init__(**snake_case_ , pad_token_id=snake_case_ , bos_token_id=snake_case_ , eos_token_id=snake_case_ )
_UpperCAmelCase = hidden_size
_UpperCAmelCase = feat_extract_norm
_UpperCAmelCase = feat_extract_activation
_UpperCAmelCase = list(snake_case_ )
_UpperCAmelCase = list(snake_case_ )
_UpperCAmelCase = list(snake_case_ )
_UpperCAmelCase = conv_bias
_UpperCAmelCase = num_conv_pos_embeddings
_UpperCAmelCase = num_conv_pos_embedding_groups
_UpperCAmelCase = len(self.conv_dim )
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = intermediate_size
_UpperCAmelCase = hidden_act
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = hidden_dropout
_UpperCAmelCase = attention_dropout
_UpperCAmelCase = activation_dropout
_UpperCAmelCase = feat_proj_dropout
_UpperCAmelCase = final_dropout
_UpperCAmelCase = layerdrop
_UpperCAmelCase = layer_norm_eps
_UpperCAmelCase = initializer_range
_UpperCAmelCase = num_ctc_classes
_UpperCAmelCase = vocab_size
_UpperCAmelCase = do_stable_layer_norm
_UpperCAmelCase = use_weighted_layer_sum
_UpperCAmelCase = classifier_proj_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)`, but is `len(config.conv_dim) ="
f' {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,'
f' `len(config.conv_kernel) = {len(self.conv_kernel )}`.' )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
_UpperCAmelCase = apply_spec_augment
_UpperCAmelCase = mask_time_prob
_UpperCAmelCase = mask_time_length
_UpperCAmelCase = mask_time_min_masks
_UpperCAmelCase = mask_feature_prob
_UpperCAmelCase = mask_feature_length
_UpperCAmelCase = mask_feature_min_masks
# parameters for pretraining with codevector quantized representations
_UpperCAmelCase = num_codevectors_per_group
_UpperCAmelCase = num_codevector_groups
_UpperCAmelCase = contrastive_logits_temperature
_UpperCAmelCase = feat_quantizer_dropout
_UpperCAmelCase = num_negatives
_UpperCAmelCase = codevector_dim
_UpperCAmelCase = proj_codevector_dim
_UpperCAmelCase = diversity_loss_weight
# ctc loss
_UpperCAmelCase = ctc_loss_reduction
_UpperCAmelCase = ctc_zero_infinity
# pretraining loss
_UpperCAmelCase = replace_prob
@property
def lowercase ( self : Optional[int] ):
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 22 |
'''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_albert import AlbertTokenizer
else:
__SCREAMING_SNAKE_CASE :List[Any] = None
__SCREAMING_SNAKE_CASE :Union[str, Any] = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE :List[str] = {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}
__SCREAMING_SNAKE_CASE :List[Any] = {
'''vocab_file''': {
'''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/spiece.model''',
'''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/spiece.model''',
'''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model''',
'''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model''',
'''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/spiece.model''',
'''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/spiece.model''',
'''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model''',
'''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model''',
},
'''tokenizer_file''': {
'''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json''',
'''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json''',
'''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json''',
'''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json''',
'''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json''',
'''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json''',
'''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json''',
'''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json''',
},
}
__SCREAMING_SNAKE_CASE :Optional[Any] = {
'''albert-base-v1''': 512,
'''albert-large-v1''': 512,
'''albert-xlarge-v1''': 512,
'''albert-xxlarge-v1''': 512,
'''albert-base-v2''': 512,
'''albert-large-v2''': 512,
'''albert-xlarge-v2''': 512,
'''albert-xxlarge-v2''': 512,
}
__SCREAMING_SNAKE_CASE :Optional[int] = '''▁'''
class A_ ( lowerCAmelCase_ ):
_lowerCamelCase : Optional[int] = VOCAB_FILES_NAMES
_lowerCamelCase : Any = PRETRAINED_VOCAB_FILES_MAP
_lowerCamelCase : Tuple = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_lowerCamelCase : int = AlbertTokenizer
def __init__( self : Optional[Any] , snake_case_ : Optional[Any]=None , snake_case_ : Optional[Any]=None , snake_case_ : Optional[Any]=True , snake_case_ : str=True , snake_case_ : Tuple=False , snake_case_ : List[Any]="[CLS]" , snake_case_ : Union[str, Any]="[SEP]" , snake_case_ : str="<unk>" , snake_case_ : Union[str, Any]="[SEP]" , snake_case_ : List[Any]="<pad>" , snake_case_ : List[str]="[CLS]" , snake_case_ : int="[MASK]" , **snake_case_ : Any , ):
# Mask token behave like a normal word, i.e. include the space before it and
# is included in the raw text, there should be a match in a non-normalized sentence.
_UpperCAmelCase = (
AddedToken(snake_case_ , lstrip=snake_case_ , rstrip=snake_case_ , normalized=snake_case_ )
if isinstance(snake_case_ , snake_case_ )
else mask_token
)
super().__init__(
snake_case_ , tokenizer_file=snake_case_ , do_lower_case=snake_case_ , remove_space=snake_case_ , keep_accents=snake_case_ , bos_token=snake_case_ , eos_token=snake_case_ , unk_token=snake_case_ , sep_token=snake_case_ , pad_token=snake_case_ , cls_token=snake_case_ , mask_token=snake_case_ , **snake_case_ , )
_UpperCAmelCase = do_lower_case
_UpperCAmelCase = remove_space
_UpperCAmelCase = keep_accents
_UpperCAmelCase = vocab_file
_UpperCAmelCase = False if not self.vocab_file else True
def lowercase ( self : Union[str, Any] , snake_case_ : List[int] , snake_case_ : Optional[List[int]] = None ):
_UpperCAmelCase = [self.sep_token_id]
_UpperCAmelCase = [self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def lowercase ( self : Dict , snake_case_ : List[int] , snake_case_ : Optional[List[int]] = None ):
_UpperCAmelCase = [self.sep_token_id]
_UpperCAmelCase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def lowercase ( self : Optional[Any] , snake_case_ : str , snake_case_ : Optional[str] = None ):
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(snake_case_ ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
return
_UpperCAmelCase = os.path.join(
snake_case_ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(snake_case_ ):
copyfile(self.vocab_file , snake_case_ )
return (out_vocab_file,)
| 22 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
__SCREAMING_SNAKE_CASE :List[Any] = logging.get_logger(__name__)
class A_ ( lowerCAmelCase_ ):
def __init__( self : str , *snake_case_ : List[str] , **snake_case_ : int ):
warnings.warn(
"The class LayoutLMv2FeatureExtractor is deprecated and will be removed in version 5 of Transformers."
" Please use LayoutLMv2ImageProcessor instead." , snake_case_ , )
super().__init__(*snake_case_ , **snake_case_ )
| 22 |
'''simple docstring'''
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
__SCREAMING_SNAKE_CASE :Dict = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE :int = {
'''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 A_ ( lowerCAmelCase_ ):
_lowerCamelCase : int = """perceiver"""
def __init__( self : Any , snake_case_ : List[Any]=2_5_6 , snake_case_ : str=1_2_8_0 , snake_case_ : Optional[int]=7_6_8 , snake_case_ : int=1 , snake_case_ : List[Any]=2_6 , snake_case_ : Dict=8 , snake_case_ : List[Any]=8 , snake_case_ : Tuple=None , snake_case_ : Tuple=None , snake_case_ : Any="kv" , snake_case_ : Any=1 , snake_case_ : List[str]=1 , snake_case_ : Optional[int]="gelu" , snake_case_ : List[Any]=0.1 , snake_case_ : Dict=0.0_2 , snake_case_ : int=1e-12 , snake_case_ : List[str]=True , snake_case_ : str=2_6_2 , snake_case_ : Optional[Any]=2_0_4_8 , snake_case_ : Union[str, Any]=5_6 , snake_case_ : Dict=[3_6_8, 4_9_6] , snake_case_ : Tuple=1_6 , snake_case_ : Union[str, Any]=1_9_2_0 , snake_case_ : List[Any]=1_6 , snake_case_ : Tuple=[1, 1_6, 2_2_4, 2_2_4] , **snake_case_ : List[Any] , ):
super().__init__(**snake_case_ )
_UpperCAmelCase = num_latents
_UpperCAmelCase = d_latents
_UpperCAmelCase = d_model
_UpperCAmelCase = num_blocks
_UpperCAmelCase = num_self_attends_per_block
_UpperCAmelCase = num_self_attention_heads
_UpperCAmelCase = num_cross_attention_heads
_UpperCAmelCase = qk_channels
_UpperCAmelCase = v_channels
_UpperCAmelCase = cross_attention_shape_for_attention
_UpperCAmelCase = self_attention_widening_factor
_UpperCAmelCase = cross_attention_widening_factor
_UpperCAmelCase = hidden_act
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = initializer_range
_UpperCAmelCase = layer_norm_eps
_UpperCAmelCase = use_query_residual
# masked language modeling attributes
_UpperCAmelCase = vocab_size
_UpperCAmelCase = max_position_embeddings
# image classification attributes
_UpperCAmelCase = image_size
# flow attributes
_UpperCAmelCase = train_size
# multimodal autoencoding attributes
_UpperCAmelCase = num_frames
_UpperCAmelCase = audio_samples_per_frame
_UpperCAmelCase = samples_per_patch
_UpperCAmelCase = output_shape
class A_ ( lowerCAmelCase_ ):
@property
def lowercase ( self : int ):
if self.task == "multiple-choice":
_UpperCAmelCase = {0: "batch", 1: "choice", 2: "sequence"}
else:
_UpperCAmelCase = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("inputs", dynamic_axis),
("attention_mask", dynamic_axis),
] )
@property
def lowercase ( self : Optional[Any] ):
return 1e-4
def lowercase ( self : List[str] , snake_case_ : Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"] , snake_case_ : int = -1 , snake_case_ : int = -1 , snake_case_ : int = -1 , snake_case_ : bool = False , snake_case_ : Optional[TensorType] = None , snake_case_ : int = 3 , snake_case_ : int = 4_0 , snake_case_ : int = 4_0 , ):
# copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified
if isinstance(snake_case_ , snake_case_ ):
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
_UpperCAmelCase = compute_effective_axis_dimension(
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 = preprocessor.num_special_tokens_to_add(snake_case_ )
_UpperCAmelCase = compute_effective_axis_dimension(
snake_case_ , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=snake_case_ )
# Generate dummy inputs according to compute batch and sequence
_UpperCAmelCase = [" ".join(["a"] ) * seq_length] * batch_size
_UpperCAmelCase = dict(preprocessor(snake_case_ , return_tensors=snake_case_ ) )
_UpperCAmelCase = inputs.pop("input_ids" )
return inputs
elif isinstance(snake_case_ , snake_case_ ) 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
_UpperCAmelCase = compute_effective_axis_dimension(snake_case_ , fixed_dimension=OnnxConfig.default_fixed_batch )
_UpperCAmelCase = self._generate_dummy_images(snake_case_ , snake_case_ , snake_case_ , snake_case_ )
_UpperCAmelCase = dict(preprocessor(images=snake_case_ , return_tensors=snake_case_ ) )
_UpperCAmelCase = inputs.pop("pixel_values" )
return inputs
else:
raise ValueError(
"Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor." )
| 22 | 1 |
'''simple docstring'''
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def UpperCAmelCase_ ( ) -> Any:
'''simple docstring'''
_UpperCAmelCase = {
"repo_name": ["test_repo1", "test_repo2", "test_repo3"],
"path": ["test_1.py", "test_2.py", "unit_test.py"],
"content": ["a " * 20, "a " * 30, "b " * 7],
}
_UpperCAmelCase = Dataset.from_dict(__lowercase )
return dataset
class A_ ( lowerCAmelCase_ ):
def lowercase ( self : Union[str, Any] ):
_UpperCAmelCase = get_dataset()
_UpperCAmelCase = make_duplicate_clusters(snake_case_ , 0.8_5 )
self.assertEqual(len(duplicate_clusters[0] ) , 2 )
def lowercase ( self : int ):
_UpperCAmelCase = get_dataset()
_UpperCAmelCase , _UpperCAmelCase = deduplicate_dataset(snake_case_ )
self.assertEqual(len(snake_case_ ) , 2 )
print(snake_case_ )
self.assertEqual(duplicate_clusters[0][0]["copies"] , 2 )
self.assertEqual(duplicate_clusters[0][0]["is_extreme"] , snake_case_ )
| 22 |
'''simple docstring'''
import warnings
from ...utils import is_sklearn_available, requires_backends
if is_sklearn_available():
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
__SCREAMING_SNAKE_CASE :List[str] = (
'''This metric will be removed from the library soon, metrics should be handled with the 🤗 Evaluate '''
'''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'''
)
def UpperCAmelCase_ ( __lowercase : Any , __lowercase : Tuple ) -> int:
'''simple docstring'''
warnings.warn(__lowercase , __lowercase )
requires_backends(__lowercase , "sklearn" )
return (preds == labels).mean()
def UpperCAmelCase_ ( __lowercase : int , __lowercase : str ) -> Optional[Any]:
'''simple docstring'''
warnings.warn(__lowercase , __lowercase )
requires_backends(__lowercase , "sklearn" )
_UpperCAmelCase = simple_accuracy(__lowercase , __lowercase )
_UpperCAmelCase = fa_score(y_true=__lowercase , y_pred=__lowercase )
return {
"acc": acc,
"f1": fa,
"acc_and_f1": (acc + fa) / 2,
}
def UpperCAmelCase_ ( __lowercase : Optional[int] , __lowercase : List[str] ) -> List[Any]:
'''simple docstring'''
warnings.warn(__lowercase , __lowercase )
requires_backends(__lowercase , "sklearn" )
_UpperCAmelCase = pearsonr(__lowercase , __lowercase )[0]
_UpperCAmelCase = spearmanr(__lowercase , __lowercase )[0]
return {
"pearson": pearson_corr,
"spearmanr": spearman_corr,
"corr": (pearson_corr + spearman_corr) / 2,
}
def UpperCAmelCase_ ( __lowercase : Optional[Any] , __lowercase : str , __lowercase : str ) -> Tuple:
'''simple docstring'''
warnings.warn(__lowercase , __lowercase )
requires_backends(__lowercase , "sklearn" )
assert len(__lowercase ) == len(__lowercase ), f'Predictions and labels have mismatched lengths {len(__lowercase )} and {len(__lowercase )}'
if task_name == "cola":
return {"mcc": matthews_corrcoef(__lowercase , __lowercase )}
elif task_name == "sst-2":
return {"acc": simple_accuracy(__lowercase , __lowercase )}
elif task_name == "mrpc":
return acc_and_fa(__lowercase , __lowercase )
elif task_name == "sts-b":
return pearson_and_spearman(__lowercase , __lowercase )
elif task_name == "qqp":
return acc_and_fa(__lowercase , __lowercase )
elif task_name == "mnli":
return {"mnli/acc": simple_accuracy(__lowercase , __lowercase )}
elif task_name == "mnli-mm":
return {"mnli-mm/acc": simple_accuracy(__lowercase , __lowercase )}
elif task_name == "qnli":
return {"acc": simple_accuracy(__lowercase , __lowercase )}
elif task_name == "rte":
return {"acc": simple_accuracy(__lowercase , __lowercase )}
elif task_name == "wnli":
return {"acc": simple_accuracy(__lowercase , __lowercase )}
elif task_name == "hans":
return {"acc": simple_accuracy(__lowercase , __lowercase )}
else:
raise KeyError(__lowercase )
def UpperCAmelCase_ ( __lowercase : List[Any] , __lowercase : Dict , __lowercase : str ) -> Union[str, Any]:
'''simple docstring'''
warnings.warn(__lowercase , __lowercase )
requires_backends(__lowercase , "sklearn" )
if len(__lowercase ) != len(__lowercase ):
raise ValueError(f'Predictions and labels have mismatched lengths {len(__lowercase )} and {len(__lowercase )}' )
if task_name == "xnli":
return {"acc": simple_accuracy(__lowercase , __lowercase )}
else:
raise KeyError(__lowercase )
| 22 | 1 |
'''simple docstring'''
import datasets
__SCREAMING_SNAKE_CASE :List[Any] = '''\
@InProceedings{conneau2018xnli,
author = "Conneau, Alexis
and Rinott, Ruty
and Lample, Guillaume
and Williams, Adina
and Bowman, Samuel R.
and Schwenk, Holger
and Stoyanov, Veselin",
title = "XNLI: Evaluating Cross-lingual Sentence Representations",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods
in Natural Language Processing",
year = "2018",
publisher = "Association for Computational Linguistics",
location = "Brussels, Belgium",
}
'''
__SCREAMING_SNAKE_CASE :Any = '''\
XNLI is a subset of a few thousand examples from MNLI which has been translated
into a 14 different languages (some low-ish resource). As with MNLI, the goal is
to predict textual entailment (does sentence A imply/contradict/neither sentence
B) and is a classification task (given two sentences, predict one of three
labels).
'''
__SCREAMING_SNAKE_CASE :Dict = '''
Computes XNLI score which is just simple accuracy.
Args:
predictions: Predicted labels.
references: Ground truth labels.
Returns:
\'accuracy\': accuracy
Examples:
>>> predictions = [0, 1]
>>> references = [0, 1]
>>> xnli_metric = datasets.load_metric("xnli")
>>> results = xnli_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'accuracy\': 1.0}
'''
def UpperCAmelCase_ ( __lowercase : Tuple , __lowercase : Optional[int] ) -> str:
'''simple docstring'''
return (preds == labels).mean()
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class A_ ( datasets.Metric ):
def lowercase ( self : Dict ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Value("int64" if self.config_name != "sts-b" else "float32" ),
"references": datasets.Value("int64" if self.config_name != "sts-b" else "float32" ),
} ) , codebase_urls=[] , reference_urls=[] , format="numpy" , )
def lowercase ( self : str , snake_case_ : str , snake_case_ : int ):
return {"accuracy": simple_accuracy(snake_case_ , snake_case_ )}
| 22 |
'''simple docstring'''
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCAmelCase_ ( __lowercase : int , __lowercase : Dict , __lowercase : str , __lowercase : Optional[Any] , __lowercase : str ) -> List[str]:
'''simple docstring'''
_UpperCAmelCase = TapasConfig.from_json_file(__lowercase )
# set absolute/relative position embeddings parameter
_UpperCAmelCase = reset_position_index_per_cell
# set remaining parameters of TapasConfig as well as the model based on the task
if task == "SQA":
_UpperCAmelCase = TapasForQuestionAnswering(config=__lowercase )
elif task == "WTQ":
# run_task_main.py hparams
_UpperCAmelCase = 4
_UpperCAmelCase = True
# hparam_utils.py hparams
_UpperCAmelCase = 0.66_4694
_UpperCAmelCase = 0.20_7951
_UpperCAmelCase = 0.12_1194
_UpperCAmelCase = True
_UpperCAmelCase = True
_UpperCAmelCase = False
_UpperCAmelCase = 0.035_2513
_UpperCAmelCase = TapasForQuestionAnswering(config=__lowercase )
elif task == "WIKISQL_SUPERVISED":
# run_task_main.py hparams
_UpperCAmelCase = 4
_UpperCAmelCase = False
# hparam_utils.py hparams
_UpperCAmelCase = 36.4519
_UpperCAmelCase = 0.90_3421
_UpperCAmelCase = 222.088
_UpperCAmelCase = True
_UpperCAmelCase = True
_UpperCAmelCase = True
_UpperCAmelCase = 0.76_3141
_UpperCAmelCase = TapasForQuestionAnswering(config=__lowercase )
elif task == "TABFACT":
_UpperCAmelCase = TapasForSequenceClassification(config=__lowercase )
elif task == "MLM":
_UpperCAmelCase = TapasForMaskedLM(config=__lowercase )
elif task == "INTERMEDIATE_PRETRAINING":
_UpperCAmelCase = TapasModel(config=__lowercase )
else:
raise ValueError(f'Task {task} not supported.' )
print(f'Building PyTorch model from configuration: {config}' )
# Load weights from tf checkpoint
load_tf_weights_in_tapas(__lowercase , __lowercase , __lowercase )
# Save pytorch-model (weights and configuration)
print(f'Save PyTorch model to {pytorch_dump_path}' )
model.save_pretrained(__lowercase )
# Save tokenizer files
print(f'Save tokenizer files to {pytorch_dump_path}' )
_UpperCAmelCase = TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + "vocab.txt" , model_max_length=512 )
tokenizer.save_pretrained(__lowercase )
print("Used relative position embeddings:" , model.config.reset_position_index_per_cell )
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE :List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--task''', default='''SQA''', type=str, help='''Model task for which to convert a checkpoint. Defaults to SQA.'''
)
parser.add_argument(
'''--reset_position_index_per_cell''',
default=False,
action='''store_true''',
help='''Whether to use relative position embeddings or not. Defaults to True.''',
)
parser.add_argument(
'''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--tapas_config_file''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained TAPAS model. \n'''
'''This specifies the model architecture.'''
),
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
__SCREAMING_SNAKE_CASE :List[str] = parser.parse_args()
convert_tf_checkpoint_to_pytorch(
args.task,
args.reset_position_index_per_cell,
args.tf_checkpoint_path,
args.tapas_config_file,
args.pytorch_dump_path,
)
| 22 | 1 |
'''simple docstring'''
__SCREAMING_SNAKE_CASE :int = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
__SCREAMING_SNAKE_CASE :Any = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def UpperCAmelCase_ ( __lowercase : dict[int, list[int]] , __lowercase : int , __lowercase : list[bool] ) -> list[int]:
'''simple docstring'''
_UpperCAmelCase = True
_UpperCAmelCase = []
for neighbour in graph[vert]:
if not visited[neighbour]:
order += topology_sort(__lowercase , __lowercase , __lowercase )
order.append(__lowercase )
return order
def UpperCAmelCase_ ( __lowercase : dict[int, list[int]] , __lowercase : int , __lowercase : list[bool] ) -> list[int]:
'''simple docstring'''
_UpperCAmelCase = True
_UpperCAmelCase = [vert]
for neighbour in reversed_graph[vert]:
if not visited[neighbour]:
component += find_components(__lowercase , __lowercase , __lowercase )
return component
def UpperCAmelCase_ ( __lowercase : dict[int, list[int]] ) -> list[list[int]]:
'''simple docstring'''
_UpperCAmelCase = len(__lowercase ) * [False]
_UpperCAmelCase = {vert: [] for vert in range(len(__lowercase ) )}
for vert, neighbours in graph.items():
for neighbour in neighbours:
reversed_graph[neighbour].append(__lowercase )
_UpperCAmelCase = []
for i, was_visited in enumerate(__lowercase ):
if not was_visited:
order += topology_sort(__lowercase , __lowercase , __lowercase )
_UpperCAmelCase = []
_UpperCAmelCase = len(__lowercase ) * [False]
for i in range(len(__lowercase ) ):
_UpperCAmelCase = order[len(__lowercase ) - i - 1]
if not visited[vert]:
_UpperCAmelCase = find_components(__lowercase , __lowercase , __lowercase )
components_list.append(__lowercase )
return components_list
| 22 |
'''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
__SCREAMING_SNAKE_CASE :str = [
'''good first issue''',
'''feature request''',
'''wip''',
]
def UpperCAmelCase_ ( ) -> Optional[Any]:
'''simple docstring'''
_UpperCAmelCase = Github(os.environ["GITHUB_TOKEN"] )
_UpperCAmelCase = g.get_repo("huggingface/accelerate" )
_UpperCAmelCase = repo.get_issues(state="open" )
for issue in open_issues:
_UpperCAmelCase = sorted([comment for comment in issue.get_comments()] , key=lambda __lowercase : i.created_at , reverse=__lowercase )
_UpperCAmelCase = comments[0] if len(__lowercase ) > 0 else None
_UpperCAmelCase = dt.utcnow()
_UpperCAmelCase = (current_time - issue.updated_at).days
_UpperCAmelCase = (current_time - issue.created_at).days
if (
last_comment is not None
and last_comment.user.login == "github-actions[bot]"
and days_since_updated > 7
and days_since_creation >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Close issue since it has been 7 days of inactivity since bot mention.
issue.edit(state="closed" )
elif (
days_since_updated > 23
and days_since_creation >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Add stale comment
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/accelerate/blob/main/CONTRIBUTING.md) "
"are likely to be ignored." )
if __name__ == "__main__":
main()
| 22 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
__SCREAMING_SNAKE_CASE :Dict = logging.get_logger(__name__)
class A_ ( lowerCAmelCase_ ):
def __init__( self : Dict , *snake_case_ : int , **snake_case_ : Optional[Any] ):
warnings.warn(
"The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
" use BeitImageProcessor instead." , snake_case_ , )
super().__init__(*snake_case_ , **snake_case_ )
| 22 |
'''simple docstring'''
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 UpperCAmelCase_ ( __lowercase : Any , __lowercase : int ) -> int:
'''simple docstring'''
_UpperCAmelCase = 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}}" )
_UpperCAmelCase = DatasetInfosDict.from_directory(__lowercase )
assert dataset_infos
assert dataset_infos["default"].dataset_size == 42
@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=42 , ),
] , )
def UpperCAmelCase_ ( __lowercase : Tuple , __lowercase : DatasetInfo ) -> Any:
'''simple docstring'''
_UpperCAmelCase = str(__lowercase )
dataset_info.write_to_directory(__lowercase )
_UpperCAmelCase = DatasetInfo.from_directory(__lowercase )
assert dataset_info == reloaded
assert os.path.exists(os.path.join(__lowercase , "dataset_info.json" ) )
def UpperCAmelCase_ ( ) -> Optional[Any]:
'''simple docstring'''
_UpperCAmelCase = 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": 42}] , download_checksums={} , download_size=1337 , post_processing_size=442 , dataset_size=1234 , size_in_bytes=1337 + 442 + 1234 , )
_UpperCAmelCase = dataset_info._to_yaml_dict()
assert sorted(__lowercase ) == 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) )
_UpperCAmelCase = yaml.safe_dump(__lowercase )
_UpperCAmelCase = yaml.safe_load(__lowercase )
assert dataset_info_yaml_dict == reloaded
def UpperCAmelCase_ ( ) -> Optional[int]:
'''simple docstring'''
_UpperCAmelCase = DatasetInfo()
_UpperCAmelCase = 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=42 , )
} ),
DatasetInfosDict(
{
"v1": DatasetInfo(dataset_size=42 ),
"v2": DatasetInfo(dataset_size=1337 ),
} ),
] , )
def UpperCAmelCase_ ( __lowercase : int , __lowercase : DatasetInfosDict ) -> Dict:
'''simple docstring'''
_UpperCAmelCase = str(__lowercase )
dataset_infos_dict.write_to_directory(__lowercase )
_UpperCAmelCase = DatasetInfosDict.from_directory(__lowercase )
# the config_name of the dataset_infos_dict take over the attribute
for config_name, dataset_info in dataset_infos_dict.items():
_UpperCAmelCase = 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
_UpperCAmelCase = 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(__lowercase , "README.md" ) )
| 22 | 1 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
_UpperCAmelCase = len(__lowercase )
_UpperCAmelCase = sum(__lowercase )
_UpperCAmelCase = [[False for x in range(s + 1 )] for y in range(n + 1 )]
for i in range(1 , n + 1 ):
_UpperCAmelCase = True
for i in range(1 , s + 1 ):
_UpperCAmelCase = False
for i in range(1 , n + 1 ):
for j in range(1 , s + 1 ):
_UpperCAmelCase = dp[i][j - 1]
if arr[i - 1] <= j:
_UpperCAmelCase = dp[i][j] or dp[i - 1][j - arr[i - 1]]
for j in range(int(s / 2 ) , -1 , -1 ):
if dp[n][j] is True:
_UpperCAmelCase = s - 2 * j
break
return diff
| 22 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : str ) -> str:
'''simple docstring'''
return " ".join(
"".join(word[::-1] ) if len(__lowercase ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(reverse_long_words('''Hey wollef sroirraw'''))
| 22 | 1 |
'''simple docstring'''
import json
import os
from datetime import date
from pathlib import Path
from tabulate import DataRow, TableFormat, tabulate
__SCREAMING_SNAKE_CASE :Optional[int] = TableFormat(
lineabove=None,
linebelowheader=None,
linebetweenrows=None,
linebelow=None,
headerrow=DataRow('''''', '''|''', '''|'''),
datarow=DataRow('''''', '''|''', '''|'''),
padding=1,
with_header_hide=None,
)
__SCREAMING_SNAKE_CASE :Any = []
__SCREAMING_SNAKE_CASE :int = []
__SCREAMING_SNAKE_CASE :str = {'''type''': '''section''', '''text''': {'''type''': '''plain_text''', '''text''': '''No failed tests! 🤗''', '''emoji''': True}}
__SCREAMING_SNAKE_CASE :Any = [
{
'''type''': '''header''',
'''text''': {
'''type''': '''plain_text''',
'''text''': F"🤗 Accelerate nightly {os.environ.get('TEST_TYPE', '')} test results",
'''emoji''': True,
},
}
]
__SCREAMING_SNAKE_CASE :List[str] = 0
for log in Path().glob('''*.log'''):
__SCREAMING_SNAKE_CASE :Optional[int] = 0
with open(log, '''r''') as f:
for line in f:
__SCREAMING_SNAKE_CASE :Tuple = json.loads(line)
if line.get('''nodeid''', '''''') != "":
__SCREAMING_SNAKE_CASE :Optional[int] = line['''nodeid''']
if line.get('''duration''', None) is not None:
__SCREAMING_SNAKE_CASE :List[str] = F"{line['duration']:.4f}"
if line.get('''outcome''', '''''') == "failed":
section_num_failed += 1
failed.append([test, duration, log.name.split('''_''')[0]])
total_num_failed += 1
group_info.append([str(log), section_num_failed, failed])
__SCREAMING_SNAKE_CASE :int = []
log.unlink()
__SCREAMING_SNAKE_CASE :Any = ''''''
__SCREAMING_SNAKE_CASE :Union[str, Any] = []
if total_num_failed > 0:
for name, num_failed, failed_tests in group_info:
if num_failed > 0:
if num_failed == 1:
message += F"*{name[1:]}: {num_failed} failed test*\n"
else:
message += F"*{name[1:]}: {num_failed} failed tests*\n"
__SCREAMING_SNAKE_CASE :int = []
__SCREAMING_SNAKE_CASE :Any = {}
for test in failed_tests:
__SCREAMING_SNAKE_CASE :Any = test[0].split('''::''')
__SCREAMING_SNAKE_CASE :Optional[Any] = data[0].split('''/''')[-1]
if data[0] not in filesafailed:
__SCREAMING_SNAKE_CASE :List[Any] = [data[1:]]
else:
filesafailed[data[0]] += [data[1:]]
failed_table.append(data)
__SCREAMING_SNAKE_CASE :Dict = [test[0] for test in failed_table]
__SCREAMING_SNAKE_CASE :int = list(set(files))
# Count number of instances in failed_tests
__SCREAMING_SNAKE_CASE :List[Any] = []
for file in individual_files:
table.append([file, len(filesafailed[file])])
__SCREAMING_SNAKE_CASE :int = tabulate(
table,
headers=['''Test Location''', '''Num Failed'''],
tablefmt=hf_table_format,
stralign='''right''',
)
message += F"\n```\n{failed_table}\n```"
all_filesafailed.append(filesafailed)
if len(message) > 3000:
__SCREAMING_SNAKE_CASE :Union[str, Any] = '''Too many failed tests, please see the full report in the Action results.'''
__SCREAMING_SNAKE_CASE :List[Any] = len(err) + 10
__SCREAMING_SNAKE_CASE :List[str] = message[: 3000 - offset] + F"\n...\n```\n{err}"
print(F"### {message}")
else:
__SCREAMING_SNAKE_CASE :Optional[Any] = '''No failed tests! 🤗'''
print(F"## {message}")
payload.append(no_error_payload)
if os.environ.get('''TEST_TYPE''', '''''') != "":
from slack_sdk import WebClient
__SCREAMING_SNAKE_CASE :List[Any] = WebClient(token=os.environ['''SLACK_API_TOKEN'''])
if message != "No failed tests! 🤗":
__SCREAMING_SNAKE_CASE :Optional[Any] = {
'''type''': '''section''',
'''text''': {
'''type''': '''mrkdwn''',
'''text''': message,
},
}
payload.append(md_report)
__SCREAMING_SNAKE_CASE :str = {
'''type''': '''section''',
'''text''': {
'''type''': '''mrkdwn''',
'''text''': '''*For more details:*''',
},
'''accessory''': {
'''type''': '''button''',
'''text''': {
'''type''': '''plain_text''',
'''text''': '''Check Action results''',
'''emoji''': True,
},
'''url''': F"https://github.com/{os.environ['GITHUB_REPOSITORY']}/actions/runs/{os.environ['GITHUB_RUN_ID']}",
},
}
payload.append(action_button)
__SCREAMING_SNAKE_CASE :List[Any] = {
'''type''': '''context''',
'''elements''': [
{
'''type''': '''plain_text''',
'''text''': F"Nightly {os.environ.get('TEST_TYPE')} test results for {date.today()}",
}
],
}
payload.append(date_report)
__SCREAMING_SNAKE_CASE :Dict = client.chat_postMessage(channel='''#accelerate-ci-daily''', text=message, blocks=payload)
__SCREAMING_SNAKE_CASE :List[Any] = response.data['''ts''']
for failed_file in all_filesafailed:
for test_location, test_failures in failed_file.items():
# Keep only the first instance of the test name
__SCREAMING_SNAKE_CASE :Tuple = ''''''
for i, row in enumerate(test_failures):
if row[0] != test_class:
__SCREAMING_SNAKE_CASE :Any = row[0]
else:
__SCREAMING_SNAKE_CASE :Union[str, Any] = ''''''
__SCREAMING_SNAKE_CASE :Dict = {
'''type''': '''section''',
'''text''': {
'''type''': '''mrkdwn''',
'''text''': F"Test location: {test_location}\n```\n{tabulate(test_failures, headers=['Class', 'Test'], tablefmt=hf_table_format, stralign='right')}\n```",
},
}
client.chat_postMessage(
channel='''#accelerate-ci-daily''',
thread_ts=ts,
blocks=[payload],
)
| 22 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : str ) -> list:
'''simple docstring'''
if n_term == "":
return []
_UpperCAmelCase = []
for temp in range(int(__lowercase ) ):
series.append(f'1/{temp + 1}' if series else "1" )
return series
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE :str = input('''Enter the last number (nth term) of the Harmonic Series''')
print('''Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n''')
print(harmonic_series(nth_term))
| 22 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional
from packaging import version
if TYPE_CHECKING:
from ... import PreTrainedTokenizer, TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import is_torch_available, logging
__SCREAMING_SNAKE_CASE :Dict = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE :Optional[int] = {
'''bigscience/bloom''': '''https://huggingface.co/bigscience/bloom/resolve/main/config.json''',
'''bigscience/bloom-560m''': '''https://huggingface.co/bigscience/bloom-560m/blob/main/config.json''',
'''bigscience/bloom-1b1''': '''https://huggingface.co/bigscience/bloom-1b1/blob/main/config.json''',
'''bigscience/bloom-1b7''': '''https://huggingface.co/bigscience/bloom-1b7/blob/main/config.json''',
'''bigscience/bloom-3b''': '''https://huggingface.co/bigscience/bloom-3b/blob/main/config.json''',
'''bigscience/bloom-7b1''': '''https://huggingface.co/bigscience/bloom-7b1/blob/main/config.json''',
}
class A_ ( lowerCAmelCase_ ):
_lowerCamelCase : str = """bloom"""
_lowerCamelCase : Tuple = ["""past_key_values"""]
_lowerCamelCase : int = {
"""num_hidden_layers""": """n_layer""",
"""num_attention_heads""": """n_head""",
}
def __init__( self : str , snake_case_ : Dict=2_5_0_8_8_0 , snake_case_ : int=6_4 , snake_case_ : Union[str, Any]=2 , snake_case_ : Optional[int]=8 , snake_case_ : Optional[Any]=1e-5 , snake_case_ : str=0.0_2 , snake_case_ : Tuple=True , snake_case_ : Tuple=1 , snake_case_ : List[str]=2 , snake_case_ : List[str]=False , snake_case_ : List[Any]=0.0 , snake_case_ : Tuple=0.0 , snake_case_ : Dict=1 , snake_case_ : Tuple=False , **snake_case_ : Any , ):
_UpperCAmelCase = vocab_size
# Backward compatibility with n_embed kwarg
_UpperCAmelCase = kwargs.pop("n_embed" , snake_case_ )
_UpperCAmelCase = hidden_size if n_embed is None else n_embed
_UpperCAmelCase = n_layer
_UpperCAmelCase = n_head
_UpperCAmelCase = layer_norm_epsilon
_UpperCAmelCase = initializer_range
_UpperCAmelCase = use_cache
_UpperCAmelCase = pretraining_tp
_UpperCAmelCase = apply_residual_connection_post_layernorm
_UpperCAmelCase = hidden_dropout
_UpperCAmelCase = attention_dropout
_UpperCAmelCase = bos_token_id
_UpperCAmelCase = eos_token_id
_UpperCAmelCase = slow_but_exact
super().__init__(bos_token_id=snake_case_ , eos_token_id=snake_case_ , **snake_case_ )
class A_ ( lowerCAmelCase_ ):
_lowerCamelCase : Dict = version.parse("""1.12""" )
def __init__( self : str , snake_case_ : PretrainedConfig , snake_case_ : str = "default" , snake_case_ : List[PatchingSpec] = None , snake_case_ : bool = False , ):
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?
_UpperCAmelCase = 0
@property
def lowercase ( self : Optional[int] ):
_UpperCAmelCase = OrderedDict({"input_ids": {0: "batch", 1: "sequence"}} )
if self.use_past:
# BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
self.fill_with_past_key_values_(snake_case_ , direction="inputs" , inverted_values_shape=snake_case_ )
_UpperCAmelCase = {0: "batch", 1: "past_sequence + sequence"}
else:
_UpperCAmelCase = {0: "batch", 1: "sequence"}
return common_inputs
@property
def lowercase ( self : List[Any] ):
return self._config.n_layer
@property
def lowercase ( self : Optional[int] ):
return self._config.n_head
@property
def lowercase ( self : List[Any] ):
return 1e-3
def lowercase ( self : Optional[int] , snake_case_ : "PreTrainedTokenizer" , snake_case_ : int = -1 , snake_case_ : int = -1 , snake_case_ : bool = False , snake_case_ : Optional["TensorType"] = None , ):
_UpperCAmelCase = 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()
_UpperCAmelCase = 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
_UpperCAmelCase , _UpperCAmelCase = common_inputs["input_ids"].shape
# Not using the same length for past_key_values
_UpperCAmelCase = seqlen + 2
_UpperCAmelCase = self._config.hidden_size // self.num_attention_heads
_UpperCAmelCase = (
batch * self.num_attention_heads,
head_dim,
past_key_values_length,
)
_UpperCAmelCase = (
batch * self.num_attention_heads,
past_key_values_length,
head_dim,
)
_UpperCAmelCase = [
(torch.zeros(snake_case_ ), torch.zeros(snake_case_ )) for _ in range(self.num_layers )
]
_UpperCAmelCase = common_inputs["attention_mask"]
if self.use_past:
_UpperCAmelCase = ordered_inputs["attention_mask"].dtype
_UpperCAmelCase = torch.cat(
[ordered_inputs["attention_mask"], torch.ones(snake_case_ , snake_case_ , dtype=snake_case_ )] , dim=1 )
return ordered_inputs
@property
def lowercase ( self : Optional[Any] ):
return 1_3
| 22 |
'''simple docstring'''
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
__SCREAMING_SNAKE_CASE :int = get_tests_dir('''fixtures/test_sentencepiece_no_bos.model''')
@require_sentencepiece
@require_tokenizers
class A_ ( lowerCAmelCase_ , unittest.TestCase ):
_lowerCamelCase : List[str] = PegasusTokenizer
_lowerCamelCase : int = PegasusTokenizerFast
_lowerCamelCase : Union[str, Any] = True
_lowerCamelCase : List[str] = True
def lowercase ( self : Optional[int] ):
super().setUp()
# We have a SentencePiece fixture for testing
_UpperCAmelCase = PegasusTokenizer(snake_case_ )
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def lowercase ( self : Tuple ):
return PegasusTokenizer.from_pretrained("google/pegasus-large" )
def lowercase ( self : Union[str, Any] , **snake_case_ : Union[str, Any] ):
return PegasusTokenizer.from_pretrained(self.tmpdirname , **snake_case_ )
def lowercase ( self : Tuple , snake_case_ : Any ):
return ("This is a test", "This is a test")
def lowercase ( self : Optional[int] ):
_UpperCAmelCase = "</s>"
_UpperCAmelCase = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case_ ) , snake_case_ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case_ ) , snake_case_ )
def lowercase ( self : Union[str, Any] ):
_UpperCAmelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , "<pad>" )
self.assertEqual(vocab_keys[1] , "</s>" )
self.assertEqual(vocab_keys[-1] , "v" )
self.assertEqual(len(snake_case_ ) , 1_1_0_3 )
def lowercase ( self : Any ):
self.assertEqual(self.get_tokenizer().vocab_size , 1_1_0_3 )
def lowercase ( self : List[Any] ):
_UpperCAmelCase = self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
_UpperCAmelCase = self.tokenizer_class.from_pretrained(self.tmpdirname )
_UpperCAmelCase = (
"Let's see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important"
" </s> <pad> <pad> <pad>"
)
_UpperCAmelCase = rust_tokenizer([raw_input_str] , return_tensors=snake_case_ , add_special_tokens=snake_case_ ).input_ids[0]
_UpperCAmelCase = py_tokenizer([raw_input_str] , return_tensors=snake_case_ , add_special_tokens=snake_case_ ).input_ids[0]
self.assertListEqual(snake_case_ , snake_case_ )
def lowercase ( self : Tuple ):
_UpperCAmelCase = self._large_tokenizer
# <mask_1> masks whole sentence while <mask_2> masks single word
_UpperCAmelCase = "<mask_1> To ensure a <mask_2> flow of bank resolutions."
_UpperCAmelCase = [2, 4_1_3, 6_1_5, 1_1_4, 3, 1_9_7_1, 1_1_3, 1_6_7_9, 1_0_7_1_0, 1_0_7, 1]
_UpperCAmelCase = tokenizer([raw_input_str] , return_tensors=snake_case_ ).input_ids[0]
self.assertListEqual(snake_case_ , snake_case_ )
def lowercase ( self : Union[str, Any] ):
_UpperCAmelCase = self._large_tokenizer
# The tracebacks for the following asserts are **better** without messages or self.assertEqual
assert tokenizer.vocab_size == 9_6_1_0_3
assert tokenizer.pad_token_id == 0
assert tokenizer.eos_token_id == 1
assert tokenizer.offset == 1_0_3
assert tokenizer.unk_token_id == tokenizer.offset + 2 == 1_0_5
assert tokenizer.unk_token == "<unk>"
assert tokenizer.model_max_length == 1_0_2_4
_UpperCAmelCase = "To ensure a smooth flow of bank resolutions."
_UpperCAmelCase = [4_1_3, 6_1_5, 1_1_4, 2_2_9_1, 1_9_7_1, 1_1_3, 1_6_7_9, 1_0_7_1_0, 1_0_7, 1]
_UpperCAmelCase = tokenizer([raw_input_str] , return_tensors=snake_case_ ).input_ids[0]
self.assertListEqual(snake_case_ , snake_case_ )
assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3] ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]
@require_torch
def lowercase ( self : int ):
_UpperCAmelCase = ["This is going to be way too long." * 1_5_0, "short example"]
_UpperCAmelCase = ["not super long but more than 5 tokens", "tiny"]
_UpperCAmelCase = self._large_tokenizer(snake_case_ , padding=snake_case_ , truncation=snake_case_ , return_tensors="pt" )
_UpperCAmelCase = self._large_tokenizer(
text_target=snake_case_ , max_length=5 , padding=snake_case_ , truncation=snake_case_ , return_tensors="pt" )
assert batch.input_ids.shape == (2, 1_0_2_4)
assert batch.attention_mask.shape == (2, 1_0_2_4)
assert targets["input_ids"].shape == (2, 5)
assert len(snake_case_ ) == 2 # input_ids, attention_mask.
@slow
def lowercase ( self : Dict ):
# fmt: off
_UpperCAmelCase = {"input_ids": [[3_8_9_7_9, 1_4_3, 1_8_4_8_5, 6_0_6, 1_3_0, 2_6_6_6_9, 8_7_6_8_6, 1_2_1, 5_4_1_8_9, 1_1_2_9, 1_1_1, 2_6_6_6_9, 8_7_6_8_6, 1_2_1, 9_1_1_4, 1_4_7_8_7, 1_2_1, 1_3_2_4_9, 1_5_8, 5_9_2, 9_5_6, 1_2_1, 1_4_6_2_1, 3_1_5_7_6, 1_4_3, 6_2_6_1_3, 1_0_8, 9_6_8_8, 9_3_0, 4_3_4_3_0, 1_1_5_6_2, 6_2_6_1_3, 3_0_4, 1_0_8, 1_1_4_4_3, 8_9_7, 1_0_8, 9_3_1_4, 1_7_4_1_5, 6_3_3_9_9, 1_0_8, 1_1_4_4_3, 7_6_1_4, 1_8_3_1_6, 1_1_8, 4_2_8_4, 7_1_4_8, 1_2_4_3_0, 1_4_3, 1_4_0_0, 2_5_7_0_3, 1_5_8, 1_1_1, 4_2_8_4, 7_1_4_8, 1_1_7_7_2, 1_4_3, 2_1_2_9_7, 1_0_6_4, 1_5_8, 1_2_2, 2_0_4, 3_5_0_6, 1_7_5_4, 1_1_3_3, 1_4_7_8_7, 1_5_8_1, 1_1_5, 3_3_2_2_4, 4_4_8_2, 1_1_1, 1_3_5_5, 1_1_0, 2_9_1_7_3, 3_1_7, 5_0_8_3_3, 1_0_8, 2_0_1_4_7, 9_4_6_6_5, 1_1_1, 7_7_1_9_8, 1_0_7, 1], [1_1_0, 6_2_6_1_3, 1_1_7, 6_3_8, 1_1_2, 1_1_3_3, 1_2_1, 2_0_0_9_8, 1_3_5_5, 7_9_0_5_0, 1_3_8_7_2, 1_3_5, 1_5_9_6, 5_3_5_4_1, 1_3_5_2, 1_4_1, 1_3_0_3_9, 5_5_4_2, 1_2_4, 3_0_2, 5_1_8, 1_1_1, 2_6_8, 2_9_5_6, 1_1_5, 1_4_9, 4_4_2_7, 1_0_7, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1_3_9, 1_2_3_5, 2_7_9_9, 1_8_2_8_9, 1_7_7_8_0, 2_0_4, 1_0_9, 9_4_7_4, 1_2_9_6, 1_0_7, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=snake_case_ , model_name="google/bigbird-pegasus-large-arxiv" , revision="ba85d0851d708441f91440d509690f1ab6353415" , )
@require_sentencepiece
@require_tokenizers
class A_ ( lowerCAmelCase_ , unittest.TestCase ):
_lowerCamelCase : List[str] = PegasusTokenizer
_lowerCamelCase : List[Any] = PegasusTokenizerFast
_lowerCamelCase : int = True
_lowerCamelCase : Union[str, Any] = True
def lowercase ( self : Any ):
super().setUp()
# We have a SentencePiece fixture for testing
_UpperCAmelCase = PegasusTokenizer(snake_case_ , offset=0 , mask_token_sent=snake_case_ , mask_token="[MASK]" )
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def lowercase ( self : Tuple ):
return PegasusTokenizer.from_pretrained("google/bigbird-pegasus-large-arxiv" )
def lowercase ( self : Optional[Any] , **snake_case_ : Dict ):
return PegasusTokenizer.from_pretrained(self.tmpdirname , **snake_case_ )
def lowercase ( self : Union[str, Any] , snake_case_ : str ):
return ("This is a test", "This is a test")
def lowercase ( self : List[str] ):
_UpperCAmelCase = self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
_UpperCAmelCase = self.tokenizer_class.from_pretrained(self.tmpdirname )
_UpperCAmelCase = (
"Let's see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"
" <pad> <pad> <pad>"
)
_UpperCAmelCase = rust_tokenizer([raw_input_str] , return_tensors=snake_case_ , add_special_tokens=snake_case_ ).input_ids[0]
_UpperCAmelCase = py_tokenizer([raw_input_str] , return_tensors=snake_case_ , add_special_tokens=snake_case_ ).input_ids[0]
self.assertListEqual(snake_case_ , snake_case_ )
@require_torch
def lowercase ( self : Tuple ):
_UpperCAmelCase = ["This is going to be way too long." * 1_0_0_0, "short example"]
_UpperCAmelCase = ["not super long but more than 5 tokens", "tiny"]
_UpperCAmelCase = self._large_tokenizer(snake_case_ , padding=snake_case_ , truncation=snake_case_ , return_tensors="pt" )
_UpperCAmelCase = self._large_tokenizer(
text_target=snake_case_ , max_length=5 , padding=snake_case_ , truncation=snake_case_ , return_tensors="pt" )
assert batch.input_ids.shape == (2, 4_0_9_6)
assert batch.attention_mask.shape == (2, 4_0_9_6)
assert targets["input_ids"].shape == (2, 5)
assert len(snake_case_ ) == 2 # input_ids, attention_mask.
def lowercase ( self : Union[str, Any] ):
_UpperCAmelCase = (
"This is an example string that is used to test the original TF implementation against the HF"
" implementation"
)
_UpperCAmelCase = self._large_tokenizer(snake_case_ ).input_ids
self.assertListEqual(
snake_case_ , [1_8_2, 1_1_7, 1_4_2, 5_8_7, 4_2_1_1, 1_2_0, 1_1_7, 2_6_3, 1_1_2, 8_0_4, 1_0_9, 8_5_6, 2_5_0_1_6, 3_1_3_7, 4_6_4, 1_0_9, 2_6_9_5_5, 3_1_3_7, 1] , )
| 22 | 1 |
'''simple docstring'''
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device
if is_torch_available():
from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
@require_torch
class A_ ( unittest.TestCase ):
def lowercase ( self : Union[str, Any] , snake_case_ : List[Any] ):
for model_result in results.values():
for batch_size, sequence_length in zip(model_result["bs"] , model_result["ss"] ):
_UpperCAmelCase = model_result["result"][batch_size][sequence_length]
self.assertIsNotNone(snake_case_ )
def lowercase ( self : Optional[int] ):
_UpperCAmelCase = "sshleifer/tiny-gpt2"
_UpperCAmelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=snake_case_ , inference=snake_case_ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=snake_case_ , )
_UpperCAmelCase = PyTorchBenchmark(snake_case_ )
_UpperCAmelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def lowercase ( self : Optional[int] ):
_UpperCAmelCase = "sgugger/tiny-distilbert-classification"
_UpperCAmelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=snake_case_ , inference=snake_case_ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=snake_case_ , only_pretrain_model=snake_case_ , )
_UpperCAmelCase = PyTorchBenchmark(snake_case_ )
_UpperCAmelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def lowercase ( self : str ):
_UpperCAmelCase = "sshleifer/tiny-gpt2"
_UpperCAmelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=snake_case_ , inference=snake_case_ , torchscript=snake_case_ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=snake_case_ , )
_UpperCAmelCase = PyTorchBenchmark(snake_case_ )
_UpperCAmelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
@unittest.skipIf(torch_device == "cpu" , "Cant do half precision" )
def lowercase ( self : str ):
_UpperCAmelCase = "sshleifer/tiny-gpt2"
_UpperCAmelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=snake_case_ , inference=snake_case_ , fpaa=snake_case_ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=snake_case_ , )
_UpperCAmelCase = PyTorchBenchmark(snake_case_ )
_UpperCAmelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def lowercase ( self : Tuple ):
_UpperCAmelCase = "sshleifer/tiny-gpt2"
_UpperCAmelCase = AutoConfig.from_pretrained(snake_case_ )
# set architectures equal to `None`
_UpperCAmelCase = None
_UpperCAmelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=snake_case_ , inference=snake_case_ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=snake_case_ , )
_UpperCAmelCase = PyTorchBenchmark(snake_case_ , configs=[config] )
_UpperCAmelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def lowercase ( self : List[str] ):
_UpperCAmelCase = "sshleifer/tiny-gpt2"
_UpperCAmelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=snake_case_ , inference=snake_case_ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=snake_case_ , )
_UpperCAmelCase = PyTorchBenchmark(snake_case_ )
_UpperCAmelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
@unittest.skipIf(torch_device == "cpu" , "Can't do half precision" )
def lowercase ( self : int ):
_UpperCAmelCase = "sshleifer/tiny-gpt2"
_UpperCAmelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=snake_case_ , inference=snake_case_ , sequence_lengths=[8] , batch_sizes=[1] , fpaa=snake_case_ , multi_process=snake_case_ , )
_UpperCAmelCase = PyTorchBenchmark(snake_case_ )
_UpperCAmelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def lowercase ( self : int ):
_UpperCAmelCase = "sshleifer/tiny-gpt2"
_UpperCAmelCase = AutoConfig.from_pretrained(snake_case_ )
_UpperCAmelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=snake_case_ , inference=snake_case_ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=snake_case_ , )
_UpperCAmelCase = PyTorchBenchmark(snake_case_ , configs=[config] )
_UpperCAmelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def lowercase ( self : Optional[int] ):
_UpperCAmelCase = "sshleifer/tinier_bart"
_UpperCAmelCase = AutoConfig.from_pretrained(snake_case_ )
_UpperCAmelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=snake_case_ , inference=snake_case_ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=snake_case_ , )
_UpperCAmelCase = PyTorchBenchmark(snake_case_ , configs=[config] )
_UpperCAmelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def lowercase ( self : Any ):
_UpperCAmelCase = "sshleifer/tiny-gpt2"
_UpperCAmelCase = AutoConfig.from_pretrained(snake_case_ )
_UpperCAmelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=snake_case_ , inference=snake_case_ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=snake_case_ , )
_UpperCAmelCase = PyTorchBenchmark(snake_case_ , configs=[config] )
_UpperCAmelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def lowercase ( self : List[str] ):
_UpperCAmelCase = "sshleifer/tinier_bart"
_UpperCAmelCase = AutoConfig.from_pretrained(snake_case_ )
_UpperCAmelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=snake_case_ , inference=snake_case_ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=snake_case_ , )
_UpperCAmelCase = PyTorchBenchmark(snake_case_ , configs=[config] )
_UpperCAmelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def lowercase ( self : Union[str, Any] ):
_UpperCAmelCase = "sshleifer/tiny-gpt2"
with tempfile.TemporaryDirectory() as tmp_dir:
_UpperCAmelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=snake_case_ , inference=snake_case_ , save_to_csv=snake_case_ , sequence_lengths=[8] , batch_sizes=[1] , inference_time_csv_file=os.path.join(snake_case_ , "inf_time.csv" ) , train_memory_csv_file=os.path.join(snake_case_ , "train_mem.csv" ) , inference_memory_csv_file=os.path.join(snake_case_ , "inf_mem.csv" ) , train_time_csv_file=os.path.join(snake_case_ , "train_time.csv" ) , env_info_csv_file=os.path.join(snake_case_ , "env.csv" ) , multi_process=snake_case_ , )
_UpperCAmelCase = PyTorchBenchmark(snake_case_ )
benchmark.run()
self.assertTrue(Path(os.path.join(snake_case_ , "inf_time.csv" ) ).exists() )
self.assertTrue(Path(os.path.join(snake_case_ , "train_time.csv" ) ).exists() )
self.assertTrue(Path(os.path.join(snake_case_ , "inf_mem.csv" ) ).exists() )
self.assertTrue(Path(os.path.join(snake_case_ , "train_mem.csv" ) ).exists() )
self.assertTrue(Path(os.path.join(snake_case_ , "env.csv" ) ).exists() )
def lowercase ( self : List[Any] ):
_UpperCAmelCase = "sshleifer/tiny-gpt2"
def _check_summary_is_not_empty(snake_case_ : Any ):
self.assertTrue(hasattr(snake_case_ , "sequential" ) )
self.assertTrue(hasattr(snake_case_ , "cumulative" ) )
self.assertTrue(hasattr(snake_case_ , "current" ) )
self.assertTrue(hasattr(snake_case_ , "total" ) )
with tempfile.TemporaryDirectory() as tmp_dir:
_UpperCAmelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=snake_case_ , inference=snake_case_ , sequence_lengths=[8] , batch_sizes=[1] , log_filename=os.path.join(snake_case_ , "log.txt" ) , log_print=snake_case_ , trace_memory_line_by_line=snake_case_ , multi_process=snake_case_ , )
_UpperCAmelCase = PyTorchBenchmark(snake_case_ )
_UpperCAmelCase = benchmark.run()
_check_summary_is_not_empty(result.inference_summary )
_check_summary_is_not_empty(result.train_summary )
self.assertTrue(Path(os.path.join(snake_case_ , "log.txt" ) ).exists() )
| 22 |
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast
@require_vision
class A_ ( unittest.TestCase ):
def lowercase ( self : int ):
_UpperCAmelCase = tempfile.mkdtemp()
_UpperCAmelCase = BlipImageProcessor()
_UpperCAmelCase = BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-BertModel" )
_UpperCAmelCase = BlipProcessor(snake_case_ , snake_case_ )
processor.save_pretrained(self.tmpdirname )
def lowercase ( self : Tuple , **snake_case_ : int ):
return AutoProcessor.from_pretrained(self.tmpdirname , **snake_case_ ).tokenizer
def lowercase ( self : Dict , **snake_case_ : Any ):
return AutoProcessor.from_pretrained(self.tmpdirname , **snake_case_ ).image_processor
def lowercase ( self : int ):
shutil.rmtree(self.tmpdirname )
def lowercase ( self : Optional[Any] ):
_UpperCAmelCase = [np.random.randint(2_5_5 , size=(3, 3_0, 4_0_0) , dtype=np.uinta )]
_UpperCAmelCase = [Image.fromarray(np.moveaxis(snake_case_ , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def lowercase ( self : int ):
_UpperCAmelCase = BlipProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
_UpperCAmelCase = self.get_tokenizer(bos_token="(BOS)" , eos_token="(EOS)" )
_UpperCAmelCase = self.get_image_processor(do_normalize=snake_case_ , padding_value=1.0 )
_UpperCAmelCase = BlipProcessor.from_pretrained(
self.tmpdirname , bos_token="(BOS)" , eos_token="(EOS)" , do_normalize=snake_case_ , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , snake_case_ )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , snake_case_ )
def lowercase ( self : Any ):
_UpperCAmelCase = self.get_image_processor()
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = BlipProcessor(tokenizer=snake_case_ , image_processor=snake_case_ )
_UpperCAmelCase = self.prepare_image_inputs()
_UpperCAmelCase = image_processor(snake_case_ , return_tensors="np" )
_UpperCAmelCase = processor(images=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 lowercase ( self : Optional[int] ):
_UpperCAmelCase = self.get_image_processor()
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = BlipProcessor(tokenizer=snake_case_ , image_processor=snake_case_ )
_UpperCAmelCase = "lower newer"
_UpperCAmelCase = processor(text=snake_case_ )
_UpperCAmelCase = tokenizer(snake_case_ , return_token_type_ids=snake_case_ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def lowercase ( self : Optional[Any] ):
_UpperCAmelCase = self.get_image_processor()
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = BlipProcessor(tokenizer=snake_case_ , image_processor=snake_case_ )
_UpperCAmelCase = "lower newer"
_UpperCAmelCase = self.prepare_image_inputs()
_UpperCAmelCase = processor(text=snake_case_ , images=snake_case_ )
self.assertListEqual(list(inputs.keys() ) , ["pixel_values", "input_ids", "attention_mask"] )
# test if it raises when no input is passed
with pytest.raises(snake_case_ ):
processor()
def lowercase ( self : Union[str, Any] ):
_UpperCAmelCase = self.get_image_processor()
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = BlipProcessor(tokenizer=snake_case_ , image_processor=snake_case_ )
_UpperCAmelCase = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
_UpperCAmelCase = processor.batch_decode(snake_case_ )
_UpperCAmelCase = tokenizer.batch_decode(snake_case_ )
self.assertListEqual(snake_case_ , snake_case_ )
def lowercase ( self : str ):
_UpperCAmelCase = self.get_image_processor()
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = BlipProcessor(tokenizer=snake_case_ , image_processor=snake_case_ )
_UpperCAmelCase = "lower newer"
_UpperCAmelCase = self.prepare_image_inputs()
_UpperCAmelCase = processor(text=snake_case_ , images=snake_case_ )
# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
self.assertListEqual(list(inputs.keys() ) , ["pixel_values", "input_ids", "attention_mask"] )
| 22 | 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,
is_vision_available,
)
__SCREAMING_SNAKE_CASE :List[Any] = {
'''configuration_owlvit''': [
'''OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''OwlViTConfig''',
'''OwlViTOnnxConfig''',
'''OwlViTTextConfig''',
'''OwlViTVisionConfig''',
],
'''processing_owlvit''': ['''OwlViTProcessor'''],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE :Optional[Any] = ['''OwlViTFeatureExtractor''']
__SCREAMING_SNAKE_CASE :Optional[int] = ['''OwlViTImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE :str = [
'''OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''OwlViTModel''',
'''OwlViTPreTrainedModel''',
'''OwlViTTextModel''',
'''OwlViTVisionModel''',
'''OwlViTForObjectDetection''',
]
if TYPE_CHECKING:
from .configuration_owlvit import (
OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
OwlViTConfig,
OwlViTOnnxConfig,
OwlViTTextConfig,
OwlViTVisionConfig,
)
from .processing_owlvit import OwlViTProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_owlvit import OwlViTFeatureExtractor
from .image_processing_owlvit import OwlViTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_owlvit import (
OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
OwlViTForObjectDetection,
OwlViTModel,
OwlViTPreTrainedModel,
OwlViTTextModel,
OwlViTVisionModel,
)
else:
import sys
__SCREAMING_SNAKE_CASE :Any = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 22 |
'''simple docstring'''
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
def UpperCAmelCase_ ( __lowercase : str ) -> List[str]:
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase = image.size
_UpperCAmelCase , _UpperCAmelCase = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32
_UpperCAmelCase = image.resize((w, h) , resample=PIL_INTERPOLATION["lanczos"] )
_UpperCAmelCase = np.array(__lowercase ).astype(np.floataa ) / 255.0
_UpperCAmelCase = image[None].transpose(0 , 3 , 1 , 2 )
_UpperCAmelCase = torch.from_numpy(__lowercase )
return 2.0 * image - 1.0
class A_ ( lowerCAmelCase_ ):
def __init__( self : Optional[Any] , snake_case_ : VQModel , snake_case_ : UNetaDModel , snake_case_ : Union[
DDIMScheduler,
PNDMScheduler,
LMSDiscreteScheduler,
EulerDiscreteScheduler,
EulerAncestralDiscreteScheduler,
DPMSolverMultistepScheduler,
] , ):
super().__init__()
self.register_modules(vqvae=snake_case_ , unet=snake_case_ , scheduler=snake_case_ )
@torch.no_grad()
def __call__( self : Any , snake_case_ : Union[torch.Tensor, PIL.Image.Image] = None , snake_case_ : Optional[int] = 1 , snake_case_ : Optional[int] = 1_0_0 , snake_case_ : Optional[float] = 0.0 , snake_case_ : Optional[Union[torch.Generator, List[torch.Generator]]] = None , snake_case_ : Optional[str] = "pil" , snake_case_ : bool = True , ):
if isinstance(snake_case_ , PIL.Image.Image ):
_UpperCAmelCase = 1
elif isinstance(snake_case_ , torch.Tensor ):
_UpperCAmelCase = image.shape[0]
else:
raise ValueError(f'`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(snake_case_ )}' )
if isinstance(snake_case_ , PIL.Image.Image ):
_UpperCAmelCase = preprocess(snake_case_ )
_UpperCAmelCase , _UpperCAmelCase = image.shape[-2:]
# in_channels should be 6: 3 for latents, 3 for low resolution image
_UpperCAmelCase = (batch_size, self.unet.config.in_channels // 2, height, width)
_UpperCAmelCase = next(self.unet.parameters() ).dtype
_UpperCAmelCase = randn_tensor(snake_case_ , generator=snake_case_ , device=self.device , dtype=snake_case_ )
_UpperCAmelCase = image.to(device=self.device , dtype=snake_case_ )
# set timesteps and move to the correct device
self.scheduler.set_timesteps(snake_case_ , device=self.device )
_UpperCAmelCase = self.scheduler.timesteps
# scale the initial noise by the standard deviation required by the scheduler
_UpperCAmelCase = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
_UpperCAmelCase = "eta" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
_UpperCAmelCase = {}
if accepts_eta:
_UpperCAmelCase = eta
for t in self.progress_bar(snake_case_ ):
# concat latents and low resolution image in the channel dimension.
_UpperCAmelCase = torch.cat([latents, image] , dim=1 )
_UpperCAmelCase = self.scheduler.scale_model_input(snake_case_ , snake_case_ )
# predict the noise residual
_UpperCAmelCase = self.unet(snake_case_ , snake_case_ ).sample
# compute the previous noisy sample x_t -> x_t-1
_UpperCAmelCase = self.scheduler.step(snake_case_ , snake_case_ , snake_case_ , **snake_case_ ).prev_sample
# decode the image latents with the VQVAE
_UpperCAmelCase = self.vqvae.decode(snake_case_ ).sample
_UpperCAmelCase = torch.clamp(snake_case_ , -1.0 , 1.0 )
_UpperCAmelCase = image / 2 + 0.5
_UpperCAmelCase = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
_UpperCAmelCase = self.numpy_to_pil(snake_case_ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=snake_case_ )
| 22 | 1 |
'''simple docstring'''
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def UpperCAmelCase_ ( __lowercase : int , __lowercase : int ) -> List[Any]:
'''simple docstring'''
assert isinstance(__lowercase , __lowercase )
assert dataset.num_rows == 4
assert dataset.num_columns == 1
assert dataset.column_names == ["text"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("keep_in_memory" , [False, True] )
def UpperCAmelCase_ ( __lowercase : str , __lowercase : int , __lowercase : int ) -> List[Any]:
'''simple docstring'''
_UpperCAmelCase = tmp_path / "cache"
_UpperCAmelCase = {"text": "string"}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
_UpperCAmelCase = TextDatasetReader(__lowercase , cache_dir=__lowercase , keep_in_memory=__lowercase ).read()
_check_text_dataset(__lowercase , __lowercase )
@pytest.mark.parametrize(
"features" , [
None,
{"text": "string"},
{"text": "int32"},
{"text": "float32"},
] , )
def UpperCAmelCase_ ( __lowercase : Optional[int] , __lowercase : str , __lowercase : Tuple ) -> str:
'''simple docstring'''
_UpperCAmelCase = tmp_path / "cache"
_UpperCAmelCase = {"text": "string"}
_UpperCAmelCase = features.copy() if features else default_expected_features
_UpperCAmelCase = (
Features({feature: Value(__lowercase ) for feature, dtype in features.items()} ) if features is not None else None
)
_UpperCAmelCase = TextDatasetReader(__lowercase , features=__lowercase , cache_dir=__lowercase ).read()
_check_text_dataset(__lowercase , __lowercase )
@pytest.mark.parametrize("split" , [None, NamedSplit("train" ), "train", "test"] )
def UpperCAmelCase_ ( __lowercase : Dict , __lowercase : Any , __lowercase : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
_UpperCAmelCase = tmp_path / "cache"
_UpperCAmelCase = {"text": "string"}
_UpperCAmelCase = TextDatasetReader(__lowercase , cache_dir=__lowercase , split=__lowercase ).read()
_check_text_dataset(__lowercase , __lowercase )
assert dataset.split == split if split else "train"
@pytest.mark.parametrize("path_type" , [str, list] )
def UpperCAmelCase_ ( __lowercase : Any , __lowercase : List[str] , __lowercase : Union[str, Any] ) -> int:
'''simple docstring'''
if issubclass(__lowercase , __lowercase ):
_UpperCAmelCase = text_path
elif issubclass(__lowercase , __lowercase ):
_UpperCAmelCase = [text_path]
_UpperCAmelCase = tmp_path / "cache"
_UpperCAmelCase = {"text": "string"}
_UpperCAmelCase = TextDatasetReader(__lowercase , cache_dir=__lowercase ).read()
_check_text_dataset(__lowercase , __lowercase )
def UpperCAmelCase_ ( __lowercase : List[Any] , __lowercase : Optional[int] , __lowercase : Optional[Any]=("train",) ) -> List[str]:
'''simple docstring'''
assert isinstance(__lowercase , __lowercase )
for split in splits:
_UpperCAmelCase = dataset_dict[split]
assert dataset.num_rows == 4
assert dataset.num_columns == 1
assert dataset.column_names == ["text"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("keep_in_memory" , [False, True] )
def UpperCAmelCase_ ( __lowercase : Any , __lowercase : Dict , __lowercase : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
_UpperCAmelCase = tmp_path / "cache"
_UpperCAmelCase = {"text": "string"}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
_UpperCAmelCase = TextDatasetReader({"train": text_path} , cache_dir=__lowercase , keep_in_memory=__lowercase ).read()
_check_text_datasetdict(__lowercase , __lowercase )
@pytest.mark.parametrize(
"features" , [
None,
{"text": "string"},
{"text": "int32"},
{"text": "float32"},
] , )
def UpperCAmelCase_ ( __lowercase : Optional[int] , __lowercase : int , __lowercase : Dict ) -> str:
'''simple docstring'''
_UpperCAmelCase = tmp_path / "cache"
# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
_UpperCAmelCase = {"text": "string"}
_UpperCAmelCase = features.copy() if features else default_expected_features
_UpperCAmelCase = (
Features({feature: Value(__lowercase ) for feature, dtype in features.items()} ) if features is not None else None
)
_UpperCAmelCase = TextDatasetReader({"train": text_path} , features=__lowercase , cache_dir=__lowercase ).read()
_check_text_datasetdict(__lowercase , __lowercase )
@pytest.mark.parametrize("split" , [None, NamedSplit("train" ), "train", "test"] )
def UpperCAmelCase_ ( __lowercase : Tuple , __lowercase : Dict , __lowercase : Any ) -> Tuple:
'''simple docstring'''
if split:
_UpperCAmelCase = {split: text_path}
else:
_UpperCAmelCase = "train"
_UpperCAmelCase = {"train": text_path, "test": text_path}
_UpperCAmelCase = tmp_path / "cache"
_UpperCAmelCase = {"text": "string"}
_UpperCAmelCase = TextDatasetReader(__lowercase , cache_dir=__lowercase ).read()
_check_text_datasetdict(__lowercase , __lowercase , splits=list(path.keys() ) )
assert all(dataset[split].split == split for split in path.keys() )
| 22 |
'''simple docstring'''
import string
from math import logaa
def UpperCAmelCase_ ( __lowercase : str , __lowercase : str ) -> int:
'''simple docstring'''
_UpperCAmelCase = document.translate(
str.maketrans("" , "" , string.punctuation ) ).replace("\n" , "" )
_UpperCAmelCase = document_without_punctuation.split(" " ) # word tokenization
return len([word for word in tokenize_document if word.lower() == term.lower()] )
def UpperCAmelCase_ ( __lowercase : str , __lowercase : str ) -> tuple[int, int]:
'''simple docstring'''
_UpperCAmelCase = corpus.lower().translate(
str.maketrans("" , "" , string.punctuation ) ) # strip all punctuation and replace it with ''
_UpperCAmelCase = corpus_without_punctuation.split("\n" )
_UpperCAmelCase = term.lower()
return (len([doc for doc in docs if term in doc] ), len(__lowercase ))
def UpperCAmelCase_ ( __lowercase : int , __lowercase : int , __lowercase : Union[str, Any]=False ) -> float:
'''simple docstring'''
if smoothing:
if n == 0:
raise ValueError("log10(0) is undefined." )
return round(1 + logaa(n / (1 + df) ) , 3 )
if df == 0:
raise ZeroDivisionError("df must be > 0" )
elif n == 0:
raise ValueError("log10(0) is undefined." )
return round(logaa(n / df ) , 3 )
def UpperCAmelCase_ ( __lowercase : int , __lowercase : int ) -> float:
'''simple docstring'''
return round(tf * idf , 3 )
| 22 | 1 |
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